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, Available online
, doi: 10.14135/j.cnki.1006-3080.20210223004
Abstract:
The main characteristic of rarely used spare parts include the sharp change of demand, and long and uncertain demand interval, which will result in the inaccurate prediction on the spare part demand such that it is difficult to make a reasonable inventory decision. Aiming at the above issues, this work proposes a novel demand forecasting and inventory optimization method to improve the accuracy of decision-making. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then, the Bootstrap augmented sample statistical method is combined to predict the probability distribution of spare parts demand. Based on the obtained demand probability statistics results, the stochastic inventory model on the total inventory cost is established and the particle swarm algorithm is further utilized to search the optimal inventory decision variable. Finally, the experimental results from two sets of practical industrial spare parts show that the proposed method has the higher prediction accuracy. Meanwhile, the obtained inventory decision can achieve lower total inventory cost on the premise of satisfying service level, which illustrates the practicality of the proposed prediction and optimization method of infrequent spare partsmethod.
The main characteristic of rarely used spare parts include the sharp change of demand, and long and uncertain demand interval, which will result in the inaccurate prediction on the spare part demand such that it is difficult to make a reasonable inventory decision. Aiming at the above issues, this work proposes a novel demand forecasting and inventory optimization method to improve the accuracy of decision-making. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then, the Bootstrap augmented sample statistical method is combined to predict the probability distribution of spare parts demand. Based on the obtained demand probability statistics results, the stochastic inventory model on the total inventory cost is established and the particle swarm algorithm is further utilized to search the optimal inventory decision variable. Finally, the experimental results from two sets of practical industrial spare parts show that the proposed method has the higher prediction accuracy. Meanwhile, the obtained inventory decision can achieve lower total inventory cost on the premise of satisfying service level, which illustrates the practicality of the proposed prediction and optimization method of infrequent spare partsmethod.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210128001
Abstract:
Zinc ingots are the main raw material for the production of galvanized sheets and its consumption may fluctuate greatly due to the contract orders and product structure, which further results in fluctuating demand. Material demand often reflects the characteristics of small sample size and large variation range, whose non-stationarity and non-linearity make the demand forecasting more difficult. Meanwhile, the inaccuracy of demand forecasting will be gradually amplified in the information transmission of the supply chain, which will inevitably affect the material procurement plan and inventory management. Therefore, the accurate material demand forecasting has important practical significance for the optimization of raw material procurement and the production management scheduling of iron and steel enterprises. In order to improve the prediction accuracy of zinc ingot demand for galvanized sheet production, this paper proposes a zinc consumption prediction modeling method based on Support Vector Regression (SVR) optimized by Improved Grey Wolf Optimization (IGWO). Aiming at the shortcomings of fast convergence and premature maturity of traditional gray wolf algorithm, the chaotic Tent mapping strategy is firstly adopted to initialize the population so as to enhance the diversity and distribution uniformity of the initial population. Secondly, an adaptive adjustment strategy of control parameters is introduced to balance the search ability and development ability of the algorithm. Finally, the differential evolution is integrated in the location update process to reduce the possibility of false convergence of the algorithm. For the improved gray wolf algorithm, a simulation experiment is made via a typical benchmark test function, whose result verify the superiority of the improved algorithm in comprehensive performance. Furthermore, based on the actual production data of a unit in a steel plant, the zinc ingot consumption is modeled and predicted, and the parameters of SVR is optimized via the IGWO algorithm. The experimental results show that IGWO-SVR has higher prediction accuracy, better stability and better generalization ability.
Zinc ingots are the main raw material for the production of galvanized sheets and its consumption may fluctuate greatly due to the contract orders and product structure, which further results in fluctuating demand. Material demand often reflects the characteristics of small sample size and large variation range, whose non-stationarity and non-linearity make the demand forecasting more difficult. Meanwhile, the inaccuracy of demand forecasting will be gradually amplified in the information transmission of the supply chain, which will inevitably affect the material procurement plan and inventory management. Therefore, the accurate material demand forecasting has important practical significance for the optimization of raw material procurement and the production management scheduling of iron and steel enterprises. In order to improve the prediction accuracy of zinc ingot demand for galvanized sheet production, this paper proposes a zinc consumption prediction modeling method based on Support Vector Regression (SVR) optimized by Improved Grey Wolf Optimization (IGWO). Aiming at the shortcomings of fast convergence and premature maturity of traditional gray wolf algorithm, the chaotic Tent mapping strategy is firstly adopted to initialize the population so as to enhance the diversity and distribution uniformity of the initial population. Secondly, an adaptive adjustment strategy of control parameters is introduced to balance the search ability and development ability of the algorithm. Finally, the differential evolution is integrated in the location update process to reduce the possibility of false convergence of the algorithm. For the improved gray wolf algorithm, a simulation experiment is made via a typical benchmark test function, whose result verify the superiority of the improved algorithm in comprehensive performance. Furthermore, based on the actual production data of a unit in a steel plant, the zinc ingot consumption is modeled and predicted, and the parameters of SVR is optimized via the IGWO algorithm. The experimental results show that IGWO-SVR has higher prediction accuracy, better stability and better generalization ability.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210313005
Abstract:
Multiple cycles of experiments and reaction performance tests respectively were conducted using a batch fluidized bed reactor and a thermogravimetric analyzer. The catalytic effect of Fe2O3 on CaSO4/Ben oxygen carriers (OCs) during chemical looping combustion (CLC) was analyzed and the reaction activation energy of CaSO4/Ben OCs with different Fe2O3 contents and CO were compared to verify. The experimental results showed that the specific surface area and pore volume of CaSO4/Ben OCs increased with the addition of Fe2O3, which improved the reduction reaction rate and maintained the high CO2 concentration in the system. Fe2O3 could inhibit CaSO4 reaction to generate CaO and sulfur-containing gases, and improve the stability of CaSO4/Ben OCs circulation reaction. w=15.0% Fe2O3 addition was the best choice. The addition of w=15.0% Fe2O3 reduced the activation energy of CaSO4/Ben OCs reacting with CO from 88.72 kJ/ mol to 43.08 kJ/mol, and the reactivity of CaSO4/Ben OCs were significantly improved.
Multiple cycles of experiments and reaction performance tests respectively were conducted using a batch fluidized bed reactor and a thermogravimetric analyzer. The catalytic effect of Fe2O3 on CaSO4/Ben oxygen carriers (OCs) during chemical looping combustion (CLC) was analyzed and the reaction activation energy of CaSO4/Ben OCs with different Fe2O3 contents and CO were compared to verify. The experimental results showed that the specific surface area and pore volume of CaSO4/Ben OCs increased with the addition of Fe2O3, which improved the reduction reaction rate and maintained the high CO2 concentration in the system. Fe2O3 could inhibit CaSO4 reaction to generate CaO and sulfur-containing gases, and improve the stability of CaSO4/Ben OCs circulation reaction. w=15.0% Fe2O3 addition was the best choice. The addition of w=15.0% Fe2O3 reduced the activation energy of CaSO4/Ben OCs reacting with CO from 88.72 kJ/ mol to 43.08 kJ/mol, and the reactivity of CaSO4/Ben OCs were significantly improved.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210110002
Abstract:
Aiming at the localization problem of illegal whistle vehicles, a fast location system of moving sound source based on distributed microphone array is proposed. GNSS clock is used to realize the time synchronization between microphones, and the sound information collected synchronously is transmitted to the cloud database. Meanwhile, cloud computing technology is applied to realize the sound source localization algorithm. Compared with the centralized microphone array, it can greatly reduce the number of microphones and computing resources, and has the advantages of cost economy and flexible deployment. Besides, the proposed system adopts a fast location algorithm based on the arrival time difference and arrival frequency, which can make full use of the arrival frequency difference information between distributed microphones caused by Doppler effect to overcome the bottleneck of the arrival time difference method that is difficult to adapt to moving sound source. This proposed method can avoid the process of eliminating Doppler effect with complex calculation and large amount of calculation, and has low computational complexity and can adapt to high-speed moving sound source. Finally, the system simulation and field experiment results show that the proposed system can realize the fast and accurate positioning of high-speed moving sound source, and can be better applied to the scene of car whistle positioning.
Aiming at the localization problem of illegal whistle vehicles, a fast location system of moving sound source based on distributed microphone array is proposed. GNSS clock is used to realize the time synchronization between microphones, and the sound information collected synchronously is transmitted to the cloud database. Meanwhile, cloud computing technology is applied to realize the sound source localization algorithm. Compared with the centralized microphone array, it can greatly reduce the number of microphones and computing resources, and has the advantages of cost economy and flexible deployment. Besides, the proposed system adopts a fast location algorithm based on the arrival time difference and arrival frequency, which can make full use of the arrival frequency difference information between distributed microphones caused by Doppler effect to overcome the bottleneck of the arrival time difference method that is difficult to adapt to moving sound source. This proposed method can avoid the process of eliminating Doppler effect with complex calculation and large amount of calculation, and has low computational complexity and can adapt to high-speed moving sound source. Finally, the system simulation and field experiment results show that the proposed system can realize the fast and accurate positioning of high-speed moving sound source, and can be better applied to the scene of car whistle positioning.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210208001
Abstract:
In view of the periodic dynamic characteristics of traffic flow, a probabilistic combination model of traffic flow based on similarity clustering is proposed, which fully excavates the similarity characteristics of different periods of traffic flow. Firstly, the adaptive K-means + + clustering method is used to cluster the historical traffic flow data, and the traffic flow data with time similarity is classified. Then, the combination model is constructed for different sequence feature data sets. Furthermore, according to the new traffic flow state data, the similarity between the new traffic flow state data and the classified data is analyzed, and the probability weight of the combined model is calculated. And then, the prediction output is obtained by fusing the probability weight of the combined model results. Finally, the simulation experiment verifies the validity and accuracy of the proposed prediction model.
In view of the periodic dynamic characteristics of traffic flow, a probabilistic combination model of traffic flow based on similarity clustering is proposed, which fully excavates the similarity characteristics of different periods of traffic flow. Firstly, the adaptive K-means + + clustering method is used to cluster the historical traffic flow data, and the traffic flow data with time similarity is classified. Then, the combination model is constructed for different sequence feature data sets. Furthermore, according to the new traffic flow state data, the similarity between the new traffic flow state data and the classified data is analyzed, and the probability weight of the combined model is calculated. And then, the prediction output is obtained by fusing the probability weight of the combined model results. Finally, the simulation experiment verifies the validity and accuracy of the proposed prediction model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210307001
Abstract:
Inspired by marine mussels, grafted catechol groups can endow biomimetic adhesives with excellent tissue adhesion capability under humid circumstances. In this paper, through the novel one-step strategy, once mechanical mixing between chitosan polymer (dissolved in Fe3+ solution with predetermined concentration, termed as CS-Fe) and 3,4-dihydroxybenzaldehyde (DBA) solution was completed, a brown-color hydrogel (termed as CS-DBA-Fe) could be instantly prepared in situ as results of Schiff base reaction between CS and DBA, plus oxidation-coordination dual interaction between Fe3+ and catechol groups. Multiple interaction, including coordination bond, covalent bond, hydrogen bond, π-π and π-cation interaction inside the CS-DBA-Fe crosslinking system were achieved simultaneously, leading to drastic versatile adhesion. What’s more, the obtained hydrogels also exhibited various merits, including tunable gelation time, adhesion strength and rheological properties, as well as outstanding surface adaptability and stability, rendering it a promising candidate of tissue adhesion materials for emergent situation. Lastly, though several traditional methods like reductive amination strategy (RA strategy) were developed, they usually involve prolonged reaction time, harsh reaction conditions and complicated purifying procedures. Herein, hydrogels prepared by RA strategy (termed as CCS-Fe) were used as the benchmark to further evaluate the adhesion strength of CS-DBA-Fe adhesives. Compared with the RA strategy, adhesives obtained by this method not only had better bonding strength (up to 48.8 kPa), but more importantly, tedious processes were avoided. Thus, preparation time was greatly shortened (from 72 h to less than 10 min). The one-step in-situ preparation of tissue adhesive provides an important alternative for the facile preparation of biomimetic tissue adhesives.
Inspired by marine mussels, grafted catechol groups can endow biomimetic adhesives with excellent tissue adhesion capability under humid circumstances. In this paper, through the novel one-step strategy, once mechanical mixing between chitosan polymer (dissolved in Fe3+ solution with predetermined concentration, termed as CS-Fe) and 3,4-dihydroxybenzaldehyde (DBA) solution was completed, a brown-color hydrogel (termed as CS-DBA-Fe) could be instantly prepared in situ as results of Schiff base reaction between CS and DBA, plus oxidation-coordination dual interaction between Fe3+ and catechol groups. Multiple interaction, including coordination bond, covalent bond, hydrogen bond, π-π and π-cation interaction inside the CS-DBA-Fe crosslinking system were achieved simultaneously, leading to drastic versatile adhesion. What’s more, the obtained hydrogels also exhibited various merits, including tunable gelation time, adhesion strength and rheological properties, as well as outstanding surface adaptability and stability, rendering it a promising candidate of tissue adhesion materials for emergent situation. Lastly, though several traditional methods like reductive amination strategy (RA strategy) were developed, they usually involve prolonged reaction time, harsh reaction conditions and complicated purifying procedures. Herein, hydrogels prepared by RA strategy (termed as CCS-Fe) were used as the benchmark to further evaluate the adhesion strength of CS-DBA-Fe adhesives. Compared with the RA strategy, adhesives obtained by this method not only had better bonding strength (up to 48.8 kPa), but more importantly, tedious processes were avoided. Thus, preparation time was greatly shortened (from 72 h to less than 10 min). The one-step in-situ preparation of tissue adhesive provides an important alternative for the facile preparation of biomimetic tissue adhesives.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210104002
Abstract:
Gender identification is a quite important task in speaker verification and can also be used as an auxiliary tool in automatic speech recognition (ASR) to improve model performance. In order to increase the accuracy of gender identification, some schemes based on deep learning have been recently reported. However, compared with the acoustic conditioned data in training, speech data in the actual application scenarios is usually masked by the background noise, such as music, environmental noise, background chatter, etc. Thus, the performance of gender identification model based on audio is seriously degraded due to the great difference between the actual speech data and the model training data. In order to solve this problem, we propose a domain adaptive model via combining generative adversarial network(GAN) and Ghost VLAD layer. The introduction of GhostVLAD can effectively reduce the interference of noise and irrelevant information in speech and the training method based on GaN can realize the adaptation of the model to the target domain data. During the confrontation training, auxiliary loss is introduced to maintain the representation ability of gender characteristics. Finally, by voxceleb1 data set as the source domain, audioset and movie data set as the target domain, the performance of the domain adaptive model is tested, from which it is shown that compared with the gender recognition model based on convolution neural network, this model can improve the accuracy of gender recognition by 5.13% and 7.72% , respectively.
Gender identification is a quite important task in speaker verification and can also be used as an auxiliary tool in automatic speech recognition (ASR) to improve model performance. In order to increase the accuracy of gender identification, some schemes based on deep learning have been recently reported. However, compared with the acoustic conditioned data in training, speech data in the actual application scenarios is usually masked by the background noise, such as music, environmental noise, background chatter, etc. Thus, the performance of gender identification model based on audio is seriously degraded due to the great difference between the actual speech data and the model training data. In order to solve this problem, we propose a domain adaptive model via combining generative adversarial network(GAN) and Ghost VLAD layer. The introduction of GhostVLAD can effectively reduce the interference of noise and irrelevant information in speech and the training method based on GaN can realize the adaptation of the model to the target domain data. During the confrontation training, auxiliary loss is introduced to maintain the representation ability of gender characteristics. Finally, by voxceleb1 data set as the source domain, audioset and movie data set as the target domain, the performance of the domain adaptive model is tested, from which it is shown that compared with the gender recognition model based on convolution neural network, this model can improve the accuracy of gender recognition by 5.13% and 7.72% , respectively.
, Available online
, doi: 10.14135/j.cnki.1006-2080.20220414001
Abstract:
Serum amyloid A (SAA) is an acute-phase protein mainly produced by the liver in response to proinflammatory cytokines. SAA genes and proteins are significantly activated during the acute phase response, which comprises a number of phenomena that occur in the presence of inflammation and infection, increased temperature and hormonal and metabolic alterations. Therefore SAA is a sensitive indicator of inflammation in the early stage of infectious diseases, which is important for diagnosis, evaluation, monitoring and treatment of inflammation. Fluorescent immunochromatography is one of the most popular strategies for point-of-care testing (POCT), which is capable of rapid screening for disease detection. Fluorescent microspheres QM-OH@PS-COOH were obtained from aggregation-induced emission (AIE) quinoline-malononitrile (QM) derivatives QM-OH. The morphology and structure of these QM fluorescent microspheres were characterized by scanning electron microscopy et al. Finally, these fluorescent microspheres were utilized to detect for SAA concertation in clinical samples via fluorescent immunochromatography. The results showed that QM-OH@PS-COOH has uniform sizes with regular shapes. Compared with commercial fluorescent microspheres, these AIE microspheres had similar density, solid content and carboxyl content. The QM-OH@PS-COOH system exhibited the detection of SAA with high sensitivity in clinical samples via fluorescent immunochromatography. Thus, this new type of QM fluorescent microspheres could be employed as an important tool for clinical diagnosis, enabling quantitatively analyze and monitor the concentration of SAA during the inflammation process. Therefore, we believe that these detection platforms based on QM-OH@PS-COOH can serve as a screening platform for early disease detection, especially self-testing in POCT.
Serum amyloid A (SAA) is an acute-phase protein mainly produced by the liver in response to proinflammatory cytokines. SAA genes and proteins are significantly activated during the acute phase response, which comprises a number of phenomena that occur in the presence of inflammation and infection, increased temperature and hormonal and metabolic alterations. Therefore SAA is a sensitive indicator of inflammation in the early stage of infectious diseases, which is important for diagnosis, evaluation, monitoring and treatment of inflammation. Fluorescent immunochromatography is one of the most popular strategies for point-of-care testing (POCT), which is capable of rapid screening for disease detection. Fluorescent microspheres QM-OH@PS-COOH were obtained from aggregation-induced emission (AIE) quinoline-malononitrile (QM) derivatives QM-OH. The morphology and structure of these QM fluorescent microspheres were characterized by scanning electron microscopy et al. Finally, these fluorescent microspheres were utilized to detect for SAA concertation in clinical samples via fluorescent immunochromatography. The results showed that QM-OH@PS-COOH has uniform sizes with regular shapes. Compared with commercial fluorescent microspheres, these AIE microspheres had similar density, solid content and carboxyl content. The QM-OH@PS-COOH system exhibited the detection of SAA with high sensitivity in clinical samples via fluorescent immunochromatography. Thus, this new type of QM fluorescent microspheres could be employed as an important tool for clinical diagnosis, enabling quantitatively analyze and monitor the concentration of SAA during the inflammation process. Therefore, we believe that these detection platforms based on QM-OH@PS-COOH can serve as a screening platform for early disease detection, especially self-testing in POCT.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220223001
Abstract:
In this paper, Panax notoginseng (P. notoginseng) suspension cells were used for the production of chlorogenic acids (CGAs), of which bioactivities were evaluated. Firstly, CGAs in P. notoginseng suspension cells were identified by liquid chromatography-tandem mass spectrometry. Secondly, the suspension culture system and elicitation mode were optimized. At last, the antioxidant and α-glucosidase inhibitory activity of CGAs from P. notoginseng cells were determined. The main results were as follows: 1) Four CGAs were inferred in P. notoginseng cells. 2) The optimal condition was obtained (B5 + 4.0 mg/L NAA + 0.2 mg/L 6-BA + 40 g/L sucrose + 2 g/L PVP, pH 7.5 and 15% inoculation size). 3) By co-culture with 30 μmol/L methyl jasmonate for 3 days, the CGAs yield could reach up to 684.07 mg/L, which was 1.44 times that of control. 4) CGAs from P. notoginseng cells showed an excellent antioxidant capacity in radical-scavenging test and, moreover, certain inhibition on α-glucosidase activity. Thus, our results indicated that the production of CGAs by P. notoginseng cells have potential applications in healthy food and daily cosmetics.
In this paper, Panax notoginseng (P. notoginseng) suspension cells were used for the production of chlorogenic acids (CGAs), of which bioactivities were evaluated. Firstly, CGAs in P. notoginseng suspension cells were identified by liquid chromatography-tandem mass spectrometry. Secondly, the suspension culture system and elicitation mode were optimized. At last, the antioxidant and α-glucosidase inhibitory activity of CGAs from P. notoginseng cells were determined. The main results were as follows: 1) Four CGAs were inferred in P. notoginseng cells. 2) The optimal condition was obtained (B5 + 4.0 mg/L NAA + 0.2 mg/L 6-BA + 40 g/L sucrose + 2 g/L PVP, pH 7.5 and 15% inoculation size). 3) By co-culture with 30 μmol/L methyl jasmonate for 3 days, the CGAs yield could reach up to 684.07 mg/L, which was 1.44 times that of control. 4) CGAs from P. notoginseng cells showed an excellent antioxidant capacity in radical-scavenging test and, moreover, certain inhibition on α-glucosidase activity. Thus, our results indicated that the production of CGAs by P. notoginseng cells have potential applications in healthy food and daily cosmetics.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210312001
Abstract:
Positive definite linear systems arise in many areas of scientific computing and engineering applications, such as solid mechanics, dynamics, nonlinear programming and partial differential equations. It is very meaningful to explore how to efficiently solve the large scale sparse saddle point problem. This paper proposes an extrapolated positive definite and skew-Hermitian (EPSS) iterative method for solving large sparse positive definite linear systems. The new method first splits the coefficient matrix into positive definite matrix and skew-Hermitian matrix, next constructs a new non-symmetric two-step iterative scheme. The new method can not only solve non-Hermitian positive definite linear equations, but also be used for solving Hermitian positive definite linear equations, which greatly accelerates the convergence speed of the iterative method. Then theoretical analysis shows that the new method is convergent. And the necessary and sufficient conditions for the convergence of the new method are given.Moreover the spectral radius of the iterative matrix of the new method is smaller than that of the iterative matrix of the positive definite and skew-Hermitian (PSS) iterative method when selecting appropriate variables. After that numerical experiments are given to show that the new method is efficient and more competitive than PSS iteration method and the extrapolated Hermitian and skew-Hermitian (EHSS) iterative method. Finally, numerical experiments analyze the sensitivity of the parameters in the EPSS iterative method and find the approximate optimal parameters.
Positive definite linear systems arise in many areas of scientific computing and engineering applications, such as solid mechanics, dynamics, nonlinear programming and partial differential equations. It is very meaningful to explore how to efficiently solve the large scale sparse saddle point problem. This paper proposes an extrapolated positive definite and skew-Hermitian (EPSS) iterative method for solving large sparse positive definite linear systems. The new method first splits the coefficient matrix into positive definite matrix and skew-Hermitian matrix, next constructs a new non-symmetric two-step iterative scheme. The new method can not only solve non-Hermitian positive definite linear equations, but also be used for solving Hermitian positive definite linear equations, which greatly accelerates the convergence speed of the iterative method. Then theoretical analysis shows that the new method is convergent. And the necessary and sufficient conditions for the convergence of the new method are given.Moreover the spectral radius of the iterative matrix of the new method is smaller than that of the iterative matrix of the positive definite and skew-Hermitian (PSS) iterative method when selecting appropriate variables. After that numerical experiments are given to show that the new method is efficient and more competitive than PSS iteration method and the extrapolated Hermitian and skew-Hermitian (EHSS) iterative method. Finally, numerical experiments analyze the sensitivity of the parameters in the EPSS iterative method and find the approximate optimal parameters.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210308001
Abstract:
In this article, a new three-parameter asymmetric generalized error distribution and its extension are introduced. This includes as special case the symmetric Normal distribution. One skewness parameter and two tail parameters are introduced into the generalized error distribution to control the asymmetric and the left tail as well as the right tail respectively. Basic properties of the distribution are studied in details, including the cumulative distribution function, the quantile function, the origin moment of each order and so no, and the sampling method of the random variable is given. Different approaches to the estimation of parameters, such as moments, maximum likelihood and Bayesian methods are discussed. Finally, two applications are made to two real data sets modeling example.
In this article, a new three-parameter asymmetric generalized error distribution and its extension are introduced. This includes as special case the symmetric Normal distribution. One skewness parameter and two tail parameters are introduced into the generalized error distribution to control the asymmetric and the left tail as well as the right tail respectively. Basic properties of the distribution are studied in details, including the cumulative distribution function, the quantile function, the origin moment of each order and so no, and the sampling method of the random variable is given. Different approaches to the estimation of parameters, such as moments, maximum likelihood and Bayesian methods are discussed. Finally, two applications are made to two real data sets modeling example.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210301001
Abstract:
Multilayer capacitors with BaTiO3 as the core dielectric material was known as the most widely used and classic perovskite ferroelectrics. Due to the high sintering temperature of BaTiO3 based ceramics, only noble metal materials could be used as internal electrodes. Therefore, it was of great practical significance to reduce the sintering temperature of BaTiO3 based ceramics to match with the base metal electrode materials with lower melting point, so as to reduce the cost, which had been a research hotspot in this field at home and abroad. In order to reduce the sintering temperature of BaTiO3 ceramics, many methods had been used, among which the simplest and most effective method was to add appropriate sintering additives. In this work, BaTiO3 ceramics were sintered at low temperatures via co-doping with CuO, B2O3 and Li2O. The phase composition, density and microstructure of the ceramics at different sintering temperatures had been investigated. The results showed that co-doping of CuO, B2O3 and Li2O could effectively reduce the sintering temperature of BaTiO3 ceramics. Single tetragonal phase BaTiO3 ceramics with high density of 5.75 g/cm3 could be obtained after sintering at 950 ℃ for 2 h and the relative density was 95.6%, while higher sintering temperature led to the decrease of the density of the ceramics. The density of sample sintered at 1100 ℃ was only 5.23 g/cm3 and the relative density was 86.9%. Meanwhile, the microstructure of BaTiO3 ceramics changed obviously with the increase of sintering temperature, and the grains grew rapidly. The low eutectic phase and solid solution reaction during 0.7%CuO-1.5%B2O3-0.3%Li2O (BCL) co-doping were the main reasons for decreasing sintering temperature.
Multilayer capacitors with BaTiO3 as the core dielectric material was known as the most widely used and classic perovskite ferroelectrics. Due to the high sintering temperature of BaTiO3 based ceramics, only noble metal materials could be used as internal electrodes. Therefore, it was of great practical significance to reduce the sintering temperature of BaTiO3 based ceramics to match with the base metal electrode materials with lower melting point, so as to reduce the cost, which had been a research hotspot in this field at home and abroad. In order to reduce the sintering temperature of BaTiO3 ceramics, many methods had been used, among which the simplest and most effective method was to add appropriate sintering additives. In this work, BaTiO3 ceramics were sintered at low temperatures via co-doping with CuO, B2O3 and Li2O. The phase composition, density and microstructure of the ceramics at different sintering temperatures had been investigated. The results showed that co-doping of CuO, B2O3 and Li2O could effectively reduce the sintering temperature of BaTiO3 ceramics. Single tetragonal phase BaTiO3 ceramics with high density of 5.75 g/cm3 could be obtained after sintering at 950 ℃ for 2 h and the relative density was 95.6%, while higher sintering temperature led to the decrease of the density of the ceramics. The density of sample sintered at 1100 ℃ was only 5.23 g/cm3 and the relative density was 86.9%. Meanwhile, the microstructure of BaTiO3 ceramics changed obviously with the increase of sintering temperature, and the grains grew rapidly. The low eutectic phase and solid solution reaction during 0.7%CuO-1.5%B2O3-0.3%Li2O (BCL) co-doping were the main reasons for decreasing sintering temperature.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210204001
Abstract:
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, Available online
, doi: 10.14135/j.cnki.1006-3080.20220105005
Abstract:
The low voltage ride through performance of doubly fed fan depends not only on the control strategy, but also on the selection of control parameters.Because the control parameter optimization algorithm takes too long to achieve the corresponding effect in real-time control, a method based on off-line parameter optimization, model training and on-line fault identification is proposed in this paper.Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and the control parameters are optimized offline according to the fault type to form the corresponding low-voltage ride through mode, and then the different fault data are classified to form the training samples of neural network.At the moment of power grid fault, the fault data can be directly used to quickly judge the fault type through the trained distributed deep neural network, and select the appropriate control strategy according to the fault type.The feasibility of this method and its advantages in control effect and speed are verified by the fault identification and parameter optimization method of doubly fed fan model.
The low voltage ride through performance of doubly fed fan depends not only on the control strategy, but also on the selection of control parameters.Because the control parameter optimization algorithm takes too long to achieve the corresponding effect in real-time control, a method based on off-line parameter optimization, model training and on-line fault identification is proposed in this paper.Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and the control parameters are optimized offline according to the fault type to form the corresponding low-voltage ride through mode, and then the different fault data are classified to form the training samples of neural network.At the moment of power grid fault, the fault data can be directly used to quickly judge the fault type through the trained distributed deep neural network, and select the appropriate control strategy according to the fault type.The feasibility of this method and its advantages in control effect and speed are verified by the fault identification and parameter optimization method of doubly fed fan model.
, Available online
, doi: 10.14135/j.cnki.1006-3080-20220123002
Abstract:
The chlorination of sucrose-6-acetates (S-6-A) to produce sucralose-6-acetate (TGS-6-A) is a key step in the synthesis of sucralose. In this work, the thermodynamic calculation of the chlorination reaction was carried out using the group contribution method. The results show that the reaction is exothermic and irreversible in the temperature range of 372 K to 389 K. The thermodynamic calculations were performed on the hydrolysis reaction of TGS-6-A and the equilibrium constants at different temperatures were obtained, which agree well with the experimental value, confirming the reliability of the calculation methods used in this work. Then the effect of temperature on reaction rate was studied by batch experiments and a chain reaction kinetic model of chlorination reaction was established. The activation energy of the main reactions was calculated as 103.87 kJ·mol−1 and 153.87 kJ·mol−1, respectively, and the activation energy of the side reaction was 87.09 kJ·mol−1. The kinetic experiment results show that the main reaction is more affected by reaction temperature, increasing the temperature and controlling the reaction time can effectively increase the yield of the target product TGS-6-A.
The chlorination of sucrose-6-acetates (S-6-A) to produce sucralose-6-acetate (TGS-6-A) is a key step in the synthesis of sucralose. In this work, the thermodynamic calculation of the chlorination reaction was carried out using the group contribution method. The results show that the reaction is exothermic and irreversible in the temperature range of 372 K to 389 K. The thermodynamic calculations were performed on the hydrolysis reaction of TGS-6-A and the equilibrium constants at different temperatures were obtained, which agree well with the experimental value, confirming the reliability of the calculation methods used in this work. Then the effect of temperature on reaction rate was studied by batch experiments and a chain reaction kinetic model of chlorination reaction was established. The activation energy of the main reactions was calculated as 103.87 kJ·mol−1 and 153.87 kJ·mol−1, respectively, and the activation energy of the side reaction was 87.09 kJ·mol−1. The kinetic experiment results show that the main reaction is more affected by reaction temperature, increasing the temperature and controlling the reaction time can effectively increase the yield of the target product TGS-6-A.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220106006
Abstract:
Saccharomyces cerevisiae is one of the cell factories in biomanufacturing because of numerous advantages towards industrial fermentations, which include robust growth in low pH, lower temperatures, high tolerance to shear stress, lack of phage contamination, and ease of separation. However, the Crabtree effect of S. cerevisiae made ethanol and glycerol be accumulated due to carbon overflow. For the production of intermediate derivatives of the TCA cycle, such as itaconic acid, the Crabtree effect must be overcome by using a suitable strategy. In this paper, the role of NOX and AOX1 on the Crabtree effect in batch fermentation of S. cerevisiae was investigated by expressing the NADH oxidase NOX and the alternative oxidase AOX1 with plasmids having different copy numbers. It was revealed that both strains expressing NOX and AOX1 in high copy vectors caused significant metabolic changes. The high copy expressing nox strains were able to oxidize cytoplasmic NADH, glycerol secretion in the medium was reduced by 43.94%, and IA concentration was not changed. In contrast, strains with high copy expression of aox1 had cytoplasmic residual AOX1, which oxidized cytoplasmic NADH and reduced glycerol accumulation. Further location of AOX1 to the mitochondria of S. cerevisiae with the mitochondrial location signals AAC2 and BCS1p reduced the effect of AOX1 on glycerol synthesis, and IA production was enhanced to 116.98 mg/L. However, none of the strains expressing AOX1, AAC2-AOX1 and BCS1p-AOX1 significantly alleviated the accumulation of ethanol in batch fermentation. This study helps to improve the production of TCA cycle derivatives from glucose by engineered S. cerevisiae, provides a reference for the production of TCA cycle derivatives from S. cerevisiae in the batch culture at high original glucose concentrations.
Saccharomyces cerevisiae is one of the cell factories in biomanufacturing because of numerous advantages towards industrial fermentations, which include robust growth in low pH, lower temperatures, high tolerance to shear stress, lack of phage contamination, and ease of separation. However, the Crabtree effect of S. cerevisiae made ethanol and glycerol be accumulated due to carbon overflow. For the production of intermediate derivatives of the TCA cycle, such as itaconic acid, the Crabtree effect must be overcome by using a suitable strategy. In this paper, the role of NOX and AOX1 on the Crabtree effect in batch fermentation of S. cerevisiae was investigated by expressing the NADH oxidase NOX and the alternative oxidase AOX1 with plasmids having different copy numbers. It was revealed that both strains expressing NOX and AOX1 in high copy vectors caused significant metabolic changes. The high copy expressing nox strains were able to oxidize cytoplasmic NADH, glycerol secretion in the medium was reduced by 43.94%, and IA concentration was not changed. In contrast, strains with high copy expression of aox1 had cytoplasmic residual AOX1, which oxidized cytoplasmic NADH and reduced glycerol accumulation. Further location of AOX1 to the mitochondria of S. cerevisiae with the mitochondrial location signals AAC2 and BCS1p reduced the effect of AOX1 on glycerol synthesis, and IA production was enhanced to 116.98 mg/L. However, none of the strains expressing AOX1, AAC2-AOX1 and BCS1p-AOX1 significantly alleviated the accumulation of ethanol in batch fermentation. This study helps to improve the production of TCA cycle derivatives from glucose by engineered S. cerevisiae, provides a reference for the production of TCA cycle derivatives from S. cerevisiae in the batch culture at high original glucose concentrations.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220302001
Abstract:
Due to inherent factors such as infrastructure construction, wireless sensor networks must consider the problem of limited network resources and uneven resource consumption. In this paper, based on swarm intelligence fuzzy control, fuzzy control is introduced into swarm intelligence artificial bee swarm routing protocol to solve the optimization problem of multipath routing planning in software-defined sensor networks. Based on the SDN-WISE software defined network architecture and swarm intelligence algorithm, and the optimal link was searched by generating artificial bees to simulate the process of honey gathering. Artificial bees adjust different data transmission links, judge regional state through fuzzy logic, and evaluate the data link with the highest value by generating fitness function, generating an optimized routing solution. The experimental results show that, compared with the classical routing algorithms is adopted in this method to optimize the routing problem solving process in the framework of loosely coupled software-defined network by integrating the agent adaptive ability of artificial bees and the fault-tolerant logic of fuzzy control. The experimental results show that, It has obvious advantages in residual energy management, network utilization, transmission delay and packet delivery rate.
Due to inherent factors such as infrastructure construction, wireless sensor networks must consider the problem of limited network resources and uneven resource consumption. In this paper, based on swarm intelligence fuzzy control, fuzzy control is introduced into swarm intelligence artificial bee swarm routing protocol to solve the optimization problem of multipath routing planning in software-defined sensor networks. Based on the SDN-WISE software defined network architecture and swarm intelligence algorithm, and the optimal link was searched by generating artificial bees to simulate the process of honey gathering. Artificial bees adjust different data transmission links, judge regional state through fuzzy logic, and evaluate the data link with the highest value by generating fitness function, generating an optimized routing solution. The experimental results show that, compared with the classical routing algorithms is adopted in this method to optimize the routing problem solving process in the framework of loosely coupled software-defined network by integrating the agent adaptive ability of artificial bees and the fault-tolerant logic of fuzzy control. The experimental results show that, It has obvious advantages in residual energy management, network utilization, transmission delay and packet delivery rate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200212001
Abstract:
This paper considers a hybrid fuzzy neural networks (FNN) for time-series prediction based on error distribution analysis. Firstly, a new hybrid FNN (HFNN) structure is established, where the last two layers is replaced by a combination of a full connection layer and nonlinear activation function. Thus, more parameters can be updated in training process to guarantee the prediction accuracy. Secondly, a novel attention loss function is proposed to make a sample with a certain error distribution get more gains in training process. Based on rule analysis with probability density function, it is seen that the proposed method can provide a more uniform and stable predicted output. The prediction errors of HFNN converge to a compact set. Finally, two benchmark problems are applied to demonstrate the hybrid model performance on time series prediction. The comparisons with other prediction models have verified the efficiency and accuracy of the proposed HFNN model.
This paper considers a hybrid fuzzy neural networks (FNN) for time-series prediction based on error distribution analysis. Firstly, a new hybrid FNN (HFNN) structure is established, where the last two layers is replaced by a combination of a full connection layer and nonlinear activation function. Thus, more parameters can be updated in training process to guarantee the prediction accuracy. Secondly, a novel attention loss function is proposed to make a sample with a certain error distribution get more gains in training process. Based on rule analysis with probability density function, it is seen that the proposed method can provide a more uniform and stable predicted output. The prediction errors of HFNN converge to a compact set. Finally, two benchmark problems are applied to demonstrate the hybrid model performance on time series prediction. The comparisons with other prediction models have verified the efficiency and accuracy of the proposed HFNN model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107003
Abstract:
Heat integration across different units is an effective measure to improve energy utilization in chemical plants. In order to obtain the actual energy-saving potential across the units and improve the fluctuation resistance of the retrofitting schemes, a retrofitting method based on the actual cold and heat composite curves is put forward. Based on the industrial data of benzene production and C8 units in a petrochemical enterprise, the heat exchanger networks of the two units are simulated in Aspen HYSYS and the pinch analysis is done with Aspen Energy Analyzer. Considering the unreasonable heat transfer of benzene production unit and the safety constraints, retrofitting schemes for the heat exchanger network of benzene production unit are proposed to reduce the steam consumption. Since the energy-saving potential of the C8 unit is limited and the unreasonable heat transfer is distributed in different heat exchangers, it is not cost-effective to retrofit the heat exchanger network of the C8 unit. The energy-saving potential of heat integration between the two units is analyzed by constructing the actual cold and hot composite curves. The advantage of using the actual cold and hot composite curves to guide the heat integration across the two units is that it uses the actual residual energy, which is more practical than the theoretical situation such as the grand composite curve. Besides, limitations and complexity of implementation are also considered to make retrofitting schemes. As a result, two heat integration retrofit schemes across the two units are proposed, compared, and discussed. The results show that the scheme with more energy saving needs more investment costs and has a slightly longer payback period, but has more economic benefits in the long term.
Heat integration across different units is an effective measure to improve energy utilization in chemical plants. In order to obtain the actual energy-saving potential across the units and improve the fluctuation resistance of the retrofitting schemes, a retrofitting method based on the actual cold and heat composite curves is put forward. Based on the industrial data of benzene production and C8 units in a petrochemical enterprise, the heat exchanger networks of the two units are simulated in Aspen HYSYS and the pinch analysis is done with Aspen Energy Analyzer. Considering the unreasonable heat transfer of benzene production unit and the safety constraints, retrofitting schemes for the heat exchanger network of benzene production unit are proposed to reduce the steam consumption. Since the energy-saving potential of the C8 unit is limited and the unreasonable heat transfer is distributed in different heat exchangers, it is not cost-effective to retrofit the heat exchanger network of the C8 unit. The energy-saving potential of heat integration between the two units is analyzed by constructing the actual cold and hot composite curves. The advantage of using the actual cold and hot composite curves to guide the heat integration across the two units is that it uses the actual residual energy, which is more practical than the theoretical situation such as the grand composite curve. Besides, limitations and complexity of implementation are also considered to make retrofitting schemes. As a result, two heat integration retrofit schemes across the two units are proposed, compared, and discussed. The results show that the scheme with more energy saving needs more investment costs and has a slightly longer payback period, but has more economic benefits in the long term.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220103001
Abstract:
The order of the stability and binding constant of curcumin encapsulated by three Tween aggregates is: Tween-85 vesicles > Tween-60 vesicles > Tween-80 micelles. By UV and fluorescence measurements, it is found that curcumin is encapsulated in the hydrophobic region of the alkyl chains of Tween aggregates with the hydrophobic interaction as the main driving force. The 1H NMR data confirm that the encapsulation position and force of curcumin are closely related to the alkyl chain structure of Tween surfactants. Compared with Tween-60 vesicles with a particle size of ~92 nm, the micelles formed by Tween-80 containing double bonds in the alkyl chain have loosely arranged hydrophobic region. Therefore, curcumin encapsulated by Tween-80 micelles exhibits lower stability, binding constant, and UV absorption and fluorescence emission intensities. Tween-85 with three unsaturated alkyl chains can generate vesicles with a particle size of ~150 nm, and its bilayer has the highest hydrophobicity which has the best encapsulation effect on curcumin.
The order of the stability and binding constant of curcumin encapsulated by three Tween aggregates is: Tween-85 vesicles > Tween-60 vesicles > Tween-80 micelles. By UV and fluorescence measurements, it is found that curcumin is encapsulated in the hydrophobic region of the alkyl chains of Tween aggregates with the hydrophobic interaction as the main driving force. The 1H NMR data confirm that the encapsulation position and force of curcumin are closely related to the alkyl chain structure of Tween surfactants. Compared with Tween-60 vesicles with a particle size of ~92 nm, the micelles formed by Tween-80 containing double bonds in the alkyl chain have loosely arranged hydrophobic region. Therefore, curcumin encapsulated by Tween-80 micelles exhibits lower stability, binding constant, and UV absorption and fluorescence emission intensities. Tween-85 with three unsaturated alkyl chains can generate vesicles with a particle size of ~150 nm, and its bilayer has the highest hydrophobicity which has the best encapsulation effect on curcumin.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107004
Abstract:
Heat exchanger network, HEN, is one of the most important parts in chemical production process. HEN optimization become an effective tool to save energy and keep sustainable development. There are many methods to optimize HEN. In principle, mathematical programming seems a comprehensive solution, while the pinch point method is still a handy tool, due to its simplicity and clear physical meaning. Special arrangement is required when phase change is considered in the system. The aim of this paper is to investigate six streams distributed in adjacent sections in an ethylene cracking process from systematic perspective, as no heat recovery is involved, and only utility is matched to meet their temperature requirement in process. Problem table is used to determine the pinch point for the design of HEN. Due to the complex phase change of the mixture in the heat exchange system, the phase change section is converted into one or more streams with constant heat capacity flow rate according to its thermal load and material characteristics, so as to determine the temperature interval. In traditional pinch point method, △Tmin is found as 11℃, and it is determined to be 9℃ with the consideration of carbon emission. The pinch point is determined by problem table at 88.3℃ for the hot stream and 79.3℃ for the cold stream. Under this condition, the minimum thermal utility required by the heat exchanger network is 12727.27 kW, and the required minimum cold utility is 38719.59kW. The total annual cost is reduced by 2620585.49 USD/a, and the carbon emission is reduced by 61453.50t/a. According to the design principles of pinch technology and stream matching criteria, the energy-efficient heat exchanger network structure has been obtained.
Heat exchanger network, HEN, is one of the most important parts in chemical production process. HEN optimization become an effective tool to save energy and keep sustainable development. There are many methods to optimize HEN. In principle, mathematical programming seems a comprehensive solution, while the pinch point method is still a handy tool, due to its simplicity and clear physical meaning. Special arrangement is required when phase change is considered in the system. The aim of this paper is to investigate six streams distributed in adjacent sections in an ethylene cracking process from systematic perspective, as no heat recovery is involved, and only utility is matched to meet their temperature requirement in process. Problem table is used to determine the pinch point for the design of HEN. Due to the complex phase change of the mixture in the heat exchange system, the phase change section is converted into one or more streams with constant heat capacity flow rate according to its thermal load and material characteristics, so as to determine the temperature interval. In traditional pinch point method, △Tmin is found as 11℃, and it is determined to be 9℃ with the consideration of carbon emission. The pinch point is determined by problem table at 88.3℃ for the hot stream and 79.3℃ for the cold stream. Under this condition, the minimum thermal utility required by the heat exchanger network is 12727.27 kW, and the required minimum cold utility is 38719.59kW. The total annual cost is reduced by 2620585.49 USD/a, and the carbon emission is reduced by 61453.50t/a. According to the design principles of pinch technology and stream matching criteria, the energy-efficient heat exchanger network structure has been obtained.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210505001
Abstract:
1,2,3,4-Tetrahydro-isoquinolines are a significant class of building blocks used in the pharmaceutical and agrochemical industries, and existed widely in a variety of chiral amine drugs. Among them, (S)-1-Phenyl-1,2,3,4-tetrahydro-isoquinoline ((S)-1-Ph-THIQ) is the key precursor for the synthesis of Solifenacin, a drug for the treatment of overactive bladder. Imine reductase (IRED)-catalyzed asymmetric reduction of 1-phenyl-3,4-dihydroisoquinoline (1-Ph-DHIQ) is a green and promising route towards chiral 1-Ph-THIQ. However, currently there is only a limited number of reported IREDs that could catalyze the synthesis of chiral 1-Ph-THIQ from 1-Ph-DHIQ, and they may suffer from issues including low activity, poor stereoselectivity, and substrate inhibition. In this study, we first discovered an IRED AdIR1 with considerable properties by screening a panel of IREDs and identified key residues which may affect the activity via homo-modelling and structure comparison. Protein engineering was performed to generate mutant F172Y with elevated catalytic efficiency, which was then characterized in terms of kinetic parameters and thermostability. Finally, preparative synthesis of (S)-1-Ph-THIQ on gram-scale was achieved employing mutant F172Y, demonstrating the considerable applicability of this biocatalytic route in the synthesis of (S)-1-Ph-THIQ.
1,2,3,4-Tetrahydro-isoquinolines are a significant class of building blocks used in the pharmaceutical and agrochemical industries, and existed widely in a variety of chiral amine drugs. Among them, (S)-1-Phenyl-1,2,3,4-tetrahydro-isoquinoline ((S)-1-Ph-THIQ) is the key precursor for the synthesis of Solifenacin, a drug for the treatment of overactive bladder. Imine reductase (IRED)-catalyzed asymmetric reduction of 1-phenyl-3,4-dihydroisoquinoline (1-Ph-DHIQ) is a green and promising route towards chiral 1-Ph-THIQ. However, currently there is only a limited number of reported IREDs that could catalyze the synthesis of chiral 1-Ph-THIQ from 1-Ph-DHIQ, and they may suffer from issues including low activity, poor stereoselectivity, and substrate inhibition. In this study, we first discovered an IRED AdIR1 with considerable properties by screening a panel of IREDs and identified key residues which may affect the activity via homo-modelling and structure comparison. Protein engineering was performed to generate mutant F172Y with elevated catalytic efficiency, which was then characterized in terms of kinetic parameters and thermostability. Finally, preparative synthesis of (S)-1-Ph-THIQ on gram-scale was achieved employing mutant F172Y, demonstrating the considerable applicability of this biocatalytic route in the synthesis of (S)-1-Ph-THIQ.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211024001
Abstract:
High temperature and high dielectric constant polymer nanocomposites have attracted widespread attention in pulse power system. such as mobile electronics, electric vehicles and electronic equipment ,which due to their processing flexibility, light weight, and low cost. Herein, a new type of thermosetting benzoxazole high-temperature resistant resin NPBO was synthesized by chemical methods. The chemical structure and thermal curing behavior of NPBO were studied by H-NMR , EI-MS spectra and DSC, it proved excellent thermal properties. At the same time, the stepwise reaction and chemical grafting were used to prepare polyurethane-coated barium titanate core-shell hybrid nanoparticles (PU@BT), and then the PU @BT and NPBO resins were compounded according to different components to prepare PU@BT/NPBO nanocomposites. Use scanning electron microscope (SEM) and transmission electron microscope (TEM) to observe the morphology of PU@BT. and particles are evenly coated and showed a good dispersion performance. Finally, the dielectric properties of the composite materials are measured by a broadband dielectric spectrometer. It is found that as the volume fraction of PU@BT increases from 0 to 10%, the dielectric constant of the composite material increases significantly. At 1 kHz, the dielectric constant of NPBO is 3.3, and when 10% PU@BT is added, the dielectric constant of the composite is 7.3, which is an increase of 1.21 times. The composite material provides a theoretical basis for its application in the field of dielectrics.
High temperature and high dielectric constant polymer nanocomposites have attracted widespread attention in pulse power system. such as mobile electronics, electric vehicles and electronic equipment ,which due to their processing flexibility, light weight, and low cost. Herein, a new type of thermosetting benzoxazole high-temperature resistant resin NPBO was synthesized by chemical methods. The chemical structure and thermal curing behavior of NPBO were studied by H-NMR , EI-MS spectra and DSC, it proved excellent thermal properties. At the same time, the stepwise reaction and chemical grafting were used to prepare polyurethane-coated barium titanate core-shell hybrid nanoparticles (PU@BT), and then the PU @BT and NPBO resins were compounded according to different components to prepare PU@BT/NPBO nanocomposites. Use scanning electron microscope (SEM) and transmission electron microscope (TEM) to observe the morphology of PU@BT. and particles are evenly coated and showed a good dispersion performance. Finally, the dielectric properties of the composite materials are measured by a broadband dielectric spectrometer. It is found that as the volume fraction of PU@BT increases from 0 to 10%, the dielectric constant of the composite material increases significantly. At 1 kHz, the dielectric constant of NPBO is 3.3, and when 10% PU@BT is added, the dielectric constant of the composite is 7.3, which is an increase of 1.21 times. The composite material provides a theoretical basis for its application in the field of dielectrics.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220221003
Abstract:
PBT polyether polyurethanes with different active hydrogen components were prepared by a two-step method using 3, 3-diazymoxy-tetrahydrofuran copolymer (PBT) as the soft segment of polyether polyurethanes, toluene diisocyanate (TDI) as the curing agent, diethylene glycol (DEG) as the chain extender and trimethylol propane (TMP) as the crosslinking agent. The curing reaction kinetics and mechanical properties of PBT/TDI, PBT/TDI/DEG, PBT/TDI/TMP and PBT/TDI/DEG/TMP systems were studied by Fourier Transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), electronic universal testing machine and swelling ratio test. The results show that the curing reactions of PBT / TDI, PBT / TDI / DEG, PBT / TDI / TMP / and PBT / TDI / DEG / TMP systems are second-order reactions, and the activation energies of these systems are 135.98, 165.57, 164.93 and 164.29 kJ / mol respectively. The addition of DEG can significantly increase the elongation at break of the adhesive matrix, but the tensile strength decreases; The addition of TMP can improve the tensile strength of the adhesive matrix and reduce the elongation at break; when DEG and TMP exist simultaneously, the tensile strength of the adhesive matrix increased and the elongation at break decreased. DEG and TMP can both improve the crosslinking density of the curing systems.
PBT polyether polyurethanes with different active hydrogen components were prepared by a two-step method using 3, 3-diazymoxy-tetrahydrofuran copolymer (PBT) as the soft segment of polyether polyurethanes, toluene diisocyanate (TDI) as the curing agent, diethylene glycol (DEG) as the chain extender and trimethylol propane (TMP) as the crosslinking agent. The curing reaction kinetics and mechanical properties of PBT/TDI, PBT/TDI/DEG, PBT/TDI/TMP and PBT/TDI/DEG/TMP systems were studied by Fourier Transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), electronic universal testing machine and swelling ratio test. The results show that the curing reactions of PBT / TDI, PBT / TDI / DEG, PBT / TDI / TMP / and PBT / TDI / DEG / TMP systems are second-order reactions, and the activation energies of these systems are 135.98, 165.57, 164.93 and 164.29 kJ / mol respectively. The addition of DEG can significantly increase the elongation at break of the adhesive matrix, but the tensile strength decreases; The addition of TMP can improve the tensile strength of the adhesive matrix and reduce the elongation at break; when DEG and TMP exist simultaneously, the tensile strength of the adhesive matrix increased and the elongation at break decreased. DEG and TMP can both improve the crosslinking density of the curing systems.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210530002
Abstract:
Coupled process of CaCl2 waste mineralization by reaction extraction crystallization has the function of waste recycling and mineralization of CO2, which has a broad application prospect. The key to low cost operation of reaction extraction crystallization coupling mineralization process is the effective regeneration of organic amine extractant. Solid acid catalyst was used to strengthen the pyrolysis regeneration process to realize the regeneration of organic amine.At the same time, the coupled process also producedvaluable HCl gas,, which improved the economyof the process. The effect of heating temperature, carrier gas flow, stirring speed, diluent amount and catalyst amount on the thermal dissociation of trioctylamine hydrochloride by 5A molecular sieve were investigated. The results showed that the pyrolysis of trioctylamine hydrochloride catalyzed by 5A molecular sieve conformed to the first-order kinetic model. The thermal dissociation reaction rate was accelerated and the conversion rate was increased with the increase of thermal dissociation temperature, carrier gas flow, the increase of diluent naphthalene and catalyst amount, and the effect of rotational speed on the thermal dissociation reaction was not obvious. Considering the conversion rate and energy consumption, the optimized pyrolysis conditions were as follws: reaction temperature 180 ℃, carrier gas flow 300 mL/min, rotating speed 150 rpm, mass ratio of triactylamine hydrochloride to naphthalene 1:4, mass ratio to 5A molecular sieve 10:1, 4-hour conversion rate is 95%, and 8-hour conversion rate is 99%. The 5 cycles experiments showed that 5A zeolite still had good catalytic activity.
Coupled process of CaCl2 waste mineralization by reaction extraction crystallization has the function of waste recycling and mineralization of CO2, which has a broad application prospect. The key to low cost operation of reaction extraction crystallization coupling mineralization process is the effective regeneration of organic amine extractant. Solid acid catalyst was used to strengthen the pyrolysis regeneration process to realize the regeneration of organic amine.At the same time, the coupled process also producedvaluable HCl gas,, which improved the economyof the process. The effect of heating temperature, carrier gas flow, stirring speed, diluent amount and catalyst amount on the thermal dissociation of trioctylamine hydrochloride by 5A molecular sieve were investigated. The results showed that the pyrolysis of trioctylamine hydrochloride catalyzed by 5A molecular sieve conformed to the first-order kinetic model. The thermal dissociation reaction rate was accelerated and the conversion rate was increased with the increase of thermal dissociation temperature, carrier gas flow, the increase of diluent naphthalene and catalyst amount, and the effect of rotational speed on the thermal dissociation reaction was not obvious. Considering the conversion rate and energy consumption, the optimized pyrolysis conditions were as follws: reaction temperature 180 ℃, carrier gas flow 300 mL/min, rotating speed 150 rpm, mass ratio of triactylamine hydrochloride to naphthalene 1:4, mass ratio to 5A molecular sieve 10:1, 4-hour conversion rate is 95%, and 8-hour conversion rate is 99%. The 5 cycles experiments showed that 5A zeolite still had good catalytic activity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220123003
Abstract:
The electrochemical method has been proved to be an effective method to remove ammonia, but the research on the energy consumption control has been neglected. This research uses artificial intelligence and back propagation neural network to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with a back propagation neural network (BPNN) algorithm model. First, 4 hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm to optimize the model and predict the ammonia removal rate. Through parameter analysis and comparison of response surface models, the BPNN model proposed in this paper has better coefficient of determination and lower mean square error. According to the water quality changes and the determined target of ammonia removal rate, the current control strategy in the electrochemical can be obtained through the BPNN model. Finally, the proposed intelligent control strategy is applied to the electrochemical system for ammonia removal, reducing the negative impact of water quality changes, and can also reduce energy consumption by 38% compared with the original strategy. This work proves the application potential of artificial intelligence and back propagation neural network in the electrochemical of ammonia removal, and provides the possibility to automate the water treatment process.
The electrochemical method has been proved to be an effective method to remove ammonia, but the research on the energy consumption control has been neglected. This research uses artificial intelligence and back propagation neural network to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with a back propagation neural network (BPNN) algorithm model. First, 4 hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm to optimize the model and predict the ammonia removal rate. Through parameter analysis and comparison of response surface models, the BPNN model proposed in this paper has better coefficient of determination and lower mean square error. According to the water quality changes and the determined target of ammonia removal rate, the current control strategy in the electrochemical can be obtained through the BPNN model. Finally, the proposed intelligent control strategy is applied to the electrochemical system for ammonia removal, reducing the negative impact of water quality changes, and can also reduce energy consumption by 38% compared with the original strategy. This work proves the application potential of artificial intelligence and back propagation neural network in the electrochemical of ammonia removal, and provides the possibility to automate the water treatment process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220124003
Abstract:
Antifungal effects of 7 different active components from plant essential oils (including cinnamaldehyde, citral, carvacrol, linalool, thymol, menthol, perillyl alcohol) against Fusarium graminearum (F.g.) were compared by the method of inhibiting mycelial growth in vitro. Citral, carvacrol, and thymol were selected due to their lower EC50 values and formed a compound with each other, respectively. The compound composed of carvacrol and thymol was considered the most excellent paring with the best inhibitory effect against F.g. in vitro. Additionally, the mass ratio of carvacrol and thymol in the compound was optimized and finalized the formulation of natural compound fungicide. Results showed that when the mass ratio of carvacrol and thymol was 1∶2, the fungicide had the best antifungal effect against F.g. and the synergistic index (S.I.) is 1.45, which showed a synergistic effect. The possible antifungal mechanisms of carvacrol and thymol compound were also analyzed. The prepared natural compound fungicide could change the permeability of F.g.’s cell membrane, reflected by the change in conductivity. Furthermore, the effects of the prepared natural compounded fungicide on wheat coleoptiles against F.g. were studied. It could significantly inhibit the growth of lesions on wheat coleoptiles. When fungicide, which concentration was 200 μg·mL−1, was administered to wheat coleoptiles infected by F.g., the control rate of the protective group and the curative group were 79.08 % and 84.54 %, respectively. This research provided theoretical guidance for developing natural compound fungicides with precise efficacy. The possibility of further application of active components of plant essential oils has also been discussed.
Antifungal effects of 7 different active components from plant essential oils (including cinnamaldehyde, citral, carvacrol, linalool, thymol, menthol, perillyl alcohol) against Fusarium graminearum (F.g.) were compared by the method of inhibiting mycelial growth in vitro. Citral, carvacrol, and thymol were selected due to their lower EC50 values and formed a compound with each other, respectively. The compound composed of carvacrol and thymol was considered the most excellent paring with the best inhibitory effect against F.g. in vitro. Additionally, the mass ratio of carvacrol and thymol in the compound was optimized and finalized the formulation of natural compound fungicide. Results showed that when the mass ratio of carvacrol and thymol was 1∶2, the fungicide had the best antifungal effect against F.g. and the synergistic index (S.I.) is 1.45, which showed a synergistic effect. The possible antifungal mechanisms of carvacrol and thymol compound were also analyzed. The prepared natural compound fungicide could change the permeability of F.g.’s cell membrane, reflected by the change in conductivity. Furthermore, the effects of the prepared natural compounded fungicide on wheat coleoptiles against F.g. were studied. It could significantly inhibit the growth of lesions on wheat coleoptiles. When fungicide, which concentration was 200 μg·mL−1, was administered to wheat coleoptiles infected by F.g., the control rate of the protective group and the curative group were 79.08 % and 84.54 %, respectively. This research provided theoretical guidance for developing natural compound fungicides with precise efficacy. The possibility of further application of active components of plant essential oils has also been discussed.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210225007
Abstract:
With the development of the artificial intelligence and digital audio technology, music information retrieval (MIR) has gradually become a research hotspot. Meanwhile, music emotion recognition (MER) is becoming an important research direction, due to its great research value for video soundtracks. However, there have been relatively few researching results on music emotion recognition. Although some researchers combine Mel Frequency Cepstral coefficient (MFCC) and Residual Phase (RP) to extract music emotional features and improve classification accuracy, the training models in traditional deep learning takes longer time. In order to improve the efficiency of feature mining of music emotional features, Mel frequency cepstral coefficient (MFCC) and residual phase (RP) are weighted and combined in this work to extract music emotion features so that the mining efficiency of music emotion features can be effectively improved. At the same time, in order to improve the classification accuracy of music emotion and shorten the training time of the model, by integrating the Long Short-Term Memory (LSTM) and the Broad Learning System (BLS), a new wide and deep learning network (LSTM-BLS) is further built to train music emotion recognition and classification by using LSTM as the feature mapping node of BLS. The network structure of this model makes full use of the ability of BLS to quickly process complex data. Its advantages are simple structure and short model training time, thereby improving recognition efficiency, and LSTM has excellent performance in extracting time series features from time series data. The time sequence relationship of music can be extracted so that the emotional characteristics of the music can be preserved to the greatest extent. Finally, the experimental results on the emotion dataset show that the proposed algorithm can achieve higher recognition accuracy than other complex networks and provide new feasible ideas for the music emotion recognition.
With the development of the artificial intelligence and digital audio technology, music information retrieval (MIR) has gradually become a research hotspot. Meanwhile, music emotion recognition (MER) is becoming an important research direction, due to its great research value for video soundtracks. However, there have been relatively few researching results on music emotion recognition. Although some researchers combine Mel Frequency Cepstral coefficient (MFCC) and Residual Phase (RP) to extract music emotional features and improve classification accuracy, the training models in traditional deep learning takes longer time. In order to improve the efficiency of feature mining of music emotional features, Mel frequency cepstral coefficient (MFCC) and residual phase (RP) are weighted and combined in this work to extract music emotion features so that the mining efficiency of music emotion features can be effectively improved. At the same time, in order to improve the classification accuracy of music emotion and shorten the training time of the model, by integrating the Long Short-Term Memory (LSTM) and the Broad Learning System (BLS), a new wide and deep learning network (LSTM-BLS) is further built to train music emotion recognition and classification by using LSTM as the feature mapping node of BLS. The network structure of this model makes full use of the ability of BLS to quickly process complex data. Its advantages are simple structure and short model training time, thereby improving recognition efficiency, and LSTM has excellent performance in extracting time series features from time series data. The time sequence relationship of music can be extracted so that the emotional characteristics of the music can be preserved to the greatest extent. Finally, the experimental results on the emotion dataset show that the proposed algorithm can achieve higher recognition accuracy than other complex networks and provide new feasible ideas for the music emotion recognition.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220117001
Abstract:
An improved attention algorithm based on the MaskRCNN network to improve the effect of the semantic segmentation of the test paper, because separating the printed and handwritten regions is a key step to achieve the semantic segmentation of the test paper. The algorithm embeds the Subspace Multiscale Feature Fusion (SMFF) module into the feature pyramid structure of the MaskRCNN network, which calculates attention features based on the subspace, and reduces the spatial and channel redundancy in the feature map. Fusion can effectively extract features of text regions of different sizes and enhance the correlation between features. The experimental results show that the average accuracy of the MaskRCNN network model based on the SMFF module is 15.8% and 10.2% higher than that of the original MaskRCNN network model in the target detection and semantic segmentation tasks of the test paper image dataset, which has a large performance improvement than the MaskRCNN based on the commonly used attention module.
An improved attention algorithm based on the MaskRCNN network to improve the effect of the semantic segmentation of the test paper, because separating the printed and handwritten regions is a key step to achieve the semantic segmentation of the test paper. The algorithm embeds the Subspace Multiscale Feature Fusion (SMFF) module into the feature pyramid structure of the MaskRCNN network, which calculates attention features based on the subspace, and reduces the spatial and channel redundancy in the feature map. Fusion can effectively extract features of text regions of different sizes and enhance the correlation between features. The experimental results show that the average accuracy of the MaskRCNN network model based on the SMFF module is 15.8% and 10.2% higher than that of the original MaskRCNN network model in the target detection and semantic segmentation tasks of the test paper image dataset, which has a large performance improvement than the MaskRCNN based on the commonly used attention module.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220211001
Abstract:
Due to multi-path effect and electromagnetic interference, RSS-based indoor positioning systems show poor accuracy. In this paper, an indoor positioning scheme of a heterogeneous wireless sensor network (WSN) based on the RSS and inertial measurement is proposed, which uses the distributed consensus cubature information filters with credibility evaluation to estimate target positions collaboratively. An event triggering mechanism is introduced, where the sensor nodes are awaken to serve only if their RSS constraints are satisfied. The simulation and experiment results of a mobile car show that the positioning accuracy and robustness of the proposed method improve significantly. Moreover, the distributed positioning sheme and event-triggering mechanism help to reduce network energy consumption effectively.
Due to multi-path effect and electromagnetic interference, RSS-based indoor positioning systems show poor accuracy. In this paper, an indoor positioning scheme of a heterogeneous wireless sensor network (WSN) based on the RSS and inertial measurement is proposed, which uses the distributed consensus cubature information filters with credibility evaluation to estimate target positions collaboratively. An event triggering mechanism is introduced, where the sensor nodes are awaken to serve only if their RSS constraints are satisfied. The simulation and experiment results of a mobile car show that the positioning accuracy and robustness of the proposed method improve significantly. Moreover, the distributed positioning sheme and event-triggering mechanism help to reduce network energy consumption effectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211118002
Abstract:
Traditional linear multivariate Granger causality test introduces conditional variables to determine whether the causal relationships exist between every two variables or not. However, the traditional way of selecting conditional variables is manual, which lacks of reasonable rules. To deal with the problem, an improved nonlinear multivariate Granger causality test method with selecting conditional variables is proposed in this paper. The proposed method in this paper combines traditional Granger test and multivariable Granger test. This method uses nonlinear Granger causality test to construct a preliminary structure by analyzing the potential relationships between variables to determine which variables are suitable as conditional variables, then nonlinear multivariable Granger causality test can be further used on these preprocessed conditional variables; Two kinds of topological structures are introduced to avoid the repeated inspection of some real relationships that do not produce pseudo causality problems. In our method, support vector regression is used as the way to cope with the nonlinearity. The experimental results on numerical simulation and wastewater treatment benchmark simulation model show that the influence of irrelevant variables is reduced by the proposed method in this paper via selecting condition variables and the causal relationship between variables could be analyzed more accurately. Moreover, the proposed method can adapt to nonlinear conditions and has better performance in terms of computational intensity.
Traditional linear multivariate Granger causality test introduces conditional variables to determine whether the causal relationships exist between every two variables or not. However, the traditional way of selecting conditional variables is manual, which lacks of reasonable rules. To deal with the problem, an improved nonlinear multivariate Granger causality test method with selecting conditional variables is proposed in this paper. The proposed method in this paper combines traditional Granger test and multivariable Granger test. This method uses nonlinear Granger causality test to construct a preliminary structure by analyzing the potential relationships between variables to determine which variables are suitable as conditional variables, then nonlinear multivariable Granger causality test can be further used on these preprocessed conditional variables; Two kinds of topological structures are introduced to avoid the repeated inspection of some real relationships that do not produce pseudo causality problems. In our method, support vector regression is used as the way to cope with the nonlinearity. The experimental results on numerical simulation and wastewater treatment benchmark simulation model show that the influence of irrelevant variables is reduced by the proposed method in this paper via selecting condition variables and the causal relationship between variables could be analyzed more accurately. Moreover, the proposed method can adapt to nonlinear conditions and has better performance in terms of computational intensity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210319001
Abstract:
Supersonic impinging jets are widely occurred in aeronautics, especially in vertically takeoff and landing of aircrafts. In this paper, large eddy simulation of a supersonic jet impinging on a large plate is presented. The nozzle-to-plate space is 2.08 times of nozzle exit diameter, and the nozzle-pressure ratio is equal to 4.03. Unit ring vortex forcing method for the inflow is used in LES to trigger the turbulence. Results indicate that the position and strength of shock wave are periodical. As the Mach disc oscillates in the axial direction, the underexpanded gas has more sufficient space for expansion, so it can reach a higher speed. In the wall jet zone, the large-scale annular vortical structures are continuous. Along the radial direction, the large-scale vortices break up and generate smaller vortices. Sound wave propagation to the upstream from the wall is observed. After reflection from the lip of the nozzle, it propagates to the downstream near the shear layer, thus a feedback loop is formed. It dominates the generation of monotone. Fast Fourier transform is applied to the pressure fluctuation. The results verify that the feedback loop has the same frequency as the tone. Proper orthogonal decomposition is employed to analyze the velocity fluctuation. The modes and their energy contribution rates are calculated. The jet boundary, Mach disc and the oblique shock wave, the recirculation area and the wall jet all have strong correlation. The generation and evolution of large scale turbulence structures in turbulent field are presented and analyzed.
Supersonic impinging jets are widely occurred in aeronautics, especially in vertically takeoff and landing of aircrafts. In this paper, large eddy simulation of a supersonic jet impinging on a large plate is presented. The nozzle-to-plate space is 2.08 times of nozzle exit diameter, and the nozzle-pressure ratio is equal to 4.03. Unit ring vortex forcing method for the inflow is used in LES to trigger the turbulence. Results indicate that the position and strength of shock wave are periodical. As the Mach disc oscillates in the axial direction, the underexpanded gas has more sufficient space for expansion, so it can reach a higher speed. In the wall jet zone, the large-scale annular vortical structures are continuous. Along the radial direction, the large-scale vortices break up and generate smaller vortices. Sound wave propagation to the upstream from the wall is observed. After reflection from the lip of the nozzle, it propagates to the downstream near the shear layer, thus a feedback loop is formed. It dominates the generation of monotone. Fast Fourier transform is applied to the pressure fluctuation. The results verify that the feedback loop has the same frequency as the tone. Proper orthogonal decomposition is employed to analyze the velocity fluctuation. The modes and their energy contribution rates are calculated. The jet boundary, Mach disc and the oblique shock wave, the recirculation area and the wall jet all have strong correlation. The generation and evolution of large scale turbulence structures in turbulent field are presented and analyzed.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220115004
Abstract:
MiRNA is a single-stranded and small non-coding RNA, which is closely related to human diseases. Predicting miRNA-disease associations can help understand the pathogenesis of diseases at the molecular level, so as to provide basis for studying the prognosis, diagnosis, evaluation and treatment of diseases. In miRNA-disease association prediction, most methods used miRNA functional similarity and disease semantic similarity as input, they ignored the miRNA sequence similarity, disease functional similarity and hamming similarity. And in the feature extraction process, they not considered the information complementarity between the linear features and nonlinear features, which would affect the quality of feature extraction of miRNA and disease. Therefore, we propose a novel miRNA-disease association prediction model GCNMSF. First, we introduce the miRNA sequence similarity, disease semantic similarity and hamming similarity, and use similarity kernel fusion method to integrate multi-source similarities of miRNA and disease respectively. Then, we use the graph convolutional network to learn nonlinear features. And the convolutional attention block is embedded into GCN to optimize feature distribution. At the same time, the non-negative matrix factorization method is introduced to learn linear features of miRNA and disease to enrich the feature space which can improve the ability of predicting miRNA-disease associations. Finally, we fused the linear and nonlinear features of miRNA and disease to predict miRNA-disease associations. We use five-fold cross validation to evaluate GCNMSF and the experimental results show that our model is better than the existing methods. In addition, we conduct ablation experiment and case studies to evaluate the effectiveness and applicability of the model. The results of ablation experiment verify the fusion of multi-source similarity information and the combination of linear and nonlinear features are helpful for miRNA-disease association prediction. The case studies of lung and breast cancers further confirmed that GCNMSF can not only predict the potential miRNA-disease associations, but also discover the miRNA-disease associations of unknown diseases.
MiRNA is a single-stranded and small non-coding RNA, which is closely related to human diseases. Predicting miRNA-disease associations can help understand the pathogenesis of diseases at the molecular level, so as to provide basis for studying the prognosis, diagnosis, evaluation and treatment of diseases. In miRNA-disease association prediction, most methods used miRNA functional similarity and disease semantic similarity as input, they ignored the miRNA sequence similarity, disease functional similarity and hamming similarity. And in the feature extraction process, they not considered the information complementarity between the linear features and nonlinear features, which would affect the quality of feature extraction of miRNA and disease. Therefore, we propose a novel miRNA-disease association prediction model GCNMSF. First, we introduce the miRNA sequence similarity, disease semantic similarity and hamming similarity, and use similarity kernel fusion method to integrate multi-source similarities of miRNA and disease respectively. Then, we use the graph convolutional network to learn nonlinear features. And the convolutional attention block is embedded into GCN to optimize feature distribution. At the same time, the non-negative matrix factorization method is introduced to learn linear features of miRNA and disease to enrich the feature space which can improve the ability of predicting miRNA-disease associations. Finally, we fused the linear and nonlinear features of miRNA and disease to predict miRNA-disease associations. We use five-fold cross validation to evaluate GCNMSF and the experimental results show that our model is better than the existing methods. In addition, we conduct ablation experiment and case studies to evaluate the effectiveness and applicability of the model. The results of ablation experiment verify the fusion of multi-source similarity information and the combination of linear and nonlinear features are helpful for miRNA-disease association prediction. The case studies of lung and breast cancers further confirmed that GCNMSF can not only predict the potential miRNA-disease associations, but also discover the miRNA-disease associations of unknown diseases.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211221001
Abstract:
One of the important way of 3D model making is 3D reconstruction. At present, 3D scene reconstruction with moving object interference is a research hotspot. To solve this problem, this paper proposes a 3D reconstruction framework named ORBTSDF-SCNet. This framework combines SLAM (Simultaneous Localization And Mapping), TSDF(Truncated Signed Distance Function) and SCNet(Sample Consistency Networks) technology to complete 3D scene reconstruction with moving object interference. In this framework, firstly, aiming at the fact that SLAM system can only output point cloud and can not directly generate 3D model, this paper proposes a 3D reconstruction method ORBTSDF.In this method, depth camera or binocular camera obtains RGBD image of the moving objects and scene, the tracking thread of ORB_SLAM2 is applied to obtains pose information in real time, the surface reconstruction algorithm TSDF is adopted to realize 3D model reconstruction combined with depth image.At the same time, in order to eliminate the interference of moving objects in 3D scene reconstruction, such as image smear, low accuracy or reconstruction failure etc., a deep learning instance segmentation network SCNet is used to detect and segment moving objects. By combining with some optimization strategies, the error of detection and instance segmentation , the alignment error of depth map and RGB map are reduced. When the instance of the moving object is removed, the RGBD image is transmitted back to the part of ORBTSDF to form a 3D scene reconstruction without moving objects. Comparative experiments on ICL-NUM and TUM datasets shows the effectiveness of the proposed method.
One of the important way of 3D model making is 3D reconstruction. At present, 3D scene reconstruction with moving object interference is a research hotspot. To solve this problem, this paper proposes a 3D reconstruction framework named ORBTSDF-SCNet. This framework combines SLAM (Simultaneous Localization And Mapping), TSDF(Truncated Signed Distance Function) and SCNet(Sample Consistency Networks) technology to complete 3D scene reconstruction with moving object interference. In this framework, firstly, aiming at the fact that SLAM system can only output point cloud and can not directly generate 3D model, this paper proposes a 3D reconstruction method ORBTSDF.In this method, depth camera or binocular camera obtains RGBD image of the moving objects and scene, the tracking thread of ORB_SLAM2 is applied to obtains pose information in real time, the surface reconstruction algorithm TSDF is adopted to realize 3D model reconstruction combined with depth image.At the same time, in order to eliminate the interference of moving objects in 3D scene reconstruction, such as image smear, low accuracy or reconstruction failure etc., a deep learning instance segmentation network SCNet is used to detect and segment moving objects. By combining with some optimization strategies, the error of detection and instance segmentation , the alignment error of depth map and RGB map are reduced. When the instance of the moving object is removed, the RGBD image is transmitted back to the part of ORBTSDF to form a 3D scene reconstruction without moving objects. Comparative experiments on ICL-NUM and TUM datasets shows the effectiveness of the proposed method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107001
Abstract:
In order to study the variation of the crack growth behavior of oxygen-sensitive materials under the interaction of creep-oxidation, the physical mechanism of dynamic embrittlement was used to establish a mathematical model of creep coupled oxidation damage. The creep-oxidation crack growth of nickel-based alloy was analyzed by Abaqus and Voronoi diagram techniques. Meanwhile, the effects of load level, Grain boundary direction at initial crack, oxygen diffusion rate and creep properties on crack growth were analyzed. Results show that when the load is small, the oxidation promotion effect is significant when the crack propagates; creep gradually tends to dominate when the load increases. Since oxygen is easier to diffuse, the crack initiation time of straight grain boundary cracks is shorter than that of oblique grain boundary cracks. As the oxygen diffusion rate increases, the crack initiation time decreases, and the increase of load will cause the crack initiation time of the straight grain boundary cracks to stabilize. Creep constitutive parameters have almost no effect on the law of crack initiation time with load. The better the creep property of the materials or service conditions, the more obvious the effect of oxidation.
In order to study the variation of the crack growth behavior of oxygen-sensitive materials under the interaction of creep-oxidation, the physical mechanism of dynamic embrittlement was used to establish a mathematical model of creep coupled oxidation damage. The creep-oxidation crack growth of nickel-based alloy was analyzed by Abaqus and Voronoi diagram techniques. Meanwhile, the effects of load level, Grain boundary direction at initial crack, oxygen diffusion rate and creep properties on crack growth were analyzed. Results show that when the load is small, the oxidation promotion effect is significant when the crack propagates; creep gradually tends to dominate when the load increases. Since oxygen is easier to diffuse, the crack initiation time of straight grain boundary cracks is shorter than that of oblique grain boundary cracks. As the oxygen diffusion rate increases, the crack initiation time decreases, and the increase of load will cause the crack initiation time of the straight grain boundary cracks to stabilize. Creep constitutive parameters have almost no effect on the law of crack initiation time with load. The better the creep property of the materials or service conditions, the more obvious the effect of oxidation.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220120001
Abstract:
In order to ensure the safe and stable operation of the production process and avoid losses due to failures, timely detection of abnormal conditions and accurate diagnosis of abnormal conditions are of very important research significance. Aiming at the complexity of the chemical process, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in the chemical production process. This model effectively combines the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, reducing the loss of feature information and achieving a higher fault detection rate. The one-dimensional dense convolutional neural network is used as the main body of CNN, combined with the LSTM network's sensitivity to sequence information changes, to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions. Taking the Eastman Process of Tennessee (TE) process in Tennessee as the research object, the PLSTM-CNN model is significantly better than the traditional recurrent neural network in indicators such as the average failure detection rate and the false negative rate.
In order to ensure the safe and stable operation of the production process and avoid losses due to failures, timely detection of abnormal conditions and accurate diagnosis of abnormal conditions are of very important research significance. Aiming at the complexity of the chemical process, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in the chemical production process. This model effectively combines the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, reducing the loss of feature information and achieving a higher fault detection rate. The one-dimensional dense convolutional neural network is used as the main body of CNN, combined with the LSTM network's sensitivity to sequence information changes, to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions. Taking the Eastman Process of Tennessee (TE) process in Tennessee as the research object, the PLSTM-CNN model is significantly better than the traditional recurrent neural network in indicators such as the average failure detection rate and the false negative rate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210928002
Abstract:
Aimed at solving the problems of complex background and variable lighting in laboratory scene understanding, based on the complementary characteristics of RGB image information and depth image information in scene understanding, a perceptual attention and lightweight spatial fusion network model is proposed. In the perceptual attention module of this model, the RGB image and the depth image in the network are used to implement the multi-level assistance to the RGB information by the depth information in a weighted mode. In the lightweight spatial pyramid pooling module, increasing the level of the joint atrous space convolution not only effectively aggregates multi-scale features, but also reduces the parameter amount of the traditional spatial pyramid pooling module by around 89%, enabling the RGB image information and depth image information to fuse more adequately. The model performs better on the two public datasets of indoor scenes than among the classic algorithm. The analysis of each module through ablation experiments verifies that the mean intersections over union of the algorithm proposed in this paper increase by 4.3% and 3.5% respectively. Finally, a test based on the dataset of biological laboratory on the more complex scenes is carried out, which shows that the model can effectively realize the scene understanding of biological laboratory.
Aimed at solving the problems of complex background and variable lighting in laboratory scene understanding, based on the complementary characteristics of RGB image information and depth image information in scene understanding, a perceptual attention and lightweight spatial fusion network model is proposed. In the perceptual attention module of this model, the RGB image and the depth image in the network are used to implement the multi-level assistance to the RGB information by the depth information in a weighted mode. In the lightweight spatial pyramid pooling module, increasing the level of the joint atrous space convolution not only effectively aggregates multi-scale features, but also reduces the parameter amount of the traditional spatial pyramid pooling module by around 89%, enabling the RGB image information and depth image information to fuse more adequately. The model performs better on the two public datasets of indoor scenes than among the classic algorithm. The analysis of each module through ablation experiments verifies that the mean intersections over union of the algorithm proposed in this paper increase by 4.3% and 3.5% respectively. Finally, a test based on the dataset of biological laboratory on the more complex scenes is carried out, which shows that the model can effectively realize the scene understanding of biological laboratory.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210922003
Abstract:
Mental workload can be employed as an indicator of the brain effort. It reflects people’s capability of processing information when performing a task. Recently, mental workload assessment has been widely studied in various tasks, such as simulated flight tasks, cognitive tasks and so on. The traditional methods of evaluating mental workload include subjective scale method, task performance method and physiological signal parameters method. NIRS has many advantages over other physiological techniques as it has better spatial resolution than EEG and better temporal resolution than fMRI. Besides, it is portable and lightweight with simple data acquisition and less exposed to electrical artifacts. In this paper, near-infrared spectrum signals (NIRS) are selected to build a mental workload assessment model. In recent years, deep learning with convolutional neural networks has revolutionized signal processing through end-to-end learning due to its efficiency and convenience. In order to eliminate the redundant information and extract features from the multi-channel near-infrared spectrum signals (NIRS), a novel mental workload recognition model was created based on the hybrid autoencoders. First, the original signals were sent to the stack autoencoder for channel dimensionality reduction, then these processed signals were fed to the convolutional autoencoder to extract the abstract features. Then we employed three base classifiers, i.e., the Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), for building models. Finally, the integration strategies of soft voting and hard voting were applied to improve the assessment accuracy for mental workload. The results show that proper way of compressing signal channels helps to improve the recognition accuracy of the model. The best accuracy of our proposed model for classifying three levels of mental workload can reach 95.12%, which is significantly improved compared to similar studies.
Mental workload can be employed as an indicator of the brain effort. It reflects people’s capability of processing information when performing a task. Recently, mental workload assessment has been widely studied in various tasks, such as simulated flight tasks, cognitive tasks and so on. The traditional methods of evaluating mental workload include subjective scale method, task performance method and physiological signal parameters method. NIRS has many advantages over other physiological techniques as it has better spatial resolution than EEG and better temporal resolution than fMRI. Besides, it is portable and lightweight with simple data acquisition and less exposed to electrical artifacts. In this paper, near-infrared spectrum signals (NIRS) are selected to build a mental workload assessment model. In recent years, deep learning with convolutional neural networks has revolutionized signal processing through end-to-end learning due to its efficiency and convenience. In order to eliminate the redundant information and extract features from the multi-channel near-infrared spectrum signals (NIRS), a novel mental workload recognition model was created based on the hybrid autoencoders. First, the original signals were sent to the stack autoencoder for channel dimensionality reduction, then these processed signals were fed to the convolutional autoencoder to extract the abstract features. Then we employed three base classifiers, i.e., the Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), for building models. Finally, the integration strategies of soft voting and hard voting were applied to improve the assessment accuracy for mental workload. The results show that proper way of compressing signal channels helps to improve the recognition accuracy of the model. The best accuracy of our proposed model for classifying three levels of mental workload can reach 95.12%, which is significantly improved compared to similar studies.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210916002
Abstract:
Optical chemical structure recognition from scientific publications is an essential part of rediscovering a chemical structure. Rule-based approaches and emerging deep learning methods both face certain problems, such as a low recognition rate. In this paper, we propose DeepOCSR, a deep learning method for optical chemical structure recognition. Based on the encoder–decoder architecture, this method introduces Transformer and ResNeSt models for converting chemical structure images from publications into SMILES sequences. To train and verify our method, two novel chemical structure datasets were constructed, one of which contained common substituents in the chemical literature. Our proposed method has been extensively tested against existing publicly available deep-learning approaches. The experimental results show that our method outperforms the compared approaches in several pivotal evaluation metrics, including similarity and validity, proving the effectiveness of our method.
Optical chemical structure recognition from scientific publications is an essential part of rediscovering a chemical structure. Rule-based approaches and emerging deep learning methods both face certain problems, such as a low recognition rate. In this paper, we propose DeepOCSR, a deep learning method for optical chemical structure recognition. Based on the encoder–decoder architecture, this method introduces Transformer and ResNeSt models for converting chemical structure images from publications into SMILES sequences. To train and verify our method, two novel chemical structure datasets were constructed, one of which contained common substituents in the chemical literature. Our proposed method has been extensively tested against existing publicly available deep-learning approaches. The experimental results show that our method outperforms the compared approaches in several pivotal evaluation metrics, including similarity and validity, proving the effectiveness of our method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210920001
Abstract:
In the process of the slurry breakup, the throat of the liquid bridge keeps shrinking. When the minimum characteristic diameter of the liquid bridge is close to the size of the solid particle, the slurry will exhibit the complex variation characteristics in the process of time, which is significantly different from pure liquid. Therefore, the study of the micro breakup characteristics of the slurry is helpful to reveal the atomization mechanism and improve the simulation model of slurry. Here Shenhua coal and Huadian coal are used as the raw materials to prepare coal water slurry with a mass concentration range of 58% -62 %(mass fraction). The influence of the physical and chemical parameters of coal water slurry on its microscopic breakup process has been studied by the rotary rheometer, the static surface tension meter, the dynamic surface tension meter, the high-speed camera, the image processing software, and so on. Coal water slurry is a shear thinning non-Newtonian fluid. So in this paper the Herschel-Bulkley model is used to establish the rheological relationship of coal water slurry. Unlike the static surface tension, the dynamic surface tension of coal water slurry decreases with the increase of the characteristic bubble time. After increasing, the minimum surface tension appears around between 100 ms and 200 ms. Finally based on the rheological properties and the dynamic surface tension of coal water slurry, the relationship between the change of the characteristic diameter of coal water slurry micro-breakup and the time of breakup is obtained.
In the process of the slurry breakup, the throat of the liquid bridge keeps shrinking. When the minimum characteristic diameter of the liquid bridge is close to the size of the solid particle, the slurry will exhibit the complex variation characteristics in the process of time, which is significantly different from pure liquid. Therefore, the study of the micro breakup characteristics of the slurry is helpful to reveal the atomization mechanism and improve the simulation model of slurry. Here Shenhua coal and Huadian coal are used as the raw materials to prepare coal water slurry with a mass concentration range of 58% -62 %(mass fraction). The influence of the physical and chemical parameters of coal water slurry on its microscopic breakup process has been studied by the rotary rheometer, the static surface tension meter, the dynamic surface tension meter, the high-speed camera, the image processing software, and so on. Coal water slurry is a shear thinning non-Newtonian fluid. So in this paper the Herschel-Bulkley model is used to establish the rheological relationship of coal water slurry. Unlike the static surface tension, the dynamic surface tension of coal water slurry decreases with the increase of the characteristic bubble time. After increasing, the minimum surface tension appears around between 100 ms and 200 ms. Finally based on the rheological properties and the dynamic surface tension of coal water slurry, the relationship between the change of the characteristic diameter of coal water slurry micro-breakup and the time of breakup is obtained.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211014002
Abstract:
Pitch-based spherical activated carbon (PSAC) is widely used in medical treatment, environmental protection and other fields because of its advantages of high specific surface area, high mechanical strength, high packing intensity and low fluid resistance. A series of SnOx-CeOx/PSAC catalysts were prepared by impregnation method using PSAC prepared from high softening point petroleum pitch as support. And their catalytic performance were evaluated by the low-temperature selective catalytic reduction (SCR) of NO with NH3. The obtained samples were mainly characterized by nitrogen adsorption/desorption, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that SnOx-CeOx/PSAC catalyst exhibits higher SCR activity in comparison with CeOx/PSAC catalyst, and the trend of NO conversion firstly increases and then decreases with the increasing of metal loading. The Sn(5%)Ce(13%)/PSAC catalyst exhibits the highest NO removal activity, where the highest NO conversion can reach about 98% in the temperature range of 100~300 oC. The reason is mainly attributed to the improved dispersion of cerium oxide on the surface of PSAC by addition of SnOx, and the formation of solid solution between SnOx and CeOx with the fluorite-type structure, which may be caused by the incorporation of Sn4+ into the crystal lattice of CeO2. Furthermore, there are a certain amount of Ce3+, and higher percentage of surface chemisorbed oxygen on the catalyst surface because of the synergistic effect between tin and cerium oxides. These factors result in the excellent NH3-SCR performance of the Sn(5%)Ce(13%)/PSAC catalyst. Compared with CeOx/PSAC catalyst, SnOx-CeOx/PSAC catalyst exhibits a higher resistance to SO2 poisoning. NO conversion of Sn(5%)Ce(13%)/PSAC catalyst is still about 80% at 260 oC after the introduction of SO2 in the feed gas for 420 min.
Pitch-based spherical activated carbon (PSAC) is widely used in medical treatment, environmental protection and other fields because of its advantages of high specific surface area, high mechanical strength, high packing intensity and low fluid resistance. A series of SnOx-CeOx/PSAC catalysts were prepared by impregnation method using PSAC prepared from high softening point petroleum pitch as support. And their catalytic performance were evaluated by the low-temperature selective catalytic reduction (SCR) of NO with NH3. The obtained samples were mainly characterized by nitrogen adsorption/desorption, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that SnOx-CeOx/PSAC catalyst exhibits higher SCR activity in comparison with CeOx/PSAC catalyst, and the trend of NO conversion firstly increases and then decreases with the increasing of metal loading. The Sn(5%)Ce(13%)/PSAC catalyst exhibits the highest NO removal activity, where the highest NO conversion can reach about 98% in the temperature range of 100~300 oC. The reason is mainly attributed to the improved dispersion of cerium oxide on the surface of PSAC by addition of SnOx, and the formation of solid solution between SnOx and CeOx with the fluorite-type structure, which may be caused by the incorporation of Sn4+ into the crystal lattice of CeO2. Furthermore, there are a certain amount of Ce3+, and higher percentage of surface chemisorbed oxygen on the catalyst surface because of the synergistic effect between tin and cerium oxides. These factors result in the excellent NH3-SCR performance of the Sn(5%)Ce(13%)/PSAC catalyst. Compared with CeOx/PSAC catalyst, SnOx-CeOx/PSAC catalyst exhibits a higher resistance to SO2 poisoning. NO conversion of Sn(5%)Ce(13%)/PSAC catalyst is still about 80% at 260 oC after the introduction of SO2 in the feed gas for 420 min.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211111001
Abstract:
Solid dispersions (SDs) is one of the main technologies to improve the dissolution of poorly soluble drugs in drug research and development. However, supersaturated high-energy amorphous state drug in SDs are often associated with a tendency to recrystallized during long term storage. The carrier of SDs plays a key role in maintaining the amorphous state of the drug. Traditionally, the screening of the carrier in the development process is a time-consuming process. The effect of polymer carriers on the long-term physical stability of the amorphous state of Erlotinib (ERL) in SDs were studied. ERL SDs were prepared with different ratios of HPMC, HPMCAS, PVP, PVP/VA, Eudragit, and Soluplus by solvent evaporation method. Through the Flory-Huggins interaction parameter χ and anti-solvent microscopic observations, the compatibility of the polymer with ERL and polymer's influence on the crystallization of ERL were predicted. Focused Beam Reflectance Measurement (FBRM) system was used to analyzed morphologically the regulating effect of the polymer on the crystallization process. Then the amorphous state formed by different proportions of SDs were characterized by Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy(FT-IR). The physical stability of amorphous state of SDs in accelerated test condition were determined by PXRD. The results showed that HPMC is a suitable carrier for the preparation of ERL amorphous SDs. The combination of the interaction parameter χ, anti-solvent microscopic observation and FBRM analysis is an effective way to select suitable carrier for amorphous SDs. A full understanding of the impact of polymers on amorphous SDs is of positive significance for the rapid development of poorly soluble drugs.
Solid dispersions (SDs) is one of the main technologies to improve the dissolution of poorly soluble drugs in drug research and development. However, supersaturated high-energy amorphous state drug in SDs are often associated with a tendency to recrystallized during long term storage. The carrier of SDs plays a key role in maintaining the amorphous state of the drug. Traditionally, the screening of the carrier in the development process is a time-consuming process. The effect of polymer carriers on the long-term physical stability of the amorphous state of Erlotinib (ERL) in SDs were studied. ERL SDs were prepared with different ratios of HPMC, HPMCAS, PVP, PVP/VA, Eudragit, and Soluplus by solvent evaporation method. Through the Flory-Huggins interaction parameter χ and anti-solvent microscopic observations, the compatibility of the polymer with ERL and polymer's influence on the crystallization of ERL were predicted. Focused Beam Reflectance Measurement (FBRM) system was used to analyzed morphologically the regulating effect of the polymer on the crystallization process. Then the amorphous state formed by different proportions of SDs were characterized by Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy(FT-IR). The physical stability of amorphous state of SDs in accelerated test condition were determined by PXRD. The results showed that HPMC is a suitable carrier for the preparation of ERL amorphous SDs. The combination of the interaction parameter χ, anti-solvent microscopic observation and FBRM analysis is an effective way to select suitable carrier for amorphous SDs. A full understanding of the impact of polymers on amorphous SDs is of positive significance for the rapid development of poorly soluble drugs.
, Available online
, doi: 10.14135/j.cnki.1006-3080、20211217001
Abstract:
Anionic surfactant-sodium dodecyl benzene sulfonate (SDBS), sodium lauryl sulfate (SLS), sodium N-lauroyl sarcosinate (NLSS), sodium dodecyl sulfonate (SDS), zwitterionic surfactant-cocoamide propylene betaine (CAB), and non-ionic surfactant-fatty alcohol polyoxyethylene ether (AEO), Tween 20(T20) have been used for combination experiment in binary surfactant systems. Foamability which defined by the ratio of foam volume and initial liquid volume has been characterized as well as the relevant surface activity parameters such as .interaction parameter(βm), the mole fraction in micelle and surface(Xa), and the Gibbs free energy(ΔGm0). Further exploration was performed to evaluate the effect of the interaction of the binary surfactant mixture on the foamability. The results show that during the combination process of anionic and zwitterionic surfactants, coagulation occurs when the molar concentration ratio closes to 1:1. When the surfactant with linear hydrophobic group and the nonionic surface with larger hydrophilic group are mixed, competitive adsorption occurs, which weakens the foamability of the surfactant. Synergistic effects can be produced when anionic surfactants with similar structures are compounded. The mixture of sodium dodecylbenzene sulfonate (SDBS) and sodium lauroyl sarcosinate (NLSS) has the best foaming performance when the molar concentration ratio is 3:7, which has the best foam quality(Q=12.9). The interaction parameter is βm=-9.83 (XSDBSm=0.4) calculated by the theory of synergy, which has a strong Synergistic effect. The Gibbs free energy is ΔGm0=-13.6kJ/mol, proving that the formation of micelles is a spontaneous process.
Anionic surfactant-sodium dodecyl benzene sulfonate (SDBS), sodium lauryl sulfate (SLS), sodium N-lauroyl sarcosinate (NLSS), sodium dodecyl sulfonate (SDS), zwitterionic surfactant-cocoamide propylene betaine (CAB), and non-ionic surfactant-fatty alcohol polyoxyethylene ether (AEO), Tween 20(T20) have been used for combination experiment in binary surfactant systems. Foamability which defined by the ratio of foam volume and initial liquid volume has been characterized as well as the relevant surface activity parameters such as .interaction parameter(βm), the mole fraction in micelle and surface(Xa), and the Gibbs free energy(ΔGm0). Further exploration was performed to evaluate the effect of the interaction of the binary surfactant mixture on the foamability. The results show that during the combination process of anionic and zwitterionic surfactants, coagulation occurs when the molar concentration ratio closes to 1:1. When the surfactant with linear hydrophobic group and the nonionic surface with larger hydrophilic group are mixed, competitive adsorption occurs, which weakens the foamability of the surfactant. Synergistic effects can be produced when anionic surfactants with similar structures are compounded. The mixture of sodium dodecylbenzene sulfonate (SDBS) and sodium lauroyl sarcosinate (NLSS) has the best foaming performance when the molar concentration ratio is 3:7, which has the best foam quality(Q=12.9). The interaction parameter is βm=-9.83 (XSDBSm=0.4) calculated by the theory of synergy, which has a strong Synergistic effect. The Gibbs free energy is ΔGm0=-13.6kJ/mol, proving that the formation of micelles is a spontaneous process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211029001
Abstract:
Based on the method of Grand Canonical Monte Carlo and molecular dynamic simulation, the adsorption, diffusion and permeation behavior of H2O in PBT polyether polyurethane (PUPBT) elastomer were simulated. The results show that the heat of adsorption of H2O on PUPBT at 298, 318, 338 and 358k is 41.15, 40.23, 36.84 and 34.16 kJ/mol respectively in the fugacity range of 0~1000 kPa. The adsorption equilibrium has been reached when the temperature is 298 K. With the increase of temperature, the adsorption capacity of PUPBT toward H2O is declined. The adsorption of H2O on PUPBT is not a uniform adsorption, H2O molecules Adsorbed to the lower potential energy region near the center of the holes in the polymer. The results of diffusion simulation show that under the environmental conditions of 298, 318, 338 and 358 K when the pressure is 101 kPa, the free volume fraction of H2O/PUPBT was 14.37%, 15.55%, 17.00% and 17.85%, respectively, when the diffusion coefficients of H2O into PUPBT are 1.488×10−6、1.999×10−6、3.086×10−6 and 3.462×10-6 cm2/s. And the diffusion of H2O into PUPBT is not a uniform diffusion, but a jump-motion diffusion in free volumes. The solubility coefficient of H2O molecules in PUPBT is the major factor that affects the permeability coefficient of the system. With the increase of temperature, the permeability coefficient of H2O into PUPBT decreases gradually.
Based on the method of Grand Canonical Monte Carlo and molecular dynamic simulation, the adsorption, diffusion and permeation behavior of H2O in PBT polyether polyurethane (PUPBT) elastomer were simulated. The results show that the heat of adsorption of H2O on PUPBT at 298, 318, 338 and 358k is 41.15, 40.23, 36.84 and 34.16 kJ/mol respectively in the fugacity range of 0~1000 kPa. The adsorption equilibrium has been reached when the temperature is 298 K. With the increase of temperature, the adsorption capacity of PUPBT toward H2O is declined. The adsorption of H2O on PUPBT is not a uniform adsorption, H2O molecules Adsorbed to the lower potential energy region near the center of the holes in the polymer. The results of diffusion simulation show that under the environmental conditions of 298, 318, 338 and 358 K when the pressure is 101 kPa, the free volume fraction of H2O/PUPBT was 14.37%, 15.55%, 17.00% and 17.85%, respectively, when the diffusion coefficients of H2O into PUPBT are 1.488×10−6、1.999×10−6、3.086×10−6 and 3.462×10-6 cm2/s. And the diffusion of H2O into PUPBT is not a uniform diffusion, but a jump-motion diffusion in free volumes. The solubility coefficient of H2O molecules in PUPBT is the major factor that affects the permeability coefficient of the system. With the increase of temperature, the permeability coefficient of H2O into PUPBT decreases gradually.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211130001
Abstract:
A gas-liquid-solid three-phase ebullated-bed reactor with an inner diameter of 286 mm and a height of 7.2 m was used to conduct intermittent liquid phase and continuous gas phase operations. The three-phase system was composed of water, air, and Al2O3 spherical particles. The macroscopic liquid circulation velocity was measured at a solid holdup of 12% ~ 30% and the superficial gas velocity of 0.086 ~ 0.216 m/s. In this study, the tracer method was used to determine the concentration curves of multiple tracers at the inlet and outlet of the reactor. The axial dispersion coefficient of the liquid phase was solved by MATLAB software. Substituting it into the definition of Einstein's diffusion coefficient to get the liquid circulation velocity. The experimental results show that at a certain solid holdup, as the superficial gas velocity increases, small bubbles gradually gather into large bubbles. The rising velocity of the bubbles continues to increase, and the liquid circulation velocity also increases accordingly. Increasing the superficial gas velocity can significantly increase the liquid circulation velocity. At a constant superficial gas velocity, as the solid holdup increases, the large bubble holdup increases, and the bubble rise velocity also increases. As a result, the liquid circulation velocity also increases. However, because the increase of solid holdup will hinder the circulation of liquid to a certain extent, as the solid holdup increases, the increase of the liquid circulation velocity continues to decrease. It shows that there may be an optimal value for the solid holdup.
A gas-liquid-solid three-phase ebullated-bed reactor with an inner diameter of 286 mm and a height of 7.2 m was used to conduct intermittent liquid phase and continuous gas phase operations. The three-phase system was composed of water, air, and Al2O3 spherical particles. The macroscopic liquid circulation velocity was measured at a solid holdup of 12% ~ 30% and the superficial gas velocity of 0.086 ~ 0.216 m/s. In this study, the tracer method was used to determine the concentration curves of multiple tracers at the inlet and outlet of the reactor. The axial dispersion coefficient of the liquid phase was solved by MATLAB software. Substituting it into the definition of Einstein's diffusion coefficient to get the liquid circulation velocity. The experimental results show that at a certain solid holdup, as the superficial gas velocity increases, small bubbles gradually gather into large bubbles. The rising velocity of the bubbles continues to increase, and the liquid circulation velocity also increases accordingly. Increasing the superficial gas velocity can significantly increase the liquid circulation velocity. At a constant superficial gas velocity, as the solid holdup increases, the large bubble holdup increases, and the bubble rise velocity also increases. As a result, the liquid circulation velocity also increases. However, because the increase of solid holdup will hinder the circulation of liquid to a certain extent, as the solid holdup increases, the increase of the liquid circulation velocity continues to decrease. It shows that there may be an optimal value for the solid holdup.
, Available online
, doi: 10.14135/j.cki.1006-3080.20211208001
Abstract:
The aim of this study was to quantify the effects of multiple factors on fermentation broth rheology. Industrial fed-batch fermentations of Acremonium chrysogenum were conducted, and rheology properties of samples were adequately described by power law model. Nonlinear modeling taking only fungal morphology and cell concentration into consideration led to poor correlation and little prediction function. One of the reasons probably was that the model was oversimplified and some inconspicuous but significant factors were omitted. Consequently, extra elements such as substrate concentration, feed mode, media composition were taken into account, following tremendously increased sample library and existence of variables multicollinearity. Two major morphologies of A. chrysogenum were observed in fermentation broth, i.e.freely dispersed arthrospores and filamentous mycelium. It was found that the number of arthrospores was the major factor contributing to rheology properties, based on the standard partial regression coefficients. Using the partial least squares regression (PLSR) model, good prediction of flow index(n) and consistency index(K) can be made from linear recombination of variables, with R2=0.94, R2=0.91 respectively.
The aim of this study was to quantify the effects of multiple factors on fermentation broth rheology. Industrial fed-batch fermentations of Acremonium chrysogenum were conducted, and rheology properties of samples were adequately described by power law model. Nonlinear modeling taking only fungal morphology and cell concentration into consideration led to poor correlation and little prediction function. One of the reasons probably was that the model was oversimplified and some inconspicuous but significant factors were omitted. Consequently, extra elements such as substrate concentration, feed mode, media composition were taken into account, following tremendously increased sample library and existence of variables multicollinearity. Two major morphologies of A. chrysogenum were observed in fermentation broth, i.e.freely dispersed arthrospores and filamentous mycelium. It was found that the number of arthrospores was the major factor contributing to rheology properties, based on the standard partial regression coefficients. Using the partial least squares regression (PLSR) model, good prediction of flow index(n) and consistency index(K) can be made from linear recombination of variables, with R2=0.94, R2=0.91 respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.^20211209002
Abstract:
Changes in the choroid are closely related to many ophthalmic diseases. Doctors often need to manually split the choroid layer in the optical tomography image (OCT) during diagnosis, and then quantify the health of the choroid, but manual segmentation is time-consuming and laborious. The difficulty of automatic segmentation of choroid lies in the blurred boundary of the OCT images, for it is difficult to capture the context information, and secondly, the choroidal structure is similar to the retina structure, which is easy to confuse. In order to solve this difficulty, the residual codec model of fusion coordinate parallel attention module and dense atrous convolution module is proposed. A bridge structure is designed, which combines attention mechanism and atrous convolution to suppress shallow noise while increasing the model's receptive fields. In order to make the model pay attention to the choroidal structure information, a hybrid loss function with structural similarity is introduced. The experimental results show that the model can effectively improve the segmentation accuracy of the choroid, and the Dice coefficient and Jaccard similarity reached 97.63 percent and 95.28 percent on the OCT images data set.
Changes in the choroid are closely related to many ophthalmic diseases. Doctors often need to manually split the choroid layer in the optical tomography image (OCT) during diagnosis, and then quantify the health of the choroid, but manual segmentation is time-consuming and laborious. The difficulty of automatic segmentation of choroid lies in the blurred boundary of the OCT images, for it is difficult to capture the context information, and secondly, the choroidal structure is similar to the retina structure, which is easy to confuse. In order to solve this difficulty, the residual codec model of fusion coordinate parallel attention module and dense atrous convolution module is proposed. A bridge structure is designed, which combines attention mechanism and atrous convolution to suppress shallow noise while increasing the model's receptive fields. In order to make the model pay attention to the choroidal structure information, a hybrid loss function with structural similarity is introduced. The experimental results show that the model can effectively improve the segmentation accuracy of the choroid, and the Dice coefficient and Jaccard similarity reached 97.63 percent and 95.28 percent on the OCT images data set.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211231001
Abstract:
Process monitoring is a crucial part of ensuring the safety and quality of industrial production. A sparse D-vine Copula-based (SDVC) process monitoring method is proposed for the problem of nonlinearity and non-Gaussian properties of high-dimensional data in industrial processes. Firstly, considering that the traditional Vine Copula structure optimization method tends to cause estimation errors to accumulate in the Vine structure and the computational burden grows sharply with the increase of data dimensionality. The prior probability of bivariate Copula is modified so that the bivariate Copula in high-level structure tree is more inclined to be optimized to independent states, and the sparse optimization of the high-level tree structure is achieved. Secondly, the vine structure node order determination method is improved. It is expanded sequentially according to the sum of correlations among nodes, making it more applicable to D-vine modeling of horizontal structure. Finally, the high density region (HDR) and density quantile theory are introduced to determine the control boundary and construct generalized local probability (GLP) index to realize real-time monitoring of industrial processes. The superior performance of the proposed method was verified through the Tennessee-Eastman (TE) and acetic acid dehydration industrial processes.
Process monitoring is a crucial part of ensuring the safety and quality of industrial production. A sparse D-vine Copula-based (SDVC) process monitoring method is proposed for the problem of nonlinearity and non-Gaussian properties of high-dimensional data in industrial processes. Firstly, considering that the traditional Vine Copula structure optimization method tends to cause estimation errors to accumulate in the Vine structure and the computational burden grows sharply with the increase of data dimensionality. The prior probability of bivariate Copula is modified so that the bivariate Copula in high-level structure tree is more inclined to be optimized to independent states, and the sparse optimization of the high-level tree structure is achieved. Secondly, the vine structure node order determination method is improved. It is expanded sequentially according to the sum of correlations among nodes, making it more applicable to D-vine modeling of horizontal structure. Finally, the high density region (HDR) and density quantile theory are introduced to determine the control boundary and construct generalized local probability (GLP) index to realize real-time monitoring of industrial processes. The superior performance of the proposed method was verified through the Tennessee-Eastman (TE) and acetic acid dehydration industrial processes.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210831004
Abstract:
Cyber-physical systems (CPS) are tight integration of embedded computers and physical devices, which has a wide of applications in many areas such as process industry, smart energy, medical care, and national defense. However, it is a challenging task to design CPS software that meets both functional and performance requirements, since various physical devices and software in CSPs are interconnected and complex in structures and behaviors. The CPS that controls the operation of physical devices is usually running in dynamical environment. The environmental parameters will affect the structures and behaviors of CPS. This paper proposes a data-based adaptive software structure model design method. In this method, the software architecture model of CPS is constructed by the hierarchical combination of unit modules. The multi-level formal models for CPS software are based on formalisms of Petri net and temporal logic, in order to precisely specify CPS software architecture model, properties, and refine the relation between different levels. The adaptive evolution of CPS is realized by taking advantage of formal semantics, aspect-oriented method, and data analysis algorithms, which abstracts the function of environmental factors into aspect model and obtains a comprehensive CPS model and basic model. The formal method based on Petri nets and temporal logic provides mathematical expression and analysis means for CPS model. Theoretical analysis and experiments show that the designed method is feasible and efficient.
Cyber-physical systems (CPS) are tight integration of embedded computers and physical devices, which has a wide of applications in many areas such as process industry, smart energy, medical care, and national defense. However, it is a challenging task to design CPS software that meets both functional and performance requirements, since various physical devices and software in CSPs are interconnected and complex in structures and behaviors. The CPS that controls the operation of physical devices is usually running in dynamical environment. The environmental parameters will affect the structures and behaviors of CPS. This paper proposes a data-based adaptive software structure model design method. In this method, the software architecture model of CPS is constructed by the hierarchical combination of unit modules. The multi-level formal models for CPS software are based on formalisms of Petri net and temporal logic, in order to precisely specify CPS software architecture model, properties, and refine the relation between different levels. The adaptive evolution of CPS is realized by taking advantage of formal semantics, aspect-oriented method, and data analysis algorithms, which abstracts the function of environmental factors into aspect model and obtains a comprehensive CPS model and basic model. The formal method based on Petri nets and temporal logic provides mathematical expression and analysis means for CPS model. Theoretical analysis and experiments show that the designed method is feasible and efficient.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211202003
Abstract:
Electroencephalogram (EEG) functional connectivity microstates represent quasi-stable global neuronal activity and are considered the building blocks of brain dynamics. Therefore, microstate sequence analysis is a promising method to understand the brain dynamics behind various emotional states. Recent studies have shown that the sequence of EEG microstates is non-Markov and non-stationary, which also explains the importance of temporal dynamics between different emotional states. However, the microstate features based on probability statistics can not well represent the dynamic characteristics of EEG signals. These findings inspire us to use recurrence analysis to model time series of microstates to capture non-obvious correlations in time series. In conclusion, we propose an emotion decoding model based on recurrence analysis of EEG functional connectivity microstate sequences. Firstly, the functional connection microstate pattern of each frame signal is established by using the correlation of time-domain signals between each channel, and the typical microstate patterns are obtained by clustering. Then, the original EEG signals were mapped to microstate time series according to typical microstate patterns, and the time series were analyzed recursively to construct recurrence plots to characterize the EEG dynamic characteristics. Finally, Convolutional Neural Networks (CNNs) are used to predict the regression of emotions based on the valence or arousal value. On open dataset DEAP, the regression effect of the Mean Square Error (MSE) of the model in the two dimensions of valence and arousal is 3.45±1.42 and 2.79±1.48, respectively, which is better than the MSE of 3.87±1.67 and 3.25±1.71 based on the traditional statistical characteristics of microstate features.
Electroencephalogram (EEG) functional connectivity microstates represent quasi-stable global neuronal activity and are considered the building blocks of brain dynamics. Therefore, microstate sequence analysis is a promising method to understand the brain dynamics behind various emotional states. Recent studies have shown that the sequence of EEG microstates is non-Markov and non-stationary, which also explains the importance of temporal dynamics between different emotional states. However, the microstate features based on probability statistics can not well represent the dynamic characteristics of EEG signals. These findings inspire us to use recurrence analysis to model time series of microstates to capture non-obvious correlations in time series. In conclusion, we propose an emotion decoding model based on recurrence analysis of EEG functional connectivity microstate sequences. Firstly, the functional connection microstate pattern of each frame signal is established by using the correlation of time-domain signals between each channel, and the typical microstate patterns are obtained by clustering. Then, the original EEG signals were mapped to microstate time series according to typical microstate patterns, and the time series were analyzed recursively to construct recurrence plots to characterize the EEG dynamic characteristics. Finally, Convolutional Neural Networks (CNNs) are used to predict the regression of emotions based on the valence or arousal value. On open dataset DEAP, the regression effect of the Mean Square Error (MSE) of the model in the two dimensions of valence and arousal is 3.45±1.42 and 2.79±1.48, respectively, which is better than the MSE of 3.87±1.67 and 3.25±1.71 based on the traditional statistical characteristics of microstate features.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211215002
Abstract:
An adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM) is proposed to solve the problems of high detection delay, missed detection and false alarm in the active detection method of concept drift. WMDDM algorithm has a weight adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give the old data lower weights and dynamically adjust according to the changes in the data stream in order to adapt to the concept drift faster. The warning level and drift level of the weighted classification accuracy are obtained by McDiarmid's inequality. When it is detected that the weighted classification accuracy rate drops outside the drift level, the detection result is fed back to the classifier. When it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow through the triggered weight adjustment mechanism. The experiment uses 4 artificial data sets (two mutation drift data sets, two gradual drift data sets) and 1 real data set, which are mainly compared with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on the Hoeffding’s inequality (HDDM) and other algorithms. Experimental results show that the WMDDM algorithm has the lowest false alarm rate and missed detection rate, and the average detection delay and accuracy rate rank the top 2 among the six algorithms. Finally, WMDDM algorithm is used to classify real data sets and compared with FHDDM algorithm. The results show that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is suitable for abrupt and gradual conceptual drift, and has strong robustness.
An adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM) is proposed to solve the problems of high detection delay, missed detection and false alarm in the active detection method of concept drift. WMDDM algorithm has a weight adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give the old data lower weights and dynamically adjust according to the changes in the data stream in order to adapt to the concept drift faster. The warning level and drift level of the weighted classification accuracy are obtained by McDiarmid's inequality. When it is detected that the weighted classification accuracy rate drops outside the drift level, the detection result is fed back to the classifier. When it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow through the triggered weight adjustment mechanism. The experiment uses 4 artificial data sets (two mutation drift data sets, two gradual drift data sets) and 1 real data set, which are mainly compared with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on the Hoeffding’s inequality (HDDM) and other algorithms. Experimental results show that the WMDDM algorithm has the lowest false alarm rate and missed detection rate, and the average detection delay and accuracy rate rank the top 2 among the six algorithms. Finally, WMDDM algorithm is used to classify real data sets and compared with FHDDM algorithm. The results show that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is suitable for abrupt and gradual conceptual drift, and has strong robustness.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211227001
Abstract:
Optimization of heat exchanger network is an effective way of energy recovery. However, the model with large optimization space of heat exchanger network is often a complex mixed-integer nonlinear programming (MINLP) model with nonlinear and non-convexity constraints, and difficult to get a feasible solution. In this paper, based on the stage-wise superstructure, a new type of heat exchanger network contains flow split, reflux and non-isothermal mixing was built, and while increasing the optimization space of heat exchange network, linear constraints were set to greatly improve the solvability of MINLP model. Two cases in literature were used to verify the contribution of flow split, reflux, isothermal mixing and non-isothermal mixing to the optimization of heat exchanger network, and the effectiveness and applicability of the model.
Optimization of heat exchanger network is an effective way of energy recovery. However, the model with large optimization space of heat exchanger network is often a complex mixed-integer nonlinear programming (MINLP) model with nonlinear and non-convexity constraints, and difficult to get a feasible solution. In this paper, based on the stage-wise superstructure, a new type of heat exchanger network contains flow split, reflux and non-isothermal mixing was built, and while increasing the optimization space of heat exchange network, linear constraints were set to greatly improve the solvability of MINLP model. Two cases in literature were used to verify the contribution of flow split, reflux, isothermal mixing and non-isothermal mixing to the optimization of heat exchanger network, and the effectiveness and applicability of the model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211030001
Abstract:
With the development of the logistics industry, cold chain logistics have been studied by more and more scholarsas an important branch of the logistics industry. Because the waste of resources in cold chain logistics and distribution is a problem that cannot be underestimated, we use the optimization algorithm to solve the multi-objective optimization model to provide an effective distribution plan for solving the problem of resource waste in this paper. We establish a multi-objective cold chain logistics optimization model with minimizing distribution costs and maximizing customer satisfaction as the objective function in this paper. Customer satisfaction is reflected by the relationship between the delivery vehicle’s arrival time at the customer’s point and the customer’s specific time window; delivery costs are composed of transportation costs, cargo damage costs, cooling costs, and time penalty costs. We adopt the improved five-elements cycle optimization (FECO), which is the five-elements cycle optimization algorithm of dual-mode updating individuals (FECO-DMUI) for multi-objective cold chain logistics optimization model in this paper. The chain logistics optimization model is solved by FECO-DMUI algorithm and compared with FECO algorithm, NSGA-II, whale optimization algorithm and gray wolf optimization algorithm. The effectiveness of the model and algorithm is verified through specific examples, and the FECO-DMUI algorithm can be used to obtain the optimal solution set for path optimization more efficiently in the multi-objective cold chain distribution problem.
With the development of the logistics industry, cold chain logistics have been studied by more and more scholarsas an important branch of the logistics industry. Because the waste of resources in cold chain logistics and distribution is a problem that cannot be underestimated, we use the optimization algorithm to solve the multi-objective optimization model to provide an effective distribution plan for solving the problem of resource waste in this paper. We establish a multi-objective cold chain logistics optimization model with minimizing distribution costs and maximizing customer satisfaction as the objective function in this paper. Customer satisfaction is reflected by the relationship between the delivery vehicle’s arrival time at the customer’s point and the customer’s specific time window; delivery costs are composed of transportation costs, cargo damage costs, cooling costs, and time penalty costs. We adopt the improved five-elements cycle optimization (FECO), which is the five-elements cycle optimization algorithm of dual-mode updating individuals (FECO-DMUI) for multi-objective cold chain logistics optimization model in this paper. The chain logistics optimization model is solved by FECO-DMUI algorithm and compared with FECO algorithm, NSGA-II, whale optimization algorithm and gray wolf optimization algorithm. The effectiveness of the model and algorithm is verified through specific examples, and the FECO-DMUI algorithm can be used to obtain the optimal solution set for path optimization more efficiently in the multi-objective cold chain distribution problem.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211102001
Abstract:
In multivariate time series prediction, it is difficult to capture short-term mutation during long time series, which leads to significant prediction errors. A short-term information enhancement model called clockwork triggered long short term memory (CWTLSTM) neural network is proposed in this paper. The new model groups neurons in the network and assigns different activation frequencies to each group. The neurons in each group can be activated only when the time step is equal to an integer multiple of their specified period. According to the number of the group period, the network is divided into backbone network chain and short-term input enhancement chain. When the short-term input enhancement chain is activated on the time step close to the output position, the input information at that point will be transmitted to the backbone network chain uniaxially, and the weight of short-term input data will be enhanced. So the model can quickly respond to the data fluctuation caused by short-term mutation information, on the basis of storing long-term information. The prediction performance of CWTLSTM was verified by air pollution data set and cement cooler data set, compared with LSTM, XGboost and CWRNN models. The results show that the proposed model has good performance in reducing forecasting error and forecasting future trend. In the experiment, the parameter sensitivity of the model to the periodic allocation strategy is also analyzed, which verifies the role of CWTLSTM in short-term information enhancement to a certain extent.
In multivariate time series prediction, it is difficult to capture short-term mutation during long time series, which leads to significant prediction errors. A short-term information enhancement model called clockwork triggered long short term memory (CWTLSTM) neural network is proposed in this paper. The new model groups neurons in the network and assigns different activation frequencies to each group. The neurons in each group can be activated only when the time step is equal to an integer multiple of their specified period. According to the number of the group period, the network is divided into backbone network chain and short-term input enhancement chain. When the short-term input enhancement chain is activated on the time step close to the output position, the input information at that point will be transmitted to the backbone network chain uniaxially, and the weight of short-term input data will be enhanced. So the model can quickly respond to the data fluctuation caused by short-term mutation information, on the basis of storing long-term information. The prediction performance of CWTLSTM was verified by air pollution data set and cement cooler data set, compared with LSTM, XGboost and CWRNN models. The results show that the proposed model has good performance in reducing forecasting error and forecasting future trend. In the experiment, the parameter sensitivity of the model to the periodic allocation strategy is also analyzed, which verifies the role of CWTLSTM in short-term information enhancement to a certain extent.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211129001
Abstract:
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often lack of complete facts. In this paper, we study the problem of predicting missing links by learning embeddings of entities and relations in graph knowledge. We introduce a mirror space translation method to learning the symmetric/antisymmetric patterns. Relations are still modelled as translations in our new space, while entities are modelled as points that have mirror points. Within this space, translation-based models gain the ability to model symmetry/antisymmetry relations. Our proposed model MTransE applies the concept of mirrored space to TransE, with experiments on four well-known datasets, shows the performance over other baseline models.
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often lack of complete facts. In this paper, we study the problem of predicting missing links by learning embeddings of entities and relations in graph knowledge. We introduce a mirror space translation method to learning the symmetric/antisymmetric patterns. Relations are still modelled as translations in our new space, while entities are modelled as points that have mirror points. Within this space, translation-based models gain the ability to model symmetry/antisymmetry relations. Our proposed model MTransE applies the concept of mirrored space to TransE, with experiments on four well-known datasets, shows the performance over other baseline models.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220122003
Abstract:
Over recent years, immunotherapy has developed rapidly, which has changed the way of cancer treatment. However, most patients cannot benefit from immunotherapy, which may be due to insufficient reprogramming of the immunosuppressive tumor microenvironment (TME) and thus limited reinvigoration of antitumor immunity. In TME, adenosine, a metabolite of ATP, is an effective immunoregulatory factor. Extracellular 5'-nucleotidase (CD73) is the rate-limiting molecule in the process of adenosine production. Overexpression of CD73 on tumor cells and immune cells leads to a higher concentration of adenosine in the TME. The high concentration of adenosine suppresses the anti-tumor immune response, promotes tumor cells proliferate, metastasis and angiogenesis. Therefore, anti-CD73 therapy is expected to become a promising strategy for cancer immunotherapy. Several anti-CD73 mAbs (MEDI9447, BMS986179, SRF373/NZV930, CPI-006/CPX-006, IPH5301, TJ004309) and small molecule CD73 inhibitors ((LY3475070, AB680, CB-708) are being investigated in early phase clinical trials. But so far, there is no CD73 targeted product for the treatment of cancer on the market. In this study, firstly we performed the screening of our compound library containing 876 listed drugs to identify candidate inhibitors targeting CD73. Preliminary experimental results showed that Cisatracurium besylate (51w89) could inhibit the enzyme activity of recombinant CD73 with an IC50 value of 13.30 μmol/L. To verify the interactions between 51w89 and CD73 and evaluate their binding affinities, we performed surface plasmon resonance (SPR) experiments using a Biacore T200 (GE Healthcare). The binding affinity (KD value) of 51w89 binding to CD73 was 20.45 μmol/L. Encouraged by these results at the molecular level, we next evaluated the inhibitory effect of 51w89 against CD73 in MDA-MB-231 cells. The results show that the IC50 of 51w89 against CD73 in MDA-MB-231 cells was 17.70 μmol/L, which was close to the IC50 value at the molecular level. Subsequently, we proved the role of CD73 in the migration of MDA-MB-231 cells through scratch tests, transwell tests and siRNA tests, and found that 51w89 could inhibit the migration of MDA-MB-231 cells. Moreover, we found that 51w89 could limit the inhibitory effect of AMP on CD8+ T cells. Taken together, we speculated that 51w89 could be used as a potent small-molecule inhibitor of CD73 for subsequent antitumor research.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211222001
Abstract:
The paper establishes the function of time between the change of water temperature of the gas water heater through the establishment of heat transfer model. In addition, the function of water temperature of hot water pipe is calculated and the importance of intelligent water pump system for water saving is analyzed. In particular, the integration of non-elementary functions by curve fitting is worth reference to engineering technicians. Finally, several practical methods to shorten the waiting time of hot water are given: 1. Improve the outlet temperature of gas water heater; 2. Install hot water storage device in the bathroom; 3. Install gas water heater near the bathroom; 4. Hot water pipe add insulation material and hot water circulation pump; 5. Accelerate the pumping speed of hot water circulation pump; 6. Fully realize zero hot water waiting need to open the gas water heater and circulation pump, keep the hot water pipe without water.
The paper establishes the function of time between the change of water temperature of the gas water heater through the establishment of heat transfer model. In addition, the function of water temperature of hot water pipe is calculated and the importance of intelligent water pump system for water saving is analyzed. In particular, the integration of non-elementary functions by curve fitting is worth reference to engineering technicians. Finally, several practical methods to shorten the waiting time of hot water are given: 1. Improve the outlet temperature of gas water heater; 2. Install hot water storage device in the bathroom; 3. Install gas water heater near the bathroom; 4. Hot water pipe add insulation material and hot water circulation pump; 5. Accelerate the pumping speed of hot water circulation pump; 6. Fully realize zero hot water waiting need to open the gas water heater and circulation pump, keep the hot water pipe without water.
, Available online
, doi: 10.14135/J.cnki.1006-3080.20211018003
Abstract:
Single-walled carbon nanotubes (SWCNTs) can combine with proteins in organisms, creating potential biosafety risks. In this paper, SWCNTs and two separated single-chiral single-walled carbon nanotubes ((6,5)-SWCNT, (8,3)-SWCNT) were combined with Bovine hemoglobin (BHb) respectively, and SWCNTs interaction with BHb were analyzed by fluorescence spectroscopy. The results showed that the fluorescence quenching of BHb by SWCNTs was resulted from the combination of dynamic quenching and static quenching. The fluorescence quenching of BHb by single chiral (6,5)-SWCNT and (8,3)-SWCNT was static quenching. The order of the binding constants of BHb and different SWCNTs was as follows: SWCNTs>(6,5)-SWCNT>(8,3)-SWCNT. Van der Waals force, hydrogen bond and hydrophobic interaction were the main forces in the interaction. The result of this article will assist the revealing of the potential biosafety risks of SWCNTs.
Single-walled carbon nanotubes (SWCNTs) can combine with proteins in organisms, creating potential biosafety risks. In this paper, SWCNTs and two separated single-chiral single-walled carbon nanotubes ((6,5)-SWCNT, (8,3)-SWCNT) were combined with Bovine hemoglobin (BHb) respectively, and SWCNTs interaction with BHb were analyzed by fluorescence spectroscopy. The results showed that the fluorescence quenching of BHb by SWCNTs was resulted from the combination of dynamic quenching and static quenching. The fluorescence quenching of BHb by single chiral (6,5)-SWCNT and (8,3)-SWCNT was static quenching. The order of the binding constants of BHb and different SWCNTs was as follows: SWCNTs>(6,5)-SWCNT>(8,3)-SWCNT. Van der Waals force, hydrogen bond and hydrophobic interaction were the main forces in the interaction. The result of this article will assist the revealing of the potential biosafety risks of SWCNTs.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211014001
Abstract:
The service life of metalworking fluids can be shortened by existence of microorganism, and it is necessary to explore the composition of microbial communities in metalworking fluids. Three different methods were measured to determine the best one to separate microorganisms from metal working fluids. The concentration of microorganisms can be increased by the mikrocount combi method which has the optimal separation result. Under the help of Illumina MiSeq high-throughput sequencing, the composition of the microbial diversity of metalworking fluid samples at the 6 levels of phylum, class, order, family, genus, and species were completed respectively. Moreover, bacteria were detected in six groups of samples, while fungus were discovered in only two groups. Meanwhile, it was easier for bacteria to thrive in metalworking fluids than fungi, and fungi was only existed in samples with high bacterial contamination. A total of 2 phyla, 2 classes, 5 orders, 6 families, 10 genera and 14 species of bacteria were detected in all samples, while 4 phyla, 8 classes, 10 orders, 14 families, 15 genera and 17 species of fungi were also detected, which means that the fungal diversity is more abundant. Citrobacter_freundii_g_Citrobacter, unclassified_g_Citrobacter, unclassified_f_Enterobacteriaceae were identified as the dominant bacteria, and most of bacteria detected were Gram-negative. The composition of the metalworking fluid will affect the type of bacteria. All detected bacteria can destroy the stability of the metal working fluid through different ways, which shorten its service life. The dominant fungi were unclassified_k_Fungi and Fusarium_petroliphilum. The health of operators will be harmed by metalworking fluids with microbial contamination.
The service life of metalworking fluids can be shortened by existence of microorganism, and it is necessary to explore the composition of microbial communities in metalworking fluids. Three different methods were measured to determine the best one to separate microorganisms from metal working fluids. The concentration of microorganisms can be increased by the mikrocount combi method which has the optimal separation result. Under the help of Illumina MiSeq high-throughput sequencing, the composition of the microbial diversity of metalworking fluid samples at the 6 levels of phylum, class, order, family, genus, and species were completed respectively. Moreover, bacteria were detected in six groups of samples, while fungus were discovered in only two groups. Meanwhile, it was easier for bacteria to thrive in metalworking fluids than fungi, and fungi was only existed in samples with high bacterial contamination. A total of 2 phyla, 2 classes, 5 orders, 6 families, 10 genera and 14 species of bacteria were detected in all samples, while 4 phyla, 8 classes, 10 orders, 14 families, 15 genera and 17 species of fungi were also detected, which means that the fungal diversity is more abundant. Citrobacter_freundii_g_Citrobacter, unclassified_g_Citrobacter, unclassified_f_Enterobacteriaceae were identified as the dominant bacteria, and most of bacteria detected were Gram-negative. The composition of the metalworking fluid will affect the type of bacteria. All detected bacteria can destroy the stability of the metal working fluid through different ways, which shorten its service life. The dominant fungi were unclassified_k_Fungi and Fusarium_petroliphilum. The health of operators will be harmed by metalworking fluids with microbial contamination.
, Available online
, doi: 10.14135/j.cnki.1006-3080.2021092400
Abstract:
Clostridium histolyticum collagenase H (ColH) recognizes the Y-Gly of collagen and hydrolyzes it into small peptides. The high molecular weight ColH(116 kDa) secreting strain was successfully constructed by fusing colH gene with signal peptide sequence of outer membrane protein A. In this study, we found that the secretion of ColH was effected by the position of the signal peptide at the N-terminal, and the presence of excess amino acid fragments at the N-terminal significantly reduced the secretion function of the signal peptide-guided collagenase. Orthogonal experiment and single factor experiment were used to optimize the induction conditions and medium additives to improve the secretory expression. Under the conditions of inducing temperature of 25 ℃, the cell density(OD600) of 0.9, IPTG concentration of 0.1 mmol/L, liquid volume of 20%, magnesium ion concentration of 10 mmol/L, and 2% glycine added at 2.5 h after induction, the highest extracellular collagenase activity was 0.68 U/mL after induction for 20 h, which was 38.1 times of that before optimization, and the secretory expression level was greatly increased. Glycine added into the culture medium is a common strategy to promote the secretion of recombinant protein. Experimental results showed that the amount and time of glycine added after induction had the greatest influence on the secretion of collagenase. The addition of calcium and magnesium ions in the medium can promote the growth of E.coli, the results also showed that but only the addition of magnesium ion can promote the secretion of ColH.
Clostridium histolyticum collagenase H (ColH) recognizes the Y-Gly of collagen and hydrolyzes it into small peptides. The high molecular weight ColH(116 kDa) secreting strain was successfully constructed by fusing colH gene with signal peptide sequence of outer membrane protein A. In this study, we found that the secretion of ColH was effected by the position of the signal peptide at the N-terminal, and the presence of excess amino acid fragments at the N-terminal significantly reduced the secretion function of the signal peptide-guided collagenase. Orthogonal experiment and single factor experiment were used to optimize the induction conditions and medium additives to improve the secretory expression. Under the conditions of inducing temperature of 25 ℃, the cell density(OD600) of 0.9, IPTG concentration of 0.1 mmol/L, liquid volume of 20%, magnesium ion concentration of 10 mmol/L, and 2% glycine added at 2.5 h after induction, the highest extracellular collagenase activity was 0.68 U/mL after induction for 20 h, which was 38.1 times of that before optimization, and the secretory expression level was greatly increased. Glycine added into the culture medium is a common strategy to promote the secretion of recombinant protein. Experimental results showed that the amount and time of glycine added after induction had the greatest influence on the secretion of collagenase. The addition of calcium and magnesium ions in the medium can promote the growth of E.coli, the results also showed that but only the addition of magnesium ion can promote the secretion of ColH.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211116001
Abstract:
Gasification temperature is the most important operating parameter of entrained flow gasifier. However, the coal gasifier unit lacks long-term and reliable gasification temperature measurement. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data such as gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by using theoretical calculation model and BP neural based on genetic algorithm model(GABP). The results of prediction were compared with industrial measurement data. The results show that the outlet temperature of gasifier can be obtained by theoretical calculation of quench system, but the accuracy and stability of prediction results are poor due to low sensitivity of measurement parameters. GABP neural network model can greatly improve the prediction performance. Base on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and the lack of coal property data. Taking quench system parameters as the input of GABP neural network can greatly improve the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both of the train set and verification set have excellent prediction results, the average absolute errors of GABP model with quench system parameters as input are about 5 K. GABP model has good performances in the face of complex working conditions. Carry out predictions under different conditions, the results under steady and variable coal load have good prediction precision and stability, meet the demand of online monitoring of gasifier temperature.
Gasification temperature is the most important operating parameter of entrained flow gasifier. However, the coal gasifier unit lacks long-term and reliable gasification temperature measurement. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data such as gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by using theoretical calculation model and BP neural based on genetic algorithm model(GABP). The results of prediction were compared with industrial measurement data. The results show that the outlet temperature of gasifier can be obtained by theoretical calculation of quench system, but the accuracy and stability of prediction results are poor due to low sensitivity of measurement parameters. GABP neural network model can greatly improve the prediction performance. Base on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and the lack of coal property data. Taking quench system parameters as the input of GABP neural network can greatly improve the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both of the train set and verification set have excellent prediction results, the average absolute errors of GABP model with quench system parameters as input are about 5 K. GABP model has good performances in the face of complex working conditions. Carry out predictions under different conditions, the results under steady and variable coal load have good prediction precision and stability, meet the demand of online monitoring of gasifier temperature.
Research on Chinese Named Entity Recognition Based on Hierarchical Adjustment of Lexicon Information
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211105003
Abstract:
In the task of Chinese named entity recognition, word information fusion vocabulary information can enrich text features, but a word may correspond to multiple candidate words, which is prone to vocabulary conflict. The fusion of irrelevant vocabulary information will affect the recognition effect of the model. In this paper, a Chinese named entity recognition method based on hierarchical adjustment of dictionary information is proposed. All potential words are layered according to the word length, and the weight of low-level words is adjusted through high-level word feedback to retain more useful information, so as to alleviate the problem of semantic deviation and reduce the impact of word conflict. Then, the word information is spliced into the word information to enhance the text feature representation. Experiments are carried out on resume and Weibo data sets. The experimental results show that this method has better effect than the traditional method.
In the task of Chinese named entity recognition, word information fusion vocabulary information can enrich text features, but a word may correspond to multiple candidate words, which is prone to vocabulary conflict. The fusion of irrelevant vocabulary information will affect the recognition effect of the model. In this paper, a Chinese named entity recognition method based on hierarchical adjustment of dictionary information is proposed. All potential words are layered according to the word length, and the weight of low-level words is adjusted through high-level word feedback to retain more useful information, so as to alleviate the problem of semantic deviation and reduce the impact of word conflict. Then, the word information is spliced into the word information to enhance the text feature representation. Experiments are carried out on resume and Weibo data sets. The experimental results show that this method has better effect than the traditional method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210909003
Abstract:
Recently, the method of combining BERT(Bidirectional Encoder Representations from Transformers) and neural network model has been widely used in the field of Chinese medical named entity recognition. However, BERT was segmented at the granularity of characters in Chinese, and Chinese word segmentation was not considered. And neural network models were often locally unstable, and even small disturbances may mislead them, resulting in poor model robustness. In order to solve these two problems, a Chinese medical named entity recognition model based on RoBERTa(A Robustly Optimized BERT Pre-training Approach) and adversarial training, namely AT-RBC (Adversarial Training with RoBERTa-wwm-ext-large+BiLSTM+CRF), was proposed. Firstly, use RoBERTa-wwm-ext-large(A Robustly Optimized BERT Pre-training Approach-whole word masking-extended data-large) pre-trained model to obtain initial vector representation of input text. Secondly, some perturbations were added to the initial vector representation to generate adversarial samples. Finally, the initial vector representation and adversarial samples were sequentially inputted to bidirectional long short-term memory network and conditional random field to obtain the final prediction. Experiments on the CCKS 2019 data set show that the F1 score of the improved model reaches 88.96%, achieving good results. Experiments were also conducted on the Resume data set, and the F1 value reaches 97.14%, which proved the effectiveness of the improved model.
Recently, the method of combining BERT(Bidirectional Encoder Representations from Transformers) and neural network model has been widely used in the field of Chinese medical named entity recognition. However, BERT was segmented at the granularity of characters in Chinese, and Chinese word segmentation was not considered. And neural network models were often locally unstable, and even small disturbances may mislead them, resulting in poor model robustness. In order to solve these two problems, a Chinese medical named entity recognition model based on RoBERTa(A Robustly Optimized BERT Pre-training Approach) and adversarial training, namely AT-RBC (Adversarial Training with RoBERTa-wwm-ext-large+BiLSTM+CRF), was proposed. Firstly, use RoBERTa-wwm-ext-large(A Robustly Optimized BERT Pre-training Approach-whole word masking-extended data-large) pre-trained model to obtain initial vector representation of input text. Secondly, some perturbations were added to the initial vector representation to generate adversarial samples. Finally, the initial vector representation and adversarial samples were sequentially inputted to bidirectional long short-term memory network and conditional random field to obtain the final prediction. Experiments on the CCKS 2019 data set show that the F1 score of the improved model reaches 88.96%, achieving good results. Experiments were also conducted on the Resume data set, and the F1 value reaches 97.14%, which proved the effectiveness of the improved model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210921001
Abstract:
Microservice architecture builds applications as independent components and runs each application process as a service. The decoupling and independent development of microservices make the flexibility and speed of software update possible. Meanwhile, it also brings many problems, such as service decomposition, transmission delay, and reliability. This paper uses PrT net (Predicated Petri net) to model the microservice composition by event bus to establish the dependency among microservices, transmission latency, and reliability of microservice composition. The event listening mechanism is a delegated event handling mechanism. When a specified event occurs in the event source, it will notify the specified event listener to perform the corresponding operation. For event-based communication, when the event occurs, the microservice will publish the event. Then, we propose a BP (primary and backup) replication allocation strategy meeting the sub-deadline through microservice instances of the primary and backup replica to improve the overall reliability of microservice composition. In this paper, the PB replica deployment strategy is analyzed from two cases: single task and multi task PB replica. By deploying the primary and backup replica of the task in different containers or host resources, the goal of improving the reliability of cloud applications has been achieved. The related properties of constructed models are established by using the related theories of PrT net. Through semantic and syntax analysis, the correctness of the PrT net modeling is analyzed. Finally, several experiments are carried out to verify the effectiveness of the modeling and analysis method. Experimental results show that the proposed microservice reliability strategy is effective by taking the guarantee ratio as the reliability parameter.
Microservice architecture builds applications as independent components and runs each application process as a service. The decoupling and independent development of microservices make the flexibility and speed of software update possible. Meanwhile, it also brings many problems, such as service decomposition, transmission delay, and reliability. This paper uses PrT net (Predicated Petri net) to model the microservice composition by event bus to establish the dependency among microservices, transmission latency, and reliability of microservice composition. The event listening mechanism is a delegated event handling mechanism. When a specified event occurs in the event source, it will notify the specified event listener to perform the corresponding operation. For event-based communication, when the event occurs, the microservice will publish the event. Then, we propose a BP (primary and backup) replication allocation strategy meeting the sub-deadline through microservice instances of the primary and backup replica to improve the overall reliability of microservice composition. In this paper, the PB replica deployment strategy is analyzed from two cases: single task and multi task PB replica. By deploying the primary and backup replica of the task in different containers or host resources, the goal of improving the reliability of cloud applications has been achieved. The related properties of constructed models are established by using the related theories of PrT net. Through semantic and syntax analysis, the correctness of the PrT net modeling is analyzed. Finally, several experiments are carried out to verify the effectiveness of the modeling and analysis method. Experimental results show that the proposed microservice reliability strategy is effective by taking the guarantee ratio as the reliability parameter.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210824001
Abstract:
Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and non-invasive. EBC was considered to be the ideal sample for the study of pulmonary diseases. Proteomics is one of the novel methods to develop disease biomarkers, and the proteomics of EBC is widely studied due to its tremendous biological potential. It can reflect different disease status by analyzing the components of EBC protein, explore potential biomarkers, and improve the diagnostic ability of lung cancer and other diseases. In this study, an EBC proteomics method based on data independent acquisition (DIA) was established to overcome the disadvantage of low protein concentration of EBC, and 2052 proteins were identified. On this basis, the weighted gene co-expression network analysis (WGCNA) was carried out. WGCNA is a novel bioinformatic analysis technology, which allows multiple analysis of different omics information. A total of 61 hub proteins were screened by cluster analysis, and the hub proteins were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interactions (PPIs) analysis. The results showed that the hub proteins mainly existed in the nucleus and cytoplasm, and participated in the metabolic pathways related to human diseases, which indicated that the hub proteins could reflect the disease status and hold the potential to be biomarkers. In conclusion, the DIA-based EBC proteomics combined with WGCNA analysis, could effectively explore the potential biological functions of EBC, which could be applied to a large-scale clinical research and contribute to the exploration of biomarkers in the future.
Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and non-invasive. EBC was considered to be the ideal sample for the study of pulmonary diseases. Proteomics is one of the novel methods to develop disease biomarkers, and the proteomics of EBC is widely studied due to its tremendous biological potential. It can reflect different disease status by analyzing the components of EBC protein, explore potential biomarkers, and improve the diagnostic ability of lung cancer and other diseases. In this study, an EBC proteomics method based on data independent acquisition (DIA) was established to overcome the disadvantage of low protein concentration of EBC, and 2052 proteins were identified. On this basis, the weighted gene co-expression network analysis (WGCNA) was carried out. WGCNA is a novel bioinformatic analysis technology, which allows multiple analysis of different omics information. A total of 61 hub proteins were screened by cluster analysis, and the hub proteins were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interactions (PPIs) analysis. The results showed that the hub proteins mainly existed in the nucleus and cytoplasm, and participated in the metabolic pathways related to human diseases, which indicated that the hub proteins could reflect the disease status and hold the potential to be biomarkers. In conclusion, the DIA-based EBC proteomics combined with WGCNA analysis, could effectively explore the potential biological functions of EBC, which could be applied to a large-scale clinical research and contribute to the exploration of biomarkers in the future.
, Available online
, doi: 10.14135/j.cnki/1006-3080.20211101003
Abstract:
The hydrodynamics and the suspension state of the Mg(OH)2 particles was studied by multiple reference frame (MRF) and the standard k – ε model in Fluent software. The effects on impeller height and stirring speed were discussed. The simulated results showed that the appropriate increase of impeller installation height was benefit for the settling and discharge of the coarse particles. which mainly attributed to the decrease of flow velocity in the bottom zone of the crystallizer. However, when the installation height was too high, the flow velocity in the baffle cylinder was increased, which caused the nonuniformity of the flow velocity. The low stirring speed was disadvantageous for the suspension of Mg(OH)2 particles. With the increasing stirring speed, the nonuniformity of the flow velocity in the settling zone was significantly improved and the particles in the crystallizer were more uniform in suspension state. The optimal installation height of impeller and stirring rate were determined to be 3.3m and 70rpm, respectively. The related results provided theoretical support for the structure and operation optimization of DTB type crystallizer for the project of 130,000 t per year magnesium hydroxide production.
The hydrodynamics and the suspension state of the Mg(OH)2 particles was studied by multiple reference frame (MRF) and the standard k – ε model in Fluent software. The effects on impeller height and stirring speed were discussed. The simulated results showed that the appropriate increase of impeller installation height was benefit for the settling and discharge of the coarse particles. which mainly attributed to the decrease of flow velocity in the bottom zone of the crystallizer. However, when the installation height was too high, the flow velocity in the baffle cylinder was increased, which caused the nonuniformity of the flow velocity. The low stirring speed was disadvantageous for the suspension of Mg(OH)2 particles. With the increasing stirring speed, the nonuniformity of the flow velocity in the settling zone was significantly improved and the particles in the crystallizer were more uniform in suspension state. The optimal installation height of impeller and stirring rate were determined to be 3.3m and 70rpm, respectively. The related results provided theoretical support for the structure and operation optimization of DTB type crystallizer for the project of 130,000 t per year magnesium hydroxide production.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210823001
Abstract:
Organic molecules with fluorine usually possess unique physical, chemical, and biological properties, thus playing an important role in material science and pharmaceutical chemistry. Meanwhile, functionalization of organic molecules via C−H activation has drawn extremely broad attention in recent years. Therefore, C−H fluorination for the synthesis of fluorine-containing molecules is a very important and challenging project in organic synthesis. Directing groups such as pyridine and amide have been utilized to facilitate C−H fluorinations, however, most of the directing groups are usually installed into the substrates before the fluorination and uninstalled after the fluorination, thus reducing the step economy of the reaction. Carboxylic group is ubiquitous in organic molecules and it can dramatically increase the step economy if it is employed as native directing group. Indeed, it has been utilized as directing group in C−H activations such as arylation, olefination, acetoxylation. While carboxylic group directed C−H fluorination remains a challenge. In this research, by using the carboxylic group as a directing group, after optimization of the reaction conditions including additive, solvent, fluorination reagent and ligand, we realized the Pd-catalyzed ortho-C(sp2)-H fluorination of benzoic acid, which affords the ortho-mono-fluorinated product in up to 13% isolated yield. A pyridone ligand with a nitro group at the C-5 position and an amide group at the C-3 position was found to be able to promote this transformation. We believe these results will benefit future development of carboxylic group directed C−H fluorination.
Organic molecules with fluorine usually possess unique physical, chemical, and biological properties, thus playing an important role in material science and pharmaceutical chemistry. Meanwhile, functionalization of organic molecules via C−H activation has drawn extremely broad attention in recent years. Therefore, C−H fluorination for the synthesis of fluorine-containing molecules is a very important and challenging project in organic synthesis. Directing groups such as pyridine and amide have been utilized to facilitate C−H fluorinations, however, most of the directing groups are usually installed into the substrates before the fluorination and uninstalled after the fluorination, thus reducing the step economy of the reaction. Carboxylic group is ubiquitous in organic molecules and it can dramatically increase the step economy if it is employed as native directing group. Indeed, it has been utilized as directing group in C−H activations such as arylation, olefination, acetoxylation. While carboxylic group directed C−H fluorination remains a challenge. In this research, by using the carboxylic group as a directing group, after optimization of the reaction conditions including additive, solvent, fluorination reagent and ligand, we realized the Pd-catalyzed ortho-C(sp2)-H fluorination of benzoic acid, which affords the ortho-mono-fluorinated product in up to 13% isolated yield. A pyridone ligand with a nitro group at the C-5 position and an amide group at the C-3 position was found to be able to promote this transformation. We believe these results will benefit future development of carboxylic group directed C−H fluorination.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210702002
Abstract:
This paper investigates the periodic event-triggered sliding mode control (SMC) of the permanent magnet synchronous motor (PMSM). The periodic event-triggered mechanism is introduced to decide whether to send the system state to the controller through the network for saving communication resources. Firstly, the sliding mode controller is synthesized based on the traditional event-triggered mechanism. By designing the event-triggered conditions, sufficient criteria for the existence of the actual sliding mode are provided, and the robust actual stability of the controlled system is guaranteed. And then, the SMC problem is considered for the periodic event-triggered scheme. Considering the characteristics of the periodic event-triggered mechanism, the upper bound of the error between two adjacent sampling times is estimated. Selection criteria of the sampling period and the control gain are provided for ensuring the robust actual stability of the controlled system and the existence of the practical sliding mode. Finally, simulation results illustrate the effectiveness of the proposed controller.
This paper investigates the periodic event-triggered sliding mode control (SMC) of the permanent magnet synchronous motor (PMSM). The periodic event-triggered mechanism is introduced to decide whether to send the system state to the controller through the network for saving communication resources. Firstly, the sliding mode controller is synthesized based on the traditional event-triggered mechanism. By designing the event-triggered conditions, sufficient criteria for the existence of the actual sliding mode are provided, and the robust actual stability of the controlled system is guaranteed. And then, the SMC problem is considered for the periodic event-triggered scheme. Considering the characteristics of the periodic event-triggered mechanism, the upper bound of the error between two adjacent sampling times is estimated. Selection criteria of the sampling period and the control gain are provided for ensuring the robust actual stability of the controlled system and the existence of the practical sliding mode. Finally, simulation results illustrate the effectiveness of the proposed controller.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210313004
Abstract:
The effects of temperature, pressure and feed ratio on the methane reformer were studied based on a kinetic model. The conversion rates of CH4, H2O and CO2 all increase with the increase of temperature at p=3.2 MPa. Compared with the steam reforming of methane, the reaction temperature required for the conversion of CH4 and CO2 is higher and the conversion of CO2 begins at 650 ℃.The effect of temperature on the reaction rate of dry reforming of methane is more significant at relatively high reaction temperature and pressure. With the increase of pressure, the conversion rates of CH4, H2O and CO2 decrease rapidly. When the pressure reaches 3.5 MPa, the conversion rates of CH4, H2O and CO2 are all less than 40%. However, the influence of pressure on n(H2)/n(CO) is not obvious. The increase of CO2 in the reaction system is beneficial to improve the conversion rate of CH4, but significantly reduces the conversion rate of H2O at p=3.2 MPa.CO2 conversion increases rapidly at first and then keeps stable with the increase of n(CO2)/n(CH4). CH4 and H2O conversion both increase with the increase of n(H2O)/n(CH4). The analysis of feed ratio and reaction temperature showed that n(H2)/n(CO) can be adjusted by adjusting the temperature and the relative concentration of H2O and CO2 in the feed gas to carry out the subsequent industrial production.
The effects of temperature, pressure and feed ratio on the methane reformer were studied based on a kinetic model. The conversion rates of CH4, H2O and CO2 all increase with the increase of temperature at p=3.2 MPa. Compared with the steam reforming of methane, the reaction temperature required for the conversion of CH4 and CO2 is higher and the conversion of CO2 begins at 650 ℃.The effect of temperature on the reaction rate of dry reforming of methane is more significant at relatively high reaction temperature and pressure. With the increase of pressure, the conversion rates of CH4, H2O and CO2 decrease rapidly. When the pressure reaches 3.5 MPa, the conversion rates of CH4, H2O and CO2 are all less than 40%. However, the influence of pressure on n(H2)/n(CO) is not obvious. The increase of CO2 in the reaction system is beneficial to improve the conversion rate of CH4, but significantly reduces the conversion rate of H2O at p=3.2 MPa.CO2 conversion increases rapidly at first and then keeps stable with the increase of n(CO2)/n(CH4). CH4 and H2O conversion both increase with the increase of n(H2O)/n(CH4). The analysis of feed ratio and reaction temperature showed that n(H2)/n(CO) can be adjusted by adjusting the temperature and the relative concentration of H2O and CO2 in the feed gas to carry out the subsequent industrial production.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210308002
Abstract:
Power-gating-aware design has been an active area of research in the last decade, aiming at reducing power dissipation while meeting a desired system throughput. In this study, an algorithm integrating both scheduling and binding processes is developed with the fine-grained functional unit (FU) power-gating technique, to achieve maximum leakage energy reduction. Firstly, the break-even points of FUs are analyzed, and the leakage energy reduction problem is formulated as an idle interval partition problem. Secondly, the idle interval length of each possible scheduling result is estimated. Finally, operations are scheduled to the control steps with maximization of the leakage energy saving. The experimental results show that our proposed algorithms can significantly reduce leakage energy while maintaining the system performance and circuit area, and therefore, provides a suit-able design solution for the circuits used in satellites.
Power-gating-aware design has been an active area of research in the last decade, aiming at reducing power dissipation while meeting a desired system throughput. In this study, an algorithm integrating both scheduling and binding processes is developed with the fine-grained functional unit (FU) power-gating technique, to achieve maximum leakage energy reduction. Firstly, the break-even points of FUs are analyzed, and the leakage energy reduction problem is formulated as an idle interval partition problem. Secondly, the idle interval length of each possible scheduling result is estimated. Finally, operations are scheduled to the control steps with maximization of the leakage energy saving. The experimental results show that our proposed algorithms can significantly reduce leakage energy while maintaining the system performance and circuit area, and therefore, provides a suit-able design solution for the circuits used in satellites.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210407001
Abstract:
Cucurbiturils (CB[n]s) is a hollow macrocyclic molecule formed by the condensation of glycoluril and formaldehyde under acidic conditions. The glycoluril units are linked together by methylene bridges, and cucurbiturils have hydrophobic cavity and polar carbonyl groups on both portals. Cucurbituril has strong inclusion ability for positively charged guest molecules such as protonated organic amines, pyridinium, and viologen. The study of the inclusion behaviors of aryl substituted viologen with cucurbit[8]uril urea (CB[8]) is of great significance for the further construction of related supramolecular polymers and even stimulus responsive materials. In this work, asymmetric 1-ethyl-1'-benzyl-4,4'-bipyridine bromide (EBV) was used to investigate its inclusion behaviors with CB[8] in aqueous solution by means of 1H-NMR spectroscopy, isothermal titration calorimetry (ITC) and high resolution electrospray ionization mass spectrometry (ESI-HRMS). The results show that the benzyl unit of EBV will firstly enter into the cavity of CB[8] to form a 1∶1 inclusion complex, and a 1∶2 supramolecular system where one CB[8] molecule encircles two benzyl groups will ultimately form. The constant of the first 1∶1 inclusion process is 1.65(±1.22)×107 M−1, the corresponding ΔH and −TΔS are −26.2(±1.26) kJ/mol and 14.6 kJ/mol, respectively. and the apparent inclusion constant of the whole process is 1.34(±0.193)×1013 M−2, the corresponding ΔH and −TΔS are −64.4(±3.19) kJ/mol and −9.43 kJ/mol, respectively, indicating that the host-guest complexation is driven by both enthalpy and entropy.
Cucurbiturils (CB[n]s) is a hollow macrocyclic molecule formed by the condensation of glycoluril and formaldehyde under acidic conditions. The glycoluril units are linked together by methylene bridges, and cucurbiturils have hydrophobic cavity and polar carbonyl groups on both portals. Cucurbituril has strong inclusion ability for positively charged guest molecules such as protonated organic amines, pyridinium, and viologen. The study of the inclusion behaviors of aryl substituted viologen with cucurbit[8]uril urea (CB[8]) is of great significance for the further construction of related supramolecular polymers and even stimulus responsive materials. In this work, asymmetric 1-ethyl-1'-benzyl-4,4'-bipyridine bromide (EBV) was used to investigate its inclusion behaviors with CB[8] in aqueous solution by means of 1H-NMR spectroscopy, isothermal titration calorimetry (ITC) and high resolution electrospray ionization mass spectrometry (ESI-HRMS). The results show that the benzyl unit of EBV will firstly enter into the cavity of CB[8] to form a 1∶1 inclusion complex, and a 1∶2 supramolecular system where one CB[8] molecule encircles two benzyl groups will ultimately form. The constant of the first 1∶1 inclusion process is 1.65(±1.22)×107 M−1, the corresponding ΔH and −TΔS are −26.2(±1.26) kJ/mol and 14.6 kJ/mol, respectively. and the apparent inclusion constant of the whole process is 1.34(±0.193)×1013 M−2, the corresponding ΔH and −TΔS are −64.4(±3.19) kJ/mol and −9.43 kJ/mol, respectively, indicating that the host-guest complexation is driven by both enthalpy and entropy.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210308003
Abstract:
The effects of reaction conditions on hydrogen production from methanol steam reforming were discussed. The experimental results showed that the optimum temperature of the reaction was about 240 ℃. The high temperature would make the CO selectivity higher, and the low temperature would make the conversion of CH3OH lower.When H2O/CH3OH molar ratio increases, the conversion of CH3OH increases and the selectivity of CO decreases. However, if H2O/CH3OH molar ratio is too high, more energy will be consumed.Under the premise of ensuring the conversion rate of CH3OH, the reaction efficiency can be improved by appropriately increasing the liquid hourly space velocity of feed liquid.The Langmuir-Hinshelwood two-rate dynamics model equation was used to fit the experimental data of intrinsic dynamics. The calculated values of molar flow rates of CO and CO2 in the gas products at the reactor outlet were in good agreement with the experimental values, and the two-rate model could be applied.The deactivation of CuO/ZnO/Al2O3 modified catalysts at 200 ℃ and 300 ℃ was investigated. The catalysts were characterized by BET, XRF, XRD and CO-TPD, the results showed that the main reasons for the deactivation of the catalysts were besides hot sintering. The reduction of specific surface area, the reduction of mesoporous ratio, CuO loss and the increase of CuO grain size are also the specific reasons for catalyst deactivation. The high content of CO produced in the high temperature has no obvious effect on catalyst deactivation.
The effects of reaction conditions on hydrogen production from methanol steam reforming were discussed. The experimental results showed that the optimum temperature of the reaction was about 240 ℃. The high temperature would make the CO selectivity higher, and the low temperature would make the conversion of CH3OH lower.When H2O/CH3OH molar ratio increases, the conversion of CH3OH increases and the selectivity of CO decreases. However, if H2O/CH3OH molar ratio is too high, more energy will be consumed.Under the premise of ensuring the conversion rate of CH3OH, the reaction efficiency can be improved by appropriately increasing the liquid hourly space velocity of feed liquid.The Langmuir-Hinshelwood two-rate dynamics model equation was used to fit the experimental data of intrinsic dynamics. The calculated values of molar flow rates of CO and CO2 in the gas products at the reactor outlet were in good agreement with the experimental values, and the two-rate model could be applied.The deactivation of CuO/ZnO/Al2O3 modified catalysts at 200 ℃ and 300 ℃ was investigated. The catalysts were characterized by BET, XRF, XRD and CO-TPD, the results showed that the main reasons for the deactivation of the catalysts were besides hot sintering. The reduction of specific surface area, the reduction of mesoporous ratio, CuO loss and the increase of CuO grain size are also the specific reasons for catalyst deactivation. The high content of CO produced in the high temperature has no obvious effect on catalyst deactivation.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200614001
Abstract:
A detailed study of the dispersion, rheological and adsorption behaviors between multi-walled carbon nanotubes (MWNTs) and sodium carboxymethylcellulose (CMC) at different metal ions solutions were presented. The experimental results suggested that the chelation between metal ions and CMC governed the adsorption amount and adsorption conformation of CMC onto MWNTs, which had a great influence on the dispersion stability of MWNTs slurries. The MWNTs slurry with Fe2+ had smaller average size, lower viscosity and better stability, which led the slurry to evolving from shear-thinning fluid. It can be seen form UV adsorption experiment that the chelation between Fe3+ and CMC was stronger than that of other divalent ion. And the chelation increased with the increase of the radius of the divalent ion. Raman and Thermo-gravimetry (TGA) results showed that the adsorption amount of Fe3+ was lower, which provided a lower electrostatic repulsive force. In the slurry with divalent ions, adsorption amount of CMC onto MWNTs were higher in the order of Ni2+, Co2+ and Fe2+, providing higher repulsive force, larger zeta potential on MWNTs surface. That’s the reason why Fe2+ had better dispersion stability. The microstructures were measured by TEM. It was found that uniform CMC adsorption layers were formed on the surface of MWNTs with divalent ions. However, for the MWNTs with Fe3+, MWNTs were wrapped by CMC agglomerates, resulting in poor dispersion stability.
A detailed study of the dispersion, rheological and adsorption behaviors between multi-walled carbon nanotubes (MWNTs) and sodium carboxymethylcellulose (CMC) at different metal ions solutions were presented. The experimental results suggested that the chelation between metal ions and CMC governed the adsorption amount and adsorption conformation of CMC onto MWNTs, which had a great influence on the dispersion stability of MWNTs slurries. The MWNTs slurry with Fe2+ had smaller average size, lower viscosity and better stability, which led the slurry to evolving from shear-thinning fluid. It can be seen form UV adsorption experiment that the chelation between Fe3+ and CMC was stronger than that of other divalent ion. And the chelation increased with the increase of the radius of the divalent ion. Raman and Thermo-gravimetry (TGA) results showed that the adsorption amount of Fe3+ was lower, which provided a lower electrostatic repulsive force. In the slurry with divalent ions, adsorption amount of CMC onto MWNTs were higher in the order of Ni2+, Co2+ and Fe2+, providing higher repulsive force, larger zeta potential on MWNTs surface. That’s the reason why Fe2+ had better dispersion stability. The microstructures were measured by TEM. It was found that uniform CMC adsorption layers were formed on the surface of MWNTs with divalent ions. However, for the MWNTs with Fe3+, MWNTs were wrapped by CMC agglomerates, resulting in poor dispersion stability.
