Search Results (6,314)

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

People living with and beyond cancer often face ongoing challenges related to nutrition, wellbeing, and long-term health. Many individuals express a need for evidence-based, tailored dietary support, yet practical approaches to sustaining healthy eating behaviours remain limited. Culinary nutrition interventions, which integrate nutrition education with hands-on culinary skills, may help address these needs; however, their effects have not been systematically synthesised. This systematic review evaluates the impact of culinary nutrition interventions, delivered alone or in combination with physical activity or mental health components, on dietary intake, psychosocial and health-related outcomes, anthropometric measures, clinical and metabolic markers, and feasibility among individuals living with or beyond cancer. Following PRISMA guidelines, 18 studies were identified across PubMed, Scopus, EMBASE, CINAHL, and Web of Science (last searched in April 2025) and narratively synthesised. A total of 1173 participants were included, with sample sizes ranging from 4 to 190 participants per intervention. Interventions were well received and rated as highly acceptable, with strong engagement and minimal adverse effects. Across studies, statistically significant improvements were reported in dietary intake (7/13 studies), quality of life (4/5), mental health (5/6), self-efficacy (2/3), symptom management (3/4), self-reported cognitive health (1/1), food-related behaviours (2/2), selected anthropometric measures (4/8), and selected metabolic biomarkers (4/6). The evidence suggests that culinary nutrition interventions hold promise as supportive, behaviour-focused strategies aligned with oncology nutrition guidelines and responsive to patient needs. However, due to heterogeneity across interventions and outcomes, and variability in methodological quality as assessed using the Cochrane risk of bias tool, quantification of effects was not possible, limiting interpretation of the evidence. Further high-quality studies using comparable outcome measures and longer-term follow-up are needed to quantify the magnitude of effects, assess their durability over time, and inform the integration of culinary nutrition programmes into cancer care. This systematic review is registered under the PROSPERO ID CRD42024567041 and was funded by the RCSI Research Summer School Fund. Full article

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition. Full article

Glioblastoma is one of the most highly aggressive types of brain tumor in adults. With limited treatment options, current therapies remain insufficient due to its invasiveness and immune evasion, highlighting the urgent need for new treatments. Bifunctional molecules targeting multiple aspects of the disease could be promising to overcome drug resistance and tumor heterogeneity. Metformin has demonstrated protective effects against brain tumors but requires high doses for efficacy, making it of great interest for molecular optimization. In this context, we synthesized a series of nine metformin–phenolic molecules, combining the metformin guanidine framework with phenolic acids, which have well-established properties in inhibiting cancer cell migration and adhesion. Their impact on cytotoxicity, reactive oxygen species inhibition, and signaling pathways was investigated for glioma cell lines and stem cells. Two of these hybrids, 5a and 5h, particularly enhanced cytotoxicity in glioblastoma cells, selectively targeting cancer cells while sparing healthy ones. Their mechanism of action differed significantly from metformin. Unlike metformin, which mainly triggers metabolic stress, the hybrids broadly inhibit RTK–MAPK–PI3K signaling, leading to cell cycle arrest and apoptosis. The results suggest that these compounds could offer a more effective and synergistic approach for glioblastoma treatment. Full article

Background/Objectives: Autologous conditioned serum (ACS) is an intra-articular orthobiologic for osteoarthritis (OA) intended to shift the joint cytokine milieu toward an anti-inflammatory, pro-regenerative profile. In the present study, we compared the molecular composition of ACS (specifically IMPACT^® ACS) from OA patients with that of healthy controls and assessed demographic and lifestyle influences on mediator levels. Methods: ACS was prepared from the whole blood of 50 OA patients and 20 healthy controls using the IMPACT^® centrifugation system (Plasmaconcept, Cologne, Germany) with glass-bead incubation and standardized handling. Cytokines, growth factors, and matrix metalloproteinases (MMPs) were quantified using multiplex immunoassays and ELISA. To account for demographic imbalances across cohorts, the primary findings were verified using age- and sex-adjusted multiple linear regression models. Results: Pro-inflammatory mediators were minimal in both cohorts, with IL-1β undetectable and IL-6 and TNF-α at very low levels. IL-1 receptor antagonist (IL-1RA) was consistently present. Notably, OA-derived ACS exhibited a catabolic shift compared to controls, characterized by significantly higher MMP-2 and MMP-3 levels. Growth factor profiling showed lower TGF-β1 and TGF-β3 in OA-derived ACS, with TGF-β2 showing no significant difference after adjustment. Exploratory stratified analyses indicated potential differences across sex, BMI, smoking status, and diet for select mediators, though subgroup sizes were limited. Conclusions: ACS prepared with a standardized IMPACT^® protocol displays a broad anti-inflammatory profile. However, increased MMPs and isoform-specific differences in TGF-β reflect a disease-associated molecular imprint. Consequently, patient-related heterogeneity supports the need for standardized reporting and motivates further research into stratified ACS therapy. Full article

Sarcopenia, defined as the progressive decline of muscle mass, strength, and function, severely compromises autonomy and quality of life in older adults. This systematic review evaluated synergistic effects of protein supplementation combined with resistance exercise on biochemical and functional biomarkers of sarcopenia. The search for scientific evidence was conducted in PubMed, Scopus, ScienceDirect, and Cochrane databases (2019–2025), applying explicit inclusion and exclusion criteria, like only randomized controlled trials in humans, published in English, Spanish, or French, were included to ensure high-quality evidence. After selection, the risk of bias of the articles was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. Seven randomized controlled trials, with a total of 260 participants, met the eligibility criteria. Interventions combining resistance exercise three times per week at 60–80% of one-repetition maximum with daily protein supplementation of at least 15 g, mainly from dairy sources, showed synergistic effects. Improvements were observed in inflammatory and anabolic biomarkers, with reductions in myostatin, activin, and IL-6, and increases in IGF-1, follistatin, and IL-10. Functional outcomes included gains in muscle strength, fat-free mass, and muscle fiber cross-sectional area. Despite heterogeneity in duration and sample size, findings support this combined approach as a promising and clinically applicable strategy to prevent and treat sarcopenia. No external funding was received, and the review is registered in PROSPERO (CRD42025640989). Full article

The considerable HIV-1 genetic diversity has several implications for viral adaptive and evolutionary capabilities. Its genetic diversity is due to its high mutational rates derived from the error-prone viral reverse transcriptase activity, which generates highly heterogeneous viral populations. Moreover, genetic diversity can also increase due to intra- or intersubtype viral genomic recombination following multiple infections. This study examines HIV-1 intersubtype recombinant viruses and their increased genomic diversity over a 12-month period in five individuals from São Paulo state, Brazil. We collected peripheral blood mononuclear cells once every three months from selected participants at five distinct visits. Molecular clones of 1.15 Kbp fragments of the Pol polyprotein, spanning the protease and a portion of the reverse transcriptase (RT) genes, were generated by bulk PCR. Pol sequences were used for evolutionary analysis, including phylogenetics (using TnT), genetic diversity (using Highlighter), and hypermutation frequency (using Hypermut). Recombination detection experiments were conducted with a jumping profile-hidden Markov model (jpHMM), SimPlot++, and RDP5. We observed great genetic diversity and frequent recombination events in all patients. Furthermore, most of the patients presented hypermutations. These findings highlight the dynamic nature of HIV-1 genetic diversity, driven by frequent recombination and hypermutation, which can accelerate viral adaptation and diversification, underscoring the challenges for treatment, prevention, and disease control. Full article

Curcumin, the major polyphenolic constituent of Curcuma longa, has been widely investigated as a hepatoprotective adjunct due to its antioxidant and immunomodulatory properties. This review evaluates the relevance of curcumin for the prevention and management of liver dysfunction and hepatitis in pigs by synthesizing available porcine evidence and integrating mechanistic insights from translational liver injury models where pig-specific data remain limited. Across experimental hepatic injury contexts, curcumin administration is most consistently associated with reduced biochemical and structural indicators of hepatocellular damage, including decreased aminotransferase activity, attenuation of lipid peroxidation, and enhancement of endogenous antioxidant defenses. These effects are mechanistically linked to suppression of pro-inflammatory signaling pathways, particularly NF-κB-related transcriptional activity and inflammasome-associated responses, together with reduced expression of key cytokines such as TNF-α, IL-1β, and IL-6. Concurrent activation of Nrf2-centered cytoprotective pathways and induction of phase II antioxidant enzymes (including HO-1, GST, and NQO1) appear to constitute a conserved axis supporting hepatic oxidative stress resilience. In swine-relevant infectious settings, available data further support antiviral activity against selected porcine pathogens, including classical swine fever virus and porcine reproductive and respiratory syndrome virus, potentially mediated through interference with lipid-dependent stages of viral replication and modulation of Kupffer cell activation. Although combination strategies with established hepatoprotective approaches are conceptually attractive, current synergy evidence remains heterogeneous and largely extrapolated. Overall, curcumin represents a plausible adjunct candidate for supporting porcine liver health; however, translation into practice will depend on resolving formulation-dependent bioavailability constraints and strengthening the pig-specific evidence base. Full article

Background: Direct oral anticoagulants (DOACs) are now the preferred anticoagulant over vitamin K antagonists for patients with atrial fibrillation (AF) and venous thromboembolism (VTE). Variability in drug exposure raises concerns about bleeding and thrombotic events, highlighting the potential value of therapeutic drug monitoring (TDM). Methods: This systematic review and meta-analysis conducted a systematic search of PubMed, Embase, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov (from inception to May 2025) and identified studies reporting DOAC levels and clinical outcomes. Two reviewers independently performed screening, data extraction, and risk-of-bias assessment (RoB 2.0, Newcastle–Ottawa Scale). Random-effects meta-analytical models generated pooled estimates, with meta-regression exploring potential sources of variability (DOAC type, drug levels) and exposure–response relationships. Results: Nineteen studies comprising 5770 patients were included in the review. The pooled event rates were 8% for major bleeding (95% CI: 0.05–0.11), 7% for thrombotic events (95% CI: 0.05–0.09), and 3% for mortality (95% CI: 0.03–0.04). Heterogeneity was substantial for bleeding and thrombotic events (I² = 95.6% and 87.3%, respectively) but negligible for mortality (I² = 0%). Meta-regression analyses showed no significant association between mean DOAC concentration and either major bleeding (β = −0.00021, p = 0.35, Adj R² ≈ 0%) or thrombotic events (β = 0.00005, p = 0.78, Adj R² ≈ 0%), indicating that variations in measured plasma levels did not meaningfully explain event rate differences across studies. Conclusions: In this systematic review and meta-analysis, measured DOAC concentrations show limited and inconsistent association with clinical outcomes. While the present synthesis does not demonstrate a statistically robust linear correlation between DOAC plasma concentrations and adverse outcomes, it highlights the multifactorial determinants of bleeding and thrombosis risk underscores the potential value of selective TDM in individualized care. Further prospective, standardized studies are needed to define clinically actionable thresholds and to validate TDM-guided strategies that optimize the delicate balance between safety and efficacy in DOAC therapy. Full article

The expansion of renewables in modern power systems and the coordinated development of upstream and downstream industrial chains are promoting a shift on the utility side from traditional settlement by energy toward operation driven by data and models. Industrial electricity consumption data exhibit pronounced multi-scale temporal structures and sectoral heterogeneity, which makes unified long-term load and generation forecasting while maintaining accuracy, interpretability, and scalability a challenge. From a modern system identification perspective, this paper proposes a System Identification in Adaptive Bayesian Framework (SIABF) for medium- and long-term industrial load forecasting based on daily freeze electricity time series. By combining daily aggregation of high-frequency data, frequency domain analysis, sparse identification, and long-term extrapolation, we first construct daily freeze series from 15 min measurements, and then we apply discrete Fourier transforms and a spectral complexity index to extract dominant periodic components and build an interpretable sinusoidal basis library. A sparse regression formulation with ${\ell }_1$ regularization is employed to select a compact set of key basis functions, yielding concise representations of sector and enterprise load profiles and naturally supporting multivariate and joint multi-sector modeling. Building on this structure, we implement a state-space-implicit physics-informed Bayesian forecasting model and evaluate it on real data from three representative sectors, namely, steel, photovoltaics, and chemical, using one year of 15 min measurements. Under a one-month-ahead evaluation using one year of 15 min measurements, the proposed framework attains a Mean Absolute Percentage Error (MAPE) of 4.5% for a representative PV-related customer case and achieves low single-digit MAPE for high-inertia sectors, often outperforming classical statistical models, sparse learning baselines, and deep learning architectures. These results should be interpreted as indicative given the limited time span and sample size, and broader multi-year, population-level validation is warranted. Full article

This study investigates the sustainable reuse of industrial enamel frit production waste generated during enamel application processes and evaluates its potential from a process-oriented glass-forming and -shaping perspective. Enamel frit waste collected from an industrial production line in Türkiye was subjected to comprehensive characterization, including XRD, XRF, TG/DTA, dilatometry, and CIE Lab* color analysis, with the primary aim of assessing forming compatibility rather than final product performance. Following calcination and controlled fritting, the waste material was processed through mold-based glass-forming experiments using firing schedules derived from thermal analysis. The results reveal pronounced chemical and thermal heterogeneity among enamel frit production wastes, leading to variable melting behavior across samples. Nevertheless, selected waste compositions exhibited sufficient viscous flow for shaping at reduced firing temperatures of approximately 850 °C. This study demonstrates that selected enamel frit production wastes—obtained from industrial enameling processes in slurry, powder, or granular form—can be reshaped into glass forms under controlled low-temperature conditions. The novelty of this study lies in investigating industrial enamel production frit waste as a reusable material within a circular economy framework, specifically focusing on its application in mold-based glass forming for artistic and educational contexts, thereby fostering collaboration between industrial waste management and glass art practice. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

Gadolinium-based contrast agents (GBCAs) are vital for glioma imaging yet pose safety, cost, and accessibility issues; predicting contrast enhancement from non-contrast MRI via machine learning (ML) provides a safer, economical alternative, as enhancement indicates tumor aggressiveness and informs treatment planning. However, scanner and population variability hinder robust model selection. To overcome this, a stability-aware framework was developed to identify reproducible ML pipelines for predicting glioma contrast enhancement across multicenter cohorts. A total of 1367 glioma cases from four TCIA datasets (UCSF-PDGM, UPENN-GB, BRATS-Africa, BRATS-TCGA-LGG) were analyzed, using non-contrast T1-weighted images as input and deriving enhancement status from paired post-contrast T1-weighted images; 108 IBSI-standardized radiomics features were extracted via PyRadiomics 3.1, then systematically combined with 48 dimensionality reduction algorithms and 25 classifiers into 1200 pipelines, evaluated through rotational validation (training on three datasets, external testing on the fourth, repeated across rotations) incorporating five-fold cross-validation and a composite score penalizing instability via standard deviation. Cross-validation accuracies spanned 0.91–0.96, with external testing yielding 0.87 (UCSF-PDGM), 0.98 (UPENN-GB), and 0.95 (BRATS-Africa), averaging ~0.93; F1, precision, and recall remained stable (0.87–0.96), while ROC-AUC varied (0.50–0.82) due to cohort heterogeneity, with the MI + ETr pipeline ranking highest for balanced accuracy and stability. This framework enables reliable, generalizable prediction of contrast enhancement from non-contrast glioma MRI, minimizing GBCA dependence and offering a scalable template for reproducible ML in neuro-oncology. Full article

Object detection systems deployed in industrial environments are often constrained by limited annotation budgets, severe class imbalance, and heterogeneous visual conditions. Active learning (AL) aims to reduce labeling costs by selecting informative samples; however, existing strategies struggle to simultaneously ensure robust performance, rare-class coverage, and stability under realistic industrial constraints. In this work, we propose a rarity-aware, stratified AL framework for industrial object detection that explicitly aligns sample selection with class imbalance and annotation efficiency. The method relies on a composite image-level score that jointly captures model uncertainty, informativeness, and complementary diversity cues, while adaptively emphasizing rare classes. Crucially, a stratified querying mechanism is introduced to explicitly regulate class-wise sample allocation during selection, playing a key role in improving performance stability and rare-class coverage under severe imbalance, without sacrificing global informativeness. The proposed approach operates purely at the data-selection level, making it detector-agnostic and directly applicable to modern object detection pipelines. Experiments conducted on two real-world industrial datasets involving lobster and snow crab parts, using YOLOv10 and YOLOv12, demonstrate improved training stability and annotation efficiency across balanced, imbalanced, and noisy settings over multiple active learning cycles up to 15% labeled data. Complementary comparisons with fully supervised training further show that using only 45–65% of the labeled data is sufficient to retain more than 97% of full-supervision mAP@50 and over 90% of mAP@50:95. Full article

Deep reinforcement learning (DRL) has been widely adopted to solve decision-making problems in complex environments, demonstrating high performance across various domains. However, DRL-based FPS agents are typically trained with a traditional, monolithic policy that integrates heterogeneous functionalities into a single network. This design hinders policy interpretability and severely limits structural flexibility, since even minor design changes in the action space often necessitate complete retraining of the entire network. These constraints are particularly problematic in game development, where behavioral characteristics are distinct and design updates are frequent. To address these issues, this study proposes a Modular Reinforcement Learning (MRL) framework. Unlike monolithic approaches, this framework decomposes complex agent behaviors into semantically distinct action modules, such as movement and attack, which are optimized in parallel with specialized reward structures. Each module learns a policy specialized for its own behavioral characteristics, and the final agent behavior is obtained by combining the outputs of these modules. This modular design enhances structural flexibility by allowing selective modification and retraining of specific functions, thereby reducing the inefficiency associated with retraining a monolithic policy. Experimental results on the 1-vs-1 training map show that the proposed modular agent achieves a maximum win rate of 83.4% against a traditional monolithic policy agent, demonstrating superior in-game performance. In addition, the retraining time required for modifying specific behaviors is reduced by up to 30%, confirming improved efficiency for development environments that require iterative behavioral updates. Full article