Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,s...Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.展开更多
The performance of data restore is one of the key indicators of user experience for backup storage systems.Compared to the traditional offline restore process,online restore reduces downtime during backup restoration,...The performance of data restore is one of the key indicators of user experience for backup storage systems.Compared to the traditional offline restore process,online restore reduces downtime during backup restoration,allowing users to operate on already restored files while other files are still being restored.This approach improves availability during restoration tasks but suffers from a critical limitation:inconsistencies between the access sequence and the restore sequence.In many cases,the file a user needs to access at a given moment may not yet be restored,resulting in significant delays and poor user experience.To this end,we present Histore,which builds on the user’s historical access sequence to schedule the restore sequence,in order to reduce users’access delayed time.Histore includes three restore approaches:(i)the frequency-based approach,which restores files based on historical file access frequencies and prioritizes ensuring the availability of frequently accessed files;(ii)the graph-based approach,which preferentially restores the frequently accessed files as well as their correlated files based on historical access patterns,and(iii)the trie-based approach,which restores particular files based on both users’real-time and historical access patterns to deduce and restore the files to be accessed in the near future.We implement a prototype of Histore and evaluate its performance from multiple perspectives.Trace-driven experiments on two datasets show that Histore significantly reduces users’delay time by 4-700×with only 1.0%-14.5%additional performance overhead.展开更多
In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability predicti...In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as socia...Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.展开更多
Background:Emerging adulthood is a critical period for ego identity exploration and consolidation,and self-presentation on social media constitutes a salient online context for this developmental process.However,limit...Background:Emerging adulthood is a critical period for ego identity exploration and consolidation,and self-presentation on social media constitutes a salient online context for this developmental process.However,limited research has explored the associations between self-presentation on WeChat Moments and ego identity.This study aims to examine these associations,focusing on the mediating role of online positive feedback and the moderating role of gender.Methods:Using a three-wave longitudinal design,this study followed 767 Chinese college students(Mean age=18.96 years)through cluster sampling.Participants completed self-report questionnaires assessing self-presentation on WeChat Moments,online positive feedback,and ego identity status.Data analyses were conducted using mediation modeling and multi-group structural equation modeling.Results:Authentic self-presentation was positively associated with identity achievement and negatively associated with identity diffusion,whereas positive self-presentation was linked to higher levels of identity foreclosure.Online positive feedback played a significant mediating role in the associations between self-presentation strategies and identity statuses,and gender differences were observed in this mediating pathway.For both males and females,authentic self-presentation was associated with higher identity achievement through online positive feedback.However,indirect associations with identity foreclosure and diffusion were observed only among females:authentic self-presentation was linked to lower levels,whereas positive self-presentation was linked to higher levels of foreclosure and diffusion through online positive feedback.No comparable indirect associations were detected among males.Conclusions:Online positive feedback is closely linked to self-presentation strategies and ego identity statuses,with these associations varying by gender.展开更多
Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lack...Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lacks formal task definitions and corresponding benchmarks.To bridge this gap,we define the Automated Online Public Opinion Report Generation(OPOR-Gen)task and construct OPOR-Bench,an event-centric dataset with 463 crisis events across 108 countries(comprising 8.8 K news articles and 185 K tweets).To evaluate report quality,we propose OPOR-Eval,a novel agent-based framework that simulates human expert evaluation.Validation experiments show OPOR-Eval achieves a high Spearman’s correlation(ρ=0.70)with human judgments,though challenges in temporal reasoning persist.This work establishes an initial foundation for advancing automated public opinion reporting research.展开更多
Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a ...Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a solid data foundation when attempting to improve the error feedback mechanisms.This paper makes a survey of 834 students across various programming courses and investigates student perceptions of error feedback mechanisms on online programming platforms.It explores the effectiveness of existing feedback,student satisfaction,and preferences for potential improvements,focusing on automatic error localization and program repair mechanisms.Results reveal a significant portion of students are dissatisfied with current feedback due to its limited informativeness.Students also express a clear demand for stronger feedback mechanisms,such as error localization and repair hints.Nevertheless,they prefer feedback that subtly guides them toward solutions,rather than providing direct and explicit answers,valuing the opportunity to enhance their debugging skills.The findings suggest a need for balanced,educational-focused feedback mechanisms that aid learning while promoting independent problem-solving.展开更多
The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling...The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users,a platform,and content creators.The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system.We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting.We analyze return-based and feature-based contracts.Under smoothness assumptions,return-based contracts achieve regretΘ(T^(2/3)).For feature-based contracts,we introduce an intrinsic dimension d and prove a regret bound O(T^(d+1)/(d+2)),which is tight for linear families.展开更多
Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and w...Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
To explore effective paths for improving college students’ mental health, this study integrates narrative therapy and painting therapy to design a 7-week online group psychological counseling program. A total of 121 ...To explore effective paths for improving college students’ mental health, this study integrates narrative therapy and painting therapy to design a 7-week online group psychological counseling program. A total of 121 volunteer college students participated as subjects, and themed painting counseling was conducted via Tencent Meeting. Five scales, including the General Self-Efficacy Scale, Self-Esteem Scale, and Self-Rating Depression Scale, were used for pre-test and post-test comparisons. The results show that after the intervention, students’ self-efficacy (t = -5.528, p = 0.000) and self-esteem level (t = -2.153, p = 0.033) significantly improved statistically, and depression and anxiety showed a positive improvement trend. The research indicates that online narrative painting therapy can effectively release students’ psychological pressure, promote in-depth self-cognition and interpersonal connection construction, providing an operable innovative paradigm for college mental health education. Its interactivity and effectiveness are compatible with the psychological needs and internet usage habits of contemporary college students.展开更多
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this...The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.展开更多
Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined ma...Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined mathematical models that often fail to capture equipment-specific degradation,(2)offline optimization methods that assume access to future data,and(3)the absence of component-level guidance.To address these challenges,we propose a data-driven framework for component-level decision-making.The framework leverages streaming sensor data to predict the remaining useful life(RUL)without relying on mathematical models,employs an online optimization algorithm suitable for practical settings,and,through remanufacturing simulations,provides guidance on which components should be replaced.In a case study on gas-insulated switchgear,the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models.Furthermore,by employing online optimization,it determined a remanufacturing timing close to the global optimum using only past and current data.In addition,unlike previous studies,the framework enables component-level decision-making,allowing for more detailed and actionable remanufacturing guidance in practical applications.展开更多
With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneo...With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.展开更多
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-base...This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.展开更多
In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is co...In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov principle.The tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.展开更多
Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it ...Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
基金funded by Malaysian Ministry of Higher Education(MOHE)under the Fundamental Research Grant Scheme(FRGS/1/2023/SSI07/MMU/02/3)which is awarded to the Multimedia University.The project is led by the second author.
文摘Background:Music has proven to be vital in enhancing resilience and promotingwell-being.Previously,the impact of music in sports environments was solely investigated,while this paper applies it to study environments,standing out as pioneering research.The study consists of a systematic development of a conceptual framework based on theories of Uses and Gratification Expectancy(UGE)and perceived motivation based on music elements.Their components are observed variables influencing students’psychological well-being(as the dependent variable).Resilience is examined as a mediator,influencing the relationships of both observed and dependent variables.The main purpose of this study is to highlight the positive effects of online music consumption on the psychological well-being of students.Methods:Semi-structured qualitative interviews were conducted with eighteen final year creative multimedia undergraduate students belonging to five central region Malaysian universities,especially on their UGE needs,and a similar concept survey instrument with two hundred participants.The interview data were analysed through thematic analysis,while the survey data through descriptive and Partial Least Squares Structural Equation Modeling(PLS-SEM).Results:The results highlight that students gain motivation from online music,which positively affects their psychological well-being(β=0.190,p=0.003,f^(2)=0.037),while resilience significantly affects this relationship(β=0.562,p<0.001,f^(2)=0.461).However,the results also predict a partial relationship between constructs based on UGE with psychological well-being,mediated by resilience,i.e.,AT-UGE(β=0.021,p=0.783,f^(2)=0.000),SIPI-UGE(β=0.228,p=0.004,f^(2)=0.044).Conclusion:The outcome of the study reflected practical,meaningful,and statistically significant results.The majority of the predictors,with the exception of one,i.e.,AT-UGE,displayed a clear positive relation of online music consumption on the Psychological Well-being of students.Future research will explore varying contextual factors impacting online music-related gratifications,motivations,and resilience,along with additional potential mediators and moderators.
基金supported in part by National Key R&D Program of China(2022YFB4501200),National Natural Science Foundation of China(62332018)Science and Technology Program(2024NSFTD0031,2024YFHZ0339 and 2025ZNSFSC0497).
文摘The performance of data restore is one of the key indicators of user experience for backup storage systems.Compared to the traditional offline restore process,online restore reduces downtime during backup restoration,allowing users to operate on already restored files while other files are still being restored.This approach improves availability during restoration tasks but suffers from a critical limitation:inconsistencies between the access sequence and the restore sequence.In many cases,the file a user needs to access at a given moment may not yet be restored,resulting in significant delays and poor user experience.To this end,we present Histore,which builds on the user’s historical access sequence to schedule the restore sequence,in order to reduce users’access delayed time.Histore includes three restore approaches:(i)the frequency-based approach,which restores files based on historical file access frequencies and prioritizes ensuring the availability of frequently accessed files;(ii)the graph-based approach,which preferentially restores the frequently accessed files as well as their correlated files based on historical access patterns,and(iii)the trie-based approach,which restores particular files based on both users’real-time and historical access patterns to deduce and restore the files to be accessed in the near future.We implement a prototype of Histore and evaluate its performance from multiple perspectives.Trace-driven experiments on two datasets show that Histore significantly reduces users’delay time by 4-700×with only 1.0%-14.5%additional performance overhead.
基金supported in part by the National Key RD Program of China under Grant 2023YFB4204400,and in part by the National Natural Science Foundation of China under Grant 52125705.
文摘In practical microgrids,current saturation of inverters and power interaction coupling of different forms of DERs complicate the system's transient behaviors.Existing methods of online transient stability prediction(TSP)are suitable for power systems consisting of homogeneous distributed energy resources(DERs),thus showing limited accuracy for stability prediction of microgrids.This paper develops a deep-learning-based TSP method for accurate online prediction of microgrids consisting of diverse forms of DERs under current saturation.First,a general key input feature selection method for microgrid TSP is systematically designed to ensure prediction accuracy.It is derived from a comprehensive mechanism analysis of the influence of DER's intrinsic and interaction characteristics under current saturation.Besides,impacts of load fluctuation and fault change are also considered to improve robust prediction performance.Second,to further improve prediction accuracy,an online TSP model based on deep learning is developed by effectively using the powerful nonlinear mapping capability of the deep belief network(DBN).Then,by combining feature selection method and deep-learning-based TSP model,an online TSP method is derived.Test results show the proposed method greatly improves accuracy of microgrid TSP under complex operating conditions.Furthermore,the method effectively avoids feature redundancy and the curse of dimensionality.Numbers of input features are independent of the scale of microgrids.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金funded by the National Key R&D Program of China under Grant(No.2022YFB3102901)National Natural Science Foundation of China(No.62072115,No.62102094)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600).
文摘Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.
基金supported by the National Social Science Fund of China(No.23BSH123).
文摘Background:Emerging adulthood is a critical period for ego identity exploration and consolidation,and self-presentation on social media constitutes a salient online context for this developmental process.However,limited research has explored the associations between self-presentation on WeChat Moments and ego identity.This study aims to examine these associations,focusing on the mediating role of online positive feedback and the moderating role of gender.Methods:Using a three-wave longitudinal design,this study followed 767 Chinese college students(Mean age=18.96 years)through cluster sampling.Participants completed self-report questionnaires assessing self-presentation on WeChat Moments,online positive feedback,and ego identity status.Data analyses were conducted using mediation modeling and multi-group structural equation modeling.Results:Authentic self-presentation was positively associated with identity achievement and negatively associated with identity diffusion,whereas positive self-presentation was linked to higher levels of identity foreclosure.Online positive feedback played a significant mediating role in the associations between self-presentation strategies and identity statuses,and gender differences were observed in this mediating pathway.For both males and females,authentic self-presentation was associated with higher identity achievement through online positive feedback.However,indirect associations with identity foreclosure and diffusion were observed only among females:authentic self-presentation was linked to lower levels,whereas positive self-presentation was linked to higher levels of foreclosure and diffusion through online positive feedback.No comparable indirect associations were detected among males.Conclusions:Online positive feedback is closely linked to self-presentation strategies and ego identity statuses,with these associations varying by gender.
基金supported by the Fundamental Research Funds for the Central Universities(No.CUC25SG013)the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),Ministry of Education(No.EIN2024C006).
文摘Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lacks formal task definitions and corresponding benchmarks.To bridge this gap,we define the Automated Online Public Opinion Report Generation(OPOR-Gen)task and construct OPOR-Bench,an event-centric dataset with 463 crisis events across 108 countries(comprising 8.8 K news articles and 185 K tweets).To evaluate report quality,we propose OPOR-Eval,a novel agent-based framework that simulates human expert evaluation.Validation experiments show OPOR-Eval achieves a high Spearman’s correlation(ρ=0.70)with human judgments,though challenges in temporal reasoning persist.This work establishes an initial foundation for advancing automated public opinion reporting research.
基金supported by the National Natural Science Foundation of China under Grant No.92582204,No.62577007,and No.62177003the Fundamental Research Funds for the Central Universities under Grant No.JKF-2025011975129.
文摘Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a solid data foundation when attempting to improve the error feedback mechanisms.This paper makes a survey of 834 students across various programming courses and investigates student perceptions of error feedback mechanisms on online programming platforms.It explores the effectiveness of existing feedback,student satisfaction,and preferences for potential improvements,focusing on automatic error localization and program repair mechanisms.Results reveal a significant portion of students are dissatisfied with current feedback due to its limited informativeness.Students also express a clear demand for stronger feedback mechanisms,such as error localization and repair hints.Nevertheless,they prefer feedback that subtly guides them toward solutions,rather than providing direct and explicit answers,valuing the opportunity to enhance their debugging skills.The findings suggest a need for balanced,educational-focused feedback mechanisms that aid learning while promoting independent problem-solving.
文摘The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms.In this paper,we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users,a platform,and content creators.The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system.We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting.We analyze return-based and feature-based contracts.Under smoothness assumptions,return-based contracts achieve regretΘ(T^(2/3)).For feature-based contracts,we introduce an intrinsic dimension d and prove a regret bound O(T^(d+1)/(d+2)),which is tight for linear families.
基金supported in part by National Natural Science Foundation of China under Grant 72401055in part by National Natural Science Foundation of China under Grant 52277083in part by the joint founding of Guangdong,and Dongguan under Grant 2023A1515110939.
文摘Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
基金General Project of the 14th Five-Year Plan for Educational Science in Liaoning Province(2025):“Teaching Research on Group Narrative Painting Training to Improve College Students’Self-Efficacy and Psychological Resilience”(Project No.:JG25DB118)。
文摘To explore effective paths for improving college students’ mental health, this study integrates narrative therapy and painting therapy to design a 7-week online group psychological counseling program. A total of 121 volunteer college students participated as subjects, and themed painting counseling was conducted via Tencent Meeting. Five scales, including the General Self-Efficacy Scale, Self-Esteem Scale, and Self-Rating Depression Scale, were used for pre-test and post-test comparisons. The results show that after the intervention, students’ self-efficacy (t = -5.528, p = 0.000) and self-esteem level (t = -2.153, p = 0.033) significantly improved statistically, and depression and anxiety showed a positive improvement trend. The research indicates that online narrative painting therapy can effectively release students’ psychological pressure, promote in-depth self-cognition and interpersonal connection construction, providing an operable innovative paradigm for college mental health education. Its interactivity and effectiveness are compatible with the psychological needs and internet usage habits of contemporary college students.
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52404343 and 52274326)the Fundamental Research Funds for the Central Universities(Grant Nos.N2425031 and N25BJD007)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M760370)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project)(Grant No.2023JH2/101800058).
文摘The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace,but it was difficult to monitor directly.To address this issue,an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms,by integrating mechanism experiment,numerical simulation,and machine learning.The experimentally determined slag layer hanging temperature was 1130℃,and the thermal conductivity ranged from 1.32 to 1.96 m^(2)℃^(-1).Based on the 3D slag-hanging numerical simulation model,a database was constructed,containing 2294 sets of mechanism cases for the slag layer.The fusion of data modeling,heat transfer theory,and expert experience enabled the online calculation of key input variables for the operation furnace profile,particularly the quantification of the“black-box”variable of gas temperature.Simulated data were used as inputs,and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile.This model facilitated the online calculation of the slag layer thickness and other key indices.The coefficient of determination of the model exceeded 0.98,indicating high accuracy.A slag layer state judgment model was constructed,categorizing states as shedding,too thin,normal,and too thick.Real-time data were applied,and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm,which was consistent with field experience.The absolute value of the Pearson correlation coefficient between slag layer thickness,thermocouple temperature,and heat load data was above 0.85,indicating that the calculated results closely aligned with the actual trends.A 3D visual online monitoring system for the operation furnace profile was created,and it has been successfully implemented at the blast furnace site.
基金supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government Ministry of Knowledge Economy(No.RS-2023-00244330)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF RS-2023-00219052RS-2024-00352587)。
文摘Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined mathematical models that often fail to capture equipment-specific degradation,(2)offline optimization methods that assume access to future data,and(3)the absence of component-level guidance.To address these challenges,we propose a data-driven framework for component-level decision-making.The framework leverages streaming sensor data to predict the remaining useful life(RUL)without relying on mathematical models,employs an online optimization algorithm suitable for practical settings,and,through remanufacturing simulations,provides guidance on which components should be replaced.In a case study on gas-insulated switchgear,the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models.Furthermore,by employing online optimization,it determined a remanufacturing timing close to the global optimum using only past and current data.In addition,unlike previous studies,the framework enables component-level decision-making,allowing for more detailed and actionable remanufacturing guidance in practical applications.
基金supported by Yunnan High-tech Industry Development Project(Grant No.201606)Yunnan Provincial Major Science and Technology Special Plan Projects(Grant Nos.202103AA080015 and 202002AD080001-5)+1 种基金Yunnan Basic Research Project(Grant No.202001AS070014)Talents and Platform Program of Science and Technology of Yunnan(Grant No.202105AC160018)。
文摘With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.
基金supported by the National Natural Science Foundation of China(62376290).
文摘This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.
基金supported in part by the National Natural Science Foundation of China(62222301,62373012,62473012,62021003)the National Science and Technology Major Project(2021ZD0112302,2021ZD0112301)the Beijing Natural Science Foundation(JQ19013)
文摘In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator faults.First, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov principle.The tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.
基金financial support by Sichuan Science and Technology,China(No.2023YFG0070).
文摘Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
