Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing ...Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.展开更多
Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to cri...Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration.However,many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters,which can easily lead to achieving a suboptimal hyper-parameter configuration.In this study,Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms.Six drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 fingerprints.This contribution aims to evaluate whether the Bernoulli Na?ve Bayes,logistic linear regression,Ada Boost decision tree,random forest,support vector machine,and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC,F1-score,Cohen’s kappa,Matthews correlation coefficient,recall,precision,and accuracy.Based on the rank normalized score approach,the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets,showing significant improvement achieved by employing the Hyperopt library.The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists,who are not familiar with writing code.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
In this paper,137“First-class universities”and“First-class discipline”construction universities in China are selected as the objects of investigation to analyzes the present situation and characteristics of the g...In this paper,137“First-class universities”and“First-class discipline”construction universities in China are selected as the objects of investigation to analyzes the present situation and characteristics of the game design of University Library in China.Taking the university library in other countries as the reference object,this paper compares the differences of the game design of University Library in China and other countries,sums up the deficiency of the gamification service practice in Chinese university libraries.At last,this paper proposes an optimization path of the gamification design of Chinese University Library from six aspects of game type,game service object,game interactive mode,game application,game development and game playability.展开更多
Paper books and documents are the main media of information service in most university libraries.In order to study the main reasons for the decline of the utilization rate of paper books in university libraries,this p...Paper books and documents are the main media of information service in most university libraries.In order to study the main reasons for the decline of the utilization rate of paper books in university libraries,this paper takes a well-known university library in China as an example,discusses the current situation of its low utilization rate of paper books and the reasons for the problems,and puts forward relevant optimization strategies,so as to promote the improvement of the construction quality of university libraries in China.展开更多
As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent task...As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.展开更多
The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to th...The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to the results,aimed at optimizing the filtered results.Based on a basic serial LMS adaptive filtering algorithm,we propose a vectorized parallel processing scheme for the LMS adaptive filtering algorithm in this work.By combining the characteristics of the algorithm processing flow and those of the parallel technologies used in vector Digital Signal Processes(DSPs),the optimizations such as loop fusion,double-word accessing,and vector shuffling of the LMS algorithm are studied in depth,and the loop unrolling optimization method is used to accelerate the calculation of the algorithm further.Experimental research was conducted on the high-performance FT-M7002 DSP platform in this paper.The results show that,compared with the running performance of the LMS adaptive filtering algorithm in Texas Instruments(TI)’s dsplib library on the TMS320C6678 processor,the optimization effect of the proposed optimization algorithm in this paper can achieve a maximum speed-up ratio of up to 6.9×for medium-scale data.The merged memory access optimization implemented on the GPU platform achieves an average 1.5x speedup compared to the basic parallel scheme.展开更多
Low-carbon,energy-saving,and health have become a common trend for the whole of mankind.However,how to balance the relationship between energy-saving and healthy indoor environment is a key issue for sustainable build...Low-carbon,energy-saving,and health have become a common trend for the whole of mankind.However,how to balance the relationship between energy-saving and healthy indoor environment is a key issue for sustainable building development.This paper extracted the prototypical form of university library atrium based on 44 library cases in Wuhan.A methodology verified with measured data for evaluating building performance was constructed,and the synergistic influence of spatial morphology parameters on the building energy efficiency(BEE)and indoor environmental quality(IEQ)was analyzed.Finally,a multi-objective fast optimization framework coupled with machine learning algorithms was used to achieve the optimal design of university library atrium.The results showed that the parameters that influence the building energy consumption,indoor thermal comfort,daylighting performance most were the height-to-width ratio,the skylight solar heat gain coefficient,and the sidewall window-to-wall ratio,respectively.The machine learning models predicted performance 400 times faster than traditional performance simulations.And compared with the worst-performance scheme,the maximum optimization rate of building energy consumption,indoor thermal comfort,daylighting performance was 29.46%,10.46%,and 65.56%,respectively.The multi-objective fast optimization framework could provide guidance for policy makers and architects to performance-based design in the early design stages of university library atrium.展开更多
基金supported by the National Natural the Science Foundation of China(51971042,51901028)the Chongqing Academician Special Fund(cstc2020yszxjcyj X0001)+1 种基金the China Scholarship Council(CSC)Norwegian University of Science and Technology(NTNU)for their financial and technical support。
文摘Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.
基金financial support provided by the National Key Research and Development Project(2019YFC0214403)Chongqing Joint Chinese Medicine Scientific Research Project(2021ZY023984)。
文摘Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration.However,many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters,which can easily lead to achieving a suboptimal hyper-parameter configuration.In this study,Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms.Six drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 fingerprints.This contribution aims to evaluate whether the Bernoulli Na?ve Bayes,logistic linear regression,Ada Boost decision tree,random forest,support vector machine,and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC,F1-score,Cohen’s kappa,Matthews correlation coefficient,recall,precision,and accuracy.Based on the rank normalized score approach,the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets,showing significant improvement achieved by employing the Hyperopt library.The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists,who are not familiar with writing code.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
文摘In this paper,137“First-class universities”and“First-class discipline”construction universities in China are selected as the objects of investigation to analyzes the present situation and characteristics of the game design of University Library in China.Taking the university library in other countries as the reference object,this paper compares the differences of the game design of University Library in China and other countries,sums up the deficiency of the gamification service practice in Chinese university libraries.At last,this paper proposes an optimization path of the gamification design of Chinese University Library from six aspects of game type,game service object,game interactive mode,game application,game development and game playability.
文摘Paper books and documents are the main media of information service in most university libraries.In order to study the main reasons for the decline of the utilization rate of paper books in university libraries,this paper takes a well-known university library in China as an example,discusses the current situation of its low utilization rate of paper books and the reasons for the problems,and puts forward relevant optimization strategies,so as to promote the improvement of the construction quality of university libraries in China.
文摘As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent tasks.By exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and devices.In this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC environment.The proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge environment.In addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)metaheuristic.Extensive experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.
基金funded by the Hunan Provincial Natural Science Foundation of China(No.2023JJ50019)the National Science and Technology Major Project(No.2022ZD0119003).
文摘The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to the results,aimed at optimizing the filtered results.Based on a basic serial LMS adaptive filtering algorithm,we propose a vectorized parallel processing scheme for the LMS adaptive filtering algorithm in this work.By combining the characteristics of the algorithm processing flow and those of the parallel technologies used in vector Digital Signal Processes(DSPs),the optimizations such as loop fusion,double-word accessing,and vector shuffling of the LMS algorithm are studied in depth,and the loop unrolling optimization method is used to accelerate the calculation of the algorithm further.Experimental research was conducted on the high-performance FT-M7002 DSP platform in this paper.The results show that,compared with the running performance of the LMS adaptive filtering algorithm in Texas Instruments(TI)’s dsplib library on the TMS320C6678 processor,the optimization effect of the proposed optimization algorithm in this paper can achieve a maximum speed-up ratio of up to 6.9×for medium-scale data.The merged memory access optimization implemented on the GPU platform achieves an average 1.5x speedup compared to the basic parallel scheme.
基金supported by the National Natural Science Foundation of China(Grant No.52378020)Open Foundation of the State Key Laboratory of Subtropical Building and Urban Science(Grant No.2023KA02)the Program for HUST Academic Frontier Youth Team(Grant No.2019QYTD10)。
文摘Low-carbon,energy-saving,and health have become a common trend for the whole of mankind.However,how to balance the relationship between energy-saving and healthy indoor environment is a key issue for sustainable building development.This paper extracted the prototypical form of university library atrium based on 44 library cases in Wuhan.A methodology verified with measured data for evaluating building performance was constructed,and the synergistic influence of spatial morphology parameters on the building energy efficiency(BEE)and indoor environmental quality(IEQ)was analyzed.Finally,a multi-objective fast optimization framework coupled with machine learning algorithms was used to achieve the optimal design of university library atrium.The results showed that the parameters that influence the building energy consumption,indoor thermal comfort,daylighting performance most were the height-to-width ratio,the skylight solar heat gain coefficient,and the sidewall window-to-wall ratio,respectively.The machine learning models predicted performance 400 times faster than traditional performance simulations.And compared with the worst-performance scheme,the maximum optimization rate of building energy consumption,indoor thermal comfort,daylighting performance was 29.46%,10.46%,and 65.56%,respectively.The multi-objective fast optimization framework could provide guidance for policy makers and architects to performance-based design in the early design stages of university library atrium.
