Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life ...Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.展开更多
Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a b...Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.展开更多
FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum S...FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.展开更多
The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated dee...The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.展开更多
College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philos...College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philosophies,traditional college English teaching methods no longer meet the learning needs of contemporary students.Situational cognitive learning theory emphasizes learner-centered approaches and highlights the contextual and practical application of knowledge,offering innovative perspectives for reforming college English teaching.When applied effectively,situational cognitive learning theory can optimize teaching methods and significantly improve learning outcomes.This paper explores the connotation and characteristics of situational cognitive learning theory,evaluates its applicability in college English teaching,and discusses its practical implementation in this context.The aim is to provide theoretical and practical references for improving the quality of college English education.展开更多
Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthca...Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthcare,there remains a lack of comprehensive analysis that integrates wearable signals,data processing techniques,and the broader applications,benefits,and challenges of DL methods.Addressing this limitation,our study provides an extensive review of DL’s role in cognitive healthcare,with a particular emphasis on wearables,data processing,and the inherent challenges in this field.This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues.By enhancing the understanding and analysis of wearable signal modalities,DL models can achieve remarkable accuracy in cognitive healthcare.Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-term Memory(LSTM)networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders.Beyond cognitive impairment detection,DL has been applied to emotion recognition,sleep analysis,stress monitoring,and neurofeedback.These applications lead to advanced diagnosis,personalized treatment,early intervention,assistive technologies,remote monitoring,and reduced healthcare costs.Nevertheless,the integration of DL and wearable technologies presents several challenges,such as data quality,privacy,interpretability,model generalizability,ethical concerns,and clinical adoption.These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI.The findings of this review aim to benefit clinicians,healthcare professionals,and society by facilitating better patient outcomes in cognitive healthcare.展开更多
Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is r...Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.展开更多
Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health...Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health,ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions.This highlights the potential of ECG as a biomarker to improve detection,therapy monitoring and risk stratification in patients with neurocognitive disorders,an area that remains underexplored.Aims We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.Methods ECG features and demographic data were used to predict neurocognitive disorders,as defined by the International Classification of Diseases 10th revision,focusing on dementia,delirium and Parkinson's disease.Internal and external validations were performed using the Medical Information Mart for Intensive CareⅣand ECG-View datasets.Predictive performance was assessed by the area under the receiver operating characteristic curve(AUROC)scores,and Shapley values were used to interpret feature contributions.Results Significant predictive performance was observed for several neurocognitive disorders.The highest predictive performance was observed for F03:dementia,with an internal AUROC of 0.848(95%confidence interval(CI)0.848 to 0.848)and an external AUROC of 0.865(95%CI 0.864 to 0.965),followed by G30:Alzheimer's disease,with an internal AUROC of 0.809(95%CI 0.808 to 0.810)and an external AUROC of 0.863(95%CI 0.863 to 0.864).Feature importance analysis revealed both established and novel ECG correlates.Conclusions These findings suggest that ECG holds promise as a non-invasive,explainable biomarker for selected neurocognitive disorders.This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications,including early detection and personalised monitoring.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face...Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subn...Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subnetworks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices,thereby making SL particularly suitable for resource-constrained devices.Although SL prevents the direct transmission of raw data,it does not alleviate entirely the risk of privacy breaches.In fact,the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data.Moreover,achieving a balance between model utility and data privacy has emerged as a challenging problem.In this article,we propose a novel defense approach that combines:(i)Adversarial learning,and(ii)Network channel pruning.In particular,the proposed adversarial learning approach is specifically designed to reduce the risk of private data exposure while maintaining high performance for the utility task.On the other hand,the suggested channel pruning enables the model to adaptively adjust and reactivate pruned channels while conducting adversarial training.The integration of these two techniques reduces the informativeness of the intermediate data transmitted by the client sub-model,thereby enhancing its robustness against attribute inference attacks without adding significant computational overhead,making it wellsuited for IoT devices,mobile platforms,and Internet of Vehicles(IoV)scenarios.The proposed defense approach was evaluated using EfficientNet-B0,a widely adopted compact model,along with three benchmark datasets.The obtained results showcased its superior defense capability against attribute inference attacks compared to existing state-of-the-art methods.This research’s findings demonstrated the effectiveness of the proposed channel pruning-based adversarial training approach in achieving the intended compromise between utility and privacy within SL frameworks.In fact,the classification accuracy attained by the attackers witnessed a drastic decrease of 70%.展开更多
Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Re...Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.展开更多
The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accom...Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.S...Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.展开更多
基金supported by the National Natural Science Foundation of China,Nos.81730033,82171193(to XG)the Key Talent Project for Strengthening Health during the 13^(th)Five-Year Plan Period,No.ZDRCA2016069(to XG)+1 种基金the National Key R&D Program of China,No.2018YFC2001901(to XG)Jiangsu Provincial Medical Key Discipline,No.ZDXK202232(to XG)。
文摘Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.
基金jointly funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0102900)the"New Generation Artificial Intelligence"Key Field Research and Development Plan of Guangdong Province(2021B0101410002)。
文摘Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.
文摘The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.
文摘College English teaching is a crucial component of higher education.Enhancing college students’English learning outcomes has long been a primary focus for educators.With the continuous evolution of educational philosophies,traditional college English teaching methods no longer meet the learning needs of contemporary students.Situational cognitive learning theory emphasizes learner-centered approaches and highlights the contextual and practical application of knowledge,offering innovative perspectives for reforming college English teaching.When applied effectively,situational cognitive learning theory can optimize teaching methods and significantly improve learning outcomes.This paper explores the connotation and characteristics of situational cognitive learning theory,evaluates its applicability in college English teaching,and discusses its practical implementation in this context.The aim is to provide theoretical and practical references for improving the quality of college English education.
基金the Asian Institute of Technology,Khlong Nueng,Thailand for their support in carrying out this study。
文摘Deep Learning(DL)offers promising solutions for analyzing wearable signals and gaining valuable insights into cognitive disorders.While previous review studies have explored various aspects of DL in cognitive healthcare,there remains a lack of comprehensive analysis that integrates wearable signals,data processing techniques,and the broader applications,benefits,and challenges of DL methods.Addressing this limitation,our study provides an extensive review of DL’s role in cognitive healthcare,with a particular emphasis on wearables,data processing,and the inherent challenges in this field.This review also highlights the considerable promise of DL approaches in addressing a broad spectrum of cognitive issues.By enhancing the understanding and analysis of wearable signal modalities,DL models can achieve remarkable accuracy in cognitive healthcare.Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-term Memory(LSTM)networks have demonstrated improved performance and effectiveness in the early diagnosis and progression monitoring of neurological disorders.Beyond cognitive impairment detection,DL has been applied to emotion recognition,sleep analysis,stress monitoring,and neurofeedback.These applications lead to advanced diagnosis,personalized treatment,early intervention,assistive technologies,remote monitoring,and reduced healthcare costs.Nevertheless,the integration of DL and wearable technologies presents several challenges,such as data quality,privacy,interpretability,model generalizability,ethical concerns,and clinical adoption.These challenges emphasize the importance of conducting future research in areas such as multimodal signal analysis and explainable AI.The findings of this review aim to benefit clinicians,healthcare professionals,and society by facilitating better patient outcomes in cognitive healthcare.
基金funded by the National Natural Science Foundation of China(No.82405530,81973921 and 72374068)the Science and Technology Research Project of Hubei Provincial Department of Education(No.B2023098)。
文摘Objective:As an age-related neurodegenerative disease,the prevalence of mild cognitive impairment(MCI)increases with age.Within the framework of traditional Chinese medicine,spleen-kidney deficiency syndrome(SKDS)is recognized as the most frequent MCI subtype.Due to the covert and gradual onset of MCI,in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes.There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS(MCI-SKDS).Methods:This investigation enrolled 312 elderly individuals diagnosed with MCI,who were randomly distributed into training and test datasets at a 3:1 ratio.Five machine learning methods,including logistic regression(LR),decision tree(DT),naive Bayes(NB),support vector machine(SVM),and gradient boosting(GB),were used to build a diagnostic prediction model for MCI-SKDS.Accuracy,sensitivity,specificity,precision,F1 score,and area under the curve were used to evaluate model performance.Furthermore,the clinical applicability of the model was evaluated through decision curve analysis(DCA).Results:The accuracy,precision,specificity and F1 score of the DT model performed best in the training set(test set),with scores of 0.904(0.845),0.875(0.795),0.973(0.875)and 0.973(0.875).The sensitivity of the training set(test set)of the SVM model performed best among the five models with a score of 0.865(0.821).The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset.The DCA of all models showed good clinical application value.The study identified ten indicators that were significant predictors of MCI-SKDS.Conclusion:The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical;the model demonstrates good predictive value and clinical applicability,and the DT model had the best performance.
文摘Background Electrocardiogram(ECG)analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders.Given the close connection between cardiovascular and neurocognitive health,ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions.This highlights the potential of ECG as a biomarker to improve detection,therapy monitoring and risk stratification in patients with neurocognitive disorders,an area that remains underexplored.Aims We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.Methods ECG features and demographic data were used to predict neurocognitive disorders,as defined by the International Classification of Diseases 10th revision,focusing on dementia,delirium and Parkinson's disease.Internal and external validations were performed using the Medical Information Mart for Intensive CareⅣand ECG-View datasets.Predictive performance was assessed by the area under the receiver operating characteristic curve(AUROC)scores,and Shapley values were used to interpret feature contributions.Results Significant predictive performance was observed for several neurocognitive disorders.The highest predictive performance was observed for F03:dementia,with an internal AUROC of 0.848(95%confidence interval(CI)0.848 to 0.848)and an external AUROC of 0.865(95%CI 0.864 to 0.965),followed by G30:Alzheimer's disease,with an internal AUROC of 0.809(95%CI 0.808 to 0.810)and an external AUROC of 0.863(95%CI 0.863 to 0.864).Feature importance analysis revealed both established and novel ECG correlates.Conclusions These findings suggest that ECG holds promise as a non-invasive,explainable biomarker for selected neurocognitive disorders.This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications,including early detection and personalised monitoring.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
基金Supported by CAS Basic and Interdisciplinary Frontier Scientific Research Pilot Project(XDB1190300,XDB1190302)Youth Innovation Promotion Association CAS(Y2021056)+1 种基金Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2022007)The special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001007)。
文摘Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
基金supported by a grant(No.CRPG-25-2054)under the Cybersecurity Research and Innovation Pioneers Initiative,provided by the National Cybersecurity Authority(NCA)in the Kingdom of Saudi Arabia.
文摘Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subnetworks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices,thereby making SL particularly suitable for resource-constrained devices.Although SL prevents the direct transmission of raw data,it does not alleviate entirely the risk of privacy breaches.In fact,the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data.Moreover,achieving a balance between model utility and data privacy has emerged as a challenging problem.In this article,we propose a novel defense approach that combines:(i)Adversarial learning,and(ii)Network channel pruning.In particular,the proposed adversarial learning approach is specifically designed to reduce the risk of private data exposure while maintaining high performance for the utility task.On the other hand,the suggested channel pruning enables the model to adaptively adjust and reactivate pruned channels while conducting adversarial training.The integration of these two techniques reduces the informativeness of the intermediate data transmitted by the client sub-model,thereby enhancing its robustness against attribute inference attacks without adding significant computational overhead,making it wellsuited for IoT devices,mobile platforms,and Internet of Vehicles(IoV)scenarios.The proposed defense approach was evaluated using EfficientNet-B0,a widely adopted compact model,along with three benchmark datasets.The obtained results showcased its superior defense capability against attribute inference attacks compared to existing state-of-the-art methods.This research’s findings demonstrated the effectiveness of the proposed channel pruning-based adversarial training approach in achieving the intended compromise between utility and privacy within SL frameworks.In fact,the classification accuracy attained by the attackers witnessed a drastic decrease of 70%.
文摘Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
基金supported by The Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00244901)(to RB)。
文摘Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.
