Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th...Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasi...Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasing year by year, so it is particularly important to improve the clinical research level of medical devices. Medical statistics is one of the effective means to ensure scientific and reasonable clinical trial design and reliable test results. SAS software is important software for statistical analysis of clinical trials of medical equipment. Starting from the specific application of SAS software, this paper focuses on correctly understanding and selecting various results of SAS statistics, and provides some practical experience for those who learn to apply SAS, so as to make the clinical statistics of medical devices possible. The trial is more accurate and scientific.展开更多
[Objective] To establish the traceability mechanism of agricultural products safety, and the application of promote domestic based software in the supervision area of agricultural products quality and safety. [Method]...[Objective] To establish the traceability mechanism of agricultural products safety, and the application of promote domestic based software in the supervision area of agricultural products quality and safety. [Method] Through the analysis on the circulation characteristics of agricultural products, like fruits, vegetables, livestock and poultry, the agricultural products quality safety management and traceability query business component libraries were designed. Based on the run-time-supporting environment provided by domestic based software, traceability management system of agricultural products quality and safety was constructed. [Result] The traceability management system provided the information interaction and comprehensive management platform of agricultural product quality and safety based on domestic based software for the government, enterprises and consumers. [Conclusion] Through the application demonstration, the quality control and information traceability of full circulation of agricultural products was achieved effective and reliably, and the management level of agricultural products quality and safety was improved.展开更多
A novel dynamic software allocation algorithm suitable for pervasive computing environments is proposed to minimize power consumption of mobile devices. Considering the power cost incurred by the computation, communic...A novel dynamic software allocation algorithm suitable for pervasive computing environments is proposed to minimize power consumption of mobile devices. Considering the power cost incurred by the computation, communication and migration of software components, a power consumption model of component assignments between a mobile device and a server is set up. Also, the mobility of components and the mobility relationships between components are taken into account in software allocation. By using network flow theory, the optimization problem of power conservation is transformed into the optimal bipartition problem of a flow network which can be partitioned by the max-flow rain-cut algorithm. Simulation results show that the proposed algorithm can save si^nificantlv more energy than existing algorithms.展开更多
Objective： To explore and investigate the selection of effective antisense oligodeoxynuleotides with the help of computer and RNAstructure folding software. Methods： Bcl-2 gene was used as the target gene and five a...Objective： To explore and investigate the selection of effective antisense oligodeoxynuleotides with the help of computer and RNAstructure folding software. Methods： Bcl-2 gene was used as the target gene and five antisense oligodeoxynuleotides were designed to be bound to Bcl-2 mRNA optimal secondary structure regions that were predicted free from intramolecular fold or instability of free energy. The five antisense oligodeoxynucleotides were studied with experimental assay of leukemia cells, including cell grow assay with tropan blue exclusion, expression of Bcl-2 protein detected with immunochemistry and flowcytometry, Bcl-2 mRNA content detected with RT-PCR technique, as well as apoptosis observed and determined with morphonological method, electrophoresis and flowcytometry. Results： The results showed that two of the five antisense oligodeoxynucleotides were effective antisense oligodeoxynucleotides, which were able to inhibit cell growth in leukemia, to decrease the level of Bcl-2 mRNA and protein, to induce apoptosis of leukemia cells significantly. Conclusion： The computational prediction of antisense efficacy is faster than other methods and more efficient, which can potentially speed the development of sequences for both research and clinical applications.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.展开更多
Satellite communication networks have been evolving from standalone networks with ad-hoc infrastructures to possibly interconnected portions of a wider Future Internet architecture. Experts belonging to the fifth-gene...Satellite communication networks have been evolving from standalone networks with ad-hoc infrastructures to possibly interconnected portions of a wider Future Internet architecture. Experts belonging to the fifth-generation(5 G) standardization committees are considering satellites as a technology to integrate in the 5 G environment. Software Defined Networking(SDN) is one of the paradigms of the next generation of mobile and fixed communications. It can be employed to perform different control functionalities, such as routing, because it allows traffic flow identification based on different parameters and traffic flow management in a centralized way. A centralized set of controllers makes the decisions and sends the corresponding forwarding rules for each traffic flow to the involved intermediate nodes that practically forward data up to the destination. The time to perform this process in integrated terrestrial-satellite networks could be not negligible due to satellite link delays. The aim of this paper is to introduce an SDN-based terrestrial satellite network architecture and to estimate the mean time to deliver the data of a new traffic flow from the source to the destination including the time required to transfer SDN control actions. The practical effect is to identify the maximum performance than can be expected.展开更多
Through reusing software test components, automated software testing generally costs less than manual software testing. There has been much research on how to develop the reusable test components, but few fall on how ...Through reusing software test components, automated software testing generally costs less than manual software testing. There has been much research on how to develop the reusable test components, but few fall on how to estimate the reusability of test conlponents for automated testing. The purpose of this paper is to present a method of minimum reusability estimation for automated testing based on the return on investment （ROI） model. Minimum reusability is a benchmark for the whole automated testing process. If the reusability in one test execution is less than the minimum reusability, some new strategies must be adopted ill the next test execution to increase the reusability. Only by this way, we can reduce unnecessary costs and finally get a return on the investment of automated testing.展开更多
In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the grow...In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.展开更多
基金Project supported by the Project of the Anhui Provincial Natural Science Foundation(Grant No.2308085MA19)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0410401)+2 种基金the National Natural Science Foundation of China(Grant No.52202120)the National Key Research and Development Program of China(Grant No.2023YFA1609800)USTC Research Funds of the Double First-Class Initiative(Grant No.YD2310002013)。
文摘Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
文摘Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasing year by year, so it is particularly important to improve the clinical research level of medical devices. Medical statistics is one of the effective means to ensure scientific and reasonable clinical trial design and reliable test results. SAS software is important software for statistical analysis of clinical trials of medical equipment. Starting from the specific application of SAS software, this paper focuses on correctly understanding and selecting various results of SAS statistics, and provides some practical experience for those who learn to apply SAS, so as to make the clinical statistics of medical devices possible. The trial is more accurate and scientific.
基金Supported by Common Chips and Basic Software Products(2010ZX01045-001-004-3)~~
文摘[Objective] To establish the traceability mechanism of agricultural products safety, and the application of promote domestic based software in the supervision area of agricultural products quality and safety. [Method] Through the analysis on the circulation characteristics of agricultural products, like fruits, vegetables, livestock and poultry, the agricultural products quality safety management and traceability query business component libraries were designed. Based on the run-time-supporting environment provided by domestic based software, traceability management system of agricultural products quality and safety was constructed. [Result] The traceability management system provided the information interaction and comprehensive management platform of agricultural product quality and safety based on domestic based software for the government, enterprises and consumers. [Conclusion] Through the application demonstration, the quality control and information traceability of full circulation of agricultural products was achieved effective and reliably, and the management level of agricultural products quality and safety was improved.
基金The National Natural Science Foundation of China(No60503041)the Science and Technology Commission of ShanghaiInternational Cooperation Project (No05SN07114)
文摘A novel dynamic software allocation algorithm suitable for pervasive computing environments is proposed to minimize power consumption of mobile devices. Considering the power cost incurred by the computation, communication and migration of software components, a power consumption model of component assignments between a mobile device and a server is set up. Also, the mobility of components and the mobility relationships between components are taken into account in software allocation. By using network flow theory, the optimization problem of power conservation is transformed into the optimal bipartition problem of a flow network which can be partitioned by the max-flow rain-cut algorithm. Simulation results show that the proposed algorithm can save si^nificantlv more energy than existing algorithms.
文摘Objective： To explore and investigate the selection of effective antisense oligodeoxynuleotides with the help of computer and RNAstructure folding software. Methods： Bcl-2 gene was used as the target gene and five antisense oligodeoxynuleotides were designed to be bound to Bcl-2 mRNA optimal secondary structure regions that were predicted free from intramolecular fold or instability of free energy. The five antisense oligodeoxynucleotides were studied with experimental assay of leukemia cells, including cell grow assay with tropan blue exclusion, expression of Bcl-2 protein detected with immunochemistry and flowcytometry, Bcl-2 mRNA content detected with RT-PCR technique, as well as apoptosis observed and determined with morphonological method, electrophoresis and flowcytometry. Results： The results showed that two of the five antisense oligodeoxynucleotides were effective antisense oligodeoxynucleotides, which were able to inhibit cell growth in leukemia, to decrease the level of Bcl-2 mRNA and protein, to induce apoptosis of leukemia cells significantly. Conclusion： The computational prediction of antisense efficacy is faster than other methods and more efficient, which can potentially speed the development of sequences for both research and clinical applications.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
文摘Satellite communication networks have been evolving from standalone networks with ad-hoc infrastructures to possibly interconnected portions of a wider Future Internet architecture. Experts belonging to the fifth-generation(5 G) standardization committees are considering satellites as a technology to integrate in the 5 G environment. Software Defined Networking(SDN) is one of the paradigms of the next generation of mobile and fixed communications. It can be employed to perform different control functionalities, such as routing, because it allows traffic flow identification based on different parameters and traffic flow management in a centralized way. A centralized set of controllers makes the decisions and sends the corresponding forwarding rules for each traffic flow to the involved intermediate nodes that practically forward data up to the destination. The time to perform this process in integrated terrestrial-satellite networks could be not negligible due to satellite link delays. The aim of this paper is to introduce an SDN-based terrestrial satellite network architecture and to estimate the mean time to deliver the data of a new traffic flow from the source to the destination including the time required to transfer SDN control actions. The practical effect is to identify the maximum performance than can be expected.
基金Foundation item： the National Natural Science Foundation of China （No. 90718037）
文摘Through reusing software test components, automated software testing generally costs less than manual software testing. There has been much research on how to develop the reusable test components, but few fall on how to estimate the reusability of test conlponents for automated testing. The purpose of this paper is to present a method of minimum reusability estimation for automated testing based on the return on investment （ROI） model. Minimum reusability is a benchmark for the whole automated testing process. If the reusability in one test execution is less than the minimum reusability, some new strategies must be adopted ill the next test execution to increase the reusability. Only by this way, we can reduce unnecessary costs and finally get a return on the investment of automated testing.
基金This work is supported by the Fundamental Research Funds for the Central Universities.
文摘In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.
