In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,a...In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.展开更多
The collaborative development of future learning centers in engineering university is a crucial component of integrating education,technology,and talent development.Its essence lies in cultivating top-notch innovative...The collaborative development of future learning centers in engineering university is a crucial component of integrating education,technology,and talent development.Its essence lies in cultivating top-notch innovative engineering talents with both scientific and technological literacy and humanistic feelings for the great rejuvenation of the Chinese nation through a historical narrative and a deep understanding of engineering talent cultivation in China.By tracing back to the formation of educator Mei Yiqi’s engineering intentions,the integration of engineering knowledge,and the history of engineering education governance,this paper proposes the key to the collaborative development of future learning centers in engineering university in the era of artificial intelligence.Firstly,it is necessary to update educational concepts and view learners as dynamically self-empowered individuals with plural existences.Secondly,attention should be paid to dynamic knowledge of possibilities,and cross-subject and interdisciplinary cognition and reflection should be carried out based on the types of engineering knowledge.Thirdly,it is essential to establish an engineering talent cultivation model with problem-guided knowledge learning as the core.With the support of digital network platforms and artificial intelligence technology,an infinite link of ideas and knowledge can be established through comprehensive textual methods,constructing learners’own“infinite library”and a spiritual world that is proactive,creative,and cultural.展开更多
Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a part...Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.展开更多
The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionar...The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionary impact of deep learning,with AlphaFold’s breakthrough in protein structure prediction exemplifying its potential.This review explores the historical evolution of deep learning,from its foundational theories in neural networks and connectionism to its modern applications in various fields.Focus is given to its use in geotechnical engineering,particularly in geological disaster prediction,tunnel safety monitoring,and structural design optimization.The integration of deep learning models such as Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Transformers has enabled significant progress in analyzing complex,unstructured data,offering innovative solutions to longstanding engineering challenges.The review also examines the opportunities and challenges faced by the field,advocating for interdisciplinary collaboration and open data sharing to further unlock deep learning’s potential in advancing both scientific and engineering disciplines.As deep learning continues to evolve,it promises to drive further innovation,shaping the future of engineering practices and scientific discovery.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ...Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.展开更多
The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GE...The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.展开更多
Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and ...Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.展开更多
Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.How...Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes.展开更多
Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems...Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.展开更多
Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of e...Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.展开更多
In order to improve Englishlistening more effectively, the application status of metacognitive strategies and autonomouslearning competence is exploredbased on a questionnaire investigation among 120 civil engineering...In order to improve Englishlistening more effectively, the application status of metacognitive strategies and autonomouslearning competence is exploredbased on a questionnaire investigation among 120 civil engineering major in BJUT. The methods of correlation analysis and an independent sample t-test are employed.The results indicated that 1) their metacognitivestrategies, and the planning, monitoring strategy have positive significant correlations with the students'listening performance and listening auton-omous learning competence;2) There are significant differences in the use of metacognitivestrategies,the level of autonomous learn-ing competence between different levels of listening performance;3) The more accuracy of self-judgment is, the higher level Eng-lish listening performance is.展开更多
New methodologies in science (also mathemat- ics) learning process and scientific thinking in the classroom activity of engineer students with ICT (information and communication technology, including also graphic calc...New methodologies in science (also mathemat- ics) learning process and scientific thinking in the classroom activity of engineer students with ICT (information and communication technology, including also graphic calculator) are presented: visual modelling with ICT, action research with graphic calculator, insight in classroom, com- munications and reflection of integrative ac- tions. How can we show our students the beauty of science (and mathematics) with ICT and the way scientists think and try to find the truth? Is it possible to create the motivation in science learning for students using ICT or graphic cal- culator? How can we organize the engineer training on such professional activity in class- room? In this paper we try to answer the ques- tions using methodology of visual modelling and technology of resource lessons in high en- gineering school.展开更多
Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity,as human vulnerabilities are often the weakest link in the entire network.Such vulnerabilities are becoming increasingl...Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity,as human vulnerabilities are often the weakest link in the entire network.Such vulnerabilities are becoming increasingly susceptible to network security risks.Addressing the social engineering attack defense problem has been the focus of many studies.However,two main challenges hinder its successful resolution.Firstly,the vulnerabilities in social engineering attacks are unique due to multistage attacks,leading to incorrect social engineering defense strategies.Secondly,social engineering attacks are real-time,and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions.This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning(IQL)reinforcement learning method on human vulnerability attributes.The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy.Furthermore,the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks.The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning(QL)and deep Q-network(DQN)approaches in terms of time efficiency,taking 9.1%and 19.4%less time,respectively.Moreover,the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes.This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.展开更多
The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where...The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods.We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities.This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach,which enables non-computer scientists to code.Therein,the learning content is provided in small tasks and embedded in learning factory scenarios.Therefore,different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated,yet condensed skills catalogue.This enables the development of training and education units that focus on the most important skills demanded on the market.To achieve this objective,individual learning scenarios are developed.Of course,proper basic skills in coding cannot be learned over night but software programming is also no sorcery.展开更多
With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering s...With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.展开更多
In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social...In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.展开更多
文摘In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.
基金2024 General Research Topic of the Chinese Society of Educational Development Strategy,“Research on the Collaborative Development of‘Future Learning Centers’in Engineering University under New Strategic Needs”(Project No.:ZLB20240820)。
文摘The collaborative development of future learning centers in engineering university is a crucial component of integrating education,technology,and talent development.Its essence lies in cultivating top-notch innovative engineering talents with both scientific and technological literacy and humanistic feelings for the great rejuvenation of the Chinese nation through a historical narrative and a deep understanding of engineering talent cultivation in China.By tracing back to the formation of educator Mei Yiqi’s engineering intentions,the integration of engineering knowledge,and the history of engineering education governance,this paper proposes the key to the collaborative development of future learning centers in engineering university in the era of artificial intelligence.Firstly,it is necessary to update educational concepts and view learners as dynamically self-empowered individuals with plural existences.Secondly,attention should be paid to dynamic knowledge of possibilities,and cross-subject and interdisciplinary cognition and reflection should be carried out based on the types of engineering knowledge.Thirdly,it is essential to establish an engineering talent cultivation model with problem-guided knowledge learning as the core.With the support of digital network platforms and artificial intelligence technology,an infinite link of ideas and knowledge can be established through comprehensive textual methods,constructing learners’own“infinite library”and a spiritual world that is proactive,creative,and cultural.
文摘Machine learning(ML)has been increasingly adopted to solve engineering problems with performance gauged by accuracy,efficiency,and security.Notably,blockchain technology(BT)has been added to ML when security is a particular concern.Nevertheless,there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security,making the traditional ML process vulnerable to off-chain risks.Therefore,the research objective is to develop a novel ML on blockchain(MLOB)framework to ensure both the data and computational process security.The central tenet is to place them both on the blockchain,execute them as blockchain smart contracts,and protect the execution records on-chain.The framework is established by developing a prototype and further calibrated using a case study of industrial inspection.It is shown that the MLOB framework,compared with existing ML and BT isolated solutions,is superior in terms of security(successfully defending against corruption on six designed attack scenario),maintaining accuracy(0.01%difference with baseline),albeit with a slightly compromised efficiency(0.231 second latency increased).The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands.This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration.With proper adaption,the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.
基金support provided by the Hebei Province Full-Time Recruitment of National High-Level Innovative Talents Research Project(Grant No.2023HBQZYCSB004).
文摘The rise of deep learning has brought about transformative advancements in both scientific research and engineering applications.The 2024 Nobel Prizes,particularly in Physics and Chemistry,highlighted the revolutionary impact of deep learning,with AlphaFold’s breakthrough in protein structure prediction exemplifying its potential.This review explores the historical evolution of deep learning,from its foundational theories in neural networks and connectionism to its modern applications in various fields.Focus is given to its use in geotechnical engineering,particularly in geological disaster prediction,tunnel safety monitoring,and structural design optimization.The integration of deep learning models such as Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Transformers has enabled significant progress in analyzing complex,unstructured data,offering innovative solutions to longstanding engineering challenges.The review also examines the opportunities and challenges faced by the field,advocating for interdisciplinary collaboration and open data sharing to further unlock deep learning’s potential in advancing both scientific and engineering disciplines.As deep learning continues to evolve,it promises to drive further innovation,shaping the future of engineering practices and scientific discovery.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
文摘Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.
文摘The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.
基金The authors acknowledge funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation(818607)Pieter P.Plehiers and Ruben Van de Vijver acknowledge financial support,respectively,from a doctoral(1150817N)a postdoctoral(3E013419)fellowship from the Research Foundation-Flanders(FWO).
文摘Chemical engineers rely on models for design,research,and daily decision-making,often with potentially large financial and safety implications.Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations.In the last five years,the increasing availability of data and computational resources has led to a resurgence in machine learning-based research.Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases,benchmarks,and representations for chemical applications and new machine learning frameworks.Machine learning has significant advantages over traditional modeling techniques,including flexibility,accuracy,and execution speed.These strengths also come with weaknesses,such as the lack of interpretability of these black-box models.The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns,learn from data,and become more intelligent over time.The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis.Nevertheless,machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
基金supported by the National Key R&D Project(No.2020YFB1804803)the Research and Development Program in Key Areas of Guangdong Province(No.2018B010113001)。
文摘Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes.
基金funded by Firat University Scientific Research Projects Management Unit for the scientific research project of Feyza AltunbeyÖzbay,numbered MF.23.49.
文摘Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.
文摘Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.
文摘In order to improve Englishlistening more effectively, the application status of metacognitive strategies and autonomouslearning competence is exploredbased on a questionnaire investigation among 120 civil engineering major in BJUT. The methods of correlation analysis and an independent sample t-test are employed.The results indicated that 1) their metacognitivestrategies, and the planning, monitoring strategy have positive significant correlations with the students'listening performance and listening auton-omous learning competence;2) There are significant differences in the use of metacognitivestrategies,the level of autonomous learn-ing competence between different levels of listening performance;3) The more accuracy of self-judgment is, the higher level Eng-lish listening performance is.
文摘New methodologies in science (also mathemat- ics) learning process and scientific thinking in the classroom activity of engineer students with ICT (information and communication technology, including also graphic calculator) are presented: visual modelling with ICT, action research with graphic calculator, insight in classroom, com- munications and reflection of integrative ac- tions. How can we show our students the beauty of science (and mathematics) with ICT and the way scientists think and try to find the truth? Is it possible to create the motivation in science learning for students using ICT or graphic cal- culator? How can we organize the engineer training on such professional activity in class- room? In this paper we try to answer the ques- tions using methodology of visual modelling and technology of resource lessons in high en- gineering school.
基金funded by the Beijing Natural Science Foundation (4202002).
文摘Social engineering attacks are considered one of the most hazardous cyberattacks in cybersecurity,as human vulnerabilities are often the weakest link in the entire network.Such vulnerabilities are becoming increasingly susceptible to network security risks.Addressing the social engineering attack defense problem has been the focus of many studies.However,two main challenges hinder its successful resolution.Firstly,the vulnerabilities in social engineering attacks are unique due to multistage attacks,leading to incorrect social engineering defense strategies.Secondly,social engineering attacks are real-time,and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions.This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning(IQL)reinforcement learning method on human vulnerability attributes.The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy.Furthermore,the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks.The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning(QL)and deep Q-network(DQN)approaches in terms of time efficiency,taking 9.1%and 19.4%less time,respectively.Moreover,the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes.This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain.
基金supported in part by the Junior Research Group“ProMUT”(01UU1705B)which is funded by the German Federal Ministry of Education and Research as part of its funding initiative“Social-Ecological Research”the German Internet Institute(16DII116).
文摘The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities.Accordingly,an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods.We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities.This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach,which enables non-computer scientists to code.Therein,the learning content is provided in small tasks and embedded in learning factory scenarios.Therefore,different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated,yet condensed skills catalogue.This enables the development of training and education units that focus on the most important skills demanded on the market.To achieve this objective,individual learning scenarios are developed.Of course,proper basic skills in coding cannot be learned over night but software programming is also no sorcery.
基金partially funded by the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(No.76250-12230036)the 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.
基金The authors acknowledge the funding support ofFRGS/1/2021/ICT07/UTAR/02/3 and IPSR/RMC/UTARRF/2020-C2/G01 for this study.
文摘In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.
