Search Results (760)

Recent evidence links lens epithelial cell (LEC) dysfunction and cellular senescence—an irreversible cell cycle arrest with a pro-inflammatory secretory phenotype—to age-related cataract (ARC) progression. This systematic review synthesizes current knowledge on LEC senescence, its molecular features, and laboratory methods for senescence assessment in the ARC. Following PRISMA guidelines, a comprehensive search of PubMed, Scopus and Cochrane databases retrieved 3417 records from inception to 9 February 2025, with 14 studies ultimately included (821 patients and multiple in vitro LEC models). The following multiple senescence expression pathways were identified: SA-β-gal activity, p53/p21 and p16INK4A pathway activation, mitochondrial dysfunction, oxidative stress, and secretion of senescence-associated secretory phenotype (SASP) factors. Notably, cortical cataract demonstrated direct association with local senescent cell accumulation, while nuclear cataract reflected cumulative oxidative damage from impaired LEC-mediated antioxidant defense. Senescence markers correlated positively with cataract severity across multiple studies. Several potential therapeutic targets emerged, including metformin (AMPK activation/autophagic restoration), circMRE11A silencing, NLRP3 inflammasome inhibition, and modulation of FYCO1/PAK1 and MMP2 pathways. This review establishes LEC senescence as a central process in ARC pathogenesis and highlights promising senotherapeutic approaches. Future research should prioritize human surgical samples, develop standardized senescence detection panels (SA-β-gal + p21/p16 + SASP factors), and conduct longitudinal studies to establish causal relationships between senescence accumulation and cataract progression. Full article

In recent years, Large Language Models (LLMs) have demonstrated strong capabilities in contextual reasoning and knowledge retrieval. However, their application in industrial domains is limited by concerns regarding data security, reliance on cloud infrastructure, and high operational costs. To address these challenges, this study proposes the use of the Model Context Protocol (MCP) as a middleware framework that enables the deployment of offline-operable Small Language Models (SLMs) for industrial data processing. MCP facilitates structured interaction between SLMs and external resources (e.g., databases, APIs, and processors), allowing secure and controlled data access without exposing proprietary systems. As illustrated in the proposed framework, user input is first processed by the SLM (Qwen-7B) for intent determination. When external data is required, MCP coordinates the invocation of relevant resources and integrates the returned results into the model. The SLM then generates the final response. This approach enables SLMs to perform local computation for contextual analysis and decision support while maintaining low computational requirements and full data locality. The proposed system eliminates dependence on cloud-based LLM services and enhances security and cost efficiency. Experimental results demonstrate that the MCP-based architecture provides a practical and effective solution for deploying intelligent assistants in industrial environments without relying on large-scale external AI services. Full article

Oxidative stress from exogenous insults is a major driver of skin aging and hyperpigmentation. Plant-derived bioactive compounds represent promising multifunctional agents with protective effects on skin. They meet the demand for natural, safe skin-protective agents with well-defined action mechanisms. However, current studies lack an integrated understanding of their dual cellular protective mechanisms: antioxidation and autophagy. A unified “component–pathway–efficacy” regulatory network remains lacking, which limits mechanistic insights into skin protection. To address this gap, this comprehensive narrative review retrieved literature from four authoritative databases: PubMed, Web of Science, Scopus, and Wiley Online Library. With targeted keyword retrieval, 129 core studies published between 2021 and 2025 were selected for synthesis. The selection was based on relevance, methodological rigor, and scientific impact. This review constructs a novel “antioxidation–autophagy” synergistic regulatory model. It also establishes a consolidated dual-mechanism framework outlining the “component–pathway–efficacy” axis. This framework reduces knowledge fragmentation across natural product research, skin biology and translational molecular biology. This work integrates the dual protective mechanisms of plant-derived bioactive compounds for skin protection and translational applications. It provides a theoretical basis for understanding their molecular regulatory logic and facilitates further mechanistic studies and translational research on skin protection. Full article

The operation and maintenance (O&M) management of prefabricated buildings often struggles with fragmented knowledge and low reusability, relying predominantly on expert experience. While large language models (LLMs) offer a potential solution, their inherent hallucination issues significantly hinder practical application. To address these issues, this study proposes a knowledge base-augmented OM-GPT for prefabricated buildings O&M, built on a hybrid architecture that combines domain-specific fine-tuning with graph-based retrieval-augmented generation (GraphRAG). Specifically, it first fine-tuned the LLM Qwen2.5 using specialized O&M data to enhance its understanding of O&M tasks. It then constructed a multi-relational knowledge graph within a GraphRAG framework to effectively mitigate model hallucinations. Experimental results demonstrate that the Fine-Tuned Model achieved excellent Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores, validating the success of domain adaptation. In a five-dimensional evaluation, knowledge base-augmented OM-GPT significantly outperformed both GPT-4 and DeepSeek. Furthermore, two-way ANOVA tests confirmed the model’s advantages generalize across all five evaluation dimensions. Full article

Amid the wave of digital transformation and customer service automation, traditional chatbots are increasingly challenged by their inability to handle unstructured data and complex queries. This issue is particularly critical in the import trade industry, where customer service representatives must respond promptly to diverse inquiries involving quality anomalies, order tracking, and product substitution. Existing rule-based or keyword-driven chatbots often fail to provide accurate responses, resulting in reduced customer satisfaction and increased operational burdens. This study proposes and implements a “Retrieval-Augmented Generation (RAG)-based Customer Service Chatbot,” integrating the RAG framework with a Neo4j-based knowledge graph, specifically tailored for the import trade domain. The system constructs a dedicated QA dataset, knowledge graph, and dynamic learning mechanism. It semantically vectorizes internal documents, meeting records, quality assurance procedures, and historical dialogues, establishing interrelated knowledge nodes to enhance the chatbot’s comprehension and response accuracy. The study also incorporates GPT-based response evaluation and a high-score caching strategy, enabling dynamic learning and knowledge enhancement. Experiments were conducted using 101 representative enterprise-level queries across six categories, reflecting real-world operational scenarios and inquiry needs. The results demonstrate that the combination of knowledge graphs and RAG technology effectively reduces AI hallucinations and improves response coverage and accuracy, thereby addressing complex problems in customer service applications. This paper not only presents a feasible AI implementation model for the import trading industry but also offers a practical architectural reference for domain-specific knowledge management in the import trade and allied sectors. Full article

The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in customer-facing roles, their potential to enhance internal operational efficiency remains largely underexplored. This study presents the design and evaluation of an intelligent assistant specifically for travel agency operations, built upon a Retrieval-Augmented Generation (RAG) architecture using Gemini 2.0 Flash. The system integrates heterogeneous data sources, including structured product catalogs and unstructured documentation processed via Optical Character Recognition (OCR), into a unified interface comprising work assistance, interactive training, and evaluation modules. Results demonstrate information retrieval times not greater than 45 s, ensuring its daily usability, while maintaining 95% accuracy. Furthermore, the system democratizes tacit senior expertise and accelerates new employee onboarding. This research validates RAG architectures as a powerful solution to knowledge fragmentation, shifting the strategic AI focus from customer automation to employee empowerment and operational optimization. Full article

Patents are essential carriers of technological innovation, and their efficient transfer is critical for accelerating technological iteration in the lithium battery industry and supporting sustainability in the new energy sector. However, existing patent recommendation methods lack frameworks for handling heterogeneous enterprise demands, which limits the accuracy of supply–demand matching. This study proposes a knowledge graph-based differentiated patent recommendation framework for enterprise technological demands in the lithium battery domain. A five-element content framework—material, method, efficacy, product, and application—is constructed from both the supply and demand sides. Enterprise demands are classified into complete and incomplete types based on element coverage, and patent supply knowledge graphs are built for potentially relevant patents. Two differentiated recommendation methods are then developed. For complete demands, the Precision Recommendation Method for Complete Technological Demands integrates BERT-based semantic encoding, TransE-based structural modeling, and RAG-based constraint retrieval to achieve precise matching under full element coverage. For incomplete demands, the Fuzzy Recommendation Method for Incomplete Technological Demands incorporates multi-source enterprise data to enrich demand categories and constructs augmented query contexts to generate diversified candidate patent sets. Empirical validation based on 25 demand-driven patent transfer cases shows that the PR-CTD method exactly identifies the actual transferred patents in three cases. The FR-ITD method ranks 6 out of 14 actual transferred patents within the Top-5 results, while the remaining cases are all within the Top 13. These results demonstrate the effectiveness of the proposed framework in real-world patent transfer scenarios. This study provides a novel theoretical perspective for the structured modeling of heterogeneous technological demands and supply–demand semantic matching. It also offers practical value by improving the efficiency of patent retrieval and matching, thereby supporting patent technology transfer in the lithium battery industry. Full article

The coordination of multiple unmanned aerial vehicles traditionally relies on pre-defined control strategies and complex programming implementations, making adaptation to dynamic environments and tasks challenging. The purpose of this study is to explore intent-driven control supported by large language models to address these limitations. The codified objective is to develop a framework capable of interpreting high-level human intent and automatically translating it into executable control instructions for vehicle swarms. As a first approach to the methodology, we present a dual-layer intent-driven cooperative control framework that separates cognitive planning from real-time execution. The design tools include a hierarchical interface, standardized application programming interfaces, retrieval-augmented generation for incorporating domain knowledge, and multimodal prompt engineering to process natural-language instructions and sensor data into Python code. The main findings demonstrate that this framework achieves high code-generation accuracy in typical scenarios, enhances programming efficiency compared to manual methods, and enables adaptive optimization of cooperative strategies through the monitoring of emergent behaviors. In summary, this study contributes an intent-driven solution that simplifies the programming complexity of cooperative swarm control, lowering the technical barrier for deploying advanced autonomous aerial systems. Full article

The rapid evolution of the Open Radio Access Network (O-RAN) architecture and the exponential growth in specification complexity create significant barriers for researchers translating 5G standards into practical implementations. Existing evaluation frameworks for large language models, such as ORAN-Bench-13K, focus predominantly on the theoretical comprehension of regulatory documents while neglecting the critical aspect of software execution. This disparity results in a profound semantic gap, defined here as the structural and conceptual misalignment between abstract normative requirements and their concrete realization in the source code of open platforms like srsRAN. To bridge this divide and enable advanced cognitive reasoning, this paper presents a Hybrid Retrieval-Augmented Generation (RAG) framework designed to unify two heterogeneous knowledge domains: the O-RAN/3GPP specification corpus and the srsRAN C++ codebase. The proposed architecture leverages a hierarchical Parent–Child Chunking strategy to preserve the structural integrity of complex code and normative protocols. Additionally, it introduces a probabilistic Semantic Query Routing mechanism that dynamically selects the relevant context domain based on query intent. This routing actively mitigates semantic interference—a phenomenon where merging conflicting cross-domain terminology introduces informational noise, which our baseline tests showed degrades response accuracy by 4.7%. Empirical evaluation demonstrates that the hybrid approach successfully overcomes this, achieving an overall accuracy of 76.70% and outperforming the standard RAG baseline of 72.00%. Furthermore, system performance analysis reveals that effective context filtering reduces the average response generation latency to 3.47 s, compared to 3.73 s for traditional RAG methods, rendering the framework highly suitable for real-time telecommunications engineering tasks. Full article

Cybersecurity operations in IoT and edge environments require fast, evidence-grounded decisions under strict resource and trust constraints. While large language models can support triage and incident analysis, their parametric knowledge may be outdated and prone to hallucination. Retrieval-augmented generation (RAG) improves grounding by conditioning responses on retrieved evidence, but also introduces new risks such as knowledge-base poisoning, indirect prompt injection, and embedding leakage. Federated learning enables collaborative adaptation without centralizing sensitive data, motivating federated RAG (FedRAG) architectures for distributed cybersecurity deployments. This study presents a deployment-oriented scoping review of FedRAG for cybersecurity. The review follows PRISMA-ScR reporting guidance and synthesizes 82 studies published between 2020 and 2026, identified through keyword search and citation snowballing over OpenAlex, arXiv, and Crossref. We develop a taxonomy that clarifies the components of federated systems, deployment locations, trust boundaries, and protected assets. We further map the combined RAG+FL attack surface, summarize practical defenses and system patterns, and distill actionable guidance for secure, privacy-preserving, and efficient FedRAG deployment in real-world IoT and edge scenarios. Our synthesis highlights recurring trade-offs among robustness, privacy, latency, communication overhead, and maintainability, and identifies open research priorities in benchmark design, governance mechanisms, and cross-silo evaluation protocols for practical deployment. Full article

Retrieval-augmented generation (RAG) grounds large language models (LLMs) with external evidence. Dynamic knowledge tasks, however, require systems to decide not only what to retrieve but also when to refresh, how to arbitrate conflicts, and how to preserve an auditable record of the evidence used to answer a query. We present LedgerRAG, a trigger-aware retrieval chain framework that maintains an explicit claim-level evidence ledger and uses coverage, temporal validity, authority, and conflict signals to control retrieval, refresh, and stopping decisions. We expand the evaluation with a query-level BM25 baseline, a dense retriever setting, and task-aligned proxy baselines representing graph-style retrieval, temporal-only retrieval, and conflict-focused retrieval. The revised results show that LedgerRAG’s clearest advantage lies in conflict governance and auditable evidence control, achieving near-perfect ConFLICT adjudication (CRAcc = 0.993) under authority-aware routing while yielding more modest gains and explicit trade-offs in regulation-change and streaming settings. Full article

We present a Retrieval-Augmented Generation (RAG)-based question-answering system for nuclear energy science communication, characterizing retrieval quality in generated responses. The system introduces a dual-similarity analysis that jointly measures (i) question-to-context (Q→C) and (ii) answer-to-context (A→C) semantic consistency, serving as “retrieval-side semantic alignment signal” and “post-generation semantic alignment indicator” respectively. Built with LangChain, FAISS retrieval, and a large language model, our pipeline separates offline indexing from online inference and is grounded on authoritative Taiwanese Nuclear Safety Commission documents. We evaluate two settings: (a) in-domain prompts derived from the corpus and (b) out-of-domain, randomly generated nuclear energy questions. Results show that generated answers are, on average, more semantically similar to retrieved contexts than the original questions under the present setup, while the overall association between retrieval-side and answer-side signals remains stronger in the in-domain setting. Out-of-domain questions show weaker but still observable answer-to-context alignment patterns, contingent on corpus overlap. These findings suggest that combining RAG with dual-similarity analysis offers a practical and audit-oriented approach for educational Q&A, and we discuss potential improvements in versioned regulations, re-ranking, and abstention strategies. In this study, the RAG technique and dual-similarity analysis are combined together to promote nuclear energy knowledge. The research flow chat of this study can be applied to many other fields of scientific knowledge. Full article

Construction sites implement various safety management activities, including toolbox meetings, risk assessments, and safety knowledge assessments, to reduce accidents. Multiple-choice question (MCQ)-based assessments are widely used to evaluate worker safety competencies. However, the effectiveness of MCQ assessments depends critically on distractor quality; incorrect options must be plausible enough to challenge uninformed respondents while remaining clearly distinguishable from knowledgeable ones. Manual distractor creation requires substantial expertise and is prone to inconsistency, whereas large language models (LLMs) often generate options that lack domain relevance. This paper proposes context-aware multipath adaptive safety scoring (CoMPASS), an algorithm that integrates construction safety domain knowledge with LLM capabilities for MCQ distractor generation. CoMPASS operates through two pathways: CoMPASS-H leverages a hierarchical hazard factor ontology for hazard identification questions, whereas CoMPASS-R uses hybrid retrieval-augmented generation (RAG) for risk control questions. An evaluation using 50 real construction accident cases with a robotic assessment test (RAT) using frontier LLMs as virtual examinees demonstrated that CoMPASS-R achieved a 90% quality pass rate, whereas all baseline methods failed to meet the composite quality criteria. The proposed framework provides a scalable approach to generating assessment content that supports effective safety management at construction sites. Full article

Advances in Generative Artificial Intelligence have enabled the development of models capable of generating text, images, and audio that are similar to what humans can create. These models often have valuable general knowledge thanks to their training on large datasets. Through fine-tuning or prompt-based adaptation, this knowledge can be applied to specific tasks. In this work, we propose a modular approach to automated news generation using Large Language Models, composed of an information retrieval module and a text generation module. The proposed system leverages both publicly available (open-weight) and proprietary Large Language Models, enabling a comparative evaluation of their behavior within the proposed news generation pipeline. We describe the experiments carried out with a total of five representative Large Language Models spanning both categories, detailing their configurations and performance. The results demonstrate the feasibility of using Large Language Models to automate this task and identify systematic differences in behavior across model categories, as well as the problems that remain to be solved to enable fully autonomous news generation. Full article

When LLMs are applied in the veterinary field, they often produce serious hallucinations and logical restrictions, especially in the accurate diagnosis of bovine disease, where accuracy is crucial. To meet this challenge, this paper proposes GraphRAG-Vet, a Knowledge Graph Retrieval-Augmented Generation framework specifically designed for the dairy industry. First, we constructed a domain knowledge map comprising 2500 elements and 3000 relationships, covering high-frequency diseases in cows such as mastitis and ketosis. Second, the semantic-to-password parsing module is designed to retrieve disease symptom subgraphs from the Neo4j database accurately. Finally, the hard constraint injection mechanism is introduced to force LLMs to generate diagnoses strictly in accordance with the retrieved graph context, thereby implementing the “refuse to answer” function for foreign queries. The experimental results showed that GraphRAG-Vet achieved 100% accuracy in diagnosing core infectious diseases and had an almost-zero hallucination rate compared with baseline LLMs. This study provides a reliable, low-resource solution for automated veterinary consultation. Full article