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Enhancing Power Enterprise Inspection and Supervision: A LoRA-Based Lightweight LLM Framework Integrating Retrieval-Augmented Generation and Prompt Engineering 认领 引用
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作者 Jianfeng Liu Yongjiao Yang +4 位作者 Kangyi Yang Changhua Hu Zijia Xu Qingguo Shi Yi Su 《Computers, Materials & Continua》 SCIE EI 2026年第8期2256-2275,共20页
Power enterprise inspection and supervision require greater intelligence,efficiency,and standardization;however,existing approaches are limited by inefficient knowledge retrieval,inaccurate issue identification,and in... Power enterprise inspection and supervision require greater intelligence,efficiency,and standardization;however,existing approaches are limited by inefficient knowledge retrieval,inaccurate issue identification,and insufficient support for standardized reporting and rectification tracking.This study proposes a lightweight,domain-adaptive large language model(LLM)framework based on Low-Rank Adaptation(LoRA),integrating Retrieval-Augmented Generation(RAG)and structured prompt engineering to enable evidence-grounded inspection tasks.The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base,enhancing output traceability,consistency,and task generalization.A key contribution is the introduction of a Sensitive Information Control Gate,which enforces role-based access control and automated redaction,ensuring secure and compliant generation in regulated environments while preserving traceability.Experimental results demonstrate that the proposed method achieves improved performance over the base model and demonstrates competitive effectiveness under the evaluated conditions,supported by statistical analysis(paired t-test,p<0.01,bootstrap 95%confidence intervals),while maintaining high parameter efficiency with only 0.4%–0.5%trainable parameters. 展开更多
关键词 Large language models LoRA fine-tuning retrieval-augmented generation prompt engineering inspection and supervision power enterprise governance
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Chronological Passage Assembly for Retrieval-Augmented Generation in Narrative Question Answering 认领 引用
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作者 Byeongjeong Kim Jeonghyun Park +1 位作者 Joonho Yang Hwanhee Lee 《Computers, Materials & Continua》 SCIE EI 2026年第9期2167-2187,共21页
Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets.Existing ... Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets.Existing retrieval-augmented generation(RAG)methods typically retrieve document snippets independently,which can fragment narratives and harm temporal dependencies.We propose ChronoRAG,a retrieval framework for narrative question answering that first converts sequential document chunks into concise relation descriptions and then retrieves relevant units together with their adjacent chronological context.This design preserves retrieval precision while providing the generator with coherent local narrative structure.Experiments on NarrativeQA and GutenQA show that ChronoRAG improves performance on NarrativeQA and remains competitive on GutenQA,with particularly strong gains on questions that require chronology-sensitive context.These results suggest that explicitly modeling local event order is a useful retrieval signal for narrative question answering. 展开更多
关键词 Retrieval-augmented generation narrative question answering long-context reasoning temporal reasoning knowledge graphs
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Nursing Retrieval-Augmented Generation:Retrieval augmented generation for nursing question answering with large language models 认领 引用 被引量：1
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作者 Liping Xiong Qiqiao Zeng +1 位作者 Weixiang Luo Ronghui Liu 《International Journal of Nursing Sciences》 CSCD 2025年第6期516-523,I0001,共8页
Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Me... Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice. 展开更多
关键词 Evidence-based nursing Large language models Nursing knowledge base Question-answering system Retrieval-augmented generation
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A Dynamic Knowledge Base Updating Mechanism-Based Retrieval-Augmented Generation Framework for Intelligent Question-and-Answer Systems 认领 引用 被引量：2
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作者 Yu Li 《Journal of Computer and Communications》 2025年第1期41-58,共18页
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati... In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries. 展开更多
关键词 Retrieval-Augmented Generation Question-and-Answer Large Language Models Dynamic Knowledge Base Updating Mechanism Weighted Context-Aware Similarity
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Improving Clinical Support through Retrieval-Augmented Generation Powered Virtual Health Assistants 认领 引用
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作者 Biju Baburajan Anandavally 《Journal of Computer and Communications》 2024年第11期86-94,共9页
This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with p... This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery. 展开更多
关键词 Retrieval-Augmented Generation (RAG) GPT-4 Healthcare Assistants Artificial Intelligence
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Bailicai:A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications 认领 引用
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作者 Long Cui Yongbin Liu +4 位作者 Chunping Ouyang Ying Yu Jiangtao Zhang Yaping Wan Fei Yang 《Big Data Mining and Analytics》 EI CSCD 2026年第2期376-392,共17页
Large language models(LLMs)excel in various natural language processing tasks and are increasingly applied in specialized fields like medicine.However,their deployment in the medical domain is challenged by limited do... Large language models(LLMs)excel in various natural language processing tasks and are increasingly applied in specialized fields like medicine.However,their deployment in the medical domain is challenged by limited domain-specific data and the tendency to generate inaccurate information,known as“hallucinations.”While domainspecific fine-tuning has improved open-source LLMs,they still underperform compared to proprietary models like ChatGPT and PaLM.To address this gap,retrieval-augmented generation(RAG)techniques have been explored to enhance LLMs by integrating external knowledge bases.Nevertheless,the success of RAG depends on the quality of retrieved documents,and its application within the medical field remains in the early stages.In this paper,we introduce the“Bailicai”framework as an exploratory approach to integrating RAG with LLMs in the medical field.The framework employs fine-tuning to improve the RAG process,where“falsely relevant”and“completely irrelevant”interference documents are intentionally included in the training data.This enables Bailicai to develop the ability to assess the quality of retrieved documents and selectively incorporate them.The framework is organized into four modules:(1)medical knowledge injection,(2)self-knowledge boundary identification,(3)directed acyclic graph task decomposition,and(4)retrieval-augmented generation.Through the synergy of these modules,Bailicai achieves superior performance on multiple medical benchmarks,outperforming existing large models in the medical domain,RAG-based methods,and proprietary models such as GPT-3.5.Furthermore,Bailicai effectively mitigates the hallucination problem common in LLMs applied to medical tasks and enhances the robustness of RAG when dealing with irrelevant or misleading documents,enabling more accurate information retrieval and integration. 展开更多
关键词 large language models(LLMs) retrieval-augmented generation(RAG) domain-specific language models
Editorial:Special Section on Challenges and Opportunities in Retrieval-Augmented Generation for LLMs:Techniques,Trends,and Applications 认领 引用
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作者 Philip S.Yu Haofen Wang Feida Zhu 《Big Data Mining and Analytics》 EI CSCD 2026年第2期339-340,共2页
Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fun... Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fundamental challenges in knowledge-intensive tasks.This special issue serves as a dedicated platform to showcase these cutting-edge advancements.It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG. 展开更多
关键词 llms external knowledge integrating external knowledge accuracy knowledge intensive tasks artificial intelligence retrieval augmented generation generative artificial intelligence ai landscaperag
The Semantic Design Space of Retrieval-Augmented Recommender Systems:A Systematic Review of LLM-Based Approaches 认领 引用
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作者 Minhyeok Choi Imran Ahsan +2 位作者 Hyunwook Yu Taeyoung Choe Mucheol Kim 《Computers, Materials & Continua》 SCIE EI 2026年第7期77-119,共43页
Large language models(LLMs)are increasingly integrated into recommender systems to support semantic reasoning,natural language understanding,and user-adaptive personalization.However,their reliance on static parametri... Large language models(LLMs)are increasingly integrated into recommender systems to support semantic reasoning,natural language understanding,and user-adaptive personalization.However,their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments,particularly under long-tail and cold-start conditions.Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external,non-parametric knowledge sources.This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals,focusing on retrieval-augmented and LLM-enhanced recommendation.We analyze these works through a three-dimensional framework covering:(i)domain application,(ii)semantic feature and representation design,and(iii)algorithmic strategies for retrieval and personalization.The review shows that current research is concentrated in general recommendation and information retrieval,that similarityetrieval,user-item interaction,and textual content signals dominate semantic modeling,and that LLM and BERT-style encoders form the primary representation backbones,while graph-based,multimodal,and hybrid approaches remain comparatively underexplored.Algorithmically,most systems adopt generic LLM-centric modeling with limited use of retrieval optimization,reinforcement learning,or structure-aware strategies,and only sporadic attention to explicit cold-start,hallucination,and robustness treatment.By mapping co-occurrence patterns between domains,semantic features,representation choices,and strategy families,this review identifies concrete gaps and transfer opportunities for future work on retrieval-augmented recommendation and provides a structured reference for designing more context-aware,explainable,and data-efficient LLM-based recommender systems. 展开更多
关键词 Large language models(LLMs) recommender system retrieval-augmented generation(RAG) semantic features
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Retrieval-Augmented Large Language Model for AWS Cloud Threat Detection and Modelling:Cloudtrail Mitre ATT&CK Mapping 认领 引用
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作者 Goodness Adediran Kenny Awuson-David Yussuf Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第5期2307-2331,共25页
Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased th... Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased threat detection methods have been enhanced with machine learning and Large Language Models(LLMs),these approaches remain limited in addressing emerging threats.This study evaluates a two-step Retrieval Augmented Generation(RAG)approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance.The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework,AWS Threat Technique Catalogue,and threat reports to overcome limitations of static pre-trained LLMs.We constructed an evaluation dataset of 200 unique CloudTrail events(122 malicious,78 benign)using the Stratus Red Team adversary emulation framework,covering 9 MITRE ATT&CK techniques across 8 tactics.Events were sampled from 1724 total events using stratified sampling.Ground truth labels were created through systematic expert annotation with 90%inter-annotator agreement.The RAG-enabled model achieved estimated 78%accuracy,85%precision,and 79%F1-score,representing 70.5%accuracy improvement and 76.4%F1-score improvement over baseline Gemini 2.5 Pro(46%accuracy,45%F1-score).Performance are based on evaluation results on 200-event dataset.Cost-latency analysis revealed processing time of 4.1 s and cost of$0.00376 per event,comparable to commercial SIEM solutions while providing superior MITRE ATT&CK attribution.The findings demonstrate that RAG substantially enhances context-aware threat detection,providing actionable insights for cloud security operations. 展开更多
关键词 Retrieval-augmented generation Amazon web services LLM cloud service provider threat detection threat modelling MITRE ATT&CK RAG-enabled model RAG-enabled LLM system
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From Documents to Decisions:Enterprise-Grade LLM Systems for Zero-Hallucination,Attributed Generation,and Regulatory Alignment 认领 引用
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作者 Yenjou Wang Chihtan Cheng Jia-Wei Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期270-324,共55页
As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have sh... As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency,traceability,and governability.This paper systematically reviews key technological developments relevant to enterprise requirements,including document perception,retrieval-augmented generation(RAG),hybrid RAG-KG architectures,fine-grained attribution evaluation,and multi-agent coordination.The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability,but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making.In response,a risk-controlled data flywheel architecture is proposed that integrates perception,reasoning,verification,and governance layers.By converting reasoning outputs into observable risk signals and feeding them back into retrieval and structural components,this architecture establishes a continuous improvement loop.This approach provides a systematic deployment blueprint for enterprise-grade LLM systems,emphasizing traceability,accountability,and sustainable optimization in high-risk and long-term operational contexts. 展开更多
关键词 Large language models(LLMs) retrieval-augmented generation(RAG) knowledge graph(KG) optical character recognition(OCR) enterprise AI systems risk-controlled architecture governance and compliance attribution and faithfulness multi-agent systems data flywheel
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High efficiency of thalassemia prevention by next-generation sequencing:a real-world cohort study in two centers of China 认领 引用 被引量：3
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作者 Jinman Zhang Wenqian Zhang +18 位作者 Haoqing Zhang Aiqi Cai Caiyun Li Ling Liu Jufang Tan Yang Yang Wen Yuan Jing He Shiping Chen Yingli Cao Yan Zhang Jie Zhang Rui Zhou Shuai Hou Dongqun Huang Danjing Chen Zhiyu Peng Dongzhu Lei Baosheng Zhu 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第1期87-96,共10页
The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of ... The occurrence of severe thalassemia,an inherited blood disorder that is either blood-transfusiondependent or fatal,can be mitigated through carrier screening.Here,we aim to evaluate the effectiveness and outcomes of pre-conceptional and early pregnancy screening initiatives for severe thalassemia prevention in a diverse population of 28,043 women.Using next-generation sequencing(NGS),we identify 4,226(15.07%)thalassemia carriers across 29 ethnic groups and categorize them into high-(0.75%),low-(25.86%),and unknown-risk(69.19%)groups based on their spouses'screening results.Post-screening follow-up reveals 59 fetuses with severe thalassemia exclusively in high-risk couples,underscoring the efficacy of risk classification.Among 25,053 live births over 6 months of age,two severe thalassemia infants were born to unknown-risk couples,which was attributed to incomplete screening and late NGS-based testing for a rare variant.Notably,64 rare variants are identified in 287 individuals,highlighting the genetic heterogeneity of thalassemia.We also observe that migrant flow significantly impacts carrier rates,with 93.90%of migrants to Chenzhou originating from high-prevalence regions in southern China.Our study demonstrates that NGS-based screening during pre-conception and early pregnancy is effective for severe thalassemia prevention,emphasizing the need for continuous screening efforts in areas with high and underestimated prevalence. 展开更多
关键词 Thalassemia Carrier screening Next generation sequencing Rare thalassemia Clinical effectiveness Blood-transfusion-dependent
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The Agentic-AI Core:An AI-Empowered,Mission-Oriented Core Network for Next-Generation Mobile Telecommunications 认领 引用 被引量：1
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作者 Xu Li Weisen Shi +3 位作者 Hang Zhang Chenghui Peng Shaoyun Wu Wen Tong 《Engineering》 SCIE EI CSCD 2026年第1期104-119,共16页
While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easi... While the complexity of fifth-generation wireless networks is being widely commented upon,there is great anticipation for the arrival of the sixth generation(6G),with its enriched capabilities and features.It can easily be imagined that,without proper design,the enrichment of 6G will further increase system complexity.To address this issue,we propose the Agentic-AI Core(A-Core),an artificial intelligence(AI)-empowered,mission-oriented core network architecture for next-generation mobile telecommunications.In A-Core,network capabilities can be added and updated on the fly and further programmed into missions for enabling and offering diverse services to customers.These missions are created and executed by autonomous network agents according to the customer's intent,which may be expressed in natural language.The agents resolve intents from customers into workflows of network capabilities by leveraging a large-scale network AI model and follow the workflows to execute the mission.As an open,agile system architecture,A-Core holds promise for accelerating innovation and greatly reducing standard release times.The advantages of A-Core are demonstrated through two use cases. 展开更多
关键词 Sixth generation Core network Generative artificial intelligence Artificial intelligence agent
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Design of rare earth borate short-wave UV nonlinear optical crystals with strengthened second harmonic generation activities via cationic modification strategy 认领 引用 被引量：1
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作者 Huijian Zhao Jie Song +4 位作者 Shuaifeng Li Xianghao Kong Conggang Li Ning Ye Zhanggui Hu 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第6期662-666,共5页
Ultraviolet(UV)nonlinear optical(NLO)crystals have received substantial interest in advanced laser technology.However,tailoring a UV NLO material with a large second harmonic generation(SHG)response and good UV transp... Ultraviolet(UV)nonlinear optical(NLO)crystals have received substantial interest in advanced laser technology.However,tailoring a UV NLO material with a large second harmonic generation(SHG)response and good UV transparency remains a challenge.Here,inspired by the classic A3-RE2-[BO3]3 parent template,two new rare-earth borate NLO crystals,RbNa2La2(BO3)3(RNLBO-Ⅰ)and Rb0.681Na2.319La2(BO3)3(RNLBO-Ⅱ),were extracted by merging larger ionic radius cations Rb+and La3+simultaneously using a chemical substitution-oriented strategy.As expected,both compounds achieve significant enhancements in SHG activities,reaching 4.5×and 4.3×KDP,respectively,exceeding three times that of the isomorphic Na3Gd2B3O9.Notably,RNLBO-Ⅰdisplayed the highest SHG response among alkali metal RE-borate NLO crystals containing isolated[BO3]groups in the short-wave UV region.Moreover,RNLBO-Ⅰand-Ⅱdemonstrated short UV cutoff edges at 213 and 207 nm,corresponding to wide bandgaps of 5.3 and 5.6 eV,respectively.Additionally,theoretical calculations and dipole moment analysis were conducted to clarify the origin of the enhanced SHG activities of RNLBO-Ⅰand-Ⅱ.The optimal balance between SHG intensity and UV transparency in RNLBO-Ⅰand-Ⅱunderscores their potential as UV NLO candidates and offers valuable insights for fabricating new advanced UV NLO materials. 展开更多
关键词 Rare-earth borates Second harmonic generation Chemical substitution strategy Microscopic polarizability UV nonlinear optical crystals
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Multimodal Trajectory Generation for Robotic Motion Planning Using Transformer-Based Fusion and Adversarial Learning 认领 引用 被引量：1
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作者 Shtwai Alsubai Ahmad Almadhor +3 位作者 Abdullah Al Hejaili Najib Ben Aoun Tahani Alsubait Vincent Karovic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期848-869,共22页
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel... In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics. 展开更多
关键词 Multimodal trajectory generation robotic motion planning transformer networks sensor fusion reinforcement learning generative adversarial networks
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Role of trajectory symmetry in solid high-order harmonic generation 认领 引用
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作者 Shuang Wang Yu Zhao +3 位作者 Hui-Rong Wang Yun-He Xing Xiao-Xin Huo Jun Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期425-431,共7页
High-order harmonic generation(HHG)from a ZnO crystal has been investigated theoretically using a two-band model driven by a few-cycle laser pulse.We observe that harmonics in the cut-off region exhibit periodic frequ... High-order harmonic generation(HHG)from a ZnO crystal has been investigated theoretically using a two-band model driven by a few-cycle laser pulse.We observe that harmonics in the cut-off region exhibit periodic frequency shifts with changes in the carrier envelope phase(CEP)of the laser field.When the CEP of the laser pulse is an integer multiple of π,the cut-off region is dominated by even-order harmonics rather than odd-order harmonics.To illustrate the physical mechanism behind the even-order harmonics,we track the trajectories of electrons and holes between two successive halfcycles by performing time-frequency analysis and applying the recollision model.The results show that the maximum electron displacement is symmetric between successive half-cycles for odd-order harmonics.In contrast,the half-cycle symmetry of the maximum displacement is broken in the case of even-order harmonics. 展开更多
关键词 high-order harmonic generation recollision model trajectory analyses
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Data-driven intelligent analysis of hydrocarbon generation kinetics and differential characteristics during pyrolysis of coal macerals 认领 引用
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作者 Yijie Wen Zongsen Yao +5 位作者 Fan Yang Yi Cui Shouqing Yan Bin Yu Zijian Zhang Shu Tao 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2026年第2期427-447,共21页
The heterogeneity of macerals represents a key challenge to accurately evaluating the hydrocarbon generation potential of coal.Conventional methods often overlook these differences,leading to biased understanding of i... The heterogeneity of macerals represents a key challenge to accurately evaluating the hydrocarbon generation potential of coal.Conventional methods often overlook these differences,leading to biased understanding of its hydrocarbon generation characteristics.Therefore,this study integrates maceral identification,thermal simulation experiments,and machine learning algorithms to develop the extreme gradient boosting(XGBoost)prediction models for the yields of gaseous and liquid hydrocarbons.This approach enables enabling quantitative characterization of the hydrocarbon generation behavior of different macerals and identification of their primary controlling factors of coal in Xishanyao(J2x)Formation of Taibei Sag,China.The results indicate that the correlation coefficients of the prediction models for gaseous and liquid hydrocarbon yields are 0.98 and 0.78,respectively,and the difference in prediction accuracy between the two productions arises from differences in the primary controlling factors of hydrocarbon generation.SHAP and ANOVA analyses indicate that temperature is the primary controlling factor for gaseous hydrocarbon generation,whereas liquid hydrocarbon yields are synergistically controlled by temperature and macerals type.Among the macerals,sporinite is the favorable oil-prone component,while cutinite is characterized by“early oil and late gas.”Collotelinite is the principal gas-prone component,whereas collodetrinite and corpogelinite display relatively balanced potential for oil and gas.The differentiated hydrocarbon generation characteristics of the various macerals is essentially governed by differences in their molecular structures.The aliphatic chain structures primarily control oil generation,aromaticity governs gas generation,and bond types determine the distribution of the hydrocarbon generation window.Based on the above results,the study further delineates three types of favorable hydrocarbon-generating zones,namely Class Ⅰ and Class Ⅱ oil-gas co-generation zones and Class Ⅱ oil-generating zones. 展开更多
关键词 Taibei Sag Maceral Hydrocarbon generation kinetics Machine learning
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Investigating the gas generation during Li plating in fast-charging Li-ion batteries 认领 引用
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作者 Yuanmao Chen Luoyi Ding +7 位作者 Qinghui Zeng Yongteng Dong Xinyang Yue Xianping Si Donglin Zhang Qingtuan Qu Zheng Liang Wei Hao 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第7期696-701,共6页
Lithium plating and gas evolution during fast charging of graphite-based lithium-ion batteries(LIBs)are among the pivotal challenges contributing to rapid capacity loss.However,the mechanisms underlying gas generation... Lithium plating and gas evolution during fast charging of graphite-based lithium-ion batteries(LIBs)are among the pivotal challenges contributing to rapid capacity loss.However,the mechanisms underlying gas generation and corresponding mitigation strategies in electrolytes comprising mixed organic molecules and Li salts remain underexplored.Herein,we employed first-principles studies to simulate the lithiation process of electrolytes and predicted gas formation at anode interfaces with Li plating.Our results emphasize the critical role of Li salts in initiating solvent molecule decomposition and the exacerbation of interfacial degradation under conditions of elevated temperature and prolonged annealing,giving rise to the production of CO,C2H4,CH4,and H2,along with a significant increase in SEI's electronic conductivity.Moreover,our computations highlight that ethylene carbonate(EC)in commercial electrolytes is the overarching cause of interface instability and gas evolution.Experimental validations demonstrate that reducing the EC content in electrolytes results in an enhancement of the specific capacity of LiNi0.8Co0.1Mn0.1O2|graphite full cells from 158.13 m Ah/g to 182.53 m Ah/g,and an improvement in capacity retention from 72.0%to 80.4%over 130 cycling at 3 C.This research provides a theoretical framework for designing fast-charging electrolytes with stable interfaces and minimal gas generation. 展开更多
关键词 Li-ion battery Gas generation Fast charging Li plating Electrolyte engineering
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High harmonic generation in solids driven by optical skyrmions 认领 引用
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作者 Jianing Zhang Zijian Lyu +4 位作者 Xiulan Liu Guanglu Yuan Zhengliang Li Yunquan Liu Liang-You Peng 《Advanced Photonics Nexus》 CSCD 2026年第1期224-235,共12页
With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation ... With their intricate vectorial structures in space,optical skyrmions have significantly expanded the landscape of topological optics and light-matter interactions.We theoretically investigate high harmonic generation in crystals driven by optical skyrmions.We find that although the skyrmion number is not conserved,the resulting high-order harmonics can exhibit a distinctive multi-vortex structure,whose features are shaped by both the topology of the optical skyrmions and the rotational symmetry of the crystal.The position of the vortex centers can be effectively tuned by employing different types of optical skyrmions.To elucidate the underlying physics,we develop a multi-absorption channel model based on the conservation laws of spin and orbital angular momentum.Our work explores the role of optical topology in extreme nonlinear light-matter interactions,offering new opportunities for the formation and manipulation of optical vortices and novel structured light fields in the visible and ultraviolet regimes. 展开更多
关键词 skyrmions high harmonic generation optical vortex spin conservation law
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Logic-Aware Security Playbook Generation for SOAR Using Adversarial Representation Learning 认领 引用
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作者 Hangyu Hu Liangrui Zhang +4 位作者 Xiaowei Huang Xingmiao Yao Youyang Qu Xia Wu Guangmin Hu 《Computers, Materials & Continua》 SCIE EI 2026年第9期1219-1237,共19页
With the evolution of information technology toward more advanced intelligence and automation,Security Orchestration,Automation,and Response(SOAR)has become a critical foundation for security incident handling,owing t... With the evolution of information technology toward more advanced intelligence and automation,Security Orchestration,Automation,and Response(SOAR)has become a critical foundation for security incident handling,owing to its intelligent orchestration capabilities.Security playbooks,as the core mechanism for automated response in SOAR,require well-designed workflows and precise action matching to ensure efficient and accurate alert handling.However,with the rising sophistication of attacks and the expanding scale of security alerts,traditional expert-driven playbook recommendation approaches often degrade in recommendation quality or completely fail when existing playbook repositories cannot adequately cover unknown or novel alert scenarios.Generative Adversarial Network(GAN)offers a promising solution by capturing feature associations from existing playbooks and autonomously generating validated new playbooks tailored to previously unseen alert characteristics.Motivated by this,we propose a logic-aware,two-stage GAN-based playbook generation method in this paper.In the first stage,alert features are projected into a modeled playbook feature space to perform preliminary similarity matching.In the second stage,a hybrid strategy combining similarity-based recommendation and GAN-driven generation is used to produce and refine playbooks while preserving logical workflow integrity.Experimental results demonstrate that the proposed approach not only delivers high-precision playbook recommendations for known alert scenarios but also efficiently generates reliable playbooks for unseen alerts,achieving an average alert handling success rate of 86.55%,and thereby fulfilling response requirements in previously uncovered scenarios. 展开更多
关键词 SOAR security intelligent recommendation playbook generation generative adversarial network
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Measuring the Coulomb time shift in high-order harmonic generation 认领 引用
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作者 Shengjun Yue Siqi Song +3 位作者 Ruofeng Zhong Jiangkun Li Haiyuan Yu Hongchuan Du 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期61-70,共10页
High-order harmonic generation(HHG),a key nonlinear phenomenon in strong-field physics,enables ultrafast detection on the attosecond timescale.Quantifying ionizationecombination times is essential for trajectory-resol... High-order harmonic generation(HHG),a key nonlinear phenomenon in strong-field physics,enables ultrafast detection on the attosecond timescale.Quantifying ionizationecombination times is essential for trajectory-resolved highharmonic spectroscopy and for benchmarking its temporal resolution.In this review,we summarize our recent studies[Phys.Rev.A 105 L041103(2022),Phys.Rev.A 106023117(2022),Phys.Rev.A 107063102(2023),Phys.Rev.A111039902(2025)]on the role of electron-core interactions in HHG.Employing the classical trajectory model,analytical R-matrix theory,and numerical solutions of time-dependent Schrodinger equations for helium,we reveal how Coulomb attraction induces subtle shifts in ionization and recombination times.Such effects emerge as observable signatures under orthogonally polarized bichromatic fields at high probe frequencies.Because of the direct experimental relevance of these findings,this review seeks to stimulate further experimental efforts to control and resolve electron dynamics in HHG.In the future,it will be of great interest to(i)refine retrieval methods by incorporating Coulomb corrections beyond the staticfield approximation,and(ii)advance two-color detection techniques with the capability to reconstruct complete quantum trajectories in HHG. 展开更多
关键词 high-order harmonic generation ultrafast electron dynamics Coulomb effects attosecond science
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