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Empowering PV system modeling and forecasting:a review of public benchmark datasets 认领 引用
1
作者 Bochao Zhao Yaqing Wang +1 位作者 Wenpeng Luan Hanju Cai 《Global Energy Interconnection》 EI CSCD 2026年第2期219-242,共24页
Driven by the high penetration of renewable energy,the inherent intermittency of photovoltaic(PV)generation poses severe challenges to grid stability.To manage this volatility and ensure reliable grid integration,prec... Driven by the high penetration of renewable energy,the inherent intermittency of photovoltaic(PV)generation poses severe challenges to grid stability.To manage this volatility and ensure reliable grid integration,precise PV system modeling and power forecasting have emerged as critical solutions.However,existing research predominantly focuses on algorithmic innovations and model architectures,frequently overlooking the foundational role of dataset selection.Because capturing the complex spatiotemporal dynamics of solar generation increasingly requires the integration of diverse data types,understanding how to select and fuse these multimodal sources is crucial for determining the upper bound of predictive performance.To address the persistent fragmentation of data resources in PV predictive modeling,this paper delivers a comprehensive taxonomy of publicly available benchmark datasets,establishing a roadmap for future data-driven research.We categorize these valuable resources into three core pillars:1)meteorological datasets(encompassing observational,synthetic,hybrid,and reanalysis types);2)PV generation datasets(grouped by temporal resolution);and 3)static system parameters(including plant-level geospatial data and module-level physical properties).Building upon this categorization,this review thoroughly examines multimodal data fusion strategies across various forecasting horizons and elucidates the specific data dependencies of persistence,physical,and data-driven modeling paradigms.Furthermore,we critically analyze key challenges in multi-source data fusion,particularly spatiotemporal misalignment and the lack of standardized quality control flags.Ultimately,this work provides researchers with an authoritative guide for robust data selection and model construction. 展开更多
关键词 PV system modeling PV forecasting Meteorological datasets PV generation datasets Static system parameters
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Quantitative Profiling of Tabular Biomedical Benchmark Datasets:A Meta-Learning Perspective for Algorithm Selection 认领 引用
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作者 Yiyan Zhang Yi Xin Qin Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1291-1309,共19页
Medical data has specificity compared to other fields of data,and the description of medical data characteristics is still in a qualitative stage.This study included 293 sub-datasets of 138 independent datasets.First,... Medical data has specificity compared to other fields of data,and the description of medical data characteristics is still in a qualitative stage.This study included 293 sub-datasets of 138 independent datasets.First,data preprocessing was performed using methods such as incomplete data removal,inconsistent data normalization,and data integration.Then,the characteristics of 293 research datasets were quantified using 26 indicators in three categories:simple indicators,statistical indicators,and informational indicators.Furthermore,statistical analysis was performed on the above-mentioned quantitative characteristics,and stepwise regression and decision tree methods were used for modeling learning.The characteristics of the biological and medical datasets in the study were compared with those of other fields’datasets.By comparing the results of statistical analysis and learning modeling,the study found that the sample size of medical datasets included in the UCI database analyzed in this paper is small,most within 1000.The harmonic mean or geometric mean of continuous variables is significantly higher than the data from other fields.That is to say,the scope of the continuous variable range is large.This study uses quantitative indicators to describe the characteristics of medical datasets to avoid the decrease in credibility caused by subjective analysis,and lays a foundation for further algorithm applicability research. 展开更多
关键词 Characteristics of datasets medical data quantitative representation decision tree
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Multilingual opinion mining of imbalanced customer review datasets using GPT and ensemble hybrid deep learning model 认领 引用
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作者 Nouri Hicham Habbat Nassera 《Data Science and Management》 EI CSCD 2026年第1期26-44,共19页
This study introduces stacked deep learning for multilingual opinion mining.This framework incorporates RoBERTa-GRU,RoBERTa-LSTM,RoBERTa-BiGRU,and RoBERTa-BiLSTM hybrid models,optimized using the Adam optimizer.The me... This study introduces stacked deep learning for multilingual opinion mining.This framework incorporates RoBERTa-GRU,RoBERTa-LSTM,RoBERTa-BiGRU,and RoBERTa-BiLSTM hybrid models,optimized using the Adam optimizer.The methodology can handle three languages:French,English,and Arabic.This research explores the challenges of imbalanced datasets in opinion mining,employing oversampling approaches like advanced easier data augmentation,synthetic minority over-sampling technique,and generative pre-trained transformer to balance the datasets and improve classification efficiency.We used the assessment metrics of Cohen’s kappa,the receiver operating characteristic-area under curve,accuracy,and Matthews correlation coefficient,along with k-fold validation,to evaluate the sentiment analysis performance across three languages and six datasets.Moreover,we computed performance metrics for all models while scaling the dataset for training and testing.We also determined the memory usage and execution time for each model.Utilizing a stacked deep learning algorithm,the suggested methodology for multilingual opinion mining demonstrated high efficacy in extracting meaningful insights from social media data across several languages.The technique produced significant outcomes,rendering it a potentially valuable instrument for enhancing performance and customer satisfaction by identifying patterns and trends in public sentiment. 展开更多
关键词 Natural language processing(NLP) Stacking deep learning Imbalanced datasets GPT
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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Layered Feature Engineering for E-Commerce Purchase Prediction:A Hierarchical Evaluation on Taobao User Behavior Datasets 认领 引用
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作者 Liqiu Suo Lin Xia +1 位作者 Yoona Chung Eunchan Kim 《Computers, Materials & Continua》 SCIE EI 2026年第4期1865-1889,共25页
Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three ... Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design. 展开更多
关键词 Hierarchical feature engineering purchase prediction user behavior dataset feature importance e-commerce platform Taobao
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Fine-Med-Mental-T&P:a dual-track approach for high-quality instructional datasets of mental disorders in traditional Chinese medicine 认领 引用
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作者 Yanbai Wei Xiaoshuo Jing Junfeng Yan 《Digital Chinese Medicine》 CAS CSCD 2026年第1期31-42,共12页
Objective To investigate methods for constructing a high-quality instructional dataset for traditional Chinese medicine(TCM)mental disorders and to validate its efficacy.Methods We proposed the Fine-Med-Mental-T&P... Objective To investigate methods for constructing a high-quality instructional dataset for traditional Chinese medicine(TCM)mental disorders and to validate its efficacy.Methods We proposed the Fine-Med-Mental-T&P methodology for constructing high-quality instruction datasets in TCM mental disorders.This approach integrates theoretical knowledge and practical case studies through a dual-track strategy.(i)Theoretical track:textbooks and guidelines on TCM mental disorders were manually segmented.Initial responses were generated using DeepSeek-V3,followed by refinement by the Qwen3-32B model to align the expression with human preferences.A screening algorithm was then applied to select 16000 high-quality instruction pairs.(ii)Practical track:starting from over 600 real clinical case seeds,diagnostic and therapeutic instruction pairs were generated using DeepSeek-V3 and subsequently screened through manual evaluation,resulting in 4000 high-quality practiceoriented instruction pairs.The integration of both tracks yielded the Med-Mental-Instruct-T&P dataset,comprising a total of 20000 instruction pairs.To validate the dataset’s effectiveness,three experimental evaluations(both manual and automated)were conducted:(i)comparative studies to compare the performance of models fine-tuned on different datasets;(ii)benchmarking to compare against mainstream TCM-specific large language models(LLMs);(iii)data ablation study to investigate the relationship between data volume and model performance.Results Experimental results demonstrate the superior performance of T&P-model finetuned on the Med-Mental-Instruct-T&P dataset.In the comparative study,the T&P-model significantly outperformed the baseline models trained solely on self-generated or purely human-curated baseline data.This superiority was evident in both automated metrics(ROUGEL>0.55)and expert manual evaluations(scoring above 7/10 across accuracy).In benchmark comparisons,the T&P-model also excelled against existing mainstream TCM LLMs(e.g.,HuatuoGPT and ZuoyiGPT).It showed particularly strong capabilities in handling diverse clinical presentations,including challenging disorders such as insomnia and coma,showcasing its robustness and versatility.Data ablation studies showed that T&P-model performance had an overall upward trend with minor fluctuations when training data increased from 10%to 50%;beyond 50%,performance improvement slowed significantly,with metrics plateauing and approaching a saturation point. 展开更多
关键词 Mental disorder Traditional Chinese medicine(TCM) Instruction dataset construction Instruction tuning Large language model
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Comprehensive guidelines for establishing sample diversity and data sufficiency in artificial intelligence diagnostic datasets for cervical liquid-based cytology 认领 引用
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作者 Zhejiang Society for Mathematical Medicine Artificial Intelligence Medical Device Innovation Cooperation Platform Data Governance Working Group of the Center for Medical Device Evaluation,National Medical Products Administration +1 位作者 Haimiao Xu Mulan Jin 《Intelligent Oncology》 CAS 2026年第3期76-84,共9页
Liquid-based cytology(LBC)has become a core technology in cervical cancer screening,and artificial intelligence(AI)shows great potential in addressing issues such as the global shortage of cytopathologists and large v... Liquid-based cytology(LBC)has become a core technology in cervical cancer screening,and artificial intelligence(AI)shows great potential in addressing issues such as the global shortage of cytopathologists and large variations in diagnostic results.However,the clinical reliability of AI systems in the field of cervical cytology fundamentally depends on the quality,diversity,and representativeness of their training and validation datasets.Drawing on evidence-based medicine principles,the latest research progress in China and globally,and clinical practices,these guidelines establish standardized requirements for sample diversity and data sufficiency in cervical LBC AI datasets to reduce algorithmic bias.The objective is to improve the real-world applicability of models and ensure their safe clinical application.The guidelines strictly comply with standardized guideline development specifications,with transparent expert panel organization,systematic literature retrieval,standardized evidence grading,three-round Delphi expert consensus,external peer review,and dynamic update mechanisms.All quantitative threshold indicators in the recommendations are jointly formulated based on highquality clinical evidence and expert consensus,with clear evidence sources and consensus construction processes to enhance the transparency,credibility,and operability of the guideline. 展开更多
关键词 Cervical cytology dataset design Cervical cancer screening Data sufficiency standards Cytology AI clinical validation Algorithmic bias mitigation The Bethesda System
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A Comprehensive Review of Face Detection Techniques for Occluded Faces:Methods,Datasets,and Open Challenges 认领 引用 被引量：1
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作者 Thaer Thaher Majdi Mafarja +2 位作者 Muhammed Saffarini Abdul Hakim H.M.Mohamed Ayman A.El-Saleh 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2615-2673,共59页
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill... Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios. 展开更多
关键词 Occluded face detection feature-based deep learning machine learning hybrid approaches datasets
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A critical evaluation of deep-learning based phylogenetic inference programs using simulated datasets 认领 引用
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作者 Yixiao Zhu Yonglin Li +2 位作者 Chuhao Li Xing-Xing Shen Xiaofan Zhou 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2025年第5期714-717,共4页
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o... Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively. 展开更多
关键词 phylogenetic inference explicit models sequence evolution deep learning deep learning dl techniques molecular sequences simulated datasets phylogenetic methods such evolutionary biologymany
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The Development of Artificial Intelligence:Toward Consistency in the Logical Structures of Datasets,AI Models,Model Building,and Hardware? 认领 引用
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作者 Li Guo Jinghai Li 《Engineering》 SCIE EI CSCD 2025年第7期13-17,共5页
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu... The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration. 展开更多
关键词 consistency datasets model building ai models artificial intelligence ai explore potential directions hardware artificial intelligence
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DCS-SOCP-SVM:A Novel Integrated Sampling and Classification Algorithm for Imbalanced Datasets 认领 引用
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作者 Xuewen Mu Bingcong Zhao 《Computers, Materials & Continua》 SCIE EI 2025年第5期2143-2159,共17页
When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes... When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets. 展开更多
关键词 DCS-SOCP-SVM imbalanced datasets sampling method ensemble method integrated algorithm
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Standardizing Healthcare Datasets in China:Challenges and Strategies 认领 引用 被引量：1
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作者 Zheng-Yong Hu Xiao-Lei Xiu +2 位作者 Jing-Yu Zhang Wan-Fei Hu Si-Zhu Wu 《Chinese Medical Sciences Journal》 CAS CSCD 2025年第4期253-267,I0001,共15页
Standardized datasets are foundational to healthcare informatization by enhancing data quality and unleashing the value of data elements.Using bibliometrics and content analysis,this study examines China's healthc... Standardized datasets are foundational to healthcare informatization by enhancing data quality and unleashing the value of data elements.Using bibliometrics and content analysis,this study examines China's healthcare dataset standards from 2011 to 2025.It analyzes their evolution across types,applications,institutions,and themes,highlighting key achievements including substantial growth in quantity,optimized typology,expansion into innovative application scenarios such as health decision support,and broadened institutional involvement.The study also identifies critical challenges,including imbalanced development,insufficient quality control,and a lack of essential metadata—such as authoritative data element mappings and privacy annotations—which hampers the delivery of intelligent services.To address these challenges,the study proposes a multi-faceted strategy focused on optimizing the standard system's architecture,enhancing quality and implementation,and advancing both data governance—through authoritative tracing and privacy protection—and intelligent service provision.These strategies aim to promote the application of dataset standards,thereby fostering and securing the development of new productive forces in healthcare. 展开更多
关键词 healthcare dataset standards data standardization data management
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A Comprehensive Review of Face Detection/Recognition Algorithms and Competitive Datasets to Optimize Machine Vision 认领 引用
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作者 Mahmood Ul Haq Muhammad Athar Javed Sethi +3 位作者 Sadique Ahmad Naveed Ahmad Muhammad Shahid Anwar Alpamis Kutlimuratov 《Computers, Materials & Continua》 SCIE EI 2025年第7期1-24,共24页
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi... Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detectionecognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research. 展开更多
关键词 Face recognition algorithms face detection techniques face recognition/detection datasets
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Development and validation of AI delineation of the thoracic RTOG organs at risk with deep learning on multi-institutional datasets 认领 引用
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作者 Xianghua Ye Dazhou Guo +32 位作者 Lujun Zhao Congying Xie Dandan Zheng Haihua Yang Xiangzhi Zhu Xin Sun Pingping Dong Huanhuan Li Weiwei Kong Jianzhong Cao Honglei Chen Juntao Ran Kai Ren Hongxin Su Hao Hu Cuimeng Tian Tianlu Wang Qiang Zeng Xiao Hu Ping Peng Junhua Zhang Li Zhang Tingting Zhang Lue Zhou Wenchao Guo Zhanghexuan Ji Puyang Wang Hua Zhang Jiali Liu Le Lu Senxiang Yan Dakai Jin Feng-MingKong 《Intelligent Oncology》 2025年第1期61-71,共11页
Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substan... Introduction:Accurate contouring of thoracic organs at risk(OARs)is essential for minimizing complications in radiation treatment.Manual contouring of thoracic OARs is not only time-consuming but also prone to substantial user variation.To enhance the efficiency and consistency,we developed a unified deep learning(DL)OAR contouring model,DeepOAR,that was trained using multiple partially labeled datasets for segmenting a comprehensive set of thoracic OARs following the Radiation Therapy Oncology Group(RTOG)-guided OAR atlas.This DL model supports the segmentation of six required and eight optional OARs guided by the NRG-RTOG 1106 trial,providing precise and reproducible OARs contouring that are ready to be used in radiotherapy practice.Materials and methods:Following the OAR contouring recommendation of the NRG-RTOG 1106 trial,we collected and curated three private datasets and two public datasets,comprising a total of 531 patients with partially annotated thoracic OARs.These partially annotated datasets were utilized to develop DeepOAR,which consisted of a shared encoder and 14 separate decoders,with each decoder dedicated to one specific OAR.For model training,we utilized all patients from the two public datasets and 75%of the patients from the private datasets.We reserved the remaining 25%of the private datasets for independent testing.A multi-user study involving 21 radiation oncologists was conducted on 40 randomly selected patients from the independent testing dataset to evaluate the clinical applicability of DeepOAR.The Dice coefficient score(DSC)and average surface distance(ASD)were computed to evaluate the quantitative delineation performance of the model.Results:DeepOAR outperformed nnUNet(the benchmark medical segmentation model)across all 14 OARs,achieving mean DSC and ASD values of 88.4%and 1.0 mm,respectively,in the independent testing set.Multi-user validation demonstrated that 89.7%of DeepOAR-generated OARs were clinically acceptable or required only minor revisions.A comparison using two randomly selected patients showed that the delineation variability of DeepOAR was significantly smaller than the inter-user variation among radiation oncologists.Human editing of DeepOAR’s predictions could further improve OAR delineation accuracy by an average of 3%increase in DSC and 40%reduction in ASD while significantly reducing the workload of radiation oncologists for contouring 14 thoracic OARs by an average of 77.0%.Conclusion:We developed DeepOAR,a DL-based unified contouring model trained using multiple partially labeled datasets,to delineate a comprehensive set of 14 thoracic OARs following the RTOG-guided OAR atlas.Both qualitative and quantitative results demonstrated the strong clinical applicability of DeepOAR for the OAR delineation process in thoracic cancer radiotherapy workflows,along with improved efficiency,comprehensiveness,and quality. 展开更多
关键词 NRG-RTOG 1106 OAR segmentation Deep learning Partially labeled datasets
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Impacts of random negative training datasets on machine learning-based geologic hazard susceptibility assessment 认领 引用 被引量：2
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作者 Hao Cheng Wei Hong +3 位作者 Zhen-kai Zhang Zeng-lin Hong Zi-yao Wang Yu-xuan Dong 《China Geology》 CAS CSCD 2025年第4期676-690,共15页
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,... This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures. 展开更多
关键词 Landslides Debris flows Collapses Ground fissures Geologic hazard prevention and control engineering Geologic hazard susceptibility assessment Negative training dataset Average spatial correlation Random forest algorithm Risk and return analysis Geological survey engineering Loess Plateau area
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Impact of climate changes on Arizona State precipitation patterns using high-resolution climatic gridded datasets 认领 引用 被引量：1
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作者 Hayder H.Kareem Shahla Abdulqader Nassrullah 《Journal of Groundwater Science and Engineering》 2025年第1期34-46,共13页
Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geograph... Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geographical study was conducted in Arizona State,USA,to examine monthly precipi-tation concentration rates over time.This analysis used a high-resolution 0.50×0.50 grid for monthly precip-itation data from 1961 to 2022,Provided by the Climatic Research Unit.The study aimed to analyze climatic changes affected the first and last five years of each decade,as well as the entire decade,during the specified period.GIS was used to meet the objectives of this study.Arizona experienced 51–568 mm,67–560 mm,63–622 mm,and 52–590 mm of rainfall in the sixth,seventh,eighth,and ninth decades of the second millennium,respectively.Both the first and second five year periods of each decade showed accept-able rainfall amounts despite fluctuations.However,rainfall decreased in the first and second decades of the third millennium.and in the first two years of the third decade.Rainfall amounts dropped to 42–472 mm,55–469 mm,and 74–498 mm,respectively,indicating a downward trend in precipitation.The central part of the state received the highest rainfall,while the eastern and western regions(spanning north to south)had significantly less.Over the decades of the third millennium,the average annual rainfall every five years was relatively low,showing a declining trend due to severe climate changes,generally ranging between 35 mm and 498 mm.The central regions consistently received more rainfall than the eastern and western outskirts.Arizona is currently experiencing a decrease in rainfall due to climate change,a situation that could deterio-rate further.This highlights the need to optimize the use of existing rainfall and explore alternative water sources. 展开更多
关键词 Spatial Analysis Climate Impact Precipitation Rates CRU Dataset GIS Arizona State USA
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Study on the Structure of a Horizontal Shear Line over the Tibetan Plateau Based on CRA-Interim Datasets and Its Comparison with ERA-Interim Datasets 认领 引用 被引量：1
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作者 YAO Xiu-ping ZHANG Shuo +2 位作者 BAO Xiao-hong SHI Chun-xiang LIU Jing-wei 《Journal of Tropical Meteorology》 SCIE 2020年第4期483-494,共12页
The CRA-Interim trial production of the global atmospheric reanalysis for 10 years from 2007 to 2016 was carried out by the China Meteorological Administration in 2017.The structural characteristics of the horizontal ... The CRA-Interim trial production of the global atmospheric reanalysis for 10 years from 2007 to 2016 was carried out by the China Meteorological Administration in 2017.The structural characteristics of the horizontal shear line over the Tibetan Plateau(TPHSL)based on the CRA-Interim datasets are examined by objectively identifying the shear line,and are compared with the analysis results of the European Centre for Medium-Range Weather Forecasts reanalysis data(ERA-Interim).The case occurred at 18UTC on July 5,2016.The results show that both of the ERA-Interim and CRA-Interim datasets can well reveal the circulation background and the dynamic and thermal structure characteristics of TPHSL,and they have shown some similar features.The middle and high latitudes at 500 hPa are characterized by the circulation situation of"two troughs and two ridges",and at 200 hPa,the TPHSL is located in the northeast quadrant of the South Asian High Pressure(SAHP).The TPHSL locates in the positive vorticity zone and passes through the positive vorticity center corresponding to the ascending motion.Near the TPHSL,the contours of pseudo-equivalent potential temperature(θse)tend to be intensive,with a high-value center on the south side of the TPHSL.The TPHSL can extend to460 hPa and vertically inclines northward.There is a positive vorticity zone near the TPHSL which is also characterized by the northward inclination with the height,the ascending motion near the TPHSL can extend to 300 hPa,and the atmospheric layer above the TPHSL is stable.However,the intensities of the TPHSL’s structure characteristics analyzed with the two datasets are different,revealing the relatively strong intensity of geopotential height field,vertical velocity field,vorticity field and divergence field from the CRA-Interim datasets.In addition,the vertical profiles of the dynamic and water vapor thermal physical quantities of the two datasets are also consistent in the east and west part of the TPHSL.In summary,the reliable and usable CRA-Interim datasets show excellent properties in the analysis on the structural characteristics of a horizontal shear line over the Tibetan Plateau. 展开更多
关键词 CRA-Interim datasets ERA-Interim datasets horizontal shear line over the Tibetan Plateau structure
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Comparisons of cropland area from multiple datasets over the past 300 years in the traditional cultivated region of China 认领 引用 被引量：28
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作者 HE Fanneng LI Shicheng +2 位作者 ZHANG Xuezhen GE Quansheng DAI Junhu 《Journal of Geographical Sciences》 SCIE CSCD 2013年第6期978-990,共13页
Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical l... Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical land use datasets, were little assessed about their accuracies in regional scale. Here, we carried out some assessments for the traditional cultivated region of China （TCRC） over last 300 years, by comparing SAGE2010 and HYDE （v3.1） with Chinese Historical Cropland Dataset （CHCD）. The comparisons were performed at three spatial scales： entire study area, provincial area and 60 km by 60 km grid cell. The results show that （1） the cropland area from SAGE2010 was much more than that from CHCD moreover, the growth at a rate of 0.51% from 1700 to 1950 and -0.34% after 1950 were also inconsistent with that from CHCD. （2） HYDE dataset （v3.1） was closer to CHCD dataset than SAGE dataset on entire study area. However, the large biases could be detected at provincial scale and 60 km by 60 km grid cell scale. The percent of grid cells having biases greater than 70% （〈-70% or 〉70%） and 90% （〈-90% or 〉90%） accounted for 56%-63% and 40%-45% of the total grid cells respectively while those having biases range from -10% to 10% and from -30% to 30% account for only 5%-6% and 17% of the total grid cells respectively. （3） Using local historical archives to reconstruct historical dataset with high accuracy would be a valu- able way to improve the accuracy of climate and ecological simulation. 展开更多
关键词 cropland datasets comparisons past 300 years traditional cultivated region China
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The Assessment of Global Surface Temperature Change from 1850s:The C-LSAT2.0 Ensemble and the CMST-Interim Datasets 认领 引用 被引量：16
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作者 Wenbin SUN Qingxiang LI +6 位作者 Boyin HUANG Jiayi CHENG Zhaoyang SONG Haiyan LI Wenjie DONG Panmao ZHAI Phil JONES 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2021年第5期875-888,共14页
Based on C-LSAT2.0,using high-and low-frequency components reconstruction methods,combined with observation constraint masking,a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been develo... Based on C-LSAT2.0,using high-and low-frequency components reconstruction methods,combined with observation constraint masking,a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed.These ensemble versions have been merged with the ERSSTv5 ensemble dataset,and an upgraded version of the CMSTInterim dataset with 5°×5°resolution has been developed.The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data.After reconstruction,the data coverage before 1950 increased from 78%−81%of the original CMST to 81%−89%.The total coverage after 1955 reached about 93%,including more than 98%in the Northern Hemisphere and 81%−89%in the Southern Hemisphere.Through the reconstruction ensemble experiments with different parameters,a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMSTInterim.In comparison with the original CMST,the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century,which shows that the global warming trend is further amplified.The global warming trends are updated from 0.085±0.004℃(10 yr)–1and 0.128±0.006℃(10 yr)–1to 0.089±0.004℃(10 yr)–1and 0.137±0.007℃(10 yr)–1,respectively,since the start and the second half of 20th century. 展开更多
关键词 C-LSAT2.0 ensemble datasets CMST-Interim EOTs high-and low-frequency components reconstruction
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The spatial local accuracy of land cover datasets over the Qiangtang Plateau, High Asia 认领 引用 被引量：5
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作者 LIU Qionghuan ZHANG Yili +2 位作者 LIU Linshan LI Lanhui QI Wei 《Journal of Geographical Sciences》 SCIE CSCD 2019年第11期1841-1858,共18页
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere ... We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS),Global Land cover mapping at 30 m resolution (GlobeLand30),MODIS Land Cover Type product (MCD12Q1),Climate Change Initiative Land Cover (CCI-LC),Global Land Cover 2000 (GLC2000),University of Maryland (UMD),and GlobCover 2009 (Glob- Cover).We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources.We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation.The results of this study reveal that distinct differences,even inverse time series trends,in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015,with the exception of category areal discordance between the seven datasets.We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns,that is,high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions.The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%,and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts,yielding maximum overall accuracy (OA) values of 77.39% and 61.43%,respectively.Finally,5.63% of this area is characterized by both high assessment and accuracy (HH) values,mainly located in central and eastern regions of the Qiangtang Plateau,while most low accuracy regions are found in northern,northeastern,and western regions. 展开更多
关键词 land cover datasets spatial accuracy assessment remote sensing Qiangtang Plateau High Asia
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