Double self-adaptive fuzzy PID algorithm-based control strategy was proposed to construct quasi-cascade control system to control the speed of the acid-pickling process of titanium plates and strips. It is very useful...Double self-adaptive fuzzy PID algorithm-based control strategy was proposed to construct quasi-cascade control system to control the speed of the acid-pickling process of titanium plates and strips. It is very useful in overcoming non-linear dynamic behavior, uncertain and time-varying parameters, un-modeled dynamics, and couples between the automatic turbulence control (ATC) and the automatic acid temperature control (AATC) with varying parameters during the operation process. The quasi-cascade control system of inner and outer loop self-adaptive fuzzy PID controller was built, which could effectively control the pickling speed of plates and strips. The simulated results and real application indicate that the plates and strips acid pickling speed control system has good performances of adaptively tracking the parameter variations and anti-disturbances, which ensures the match of acid pickling temperature and turbulence of flowing with acid pickling speed, improving the surface quality of plates and strips acid pickling, and energy efficiency.展开更多
In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data ...In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.展开更多
BACKGROUND： The estimation of liver fibrosis is usually dependent on liver biopsy evaluation. Because of its disadvantages and side effects, researchers try to find non-invasive methods for the assessment of liver in...BACKGROUND： The estimation of liver fibrosis is usually dependent on liver biopsy evaluation. Because of its disadvantages and side effects, researchers try to find non-invasive methods for the assessment of liver injuries. Hyaluronic acid has been proposed as an index for scoring the severity of fibrosis, alone or in algorithm models. The algorithm model in which hyaluronic acid was used as a major constituent was more reliable and accurate in diagnosis than hyaluronic acid alone. This review described various hyaluronic acid algorithm-based models for assessing liver fibrosis.DATA SOURCE： A Pub Med database search was performed to identify the articles relevant to hyaluronic acid algorithmbased models for estimating liver fibrosis.RESULT： The use of hyaluronic acid in an algorithm model is an extra and valuable tool for assessing liver fibrosis.CONCLUSIONS： Although hyaluronic acid algorithm-based models have good diagnostic power in liver fibrosis assessment, they cannot render the need for liver biopsy obsolete and it is better to use them in parallel with liver biopsy. They can be used when frequent liver biopsy is not possible in situations such as highlighting the efficacy of treatment protocol for liver fibrosis.展开更多
In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depend...In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depends not only on complex mathematical models but also on encryption keys.Amino acid encryption is a promising model for data security.In this paper,we propose an amino acid encryption model with two encryption keys.The first key is generated randomly using the genetic algorithm.The second key is called the protein key which is generated from converting DNA to a protein message.Then,the protein message and the first key are used in the modified Playfair matrix to generate the cypher message.The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack.In addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit binary.The performance of the proposed model is compared with other models using encryption time and decryption time.The model also balances all three principles in the CIA triad.展开更多
In this study,an Artificial Neural Network-Genetic Algorithm(ANN-GA)approachwas successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids(UFAs)in the microalgae ...In this study,an Artificial Neural Network-Genetic Algorithm(ANN-GA)approachwas successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids(UFAs)in the microalgae P.kessleri UCM 001.The optimized model recommended specific cultivation conditions,including glucose at 29 g/L,NaNO_(3)at 2.4 g/L,K_(2)HPO_(4)at 0.4 g/L,red LED light,an intensity of 1000 lx,and an 8:16-h light-dark cycle.Through ANN-GA optimisation,a remarkable 66.79%increase in UFAs production in P.kessleri UCM 001 was achieved,compared to previous studies.This underscores the potential of this technology for enhancing valuable lipid production.Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions,as optimized by ANN-GA,induced alterations in UFAs production and composition in P.kessleri UCM 001.This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications.Themicroalgaewere isolated froma high-mountain lake in Colombia,highlighting their adaptation to extreme conditions.This underscores their potential for sustainable lipid and biomaterial production.This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae,offering a promising avenue for obtaining valuable lipids.Themicroalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications.展开更多
Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a se...Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.展开更多
Most of the human genetic variations are single nucleotide polymorphisms (SNPs), and among them, non-synonymous SNPs, also known as SAPs, attract extensive interest. SAPs can be neural or disease associated. Many stud...Most of the human genetic variations are single nucleotide polymorphisms (SNPs), and among them, non-synonymous SNPs, also known as SAPs, attract extensive interest. SAPs can be neural or disease associated. Many studies have been done to distinguish deleterious SAPs from neutral ones. Since many previous studies were based on both structural and sequence features of the SAP, these methods are not applicable when protein structures are not available. In the current paper, we developed a method based on UMDA and SVM using protein sequence information to predict SAP’s disease association. We extracted a set of features that are independent of protein structure for each SAP. Then a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease associa-tion of SAPs. The SVM method reaches good prediction accuracy. Since the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance, we introduced UMDA-based wrapper approach to search for the ‘best’ solution. The UMDA-based method greatly improved prediction performance. Com-pared with current method, our method achieved better performance.展开更多
基金Project(51090385) supported by the National Natural Science Foundation of ChinaProject(2001IB001) supported by Yunnan Provincial Science and Technology Fund, China
文摘Double self-adaptive fuzzy PID algorithm-based control strategy was proposed to construct quasi-cascade control system to control the speed of the acid-pickling process of titanium plates and strips. It is very useful in overcoming non-linear dynamic behavior, uncertain and time-varying parameters, un-modeled dynamics, and couples between the automatic turbulence control (ATC) and the automatic acid temperature control (AATC) with varying parameters during the operation process. The quasi-cascade control system of inner and outer loop self-adaptive fuzzy PID controller was built, which could effectively control the pickling speed of plates and strips. The simulated results and real application indicate that the plates and strips acid pickling speed control system has good performances of adaptively tracking the parameter variations and anti-disturbances, which ensures the match of acid pickling temperature and turbulence of flowing with acid pickling speed, improving the surface quality of plates and strips acid pickling, and energy efficiency.
基金Heilongjiang Natural Science Foundation (F0318).
文摘In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.
基金supported by a grant from the Babol University of Medical Sciences,Babol,Iran(No.2093)
文摘BACKGROUND： The estimation of liver fibrosis is usually dependent on liver biopsy evaluation. Because of its disadvantages and side effects, researchers try to find non-invasive methods for the assessment of liver injuries. Hyaluronic acid has been proposed as an index for scoring the severity of fibrosis, alone or in algorithm models. The algorithm model in which hyaluronic acid was used as a major constituent was more reliable and accurate in diagnosis than hyaluronic acid alone. This review described various hyaluronic acid algorithm-based models for assessing liver fibrosis.DATA SOURCE： A Pub Med database search was performed to identify the articles relevant to hyaluronic acid algorithmbased models for estimating liver fibrosis.RESULT： The use of hyaluronic acid in an algorithm model is an extra and valuable tool for assessing liver fibrosis.CONCLUSIONS： Although hyaluronic acid algorithm-based models have good diagnostic power in liver fibrosis assessment, they cannot render the need for liver biopsy obsolete and it is better to use them in parallel with liver biopsy. They can be used when frequent liver biopsy is not possible in situations such as highlighting the efficacy of treatment protocol for liver fibrosis.
文摘In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depends not only on complex mathematical models but also on encryption keys.Amino acid encryption is a promising model for data security.In this paper,we propose an amino acid encryption model with two encryption keys.The first key is generated randomly using the genetic algorithm.The second key is called the protein key which is generated from converting DNA to a protein message.Then,the protein message and the first key are used in the modified Playfair matrix to generate the cypher message.The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack.In addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit binary.The performance of the proposed model is compared with other models using encryption time and decryption time.The model also balances all three principles in the CIA triad.
文摘In this study,an Artificial Neural Network-Genetic Algorithm(ANN-GA)approachwas successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids(UFAs)in the microalgae P.kessleri UCM 001.The optimized model recommended specific cultivation conditions,including glucose at 29 g/L,NaNO_(3)at 2.4 g/L,K_(2)HPO_(4)at 0.4 g/L,red LED light,an intensity of 1000 lx,and an 8:16-h light-dark cycle.Through ANN-GA optimisation,a remarkable 66.79%increase in UFAs production in P.kessleri UCM 001 was achieved,compared to previous studies.This underscores the potential of this technology for enhancing valuable lipid production.Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions,as optimized by ANN-GA,induced alterations in UFAs production and composition in P.kessleri UCM 001.This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications.Themicroalgaewere isolated froma high-mountain lake in Colombia,highlighting their adaptation to extreme conditions.This underscores their potential for sustainable lipid and biomaterial production.This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae,offering a promising avenue for obtaining valuable lipids.Themicroalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications.
基金National Key Technologies Research and Development Program in the 10th Five-year Phan(No.2001BA204B01)National Outstanding Youth Science Foundation of China(No.60025308)
文摘Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.
文摘Most of the human genetic variations are single nucleotide polymorphisms (SNPs), and among them, non-synonymous SNPs, also known as SAPs, attract extensive interest. SAPs can be neural or disease associated. Many studies have been done to distinguish deleterious SAPs from neutral ones. Since many previous studies were based on both structural and sequence features of the SAP, these methods are not applicable when protein structures are not available. In the current paper, we developed a method based on UMDA and SVM using protein sequence information to predict SAP’s disease association. We extracted a set of features that are independent of protein structure for each SAP. Then a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease associa-tion of SAPs. The SVM method reaches good prediction accuracy. Since the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance, we introduced UMDA-based wrapper approach to search for the ‘best’ solution. The UMDA-based method greatly improved prediction performance. Com-pared with current method, our method achieved better performance.
