Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for busine...Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for business English talents.Combining this with the existing problems in China’s current training models,it proposes a reform path for talent training models that are adapted to the construction of the“Belt and Road”Initiative.The aim is to provide theoretical references and practical guidance for enhancing the international competitiveness of business English talents.展开更多
Bronchiectasis is a chronic inflammatory airway disease,and patients often suffer from recurrent airway infections leading to decreased lung function and impaired quality of life.In this study,the effects of supervise...Bronchiectasis is a chronic inflammatory airway disease,and patients often suffer from recurrent airway infections leading to decreased lung function and impaired quality of life.In this study,the effects of supervised pulmonary rehabilitation training on pulmonary function,training compliance,and quality of life in patients with bronchiectasis under home rehabilitation mode are investigated.Ninety stable patients were selected,and the observation group adopted the home-supervised mode of pulmonary rehabilitation training.The results showed that the observation group’s pulmonary function indexes,quality of life,and training adherence were better than those of the control group.The differences were statistically significant(P<0.05).The supervised pulmonary rehabilitation training in home rehabilitation mode can effectively improve patients’pulmonary function and quality of life,and improve training compliance,which has good clinical application value.展开更多
Large models have been widely used in the field of neural language processing,information retrieving,etc.With the development of the large models,not only is the parameter scale increased,but the model architecture ha...Large models have been widely used in the field of neural language processing,information retrieving,etc.With the development of the large models,not only is the parameter scale increased,but the model architecture has also become more complex.For example,the multi-modal transformer-based model mainly has concurrent branches,which we denoted as the concurrent branch model(CBM).Many CBMs have enlarged to tens of billions of parameters,and require distributed resources to train this kind of model.Existing distributed training systems cannot fully handle this type of model architecture because there are interactions between branches.Inspired by the unbalanced resource usage of pipeline parallelism,we prefer to organize different branches with a fine-grained bidirectional pipeline schedule of communication and computation.However,improper coordination between branches leads to idle time for computation and low training efficiency.In this paper,we present Flexpipe,a pipeline engine for c3oncurrent-branch models.We first introduce a branch-aware pipeline parallelism(BAPP)to make full use of the concurrent characteristic of the model architecture.Then,based on a multi-branch pipeline simulator,we propose an adaptive interaction coordinator,which facilitates the low-overhead branch interactions during the distributed model training.We evaluate our approach on popular concurrent branch models combined with modern training systems.Compared with the Chimera,the experiential results show that our method improves the end-to-end training throughput by 20%on average.展开更多
With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,...With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.展开更多
In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training pr...In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training programs.The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs,with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The training-performance model used in the proposed optimal control framework is a conceptual extension of the Banister impulse-response model that describes the dynamics of performance,training load(served as the control variable),fitness(the overall positive effects on performance),and fatigue(the overall negative effects on performance).The objective functional of the proposed optimal control framework is to maximize the fitness and minimize the fatigue on the competition day with the goal of maximizing the performance on the competition day while minimizing the cumulative training load during the training course.The Forward-Backward Sweep Method is used to solve the proposed optimal control framework to obtain the optimal solutions of performance,training load,fitness,and fatigue.The simulation results show that the performance on the competition day is higher while the cumulative training load during the training course is lower with using optimal control theory than those without,successfully showing the feasibility and benefits of using the proposed optimal control framework to design optimal training programs for helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The present feasibility study lays the foundation of the combined use of optimal control theory and training-performance models to design personalized optimal training programs in real applications in athletic training and sports science for helping athletes achieve the best performances in competitions while prevent overtraining and the risk of overuse injuries.展开更多
Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the m...Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.展开更多
Background:Oxidative stress and neuroinflammation are key factors in the pathophysiology of Alzheimer's disease(AD).Exercise and Aklil-ol-Malek may reduce AD symptoms.Therefore,the current study investigated the ef...Background:Oxidative stress and neuroinflammation are key factors in the pathophysiology of Alzheimer's disease(AD).Exercise and Aklil-ol-Malek may reduce AD symptoms.Therefore,the current study investigated the effect of weight training and Aklil-ol-Malek consumption on histopathological and inflammatory changes in hippocampal tissue of male AD model rats.Method:We prepared 558-week-old male Wistar rats and transferred them to an animal laboratory.The rats were randomly divided intofive groups:healthy control group,Alzheimer's control group,Alzheimer's group+weight training,Alzheimer's group+Aklil-ol-Malek supplement,and Alzheimer's group+Aklilol-Malek supplement+weight training.AD was induced in the 4 groups.The weight training protocol and Aklil-ol-Malek supplementation were examined as an intervention.The designated groups were administered Aklil-ol-Malek supplements.The anesthetized rats'hippocampi were extracted for further analysis 72 hours after the last session of the protocol.After the induction of AD and supplementation,two-way analysis of variance was used to examine the differences between groups(p<0.05).Results:The results showed a decrease in the expression of CRP and NFE2L2 genes in rats in the Aklil-olMalek and weight training group compared with thefindings in rats in the Alzheimer's group.Changes in the expression of BACE1 were not significant in rats in the weight training with Aklil-ol-Malek group.Conclusion:An intervention receiving exercise and Aklil-ol-Malek extract positively improved health and reduced AD progression.These results were likely to have been caused by the physiological effects of exercise and the antioxidant properties of Aklil-ol-Malek.展开更多
Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label pre...Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label predictor by minimising the objective function while maximising the objective function to update the domain discriminator's parameters.This strategy results in an excessive maximisation of the domain discriminator's loss.To address this,a friendly adversarial training-based tri-training furnace condition diagnosis model was proposed.This model employed a convolutional neural network-long short-term memory-attention mechanism network as a single-view feature extractor and used decision tree methods as three classifiers to compute the cosine similarity between features and representative vectors of each class.During the knowledge transfer process,the classifiers in this model have a specific goal;they not only seek to maximise the entropy of the target domain samples but also aim to minimise the entropy of the target domain samples when they are misclassified,thus resolving the trade-off in traditional models where robustness is improved at the expense of accuracy.Experimental results indicate that the diagnostic accuracy of this model reaches 96%,with an approximately 8%improvement over existing methods due to the inner optimisation approach.This model provides an effective and feasible solution for the efficient monitoring and diagnosis of blast furnace processes.展开更多
In the context of the rapid advancement of intelligent manufacturing,ensuring the alignment of the skill levels of embedded system developers with industry requirements has emerged as a crucial aspect in the reform of...In the context of the rapid advancement of intelligent manufacturing,ensuring the alignment of the skill levels of embedded system developers with industry requirements has emerged as a crucial aspect in the reform of vocational education.This research delves into a three-stage progressive talent cultivation model denoted as“Cultivation–Growth–Incubation”,which is founded on the Shi Zhenjiang(Z.S.)Intelligent Embedded System Development Master Skills Studio.By means of hierarchical training,project-driven strategies,and industry-academia cooperation,this model effectively elevates students’application capabilities and innovative competencies in embedded systems.Case analyses illustrate the practical efficacy of the model,providing valuable references for the establishment of master skills studios in vocational education.展开更多
The development of Meizhou Hakka cuisine relies on the role of professional cooking talents.Higher vocational colleges serve as the platform for cultivating cooking talents.Among various training models,the implementa...The development of Meizhou Hakka cuisine relies on the role of professional cooking talents.Higher vocational colleges serve as the platform for cultivating cooking talents.Among various training models,the implementation of the progressive talent training model featuring the integration of industry and education and work-study alternation is conducive to carrying out talent cultivation activities,improving the effectiveness of professional talent development,and effectively meeting the needs of market development.From the perspective of Meizhou Hakka cuisine cooking talents,this paper analyzes the problems existing in the implementation of the industry-education integration and work-study alternation model,and puts forward specific practical strategies for talent cultivation.The purpose is to enhance the training effect of Hakka cuisine cooking talents and provide reference for the subsequent optimization of professional teaching.展开更多
With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as dela...With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeuti...With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
文摘Based on the complexity and regional differences of the political,economic,and cultural environments of countries along the“Belt and Road,”this paper analyzes the new characteristics of the current demand for business English talents.Combining this with the existing problems in China’s current training models,it proposes a reform path for talent training models that are adapted to the construction of the“Belt and Road”Initiative.The aim is to provide theoretical references and practical guidance for enhancing the international competitiveness of business English talents.
文摘Bronchiectasis is a chronic inflammatory airway disease,and patients often suffer from recurrent airway infections leading to decreased lung function and impaired quality of life.In this study,the effects of supervised pulmonary rehabilitation training on pulmonary function,training compliance,and quality of life in patients with bronchiectasis under home rehabilitation mode are investigated.Ninety stable patients were selected,and the observation group adopted the home-supervised mode of pulmonary rehabilitation training.The results showed that the observation group’s pulmonary function indexes,quality of life,and training adherence were better than those of the control group.The differences were statistically significant(P<0.05).The supervised pulmonary rehabilitation training in home rehabilitation mode can effectively improve patients’pulmonary function and quality of life,and improve training compliance,which has good clinical application value.
基金supported by the National Key R&D Program of China(No.2023YFB3001704)NSFC for Young Scientists Fund(No.62402266)NSFC for Distinguished Young Scholar(No.62225206).
文摘Large models have been widely used in the field of neural language processing,information retrieving,etc.With the development of the large models,not only is the parameter scale increased,but the model architecture has also become more complex.For example,the multi-modal transformer-based model mainly has concurrent branches,which we denoted as the concurrent branch model(CBM).Many CBMs have enlarged to tens of billions of parameters,and require distributed resources to train this kind of model.Existing distributed training systems cannot fully handle this type of model architecture because there are interactions between branches.Inspired by the unbalanced resource usage of pipeline parallelism,we prefer to organize different branches with a fine-grained bidirectional pipeline schedule of communication and computation.However,improper coordination between branches leads to idle time for computation and low training efficiency.In this paper,we present Flexpipe,a pipeline engine for c3oncurrent-branch models.We first introduce a branch-aware pipeline parallelism(BAPP)to make full use of the concurrent characteristic of the model architecture.Then,based on a multi-branch pipeline simulator,we propose an adaptive interaction coordinator,which facilitates the low-overhead branch interactions during the distributed model training.We evaluate our approach on popular concurrent branch models combined with modern training systems.Compared with the Chimera,the experiential results show that our method improves the end-to-end training throughput by 20%on average.
文摘With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.
基金funded by the National Science and Technology Council,grant number NSTC 113-2221-E-002-136-.
文摘In order to help athletes optimize their performances in competitions while prevent overtraining and the risk of overuse injuries,it is important to develop science-based strategies for optimally designing training programs.The purpose of the present study is to develop a novel method by the combined use of optimal control theory and a training-performance model for designing optimal training programs,with the hope of helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The training-performance model used in the proposed optimal control framework is a conceptual extension of the Banister impulse-response model that describes the dynamics of performance,training load(served as the control variable),fitness(the overall positive effects on performance),and fatigue(the overall negative effects on performance).The objective functional of the proposed optimal control framework is to maximize the fitness and minimize the fatigue on the competition day with the goal of maximizing the performance on the competition day while minimizing the cumulative training load during the training course.The Forward-Backward Sweep Method is used to solve the proposed optimal control framework to obtain the optimal solutions of performance,training load,fitness,and fatigue.The simulation results show that the performance on the competition day is higher while the cumulative training load during the training course is lower with using optimal control theory than those without,successfully showing the feasibility and benefits of using the proposed optimal control framework to design optimal training programs for helping athletes achieve the best performance exactly on the competition day while properly manage training load during the training course for preventing overtraining.The present feasibility study lays the foundation of the combined use of optimal control theory and training-performance models to design personalized optimal training programs in real applications in athletic training and sports science for helping athletes achieve the best performances in competitions while prevent overtraining and the risk of overuse injuries.
基金support from Strategic Project of Precision Surgery,Tsinghua UniversityInitiative Scientific Research Program,Institute for Intelligent Healthcare,Tsinghua University+5 种基金Tsinghua-Foshan Institute of Advanced ManufacturingNational Natural Science Foundation of China(61735016)Beijing Nova Program(20230484308)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)Youth Elite Program of Beijing Friendship Hospital(YYQCJH2022-9)Science and Technology Program of Beijing Tongzhou District(KJ2023CX012).
文摘Foundation models(FMs)have rapidly evolved and have achieved signicant accomplishments in computer vision tasks.Specically,the prompt mechanism conveniently allows users to integrate image prior information into the model,making it possible to apply models without any training.Therefore,we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic(PA)image processing.We employed the Segment Anything Model(SAM)by setting simple prompts and integrating the model's outputs with prior knowledge of the imaged objects to accomplish various tasks,including:(1)removing the skin signal in three-dimensional PA image rendering;(2)dual speed-of-sound reconstruction,and(3)segmentation ofnger blood vessels.Through these demonstrations,we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training.This potentially allows for a hands-on,convenient approach to achieving efficient and accurate segmentation of PA images.This paper serves as a comprehensive tutorial,facilitating the mastery of the technique through the provision of code and sample datasets.
文摘Background:Oxidative stress and neuroinflammation are key factors in the pathophysiology of Alzheimer's disease(AD).Exercise and Aklil-ol-Malek may reduce AD symptoms.Therefore,the current study investigated the effect of weight training and Aklil-ol-Malek consumption on histopathological and inflammatory changes in hippocampal tissue of male AD model rats.Method:We prepared 558-week-old male Wistar rats and transferred them to an animal laboratory.The rats were randomly divided intofive groups:healthy control group,Alzheimer's control group,Alzheimer's group+weight training,Alzheimer's group+Aklil-ol-Malek supplement,and Alzheimer's group+Aklilol-Malek supplement+weight training.AD was induced in the 4 groups.The weight training protocol and Aklil-ol-Malek supplementation were examined as an intervention.The designated groups were administered Aklil-ol-Malek supplements.The anesthetized rats'hippocampi were extracted for further analysis 72 hours after the last session of the protocol.After the induction of AD and supplementation,two-way analysis of variance was used to examine the differences between groups(p<0.05).Results:The results showed a decrease in the expression of CRP and NFE2L2 genes in rats in the Aklil-olMalek and weight training group compared with thefindings in rats in the Alzheimer's group.Changes in the expression of BACE1 were not significant in rats in the weight training with Aklil-ol-Malek group.Conclusion:An intervention receiving exercise and Aklil-ol-Malek extract positively improved health and reduced AD progression.These results were likely to have been caused by the physiological effects of exercise and the antioxidant properties of Aklil-ol-Malek.
基金Thanks are given to Hebei Province Innovation Capacity Enhancement Programme Project(23560301D)the Natural Science Foundation of Hebei Province(E2024105036)the Tangshan Talent Funding Project(B202302007).
文摘Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label predictor by minimising the objective function while maximising the objective function to update the domain discriminator's parameters.This strategy results in an excessive maximisation of the domain discriminator's loss.To address this,a friendly adversarial training-based tri-training furnace condition diagnosis model was proposed.This model employed a convolutional neural network-long short-term memory-attention mechanism network as a single-view feature extractor and used decision tree methods as three classifiers to compute the cosine similarity between features and representative vectors of each class.During the knowledge transfer process,the classifiers in this model have a specific goal;they not only seek to maximise the entropy of the target domain samples but also aim to minimise the entropy of the target domain samples when they are misclassified,thus resolving the trade-off in traditional models where robustness is improved at the expense of accuracy.Experimental results indicate that the diagnostic accuracy of this model reaches 96%,with an approximately 8%improvement over existing methods due to the inner optimisation approach.This model provides an effective and feasible solution for the efficient monitoring and diagnosis of blast furnace processes.
基金The 2025 Guangdong Polytechnic College Innovation-driven School Strengthening Project“Construction of Shi Zhenjiang’s Master Studio for Intelligent Embedded System Development Skills”(Project No.:2025CQ06-05)。
文摘In the context of the rapid advancement of intelligent manufacturing,ensuring the alignment of the skill levels of embedded system developers with industry requirements has emerged as a crucial aspect in the reform of vocational education.This research delves into a three-stage progressive talent cultivation model denoted as“Cultivation–Growth–Incubation”,which is founded on the Shi Zhenjiang(Z.S.)Intelligent Embedded System Development Master Skills Studio.By means of hierarchical training,project-driven strategies,and industry-academia cooperation,this model effectively elevates students’application capabilities and innovative competencies in embedded systems.Case analyses illustrate the practical efficacy of the model,providing valuable references for the establishment of master skills studios in vocational education.
基金2025 Meizhou Municipal Planning Project of Philosophy and Social Sciences(Project No.:mzsklx2025101)。
文摘The development of Meizhou Hakka cuisine relies on the role of professional cooking talents.Higher vocational colleges serve as the platform for cultivating cooking talents.Among various training models,the implementation of the progressive talent training model featuring the integration of industry and education and work-study alternation is conducive to carrying out talent cultivation activities,improving the effectiveness of professional talent development,and effectively meeting the needs of market development.From the perspective of Meizhou Hakka cuisine cooking talents,this paper analyzes the problems existing in the implementation of the industry-education integration and work-study alternation model,and puts forward specific practical strategies for talent cultivation.The purpose is to enhance the training effect of Hakka cuisine cooking talents and provide reference for the subsequent optimization of professional teaching.
基金funded by Anhui NARI ZT Electric Co.,Ltd.,entitled“Research on the Shared Operation and Maintenance Service Model for Metering Equipment and Platform Development for the Modern Industrial Chain”(Grant No.524636250005).
文摘With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
文摘With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
