针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征...针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征变量,并通过逐次变分模态分解对筛选后的辅助变量进行分解重构,保留相关性较大的重构分量作为输入变量。随后采用双向时间卷积网络、双向门控循环单元与多头自注意力机制构建融合神经网络模型,通过CFOA对模型超参数寻优以进一步提高精度。最后使用某660 MW燃煤电厂历史运行数据进行对比实验,实验结果表明,该模型在出口SO_(2)浓度剧烈波动的工况下仍能实现较好的预测效果。同多种模型对比,该模型具有更小的误差和更高的预测精度,体现出其在复杂变化环境中的鲁棒性和可靠性。展开更多
To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synt...To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.展开更多
A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,...A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV.展开更多
For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare...For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.展开更多
Magnesium hydride(MgH_(2)),a promising high-capacity hydrogen storage material,is hindered by slow dehydrogenation kinetics.AIdriven catalyst discovery to address this is often hampered by the laborious extraction of ...Magnesium hydride(MgH_(2)),a promising high-capacity hydrogen storage material,is hindered by slow dehydrogenation kinetics.AIdriven catalyst discovery to address this is often hampered by the laborious extraction of data from unstructured literature.To overcome this,we introduce a transformative“LLM to Agent”framework that synergistically integrates Large Language Models(LLMs)for automated data curation with Machine Learning(ML)for predictive design.We automatically constructed a comprehensive database of 809 MgH_(2)catalysts(6555 data rows)with high fidelity and an~40-fold acceleration over manual methods.The resulting ML models achieved high accuracy(average R^(2)>0.91)in predicting dehydrogenation temperature and activation energy,subsequently guiding a Genetic Algorithm(GA)in an exploratory inverse design that autonomously uncovered key design principles for high-performance catalysts.Encouragingly,a strong alignment was found between these AI-discovered principles and the design strategies of recently reported,state-of-the-art experimental systems,providing substantial evidence for the validity of our approach.The framework culminates in Cat-Advisor,a novel,domain-adapted multi-agent system.Cat-Advisor translates ML predictions and retrieval-augmented knowledge into actionable design guidance,demonstrating capabilities that surpass those of general-purpose LLMs in this specialized domain.This work delivers a practical AI toolkit for accelerated materials discovery and advances the emerging Agent-based paradigm for designing next-generation energy technologies.展开更多
文摘针对燃煤电厂参与调峰负荷波动较大,出口SO_(2)浓度控制效果不佳的问题,建立了一种基于捕鱼优化算法(catch fish optimization algorithm,CFOA)优化融合神经网络的出口SO_(2)浓度预测模型。首先使用互信息算法筛选由机理分析得到的特征变量,并通过逐次变分模态分解对筛选后的辅助变量进行分解重构,保留相关性较大的重构分量作为输入变量。随后采用双向时间卷积网络、双向门控循环单元与多头自注意力机制构建融合神经网络模型,通过CFOA对模型超参数寻优以进一步提高精度。最后使用某660 MW燃煤电厂历史运行数据进行对比实验,实验结果表明,该模型在出口SO_(2)浓度剧烈波动的工况下仍能实现较好的预测效果。同多种模型对比,该模型具有更小的误差和更高的预测精度,体现出其在复杂变化环境中的鲁棒性和可靠性。
基金financial supports from the National Natural Science Foundation of China-Youth Project(51801076)the Provincial Colleges and Universities Natural Science Research Project of Jiangsu Province(18KJB430009)+1 种基金the Postdoctoral Research Support Project of Jiangsu Province(1601055C)the Senior Talents Research Startup of Jiangsu University(14JDG126)。
文摘To solve the problems of deformation,micro-cracks,and residual tensile stress in laser cladding coatings,the technique of laser cladding with Fe-based memory alloy can be considered.However,the process of in-situ synthesis of Fe-based memory alloy coatings is extremely complex.At present,there is no clear guidance scheme for its preparation process,which limits its promotion and application to some extent.Therefore,in this study,response surface methodology(RSM)was used to model the response surface between the target values and the cladding process parameters.The NSGA-2 algorithm was employed to optimize the process parameters.The results indicate that the composite optimization method consisting of RSM and the NSGA-2 algorithm can establish a more accurate model,with an error of less than 4.5%between the predicted and actual values.Based on this established model,the optimal scheme for process parameters corresponding to different target results can be rapidly obtained.The prepared coating exhibits a uniform structure,with no defects such as pores,cracks,and deformation.The surface roughness and microhardness of the coating are enhanced,the shaping quality of the coating is effectively improved,and the electrochemical corrosion performance of the coating in 3.5%NaCl solution is obviously better than that of the substrate,providing an important guide for engineering applications.
基金supported by the National Natural Science Foundation of China(32273037 and 32102636)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030007)+4 种基金Laboratory of Lingnan Modern Agriculture Project(NT2021007)the Guangdong Science and Technology Innovation Leading Talent Program(2019TX05N098)the 111 Center(D20008)the double first-class discipline promotion project(2023B10564003)the Department of Education of Guangdong Province(2019KZDXM004 and 2019KCXTD001).
文摘A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV.
基金supported by National Natural Science Foundation of China(No.12172157)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)Key Research and Development Planning Project of Gansu Province(No.23YFWA0007).
文摘For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.
基金supported by the Natural Science Foundation of Hebei Province(E2023502006)Fundamental Research Fund for the Central Universities(2025MS131).
文摘Magnesium hydride(MgH_(2)),a promising high-capacity hydrogen storage material,is hindered by slow dehydrogenation kinetics.AIdriven catalyst discovery to address this is often hampered by the laborious extraction of data from unstructured literature.To overcome this,we introduce a transformative“LLM to Agent”framework that synergistically integrates Large Language Models(LLMs)for automated data curation with Machine Learning(ML)for predictive design.We automatically constructed a comprehensive database of 809 MgH_(2)catalysts(6555 data rows)with high fidelity and an~40-fold acceleration over manual methods.The resulting ML models achieved high accuracy(average R^(2)>0.91)in predicting dehydrogenation temperature and activation energy,subsequently guiding a Genetic Algorithm(GA)in an exploratory inverse design that autonomously uncovered key design principles for high-performance catalysts.Encouragingly,a strong alignment was found between these AI-discovered principles and the design strategies of recently reported,state-of-the-art experimental systems,providing substantial evidence for the validity of our approach.The framework culminates in Cat-Advisor,a novel,domain-adapted multi-agent system.Cat-Advisor translates ML predictions and retrieval-augmented knowledge into actionable design guidance,demonstrating capabilities that surpass those of general-purpose LLMs in this specialized domain.This work delivers a practical AI toolkit for accelerated materials discovery and advances the emerging Agent-based paradigm for designing next-generation energy technologies.
