Sporosarcina pasteurii was employed as the strain,with an in-situ magnetization construction,to obtain magnetic microorganisms and oriented self-healing mortar specimens based on them.The magnetic field was used to ac...Sporosarcina pasteurii was employed as the strain,with an in-situ magnetization construction,to obtain magnetic microorganisms and oriented self-healing mortar specimens based on them.The magnetic field was used to achieve the directional migration of magnetic microorganisms during the oriented selfhealing of mortar cracks,improving the rate of self-healing of cracks.The experimental results demonstrate that the magnetic microorganisms are composed of Fe_(3)O_(4)nanosheets attached to the surface of Sporosarcina pasteurii,whose mineralization products are comprised of vaterite primarily.Compared with the pure microbial group,the magnetic microbial group exhibits a faster repair rate,shortening the repair time required to achieve an area repair efficiency of over 90%from 28 days to 14 days,thereby doubling the repair rate.Meanwhile,the area repair efficiency of the magnetic microbial group at 7,14,and 28 days are increased by 50.3%,11.2%,and 4.6%,respectively,compared to the pure microbial group,which are due to the magnetic microorganisms'superior directional migration and mineralization ability,exceeding that of the ordinary microorganisms.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
We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages...We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages.The experimental results show that the performance and self-healing effect of composite micro-organisms are significantly better than those of single microorganisms.For cracks with widths of 0.2-0.4 mm,the repair effect of the composite microorganisms at 28 days is 42.7%and 71.2%higher than that of pure Bacillus and pure yeast,respectively.The repairing rate of the area with the widths of the cracks of 0.2-0.4,0.4-0.6,and 0.6-0.8 mm are 100%,77.3%,and 53.4%,respectively.The area repair rates corresponding to cracking ages of 56,90,and 180 days are 73.3%,55.4%,and 30.8%,respectively.展开更多
In this study,a facile method was employed to synthesize strong,yet highly elastic polyurethane-urea(PUU)with typical characteristics and 94% optical transmittance.Graphene platelets(GNPs)were prepared and modified vi...In this study,a facile method was employed to synthesize strong,yet highly elastic polyurethane-urea(PUU)with typical characteristics and 94% optical transmittance.Graphene platelets(GNPs)were prepared and modified via a scalable and eco-friendly mechanochemical approach.The produced GNPs is at 1.6-nm thickness with high electrical conductivity of~950 S/m.The structure-property relations of PUU/GNP nanocomposites were comprehensively investigated through morphology and mechanical properties measurements.The strong interface and high-density hydrogen bonds between modified GNPs(M-GNPs)and PUU significantly enhanced the mechanical properties of the PUU nanocomposite.The PUU composite showed 66.7%and 36.2%increments in tensile and impact strengths,respectively,at 0.2 wt% M-GNPs.The reversible hydrogen bond between M-GNPs and PUU endowed the nanocomposite with self-healing properties achieving 97.8% healing efficiency of the strength after 5 h at 120℃.This study demonstrates the importance of surface modification and provides a simple yet robust approach for preparing high-performance and functional PUU/graphene composites.展开更多
Azobenzene-based polymer actuators show great promise for photoactuation owing to their unique photoisomerization behavior and tailorable molecular programmability.However,conventional systems are limited by inadequat...Azobenzene-based polymer actuators show great promise for photoactuation owing to their unique photoisomerization behavior and tailorable molecular programmability.However,conventional systems are limited by inadequate mechanical robustness,self-healing,and recyclability,hindering their practical implementation.Herein,we present a high-performance azobenzene-functionalized polyurethane(AzoPU)elastomer actuator designed via molecular engineering of photoactive azobenzene moieties and dynamic disulfide bonds.AzoPU exhibits exceptional mechanical properties with retained performance after multiple reshaping cycles,enabled by well-engineered hard-soft segments and synergistic stress dissipation from weak covalent bonds/hierarchical hydrogen bonds.It achieves over 93%self-healing efficiency at room temperature owing to the synergistic interplay of disulfide bonds in the polymer backbone and intermolecular hydrogen bonds.Furthermore,it demonstrates remarkable light-triggered actuation behavior,achieving a phototropic bending angle exceeding 180°toward the light source within 45 s.To showcase its practical potential,proof-of-concept photoactuated devices with flower-,hook-,and gripper-like and local-orientation processed strip-shaped structures were fabricated,which exhibited rapid and reversible light-triggered deformation.This study proposes a novel strategy for the development of intelligent polymeric materials that integrate light responsiveness,self-healing,and recyclability,thus holding great promise for applications in flexible electronics,smart actuators,and sustainable functional materials.展开更多
Thermosetting polymers exhibit outstanding mechanical properties,thermal stability,and chemical resistance due to their permanently cross-linked network structures.However,the irreversible nature of covalent cross-lin...Thermosetting polymers exhibit outstanding mechanical properties,thermal stability,and chemical resistance due to their permanently cross-linked network structures.However,the irreversible nature of covalent cross-linking renders these materials non-reprocessable and non-recyclable,posing significant environmental challenges.Although healable polymers based on dynamic covalent bonds and supramolecular interactions have emerged as promising alternatives,a broadly applicable strategy utilizing metal-ligand coordination in thermoset systems remains underexplored.In this work,we present a robust and healable thermoset system fabricated via ring-opening metathesis polymerization(ROMP)of commercially available chelating norbornene comonomers.Cross-linking is accomplished through O-donor coordination to Lewis acidic metal centers,yielding polydicyclopentadiene(PDCPD)-based networks that demonstrate high mechanical strength(up to 60.8 MPa)and effective self-healing performance.This methodology offers a simple and scalable approach to developing high-performance,sustainable thermosetting materials.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
Heavy-ion collisions(HICs)is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory.At HIRFL-CSR energies,HICs can create nuclear matter with 2-3 times...Heavy-ion collisions(HICs)is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory.At HIRFL-CSR energies,HICs can create nuclear matter with 2-3 times the saturation density(ρ_(0)).The HIRFL-CSR external-target experiment(CEE)is a large-acceptance spectrometer designed to explore frontier topics in high-energy nuclear physics,such as the QCD phase structure and nuclear matter equation of states.In this letter,we introduce simulation and analysis software for the CEE experiment(CeeROOT).Based on the CEE conceptual design and CeeROOT software,the configurations of its subdetectors were optimized by considering foreseeable physical constraints.The final detector layout of the CEE spectrometer and its acceptances were validated through simulations of U+U collisions at 500 MeV/u and pp collisions at 2.8 GeV,which demonstrated that the CEE experiment will serve as a detector with wide acceptance and multi-particle identification capabilities for studying high-energy nuclear physics topics at HIRFL-CSR energies with pp,pA,and A A collisions.展开更多
While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in...While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.展开更多
In the modern era of ubiquitous and highly interconnected information technology,cybersecurity threats stemming from software code vulnerabilities have become increasingly severe,posing significant risks to the confid...In the modern era of ubiquitous and highly interconnected information technology,cybersecurity threats stemming from software code vulnerabilities have become increasingly severe,posing significant risks to the confidentiality,integrity,and availability of modern information systems.To enhance software code quality,enterprises often integrate static code analysis tools into Continuous Integration(CI) pipelines.However,the high rates of false positives and false negatives remain a challenge.The advent of large language models(LLMs),such as ChatGPT,presents a new opportunity to address these challenges.In this paper,we propose AI-SCDF,a framework that utilizes the custombuilt Nebula-Coder AI model for detecting and fixing code security issues in real time during the developer ' s personal build process.We construct a static code checking rule knowledge base through summarizing and classifying Common Weakness Enumeration(CWE) code security problems identified by security and quality assurance teams.The rule knowledge base is combined with CodeFuse-processed code contexts to serve as input for an AI code security detection microservice,which assists in identifying code quality and security issues.If any abnormalities are detected,they are addressed by an AI code security patching microservice,which alerts the developer and requests confirmation before committing the code into the repository.Experimental results show that our approach effectively improves code quality.We also develop a VS Code plugin for code alert detection and fix based on LLMs,which facilitates test shift-left and lowers the risk of software development.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
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper...With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method.展开更多
Although poly(urethane-urea)elastomers(PUEs)possess excellent mechanical properties and durability,their inherent flammability and inability to self-repair after damage significantly limits their applications in high-...Although poly(urethane-urea)elastomers(PUEs)possess excellent mechanical properties and durability,their inherent flammability and inability to self-repair after damage significantly limits their applications in high-end fields.To address this challenge,this study employs a supramolecular chemistry approach by simultaneously incorporating multiple hydrogen bonds as dynamic cross-linking points and a phosphorus-nitrogen synergistic flame-retardant structure into the poly(urethane-urea)network.The multiple hydrogen bonds endow the material with efficient intrinsic self-healing capability,while the phosphorus-nitrogen flame retardant ensures outstanding thermal stability and flame resistance,leading to the successful synthesis of a high-performance multifunctional poly(urethane-urea)elastomer.Experimental results demonstrated that when the content of the flame retardant diethyl(2-((2-aminoethyl)amino)ethyl)phosphoramidate(DEPTA)was 10 wt%,the resulting PUE/10%DEPTA achieved a V-0 rating in the vertical burning test,with a limiting oxygen index(LOI)of 30%.Concurrently,the elastomer maintained good toughness,exhibiting a tensile strength of 27.3 MPa,an elongation at break of 601%,and a self-healing efficiency of up to 94.46%.This breakthrough shows significant promise for advanced engineering applications that demand fire safety,structural durability,and extended service life through self-repair.展开更多
This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-...This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-based FEM platform resolves multiphysics phenomena including nutrient diffusion,microbial CaCO_(3) precipitation,and stiffness recovery.These simulations,together with experimental data,are used to train ML models(Random Forest yielding normalized RMSE≈0.10)capable of predicting performance over a wide range of design parameters.Feature importance analysis identifies curing temperature,calcium carbonate precipitation rate,crack width,bacterial strain,and encapsulation method as the most influential parameters.The coupled FEM-ML approach enables sensitivity analysis,design optimization,and prediction beyond the training dataset(consistently exceeding 90%healing efficiency).Experimental validation confirms model robustness in both crack closure and strength recovery.This FEM–ML pipeline thus offers a generalizable,interpretable,and scalable strategy for the design of intelligent,self-adaptive construction materials.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
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
Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically anal...Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.展开更多
Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial t...Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial to build a faculty team with knowledge in industry application fields and experience in domestic software development.This paper first analyzes the new requirements for the faculty imposed by the cultivation of specialized software talents and the existing problems in the current faculty.Then,in response to these issues,it introduces the reforms and explorations carried out by the School of Software Engineering at Beijing Jiaotong University in the construction of the faculty for cultivating specialized software talents.The aim is to build a high-caliber and diversified faculty that boasts strong political qualities,interdisciplinary integration,complementary advantages between full-time and part-time faculty,and in-depth integration of industry and education.展开更多
基金Funded by the National Key R&D Program of China(No.2023YFC3806100)the National Nature Science Foundation of China(Nos.52278269,52278268,52178264,and 52108238)+2 种基金the Tianjin Outstanding Young Scholars Science Fund Project(No.22JCJQJC00020)the Key Project of Tianjin Natural Science Foundation(No.23JCZDJC00430)the Joint Research Center of China and Foreign Countries Special Fund of Tianjin Innovation Platform(No.24PTLYHZ00240)。
文摘Sporosarcina pasteurii was employed as the strain,with an in-situ magnetization construction,to obtain magnetic microorganisms and oriented self-healing mortar specimens based on them.The magnetic field was used to achieve the directional migration of magnetic microorganisms during the oriented selfhealing of mortar cracks,improving the rate of self-healing of cracks.The experimental results demonstrate that the magnetic microorganisms are composed of Fe_(3)O_(4)nanosheets attached to the surface of Sporosarcina pasteurii,whose mineralization products are comprised of vaterite primarily.Compared with the pure microbial group,the magnetic microbial group exhibits a faster repair rate,shortening the repair time required to achieve an area repair efficiency of over 90%from 28 days to 14 days,thereby doubling the repair rate.Meanwhile,the area repair efficiency of the magnetic microbial group at 7,14,and 28 days are increased by 50.3%,11.2%,and 4.6%,respectively,compared to the pure microbial group,which are due to the magnetic microorganisms'superior directional migration and mineralization ability,exceeding that of the ordinary microorganisms.
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Funded by the National Key R&D Program of China(No.2023YFC3806100)the National Nature Science Foundation of China(No.52278269,52278268)+2 种基金the Tianjin Outstanding Young Scholars Science Fund Project(No.22JCJQJC00020)the Key Project of Tianjin Natural Science Foundation(No.23JCZDJC00430)the Joint Research Center of China and Foreign Countries Special Fund of Tianjin Innovation Platform(No.24PTLYHZ00240)。
文摘We mixed Bacillus subtilis and brewing yeast to prepare composite microbial self-healing materials,and studied the self-healing effect of composite microorganisms in mortar cracks of different widths and cracking ages.The experimental results show that the performance and self-healing effect of composite micro-organisms are significantly better than those of single microorganisms.For cracks with widths of 0.2-0.4 mm,the repair effect of the composite microorganisms at 28 days is 42.7%and 71.2%higher than that of pure Bacillus and pure yeast,respectively.The repairing rate of the area with the widths of the cracks of 0.2-0.4,0.4-0.6,and 0.6-0.8 mm are 100%,77.3%,and 53.4%,respectively.The area repair rates corresponding to cracking ages of 56,90,and 180 days are 73.3%,55.4%,and 30.8%,respectively.
基金The National Natural Science Foundation of China(No.52173077)the Liaoning Provincial Department of Education Series Project(No.LJKZ0187)+1 种基金Natural Science Foundation of Liaoning Province(No.2023-MS-239)Liaoning BaiQianWan Talents Program(No.2021921081)。
文摘In this study,a facile method was employed to synthesize strong,yet highly elastic polyurethane-urea(PUU)with typical characteristics and 94% optical transmittance.Graphene platelets(GNPs)were prepared and modified via a scalable and eco-friendly mechanochemical approach.The produced GNPs is at 1.6-nm thickness with high electrical conductivity of~950 S/m.The structure-property relations of PUU/GNP nanocomposites were comprehensively investigated through morphology and mechanical properties measurements.The strong interface and high-density hydrogen bonds between modified GNPs(M-GNPs)and PUU significantly enhanced the mechanical properties of the PUU nanocomposite.The PUU composite showed 66.7%and 36.2%increments in tensile and impact strengths,respectively,at 0.2 wt% M-GNPs.The reversible hydrogen bond between M-GNPs and PUU endowed the nanocomposite with self-healing properties achieving 97.8% healing efficiency of the strength after 5 h at 120℃.This study demonstrates the importance of surface modification and provides a simple yet robust approach for preparing high-performance and functional PUU/graphene composites.
基金financially supported by the National Natural Science Foundation of China(No.52503154)Shandong Provincial Natural Science Foundation(Nos.ZR2022MB034 and ZR2025QC512)。
文摘Azobenzene-based polymer actuators show great promise for photoactuation owing to their unique photoisomerization behavior and tailorable molecular programmability.However,conventional systems are limited by inadequate mechanical robustness,self-healing,and recyclability,hindering their practical implementation.Herein,we present a high-performance azobenzene-functionalized polyurethane(AzoPU)elastomer actuator designed via molecular engineering of photoactive azobenzene moieties and dynamic disulfide bonds.AzoPU exhibits exceptional mechanical properties with retained performance after multiple reshaping cycles,enabled by well-engineered hard-soft segments and synergistic stress dissipation from weak covalent bonds/hierarchical hydrogen bonds.It achieves over 93%self-healing efficiency at room temperature owing to the synergistic interplay of disulfide bonds in the polymer backbone and intermolecular hydrogen bonds.Furthermore,it demonstrates remarkable light-triggered actuation behavior,achieving a phototropic bending angle exceeding 180°toward the light source within 45 s.To showcase its practical potential,proof-of-concept photoactuated devices with flower-,hook-,and gripper-like and local-orientation processed strip-shaped structures were fabricated,which exhibited rapid and reversible light-triggered deformation.This study proposes a novel strategy for the development of intelligent polymeric materials that integrate light responsiveness,self-healing,and recyclability,thus holding great promise for applications in flexible electronics,smart actuators,and sustainable functional materials.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA0540000)the National Natural Science Foundation of China(Nos.22301294,52025031 and 22261142664)the USTC Research Funds of the Double First-Class Initiative(No.YD9990002030)。
文摘Thermosetting polymers exhibit outstanding mechanical properties,thermal stability,and chemical resistance due to their permanently cross-linked network structures.However,the irreversible nature of covalent cross-linking renders these materials non-reprocessable and non-recyclable,posing significant environmental challenges.Although healable polymers based on dynamic covalent bonds and supramolecular interactions have emerged as promising alternatives,a broadly applicable strategy utilizing metal-ligand coordination in thermoset systems remains underexplored.In this work,we present a robust and healable thermoset system fabricated via ring-opening metathesis polymerization(ROMP)of commercially available chelating norbornene comonomers.Cross-linking is accomplished through O-donor coordination to Lewis acidic metal centers,yielding polydicyclopentadiene(PDCPD)-based networks that demonstrate high mechanical strength(up to 60.8 MPa)and effective self-healing performance.This methodology offers a simple and scalable approach to developing high-performance,sustainable thermosetting materials.
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 Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34030000)the National Natural Science Foundation of China(Nos.11927901 and 12475133)+1 种基金the Youth Team Program in Basic Research Fields Stably Supported by the Chinese Academy of Sciences(No.YSBR-088)the Western Light Project of the Chinese Academy of Sciences。
文摘Heavy-ion collisions(HICs)is a unique experimental tool for investigating the properties of nuclear matter under extreme conditions in the laboratory.At HIRFL-CSR energies,HICs can create nuclear matter with 2-3 times the saturation density(ρ_(0)).The HIRFL-CSR external-target experiment(CEE)is a large-acceptance spectrometer designed to explore frontier topics in high-energy nuclear physics,such as the QCD phase structure and nuclear matter equation of states.In this letter,we introduce simulation and analysis software for the CEE experiment(CeeROOT).Based on the CEE conceptual design and CeeROOT software,the configurations of its subdetectors were optimized by considering foreseeable physical constraints.The final detector layout of the CEE spectrometer and its acceptances were validated through simulations of U+U collisions at 500 MeV/u and pp collisions at 2.8 GeV,which demonstrated that the CEE experiment will serve as a detector with wide acceptance and multi-particle identification capabilities for studying high-energy nuclear physics topics at HIRFL-CSR energies with pp,pA,and A A collisions.
文摘While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.
文摘In the modern era of ubiquitous and highly interconnected information technology,cybersecurity threats stemming from software code vulnerabilities have become increasingly severe,posing significant risks to the confidentiality,integrity,and availability of modern information systems.To enhance software code quality,enterprises often integrate static code analysis tools into Continuous Integration(CI) pipelines.However,the high rates of false positives and false negatives remain a challenge.The advent of large language models(LLMs),such as ChatGPT,presents a new opportunity to address these challenges.In this paper,we propose AI-SCDF,a framework that utilizes the custombuilt Nebula-Coder AI model for detecting and fixing code security issues in real time during the developer ' s personal build process.We construct a static code checking rule knowledge base through summarizing and classifying Common Weakness Enumeration(CWE) code security problems identified by security and quality assurance teams.The rule knowledge base is combined with CodeFuse-processed code contexts to serve as input for an AI code security detection microservice,which assists in identifying code quality and security issues.If any abnormalities are detected,they are addressed by an AI code security patching microservice,which alerts the developer and requests confirmation before committing the code into the repository.Experimental results show that our approach effectively improves code quality.We also develop a VS Code plugin for code alert detection and fix based on LLMs,which facilitates test shift-left and lowers the risk of software development.
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 by the Installation of OCS Distribution Network Program Control 2.0 and Other Functions for Dongguan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.(No.:031900GS62220049).
文摘With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method.
文摘Although poly(urethane-urea)elastomers(PUEs)possess excellent mechanical properties and durability,their inherent flammability and inability to self-repair after damage significantly limits their applications in high-end fields.To address this challenge,this study employs a supramolecular chemistry approach by simultaneously incorporating multiple hydrogen bonds as dynamic cross-linking points and a phosphorus-nitrogen synergistic flame-retardant structure into the poly(urethane-urea)network.The multiple hydrogen bonds endow the material with efficient intrinsic self-healing capability,while the phosphorus-nitrogen flame retardant ensures outstanding thermal stability and flame resistance,leading to the successful synthesis of a high-performance multifunctional poly(urethane-urea)elastomer.Experimental results demonstrated that when the content of the flame retardant diethyl(2-((2-aminoethyl)amino)ethyl)phosphoramidate(DEPTA)was 10 wt%,the resulting PUE/10%DEPTA achieved a V-0 rating in the vertical burning test,with a limiting oxygen index(LOI)of 30%.Concurrently,the elastomer maintained good toughness,exhibiting a tensile strength of 27.3 MPa,an elongation at break of 601%,and a self-healing efficiency of up to 94.46%.This breakthrough shows significant promise for advanced engineering applications that demand fire safety,structural durability,and extended service life through self-repair.
基金funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No.945478(SASPRO2)supported by the ReBuilt project:Circular and Digital Renewal of Central Europe Construction and Building Sector CE0100390 ReBuiltthe Slovak Research and Development Agency under APVV-23-0383 and the Slovak Grant Agency VEGA No.2/0080/24.
文摘This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-based FEM platform resolves multiphysics phenomena including nutrient diffusion,microbial CaCO_(3) precipitation,and stiffness recovery.These simulations,together with experimental data,are used to train ML models(Random Forest yielding normalized RMSE≈0.10)capable of predicting performance over a wide range of design parameters.Feature importance analysis identifies curing temperature,calcium carbonate precipitation rate,crack width,bacterial strain,and encapsulation method as the most influential parameters.The coupled FEM-ML approach enables sensitivity analysis,design optimization,and prediction beyond the training dataset(consistently exceeding 90%healing efficiency).Experimental validation confirms model robustness in both crack closure and strength recovery.This FEM–ML pipeline thus offers a generalizable,interpretable,and scalable strategy for the design of intelligent,self-adaptive construction materials.
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.
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Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.
文摘Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial to build a faculty team with knowledge in industry application fields and experience in domestic software development.This paper first analyzes the new requirements for the faculty imposed by the cultivation of specialized software talents and the existing problems in the current faculty.Then,in response to these issues,it introduces the reforms and explorations carried out by the School of Software Engineering at Beijing Jiaotong University in the construction of the faculty for cultivating specialized software talents.The aim is to build a high-caliber and diversified faculty that boasts strong political qualities,interdisciplinary integration,complementary advantages between full-time and part-time faculty,and in-depth integration of industry and education.
