The colorectal cancer is one of the most common and lethal cancers,and colorectal polyps,as precancerous lesions,can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes,thereby promoting ...The colorectal cancer is one of the most common and lethal cancers,and colorectal polyps,as precancerous lesions,can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes,thereby promoting the irreversible progression of colorectal cancer.We propose a YOLO based model and name it EF-YOLO.It incorporates transformer to extract contextual information about the colorectal polyps.Simultaneously,leveraging the morphological characteristics of colorectal polyps,we design a brand-new module,namely advanced multi-scale aggregation(AMSA),to replace the traditional multi-scale module.The backbone adopts deformable convolutional network-maxpool(DCN-MP)to enhance feature extraction while adaptively sampling points to better match the shapes of colorectal polyps.By combining coordinate attention(CA),this model maximizes the use of positional and channel information,more effectively extracting features of colorectal polyps,directing the model’s attention toward the colorectal polyp region.EF-YOLO has made advancement on the merged Kvasir-SEG and CVC-ClinicDB dataset.Compared to the original model,the mean average precision(mAP)of EF-YOLO increases and reaches 96.60%,meeting automated colorectal polyp detection requirements.展开更多
This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post...This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post-assessment,summary)instructional model.The reform addresses the gap between syntax-based programming instruction and the need for higher-level skills in abstraction,modularity,and software architecture.The course is anchored in a semester-long,project-based learning platform centered on a Java-based Aircraft Battle Game,progressing through six iterative experiments.Each experiment targets specific competencies within the structured BOPPPS teaching cycle and is aligned with specific OBE learning outcomes.A case study on the Factory Pattern illustrates how the BOPPPS model fosters conceptual understanding and practical application.Evaluation results from the 2023 and 2024 spring semesters show improved outcomes:Project completion rose from 87%to 95%,37%of students implemented innovative features,and average final grades increased by 7%.The results affirm that the OBE+BOPPPS integration strengthens engagement,deepens understanding,and equips students with real-world software development competencies.展开更多
Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with ...Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy.The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme,while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority.Experimental results demonstrate that the Tunicate Swarm-Highest Response RatioNext reduces costs by up to 94.8%compared to meta-heuristic baselines.It also achieves competitive cost efficiency vs.a learning-based method while offering superior operational simplicity and efficiency,establishing it as a highly practical solution for dynamic cloud environments.展开更多
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ...With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.展开更多
The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and ot...The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrat...This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical paramete...Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.展开更多
The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-...The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.展开更多
This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration an...This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration and high image quality. The charge transfer process shows that interface traps, weak fringing fields and potential well between adjacent gates all cause the decrease of charge transfer efficiency(CTE). CTE and well capacity are simulated with different operating voltages and gap sizes. CTE can achieve 99.999% and the well capacity reaches up to 25 000 electrons for the gap size of 130 nm and the maximum operating voltage of 3 V.展开更多
Investigation and analysis of the current status of farmers'livelihood capital and promptly discovering and solving problems in farmers'livelihood development are of great practical significance for optimizing...Investigation and analysis of the current status of farmers'livelihood capital and promptly discovering and solving problems in farmers'livelihood development are of great practical significance for optimizing farmers'livelihood strategies and enhancing farmers'livelihood sustainable development capability.Based on the framework of sustainable livelihood analysis,taking Yangshan County as an example,this paper uses field surveys,questionnaires and interviews to summarize and analyze the current status and characteristics and main problems of local farmers'livelihood capitals on the basis of the data of 628 farmer samples.It proposes countermeasures for future development of farmers'livelihoods.Implementing these strategies will be helpful for improving the livelihoods capital structure of farmers'and enhancing their sustainable development capability.展开更多
Toric patch is a kind of rational multisided patch,which is associated with a finite integer lattice points set A.A set of weights is defined which depend on a parameter according to regular decomposition of A.When al...Toric patch is a kind of rational multisided patch,which is associated with a finite integer lattice points set A.A set of weights is defined which depend on a parameter according to regular decomposition of A.When all weights of the patch tend to infinity,we obtain the limiting form of toric patch which is called its regular control surface.The diferent weights may induce the diferent regular control surfaces of the same toric patch.It prompts us to consider that how many regular control surfaces of a toric patch.In this paper,we study the regular decompositions of A by using integer programming method firstly,and then provide the relationship between all regular decompositions of A and corresponding state polytope.Moreover,we present that the number of regular control surfaces of a toric patch associated with A is equal to the number of regular decompositions of A.An algorithm to calculate the number of regular control surfaces of toric patch is provided.The algorithm also presents a method to construct all of the regular control surfaces of a toric patch.At last,the application of proposed result in shape deformation is demonstrated by several examples.展开更多
Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems....Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems.Therefore,there is an urgent need to develop advanced tools to characterize the spatio-temporal variations of three-dimensional(3D)DO.To address this challenge,this study introduces the Light Gradient Boosting Machine(Light-GBM),combining satellite remote sensing and reanalysis data with Biogeochemical Argo data to accurately reconstruct the 3D DO structure in the Mediterranean Sea from 2010 to 2022.Various environmental parameters are incorporated as inputs,including spatiotemporal features,meteorological characteristics,and ocean color properties.The LightGBM model demonstrates excellent performance on the testing dataset with R^(2) of 0.958.The modeled DO agrees better with in-situ measurements than products from numerical models.Using the Shapley Additive exPlanations method,the contributions of input features are assessed.Sea surface temperatures provide a correlation with DO at the sea surface,while spatial coordinates supplement the view of the ocean interior.Based on the reconstructed 3D DO structure,we identify an oxygen minimum zone in the western Mediterranean that expands continuously,reaching depths of approximately 300–800 m.The western Mediterranean exhibits a significant declining trend.This study enhances marine environmental evidence by proposing a precise and cost-effective approach for reconstructing 3D DO,thereby offering insights into the dynamics of DO variations under changing climatic conditions.展开更多
For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tille...For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.展开更多
In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defe...In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.展开更多
Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high va...Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.展开更多
Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization prob...Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization problems,while MAS provides a flexible model for distributed artificial intelligence.Since their group interaction mechanisms can be borrowed from each other,many studies have attempted to combine EC and MAS.With the rapid development of the Internet of Things,the confluence of EC and MAS has become more and more important,and related articles have shown a continuously growing trend during the last decades.In this survey,we first elaborate on the mutual assistance of EC and MAS from two aspects,agent-based EC and EC-assisted MAS.Agent-based EC aims to introduce characteristics of MAS into EC to improve the performance and parallelism of EC,while EC-assisted MAS aims to use EC to better solve optimization problems in MAS.Furthermore,we review studies that combine the cooperation mechanisms of EC and MAS,which greatly leverage the strengths of both sides.A description framework is built to elaborate existing studies.Promising future research directions are also discussed in conjunction with emerging technologies and real-world applications.展开更多
The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a serie...The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy evaluation.However,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologist.In recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms.How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.展开更多
文摘The colorectal cancer is one of the most common and lethal cancers,and colorectal polyps,as precancerous lesions,can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes,thereby promoting the irreversible progression of colorectal cancer.We propose a YOLO based model and name it EF-YOLO.It incorporates transformer to extract contextual information about the colorectal polyps.Simultaneously,leveraging the morphological characteristics of colorectal polyps,we design a brand-new module,namely advanced multi-scale aggregation(AMSA),to replace the traditional multi-scale module.The backbone adopts deformable convolutional network-maxpool(DCN-MP)to enhance feature extraction while adaptively sampling points to better match the shapes of colorectal polyps.By combining coordinate attention(CA),this model maximizes the use of positional and channel information,more effectively extracting features of colorectal polyps,directing the model’s attention toward the colorectal polyp region.EF-YOLO has made advancement on the merged Kvasir-SEG and CVC-ClinicDB dataset.Compared to the original model,the mean average precision(mAP)of EF-YOLO increases and reaches 96.60%,meeting automated colorectal polyp detection requirements.
基金supported in part by the Guangdong Province Education Science Planning Project(Higher Education Project,Project No.2024GXJK410)Shenzhen Education Science“14th Five-Year Plan”2023 Annual Project on Artificial Intelligence Special Project under Grant No.rgzn23001,the Guangdong Province Higher Education Research and Reform Project under Grant No.YueJiaoGaoHan(2024)No.9+1 种基金the Guangdong Province General Colleges and Universities Innovation Team Project under No.2022KCXTD038the Guangdong Provincial Hardware and System Teaching&Research Office Quality Engineering Project under No.HITSZERP22002.
文摘This study presents a teaching reform for the Object-oriented Software Construction(OOSC)course by integrating outcome-based education(OBE)and the BOPPPS(bridge-In,objectives,pre-assessment,participatory learning,post-assessment,summary)instructional model.The reform addresses the gap between syntax-based programming instruction and the need for higher-level skills in abstraction,modularity,and software architecture.The course is anchored in a semester-long,project-based learning platform centered on a Java-based Aircraft Battle Game,progressing through six iterative experiments.Each experiment targets specific competencies within the structured BOPPPS teaching cycle and is aligned with specific OBE learning outcomes.A case study on the Factory Pattern illustrates how the BOPPPS model fosters conceptual understanding and practical application.Evaluation results from the 2023 and 2024 spring semesters show improved outcomes:Project completion rose from 87%to 95%,37%of students implemented innovative features,and average final grades increased by 7%.The results affirm that the OBE+BOPPPS integration strengthens engagement,deepens understanding,and equips students with real-world software development competencies.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13.
文摘Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy.The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme,while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority.Experimental results demonstrate that the Tunicate Swarm-Highest Response RatioNext reduces costs by up to 94.8%compared to meta-heuristic baselines.It also achieves competitive cost efficiency vs.a learning-based method while offering superior operational simplicity and efficiency,establishing it as a highly practical solution for dynamic cloud environments.
基金supported by the Shanghai Municipal Education Research Project“Exploring the Practical Application of Generative Artificial Intelligence in Cultivating Innovative Thinking and Capabilities of Interdisciplinary Application Technology Talents‘Practice Path’”(C2025299)the university-level postgraduate course project“Software Process Management”(PX-2025251502)of Shanghai Sanda Universitythe key course project at the university level of Shanghai Sanda University,“Introduction to Software Engineering”(PX-5241216).
文摘With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform.
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2025RC3166the National Natural Science Foundation of China under Grant 62572176the National Key R&D Program of China under Grant 2024YFF0618800.
文摘The rapid advancement of large language models(LLMs)has driven the pervasive adoption of AI-generated content(AIGC),while also raising concerns about misinformation,academic misconduct,biased or harmful content,and other risks.Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information.This survey reviews recent progress in detection methods,categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals.Passive detection is further divided into surface linguistic feature-based and language model-based methods,whereas active detection encompasses watermarking-based and semantic retrieval-based approaches.This taxonomy enables systematic comparison of methodological differences in model dependency,applicability,and robustness.A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks,particularly paraphrasing,which substantially compromises their effectiveness.Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization.By synthesizing current advances and limitations,this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金Liaoning Provincial Social Science Planning Fund“Research on the Educational Intelligent Evaluation System Based on the CIPP Model and Artificial Intelligence under the Background of New Engineering”(L22BTJ005)。
文摘This study explores the feasibility of constructing an intelligent educational evaluation system based on the CIPP model and artificial intelligence technology in the context of new engineering disciplines.By integrating the CIPP model with AI technology,a novel intelligent educational evaluation system was designed.Through experimental validation and case studies,the system demonstrated significant effectiveness in improving teaching quality,facilitating personalized student development,and optimizing educational resource allocation.Additionally,the study predicts potential changes this system could bring to the education industry and proposes relevant policy recommendations.Although the current research has limitations,with technological advancements in the future,this system is expected to provide stronger support for innovations in engineering education models.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Natural Science Foundation of China(Nos.51671075,51971086)the Natural Science Foundation of Heilongjiang Province,China(No.LH2022E081)。
文摘Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.
基金supported by grants from National Natural Sciences Foundation of China (No. 30801314)China Hubei Provincial Science and Technology Department (No.2008CBD088)
文摘The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram.120 adolescents (60 boys,60 girls) aged from 9.1 to 17.2 years old were randomly selected.Twenty-seven landmarks from the second to fifth cervical vertebrae on the lat-eral cephalogram were identified.In this study,the system of CACVL was developed and used to iden-tify and calculate the landmarks by fast marching method and parabolic curve fitting.The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodon-tic experts (OE) group and orthodontic novices (ON) group].The results showed that,as for the accu-racy,there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05),but there was significant difference between CACVL group and ON group,as well as OE group and ON group in both axes (P<0.05).As for the repeatability,CACVL group was more reli-able than OE group and ON group in both axes.It is concluded that CACVL has the same or higher ac-curacy,better repeatability and less workload than manual landmarking methods.It’s reliable for cervi-cal parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
基金supported by the National Natural Science Foundation of China(Nos.61306070,61404090 and 61674115)
文摘This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration and high image quality. The charge transfer process shows that interface traps, weak fringing fields and potential well between adjacent gates all cause the decrease of charge transfer efficiency(CTE). CTE and well capacity are simulated with different operating voltages and gap sizes. CTE can achieve 99.999% and the well capacity reaches up to 25 000 electrons for the gap size of 130 nm and the maximum operating voltage of 3 V.
基金Supported by Guangdong Province Philosophy and Social Science Planning Project(GD24CGL18&GD23CGL02).
文摘Investigation and analysis of the current status of farmers'livelihood capital and promptly discovering and solving problems in farmers'livelihood development are of great practical significance for optimizing farmers'livelihood strategies and enhancing farmers'livelihood sustainable development capability.Based on the framework of sustainable livelihood analysis,taking Yangshan County as an example,this paper uses field surveys,questionnaires and interviews to summarize and analyze the current status and characteristics and main problems of local farmers'livelihood capitals on the basis of the data of 628 farmer samples.It proposes countermeasures for future development of farmers'livelihoods.Implementing these strategies will be helpful for improving the livelihoods capital structure of farmers'and enhancing their sustainable development capability.
基金Supported by the National Natural Science Foundation of China(12001327,12071057)。
文摘Toric patch is a kind of rational multisided patch,which is associated with a finite integer lattice points set A.A set of weights is defined which depend on a parameter according to regular decomposition of A.When all weights of the patch tend to infinity,we obtain the limiting form of toric patch which is called its regular control surface.The diferent weights may induce the diferent regular control surfaces of the same toric patch.It prompts us to consider that how many regular control surfaces of a toric patch.In this paper,we study the regular decompositions of A by using integer programming method firstly,and then provide the relationship between all regular decompositions of A and corresponding state polytope.Moreover,we present that the number of regular control surfaces of a toric patch associated with A is equal to the number of regular decompositions of A.An algorithm to calculate the number of regular control surfaces of toric patch is provided.The algorithm also presents a method to construct all of the regular control surfaces of a toric patch.At last,the application of proposed result in shape deformation is demonstrated by several examples.
基金supported by the Central Guiding Local Science and Technology Development Fund of Shandong-Yellow River Basin(No.YDZX2023019)Shandong Natural Science Foundation of China(Nos.ZR2020QF067 and ZR2023QD073)+6 种基金the Discipline Cluster Research Project of Qingdao University“Deep mining and intelligent prediction of multimodal big data for marine ecological disasters”(No.20240604)sourced from the International Argo Program and the national programs that contribute to it(https://argo.ucsd.edu)the CMEMS(http://marine.copernicus.eu/)the CDS(https://cds.climate.copernicus.eu/)the EMODnet(https://www.emodnet-chemistry.eu/)obtained from the ERA5(https://www.ecmwf.int)derived from the Glob Colour Project(http://globcolour.info).
文摘Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems.Therefore,there is an urgent need to develop advanced tools to characterize the spatio-temporal variations of three-dimensional(3D)DO.To address this challenge,this study introduces the Light Gradient Boosting Machine(Light-GBM),combining satellite remote sensing and reanalysis data with Biogeochemical Argo data to accurately reconstruct the 3D DO structure in the Mediterranean Sea from 2010 to 2022.Various environmental parameters are incorporated as inputs,including spatiotemporal features,meteorological characteristics,and ocean color properties.The LightGBM model demonstrates excellent performance on the testing dataset with R^(2) of 0.958.The modeled DO agrees better with in-situ measurements than products from numerical models.Using the Shapley Additive exPlanations method,the contributions of input features are assessed.Sea surface temperatures provide a correlation with DO at the sea surface,while spatial coordinates supplement the view of the ocean interior.Based on the reconstructed 3D DO structure,we identify an oxygen minimum zone in the western Mediterranean that expands continuously,reaching depths of approximately 300–800 m.The western Mediterranean exhibits a significant declining trend.This study enhances marine environmental evidence by proposing a precise and cost-effective approach for reconstructing 3D DO,thereby offering insights into the dynamics of DO variations under changing climatic conditions.
基金supported by the National Natural Science Foundation of China(32370435,62106080)the Hubei Provincial Natural Science Foundation of China(2024AFB566).
文摘For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.
基金supported by the CCF-NSFOCUS‘Kunpeng’Research Fund(CCF-NSFOCUS2024012).
文摘In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.
基金Hefei Municipal Natural Science Foundation,Grant/Award Number:2022009Suqian Guiding Program Project,Grant/Award Number:Z202309Suqian Traditional Chinese Medicine Science and Technology Plan,Grant/Award Number:MS202301。
文摘Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.
基金supported in part by the National Key Research and Development Project(2023YFE0206200)the National Natural Science Foundation of China(U23B2058)+3 种基金in part by Guangdong Regional Joint Foundation Key Project(2022B1515120076)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00555463&RS-2025-25456394)the Tianjin Top Scientist Studio Project(24JRRCRC00030)the Tianjin Belt and Road Joint Laboratory(24PTLYHZ00250).
文摘Both evolutionary computation(EC)and multiagent systems(MAS)study the emergence of intelligence through the interaction and cooperation of a group of individuals.EC focuses on solving various complex optimization problems,while MAS provides a flexible model for distributed artificial intelligence.Since their group interaction mechanisms can be borrowed from each other,many studies have attempted to combine EC and MAS.With the rapid development of the Internet of Things,the confluence of EC and MAS has become more and more important,and related articles have shown a continuously growing trend during the last decades.In this survey,we first elaborate on the mutual assistance of EC and MAS from two aspects,agent-based EC and EC-assisted MAS.Agent-based EC aims to introduce characteristics of MAS into EC to improve the performance and parallelism of EC,while EC-assisted MAS aims to use EC to better solve optimization problems in MAS.Furthermore,we review studies that combine the cooperation mechanisms of EC and MAS,which greatly leverage the strengths of both sides.A description framework is built to elaborate existing studies.Promising future research directions are also discussed in conjunction with emerging technologies and real-world applications.
文摘The chest radiograph has been one of the most frequently performed radiological investigation tools.In clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy evaluation.However,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced radiologist.In recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and algorithms.How to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.
