Search Results (3,074)

Inflammatory bowel disease (IBD) poses a significant global health burden with rising incidence, particularly in Asia. This study employed an integrative network pharmacology approach combined with molecular docking to elucidate the therapeutic mechanism of ganoderic acid A (GAA) against IBD. Potential GAA targets were retrieved from pharmacogenomic databases, while IBD-related genes were curated from OMIM and GeneCards databases. Weighted gene co-expression network analysis of IBD transcriptomic datasets (GSE38713, GSE126124) identified disease-associated modules, with the yellow module exhibiting the strongest positive correlation. Functional enrichment analyses demonstrated significant involvement of overlapping targets in lipid metabolism, the inflammatory response, and the mitogen-activated protein kinase (MAPK) signaling cascade pathway. We identified 14 IBD-GAA-ferroptosis-related genes and 54 key module genes. Intersection analysis revealed 5 overlapping targets, including tumor necrosis factor-α(TNF-α), peroxisome proliferators-activated receptor γ (PPARγ), MAPK14, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic α (PIK3CA), and Caspase 3 (CASP3). Molecular docking confirmed high-affinity binding of GAA to these targets, with binding energies ranging from −7.3 to −10 kcal/mol. Crucially, experimental evaluation demonstrated the pivotal role of GAA in alleviating disease pathology. GAA treatment suppressed the significantly elevated levels of TNF-α and p-MAPK14 in the organoids using a cytokine/LPS-induced IBD model. These findings collectively suggest a potential involvement of GAA in pathways associated with ferroptosis regulation, although direct experimental evidence for ferroptosis markers remains to be established. The observed multi-target effects on immune regulation and cellular proliferation/differentiation provide a foundation for further mechanistic investigation. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

The aim of this article is to identify how research on smart buildings has evolved in the context of the energy transition, with particular emphasis on energy flexibility, grid interaction, market integration, and policy barriers. The study addresses a gap in previous reviews, which have often focused on individual technological domains, building automation, or smart-readiness assessment, while paying less attention to the conditions under which smart buildings become active energy-system resources. A systematic review protocol based on the PRISMA logic was combined with bibliometric mapping and qualitative synthesis. Bibliographic data were retrieved from Scopus on 28 February 2026 and covered 663 English-language journal articles published between 2015 and February 2026. A core set of 63 studies was selected through explicit cluster-based and relevance-based criteria for in-depth qualitative synthesis. The results show a gradual shift from component-level efficiency research towards system-level studies in which smart buildings are analyzed as flexible demand-side assets, distributed energy nodes, and participants in emerging market mechanisms. At the same time, the evidence base remains uneven: many studies rely on simulation or case-specific modeling, while empirical validation, interoperability, occupant behavior, business models, and regulatory implementation remain less mature. The article contributes by distinguishing observed bibliometric patterns from conceptual interpretation and by integrating technological, economic, behavioral, and regulatory evidence into a framework explaining the persistent implementation gap in smart building deployment. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

The gut microbiota and its metabolites, as components of the gut–bone axis, play a pivotal role in regulating skeletal homeostasis through the bidirectional communication network. In this systematic review, evidence was collected from mainstream databases following standardized inclusion/exclusion criteria for screening, to comprehensively retrieve and screen eligible studies from multiple mainstream databases according to standardized inclusion and exclusion criteria, and systematically summarize current research progress on plant-derived bioactive compounds targeting the gut–bone axis for skeletal health regulation. This review systematically explores the underlying mechanisms of the gut–bone axis and critically evaluates the regulatory effects and therapeutic potential of plant-derived bioactive compounds. Particular attention is given to targeted interventions involving prebiotics, probiotics, synbiotics, and plant-rich diets or functional foods. Among these interventions, synbiotics represent the most successful strategy and show the most prominent therapeutic possibilities in bone-related disorders. Different from single prebiotics (only nourish endogenous intestinal microbes), individual probiotics (easy to be degraded in gastrointestinal tract with poor colonization) and ordinary plant-rich diets (unfixed effective dosage and weak targeting property), synbiotics combine prebiotic carriers and viable probiotic strains to produce complementary advantages, which is the core reason for its outstanding therapeutic prospect against bone diseases. Synbiotics exert synergistic effects on gut microecology, mineral absorption, and immune regulation, leading to more robust and consistent improvements in bone health than single prebiotics, probiotics, or general plant-rich diets. They have been verified in preclinical and clinical studies to ameliorate osteoporosis and related skeletal diseases via the gut–bone axis. These strategies offer novel insights into the prevention and treatment of bone metabolic disorders, such as osteoporosis, by targeting the gut–bone axis with phytochemicals. Key outcomes of this review include that synbiotics, soy isoflavones, naringin, curcumin, and resveratrol effectively improve bone mineral density, restore gut microbiota balance, and inhibit pathological bone resorption via the gut–bone axis. Collectively, the above bioactive substances realize bone protection mainly by reshaping gut flora, elevating mineral uptake and suppressing excessive osteoclast activity. Representative cases include soy isoflavones mitigating estrogen-deficient bone loss in OVX models, naringin improving the trabecular microarchitecture, and probiotic BL-11 promoting longitudinal bone growth in children. Future directions will focus on clarifying dose–response relationships, developing standardized synbiotic formulations, constructing microbiome-guided precision diets, and conducting large-sample randomized controlled trials to translate plant-derived compounds into clinical therapies. Full article

Artificial intelligence (AI) has reshaped higher education over six decades, evolving from drill-and-practice programs to adaptive cognitive tutors and, most recently, transformer-based generative models. This article presents a critical review of publisher-built generative AI assistants, adopting an explicitly socio-technical perspective that combines a technological lens with a pedagogical one. It makes three contributions. First, it synthesizes the technical and algorithmic evolution of educational AI, from rule-based and expert systems through knowledge tracing and learning analytics to large language models and retrieval-augmented generation, and organizes these mechanisms into a taxonomy. Second, it introduces a reproducible evaluation framework that couples the TRIAD rubric (Trust, Relevance, Impact, Adoption, and Design) with a Jobs-to-Be-Done (JTBD) lens, complete with anchored scoring criteria, an evidence-and-confidence grading scheme, and reported inter-rater reliability. Third, it applies the framework to eleven assistants released by U.S. publishers, distinguishing peer-reviewed evidence from institutional reports and commercial claims. The analysis reflects a mid-2025 snapshot and is presented as a reusable template rather than a static ranking. Findings reveal substantial variation in privacy safeguards, curricular alignment, documented impact, adoption, and usability. The review identifies application scenarios and recommendations for researchers and institutional leaders seeking to guide the responsible integration of AI in higher education. Full article

Reasoning-intensive multimodal retrieval suffers from a counter-intuitive bottleneck: on MM-BRIGHT multimodal-to-text (Query+Image → Documents), the strongest dense multimodal encoder reaches only 27.6 nDCG@10 and the rest of the dense vision–language retrievers cluster between 10.0 and 23.0. The visual signal, encoded as a dense vector, adds noise rather than evidence; even augmenting strong text retrievers with raw image captions degrades performance by up to 12.0 points. We propose VISA, a Visual Symbolic Agent that re-casts multimodal-to-text as text retrieval over three parallel streams. A Vision LLM is dispatched in three roles via separate prompts: a zero-shot router that classifies the query image into up to three parser types from a fixed taxonomy of nine (chart, circuit, equation, screenshot, code, figure, diagram, map, photograph); typed parsers that extract structured text per type; and a holistic captioner. The agent constructs three text streams (raw query, query ⊕ symbolic, query ⊕ caption), scores each with a single frozen 4B-parameter retrieval LLM, and fuses the per-document scores via Reciprocal Rank Fusion or a confidence-weighted linear combination. The whole agent contains no trainable parameters. The key novelty is a change of substrate: rather than projecting the query image into a dense multimodal vector that competes with text, VISA is, to our knowledge, the first retrieval system to convert the image into typed symbolic text and keep retrieval entirely text-side, so that a frozen text retriever can match the literal tokens (axis values, variable names, function signatures) that answering documents actually contain. Across all 29 MM-BRIGHT multimodal-to-text domains, VISA achieves 32.4 nDCG@10, an absolute improvement of +4.8 over the strongest dense multimodal encoder and substantially larger margins over the remaining six dense vision–language baselines. Per-domain analysis shows VISA maintains its margin across STEM and software domains where image content is structure-heavy. In practical terms, VISA is training-free and model-agnostic: it requires no fine-tuning, reuses any off-the-shelf vision LLM and text retriever, caches all per-image parsing so re-runs cost only three query encodes, and can therefore be dropped into an existing text-retrieval stack to add reasoning-intensive multimodal capability without building or training a multimodal encoder. Full article

Industrial robot after-sales question answering often depends on multimodal evidence, such as error screenshots, interface displays, and wiring diagrams, which are difficult for conventional text-based retrieval-augmented generation (RAG) systems to exploit effectively. To address this issue, we design a dual-channel multimodal RAG system that converts image content into retrievable textual knowledge through the collaboration of optical character recognition (OCR) and structured semantic description. In the proposed system, OCR is used to extract explicit textual cues, such as error codes, parameter fields, and interface prompts, while expert-authored semantic descriptions complement implicit visual evidence, including device parts, fault phenomena, and contextual scene information. The transformed knowledge is further integrated into a hybrid retrieval pipeline that combines dense retrieval and BM25, followed by Reciprocal Rank Fusion (RRF) and Maximal Marginal Relevance (MMR) reordering to improve both relevance and contextual diversity. Experiments on a real-world industrial robot after-sales dataset show that the proposed method achieves an overall question-answering accuracy of 87.9%, outperforming the LLM-only baseline by 35.6 percentage points. For image-related questions, accuracy improves from 46.7% to 83.3%. These results indicate that the proposed framework provides a deployment-friendly and interpretable system-level alternative to end-to-end multimodal model fine-tuning for industrial after-sales question answering. Full article

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has collected tens of millions of spectra, providing an unprecedented resource for large-scale spectroscopic studies. Efficient retrieval techniques are therefore essential for exploring such massive datasets. Existing approaches often rely on predefined templates or manually labeled training samples, which can limit their applicability in large and diverse spectral archives. In this work, we present a general similarity-retrieval framework that combines self-supervised contrastive learning based on a convolutional neural network with Facebook AI Similarity Search (FAISS) for efficient large-scale spectral retrieval. The framework learns spectral representations directly from unlabeled data and enables flexible retrieval from user-defined wavelength regions based on feature similarity. We evaluate the framework on several stellar populations in LAMOST DR8. For late-type M8-star retrieval, 90.5% of the top 1000 retrieved spectra are later than M6. For M0–M5 giants, the mean retrieval accuracy across six subtypes reaches 94.8%. Using a C-H star spectrum as the query spectrum, 90.8% of the top 1000 retrieved candidates are classified as carbon stars by the LAMOST pipeline. Cross-matching with SIMBAD further confirms 255 C-H stars and 47 C-R stars among the retrieved candidates. These results demonstrate that the proposed framework can efficiently identify spectrally similar objects across large spectroscopic databases and can serve as a useful tool for searching for rare or spectrally distinctive stellar populations. Full article

This study investigates the role of the semantic layer as the contextual foundation of Retrieval-Augmented Automated Assessment, examining how its architecture and semantic extraction mechanism contribute to the reliability, validity, and consistency of automated assessment relative to expert judgment across diverse learning task types. The proposed system is implemented as an integrated architecture combining the Moodle e-learning platform, the n8n orchestration layer for workflow automation, and a dedicated semantic layer responsible for learning context extraction. Moodle manages assignments, rubrics, and student submissions, while automated workflows process the data and activate the Retrieval-Augmented Generation (RAG) mechanism to retrieve relevant learning content and perform automated evaluation. The experimental study was conducted on a purpose-built Moodle environment, designed to ensure full control over assessment processes, event logging, and AI system integration. Results for the full sample yielded a Spearman’s rank correlation coefficient of ρ = 0.874, indicating a very strong positive correlation between expert and automated rankings. Task-level analysis further revealed that system performance is closely tied to the degree of structure and formalizability of the learning tasks: lower agreement was observed for open-ended and interpretive tasks, while structured and logically defined tasks yielded results comparable to expert assessment. These findings provide preliminary evidence that, within the tested course context, the proposed methodology is especially suitable for tasks with clearly formulated criteria and a consistent logical structure, while also identifying directions for future improvement in more subjective and context-dependent assessment scenarios. Full article

Radiometabolic therapy is a mechanistically plausible but clinically underused strategy in lymphoma. Its rationale is based on the selective delivery of cytotoxic radiation to malignant lymphoid cells through antibodies, peptides, or small molecules directed against tumor-associated targets. Radioimmunotherapy with anti-CD20 agents, including 90Y-ibritumomab tiuxetan and 131I-tositumomab, demonstrated meaningful efficacy in B-cell non-Hodgkin lymphoma, particularly in indolent and relapsed/refractory settings. However, despite encouraging clinical results, its use progressively declined because of logistical, regulatory, commercial, and multidisciplinary barriers. More recently, renewed interest has emerged with the development of novel antibody–radionuclide conjugates and radioligand-based theranostic strategies targeting CD22, CD37, CD45, and CXCR4. Among these, CXCR4-directed imaging and therapy with 68Ga-pentixafor and 177Lu/90Y-pentixather illustrate image-guided patient selection and targeted radionuclide treatment in advanced hematologic malignancies. This narrative review summarizes evidence retrieved from Scopus and PubMed on radiometabolic therapy in lymphoma, with particular attention paid to established radioimmunotherapy, emerging targets, radioligand therapy, dosimetry, toxicity, and combination strategies with chemotherapy, immunotherapy, and hematopoietic stem cell transplantation. Available evidence supports the plausibility and possible clinical utility of these approaches, but remains heterogeneous and, for several newer targets, preliminary. Future development will require prospective trials, standardized imaging-based selection, individualized dosimetry, and integration within multidisciplinary lymphoma treatment pathways. 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

Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This study presents the design, implementation, and evaluation of an integrated mobile application and a hybrid Hospital Information Exchange (HIE) system to enhance healthcare accessibility and service coordination for PWDs. The platform integrates a user-centered mobile application (iOS and Android) with a hybrid data exchange architecture (MedEx Hybrid) combining an application programming interface (API) and Message Queuing Telemetry Transport (MQTT). This enables real-time and on-demand data exchange while accommodating hospitals with limited infrastructure. Key functionalities include disability registration, emergency medical service (1669) integration, appointment management, rights notification, service location mapping, teleconsultation, and peer communication. Deployment across 159 hospitals nationwide demonstrates system scalability and interoperability. The system supports secure access to electronic medical records and enables emergency responders to retrieve patient information during SOS events, improving continuity of care. Findings confirm the feasibility of the proposed system and its potential to support inclusive digital health and national healthcare interoperability. Full article