The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness dimin...The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.展开更多
针对核反应堆多物理场耦合模拟中传统程序效率低、精度不足的问题,研究基于开源耦合库preCICE及其适配器OpenFOAM-adapter,构建通用三维核热耦合程序。中子物理模块采用课题组研发的有限体积法中子输运程序,热工水力模块集成三维固体导...针对核反应堆多物理场耦合模拟中传统程序效率低、精度不足的问题,研究基于开源耦合库preCICE及其适配器OpenFOAM-adapter,构建通用三维核热耦合程序。中子物理模块采用课题组研发的有限体积法中子输运程序,热工水力模块集成三维固体导热(laplacianFoam)与流体对流换热模型(buoyantPimpleFoam),通过对preCICE官方OpenFOAM-adapter进行功能拓展,引入OpenFOAM的单元集合(cellSet)机制及其区域管理工具topoSet以界定耦合域;并在配置文件preciceDict中配置volumeCenters字段激活体积耦合模式,实现了中子学与热工水力学求解器间基于非匹配网格的数据映射。选取压水堆(Pressurized Water Reactor,PWR)单棒基准题开展网格无关性分析,对比最近邻映射、最近投影映射、径向基函数映射等数据传递方法。结果表明:程序可精确输出三维功率分布、中子通量密度场及速度场,冷却剂出口平均温度相对误差小于0.1%,包壳最高温度相对误差0.14%,计算结果与文献计算值符合较好。该程序突破传统定制化开发模式,支持异构网格差异化配置与大规模并行计算,可为反应堆安全分析、优化设计等提供参考工具。展开更多
Objective To develop QingNangTCM,a specialized large language model(LLM)tailored for expert-level traditional Chinese medicine(TCM)question-answering and clinical reasoning,addressing the scarcity of domain-specific c...Objective To develop QingNangTCM,a specialized large language model(LLM)tailored for expert-level traditional Chinese medicine(TCM)question-answering and clinical reasoning,addressing the scarcity of domain-specific corpora and specialized alignment.Methods We constructed QnTCM_Dataset,a corpus of 100000 entries,by integrating data from ShenNong_TCM_Dataset and SymMap v2.0,and synthesizing additional samples via retrieval-augmented generation(RAG)and persona-driven generation.The dataset comprehensively covers diagnostic inquiries,prescriptions,and herbal knowledge.Utilizing P-Tuning v2,we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM.A multidimensional evaluation framework,assessing accuracy,coverage,consistency,safety,professionalism,and fluency,was established using metrics such as bilingual evaluation understudy(BLEU),recall-oriented understudy for gisting evaluation(ROUGE),metric for evaluation of translation with explicit ordering(METEOR),and LLM-as-a-Judge with expert review.Qualitative analysis was conducted across four simulated clinical scenarios:symptom analysis,disease treatment,herb inquiry,and failure cases.Baseline models included GLM-4-9BChat,DeepSeek-V2,HuatuoGPT-II(7B),and GLM-4-9B-Chat(freeze-tuning).Results QingNangTCM achieved the highest scores in BLEU-1/2/3/4(0.425/0.298/0.137/0.064),ROUGE-1/2(0.368/0.157),and METEOR(0.218),demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy,coverage,and consistency.Although its ROUGE-L score(0.299)was lower than that of HuatuoGPT-II(7B)(0.351),it significantly outperformed domain-specific models in expert-validated win rates for professionalism(86%)and safety(73%).Qualitative analysis confirmed that the model strictly adheres to the“symptom-syndrome-pathogenesis-treatment”reasoning chain,though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes.展开更多
The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in S...The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.展开更多
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance o...In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.展开更多
Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automat...Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.展开更多
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehens...A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a ca...It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.展开更多
Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying ext...Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.展开更多
1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the...Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.展开更多
Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with env...Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.展开更多
Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activ...Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.展开更多
Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential facto...Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential factor for improving psychological well-being,but the underlying mechanism linking the two has not been fully explored.The current study aims to explore the relationship between positive solitude and psychological adaptation of international students,with particular emphasis on the intermediary roles of authenticity and loneliness.Methods:A total of 529 international tertiary students(Mage=23.76,SD=5.08;60.68%male)were surveyed using the Positive Solitude Scale(PSS),Authenticity Scale(AS),6-item UCLA Loneliness Scale(ULS-6),and Brief Psychological Adaptation Scale(BPAS).SPSS27.0 was used for descriptive statistical analysis and Pearson correlation analysis.PROCESS macro(Model 6)was employed to test a serial mediation model,in which authenticity and loneliness function as intermediary variables between positive solitude and psychological adaptation.Results:The correlation analysis indicated significant associations among positive solitude,authenticity,loneliness,and psychological adaptation(r=−0.544~0.511).Positive solitude was directly and positively related to psychological adaptation(β=0.132,t=3.609,p<0.001)and indirectly related to psychological adaptation through two pathways:a single mediation via authenticity(indirect effect=0.089)and a serial mediation through authenticity and loneliness(indirect effect=0.062).Loneliness did not serve as a significant mediator(indirect effect=–0.015,95%CI[–0.049,0.019]).The total indirect effect was 0.136.Conclusions:Interventions targeting international students’capacity for experiencing positive solitude and authenticity can help to reduce loneliness and increase psychological adaptation.The findings derived from this study are conducive to understanding the relationship between positive solitude and psychological adaptation,as well as its underlying mechanisms.In addition,the study offers a new perspective for educational management and psychological counseling services for international students.展开更多
Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now av...Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.展开更多
In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mec...In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62276109The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Research Group Project number(ORF-2025-585).
文摘The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.
文摘针对核反应堆多物理场耦合模拟中传统程序效率低、精度不足的问题,研究基于开源耦合库preCICE及其适配器OpenFOAM-adapter,构建通用三维核热耦合程序。中子物理模块采用课题组研发的有限体积法中子输运程序,热工水力模块集成三维固体导热(laplacianFoam)与流体对流换热模型(buoyantPimpleFoam),通过对preCICE官方OpenFOAM-adapter进行功能拓展,引入OpenFOAM的单元集合(cellSet)机制及其区域管理工具topoSet以界定耦合域;并在配置文件preciceDict中配置volumeCenters字段激活体积耦合模式,实现了中子学与热工水力学求解器间基于非匹配网格的数据映射。选取压水堆(Pressurized Water Reactor,PWR)单棒基准题开展网格无关性分析,对比最近邻映射、最近投影映射、径向基函数映射等数据传递方法。结果表明:程序可精确输出三维功率分布、中子通量密度场及速度场,冷却剂出口平均温度相对误差小于0.1%,包壳最高温度相对误差0.14%,计算结果与文献计算值符合较好。该程序突破传统定制化开发模式,支持异构网格差异化配置与大规模并行计算,可为反应堆安全分析、优化设计等提供参考工具。
基金Hebei Province Higher Education Scientific Research Project(QN2025367)Zhangjiakou City 2022 Municipal Science and Technology Plan Self-raised Fund Project(221105D)Hebei Province Education Science“14th Five-Year Plan”Project(2404224).
文摘Objective To develop QingNangTCM,a specialized large language model(LLM)tailored for expert-level traditional Chinese medicine(TCM)question-answering and clinical reasoning,addressing the scarcity of domain-specific corpora and specialized alignment.Methods We constructed QnTCM_Dataset,a corpus of 100000 entries,by integrating data from ShenNong_TCM_Dataset and SymMap v2.0,and synthesizing additional samples via retrieval-augmented generation(RAG)and persona-driven generation.The dataset comprehensively covers diagnostic inquiries,prescriptions,and herbal knowledge.Utilizing P-Tuning v2,we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM.A multidimensional evaluation framework,assessing accuracy,coverage,consistency,safety,professionalism,and fluency,was established using metrics such as bilingual evaluation understudy(BLEU),recall-oriented understudy for gisting evaluation(ROUGE),metric for evaluation of translation with explicit ordering(METEOR),and LLM-as-a-Judge with expert review.Qualitative analysis was conducted across four simulated clinical scenarios:symptom analysis,disease treatment,herb inquiry,and failure cases.Baseline models included GLM-4-9BChat,DeepSeek-V2,HuatuoGPT-II(7B),and GLM-4-9B-Chat(freeze-tuning).Results QingNangTCM achieved the highest scores in BLEU-1/2/3/4(0.425/0.298/0.137/0.064),ROUGE-1/2(0.368/0.157),and METEOR(0.218),demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy,coverage,and consistency.Although its ROUGE-L score(0.299)was lower than that of HuatuoGPT-II(7B)(0.351),it significantly outperformed domain-specific models in expert-validated win rates for professionalism(86%)and safety(73%).Qualitative analysis confirmed that the model strictly adheres to the“symptom-syndrome-pathogenesis-treatment”reasoning chain,though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes.
基金the research project LaTe4PoliticES(PID2022-138099OB-I00)funded by MCIN/AEI/10.13039/501100011033 and the European Fund for Regional Development(ERDF)-a way to make Europe.Tomás Bernal-Beltrán is supported by University of Murcia through the predoctoral programme.
文摘The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
文摘In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.
基金supported by the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金supported by the National Natural Science Foundation of China(Grant Nos.52306126,22350710788,12432010,11988102,92270203)the Xplore Prize.
文摘Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.
文摘A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金supported by the National Key Research and Development Project[Grand No.2022YFF0802304]Key Research and Development and Transformation Project of the Xizang Autonomous Region[Grand No.XZ202501ZY0119].
文摘It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.
文摘Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
基金supported by the National Natural Science Foundation of China(U24B20183)the Pioneer Leading Goose+X Science and Technology Program of Zhejiang Province(2025C02018)。
文摘Dear Editor,This letter deals with the autonomous underwater vehicle(AUV)three dimensional(3D)trajectory tracking control chronically suffering from poor accuracy and efficiency in complex hydrodynamics.A state-of-the-art predictive adaptive controller(PAC)is proposed with a distinct dual closed-loop structure.
基金Supported by Changsha Tobacco Company Science and Technology Project(2020-2024A04).
文摘Starting from the foundational static traits underlying the growth and development of flue-cured tobacco, this research conducts a systematic examination of the phenomena and theoretical principles associated with environment-driven adaptive changes during its cultivation. It was found that environmental variables-including temperature, light, and moisture-elicit directional shifts in static traits ( e.g. , chemical composition, morphological architecture, and leaf tissue structure) toward enhanced environmental adaptation, characterized by graduality, juvenility, similarity, and correlativity. Upon alterations in ambient conditions, flue-cured tobacco modulates its static traits through integrated physical, chemical, and biological-genetic mechanisms, aiming to optimize resource utilization, mitigate environmental constraints, and preserve internal homeostasis alongside metabolic balance. The investigation further reveals that the adaptive scope of flue-cured tobacco to field environments is malleable and can be extended and elevated via adaptive conditioning commencing at the juvenile stage. In addition, the adaptive alignment between static traits and environmental parameters exerts a substantial impact on the plant s growth dynamics, yield performance, and quality attributes. Beyond its relevance to flue-cured tobacco, the proposed theory offers a meaningful framework for elucidating the pervasive adaptive strategies employed by plants and broader biological systems in response to environmental contingencies.
基金supported by Science and Technology Program from the Forestry Administration of Guangdong Province(2024KJQT0012)the Guangdong Provincial Key R&D Program(2022B1111040001)+2 种基金the National Forestry Administration rare and endangered species field rescue and breeding project(Gui lin hu yu O10)the National Natural Science Foundation of China(32200337)a fellowship from the China Postdoctoral Science Foundation(2022M712003).
文摘Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.
基金supported by the 2024 Zhejiang Provincial Women’s Federation&Women’s Studies Association Research Project(202450).
文摘Objective:International students frequently face psychological adaptation difficulties while studying and living abroad.As an effective psychological resource,positive solitude has been identified as a potential factor for improving psychological well-being,but the underlying mechanism linking the two has not been fully explored.The current study aims to explore the relationship between positive solitude and psychological adaptation of international students,with particular emphasis on the intermediary roles of authenticity and loneliness.Methods:A total of 529 international tertiary students(Mage=23.76,SD=5.08;60.68%male)were surveyed using the Positive Solitude Scale(PSS),Authenticity Scale(AS),6-item UCLA Loneliness Scale(ULS-6),and Brief Psychological Adaptation Scale(BPAS).SPSS27.0 was used for descriptive statistical analysis and Pearson correlation analysis.PROCESS macro(Model 6)was employed to test a serial mediation model,in which authenticity and loneliness function as intermediary variables between positive solitude and psychological adaptation.Results:The correlation analysis indicated significant associations among positive solitude,authenticity,loneliness,and psychological adaptation(r=−0.544~0.511).Positive solitude was directly and positively related to psychological adaptation(β=0.132,t=3.609,p<0.001)and indirectly related to psychological adaptation through two pathways:a single mediation via authenticity(indirect effect=0.089)and a serial mediation through authenticity and loneliness(indirect effect=0.062).Loneliness did not serve as a significant mediator(indirect effect=–0.015,95%CI[–0.049,0.019]).The total indirect effect was 0.136.Conclusions:Interventions targeting international students’capacity for experiencing positive solitude and authenticity can help to reduce loneliness and increase psychological adaptation.The findings derived from this study are conducive to understanding the relationship between positive solitude and psychological adaptation,as well as its underlying mechanisms.In addition,the study offers a new perspective for educational management and psychological counseling services for international students.
文摘Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.
文摘In federated learning,backdoor attacks have become an important research topic with their wide application in processing sensitive datasets.Since federated learning detects or modifies local models through defense mechanisms during aggregation,it is difficult to conduct effective backdoor attacks.In addition,existing backdoor attack methods are faced with challenges,such as low backdoor accuracy,poor ability to evade anomaly detection,and unstable model training.To address these challenges,a method called adaptive simulation backdoor attack(ASBA)is proposed.Specifically,ASBA improves the stability of model training by manipulating the local training process and using an adaptive mechanism,the ability of the malicious model to evade anomaly detection by combing large simulation training and clipping,and the backdoor accuracy by introducing a stimulus model to amplify the impact of the backdoor in the global model.Extensive comparative experiments under five advanced defense scenarios show that ASBA can effectively evade anomaly detection and achieve high backdoor accuracy in the global model.Furthermore,it exhibits excellent stability and effectiveness after multiple rounds of attacks,outperforming state-of-the-art backdoor attack methods.
