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
The international development of the petroleum industry has posed an urgent demand for the internationalization capabilities of both academic and professional master’s students.However,there is currently a shortage o...The international development of the petroleum industry has posed an urgent demand for the internationalization capabilities of both academic and professional master’s students.However,there is currently a shortage of such talent in the petroleum energy sector,along with a lack of a collaborative training system.Based on this,this study focuses on featured disciplines in the petroleum energy sector and systematically constructs an international talent training model centered around the“five-element synergy”of“government-school-enterprise-teacher-student.”Firstly,it defines the connotations of the five-element synergy:“government(strategic guidance)-school(platform support)-enterprise(demand verification)-teacher(leading transformation)-student(practical co-creation).”Secondly,it sets distinct training objectives for academic(focusing on academic innovation)and professional(emphasizing engineering practice)master’s students.Furthermore,it constructs a“categorized and layered,progressive and collaborative”curriculum system,builds an international faculty team through a“recruitment+training”dual-path approach,and cultivates students’sense of professional mission to“contribute to the nation’s energy sector”through a“macro+micro”perspective.This model provides a practical pathway for international talent training in the petroleum energy sector and holds significant importance for enhancing the overseas competitiveness of petroleum enterprises and safeguarding national energy security.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
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
Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the persona...Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the personalized development needs of students,making an urgent shift toward precision and intelligence necessary.This study constructs a four-dimensional integrated framework centered on data,“Goal-Data-Intervention-Evaluation”,and proposes a data-driven training model for innovation and entrepreneurship talents in universities.By collecting multi-source data such as learning behaviors,competency assessments,and practical projects,the model conducts in-depth analysis of students’individual characteristics and development potential,enabling precise decision-making in goal setting,teaching intervention,and practical guidance.Based on data analysis,a supportive system for personalized teaching and practical activities is established.Combined with process-oriented and summative evaluations,a closed-loop feedback mechanism is formed to improve training effectiveness.This model provides a theoretical framework and practical path for the scientific,personalized,and intelligent development of innovation and entrepreneurship education in universities.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
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
Objective:To analyze the clinical effect of sensory integration training combined with cognitive training in the rehabilitation treatment of children with mental retardation.Methods:A total of 120 children with mental...Objective:To analyze the clinical effect of sensory integration training combined with cognitive training in the rehabilitation treatment of children with mental retardation.Methods:A total of 120 children with mental retardation who received rehabilitation intervention in our hospital from January 2022 to December 2025 were selected and divided into a control group and an experimental group,with 60 children in each group.The control group adopted a conventional rehabilitation training program;the experimental group adopted a combined sensory integration training and cognitive training program.The sensory integration ability,cognitive function,and daily living skills of children in the two groups were compared.Results:The sensory integration ability score of the experimental group(85.3±6.2)was significantly higher than that of the control group(72.1±7.5)(p<0.05);the cognitive function score(88.7±5.8)was significantly improved compared with that of the control group(76.4±6.9)(p<0.05);the daily living skills score(90.2±4.7)was significantly higher than that of the control group(80.5±5.3)(p<0.05).The social interaction ability of the experimental group reached 92.5%,which was significantly higher than that of the control group(81.3%)(p<0.05).Conclusion:Sensory integration training combined with cognitive training demonstrates favorable outcomes in the rehabilitation treatment of children with mental retardation,exhibiting a notable neurofunctional remodeling effect.It can optimize the multidimensional rehabilitation process,effectively enhance the comprehensive developmental potential of children,and hold significant clinical application value.展开更多
Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates dise...Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.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
Background Obesity is a risk factor for developing cardiometabolic disease.Exercise training is pivotal in the treatment of obesity and is associated with reduced cardiovascular mortality.This study examined the effec...Background Obesity is a risk factor for developing cardiometabolic disease.Exercise training is pivotal in the treatment of obesity and is associated with reduced cardiovascular mortality.This study examined the effect of high-fat feeding on cardiac morphology and mitochondrial function,alongside the mitigating effects of voluntary exercise training.Methods Six-week-old male C57Bl/6 J mice commenced a high fat diet(HFD)or chow diet and were randomized to receive locked(sedentary)or unlocked(voluntary exercise training(VET))running wheels at 10 weeks of age.Mice were monitored until 30 weeks of age and euthanized for collection of tissues.Magnetic resonance imaging was performed to assess body composition,and echocardiography was performed to assess cardiac function.Results Compared with chow-fed animals,the HFD increased body weight and adiposity and decreased cardiolipins(CL)in the heart,which are required for maintaining adequate mitochondrial respiration.Importantly,VET reversed these effects and induced physiological cardiac hypertrophy.Cardiac mitochondrial respiratory chain analysis revealed decreased complexes II and IV activity following high fat feeding,while VET enhanced complex I activity,emphasizing the cardioprotective effect of exercise training in obesity.Conclusion This study uncovers mechanisms by which obesity and exercise impact cardiac mitochondrial health and suggests the mitochondria is a therapeutic target in obesity-related cardiovascular diseases.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
Background:This paper aimed to systematically review the literature regarding the effects of resistance training(RT)performed at longer-muscle length(LML)versus shorter-muscle length(SML)on proxy measurements for long...Background:This paper aimed to systematically review the literature regarding the effects of resistance training(RT)performed at longer-muscle length(LML)versus shorter-muscle length(SML)on proxy measurements for longitudinal hypertrophy.Methods:We included studies that satisfied the following criteria:(1)be a resistance training intervention with a comparison of LML vs SML-RT;(2)assess both fascicle length(FL)and muscle size pre-and post-intervention;(3)involve healthy adults aged≥18 years;(4)be published in an English-language journal,and;(5)have a minimum training intervention duration of 4 weeks.Three databases were searched in February 2024(Google Scholar,PubMed/Medline,Scopus)for relevant articles,alongside'forward'and'backward'citation searching of articles included and additions via authors'personal knowledge.The results of studies were described narratively,compared,and contrasted.Eight studies met the inclusion criteria,totaling a sample size of 120.Results:Our results suggest that both muscle size and fascicle length increases may be greater following LML-RT versus SML-RT,suggesting LML-RT may lead to greater longitudinal hypertrophy than SML-RT.Notably,evidence is largely mixed;no studies to date have attempted to estimate serial sarcomere number changes from LML versus SML-RT,and all but one study used linear extrapolation methods to estimate FL,which has questionable validity.Therefore,the structural adaptations underlying hypertrophy from LML-RT remain undetermined.Conclusion:In conclusion,results suggest that LML-RT may be superior to SML-RT for inducing muscle hypertrophy and,more specifically,longitudinal growth,though evidence is mixed.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
The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.展开更多
Objectives:Postmenopausal women with stress urinary incontinence(SUI)exhibit low androgen expression.This study aimed to evaluate the efficacy and safety of vaginal androgen combined with pelvic floor muscle training(...Objectives:Postmenopausal women with stress urinary incontinence(SUI)exhibit low androgen expression.This study aimed to evaluate the efficacy and safety of vaginal androgen combined with pelvic floor muscle training(PFMT)in the treatment of SUI in postmenopausal women.Methods:Postmenopausal women with SUI were recruited from Hainan West Central Hospital between January 2024 and March 2025.Participants were randomly assigned in a double-blind manner to receive either vaginal androgen cream combined with PFMT(treatment group)or a visually identical placebo cream(without androgens)combined with PFMT(control group).The vaginal cream was applied to the vaginal wall at a dose of 0.5 g per application,twice weekly for a total of 10 applications,while PFMT was conducted for 8 weeks.The clinical efficacy and safety were compared between the two groups.Results:A total of 61 patients were finally enrolled,with 31 in the treatment group and 30 in the control group.At both 3-month and 6-month follow-ups,the treatment group demonstrated significantly lower values in daily pad usage(p<0.05),24-h pad test scores(p<0.05),and ICIQ-UI SF scores(p<0.05)compared to the control group.The improvement rate of urinary incontinence was significantly higher in the treatment group(p<0.05).Compared to baseline,the treatment group showed statistically significant reductions in all three outcome measures(all p<0.05).No severe adverse events were reported in either group during the treatment period.Conclusions:Androgen therapy combined with PFMT significantly improved the urinary incontinence remission rate in postmenopausal women with SUI,with no severe adverse effects observed.These findings suggest that androgen therapy may represent a novel therapeutic approach for SUI management in postmenopausal women.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.
基金Key Project of Postgraduate Education and Teaching Reform and Research at Southwest Petroleum University(2022JG003)Degree and Postgraduate Education Research Project at Association of Chinese Graduate Education(2020MSA346)+1 种基金Sichuan Provincial Society for Degree and Graduate Education(2023YB0406)Higher Education Teaching Reform Research Project at Southwest Petroleum University(X2021JGYB003)。
文摘The international development of the petroleum industry has posed an urgent demand for the internationalization capabilities of both academic and professional master’s students.However,there is currently a shortage of such talent in the petroleum energy sector,along with a lack of a collaborative training system.Based on this,this study focuses on featured disciplines in the petroleum energy sector and systematically constructs an international talent training model centered around the“five-element synergy”of“government-school-enterprise-teacher-student.”Firstly,it defines the connotations of the five-element synergy:“government(strategic guidance)-school(platform support)-enterprise(demand verification)-teacher(leading transformation)-student(practical co-creation).”Secondly,it sets distinct training objectives for academic(focusing on academic innovation)and professional(emphasizing engineering practice)master’s students.Furthermore,it constructs a“categorized and layered,progressive and collaborative”curriculum system,builds an international faculty team through a“recruitment+training”dual-path approach,and cultivates students’sense of professional mission to“contribute to the nation’s energy sector”through a“macro+micro”perspective.This model provides a practical pathway for international talent training in the petroleum energy sector and holds significant importance for enhancing the overseas competitiveness of petroleum enterprises and safeguarding national energy security.
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.
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Special Fund for Teacher Development Research Program of University of Shanghai for Science and Technology(Project No.:CFTD2025YB28)。
文摘Against the backdrop of the national innovation strategy and the digital transformation of education,the traditional“extensive”training model for innovation and entrepreneurship talents struggles to meet the personalized development needs of students,making an urgent shift toward precision and intelligence necessary.This study constructs a four-dimensional integrated framework centered on data,“Goal-Data-Intervention-Evaluation”,and proposes a data-driven training model for innovation and entrepreneurship talents in universities.By collecting multi-source data such as learning behaviors,competency assessments,and practical projects,the model conducts in-depth analysis of students’individual characteristics and development potential,enabling precise decision-making in goal setting,teaching intervention,and practical guidance.Based on data analysis,a supportive system for personalized teaching and practical activities is established.Combined with process-oriented and summative evaluations,a closed-loop feedback mechanism is formed to improve training effectiveness.This model provides a theoretical framework and practical path for the scientific,personalized,and intelligent development of innovation and entrepreneurship education in universities.
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.
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Baoding Science and Technology Plan Project(Project No.:2541ZF307)。
文摘Objective:To analyze the clinical effect of sensory integration training combined with cognitive training in the rehabilitation treatment of children with mental retardation.Methods:A total of 120 children with mental retardation who received rehabilitation intervention in our hospital from January 2022 to December 2025 were selected and divided into a control group and an experimental group,with 60 children in each group.The control group adopted a conventional rehabilitation training program;the experimental group adopted a combined sensory integration training and cognitive training program.The sensory integration ability,cognitive function,and daily living skills of children in the two groups were compared.Results:The sensory integration ability score of the experimental group(85.3±6.2)was significantly higher than that of the control group(72.1±7.5)(p<0.05);the cognitive function score(88.7±5.8)was significantly improved compared with that of the control group(76.4±6.9)(p<0.05);the daily living skills score(90.2±4.7)was significantly higher than that of the control group(80.5±5.3)(p<0.05).The social interaction ability of the experimental group reached 92.5%,which was significantly higher than that of the control group(81.3%)(p<0.05).Conclusion:Sensory integration training combined with cognitive training demonstrates favorable outcomes in the rehabilitation treatment of children with mental retardation,exhibiting a notable neurofunctional remodeling effect.It can optimize the multidimensional rehabilitation process,effectively enhance the comprehensive developmental potential of children,and hold significant clinical application value.
基金the Begum Rokeya University,Rangpur,and the United Arab Emirates University,UAE for partially supporting this work。
文摘Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.
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MAF is supported by an NHMRC Investigator Grant(APP1194141)Research in his laboratory was supported by project grants from the NHMRC(APP1042465,APP1041760,and APP1156511).
文摘Background Obesity is a risk factor for developing cardiometabolic disease.Exercise training is pivotal in the treatment of obesity and is associated with reduced cardiovascular mortality.This study examined the effect of high-fat feeding on cardiac morphology and mitochondrial function,alongside the mitigating effects of voluntary exercise training.Methods Six-week-old male C57Bl/6 J mice commenced a high fat diet(HFD)or chow diet and were randomized to receive locked(sedentary)or unlocked(voluntary exercise training(VET))running wheels at 10 weeks of age.Mice were monitored until 30 weeks of age and euthanized for collection of tissues.Magnetic resonance imaging was performed to assess body composition,and echocardiography was performed to assess cardiac function.Results Compared with chow-fed animals,the HFD increased body weight and adiposity and decreased cardiolipins(CL)in the heart,which are required for maintaining adequate mitochondrial respiration.Importantly,VET reversed these effects and induced physiological cardiac hypertrophy.Cardiac mitochondrial respiratory chain analysis revealed decreased complexes II and IV activity following high fat feeding,while VET enhanced complex I activity,emphasizing the cardioprotective effect of exercise training in obesity.Conclusion This study uncovers mechanisms by which obesity and exercise impact cardiac mitochondrial health and suggests the mitochondria is a therapeutic target in obesity-related cardiovascular diseases.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘Background:This paper aimed to systematically review the literature regarding the effects of resistance training(RT)performed at longer-muscle length(LML)versus shorter-muscle length(SML)on proxy measurements for longitudinal hypertrophy.Methods:We included studies that satisfied the following criteria:(1)be a resistance training intervention with a comparison of LML vs SML-RT;(2)assess both fascicle length(FL)and muscle size pre-and post-intervention;(3)involve healthy adults aged≥18 years;(4)be published in an English-language journal,and;(5)have a minimum training intervention duration of 4 weeks.Three databases were searched in February 2024(Google Scholar,PubMed/Medline,Scopus)for relevant articles,alongside'forward'and'backward'citation searching of articles included and additions via authors'personal knowledge.The results of studies were described narratively,compared,and contrasted.Eight studies met the inclusion criteria,totaling a sample size of 120.Results:Our results suggest that both muscle size and fascicle length increases may be greater following LML-RT versus SML-RT,suggesting LML-RT may lead to greater longitudinal hypertrophy than SML-RT.Notably,evidence is largely mixed;no studies to date have attempted to estimate serial sarcomere number changes from LML versus SML-RT,and all but one study used linear extrapolation methods to estimate FL,which has questionable validity.Therefore,the structural adaptations underlying hypertrophy from LML-RT remain undetermined.Conclusion:In conclusion,results suggest that LML-RT may be superior to SML-RT for inducing muscle hypertrophy and,more specifically,longitudinal growth,though evidence is mixed.
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.
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
基金supported by Joint Program on Health Science&Technology Innovation of Hainan Province(WSJK2024QN093)Discipline Leader Development Program for Outstanding Talents of Hainan West Central Hospital.
文摘Objectives:Postmenopausal women with stress urinary incontinence(SUI)exhibit low androgen expression.This study aimed to evaluate the efficacy and safety of vaginal androgen combined with pelvic floor muscle training(PFMT)in the treatment of SUI in postmenopausal women.Methods:Postmenopausal women with SUI were recruited from Hainan West Central Hospital between January 2024 and March 2025.Participants were randomly assigned in a double-blind manner to receive either vaginal androgen cream combined with PFMT(treatment group)or a visually identical placebo cream(without androgens)combined with PFMT(control group).The vaginal cream was applied to the vaginal wall at a dose of 0.5 g per application,twice weekly for a total of 10 applications,while PFMT was conducted for 8 weeks.The clinical efficacy and safety were compared between the two groups.Results:A total of 61 patients were finally enrolled,with 31 in the treatment group and 30 in the control group.At both 3-month and 6-month follow-ups,the treatment group demonstrated significantly lower values in daily pad usage(p<0.05),24-h pad test scores(p<0.05),and ICIQ-UI SF scores(p<0.05)compared to the control group.The improvement rate of urinary incontinence was significantly higher in the treatment group(p<0.05).Compared to baseline,the treatment group showed statistically significant reductions in all three outcome measures(all p<0.05).No severe adverse events were reported in either group during the treatment period.Conclusions:Androgen therapy combined with PFMT significantly improved the urinary incontinence remission rate in postmenopausal women with SUI,with no severe adverse effects observed.These findings suggest that androgen therapy may represent a novel therapeutic approach for SUI management in postmenopausal women.
