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Electronegativity-oriented coordination regulation of main-group metal single-atom catalysts for oxygen reduction to H2O2:A combined study of first-principles and machine learning 认领 引用
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作者 Hao Chen Haiyuan Liao +3 位作者 Qi Zhou Yang Liu Guojun Liu Yuan Yao 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第3期684-688,共5页
Electrochemical two-electron oxygen reduction reaction(2e-ORR)is a green and attractive method for hydrogen peroxide synthesis.However,rapid and efficient development of high-performance catalyst remains a great ch... Electrochemical two-electron oxygen reduction reaction(2e-ORR)is a green and attractive method for hydrogen peroxide synthesis.However,rapid and efficient development of high-performance catalyst remains a great challenge.Different from traditional trial and error methods,this study employs density functional theory and machine learning method to efficiently screen the promising main-group metal single-atom catalysts(SACs)and systematically investigate the influence of electronegativity of coordination atoms on the adsorption behavior of key intermediates in ORR process.It is found that the K SAC with N/B in the first coordination sphere and Sn SAC with N/C in the first coordination sphere and O in the second coordination sphere exhibit both excellent 2e-ORR activity and selectivity by showing extremely low overpotentials of 0.029 V and 0.064 V,respectively,as well as barrier-free processes from*OOH to H2O2.Bagging displays prominent advantages among seven popular algorithms because of its ensemble strategy.This provides a low-cost approach for designing and screening electrocatalyst candidates,and it will be informative for experimental study in the future to accelerate the development of catalysts for oxygen reduction and other types of reactions. 展开更多
关键词 Density functional theory Machine learning Main-group metal single-atom catalyst Two-electron oxygen reduction reaction Electronegativity Coordination environment
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A Deep Learning–Based Bias Correction Model for Tropical Cyclone Track and Intensity towards Forecasting of the TianXing Large Weather Model 认领 引用
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作者 Shijin YUAN Xingzhou WANG +3 位作者 Bin MU Guansong WANG Zeyi NIU Hao LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第3期612-630,共19页
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
关键词 tropical cyclone TianXing large weather model bias correction interpretability analysis deep learning-based model
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Security and privacy in edge computing:a survey of electric vehicles 认领 引用
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作者 Honghao Gao Wanqiu Huang +1 位作者 Yueshen Xu Youhuizi Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期223-235,共13页
Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)... Electric Vehicles(EVs)have developed into a complex ecosystem that includes many technical components such as task offloading on mobile devices,the Internet of Vehicles(IoV),and smart grids.Moreover,Edge Computing(EC)is a technique that relocates applications and services closer to end-users.This computing paradigm has been extensively adopted across many scenarios,effectively reducing the load on the cloud computing infrastructure and centralized server facilities.EVs are closely related to EC in many aspects since electric vehicles are typically supported by modern communication and Artificial Intelligence(AI)technologies,such as,sensor networks,computation offloading,autonomous systems,and blockchain.However,the diversity and heterogeneity of edge devices have raised many security and privacy concerns in electric vehicles,and some complex EC scenarios make addressing these issues even more challenging.In this paper,we provide a comprehensive review of the security and privacy concerns raised by EC in EVs.First,we elaborate on the development,characteristics,and applications of EC in EVs.Next,we describe the typical architectures used to ensure the security and privacy of EC in EVs.Then,we analyze the risks and challenges related to the security and privacy of EC in EVs,focusing on several significant scenarios(e.g.,offloading,the IoV,and smart grids).We also discuss current research progress on the security and privacy,covering methodologies,architectures,algorithms,insights,and performance.Finally,we discuss several future challenges and issues regarding the security and privacy of EC in EVs. 展开更多
关键词 Electric vehicle Edge computing Security and privacy Internet of vehicle Smart grid
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A multicenter randomized controlled trial and metabolomics exploration of Traditional Chinese Medicine pattern-based therapy for stable chronic obstructive pulmonary disease 认领 引用
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作者 DING Huanzhang WANG Hui +16 位作者 YANG Qinjun MA Xiao WU Di LI Qiao ZHENG Caixia LU Jiasheng WU Chengming HUANG Pingfu CHEN Zhixiang WANG Shihan FENG Jihong LIU Jian SUN Dengdi ZHU Jie TONG Jiabing GAO Yating LI Zegeng 《Journal of Traditional Chinese Medicine》 SCIE CAS CSCD 2026年第3期652-665,共14页
OBJECTIVE:To evaluate the efficacy of Traditional Chinese Medicine(TCM)pattern-based comprehensive therapy on stable chronic obstructive pulmonary disease(COPD)patients and its influence on metabolic profiles.METHODS:... OBJECTIVE:To evaluate the efficacy of Traditional Chinese Medicine(TCM)pattern-based comprehensive therapy on stable chronic obstructive pulmonary disease(COPD)patients and its influence on metabolic profiles.METHODS:In this multicenter trial,270 participants from nine hospitals were randomly divided into a trial group and a control group at a 1∶2 ratio.The trial group received Tiotropium Bromide Powder for Inhalation(TBPI)plus one of three TCM granules[Shenqi Wenfei formula(参芪温肺方),Shenqi Buzhong formula(参芪补中方),or Shenqi Tiaoshen formula(参芪调肾方)],while the control group got only TBPI.The study had a 24-week treatment and 52-week follow-up.The main outcome was the frequency of acute COPD exacerbations.Secondary outcomes included related rates,scores on COPD assessment test(CAT)and modified medical research council(m MRC)dyspnea scale,6-min walk test(6MWT)distance,and lung function,measured at weeks 0,24,and 52.Serum samples for metabolomics were taken from the trial group before/after treatment and from healthy people.RESULTS:A total of 256 out of 270 patients completed the study.The trial group had fewer acute exacerbations than the control group during 52-week follow-up(P<0.05).At 24 weeks,the trial group improved in CAT,m MRC,and 6MWT compared to the control(P<0.05),with m MRC and 6MWT improvements lasting at week 52.Lung function showed no significant group differences.Metabolomics found 32 different metabolites between healthy and pre-treatment patients,17 reversing posttreatment.Bioinformatics showed pathway changes in pre-treatment patients reversed after treatment.CONCLUSION:TCM pattern-based comprehensive therapy reduces acute exacerbation risk and improves quality of life,respiratory symptoms,and exercise tolerance in stable COPD patients,likely by modulating energy,protein,and amino acid metabolism pathways. 展开更多
关键词 pulmonary disease chronic obstructive metabolomics Traditional Chinese Medicine pattern Shenqi Wenfei formula Shenqi Buzhong formula Shenqi Tiaoshen formula
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Analysis of key technologies and intelligent development trends for tumor surgery navigation platforms 认领 引用
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作者 Yucheng Shu Jing Ling +4 位作者 Zhengyang Wu Gao Le Jiang Wu Guifeng Xia Weisheng Li 《Intelligent Oncology》 CAS 2026年第2期78-86,共9页
With the rapid development of artificial intelligence(AI)and multimodal imaging technology,intelligent surgical navigation systems have become a research hotspot in the field of precision tumor treatment.Due to clinic... With the rapid development of artificial intelligence(AI)and multimodal imaging technology,intelligent surgical navigation systems have become a research hotspot in the field of precision tumor treatment.Due to clinical characteristics of tumor surgery—such as blurred lesion boundaries,complex anatomical structures,and high requirements for functional preservation—there are higher demands for precision,real-time performance,and adaptability of surgical navigation.This article analyzes the current development status of tumor surgery navigation platforms both in China and internationally,outlines the core components of intelligent surgical navigation systems,delves into the key technical issues in the application of existing surgery navigation systems in tumor treatment and the driving role of AI technology,discusses the urgent problems to be solved in tumor surgical navigation systems,and provides an outlook on the technological development trends of intelligent tumor surgery navigation systems. 展开更多
关键词 Tumor surgery Surgical navigation Artificial intelligence Precision therapy
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From cells to patients:Multiscale computational pathology in the era of foundation models and vision-language systems 认领 引用
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作者 Zeyu Gao Jiusong Ge +8 位作者 Junbo Lu Lei Wu Hannah Clayton Di Zhang Jiashuai Liu Inês Prata Machado Yan Chen Mireia Crispín‑Ortuzar Chen Li 《Intelligent Oncology》 CAS 2026年第3期66-75,共10页
Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide im... Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide images for patient-level prognostic profiling.While traditional deep learning approaches have achieved remarkable success in specific tasks,the recent emergence of large-scale foundation models and vision-language models has precipitated a paradigm shift in the field.These data-driven systems,characterized by their robust representation learning and semantic reasoning capabilities,are redefining how we analyze pathological data across diverse spatial scales.In this review,we provide a comprehensive synthesis of this transformation through a multiscale lens.We systematically survey the application of foundation models and vision-language models in deciphering biological complexity,ranging from cell-level segmentation and tissue phenotyping to whole-slide image-level prediction and multimodal integration.Furthermore,we critically analyze the limitations of current approaches,such as interpretability,computational efficiency,and data bias,then outline promising future directions for developing holistic,context-aware systems that bridge the gap between pixel-level features and patient-centric clinical decision-making. 展开更多
关键词 Computational pathology Foundation model Multiscale modeling
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Comparison of data driven and data-mechanism hybrid driven methods for key variables prediction based on data sets with different sample sizes and noises 认领 引用
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作者 Qihang Tan Chao Wang +2 位作者 Wange Li Jinghao Sun Jun Zhao 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期57-68,共12页
Soft measurement based on data-driven models is an important method to predict key variables in process industry due to low latency demand and economics costs.However,data-driven models cannot provide accurate predict... Soft measurement based on data-driven models is an important method to predict key variables in process industry due to low latency demand and economics costs.However,data-driven models cannot provide accurate prediction on a noisy data set with a small number of samples.In response to the challenge of noisy data and lack of samples,several data-mechanism hybrid driven methods are proposed to improve key variables prediction performances on the basis of three data-driven models including random forest,extreme gradient boosting,and artificial neural network.Simultaneously,the effectiveness of hybrid driven methods proposed is validated via two cases including benzene-toluene-xylene distillation and steam methane reforming process,where data sets feature different sample sizes and noise intensity.The comparison results show that the hybrid driven methods can improve the prediction accuracy to a certain extent.The degree of improvement depends on the noise intensity,sample size,and data-driven model selected.Under conditions of noise intensity at 10%–20%and sample size ranging from 100 to 400 in this work,after adopting the hybrid driven methods,the coefficient of determination for random forest,extreme gradient boosting,and artificial neural network can be improved by 0.3%–5.2%,0.6%–17.7%,and 0.1%–36.2%compared to corresponding data driven models. 展开更多
关键词 data-mechanism hybrid driven methods different sample sizes noise dataset machine learning process industry
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Few-shot driven construction method of a large-scale light-trapped insect annotation data based on vision foundation models and self-supervised learning 认领 引用
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作者 Yanchen You Zelin Feng +7 位作者 Zhe Wang Lingyi Li Ju Luo Jun Lü Haowen Zhang Baojun Yang Shuhua Liu Qing Yao 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第7期2915-2935,共21页
The intelligent pest-monitoring light trap based on machine vision employs specific light spectra to attract pests,infrared heating to eliminate pests,and artificial intelligence models to recognize and count them.Ach... The intelligent pest-monitoring light trap based on machine vision employs specific light spectra to attract pests,infrared heating to eliminate pests,and artificial intelligence models to recognize and count them.Achieving optimal model performance requires a high-quality insect annotated dataset.However,traditional manual annotation is expert-dependent,time-consuming,and inefficient for large-scale multi-class insect labeling.This study establishes an efficient,few-shot learning approach to construct a large-scale light-trapped insect dataset through a two-stage annotation framework:detection followed by classification.Specifically,a MLTIDD addresses scale and receptive field disparities between large and tiny insects.Based on a fine-tuned Grounding DINO,SAM and SAHI are integrated to detect insects at multiple scales.Subsequently,InsectSSRL,an iBOT-based self-supervised method,learns robust insect feature representations from the extensive set of unlabeled insect sub-images detected by MLTIDD.It enhances feature extraction capability for insect subimages through three proxy tasks.This feature extractor supports a classification model to pre-classify insect sub-images.Following expert correction,labels are traced back to original images to complete annotation work for the light-trapped insect dataset.Experimental results demonstrate that under limited samples,MLTIDD achieved 79.6%average precision(AP)50-95 and 90.8%average recall(AR),surpassing DINO by 7.0 and 4.7 percentage points.InsectSSRL attained 85.87%top-1 accuracy in k-NN evaluation.In few-shot classification,Swin-T pre-trained with InsectSSRL and fine-tuned on 5%of InsectID achieved 80.35%accuracy,exceeding iBOT by 2.08 and COCO-based transfer learning by 11.3 percentage points.The proposed pipeline improved mAP50-95 by 10.91 and AR by 8.26 percentage points compared to DINO and iBOT,while reducing expert annotation time by approximately 80%relative to manual labeling. 展开更多
关键词 intelligent pest-monitoring light trap insect dataset construction few-shot learning self-supervised learning insect detection and classification automated insect annotation
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GCN and DRL based on dependent task offloading mechanism in edge computing 认领 引用
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作者 Ruiqi Tong Shaoyong Guo +2 位作者 Xuesong Qiu Feng Qi Dongxiao Yu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期397-404,共8页
Task offloading is critical for optimizing resource allocation in edge computing systems.In practical scenarios,user applications often comprise multiple interdependent tasks,where both task dependencies and paralleli... Task offloading is critical for optimizing resource allocation in edge computing systems.In practical scenarios,user applications often comprise multiple interdependent tasks,where both task dependencies and parallelism strongly affect offloading decisions.This paper presents a novel dependent task offloading framework for multiedge server environments.The task offloading problem is formulated as a Markov Decision Process(MDP)to minimize computational delay.Task dependencies are modeled using a Directed Acyclic Graph(DAG),and a Graph Convolutional Network(GCN)encoder is employed to extract DAG features as inputs for a Deep Reinforcement Learning(DRL)model.The proposed DRL-based method applies the Proximal Policy Optimization(PPO)algorithm to simultaneously select subtasks and determine their offloading decisions.Experimental evaluations across varying numbers of subtasks confirm the effectiveness of the approach,demonstrating superior performance compared to state-of-the-art solutions. 展开更多
关键词 Task offloading Edge computing Graph neural network Deep reinforcement learning
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A Workflow Scheduling Method Based on the Combination of Tunicate Swarm Algorithm and Highest Response Ratio Next Scheduling 认领 引用
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作者 Yujie Tian Ming Zhu +2 位作者 Jing Li Cong Liu Ziyang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1950-1963,共14页
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
关键词 Workflow scheduling cloud computing tunicate swarm algorithm highest response ratio next scheduling
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AI-Generated Text Detection:A Comprehensive Review of Active and Passive Approaches 认领 引用
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作者 Lingyun Xiang Nian Li +1 位作者 Yuling Liu Jiayong Hu 《Computers, Materials & Continua》 SCIE EI 2026年第3期201-229,共29页
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
关键词 AI-generated text detection large language models text classification watermarking
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Efficient management of medical image datasets for content-based case retrieval via sparse annotation and bidirectional label propagation 认领 引用
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作者 Chengyue Gong Ziyu Guan Jianguo Ju 《Data Science and Management》 EI CSCD 2026年第1期100-108,共9页
Efficient management of medical image datasets is critical for clinical decision-making.However,current methods lack fine-grained annotation management and content-based case retrieval capabilities.Existing systems st... Efficient management of medical image datasets is critical for clinical decision-making.However,current methods lack fine-grained annotation management and content-based case retrieval capabilities.Existing systems struggle to handle the annotation burden and to efficiently retrieve similar cases based on image content.To address these challenges,we propose a novel method that uses sparse annotation at the beginning,middle,and end of key disease regions,thereby reducing annotation effort while maintaining segmentation accuracy.We introduce CA-Morpher,an unsupervised image registration model enhanced by a cross-attention mechanism that effectively propagates sparse labels.Additionally,we develop a bidirectional label transfer algorithm that combines prior annotations and registration to propagate labels through bidirectional transfer and pseudo-label weighted fusion.This approach improves the management of annotated datasets by enabling content-based case retrieval,thereby enhancing overall data management efficiency.Experimental results show that our method achieves a Dice score of 76.62%,a Jaccard index of 63.25%,and a Hausdorff distance of 4.64 on the medical segmentation decathlon pancreatic tumor dataset,outperforming current sparse annotation methods and significantly improving the management and retrieval of medical image data. 展开更多
关键词 Image registration Label transfer Pseudo-label generation
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Object-oriented Software Construction Based on the Integration of OBE and BOPPPS Models 认领 引用
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作者 Jianghong Ma Cuiyun Gao +5 位作者 Min Fang Jianlong Wu Miao Zhang Zhiting Zhao Haijun Zhang Yunming Ye 《计算机教育》 2026年第3期148-157,共10页
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
关键词 OOSC Java Design pattern OBE+BOPPPS
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Prediction of the Diffusion Behavior of Interstitial Nonmetallic B and N Atoms Diffusing in FCC_CoNiV Multi‐Principal Element Alloy Based on Sublattice Preference:A Comparative Study 认领 引用
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作者 Xiaolin Zhou Minliang Gao +21 位作者 Dehua Wu Hui Guo Yang Qiao Xiangyan Su Xuan Fang Anqi Ji Xingyu Chen Xiaoqiong Zhang Hehua Que Baisheng Sa Yu Tang Chuangshi Feng Fuxiang Zhang Limei Cha Xiaolan Yang Bo Wu Chen Dong Jiankang Huang Frank Vrionis Jian Wang Yue Shen Ming Wen 《Materials Genome Engineering Advances》 CAS CSCD 2026年第2期163-180,共18页
The general predictive approach established in our previous work Qiao et al.,Materials Genome Engineering Advances.2025;3(3):e70021.was employed to study the diffusion behavior of interstitial B and N atoms in FCC_CoN... The general predictive approach established in our previous work Qiao et al.,Materials Genome Engineering Advances.2025;3(3):e70021.was employed to study the diffusion behavior of interstitial B and N atoms in FCC_CoNiV multi‐principal element alloy(MPEA)based on sublattice preference,with comparative C data from prior work,to enrich the diffusion genome database of lightweight interstitial elements.Furthermore,we employed the Kabsch algorithm to describe the lattice distortion of the octahedra containing interstitial atoms quantitatively.The results show that the number of V atoms in the local octahedral environment exerts a different regulatory effect on the diffusion behavior of interstitial atoms B and N;that is,B and C exhibit a higher diffusion barrier when migrating into V‐rich sites,whereas N exhibits such higher barrier when leaving these sites.Electron localization function(ELF)analysis shows the difference is due to the diverse bonding strengths between V atoms and interstitial atoms B,N,and C.Nonperiodic diffusion barrier waves and diffusion parameters were quantitatively predicted in detail.The fundamental understanding of interstitial diffusion mechanisms and quantitative characterization of the diffusion parameters of B,N,and C in FCC_CoNiV MPEA provide a benchmark and critical insights for tailoring alloy properties through interstitial engineering. 展开更多
关键词 diffusion behavior diffusion parameters first-principles calculations interstitial elements(B N C) multi-principal element alloys(MPEAs) sublattic epreference
Detection of Rice Bacterial Leaf Blight Using Hyperspectral Technology and Continuous Wavelet Analysis 认领 引用
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作者 Kaihao Shi Lin Yuan +5 位作者 Qimeng Yu Zhongting Shen Yingtan Yu Chenwei Nie Xingjian Zhou Jingcheng Zhang 《Phyton-International Journal of Experimental Botany》 SCIE 2025年第7期2033-2054,共22页
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
关键词 Hyperspectral continuous wavelet analysis continuous wavelet projection algorithm wavelet basis function disease monitoring
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An Enhanced Feature Neural Network and Its Application in Detection of Colorectal Polyps 认领 引用
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作者 LI Hailong LIU Guohua ZHAO Meng 《Journal of Donghua University(English Edition)》 CAS 2026年第1期32-40,共9页
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
关键词 colorectal polyp YOLO transformer deformable convolutional network-maxpool(DCN-MP) coordinate attention(CA)
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Compilers and LLMs:bidirectional impact of systems and education 认领 引用
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作者 Yu Zhang Li Zhang 《计算机教育》 2026年第6期229-236,共8页
Large language models(LLMs)are changing the way software is developed and taught.At the same time,LLMs fundamentally rely on core compiler concepts and technical foundations.This paper explores the bidirectional impac... Large language models(LLMs)are changing the way software is developed and taught.At the same time,LLMs fundamentally rely on core compiler concepts and technical foundations.This paper explores the bidirectional impact between compiler systems and LLMsÐfrom how LLMs are reshaping compiler design and usage to how compiler principles and techniques are essential for understanding,building,and teaching LLM-based systems.We further examine their implications for software engineering education and propose preliminary thoughts on integrating LLMs in future compiler courses.By bridging traditional compiler foundations with emerging AI paradigms,we advocate for reestablishing the central role of compiler education in training the next generation of intelligent system developers. 展开更多
关键词 Compiler education Large language models(LLMs) Bidirectional impact Curriculum reform
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Research and Practice of a New Training Model for Software Engineering Courses Based on Generative AI and OBE Concepts 认领 引用
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作者 Shengshai Zhang Xiaodong Yu +1 位作者 Jianhui Jiang Lixiao Zhang 《计算机教育》 2026年第3期139-147,共9页
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
关键词 Generative AI OBE Software engineering Teaching reform
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Artificial intelligence empowering software engineering education:Opportunities,challenges,and future prospects 认领 引用
19
作者 Chunwei Tian Hengxin Qiu +1 位作者 Qi Zhu Jianfang Hu 《计算机教育》 2026年第6期292-301,共10页
As a core field of information technology,the quality of software engineering education directly impacts the development of the future software industry.Current pedagogy,however,faces critical challenges including rap... As a core field of information technology,the quality of software engineering education directly impacts the development of the future software industry.Current pedagogy,however,faces critical challenges including rapid knowledge obsolescence,inadequate practical skill development,limited personalization,and complex assessment.This paper systematically explores AI's transformative potential in this domain,proposing an application framework that addresses content innovation,skill cultivation,and assessment optimization.We critically analyze implementation opportunities while addressing technical constraints,pedagogical adaptations,and ethical considerations.The contribution of this paper lies in providing a macroscopic and forwardlooking theoretical analysis framework,which offers references for in-depth research and practice of AI in the field of software engineering education. 展开更多
关键词 Artificial intelligence Software engineering education Teaching reform Practical skills Learning assessment
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