The purpose of this study is to develop a standard methodology for measuring the surface free energy (SFE),and its component parts of bamboo fiber materials.The current methods was reviewed to determine the surface te...The purpose of this study is to develop a standard methodology for measuring the surface free energy (SFE),and its component parts of bamboo fiber materials.The current methods was reviewed to determine the surface tension of natural fibers and the disadvantages of techniques used were discussed.Although numerous techniques have been employed to characterize surface tension of natural fibers,it seems that the credibility of results obtained may often be dubious.In this paper,critical surface tension estimates were obtained from computer aided machine vision based measurement.Data were then analyzed by the least squares method to estimate the components of SFE.SFE was estimated by least squares analysis and also by Schultz' method.By using the Fowkes method the polar and disperse fractions of the surface free energy of bamboo fiber materials can be obtained.Strictly speaking,this method is based on a combination of the knowledge of Fowkes theory. SFE is desirable when adhesion is required,and it avoids some of the limitations of existing studies which has been proposed.The calculation steps described in this research are only intended to explain the methods.The results show that the method that only determines SFE as a single parameter may be unable to differentiate adequately between bamboo fiber materials,but it is feasible and very efficient.In order to obtain the maximum performance from the computer aided machine vision based measurement instruments,this measurement should be recommended and kept available for reference.展开更多
In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied ...In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mm Hg compared with that by the Coriolis method.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract ke...As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).展开更多
Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degra...Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus...Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission.During the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective solution.As important parts of the face are obscured,face identification and verification becomes exceedingly difficult.The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked identification.In the first stage,we are applying face mask detector to identify the face mask.Then,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed dataset.This article employs the four pre-programmed models VGG16,VGG19,ResNet50 and ResNet101.To extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own dataset.The comparative analysis shows,that our proposed model performs better result in all four previous existing models.The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.展开更多
Digital factory technology is an advanced manufacturing technology served as to establish a bridge between the process of product development and manufacturing.In terms of application for digital factory technology in...Digital factory technology is an advanced manufacturing technology served as to establish a bridge between the process of product development and manufacturing.In terms of application for digital factory technology in machining,especially in machining of a complicated part such as a cylinder body part,a concept of digital process planning and its framework are proposed.Its components including machining domain knowledge model,machining knowledge base,machining resource base and process planning system are studied.A machining knowledge model in tree form and an object-driven knowledge reasoning mechanism are used for machining knowledge base.The process planning system is a user interface that leads a planner to finish the planning process.A case about a cylinder head part is given to demonstrate how the platform works.The framework of digital process planning is the foundation of some intelligent CAPP systems and helps to production line planning.展开更多
Digital factory technology is a research focus in academe and industry,which is an advanced manufacturing tech-nology that is proposed to bridge product development and manufacturing.For applying digital factory techn...Digital factory technology is a research focus in academe and industry,which is an advanced manufacturing tech-nology that is proposed to bridge product development and manufacturing.For applying digital factory technology in machining domain,a concept of digital process planning and its framework are suggested,its components including machining domain knowledge model,machining knowledge base,machining resource base and process planning system are studied.The framework of digital process planning is of value for implementing digital factory technology in machining industry.展开更多
With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus d...With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured.展开更多
Cyanobacteria are constructors of biological soil crusts(BSCs);their motility is thought to be crucial for surviving burial and BSC expansion.In this study,X-ray computed microtomography in combination with machine-le...Cyanobacteria are constructors of biological soil crusts(BSCs);their motility is thought to be crucial for surviving burial and BSC expansion.In this study,X-ray computed microtomography in combination with machine-learning-based image processing was employed to investigate cyanobacteria-dominated BSCs.The structural changes in these BSCs,as well as the behaviors of the dominant cyanobacterium Microcoleus vaginatus therein,in response to changes in water availability and particle burial were visualized and quantitatively analyzed.Hygroscopic swelling of cyanobacteria biomaterials increased pore-network complexity and reduced the porosity and hydraulic radius.Trichomes of M.vaginatus inside BSCs were connected to the surface by tunnel-like structures made of extracellular polymeric substances(EPSs),through which the trichomes migrated to and from the surface in bundles.Despite the generally negative effects of EPSs on hydraulic conductivity,EPS tunnels have the potential to enhance water transfer to the trichomes.Extensive hydration and particle burial led to the spreading migration of individual trichomes,forming netlike structures inside the newly deposited layer.The results highlight the significance of the structural organization of EPSs within BSCs and the importance of cyanobacterial migration in BSC expansion.展开更多
Chikungunya virus(CHIKV),transmitted by arthropods,has gained global recognition for its impact on public health.It has expanded globally,including Africa,Asia,and the Indian subcontinent,and has a helicase protein in...Chikungunya virus(CHIKV),transmitted by arthropods,has gained global recognition for its impact on public health.It has expanded globally,including Africa,Asia,and the Indian subcontinent,and has a helicase protein in its genome that is crucial for its replication.Thus,the study targeted the helicase protein of CHIKV with 745 antiviral compounds using an ML-based QSAR model and molecular docking.Top binders(5279172,78161839,6474310,and 5330286)were selected for MD simulation based on the control(Silvestrol).All compounds had the highest binding scores,with 78161839 showing the most consistent RMSD and the least conformational variation,indicating high stability.It also showed the lowest binding free energy(ΔG¼31.31 kcal/mol),indicating energetically favourable binding.PCA and FEL also depicted the stable complex confirmation of the protein and 78161839 complex during the 100 ns simulation.Overall,this study aimed to identify helicase function antiviral binders that could be experimentally tested for treating CHIKV.展开更多
基金the National Natural Science Foundation of China(No.31101085)the Scientific Research and Development Foundation for Start-up Projects of Zhejiang Agriculture and Forestry University (No.2034020044)
文摘The purpose of this study is to develop a standard methodology for measuring the surface free energy (SFE),and its component parts of bamboo fiber materials.The current methods was reviewed to determine the surface tension of natural fibers and the disadvantages of techniques used were discussed.Although numerous techniques have been employed to characterize surface tension of natural fibers,it seems that the credibility of results obtained may often be dubious.In this paper,critical surface tension estimates were obtained from computer aided machine vision based measurement.Data were then analyzed by the least squares method to estimate the components of SFE.SFE was estimated by least squares analysis and also by Schultz' method.By using the Fowkes method the polar and disperse fractions of the surface free energy of bamboo fiber materials can be obtained.Strictly speaking,this method is based on a combination of the knowledge of Fowkes theory. SFE is desirable when adhesion is required,and it avoids some of the limitations of existing studies which has been proposed.The calculation steps described in this research are only intended to explain the methods.The results show that the method that only determines SFE as a single parameter may be unable to differentiate adequately between bamboo fiber materials,but it is feasible and very efficient.In order to obtain the maximum performance from the computer aided machine vision based measurement instruments,this measurement should be recommended and kept available for reference.
基金supported by the National Natural Science Foundation of China (No.61675174)the Natural Science Foundation of Fujian Province (No.2020J01705)。
文摘In this paper, we present a method based on self-mixing interferometry combing extreme learning machine for real-time human blood pressure measurement. A signal processing method based on wavelet transform is applied to extract reversion point in the self-mixing interference signal, thus the pulse wave profile is successfully reconstructed. Considering the blood pressure values are intrinsically related to characteristic parameters of the pulse wave, 80 samples from the MIMIC-II database are used to train the extreme learning machine blood pressure model. In the experiment, 15 measured samples of pulse wave signal are used as the prediction sets. The results show that the errors of systolic and diastolic blood pressure are both within 5 mm Hg compared with that by the Coriolis method.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
基金Dr.Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting his work under Project No.(R-2022-96)。
文摘As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).
基金supported in part by the National Natural Science Foundation of China(U2034209)the Postdoctoral Science Foundation of Chongqing(cstc2021jcyj-bsh X0047)+1 种基金the Fundamental Research Funds for the Central Universities(2022CDJJMRH-008)the National Natural Science Foundation of China(62203075)
文摘Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
文摘Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security concerns.People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission.During the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective solution.As important parts of the face are obscured,face identification and verification becomes exceedingly difficult.The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked identification.In the first stage,we are applying face mask detector to identify the face mask.Then,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked Face Recognition Database(RMFRD),and Stimulated Masked Face Recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed dataset.This article employs the four pre-programmed models VGG16,VGG19,ResNet50 and ResNet101.To extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own dataset.The comparative analysis shows,that our proposed model performs better result in all four previous existing models.The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.
文摘Digital factory technology is an advanced manufacturing technology served as to establish a bridge between the process of product development and manufacturing.In terms of application for digital factory technology in machining,especially in machining of a complicated part such as a cylinder body part,a concept of digital process planning and its framework are proposed.Its components including machining domain knowledge model,machining knowledge base,machining resource base and process planning system are studied.A machining knowledge model in tree form and an object-driven knowledge reasoning mechanism are used for machining knowledge base.The process planning system is a user interface that leads a planner to finish the planning process.A case about a cylinder head part is given to demonstrate how the platform works.The framework of digital process planning is the foundation of some intelligent CAPP systems and helps to production line planning.
基金supported by Sino-German project,grant No.2002DFG00027.
文摘Digital factory technology is a research focus in academe and industry,which is an advanced manufacturing tech-nology that is proposed to bridge product development and manufacturing.For applying digital factory technology in machining domain,a concept of digital process planning and its framework are suggested,its components including machining domain knowledge model,machining knowledge base,machining resource base and process planning system are studied.The framework of digital process planning is of value for implementing digital factory technology in machining industry.
基金This study is supported by the Tamil Nadu State Council of Science and Technology.
文摘With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured.
基金supported by the Leading Talents in Sci-Technological Innovation Project of“Tianshan Talent”Training Plan of the Xinjiang Uygur Autonomous Region(2022TSYCLJ0058)the Youth Talent Training Program of Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences(E328010701)the“Tianshan Talent”Training Program(2023TSYCCX0089).
文摘Cyanobacteria are constructors of biological soil crusts(BSCs);their motility is thought to be crucial for surviving burial and BSC expansion.In this study,X-ray computed microtomography in combination with machine-learning-based image processing was employed to investigate cyanobacteria-dominated BSCs.The structural changes in these BSCs,as well as the behaviors of the dominant cyanobacterium Microcoleus vaginatus therein,in response to changes in water availability and particle burial were visualized and quantitatively analyzed.Hygroscopic swelling of cyanobacteria biomaterials increased pore-network complexity and reduced the porosity and hydraulic radius.Trichomes of M.vaginatus inside BSCs were connected to the surface by tunnel-like structures made of extracellular polymeric substances(EPSs),through which the trichomes migrated to and from the surface in bundles.Despite the generally negative effects of EPSs on hydraulic conductivity,EPS tunnels have the potential to enhance water transfer to the trichomes.Extensive hydration and particle burial led to the spreading migration of individual trichomes,forming netlike structures inside the newly deposited layer.The results highlight the significance of the structural organization of EPSs within BSCs and the importance of cyanobacterial migration in BSC expansion.
基金supported by the Al-Manara College for Medical Sciences,Department of Pharmacy,Kut University College,Department of Pharmaceutical Chemistry,College of Pharmacy,University of Baghdad,Department of pharmacy,Hilla University College,Dr Hany Akeel institute,Iraqi Medical Research center.
文摘Chikungunya virus(CHIKV),transmitted by arthropods,has gained global recognition for its impact on public health.It has expanded globally,including Africa,Asia,and the Indian subcontinent,and has a helicase protein in its genome that is crucial for its replication.Thus,the study targeted the helicase protein of CHIKV with 745 antiviral compounds using an ML-based QSAR model and molecular docking.Top binders(5279172,78161839,6474310,and 5330286)were selected for MD simulation based on the control(Silvestrol).All compounds had the highest binding scores,with 78161839 showing the most consistent RMSD and the least conformational variation,indicating high stability.It also showed the lowest binding free energy(ΔG¼31.31 kcal/mol),indicating energetically favourable binding.PCA and FEL also depicted the stable complex confirmation of the protein and 78161839 complex during the 100 ns simulation.Overall,this study aimed to identify helicase function antiviral binders that could be experimentally tested for treating CHIKV.
