Search Results (674)

In partially observable adversarial environments, heterogeneous unmanned aerial vehicle (UAV) swarms must generate tasks online from noisy observations while respecting platform capabilities, consumable resources, and structural dependencies among tasks. This paper proposes a task graph generation method that converts local observations, target beliefs, and UAV resource states into executable task graphs with explicit resource semantics and inter-task relations. The method first constructs a sufficiently expressive candidate task graph in the belief and resource spaces. An offline search teacher then evaluates future trajectory particles, resource feasibility, and structural interaction values to produce supervision for node selection, marginal task value, and relation prediction. A relation-biased graph attention network learns to generate task graphs online, and a task manager further performs task filtering, dependency repair, conflict completion, and resource checking. Simulation results under complex observation pressure and unseen adversarial strategies show that the proposed method consistently improves structural generation quality and execution feasibility. Compared with Graphormer, it improves the task-graph utility, task-edge F1-score, and executable-graph ratio by 5.83%, 5.41%, and 2.68%, respectively, while reducing the infeasible-task ratio by 35.14%. These results indicate that combining an offline search teacher with resource-constrained graph modeling provides an effective front-end task organization mechanism for heterogeneous UAV swarm planning. Full article

Background/Objectives: This review aimed to provide an updated synthesis of the evidence on the effects(s) of grapes, blueberries, and their constituent bioactives on cardiovascular health. Cardiovascular disease remains one of the most prevalent non-communicable diseases globally. Methods: A systematic literature search was conducted using PubMed, Science Direct, and Semantic Scholar. Eligible publications were restricted to studies published since 2015 focusing on grapes, blueberries, and related bioactives. A total of 37 studies were included (17 meta-analyses/systematic reviews and 20 randomised controlled trials). Compositional data on polyphenols, anthocyanins, and stilbenes (including resveratrol) from a new hybrid variety of red-fleshed table grape were also discussed in context. Results: The evidence indicates that grape- and blueberry-derived bioactives, particularly polyphenols and resveratrol, produce modest but consistent improvements in cardiovascular risk markers, particularly endothelial function. Effects were more pronounced in higher-quality trials and in metabolically at-risk populations. Certain varieties, including red-fleshed table grapes (red berry grapes), may represent effective dietary sources of these bioactives. Conclusions: Cardiovascular disease remains a common public health challenge. Increasing attention is being given to dietary and lifestyle strategies for its prevention and management. Within this context, and alongside existing recommendations to increase fruit and vegetable intake, there is scope for more specific guidance emphasising the consumption of dark-pigmented grapes, berries, and red-fleshed table grapes abundant in bioactives such as polyphenols. Full article

Dynamic hospital corridor environments are characterized by complex corridor environments, diverse target-scale variations, frequent occlusions, and dense small-object distribution, posing significant challenges to the accuracy and efficiency of the existing methods on resource-constrained platforms. To effectively address these challenges, a high-precision framework CSAWOA (Cross Search Adaptive Whale Optimization Algorithm)-YOLOv8 (You Only Look Once version 8) model for complex medical environments was introduced in this work. By jointly modelling high-level semantic information and low-level cues such as texture and colour, the proposed model achieved a more discriminative and informative feature representation. The T-CBS (Transformer-Convolutional Bottleneck Structure) module, capable of extracting shallow-level features and integrating global contextual information to address target occlusion issues, was also proposed. Furthermore, the integration of the BiFormer module yielded an enhanced feature discriminability, improving small-target recognition while reducing sensitivity to background noise. The classification function was modified, effectively solving the problem of class imbalance in complex corridor environments. The combination of these two concepts achieved an effective balance of diversity in detection and convergence speed, leading to improved optimization performance and greater resistance to local-optimum stagnation. Meanwhile, an improved version of the WOA was developed, termed CSAWOA, enabling automatic hyperparameter optimization for the improved YOLOv8 model. From the experimental results, improvements of 4.9%, 6.1%, and 8.3% in mAP, precision, and recall, respectively, compared to YOLOv8 were demonstrated, while also exhibiting better generalization. Overall, the proposed method provides a reliable and efficient approach for object detection in complex hospital corridors, offering a valuable foundation for future research and real-world healthcare applications. Full article

A Retrieval-Augmented Generation (RAG) approach is widely used as a key element for intelligent assistants. However, the knowledge extraction stage from technical text corpora is fraught with difficulties due to the presence of highly specialized terminology, tables, and abbreviations. The goal of this study is to develop methodological support for knowledge extraction for an intelligent assistant for a technical specialist in the field of microcontroller-based device design. This study systematically compares and analyzes the computational performance of knowledge extraction methods and their various combinations. The results showed that the hybrid version of the baseline methods (hybrid_v2_dense) provides the best R@1 (45.2%), MRR@5 (49.8%) and nDCG@5 (52.0%) values, while the R@5 level remains comparable to BM25. Among the extended configurations of the hybrid_v2 family, the best R@5 value (57.7%) is achieved by the hybrid_v2_dense_splade method, while the best values of R@1 (48.9%), MRR@5 (52.1%), and nDCG@5 (53.7%) are achieved by the hybrid_v2_dense_unicoil method. Based on the obtained results, an expert decision tree was formed for selecting the knowledge extraction module configuration considering hardware limitations. These results provide experimental evidence of the effectiveness of the developed methodological support for knowledge extraction for an intelligent assistant of a technical specialist. Full article

As the global population continues to age, mental health issues such as depression, anxiety, stress, loneliness, and social isolation among older adults are receiving increasing attention. The built environment is closely associated with older adults’ daily mobility, environmental perception, social participation, and mental health and well-being, but the evidence remains heterogeneous across spatial contexts, environmental indicators, and study designs. Previous umbrella reviews have summarized broad links between the built environment and healthy aging, but less attention has been paid to recent original empirical studies published after the COVID-19 pandemic, the distinction between objective environmental exposure and subjective environmental perception, and the role of social participation as a pathway linking environmental conditions to mental health and well-being. This study employs a systematic literature review approach, searching and screening peer-reviewed empirical studies published between 2015 and January 2026 that focus on the associations between the built environment and older adults’ mental health and well-being. PubMed, Scopus, and Web of Science databases were used for searching, supplemented by manual searching. After title and abstract screening and full-text evaluation, a total of 60 studies were included. Subsequently, a comprehensive analysis was conducted on aspects such as research design, spatial scale, environmental indicators, types of mental health outcomes, and potential pathways of action. In this review, core mental health and well-being outcomes included negative outcomes, such as depression, anxiety, stress, psychological distress, loneliness, and social isolation, and positive outcomes, such as life satisfaction, subjective well-being, psychological well-being, and mental well-being. Social participation was examined as a behavioral and psychosocial pathway rather than as a core outcome. Emerging methods, including street-view image analysis, FCN-based semantic segmentation, and XGBoost-SHAP, were examined because they can refine environmental exposure measurement and support variable-importance interpretation, rather than because they provide causal evidence. The main synthesis suggests that several built environment factors are associated with older adults’ mental health and well-being, although the strength and consistency of evidence vary across outcome types, spatial contexts, and study designs. (1) Exposure to green and blue spaces, quality of public open spaces, walkability and accessibility, accessibility of neighborhood facilities and services, housing and living conditions, and positive environmental perception are mostly associated with lower levels of depression, anxiety, stress, and loneliness, as well as higher levels of life satisfaction, subjective well-being, and psychological well-being. (2) Conversely, adverse environmental exposures such as proximity to roads, pollution, non-vegetated spaces, and high-intensity urbanization are more likely to exacerbate negative psychological outcomes. Existing evidence also suggests that social participation is one of the important behavioral pathways through which the built environment is linked to the mental health of older adults, but it is not the only mechanism. (3) In addition, the direction and intensity of environmental associations remain heterogeneous under different spatial scales, indicator types, and research methods. Overall, this review contributes by organizing recent empirical evidence into a built environment–social participation–mental health and well-being framework, while emphasizing that most findings should be interpreted primarily as evidence of association rather than as stable or uniform causal effects. Full article

The increasing flow of documents in digital libraries, archives and electronic document management systems makes the standardization, adaptation and automation of the process of creating metadata an urgent scientific problem. Metadata directly affects the efficiency of document search, identification, semantic interpretation, long-term storage and intersystem exchange. However, while standardized description based on MARC21, a flexible approach to creating a dynamic field, and intelligent methods based on deep learning, cover these requirements separately, the issue of their full integration into a single methodological system has not been sufficiently resolved. In this study, an integrated hybrid model for describing electronic documents based on standardized, flexible, and intelligent metadata was proposed. A mixed electronic document corpus of 1500 documents was formed for evaluation. The corpus consisted of books, dissertations, scientific articles, archival documents, and heterogeneous electronic documents, with 300 samples selected from each group. Key metadata elements for each document were manually identified and used as ground truth. According to experimental results, the MARC21-based constructor achieved 96.8% structural compatibility and 95.6% metadata completeness, but the average description time was 6.8 min. The dynamic field approach achieved 93.4% structural compatibility and 94.1% metadata completeness, and reduced the description time to 4.1 min. The deep learning-based intelligent module achieved a structural matching score of 91.7%, a metadata extraction score of 93.8% F1, and reduced the processing time to 1.9 min. The proposed hybrid model achieved a structural matching score of 95.9%, a metadata F1 score of 95.1%, and an average description time of 2.3 min. The results showed that the hybrid model is a balanced solution between metadata quality, flexibility, and automation. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Conversational search systems (CSSs) are emerging as a transformative interface for health information seeking, enabling multi-round, natural language interactions that integrate diverse medical resources. This scoping review synthesizes evidence on the capabilities, limitations, applications, and future directions of CSSs in healthcare. Following PRISMA-ScR guidelines, we systematically searched multidisciplinary databases (2010–2025), screened 3789 records, and included 325 studies addressing CSSs in health contexts. Analysis identified six thematic domains: (1) capabilities and limitations, (2) enhancement methods, (3) clinical applications, (4) trust, user experience, and interaction design, (5) readability, health literacy, and patient communication, and (6) cross-lingual and domain-specific adaptation. Findings show CSSs offer advantages in personalization, structured output, and patient education, but face challenges in accuracy, timeliness, and semantic consistency, particularly in high-risk clinical decision-making. Enhancement strategies such as retrieval-augmented generation (RAG), knowledge graphs (KG), fine-tuning, and composite approaches improve performance, while trust-building requires transparency, empathy, and ethical safeguards. Cross-lingual disparities and cultural adaptability remain critical gaps. Overall, CSSs hold substantial potential to improve health information access and literacy, but safe, equitable, and culturally sensitive integration demands multidimensional optimization in knowledge updating, bias control, and interaction design, alongside clinician oversight, to ensure reliability and maximize public health impact. Full article

Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems. Full article

Background/Objectives: This study aimed to comparatively analyze online information regarding dental implants and dentures utilizing text-mining techniques. Methods: An automated text-mining program was employed to collect and process data using the Korean keywords for “implant” and “denture.” Data sources included major search engines, social networking services, and YouTube (Google LLC, Mountain View, CA, USA). A total of 9941 data points for dental implants and 9783 for dentures were retrieved. The analytical approach included word cloud generation, term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, and sentiment analysis. Results: For implants, “dental clinic,” “treatment,” “surgery,” and “insurance” emerged as highly relevant keywords. In contrast, queries regarding dentures frequently included the term “implant,” alongside top-ranking, age-related terms such as “abnormality” and “discomfort.” TF-IDF analysis revealed that “surgery” and “procedure” ranked higher for implants, whereas “insurance” ranked higher for dentures. Sentiment analysis indicated a predominantly positive public perception of implants (63.09% positive, 36.91% negative), whereas dentures elicited a largely negative sentiment (40.70% positive, 59.30% negative). Conclusions: The text-mining analysis revealed distinct public perceptions regarding the two treatments. Implants were primarily associated with surgical procedures and positive sentiments, whereas dentures were more closely linked to insurance considerations and negative experiences. Full article

To address the challenges of small foreign matter size, severe occlusion, and complex backgrounds in Pu-erh ripe tea processing, this study drew inspiration from primate visual mechanisms and proposed an improved YOLOv13-based network, AE-YOLOv13-S. To mitigate loss of fine details, the weakening of discriminative features, and the frequent occurrence of missed and false detections, the Adaptive Sparse Self-Attention Network was introduced to optimize the backbone of the network, inspired by the sequential cognitive pattern of primates involving target search, local verification, selective integration, and final decision making. To address insufficient long-range semantic associations and the submergence of fine-grained differences in background noise, Emulating Self-Attention with Convolution was employed to optimize part of the Conv modules of the network, drawing on the hierarchical information processing mechanisms of primates from peripheral perception to central fine analysis. In response to the limitations of bounding boxes, such as approximate target enclosure, the large amount of geometric supervision noise, the obvious localization deviation, and delayed model convergence, a Scale-based Dynamic Loss, inspired by primate visual perception mechanisms, was introduced to optimize the network’s loss function. The results showed that, during training, compared with the baseline, AE-YOLOv13-S achieved lower training loss values: Box Loss declined by 6.76%, Cls Loss by 6.52%, and DFL Loss by 8.65%. On the validation dataset, the model demonstrated reductions of 6.58%, 16.39%, and 8.33% for these respective metrics. After the overall improvements, AE-YOLOv13-S achieved increases of 1.43, 4.85, and 2.69 percentage points in precision, recall, and mAP@50, respectively, with only a 0.3 G increase in FLOPs. The improved model can classify and detect foreign matter in Pu-erh ripe tea efficiently and accurately, providing not only a new technical pathway for foreign matter detection in tea processing but also a practically meaningful technical solution for intelligent quality control and food safety assurance in the tea processing chain. Full article

Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a scoping review, conducted in line with PRISMA-ScR guidance, that maps how integrated care models have evolved conceptually, what digital and AI-enabled infrastructures support them, how their clinical, economic, and equity impacts can be evaluated, and what current implementations imply for sustainable scaling. We searched PubMed, Scopus, Semantic Scholar, and Crossref (retrieval date 31 October 2025; forward screening to 31 March 2026) and added grey literature from named policy bodies. The searches identified 15,189 records, reducing to 11,789 after intra- and cross-source deduplication and grey-literature integration; 620 full texts were assessed and 192 were included in the synthesis. Four domains were synthesised: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance foundations. We chart the field using a Type I–V maturity scheme (disease, cohort, whole-system, digital-integrated, learning), benchmarked against the Rainbow, MacColl, EMRAM/AMAM, and NHS ICS models. Most deployments cluster at digitally integrated but only weakly adaptive Type IV; recurrent failure modes—temporal blind spots, maintenance debt, semantic drift, and governance gaps—block progression to Type V, and high-profile clinical-AI failures illustrate the cost of attempting Type V analytics on Type IV-or-worse infrastructure. A walk through nine world regions maps each to its current Type I–V position and shows that organisational and payment integration—not digital sophistication alone—is currently the dominant driver of progress. The COMFORTage Integrated Care Model Library is positioned as a workflow of AI agents orchestrating predictive, preventive, and personalised care across the integrated-care lifecycle rather than as a single federated-learning programme. The review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, accountable governance, and outcome-based contracting for clinically useful, equitable, and trustworthy learning systems. Full article

Camouflaged object detection (COD) is important for intelligent UAV monitoring and search-and-rescue operations. However, existing benchmarks focus primarily on natural camouflage, creating a noticeable domain shift for specific applications such as the search and rescue of individuals visually similar to their surroundings due to their clothing. To investigate this shift, we introduce CPD-UAV, a benchmark comprising 1061 high-resolution images with detailed pixel-level annotations across diverse terrains and flight altitudes. Benchmarking of seven state-of-the-art models on this dataset reveals specific challenges. Specifically, the scale variations and “vanishing boundaries” inherent in aerial perspectives can lead to boundary localization inaccuracies. Furthermore, this evaluation observes the deceptive nature of traditional metrics, such as Mean Absolute Error (MAE), when targets occupy small image proportions. To address the degradation of weak target signals during feature integration, we propose a lightweight, plug-and-play component: the Residual Gated Alignment Module (RGAM). RGAM handles scale variations by establishing semantic anchors in deep network layers, mitigating signal dilution and highlighting micro-targets against complex backgrounds. By integrating RGAM into three representative baselines, we demonstrate that the enhanced architectures achieve a competitive performance level. Quantitative results show consistent improvements in structural integrity (structure-measure, $S_m$) and boundary localization. Ultimately, this work provides a practical data platform and an effective algorithmic solution for advancing aerial monitoring systems. Full article

Composed Image Retrieval (CIR) aims to search a target database for images that best align with a user’s intent, conditioned on a reference image paired with modification requirements. Existing CIR architectures typically treat the visual reference and the textual modification as symmetrical inputs, fusing their features into a shared latent embedding space. From a user-centric perspective, however, these multi-modal inputs serve fundamentally asymmetric roles: the reference image acts as a soft semantic anchor, whereas the modification text functions as an explicit requirement specifying precise visual changes. Because current models optimize composition predominantly at a global representation level, these non-negotiable logical constraints are frequently violated during inference, leading to retrieval results that fail to satisfy the user’s explicit instructions. To mitigate this limitation, we introduce a novel, training-free verification framework for fashion retrieval that enforces textual constraint adherence without sacrificing the expressive flexibility of open-vocabulary natural language. Our approach leverages schema-conditioned large language models to extract explicit, structured logical constraints from raw queries during post-processing. A downstream vision-language agent subsequently verifies these constraints against the top retrieved candidate pool to penalize non-compliant images and optimize candidate ordering. Extensive evaluations across standard fashion benchmarks demonstrate that our plug-in framework consistently and significantly enhances the recall metrics of state-of-the-art supervised and zero-shot CIR baselines. Full article

This review offers an in-depth look at the diagnostic and therapeutic potential of MNPs as superparamagnetic and high-surface-area-to-volume entities, considering their applications in MRI, magnetic hyperthermia, and targeted drug delivery. Based on an integrative approach, which includes systematic searches in 3 main bibliographic databases, 870 articles, semantic network analysis, Retrieval-Augmented Generation (RAG), and gap classification (Miles’ taxonomy), our analysis identifies a constant gap between lab performances and in vivo applications, described through eight critical challenges. The development of MNP-based biotechnologies is largely hindered by open issues in terms of safety, standardization, and control of the nanobio interface, mainly incomplete physicochemical characterization and poor methodological harmonization, because the high sensitivity of MNPs to synthesis routes and scale is a major bottleneck for GMP-compatible translation. Moreover, the analysis of in vivo data suggests that, on average, less than 1% of the injected dose accumulates in solid tumors, whereas a substantial fraction is diverted to non-target organs, particularly those associated with the mononuclear phagocyte system, reinforcing concerns regarding off-target sequestration, incomplete clearance, and long-term safety. Other critical challenges include complex interactions with biofluids, lack of unifying conceptual frameworks, limited experimental validation, underexploited methodological integration, and geographical and biological biases. Consequently, successfully overcoming these challenges will require the early and deliberate integration of rigorous materials engineering, mechanistic biological insight, and application-oriented validation for robust, reproducible, and translatable magnetic nanoplatforms. Full article