This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from ...This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.展开更多
With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a co...With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.展开更多
The focus of green analytical chemistry(GAC)is to minimize the negative impacts of analytical procedures on human safety,human health,and the environment.Several factors,such as the reagents used,sample collection,sam...The focus of green analytical chemistry(GAC)is to minimize the negative impacts of analytical procedures on human safety,human health,and the environment.Several factors,such as the reagents used,sample collection,sample processing,instruments,energy consumed,and the quantities of hazardous materials and waste generated during analytical procedures,need to be considered in the evaluation of the greenness of analytical assays.In this study,we propose a greenness evaluation metric for analytical methods(GEMAM).The new greenness metric is simple,flexible,and comprehensive.The evaluation criteria are based on both the 12 principles of GAC(SIGNIFICANCE)and the 10 factors of sample preparation,and the results are presented on a 0–10 scale.The GEMAM calculation process is easy to perform,and its results are easy to interpret.The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.展开更多
Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors...With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors determining consumers purchasing decisions.However,due to the high moisture content,active endogenous enzymes,and rich nutrients in aquatic products,both fresh and processed products are highly susceptible to quality deterioration during procurement,distribution,and storage,which leads to a significant decline in sensory quality and nutritional value,while also compromising safety.Today,the consumption of high-quality aquatic products has become a prevailing trend.This paper reviewed the methods for freshness evaluation and quality grading of aquatic products in terms of sensory and nutritional aspects,aiming to support the market circulation principle of"higher price for better quality"and"price based on quality",and better meeting consumer demands.Therefore,it is imperative to enhance the analysis and evaluation of aquatic product quality and to continuously refine assessment systems and methods,which is crucial for promoting industry transformation and fostering a healthy market-consumer economic cycle.展开更多
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica...It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.展开更多
Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces ...Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.展开更多
In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially...In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially student contributors)in open source software(OSS)communities.Leveraging natural language processing,code semantic understanding,and learner profiling,the system functions as an intelligent tutor to scaffold three core competency domains:contribution guideline interpretation,project architecture comprehension,and personalized task matching.By transforming traditional onboarding barriers-such as complex contribution documentation and opaque project structures-into interactive learning journeys,OSSerCopilot enables newcomers to complete their first OSS contribution more easily and confidently.This paper highlights how LLM technologies can redefine software engineering education by bridging the gap between theoretical knowledge and practical OSS participation,offering implications for curriculum design,competency assessment,and sustainable OSS ecosystem cultivation.A demonstration video of the system is available at https://figshare.com/articles/media/OSSerCopilot_Introduction_mp4/29510276.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to res...The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to reshape talent cultivation but also presents significant challenges,including skill hollowing,ethical risks,and a growing disconnect between education and industry needs.Currently,graduate-level software engineering education struggles with outdated curricula and insufficient alignment with practical demands.In this paper,we propose a dual-core collaborative framework driven by“GenAI technology”and“industry demand”.Under this framework,we design a four-dimensional capability development path to enhance graduate students’innovation in software engineering practice.This path focuses on①scientific research innovation,②engineering problem-solving,③cross-domain collaborative evolution,and④ethical risk governance.The proposed approach promotes a shift from traditional knowledge transfer to human-machine collaborative innovation,aligning talent cultivation with the demands of the NQPF.展开更多
The rapid development of artificial intelligence(AI)has placed significant pressure on universities to rethink how they train software engineering students.Tools like GitHub Copilot can now generate basic code in seco...The rapid development of artificial intelligence(AI)has placed significant pressure on universities to rethink how they train software engineering students.Tools like GitHub Copilot can now generate basic code in seconds.This raises important questions:What is the value of traditional programming education?What role should instructors play when AI becomes a powerful teaching assistant?How should the goals of software engineering programs change as companies increasingly use AI to handle coding tasks?This paper explores the key challenges AI brings to software engineering education and proposes practical strategies for updating talent development models to meet these changes.展开更多
Pull-ups are a very common fitness exercise that can be seen in many gyms.For athletes,it is very important to perform pull-ups correctly and scientifically.The pull-up scoring method designed in this paper can score ...Pull-ups are a very common fitness exercise that can be seen in many gyms.For athletes,it is very important to perform pull-ups correctly and scientifically.The pull-up scoring method designed in this paper can score the quality of pull-up movement scientifically and objectively,and provide guidance to help athletes better complete the pull-up movement.In this method,the OpenPose algorithm is used to identify the coordinates of skeleton points,and then the coordinate data are processed by a Kalman filter to obtain coordinates closer to the true values.Finally,the filtered data are input into the scoring algorithm designed based on the fuzzy comprehensive evaluation algorithm,and the results of the pull-up quality score and the corresponding guidance are obtained.展开更多
Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluat...Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.展开更多
The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention a...The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.展开更多
Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characte...Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.展开更多
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
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
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.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
基金supported in part by the Education Reform Key Projects of Heilongjiang Province(Grant No.SJGZ20220011,SJGZ20220012)the Excellent Project of Ministry of Education and China Higher Education Association on Digital Ideological and Political Education in Universities(Grant No.GXSZSZJPXM001)。
文摘This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.
基金supported by the Guangdong Higher Education Association’s“14th Five Year Plan”2024 Higher Education Research Project(24GYB03)the Natural Science Foundation of Guangdong Province(2024A1515010255)。
文摘With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.:81603182 and 81703607)the Fundamental Research Funds for the Central Universities,China(Grant Nos.:DUT21RC(3)057,DUT23YG226,DUT24MS018,and DUT23YG228)+1 种基金the Natural Science Foundation of Liaoning Province,China(Grant No.:2023-MSBA-018)the Open Funding of Cancer Hospital of Dalian University of Technology,China(Grant No.:2024-ZLKF-33).
文摘The focus of green analytical chemistry(GAC)is to minimize the negative impacts of analytical procedures on human safety,human health,and the environment.Several factors,such as the reagents used,sample collection,sample processing,instruments,energy consumed,and the quantities of hazardous materials and waste generated during analytical procedures,need to be considered in the evaluation of the greenness of analytical assays.In this study,we propose a greenness evaluation metric for analytical methods(GEMAM).The new greenness metric is simple,flexible,and comprehensive.The evaluation criteria are based on both the 12 principles of GAC(SIGNIFICANCE)and the 10 factors of sample preparation,and the results are presented on a 0–10 scale.The GEMAM calculation process is easy to perform,and its results are easy to interpret.The output of GEMAM is a pictogram that can provide both qualitative and quantitative information based on color and number.
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
基金Supported by Tangshan Talent Funding Project in 2025(B202304018).
文摘With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors determining consumers purchasing decisions.However,due to the high moisture content,active endogenous enzymes,and rich nutrients in aquatic products,both fresh and processed products are highly susceptible to quality deterioration during procurement,distribution,and storage,which leads to a significant decline in sensory quality and nutritional value,while also compromising safety.Today,the consumption of high-quality aquatic products has become a prevailing trend.This paper reviewed the methods for freshness evaluation and quality grading of aquatic products in terms of sensory and nutritional aspects,aiming to support the market circulation principle of"higher price for better quality"and"price based on quality",and better meeting consumer demands.Therefore,it is imperative to enhance the analysis and evaluation of aquatic product quality and to continuously refine assessment systems and methods,which is crucial for promoting industry transformation and fostering a healthy market-consumer economic cycle.
基金the support from National Natural Science Foundation of China(No.52275153)the Frontier Technologies R&D Program of Jiangsu,China(No.BF2024068)+1 种基金The Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics),China(Nos.MCAS-I-0425K01,MCAS-I-0423G01)。
文摘It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.
文摘Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.
基金supported by the National Natural Science Foundation of China (62202022, 92582204, and 62572030)the Fundamental Research Funds for the Central Universitiesthe exploratory elective projects of the State Key Laboratory of Complex and Critical Software Environments
文摘In the context of large language model(LLM)reshaping software engineering education,this paper presents OSSerCopilot,a LLM-based tutoring system designed to address the critical challenge faced by newcomers(especially student contributors)in open source software(OSS)communities.Leveraging natural language processing,code semantic understanding,and learner profiling,the system functions as an intelligent tutor to scaffold three core competency domains:contribution guideline interpretation,project architecture comprehension,and personalized task matching.By transforming traditional onboarding barriers-such as complex contribution documentation and opaque project structures-into interactive learning journeys,OSSerCopilot enables newcomers to complete their first OSS contribution more easily and confidently.This paper highlights how LLM technologies can redefine software engineering education by bridging the gap between theoretical knowledge and practical OSS participation,offering implications for curriculum design,competency assessment,and sustainable OSS ecosystem cultivation.A demonstration video of the system is available at https://figshare.com/articles/media/OSSerCopilot_Introduction_mp4/29510276.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金supported in part by the Graduate Education Reform Research Project of Hubei University of Technology under Grant 2024YB003the Hubei University of Arts and Science,Teaching Research Project,under Grant JY2025018.
文摘The rapid development of new-quality productive forces(NQPF)has intensified the demand for high-level innovative talent.As a representative of NQPF,generative artificial intelligence(GenAI)offers powerful tools to reshape talent cultivation but also presents significant challenges,including skill hollowing,ethical risks,and a growing disconnect between education and industry needs.Currently,graduate-level software engineering education struggles with outdated curricula and insufficient alignment with practical demands.In this paper,we propose a dual-core collaborative framework driven by“GenAI technology”and“industry demand”.Under this framework,we design a four-dimensional capability development path to enhance graduate students’innovation in software engineering practice.This path focuses on①scientific research innovation,②engineering problem-solving,③cross-domain collaborative evolution,and④ethical risk governance.The proposed approach promotes a shift from traditional knowledge transfer to human-machine collaborative innovation,aligning talent cultivation with the demands of the NQPF.
基金supported in part by the Northeastern University’s 2024 Undergraduate Education and Teaching Reform Research Project:Innovation and Practice of Professional Course Teaching Paradigms in the Context of Digital Education.
文摘The rapid development of artificial intelligence(AI)has placed significant pressure on universities to rethink how they train software engineering students.Tools like GitHub Copilot can now generate basic code in seconds.This raises important questions:What is the value of traditional programming education?What role should instructors play when AI becomes a powerful teaching assistant?How should the goals of software engineering programs change as companies increasingly use AI to handle coding tasks?This paper explores the key challenges AI brings to software engineering education and proposes practical strategies for updating talent development models to meet these changes.
文摘Pull-ups are a very common fitness exercise that can be seen in many gyms.For athletes,it is very important to perform pull-ups correctly and scientifically.The pull-up scoring method designed in this paper can score the quality of pull-up movement scientifically and objectively,and provide guidance to help athletes better complete the pull-up movement.In this method,the OpenPose algorithm is used to identify the coordinates of skeleton points,and then the coordinate data are processed by a Kalman filter to obtain coordinates closer to the true values.Finally,the filtered data are input into the scoring algorithm designed based on the fuzzy comprehensive evaluation algorithm,and the results of the pull-up quality score and the corresponding guidance are obtained.
基金supported by the National Natural Science Foundation of China(Grant No.42277147)Ningbo Public Welfare Research Program(Grant No.2024S081)Ningbo Natural Science Foundation(Grant No.2024J186).
文摘Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.
基金National Natural Science Foundation of China,Grant/Award Number:82000102 and 82270112。
文摘The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.
基金Supported by the Central Public-interest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022024016)the Central Public-interest Scientific Institution Basal Research Fund,CAFS(Nos.2023TD52,2023TD76)the earmarked fund for CARS(No.CARS-49)。
文摘Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.
基金Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
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.
