In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random vari...In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.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
Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on th...Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on the toxicity of single PFAS such as perfluorooctanoic acid(PFOA)and perfluorobutanoic acid(PFBA),the knowledge of their combined effects is relatively limited.In this study,we explored the immune response of the gut in large yellow croaker(Larimichthys crocea)under the combined stress of PFOA and PFBA.Histologicalanalyses revealed that the combined effect induced intestinal vacuolization and decreased the length of intestinal villi.And it significantly activated pro-inflammatory pathways with marked upregulation of tnfα,il1β,il6 and myd88 expressions,particularly after 14 days of exposure.Gut microbiota analysis revealed substantial dysbiosis,including 1)reduced alpha diversity,2)increased abundance of potential pathogenic taxa(Proteobacteria and Spirochaetota),and 3)depletion of beneficial Firmicutes.PICRUSt-based functional prediction indicated temporal metabolic shifts,with upregulation of DNA repair pathways at day 3 and enhanced bacterial motility protein activity at days 7 and 14 of post-exposure.The Pearson correlation analysis further indicated that these immune genes had significant positive correlations with Vibrio and Brevinema,and negative correlations with Streptococcus.Our present study will provide novel insights into the microbiome-mediated immunomodulation in the larger yellow croaker exposed to combined PFAS,which will be helpful for healthy farming of economically important marine species.展开更多
This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,Calcu...This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,CalculiX,and preCICE to simulate fluid-particle-structure interactions with large deformations.Mesh motion in the fluid field is handled using the radial basis function(RBF)method.The particle phase is modeled by MPPIC,where fluid-particle interaction is described through momentum exchange,and inter-particle collisions are characterized by collision stress.The structural field is solved by nonlinear FEM to capture large deformations induced by geometric nonlinearity.Coupling among fields is realized through a partitioned,parallel,and non-intrusive iterative strategy,ensuring stable transfer and convergence of interface forces and displacements.Notably,the influence of particles on the structure is not direct but mediated by the fluid,while structural motion directly affects particle dynamics.The results demonstrate that the proposed approach effectively captures multiphysics interaction processes and provides a valuable reference for numerical modeling of coupled fluid-particle-structure systems.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that man...Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.展开更多
The giant impact hypothesis for the Moon's origin has had difficulty explaining the nearly identical isotopic compositions of Moon rocks and rocks from Earth's silicate mantle and crust.These similarities are ...The giant impact hypothesis for the Moon's origin has had difficulty explaining the nearly identical isotopic compositions of Moon rocks and rocks from Earth's silicate mantle and crust.These similarities are instead more compatible with the Darwin-Wise hypothesis that the Moon arose by fission of a rapidly spinning Earth.To overcome problems with the fission model concerning structural stability and angular momentum conservation,some authors suggested that lunar fission was feasible on a more slowly rotating Earth if assisted by a nuclear explosion near the core-mantle boundary.In this light we consider the possible roles of the large low-velocity provinces(LLVPs).These long-lived structures have been implicated in diverse geophysical processes ranging from deep mantle plumes to continental breakup and mass extinction events.While the LLVPs have been seen as possible remnants of the giant imp actor,we propose that one of them was the site of lunar ejection.Internal heating of the liquid core is suggested to have given rise to an equatorial belt just under the core-mantle boundary analogous to the one recently detected by Ma and Tkalcic[Sci Adv 10(35):eadn5562,2024].Upwellings of heat and volatiles from this belt then generated two antipodal,equatorial bulges:the precursors of the Pacific and African LLVPs.Prior to the emergence of plate tectonics,core heat was mainly dissipated by networks of deep mantle plumes extending above the proto-LLVPs.These plume networks represent conduits of weakened mantle through which proto-lunar materials could later rise in a focused ejection.Continuing heat buildup in the core eventually triggered a cataclysmic explosion in the Pacific proto-LLVP,possibly analogous to a planetary-scale kimberlite eruption.This explosion launched LLVP and overlying mantle material into a low Earth orbit,where it coalesced to form the Moon.Some possible sources of additional energy to power the explosion are considered,including nuclear fission,bolide impacts and a hypothetical gravitational decay process culminating in a'A event'.展开更多
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact...Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.展开更多
Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lack...Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lacks formal task definitions and corresponding benchmarks.To bridge this gap,we define the Automated Online Public Opinion Report Generation(OPOR-Gen)task and construct OPOR-Bench,an event-centric dataset with 463 crisis events across 108 countries(comprising 8.8 K news articles and 185 K tweets).To evaluate report quality,we propose OPOR-Eval,a novel agent-based framework that simulates human expert evaluation.Validation experiments show OPOR-Eval achieves a high Spearman’s correlation(ρ=0.70)with human judgments,though challenges in temporal reasoning persist.This work establishes an initial foundation for advancing automated public opinion reporting research.展开更多
Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstan...Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.展开更多
The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechan...The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechanism of large earthquakes.This study uses rock magnetic,microstructural,and geochemical analyses of representative samples exposed in FZ1681 within the Wenchuan Earthquake Fault Scientific Drilling borehole 2(WFSD-2)cores.Fault gouge and fault breccia have higher magnetic susceptibility values than wall rocks,and they contain abundant paramagnetic minerals and small quantities of magnetite and monoclinic pyrrhotite.The magnetite and monoclinic pyrrhotite in the fault gouge were mainly formed by coseismic frictional heating,indicating that large earthquakes with frictional heating temperatures of~500-900℃once occurred in the YBFZ.The seismogenic and coseismic environment was reducing with a relatively high sulfur content.The monoclinic pyrrhotite in the fault breccia was formed mainly by low-temperature hydrothermal fluid.This indicates that the fault zone experienced reducing and low-temperature(<400℃)hydrothermal fluid with a relatively high sulfur content after the earthquake.The YBFZ,which experiences frequent large earthquakes,is weakly oxidizing environment at different depths,but the effect of the low-temperature hydrothermal fluid is weaker at depth.展开更多
One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and ...One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and optimal ranges of the number of machines that can be used in a cascade?For the first time,the permissible and optimal ranges of the number of gas centrifuges that can be utilized in a cascade were investigated using two types of centrifuges,and the performance of small and large tapered cascades was discussed.The particle swarm optimization algorithm(PSO)has been used to optimize tapered cascades.The results show:(1)For the first centrifuge,41 cascades(91≤n≤4897)and for the second centrifuge,49 cascades(18≤n≤3839)with small and large sizes can be used in enrichment facilities,and the best cascade for them has 530(with 23 stages)and 39(with 7 stages)centrifuges,respectively.(2)For both centrifuges,when 600≤n(number of centrifuges=n),the large cascade performance changes are relatively insignificant.(3)For both types of gas centrifuges,the annual los s of separation power in enrichment facilities is approximately 1.25%-4.82%of the total separation work required.展开更多
Impacted upper ureteral stones are definedas calculi that remain lodged in the same location within the upper ureter for more than two months,1 and they are typically associated with inflammation,mucosal edema,and fib...Impacted upper ureteral stones are definedas calculi that remain lodged in the same location within the upper ureter for more than two months,1 and they are typically associated with inflammation,mucosal edema,and fibrosisof the surrounding ureteral wall.These stones often lead to significantclinical consequences,including persistent flankpain,hydronephrosis,infection,impaired renal function,and in severe cases,irreversible kidney damage.展开更多
AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospecti...AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospective cohort study enrolled 29 patients diagnosed with OA-DLBCL based on histopathological biopsy between 2006 and 2023.Patients were stratified into two subgroups:primary OA-DLBCL(no prior history of lymphoma)and secondary OA-DLBCL(history of DLBCL at non-ocular adnexal sites).OS was defined as the time interval from OA-DLBCL diagnosis to death from any cause.Survival analysis was performed using the Kaplan–Meier method,and prognostic factors affecting OS were identified using multivariate Cox proportional hazards regression with a stepwise selection approach.RESULTS:The cohort included 24 patients with primary OA-DLBCL(13 males,11 females;mean age:61.36±18.29y)and 5 patients with secondary OA-DLBCL(2 males,3 females;mean age:50.94±18.17y).Among the primary OA-DLBCL subgroup,12 patients(50%)presented with advanced disease(Ann Arbor stage IIIE–IV),and 16 patients(66%)were classified as T4 disease according to the tumor-node-metastasis(TNM)staging system.The mean final visual acuity was 1.72±1.10 in the primary group and 0.90±1.18 in the secondary group.The 5-year OS rate for the entire cohort was 27.7%.Multivariate analysis identified five factors significantly associated with poor survival outcomes:epiphora[adjusted hazard ratio(aHR),36.95],atherosclerotic cardiovascular disease(aHR,10.08),human immunodeficiency virus(HIV)infection(aHR,12.47),M1 stage(aHR,6.99),and secondary OA-DLBCL(aHR,6.03;all P<0.05).The median OS was 1.68y for primary OA-DLBCL and 1.12y for secondary OA-DLBCL.CONCLUSION:A substantial proportion of patients with primary OA-DLBCL present with advanced-stage disease at diagnosis.Epiphora,atherosclerotic cardiovascular disease,HIV infection,M1 stage,and secondary OA-DLBCL are independent prognostic factors for poor survival outcomes.These findings emphasize the urgent need for optimized therapeutic strategies and early screening protocols to improve the management of OA-DLBCL,particularly in developing countries.展开更多
Objectives:B-cell lymphoma 6(BCL6)is a transcriptional repressor whose overexpression is closely linked to the progression of diffuse large B-cell lymphoma(DLBCL),making it a promising therapeutic target.This study ai...Objectives:B-cell lymphoma 6(BCL6)is a transcriptional repressor whose overexpression is closely linked to the progression of diffuse large B-cell lymphoma(DLBCL),making it a promising therapeutic target.This study aims to identify a novel small molecule,synthesized via proteolysis-targeting chimeras(PROTACs),capable of degrading BCL6,thereby inhibiting DLBCL growth and providing a foundation for future preclinical studies.Methods:The expression of BCL6 in DLBCL was analyzed using The Cancer Genome Atlas(TCGA)database and the Human Protein Atlas.Western blotting assays confirmed BCL6 expression in tumor cell lines,leading to the identification of the small molecule compound DZ-865B.To evaluate DZ-865B’s in vitro efficacy,multiple assays were performed,including protein immunoblotting,immunofluorescence,reverse transcription quantitative PCR,EDU proliferation,and soft agar cloning assays.Results:TCGA analysis revealed significant overexpression of BCL6 in DLBCL(p<0.05),corroborated by immunohistological staining and western blotting.DZ-865B induced BCL6 degradation in DLBCL cell lines(OCI-LY-1 and SU-DHL-4)in a concentration-and time-dependent manner,and induced the degradation of nuclear BCL6 through the ubiquitin-proteasome pathway.Notably,DZ-865B did not alter BCL6 mRNA levels but modulated downstream gene expression,leading to the activation of apoptosis pathway proteins and inhibition of DNA synthesis,effectively suppressing DLBCL cell growth.Conclusion:This study demonstrates that the small molecule DZ-865B targets and degrades BCL6 in DLBCL cells,promoting apoptosis and inhibiting cellular proliferation.These findings highlight DZ-865B as a potential therapeutic agent for diffuse large B-cell lymphoma.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
As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for th...As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.展开更多
Deep Underground Science and Engineering(DUSE)is pleased to present this special issue highlighting recent advancements in underground large-scale energy storage technologies.This issue comprises 19 articles:six from ...Deep Underground Science and Engineering(DUSE)is pleased to present this special issue highlighting recent advancements in underground large-scale energy storage technologies.This issue comprises 19 articles:six from our special issue"Underground large-scale energy storage technologies in the context of carbon neutrality",11 from regular submissions on related topics,and two from early regular submissions.These contributions include five review articles,one perspective article,and 13 research articles.The increased volume of this issue and later issues reflects DUSE's commitment to addressing the rapid growth in submissions and the current backlog of high-quality papers.展开更多
A major bottleneck in large-scale eigenfrequency topology optimization is the repeated solution of the generalized eigenvalue problem.This work presents an efficient graphics processing unit(GPU)solver for threedimens...A major bottleneck in large-scale eigenfrequency topology optimization is the repeated solution of the generalized eigenvalue problem.This work presents an efficient graphics processing unit(GPU)solver for threedimensional(3D)topology optimization that maximizes the fundamental eigenfrequency.The Successive Iteration of Analysis and Design(SIAD)framework is employed to avoid solving a full eigenproblem at every iteration.The sequential approximation of the eigenpairs is solved by the GPU-accelerated multigrid-preconditioned conjugate gradient(MGPCG)method to efficiently improve the eigenvectors along with the topological evolution.The cluster-mean approach is adopted to address the non-differentiability issue caused by repeated eigenfrequencies.The corresponding sensitivity analysis method is provided.The parallelized gradient-based Zhang-Paulino-Ramos Jr.(ZPR)algorithm is employed to update the design variables.The effectiveness of the proposed solver is demonstrated through two large-scale numerical examples.The first example demonstrates the accuracy,efficiency,and scalability of the proposed solver by solving a 3D optimization problem of 50.33 million elements being solved in approximately 15.2 h over 300 iterations on a single NVIDIA Tesla V100 GPU.The second example validates the effectiveness of the proposed solver in the presence of repeated eigenfrequencies.Our findings also highlight that higher-resolution models produce distinct optimized structures with higher fundamental frequencies,underscoring the necessity of large-scale topology optimization.展开更多
文摘In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.
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 Ningbo Natural Science Foundation(Youth Foundation,No.2024J449)the Scientific Research Foundation for Introduced Talents of Ningbo University(Nos.ZX2022000602 and ZX2024000043)。
文摘Polyfluoroalkyl substances(PFAS)have emerged as persistent environmental contaminants because of their chemical stability,degradation-resistance and bioaccumulation potential.However,current studies mainly focus on the toxicity of single PFAS such as perfluorooctanoic acid(PFOA)and perfluorobutanoic acid(PFBA),the knowledge of their combined effects is relatively limited.In this study,we explored the immune response of the gut in large yellow croaker(Larimichthys crocea)under the combined stress of PFOA and PFBA.Histologicalanalyses revealed that the combined effect induced intestinal vacuolization and decreased the length of intestinal villi.And it significantly activated pro-inflammatory pathways with marked upregulation of tnfα,il1β,il6 and myd88 expressions,particularly after 14 days of exposure.Gut microbiota analysis revealed substantial dysbiosis,including 1)reduced alpha diversity,2)increased abundance of potential pathogenic taxa(Proteobacteria and Spirochaetota),and 3)depletion of beneficial Firmicutes.PICRUSt-based functional prediction indicated temporal metabolic shifts,with upregulation of DNA repair pathways at day 3 and enhanced bacterial motility protein activity at days 7 and 14 of post-exposure.The Pearson correlation analysis further indicated that these immune genes had significant positive correlations with Vibrio and Brevinema,and negative correlations with Streptococcus.Our present study will provide novel insights into the microbiome-mediated immunomodulation in the larger yellow croaker exposed to combined PFAS,which will be helpful for healthy farming of economically important marine species.
基金supported in part by the Mining Hydraulic Technology and Equipment Engineering Research Center,Liaoning Technical University,Fuxin,China(Grant No.MHTE23-R04)the Fundamental Research Funds for the Central Universities(ID N25BSS068).
文摘This study presents an implicit multiphysics coupling method integrating Computational Fluid Dynamics(CFD),the Multiphase Particle-in-Cell(MPPIC)model,and the Finite Element Method(FEM),implemented with OpenFOAM,CalculiX,and preCICE to simulate fluid-particle-structure interactions with large deformations.Mesh motion in the fluid field is handled using the radial basis function(RBF)method.The particle phase is modeled by MPPIC,where fluid-particle interaction is described through momentum exchange,and inter-particle collisions are characterized by collision stress.The structural field is solved by nonlinear FEM to capture large deformations induced by geometric nonlinearity.Coupling among fields is realized through a partitioned,parallel,and non-intrusive iterative strategy,ensuring stable transfer and convergence of interface forces and displacements.Notably,the influence of particles on the structure is not direct but mediated by the fluid,while structural motion directly affects particle dynamics.The results demonstrate that the proposed approach effectively captures multiphysics interaction processes and provides a valuable reference for numerical modeling of coupled fluid-particle-structure systems.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by 2023 Higher Education Scientific Research Planning Project of China Society of Higher Education(No.23PG0408)2023 Philosophy and Social Science Research Programs in Jiangsu Province(No.2023SJSZ0993)+2 种基金Nantong Science and Technology Project(No.JC2023070)Key Project of Jiangsu Province Education Science 14th Five-Year Plan(Grant No.B-b/2024/02/41)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202407).
文摘Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.
文摘The giant impact hypothesis for the Moon's origin has had difficulty explaining the nearly identical isotopic compositions of Moon rocks and rocks from Earth's silicate mantle and crust.These similarities are instead more compatible with the Darwin-Wise hypothesis that the Moon arose by fission of a rapidly spinning Earth.To overcome problems with the fission model concerning structural stability and angular momentum conservation,some authors suggested that lunar fission was feasible on a more slowly rotating Earth if assisted by a nuclear explosion near the core-mantle boundary.In this light we consider the possible roles of the large low-velocity provinces(LLVPs).These long-lived structures have been implicated in diverse geophysical processes ranging from deep mantle plumes to continental breakup and mass extinction events.While the LLVPs have been seen as possible remnants of the giant imp actor,we propose that one of them was the site of lunar ejection.Internal heating of the liquid core is suggested to have given rise to an equatorial belt just under the core-mantle boundary analogous to the one recently detected by Ma and Tkalcic[Sci Adv 10(35):eadn5562,2024].Upwellings of heat and volatiles from this belt then generated two antipodal,equatorial bulges:the precursors of the Pacific and African LLVPs.Prior to the emergence of plate tectonics,core heat was mainly dissipated by networks of deep mantle plumes extending above the proto-LLVPs.These plume networks represent conduits of weakened mantle through which proto-lunar materials could later rise in a focused ejection.Continuing heat buildup in the core eventually triggered a cataclysmic explosion in the Pacific proto-LLVP,possibly analogous to a planetary-scale kimberlite eruption.This explosion launched LLVP and overlying mantle material into a low Earth orbit,where it coalesced to form the Moon.Some possible sources of additional energy to power the explosion are considered,including nuclear fission,bolide impacts and a hypothetical gravitational decay process culminating in a'A event'.
基金supported by the National Key R&D Program of China[2022YFF0902703]the State Administration for Market Regulation Science and Technology Plan Project(2024MK033).
文摘Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.
基金supported by the Fundamental Research Funds for the Central Universities(No.CUC25SG013)the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),Ministry of Education(No.EIN2024C006).
文摘Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises.While large language models(LLMs)enable automated report generation,this specific domain lacks formal task definitions and corresponding benchmarks.To bridge this gap,we define the Automated Online Public Opinion Report Generation(OPOR-Gen)task and construct OPOR-Bench,an event-centric dataset with 463 crisis events across 108 countries(comprising 8.8 K news articles and 185 K tweets).To evaluate report quality,we propose OPOR-Eval,a novel agent-based framework that simulates human expert evaluation.Validation experiments show OPOR-Eval achieves a high Spearman’s correlation(ρ=0.70)with human judgments,though challenges in temporal reasoning persist.This work establishes an initial foundation for advancing automated public opinion reporting research.
文摘Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1000500)the National Natural Science Foundation of China(42172262 and 42372266)+1 种基金the China Geological Survey(DD20240041)the Fundamental Research Funds of the Institute of Geomechanics(DZLXJK202516).
文摘The Yingxiu-Beichuan fault zone(YBFZ)has long been active and experienced repeated large earthquakes.The physicochemical properties of the deep fault zone(>1000 m)are the key to understanding the deformation mechanism of large earthquakes.This study uses rock magnetic,microstructural,and geochemical analyses of representative samples exposed in FZ1681 within the Wenchuan Earthquake Fault Scientific Drilling borehole 2(WFSD-2)cores.Fault gouge and fault breccia have higher magnetic susceptibility values than wall rocks,and they contain abundant paramagnetic minerals and small quantities of magnetite and monoclinic pyrrhotite.The magnetite and monoclinic pyrrhotite in the fault gouge were mainly formed by coseismic frictional heating,indicating that large earthquakes with frictional heating temperatures of~500-900℃once occurred in the YBFZ.The seismogenic and coseismic environment was reducing with a relatively high sulfur content.The monoclinic pyrrhotite in the fault breccia was formed mainly by low-temperature hydrothermal fluid.This indicates that the fault zone experienced reducing and low-temperature(<400℃)hydrothermal fluid with a relatively high sulfur content after the earthquake.The YBFZ,which experiences frequent large earthquakes,is weakly oxidizing environment at different depths,but the effect of the low-temperature hydrothermal fluid is weaker at depth.
文摘One of the main issues in designing optimum tapered cascades for uranium enrichment for annual fuel production in a power reactor is whether to employ large(fat)or small(thin)cascades.What will be the permissible and optimal ranges of the number of machines that can be used in a cascade?For the first time,the permissible and optimal ranges of the number of gas centrifuges that can be utilized in a cascade were investigated using two types of centrifuges,and the performance of small and large tapered cascades was discussed.The particle swarm optimization algorithm(PSO)has been used to optimize tapered cascades.The results show:(1)For the first centrifuge,41 cascades(91≤n≤4897)and for the second centrifuge,49 cascades(18≤n≤3839)with small and large sizes can be used in enrichment facilities,and the best cascade for them has 530(with 23 stages)and 39(with 7 stages)centrifuges,respectively.(2)For both centrifuges,when 600≤n(number of centrifuges=n),the large cascade performance changes are relatively insignificant.(3)For both types of gas centrifuges,the annual los s of separation power in enrichment facilities is approximately 1.25%-4.82%of the total separation work required.
文摘Impacted upper ureteral stones are definedas calculi that remain lodged in the same location within the upper ureter for more than two months,1 and they are typically associated with inflammation,mucosal edema,and fibrosisof the surrounding ureteral wall.These stones often lead to significantclinical consequences,including persistent flankpain,hydronephrosis,infection,impaired renal function,and in severe cases,irreversible kidney damage.
基金Supported by the Faculty of Medicine,Prince of Songkla University.Wainipitapong S has received grants from the Faculty of Medicine,Prince of Songkla University。
文摘AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospective cohort study enrolled 29 patients diagnosed with OA-DLBCL based on histopathological biopsy between 2006 and 2023.Patients were stratified into two subgroups:primary OA-DLBCL(no prior history of lymphoma)and secondary OA-DLBCL(history of DLBCL at non-ocular adnexal sites).OS was defined as the time interval from OA-DLBCL diagnosis to death from any cause.Survival analysis was performed using the Kaplan–Meier method,and prognostic factors affecting OS were identified using multivariate Cox proportional hazards regression with a stepwise selection approach.RESULTS:The cohort included 24 patients with primary OA-DLBCL(13 males,11 females;mean age:61.36±18.29y)and 5 patients with secondary OA-DLBCL(2 males,3 females;mean age:50.94±18.17y).Among the primary OA-DLBCL subgroup,12 patients(50%)presented with advanced disease(Ann Arbor stage IIIE–IV),and 16 patients(66%)were classified as T4 disease according to the tumor-node-metastasis(TNM)staging system.The mean final visual acuity was 1.72±1.10 in the primary group and 0.90±1.18 in the secondary group.The 5-year OS rate for the entire cohort was 27.7%.Multivariate analysis identified five factors significantly associated with poor survival outcomes:epiphora[adjusted hazard ratio(aHR),36.95],atherosclerotic cardiovascular disease(aHR,10.08),human immunodeficiency virus(HIV)infection(aHR,12.47),M1 stage(aHR,6.99),and secondary OA-DLBCL(aHR,6.03;all P<0.05).The median OS was 1.68y for primary OA-DLBCL and 1.12y for secondary OA-DLBCL.CONCLUSION:A substantial proportion of patients with primary OA-DLBCL present with advanced-stage disease at diagnosis.Epiphora,atherosclerotic cardiovascular disease,HIV infection,M1 stage,and secondary OA-DLBCL are independent prognostic factors for poor survival outcomes.These findings emphasize the urgent need for optimized therapeutic strategies and early screening protocols to improve the management of OA-DLBCL,particularly in developing countries.
基金supported by the National Natural Science Foundation of China(82260716)the Key Research and Development Program of Ningxia(2023BEG02010).
文摘Objectives:B-cell lymphoma 6(BCL6)is a transcriptional repressor whose overexpression is closely linked to the progression of diffuse large B-cell lymphoma(DLBCL),making it a promising therapeutic target.This study aims to identify a novel small molecule,synthesized via proteolysis-targeting chimeras(PROTACs),capable of degrading BCL6,thereby inhibiting DLBCL growth and providing a foundation for future preclinical studies.Methods:The expression of BCL6 in DLBCL was analyzed using The Cancer Genome Atlas(TCGA)database and the Human Protein Atlas.Western blotting assays confirmed BCL6 expression in tumor cell lines,leading to the identification of the small molecule compound DZ-865B.To evaluate DZ-865B’s in vitro efficacy,multiple assays were performed,including protein immunoblotting,immunofluorescence,reverse transcription quantitative PCR,EDU proliferation,and soft agar cloning assays.Results:TCGA analysis revealed significant overexpression of BCL6 in DLBCL(p<0.05),corroborated by immunohistological staining and western blotting.DZ-865B induced BCL6 degradation in DLBCL cell lines(OCI-LY-1 and SU-DHL-4)in a concentration-and time-dependent manner,and induced the degradation of nuclear BCL6 through the ubiquitin-proteasome pathway.Notably,DZ-865B did not alter BCL6 mRNA levels but modulated downstream gene expression,leading to the activation of apoptosis pathway proteins and inhibition of DNA synthesis,effectively suppressing DLBCL cell growth.Conclusion:This study demonstrates that the small molecule DZ-865B targets and degrades BCL6 in DLBCL cells,promoting apoptosis and inhibiting cellular proliferation.These findings highlight DZ-865B as a potential therapeutic agent for diffuse large B-cell lymphoma.
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.
文摘As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly evident.However,the challenge lies in identifying the right parameters and strategies for these algorithms.In this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization challenges.It employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy search.Key parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational efficiency.We offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and independent.RA is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000.The results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and scalability.Finally,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.
文摘Deep Underground Science and Engineering(DUSE)is pleased to present this special issue highlighting recent advancements in underground large-scale energy storage technologies.This issue comprises 19 articles:six from our special issue"Underground large-scale energy storage technologies in the context of carbon neutrality",11 from regular submissions on related topics,and two from early regular submissions.These contributions include five review articles,one perspective article,and 13 research articles.The increased volume of this issue and later issues reflects DUSE's commitment to addressing the rapid growth in submissions and the current backlog of high-quality papers.
基金support from the National Natural Science Foundation of China(Award No.52105240).
文摘A major bottleneck in large-scale eigenfrequency topology optimization is the repeated solution of the generalized eigenvalue problem.This work presents an efficient graphics processing unit(GPU)solver for threedimensional(3D)topology optimization that maximizes the fundamental eigenfrequency.The Successive Iteration of Analysis and Design(SIAD)framework is employed to avoid solving a full eigenproblem at every iteration.The sequential approximation of the eigenpairs is solved by the GPU-accelerated multigrid-preconditioned conjugate gradient(MGPCG)method to efficiently improve the eigenvectors along with the topological evolution.The cluster-mean approach is adopted to address the non-differentiability issue caused by repeated eigenfrequencies.The corresponding sensitivity analysis method is provided.The parallelized gradient-based Zhang-Paulino-Ramos Jr.(ZPR)algorithm is employed to update the design variables.The effectiveness of the proposed solver is demonstrated through two large-scale numerical examples.The first example demonstrates the accuracy,efficiency,and scalability of the proposed solver by solving a 3D optimization problem of 50.33 million elements being solved in approximately 15.2 h over 300 iterations on a single NVIDIA Tesla V100 GPU.The second example validates the effectiveness of the proposed solver in the presence of repeated eigenfrequencies.Our findings also highlight that higher-resolution models produce distinct optimized structures with higher fundamental frequencies,underscoring the necessity of large-scale topology optimization.
