Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil...Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.展开更多
Temperature-programmed desorption(TPD)is a fundamental technique in surface science and heterogeneous catalysis for characterizing adsorption behavior,and for extracting key parameters such as adsorption energy.Howeve...Temperature-programmed desorption(TPD)is a fundamental technique in surface science and heterogeneous catalysis for characterizing adsorption behavior,and for extracting key parameters such as adsorption energy.However,the majority of existing TPD data is accessible in the form of published images,which lacks structured and quantitative datasets.This constrains its utility for rigorous quantitative analysis and computational modelling.Using carbon monoxide(CO)which is a widely adopted probe molecule,a curated and standardized dataset of CO-TPD is constructed,encompassing 14 transition-metal single-crystal surfaces,including copper(Cu)and ruthenium(Ru).By systematically extracting numerical data points from published spectra and applying normalization,essential spectral features such as peak shape are fully preserved.The dataset also documents relevant experimental parameters,including heating rates,and was developed using a standardized protocol for data collection and quality control.This resource serves as both a reference library to support the deconvolution of TPD spectra from complex catalysts and an experimental benchmark for calibrating parameters in theoretical models.By providing a reliable and accessible data function,this work advances the microscopic understanding and the rational design of catalyst active centers.展开更多
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
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
Objective To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases,addressing the key limitations of insufficient complementary feature extraction and inadequate cros...Objective To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases,addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods.Methods The fundus multi-label classification dataset with 12 disease categories(FMLC-12)dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition(ODIR)and Retinal Fundus Multi-Disease Image Dataset(RFMiD),yielding 6936 fundus images across 12 retinal pathology categories,and the framework was validated on both FMLC-12 and ODIR.Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine(TCM)ophthalmology,the dualbranch multi-label network(DBMNet)was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling.The architecture employed a TransNeXt backbone within a dual-branch design:one branch processed redgreen-blue(RGB)images to capture color-dependent features,such as vascular patterns and lesion morphology,while the other processed grayscale-converted images to enhance subtle textural details and contrast variations.A feature interaction module(FIM)effectively integrated the multi-scale features from both branches.Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM.The performance of DBMNet was compared against four state-of-the-art methods,including EfficientNet Ensemble,transfer learning-based convolutional neural network(CNN),BFENet,and EyeDeep-Net,using mean average precision(mAP),F1-score,and Cohen's kappa coefficient.Results The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline,increasing from 34.41%to 44.24%,and the addition of FIM further boosted mAP to 49.85%.On FMLC-12,DBMNet achieved an mAP of 49.85%,a Cohen’s kappa coefficient of 62.14%,and an F1-score of 70.21%.Compared with BFENet(mAP:45.42%,kappa:46.64%,F1-score:71.34%),DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa,while BFENet achieved a marginally higher F1-score.On ODIR,DBMNet achieved an F1-score of 85.50%,comparable to state-of-the-art methods.Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture,significantly improving multi-label fundus disease classification.The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases,providing a promising and clinically applicable pathway for standardized,intelligent fundus disease classification.展开更多
With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loose...With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.展开更多
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
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
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
Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for ...Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.展开更多
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t...Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.展开更多
Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence o...Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.展开更多
Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategi...Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategies.A high-resolution PM_(2.5) chemical composition dataset(CAQRA-aerosol)is developed in this study,which provides hourly maps of organic carbon,black carbon,ammonium,nitrate,and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km.This paper describes the method,access,and validation results of this dataset.It shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM_(2.5) composition datasets.Based on CAQRA-aerosol,spatiotemporal changes of different PM_(2.5) compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other compositions.The estimated annual rate of population-weighted concentrations of nitrate is 0.23μg m^(−3)yr^(−1) from 2015 to 2020,compared with−0.19 to−1.1μg m^(−3)yr^(−1) for other compositions.The whole dataset is freely available from the China Air Pollution Data Center(https://doi.org/10.12423/capdb_PKU.2023.DA).展开更多
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data o...This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data of the hard carbons were obtained by charge/discharge tests and the materials were characterized by XRD,gas adsorption,true density tests and SAXS.In particular,the fitting of SAXS gave a series of structural parameters which showed good characterization.The related test details are given with the structural data of the hard carbons and the electrochemical performance of the sodium-ion batteries.展开更多
The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the ...The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the Ultrasound Dataset for Thyroid Nodules(UD-TN),a comprehensive dataset containing 10,495 labeled images classified as benign or malignant based on pathology-confirmed results.To establish a benchmark,we proposed the Thyroid Ultrasound Image Neural Network(ThyUNet),a deep learning model designed for accurate nodule classification.By incorporating high-resolution feature enhancement,instance normalization,and dilated convolutions into residual blocks,ThyUNet excels in extracting fine-grained features,particularly for small nodules.Experimental results demonstrate that ThyUNet achieves state-of-the-art performance,with an accuracy of 89.7%,a sensitivity of 0.879,and a specificity of 0.910 on the testing set.These results surpass those of other advanced architectures,highlighting the model’s effectiveness.UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics.Dataset details and access instructions are available at https://github.com/18811755633/Sample-of-UD-TN.展开更多
In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deat...In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.展开更多
Background:The life-course management of children with tetralogy of Fallot(TOF)has focused on demonstrating brain structural alterations,developmental trajectories,and cognition-related changes that unfold over time.M...Background:The life-course management of children with tetralogy of Fallot(TOF)has focused on demonstrating brain structural alterations,developmental trajectories,and cognition-related changes that unfold over time.Methods:We introduce an magnetic resonance imaging(MRI)dataset comprising TOF children who underwent brain MRI scanning and cross-sectional neurocognitive follow-up.The dataset includes brain three-dimensional T1-weighted imaging(3D-T1WI),three-dimensional T2-weighted imaging(3D-T2WI),and neurodevelopmental evaluations using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition(WPPSI-IV).Results:Thirty-one children with TOF(age range:4–33 months;18 males)were recruited and completed corrective surgery at the Children’s Hospital of Nanjing Medical University,Nanjing,China.Aiming to promote the neurodevelopmental outcomes in children with TOF,we have meticulously curated a comprehensive dataset designed to dissect the complex interplay among risk factors,neuroimaging findings,and adverse neurodevelopmental outcomes.Conclusion:This article aims to introduce our open-source dataset on neurodevelopment in children with TOF,which covers the data types,data acquisition and processing methods,the procedure for accessing the data,and related publications.展开更多
文摘Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.
基金Supported by the Robotic AI-Scientist Platform of Chinese Academy of SciencesNational Natural Science Foundation of China(22372185)+2 种基金Youth Talent Development Program of SKLCC(2025BWZ009)Natural Science Foundation of Shanxi Province(202203021221219)Research on the Construction of Scientific and Technological Innovation Think Tank of Shanxi Association for Science and Technology(KXKT202542)。
文摘Temperature-programmed desorption(TPD)is a fundamental technique in surface science and heterogeneous catalysis for characterizing adsorption behavior,and for extracting key parameters such as adsorption energy.However,the majority of existing TPD data is accessible in the form of published images,which lacks structured and quantitative datasets.This constrains its utility for rigorous quantitative analysis and computational modelling.Using carbon monoxide(CO)which is a widely adopted probe molecule,a curated and standardized dataset of CO-TPD is constructed,encompassing 14 transition-metal single-crystal surfaces,including copper(Cu)and ruthenium(Ru).By systematically extracting numerical data points from published spectra and applying normalization,essential spectral features such as peak shape are fully preserved.The dataset also documents relevant experimental parameters,including heating rates,and was developed using a standardized protocol for data collection and quality control.This resource serves as both a reference library to support the deconvolution of TPD spectra from complex catalysts and an experimental benchmark for calibrating parameters in theoretical models.By providing a reliable and accessible data function,this work advances the microscopic understanding and the rational design of catalyst active centers.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金Natural Science Foundation of Hunan Province(2025JJ90031)Key Research and Development Program of Hunan Province of China(23A0273)Hunan Provincial Administration of Traditional Chinese Medicine(A2023048).
文摘Objective To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases,addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods.Methods The fundus multi-label classification dataset with 12 disease categories(FMLC-12)dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition(ODIR)and Retinal Fundus Multi-Disease Image Dataset(RFMiD),yielding 6936 fundus images across 12 retinal pathology categories,and the framework was validated on both FMLC-12 and ODIR.Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine(TCM)ophthalmology,the dualbranch multi-label network(DBMNet)was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling.The architecture employed a TransNeXt backbone within a dual-branch design:one branch processed redgreen-blue(RGB)images to capture color-dependent features,such as vascular patterns and lesion morphology,while the other processed grayscale-converted images to enhance subtle textural details and contrast variations.A feature interaction module(FIM)effectively integrated the multi-scale features from both branches.Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM.The performance of DBMNet was compared against four state-of-the-art methods,including EfficientNet Ensemble,transfer learning-based convolutional neural network(CNN),BFENet,and EyeDeep-Net,using mean average precision(mAP),F1-score,and Cohen's kappa coefficient.Results The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline,increasing from 34.41%to 44.24%,and the addition of FIM further boosted mAP to 49.85%.On FMLC-12,DBMNet achieved an mAP of 49.85%,a Cohen’s kappa coefficient of 62.14%,and an F1-score of 70.21%.Compared with BFENet(mAP:45.42%,kappa:46.64%,F1-score:71.34%),DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa,while BFENet achieved a marginally higher F1-score.On ODIR,DBMNet achieved an F1-score of 85.50%,comparable to state-of-the-art methods.Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture,significantly improving multi-label fundus disease classification.The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases,providing a promising and clinically applicable pathway for standardized,intelligent fundus disease classification.
基金funded by the Zhejiang Provincial Key Science and Technology“LingYan”Project Foundation,grant number 2023C01145Zhejiang Gongshang University Higher Education Research Projects,grant number Xgy22028.
文摘With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.
基金supported by the research fund of Hanyang University(HY-202500000001616).
文摘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.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金Key Scientific Research Project of the Hunan Provincial Department of Education(23A312).
文摘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.
基金supported by European Union’s Horizon Europe research and innovation programme,project AGILEHAND(Smart Grading,Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines)(101092043).
文摘Dear Editor,This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products,a critical step toward automating food production lines.Accurate segmentation is essential for addressing challenges such as occlusions,indistinct edges,and stacked configurations,which demand large,diverse datasets.To meet these demands,we propose two complementary approaches:a semi-automatic annotation interface using tools like the segment anything model(SAM)and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models.These methods reduce reliance on real meat,mitigate food waste,and improve scalability.Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and,when combined with real data,further boosts accuracy,paving the way for more efficient automation in the food industry.
基金the Natural Science Foundation of China(Grant Numbers 72074014 and 72004012).
文摘Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.
文摘Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.
基金support from the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)sponsored by the National Natural Science Foundation of China (Grant Nos. 42175132, 92044303, and 42205119)+2 种基金the National Key R&D Program (Grant Nos. 2020YFA0607802 and 2022YFC3703003)the CAS Information Technology Program (Grant No. CAS-WX2021SF-0107-02)the fellowship of China Postdoctoral Science Foundation (Grant No. 2022M723093)
文摘Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategies.A high-resolution PM_(2.5) chemical composition dataset(CAQRA-aerosol)is developed in this study,which provides hourly maps of organic carbon,black carbon,ammonium,nitrate,and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km.This paper describes the method,access,and validation results of this dataset.It shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM_(2.5) composition datasets.Based on CAQRA-aerosol,spatiotemporal changes of different PM_(2.5) compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other compositions.The estimated annual rate of population-weighted concentrations of nitrate is 0.23μg m^(−3)yr^(−1) from 2015 to 2020,compared with−0.19 to−1.1μg m^(−3)yr^(−1) for other compositions.The whole dataset is freely available from the China Air Pollution Data Center(https://doi.org/10.12423/capdb_PKU.2023.DA).
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
基金supported by the National Natural Science Foundation of China(22379157)CAS Project for Young Scientists in Basic Research(YSBR-102)+2 种基金Institute of Coal Chemistry,Chinese Academy of Sciences(SCJC-XCL-2023-13,SCJCXCL-2023-10)Talent Projects for Outstanding Doctoral Students to Work in Shanxi Province(E3SWR4791Z)Fundamental Research Program of Shanxi Province(202403021222485).
文摘This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data of the hard carbons were obtained by charge/discharge tests and the materials were characterized by XRD,gas adsorption,true density tests and SAXS.In particular,the fitting of SAXS gave a series of structural parameters which showed good characterization.The related test details are given with the structural data of the hard carbons and the electrochemical performance of the sodium-ion batteries.
基金the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.82402274 and 82272008)the Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194).
文摘The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging.However,publicly available thyroid ultrasound datasets remain scarce.In this study,we developed the Ultrasound Dataset for Thyroid Nodules(UD-TN),a comprehensive dataset containing 10,495 labeled images classified as benign or malignant based on pathology-confirmed results.To establish a benchmark,we proposed the Thyroid Ultrasound Image Neural Network(ThyUNet),a deep learning model designed for accurate nodule classification.By incorporating high-resolution feature enhancement,instance normalization,and dilated convolutions into residual blocks,ThyUNet excels in extracting fine-grained features,particularly for small nodules.Experimental results demonstrate that ThyUNet achieves state-of-the-art performance,with an accuracy of 89.7%,a sensitivity of 0.879,and a specificity of 0.910 on the testing set.These results surpass those of other advanced architectures,highlighting the model’s effectiveness.UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics.Dataset details and access instructions are available at https://github.com/18811755633/Sample-of-UD-TN.
基金supporting of the National Science and Technology Council NSTC(grant nos.NSTC 112-2221-E-019-023,NSTC 113-2221-E-019-039)Taiwan University of Science and Technology.
文摘In its 2023 global health statistics,the World Health Organization noted that noncommunicable diseases(NCDs)remain the leading cause of disease burden worldwide,with cardiovascular diseases(CVDs)resulting in more deaths than the three other major NCDs combined.In this study,we developed a method that can comprehensively detect which CVDs are present in a patient.Specifically,we propose a multi-label classification method that utilizes photoplethysmography(PPG)signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions:hypertension,diabetes,cerebral infarction,and cerebrovascular disease.Our approach to multi-disease classification of cardiovascular diseases(CVDs)using PPG signals achieves the highest classification performance when encompassing the broadest range of disease categories,thereby offering a more comprehensive assessment of human health.We employ a multi-label classification strategy to simultaneously predict the presence or absence of multiple diseases.Specifically,we first apply the Savitzky-Golay(S-G)filter to the PPG signals to reduce noise and then transform into statistical features.We integrate processed PPG signals with individual physiological features as a multimodal input,thereby expanding the learned feature space.Notably,even with a simple machine learning method,this approach can achieve relatively high accuracy.The proposed method achieved a maximum F1-score of 0.91,minimum Hamming loss of 0.04,and an accuracy of 0.95.Thus,our method represents an effective and rapid solution for detecting multiple diseases simultaneously,which is beneficial for comprehensively managing CVDs.
基金supported by the National Natural Science Foundation of China(82270310)Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2024ZD0527000 and No.2024ZD0527005)+2 种基金Jiangsu Provincial key research and development program(BE2023662)General project of Nanjing Health Commission(YKK22166).
文摘Background:The life-course management of children with tetralogy of Fallot(TOF)has focused on demonstrating brain structural alterations,developmental trajectories,and cognition-related changes that unfold over time.Methods:We introduce an magnetic resonance imaging(MRI)dataset comprising TOF children who underwent brain MRI scanning and cross-sectional neurocognitive follow-up.The dataset includes brain three-dimensional T1-weighted imaging(3D-T1WI),three-dimensional T2-weighted imaging(3D-T2WI),and neurodevelopmental evaluations using the Wechsler Preschool and Primary Scale of Intelligence–Fourth Edition(WPPSI-IV).Results:Thirty-one children with TOF(age range:4–33 months;18 males)were recruited and completed corrective surgery at the Children’s Hospital of Nanjing Medical University,Nanjing,China.Aiming to promote the neurodevelopmental outcomes in children with TOF,we have meticulously curated a comprehensive dataset designed to dissect the complex interplay among risk factors,neuroimaging findings,and adverse neurodevelopmental outcomes.Conclusion:This article aims to introduce our open-source dataset on neurodevelopment in children with TOF,which covers the data types,data acquisition and processing methods,the procedure for accessing the data,and related publications.
