The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress...The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.展开更多
A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the ca...A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.展开更多
Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this wor...Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.展开更多
Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patien...Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this ...Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.展开更多
The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters...The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.展开更多
Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as t...Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.展开更多
智慧交通行业具有综合性、系统性、数据驱动、服务智能化、安全性和环保性等特点,对相关企业进行价值评估可以为投资决策提供参考、为资产配置提供依据、为相关企业的并购重组提供指导、明确业务发展的方向等。在进行企业价值评估时,重...智慧交通行业具有综合性、系统性、数据驱动、服务智能化、安全性和环保性等特点,对相关企业进行价值评估可以为投资决策提供参考、为资产配置提供依据、为相关企业的并购重组提供指导、明确业务发展的方向等。在进行企业价值评估时,重要的是考虑当前实际情况,相应地调整使用的参数,选择恰当的评估方法,确保评估数据反映最新的市场动态和企业经营状况。文章以qf科技为例,使用灰色预测模型GM(1,1)对qf科技企业的未来自由现金流加以预测,通过DCF模型和经过蒙特卡洛模拟(Monte Carlo Simulation)改进后的DCF模型对qf科技企业价值进行评估,最终结果显示:通过蒙特卡洛模拟(Monte Carlo Simulation)改进后的模型评估企业价值更加合理。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52275373)the National Natural Science Foundation of China(Grant No.52105397)the Open Foundation of National Key Laboratory of Metal Forming Technology and Heavy Equipment(Grant No.S2308100.W08).
文摘The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.
基金financial support from the Start-up Grant of Nanjing Tech University(Nos.38274017103,38037037)financial support from Distinguished University Professor grant(Nanyang Technological University)+1 种基金the Agency for Science,Technology and Research(A∗STAR)under its MTC Individual Research Grants(No.M21K2c0114)RIE2025 MTC Programmatic Fund(No.M22K9b0049).
文摘A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302100)the National Natural Science Foundation of China(Grant Nos.12361131576,92265205,and 92476205).
文摘Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.
基金Fundamental Research Funds for the Central Universities in China,No.N161608001 and No.N171903002
文摘Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金National Natural Science Foundation of china(60274014,60574088)
文摘Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.
基金sponsored by the National Natural Science Foundation of China(61333002)Open Research Foundation of the State Key Laboratory of Geodesy and Earth’s Dynamics(SKLGED2018-5-4-E)+5 种基金Foundation of the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems(ACIA2017002)111 projects under Grant(B17040)Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017A02)supported by the Three Gorges Research Center for geo-hazardMinistry of Education cooperation agreements of Krasnoyarsk Science Center and Technology BureauRussian Academy of Sciences。
文摘The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.
文摘Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.
文摘智慧交通行业具有综合性、系统性、数据驱动、服务智能化、安全性和环保性等特点,对相关企业进行价值评估可以为投资决策提供参考、为资产配置提供依据、为相关企业的并购重组提供指导、明确业务发展的方向等。在进行企业价值评估时,重要的是考虑当前实际情况,相应地调整使用的参数,选择恰当的评估方法,确保评估数据反映最新的市场动态和企业经营状况。文章以qf科技为例,使用灰色预测模型GM(1,1)对qf科技企业的未来自由现金流加以预测,通过DCF模型和经过蒙特卡洛模拟(Monte Carlo Simulation)改进后的DCF模型对qf科技企业价值进行评估,最终结果显示:通过蒙特卡洛模拟(Monte Carlo Simulation)改进后的模型评估企业价值更加合理。
