Search Results (502)

Individuals may be prone or resistant to the development of type 2 diabetes. The basis for susceptibility is in part genetic, but environmental factors are likely to come into play. The gut microbiome stands at the interface of genetics and the host microenvironment. Its role in mediating susceptibility to diabetes, however, has not been resolved. Here we investigated whether the gut microbial composition contributes to susceptibility to diabetes, as distinct from disease development. We hypothesized that distinct microbial signatures modulate sensitivity or resistance to a diabetogenic diet (DD) and that separate signatures are linked to disease development. To test this hypothesis, we studied the Cohen diabetic rat model, comprising a diabetes-sensitive strain (CDs/y) and a diabetes-resistant strain (CDr/y). When exposed to DD, diabetes develops in CDs/y but not in CDr/y rats; on a regular diet (RD), both strains remain metabolically normal. To establish the contribution of the gut microbiome to susceptibility, we studied the fecal microbial composition in young, metabolically healthy CDs/y and CDr/y rats, using 16S rRNA gene sequencing, measures of α- and β-diversity, and differential taxonomic abundance. We found distinct, strain-specific gut microbiota profiles that differentiated diabetes-sensitive from -resistant animals, indicating an association between microbial composition and susceptibility. To test causality, we co-housed sensitive and resistant animals to allow passive microbial cross-transfer and fed the animals with DD. Co-housing led to partial convergence of microbial communities and significantly attenuated the diabetic phenotype in CDs/y rats, supporting a contributory and causal role for the gut microbiome in modulating sensitivity to diabetes. The resistance phenotype, on the other hand, remained unchanged. To distinguish between the contribution of the gut microbiome to susceptibility to diabetes as opposed to the development of the disease, we studied the gut microbial profiles across strains after feeding with DD or RD and the development of diabetes in CDs/y but not in CDr/y. We found distinct taxonomic signatures that differentiated diabetic from non-diabetic animals. These findings demonstrate that the gut microbiome contributes to susceptibility to diabetes with separate pathways from those linked to the development of diabetes and may represent an important modifiable determinant of diabetes risk and a target for early intervention. Full article

Background: The widespread use of Social Networks (SNS), particularly among youths, could promote Feeding and Eating Disorders (FEDs), but could also be a tool for implementing FED prevention strategies. This study aimed to identify which SNS could be most effective for implementing primary and secondary FED prevention. Methodology: A cross-sectional study was conducted via an Italian population-based survey, distributed using a snowball sampling strategy. The survey included 283 participants aged 18–35 by using the Bergen Social Media Addiction Scale (BSMAS), the SCOFF screening tool for FEDs, items from the Body Uneasiness Test (BUT), and the Mukbang Addiction Scale (MAS). Results: The sample was predominantly female (69.3%). Participants screening positive on the SCOFF were more frequently TikTok users. Stepwise logistic regression analysis showed that TikTok use was associated with SCOFF positivity (OR = 1.9) and body image concerns (e.g., spending a lot of time in front of the mirror; OR = 1.9). Instagram use was associated with body image dissatisfaction (OR = 3.9). In the overall sample, the likelihood of screening positive on the SCOFF was associated with TikTok use (OR = 1.7), higher BSMAS scores (OR = 1.1), exposure to body positivity/neutrality content (OR = 1.9), and watching Mukbang videos (OR = 1.8). Conclusions: TikTok and, to a lesser extent, Instagram appear to be widely used by young individuals vulnerable to FEDs and body image dissatisfaction. These platforms may therefore represent strategic channels for delivering educational and preventive interventions targeting eating behaviors and body image among young people. Further longitudinal research is needed to clarify causal relationships and evaluate the effectiveness of SNS-based prevention strategies. Full article

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

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article

Cross-institutional collaborative fraud detection is essential for combating increasingly sophisticated financial fraud, yet privacy regulations and data silos severely constrain knowledge sharing among institutions. This study aims to develop a privacy-preserving framework that enables effective collaborative fraud detection while protecting raw data, with particular emphasis on exploiting symmetry properties in federated architectures and graph topology analysis. We propose an Adaptive Federated Graph Attention Network (FedGAT), which employs spatio-temporal graph attention mechanisms to capture topological structures and dynamic fraud patterns within institutional transaction networks. The framework introduces a symmetric similarity matrix derived from graph topological features, where the symmetry property ($s_{ij}=s_{ji}$) ensures consistent and unbiased measurement of structural relationships between any pair of institutions. Based on this symmetric similarity metric, an adaptive weighted aggregation mechanism is designed for cross-institutional parameter fusion, enabling balanced knowledge transfer that respects the symmetric collaborative relationship among participating institutions. The symmetric information exchange protocol between local institutions and the central server further guarantees equitable contribution and benefit distribution throughout the federated learning process. The framework is evaluated on the Elliptic Bitcoin transaction dataset and the IEEE-CIS fraud detection dataset, with recall rate and false positive rate as primary performance metrics. Results show that FedGAT achieves a recall of 0.85 and a false-positive rate of 0.038 in single-institution detection, representing approximately 40% and 70% improvements over existing methods, respectively. In collaborative detection across five virtual institutions, the symmetry-aware adaptive aggregation mechanism enables all participants to achieve performance gains exceeding 15% while completely eliminating negative transfer effects observed in simple averaging approaches. This work contributes a novel symmetry-based federated learning framework that balances privacy protection with detection performance, advancing the literature on cross-institutional financial risk management. Full article

As the exponential growth in advanced compute workloads drives intra-datacenter interconnects to ever increasing bitrates, optical networking equipment has risen to the challenge by shifting from NRZ signaling to bandwidth efficient modulation methods such as PAM4. As these modulation schemes introduce an inherent SNR penalty, maintaining low bit error rates (BER) forces optical links to operate at significantly higher optical powers. However, increasing the optical power leads to photodetectors reaching one of their fundamental bottlenecks caused by the space-charge effect, limiting their ability to provide a high-speed response under high-power illumination. This work presents the design, fabrication, and characterization of a waveguide-integrated photodiode with dual optical inputs (DIPD) designed to overcome this limitation. Specifically, we demonstrate that combining a dual-fed architecture with targeted cross-sectional geometric optimizations effectively distributes the photocurrent density to delay the onset of space-charge saturation. Experimental validation demonstrates a high responsivity of ≈0.91 [A/W] (for O-band wavelengths) and a large electro-optic bandwidth (EOBW) of ≈58 [GHz], all under high-power illumination and CMOS driving voltages. Full article

Background/Objectives: Federated learning has emerged as a promising paradigm for enabling collaborative artificial intelligence in healthcare while preserving data privacy. However, most existing frameworks focus on isolated tasks and lack integrated pipelines that combine classification, segmentation, explainability, and automated clinical reporting. Methods: This study proposes FedIHRAS, a privacy-preserving federated learning framework designed for multi-institutional radiological analysis. The system integrates multi-task deep learning modules, including pathology classification using a modified ResNet-50 backbone, anatomical segmentation, explainability through Grad-CAM, and automated report generation supported by semantic aggregation using SNOMED CT. The framework employs confidence-weighted aggregation, differential privacy mechanisms, and secure aggregation protocols to ensure privacy and robustness across heterogeneous institutional datasets. Results: Experimental evaluation was conducted across four large-scale chest X-ray datasets representing simulated institutional nodes, totaling approximately 874,000 images. FedIHRAS achieved high diagnostic performance with strong cross-institutional generalization and demonstrated improved robustness under non-IID data distributions. Additional experiments showed favorable communication efficiency, effective privacy–utility trade-offs, and strong agreement with expert radiologist assessments. Conclusion: The proposed FedIHRAS framework demonstrates that federated learning can support scalable, privacy-preserving, and clinically meaningful radiological AI systems. By integrating multi-task learning, explainability, and automated reporting within a unified federated architecture, the framework addresses key limitations of existing approaches and contributes to the development of collaborative AI in healthcare. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

Renewable Energy Communities (RECs) represent a key instrument for enabling decentralized energy systems and enhancing local renewable energy utilization. Preliminary assessment of REC performance relies on simulation tools that differ in computational complexity, assumptions, and input data. Despite the growing literature, a systematic comparison of tools applied to identical community configurations is still missing. This study provides a systematic cross-comparison of four tools representing different modelling paradigms: a VBA-based prefeasibility model (MERCm), a MATLAB-based detailed framework (UNIPGm), a national open-access simulator (RECON), and a commercial platform (COMMm). The tools were applied to six residential configurations in three Italian provinces representing different solar irradiation levels. Scenarios are defined to ensure high energy sharing potential, considering a ratio of shared energy to energy fed into the grid above 60%. Key performance indicators, including physical self-consumption and shared energy, are analyzed. Results show broadly consistent trends across tools, although these findings refer to PV-only, residential RECs and may differ in more complex community configurations, with coefficients of variation below 15% for most relevant indicators, particularly shared energy, while confirming that differences in input data and modelling assumptions can still influence outcomes. These findings support the reliability of simplified simulation tools for preliminary REC feasibility assessments and provide guidance for policymakers and technical operators. Full article

The gonadal development of Japanese eels (Anguilla japonica) plays a crucial role in the success of artificial breeding. Soy isoflavones, a class of phytoestrogens commonly found in aquafeeds, have shown potential in enhancing gonad development in fish. The present study evaluated the effects of dietary soy isoflavones on gonadal development, growth performance, histology, sex hormone levels, vitellogenin content, and expression of related genes in female Japanese eel broodstock. A 4-week feeding trial was conducted with 120 two-year-old female eels randomly assigned to four groups and fed diets containing 0 (C), 0.1 (L), 0.5 (M), and 0.9 (H) mg/g of soy isoflavones. The results indicated that gonadal development was enhanced in the M and H groups, as evidenced by a significantly higher gonadosomatic index (GSI) and increased oocyte cross-sectional area (CSA) in M group, and greater nutrient accumulation in both the M and H groups. The expression of er and cyp19a genes in the ovary was downregulated in the treatment groups, leading to decreased serum estradiol (E2) and increased testosterone levels. Furthermore, hepatic vtg gene expression was upregulated in the M and H groups, though VTG protein content remained unchanged, suggesting an initiation of vitellogenesis at the transcriptional level. In conclusion, dietary soy isoflavones at 0.5–0.9 mg/g provide an effective pretreatment strategy to enhance early ovarian development in Japanese eel broodstock, potentially improving their responsiveness to subsequent hormonal induction in artificial breeding programs. Full article

Sepsis early warning is hindered by data silos, temporal leakage, and threshold choices that obscure operational performance. We present a leakage-aware federated-learning evaluation pipeline that enforces group/temporal separation and compares models at a fixed alert workload. Stage-1 benchmarks local, FedAvg, and FedProx LSTM/Transformer models on PhysioNet/CinC 2019 using the official A/B partitions in bidirectional cross-hospital evaluation (A→B/B→A) after removing ICULOS. Stage-2 constructs a Sepsis-3-aligned MIMIC-IV task using full SOFA-component features and simulated clients to emulate institutional heterogeneity. Federated training improves out-of-hospital generalization for LSTM models on PhysioNet, whereas Transformer models remain robust across 3–12 h horizons. On MIMIC-IV, fixed alert-rate evaluation (α = 5%) clarifies workload–timeliness trade-offs, and centralized XGBoost achieves the strongest stay-level detection with clinically meaningful lead times. Supplementary privacy and security stress tests further contextualize residual deployment risks. Overall, leakage control and workload-matched evaluation are essential for trustworthy, operationally actionable sepsis early warning. Full article

Addressing prevalent challenges in current cooperative task assignment methods for cross-domain unmanned swarm, such as the disconnection between decision-making and execution processes, and the inadequate incorporation of platform kinematic constraints, this study introduces an integrated decision-control cooperative task assignment approach based on a bi-level optimization framework. The proposed framework formulates a bi-level programming model that tightly couples upper-level task assignment with lower-level optimal control. The upper-level model aims to minimize the maximum task completion time by optimizing the assignment and visitation sequences of diverse target types across heterogeneous unmanned platforms. The lower-level model, given the task sequences from the upper level, addresses a minimum-time optimal control problem based on a comprehensive nonlinear kinematic model. This approach enables precise computation of task execution times, which are subsequently fed back to the decision-making layer, thereby establishing a closed-loop optimization mechanism. To solve this complex model efficiently, the lower-level employs differential flatness transformation to eliminate trigonometric functions in the kinematic equations and discretizes the continuous-time optimal control problem into a nonlinear programming problem via the Radau pseudospectral method. For the upper-level combinatorial optimization, an improved genetic algorithm is developed, integrating hybrid encoding, dual-archive elitism preservation, adaptive crossover and mutation strategies, and periodic local search. Simulation results demonstrate that, compared with traditional Euclidean-distance-based assignment methods, the proposed approach generates kinematically feasible and smooth trajectories while thoroughly accounting for the kinematic constraints of heterogeneous platforms, thereby demonstrating its effectiveness and superiority in improving the comprehensive mission performance of cross-domain unmanned swarms. Full article

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system. Full article

Moral disengagement (MD) typically peaks during adolescence. While the Dark Triad (DT) traits—Machiavellianism, psychopathy, and narcissism—are broadly linked to MD, the specific prospective pathways through which individual DT components predict distinct MD strategies remain unclear, particularly with respect to gender-specific variations in these influences among adolescents. To systematically investigate these temporal associations, this study employed Cross-Lagged Panel Network (CLPN) modeling on a sample of 1410 Chinese adolescents (M_age = 16.95, SD = 0.75) surveyed across three waves at three-month intervals. Results revealed a hierarchical pattern of DT influence: Machiavellianism exerted the strongest predictive effect on the MD system, followed by psychopathy, while narcissism showed negligible or even negative effects. Among MD strategies, euphemistic labelling, advantageous comparison and displacement of responsibility were the most responsive to DT traits. Bridge centrality analysis confirmed Machiavellianism as the primary cross-domain connector linking DT traits to MD. Weak but significant reciprocal effects were observed: MD slightly fed back onto later Machiavellianism and psychopathy, supporting a partially bidirectional process. Gender-separated networks revealed divergent pathways: Machiavellianism served as the key DT-MD bridge for males, whereas psychopathy fulfilled this role for females. These findings refine the understanding of the “dark side” of moral development by highlighting mechanism-specific MD vulnerabilities and demonstrating that the primary socio-cognitive pathway to MD is gender-contingent, thereby advancing developmental models of MD. Full article