Search Results (727)

Large language models (LLM) have achieved remarkable advances in natural-language understanding and content generation, and LLM-based agents demonstrate strong adaptability, flexibility, and robustness in handling complex tasks and enabling automated decision-making. Determining the operating mode of a power system requires repeated adjustments of boundary conditions to address violations. Conventional approaches include expert-driven power flow calculations and optimal power flow methods, the latter of which often lack clear physical interpretability during the iterative optimization process. This study proposes a novel paradigm for automated computation and adjustment of power system operating modes based on LLM-driven multi-agent systems. The approach leverages the reasoning capabilities of LLMs to enhance the adaptability of power flow adjustment strategies, while multi-agent coordination with power flow calculation modules ensures computational accuracy, enabling a natural-language-guided adaptive operational computation and adjustment process. The framework also incorporates retrieval-augmented generation techniques to access external knowledge bases and databases, further improving the agents’ understanding of system operational patterns and the accuracy of decision-making. This method constitutes an exploratory application of LLMs and multi-agent technologies in power system computational analysis, highlighting the considerable potential of LLMs to extend and enhance traditional power system analysis methodologies. Full article

Building cracks are significant indicators of structural integrity. Conventional fracture detection methodologies, however, are characterized by extended durations, significant labor requirements, and limitations in both precision and operational effectiveness. Findings are also subject to subjective and technical constraints inherent in manual assessments. To overcome these challenges, this paper introduces an enhanced YOLOv8-based methodology for developing a building crack detection system, thereby achieving high precision, operational efficiency, and cost-effectiveness. Initially, classified and segmented datasets of building fractures were obtained from field photography, online image aggregation, and open-source databases, thereby providing the basis for training the experimental model. Subsequently, the Swin Transformer window multi-head self-attention mechanism was implemented to augment small-object recognition capabilities and reduce computational demands, thereby enabling the development of an enhanced image segmentation module. Utilizing the U-Net’s segmentation capabilities, a rotated split method was implemented to quantify fracture width and derive geometric parameters from the segmented crack regions. In order to evaluate the effectiveness of the model, two experiments were conducted: one to demonstrate the performance of the classification category and the other to show the capabilities of the segmentation category. The result is that the proposed model has high accuracy and efficiency in the frac detection task. This approach effectively enhances fracture detection in structural safety evaluations of these buildings, providing technical support for relevant management decisions. Full article

Workers with upper-limb disabilities face difficulties in performing manufacturing tasks requiring fine manipulation, stable handling, and multistep procedural understanding. To address these limitations, this paper presents an integrated collaborative workcell designed to support disability-inclusive manufacturing. The system comprises four core modules: a JSON-based collaboration database that structures manufacturing processes into robot–human cooperative units; a projection-based augmented reality (AR) interface that provides spatially aligned task guidance and virtual interaction elements; a multimodal interaction channel combining gesture tracking with speech and language-based communication; and a personalization mechanism that enables users to adjust robot behaviors—such as delivery poses and user-driven task role switching—which are then stored for future operations. The system is implemented using ROS-style modular nodes with an external WPF-based projection module and evaluated through scenario-based experiments involving workers with upper-limb impairments. The experimental scenarios illustrate that the proposed workcell is capable of supporting step transitions, part handover, contextual feedback, and user-preference adaptation within a unified system framework, suggesting its feasibility as an integrated foundation for disability-inclusive human–robot collaboration in manufacturing environments. Full article

Codon optimization is widely used to improve heterologous gene expression in Escherichia coli. However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study. Full article

This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG system that integrates the LLaMA language model with the Milvus vector database, enabling efficient on device retrieval and context-grounded generation using only modest hardware resources. The system was implemented in a university-level Data Mining course and evaluated over four semesters using a quasi-experimental design with randomized assignment to experimental and control groups. Students in the experimental group had voluntary access to the RAG assistant, while the control group followed the same instructional schedule without exposure to the tool. The results show statistically significant improvements in academic performance for the experimental group, with p < 0.01 in the first semester and p < 0.001 in the subsequent three semesters. Effect sizes, measured using Hedges g to account for small cohort sizes, increased from 0.56 (moderate) to 1.52 (extremely large), demonstrating a clear and growing pedagogical impact over time. Qualitative feedback further indicates increased learner autonomy, confidence, and engagement. These findings highlight the potential of mobile RAG architectures to deliver equitable, high-quality AI support to students regardless of socioeconomic status. The proposed solution offers a practical engineering pathway for institutions seeking inclusive, scalable, and resource-efficient approaches to AI-enhanced education. Full article

Any industrial application that uses convolutional neural networks (CNNs) requires initial data and resources in order to train the models. However, the selection of models must be appropriate to the quality and quantity of the available data and computational resources. This study analyses the influence of data quantity and quality on the performance of CNN models of different complexity. Image preprocessing and image transformation data augmentation techniques are applied to generate different amounts of synthetic data with which to train the aforementioned models, shedding light on the following question: does the quality and quantity of the data or the depth of the model have more influence? Different experiments are performed using the Northeastern University (NEU) Steel Surface Defects Database, which contains surface defects found in hot-rolled steel. After analyzing the results, the authors conclude that data quality and quantity have a much greater influence than model choice. As resources and time are often limited in industry and the ultimate goal is to maximize profit by increasing efficiency, the authors encourage researchers to carefully consider the industrial application at hand and analyze the available data and resources before selecting CNN models. Full article

Background/Objectives: Obsessive–compulsive disorder (OCD) is a chronic psychiatric illness with intrusive obsessions and compulsive behaviors severely impacting daily functioning and quality of life. The purpose of this narrative review is to present an updated summary of available evidence on third-generation antipsychotics (TGAs) as augmentation strategies for SRI-refractory OCD. Methods: The literature was reviewed using the PubMed database to recognize studies on the use of TGAs in treatment-resistant OCD. Only articles in the English language and on human participants were included. Results: We included nine reports in our review. More numerous (five reports) and higher evidence-level reports were retrieved for aripiprazole, which consistently shows high response rates compared to placebo and other antipsychotics. Two cohort studies were included on brexpiprazole, with no active or placebo comparator. These showed varying but high response rates. One cohort study reported a response rate of 61.5% to cariprazine. Only one paper reported on the efficacy of lumateperone in OCD. This was a single-case report on an adolescent patient with refractory OCD responding to lumateperone monotherapy. Conclusions: The current state of evidence supports the clinical utility of TGAs, particularly aripiprazole, in augmenting SRI treatment in patients with refractory OCD. Evidence regarding cariprazine and lumateperone is scarce, but still contributes to the discussion on the use of TGAs in OCD. Full article

Informal learning in institutional settings plays a vital role in lifelong education by fostering self-directed knowledge acquisition. With the increasing integration of digital media into these environments, augmented reality (AR) has emerged as a particularly promising technology due to its ability to overlay virtual content in real-time and across multiple sensory modalities. This systematic literature review investigates the use of AR in institutional informal learning places (IILPs) from 2018 to 2025, aiming to synthesize findings across the following overall research questions: (1) In which IILP contexts has AR been implemented, and what are the characteristics of the technology? (2) What learning-relevant functions and (3) outcomes are associated with AR in these settings? (4) Which learning theories underpin the design of AR interventions? Following the PRISMA guidelines, empirical studies were identified through comprehensive database searches (Scopus, Web of Science, IEEE Xplore, FIS Bildung) and cross-referencing. Forty-four studies were analyzed via qualitative content analysis. The goal is to provide a descriptive overview of findings, patterns, and relationships. Findings indicate that AR is widely adopted across diverse domains and institutional contexts, primarily through mobile-based AR applications for K–12 learning. Native app development signals growing technological maturity. AR enhances both cognitive and emotional-motivational outcomes, though its potential to support social interaction remains insufficiently investigated. The predominant function of AR is the provision of information. Most of the examined studies are grounded in constructivist or cognitivist learning theories, particularly the Cognitive Theory of Multimedia Learning. Only limited references to emotional-motivational frameworks and minimal references to behaviorist frameworks were found. Full article

In today’s world, where the volume and variety of data are increasing at an extraordinary rate, extracting meaningful insights from data is of critical importance; however, the complexity of standard database query languages makes it difficult for users without technical expertise to access information. This study proposes an innovative Retrieval-Augmented Generation (RAG) architecture that analyzes natural language queries, identifies related database schemas, and automatically converts them to SQL. Unlike fixed schema selection (fixed-k) methods, a unique hierarchical clustering mechanism is introduced to dynamically determine the number of relevant schemas, minimizing noise. Furthermore, the architecture incorporates an iterative repair mechanism, data enrichment with sample rows, and a hybrid query strategy (Turkish + English) to overcome cross-lingual barriers. Performance evaluations on 15 databases demonstrate that the proposed method improved the schema retrieval F1 score from 0.79 to 0.88. In the SQL generation phase, the execution accuracy (EX) of the GPT-4o model increased from 0.70 to 0.78 with the proposed optimizations, representing an approximate 11% improvement relative to the baseline configuration without requiring fine-tuning. Full article

With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software development cycle. While CodeQL stands as the premier static code analysis tool currently available on the market, its high barrier to entry poses challenges for meeting the implementation requirements of shift-left security initiatives. While large language model (LLM) offers potential assistance in QL code development, the inherent complexity of code generation tasks often leads to persistent issues such as syntactic inaccuracies and references to non-existent modules, which consequently constrains their practical applicability in this domain. To address these challenges, this paper proposes CQLLM (CodeQL-enhanced Large Language Model), a novel framework for automating the generation of CodeQL security vulnerability detection code by leveraging LLM. This framework is designed to enhance both the efficiency and the accuracy of automated QL code generation, thereby advancing static code analysis for a more efficient and intelligent paradigm for vulnerability detection. First, retrieval-augmented generation (RAG) is employed to search the vector database for dependency libraries and code snippets that are highly similar to the user’s input, thereby constraining the model’s generation process and preventing the import of invalid modules. Then, the user input and the knowledge chunks retrieved by RAG are fed into a fine-tuned LLM to perform reasoning and generate QL code. By integrating external knowledge bases with the large model, the framework enhances the correctness and completeness of the generated code. Experimental results show that CQLLM significantly improves the executability of the generated QL code, with the execution success rate improving from 0.31% to 72.48%, outperforming the original model by a large margin. Meanwhile, CQLLM also enhances the effectiveness of the generated results, achieving a CWE (Common Weakness Enumeration) coverage rate of 57.4% in vulnerability detection tasks, demonstrating its practical applicability in real-world vulnerability detection. Full article

As the global population continues to expand and demand for protein increases, alternative proteins (e.g., edible insect proteins, microalgae proteins, fungal or bacterial proteins) have emerged as a significant area of research interest due to their high nutritional value and sustainability. However, these novel protein sources may contain allergenic components, such as tropomyosin and arginine kinase in insects, phycocyanin in microalgae, and ribosomal proteins in fungi, which may trigger allergic reactions and cross-reactivity with traditional allergens. In this review, we systematically retrieved published studies from databases including PubMed and Web of Science, employing keywords such as microbial proteins, edible insects, and allergenicity. Articles were screened based on their relevance to allergenic properties and processing effects, with selected studies subjected to thematic analysis. The present paper reviews the allergenic properties of edible Insects, microalgae, and microorganisms’ proteins and their molecular mechanisms, and explores the effects of various processing techniques (e.g., heat treatment, enzymatic hydrolysis, high-pressure treatment, and glycosylation) on the reduction of allergenic activity. It was determined that the impact of processing methodologies is contingent on protein structure, with certain techniques having the potential to augment sensitization through epitope exposure. Furthermore, there are still gaps in the current research on the reduction in allergenicity of microbial and algal allergens, and future research should focus on the in-depth characterization of allergenic protein structures and the development of novel sensitization reduction techniques. This review provides a significant reference point for the safe development and rational application of edible insects, microalgae, and microorganisms proteins, which is of great importance for the development of sustainable food systems. Full article

Immersive technologies such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and multisensory interaction are increasingly deployed to support the transmission and presentation of intangible cultural heritage (ICH), particularly within tourism and heritage interpretation contexts. In cultural tourism, ICH is often encountered through museums, heritage sites, festivals, and digitally mediated experiences rather than through sustained community-based transmission, raising important challenges for interaction design, accessibility, and cultural representation. This study presents a narrative review of immersive human–computer interaction (HCI) research in the context ICH, with a particular focus on tourism-facing applications. An initial dataset of 145 records was identified through a structured search of major academic databases from their inception to 2024. Following staged screening based on relevance, publication type, and temporal criteria, 97 empirical or technical studies published after 2020 were included in the final analysis. The review synthesises how immersive technologies are applied across seven ICH domains and examines their deployment in key tourism-related settings, including museum interpretation, heritage sites, and sustainable cultural tourism experiences. The findings reveal persistent tensions between technological innovation, cultural authenticity, and user engagement, challenges that are especially pronounced in tourism context. The review also maps the dominant methodological approaches, including user-centred design, participatory frameworks, and mixed-method strategies. By integrating structured screening with narrative synthesis, the review highlights fragmentation in the field, uneven methodological rigour, and gaps in both cultural adaptability and long-term sustainability, and outlines future directions for culturally responsive and inclusive immersive HCI research in ICH tourism. Full article

Deep learning applied to chest X-ray (CXR) images has gained wide attention for its potential to improve diagnostic accuracy and accessibility in resource-limited healthcare settings. This study compares two deep learning strategies for lung disease classification: a Two-Stage approach that first detects abnormalities before classifying specific pathologies and a Direct multiclass classification approach. Using a curated database of CXR images covering diverse lung diseases, including COVID-19, pneumonia, pulmonary fibrosis, and tuberculosis, we evaluate the performance of various convolutional neural network architectures, the impact of lung segmentation, and explainability techniques. Our results show that the Two-Stage framework achieves higher diagnostic performance and fewer false positives than the Direct approach. Additionally, we highlight the limitations of segmentation and data augmentation techniques, emphasizing the need for further advancements in explainability and robust model design to support real-world diagnostic applications. Finally, we conduct a complementary evaluation of bone suppression techniques to assess their potential impact on disease classification performance. Full article

The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 on the application of wearable sensor technologies and gait analysis in the early diagnosis of dementia. A targeted search of the Scopus database yielded 126 peer-reviewed studies, which were analyzed using VOSviewer for performance metrics, co-authorship networks, bibliographic coupling, co-citation, and keyword co-occurrence. The findings delineate a multidisciplinary research landscape, with major contributions spanning neurology, geriatrics, biomedical engineering, and computational sciences. Four principal thematic clusters were identified: (1) Cognitive and Clinical Aspects of Dementia, (2) Physical Activity and Mobility in Older Adults, (3) Technological and Analytical Approaches to Gait and Frailty and (4) Aging, Cognitive Decline, and Emerging Technologies. Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows. This review emphasizes the potential of sensor-derived gait metrics to augment early diagnostic protocols and advocates for interdisciplinary collaboration to advance scalable, non-invasive diagnostic solutions for neurodegenerative diseases. Full article

Background: Peripheral artery disease (PAD) represents a major global cause of mortality and disability. A primary therapeutic strategy involves promoting angiogenesis in ischemic limbs. The Xuefu Zhuyu Capsule (XFZYC) is widely used in China for treating PAD and demonstrates therapeutic potential; however, the mechanism underlying its pro-angiogenic effect remains unclear. Methods: The components of XFZYC were identified via TCMSP and HERB databases, with network pharmacology and molecular docking predicting its potential targets and pathways. For in vitro validation, drug-containing serum and blank control serum were prepared. Human Microvascular Endothelial Cells (HMEC-1) cells were treated with 1.25%, 2.5%, or 5% serum to determine the optimal concentration using tube formation assays and Western blot (WB) analysis of HIF-1α, HK2, and PFKFB3. The efficacy of XFZYC was further assessed through CCK-8, scratch wound healing, cell adhesion, and tube formation assays. Glycolytic metabolite levels and enzyme activities were measured by colorimetric assays and WB. Results: Network pharmacology screening identified 167 active components in XFZYC and 2967 potential targets. GO functional and KEGG pathway enrichment analyses suggested that XFZYC likely promotes the glycolytic pathway via the HIF-1 signaling pathway, specifically mediated by HK2 and PFKFB3. In vitro experiments confirmed that XFZYC enhanced HMEC-1 cell viability, migration, adhesion, and tube formation. Concurrently, it augmented the glycolytic capacity of HMEC-1 cells, manifested by increased glucose consumption, lactate production, enhanced activity of key glycolytic enzymes (HK, PFK, and PK), and upregulated protein expression of PFKFB3. Treatment with 3PO, a glycolytic inhibitor, significantly suppressed these drug-induced effects. Conclusions: XFZYC promotes angiogenesis in endothelial cells by modulating the glycolytic pathway, an effect primarily mediated through the upregulation of PFKFB3 expression. This study offers a preliminary exploration of the underlying mechanisms by which XFZYC may act in the treatment of PAD, thereby providing a new scientific perspective for further understanding its therapeutic effects. Full article