Search Results (562)

Background: The estimation of the postmortem interval (PMI) remains a complex challenge in forensic medicine. While macroscopic, biochemical, and molecular methods are well-documented, postmortem functional approaches at the organ level are largely underexplored. This pilot study investigated the feasibility of utilizing an isolated ex vivo kidney perfusion model to assess residual postmortem renal function—specifically glomerular filtration and tubular solute handling—as a potential chronological marker for PMI. Methods: Sixteen adult New Zealand rabbits were euthanized and randomly assigned to four postmortem interval groups (1, 5, 10, and 15 h). An unoxygenated, room-temperature crystalloid perfusion system was established to mimic natural postmortem decay. Initially, 32 kidneys were perfused; two were excluded due to anuria, resulting in 30 successfully analyzed kidneys. To strictly eliminate pseudoreplication bias, bilateral functional data were mathematically aggregated at the subject level, establishing the individual rabbit (n = 16) as the statistical unit. Results: Following statistical adjustment at the subject level, none of the measured functional parameters exhibited statistically significant chronological variation across the postmortem intervals (all p > 0.05; statistical significance defined as p < 0.05). Glomerular filtration was profoundly depressed across all groups, with adjusted inulin clearance ranging between 0.0031 and 0.0086 mL/min/g (peaking nonsignificantly at 10 h). Furthermore, active tubular reabsorption was virtually nonexistent; calculated reabsorbed loads for evaluated solutes, particularly potassium and sodium, yielded predominantly negative values. This phenomenon indicates a complete absence of physiological active reabsorption, reflecting instead a massive passive leakage of intracellular electrolytes into the tubular fluid due to cellular autolysis. Conclusions: Within this specific experimental setup, the isolated kidney perfusion model failed to demonstrate reproducible, time-dependent renal function useful for PMI estimation. These findings indirectly suggest that, unlike the prolonged supravital physiological resilience observed in skeletal muscle, highly metabolically active renal tissue rapidly loses its complex functional capacity following somatic death. Future studies exploring supravital renal function should consider targeting the immediate early postmortem period (0–1 h) or integrating advanced organ preservation techniques to unmask residual cellular capabilities. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article

Marine mucilage events are intensifying in semi-enclosed seas under accelerating climate- and nutrient-driven pressures, yet their ecosystem-level consequences for aquaculture-linked coastal habitats remain insufficiently documented. This study provides an integrated spatiotemporal assessment of water quality, phytoplankton community structure, and biometric responses of Mytilus galloprovincialis during and after the 2025 mucilage outbreak in the Gulf of Erdek (Sea of Marmara, Türkiye). Mucilage accumulation was associated with sharp increases in turbidity, total suspended solids, and particulate organic matter, alongside declines in dissolved oxygen and pH. Phytoplankton assemblages exhibited marked seasonal restructuring: the mucilage period was characterized by the coexistence of mucilage-forming taxa, non-toxic bloomers, and multiple harmful algal bloom (HAB) groups, including DSP- and ASP-related species, whereas post-mucilage conditions were dominated by non-toxic diatoms with substantially reduced HAB representation. The dinoflagellate species representing the May period in terms of abundance were Noctiluca scintillans and Prorocentrum micans; the diatom species were Chaetoceros radiatus, Cylindrotheca closterium, Pseudo-nitzschia pseudodelicatissima, and Thalassiosira rotula; and the coccolithophore was Phaeocystis pouchetii. Mussel biometric analyses revealed biometric indices and condition values markedly below regional historical baselines during the mucilage event, alongside reduced meat yield, followed by pronounced compensatory growth during the post-mucilage period. Our findings demonstrate that mucilage acts as both a physical and biological stressor, driving short-term ecological shifts in phytoplankton diversity and imposing substantial but reversible physiological impacts on mussel stocks. These results underscore the need for continuous biodiversity monitoring frameworks that integrate mucilage dynamics, HAB occurrence, and aquaculture resilience in regions vulnerable to climate-enhanced organic aggregate formation. Full article

The aggregation of flexible resources contributes to sustainability because it impacts on CO₂ emissions, enables renewable energy integration, improves network efficiency and makes the electric power system more resilient. The research project DEMAND has tested the potential of bottom-up aggregation of smart prosumers with no intermediation by a third-party balancing service provider. The present work analyzes the electrical and environmental effects of this new type of aggregation in different scenarios, taking into account both simulated data and data obtained from four pilot sites where the DEMAND system has been implemented. The effectiveness of the proposed aggregation method is evaluated through the calculation of some KPIs: power peaks, grid losses, voltage drops and CO₂ emissions. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

The proliferation of Enterprise Networks, characterized by heterogeneous devices, distributed data sources, and increasingly sophisticated cyber threats, has exposed the limitations of traditional perimeter-based security models. Guided by the principles of Zero Trust Architecture (ZTA), this paper presents a Zero-Trust (ZT)-Driven Federated Learning Algorithm for Privacy Enhancement (ZT-FL-PE), designed to safeguard model and data confidentiality in decentralized learning environments. By integrating ZTA’s “never trust, always verify” posture with Federated Learning’s (FL) decentralized training paradigm, the proposed framework eliminates the need for centralized data aggregation and significantly reduces the attack surface. The algorithm specifically targets two prominent threats to model privacy: property inference attacks (PIAs) and membership inference attacks (MIAs). We introduce adaptive verification mechanisms and privacy-preserving update transformations that enforce continuous authentication, constrain adversarial behavior, and strengthen resilience against inference-based exploitation. Experimental results demonstrate that ZT-FL-PE substantially enhances privacy protection while maintaining high model accuracy and imposing only low-to-moderate computational overhead, making it a practical and robust solution for modern ZT Enterprise environments. Full article

A systematic assessment of the human settlement suitability (HSS) and its structural resilience in metropolitan areas, from a spatial network perspective, is essential for understanding the spatial organization and evolutionary mechanisms of regional human settlement systems. It also supports the high-quality development of metropolitan areas. This study considers the Shenyang Metropolitan Area as the research object and constructs a comprehensive evaluation model of HSS from two dimensions: natural environmental suitability (NES) and human environmental suitability (HES). This study systematically analyzes the spatial distribution pattern of HSS, characteristics of its spatial association network, and its structural resilience, by integrating a modified gravity model, social network analysis (SNA), and structural resilience measurement methods. The results indicate that NES exhibits a high-west to low-east gradient, with high-value areas primarily located in peripheral regions with better ecological conditions. HES reveals a pronounced core–periphery structure, with high suitability concentrated in core cities and their adjacent suburban areas. Under the combined influence of NES and HES, the HSS forms a layered differentiation pattern dominated by core cities. The spatial association network of HSS has an overall low density and displays the coexistence of a core–periphery structure and proximity dependence, in which the HES network demonstrates strong cross-node transmission capacity, while the NES network is significantly constrained by geographical proximity. The structural resilience of the network is characterized by a moderate hierarchy, predominantly homophilic matching, limited transmission efficiency, and pronounced spatial differentiation in aggregation, indicating an overall pattern of highly connected cores with low aggregation and moderately or weakly connected nodes with high aggregation. The findings provide a scientific basis for optimizing the human settlements and enhancing regional resilience governance in metropolitan areas, while offering a novel analytical perspective for research on human settlement systems. Full article

Smart-city IoT ecosystems depend on a large number of devices with limited resources, which often lack built-in security mechanisms. While traditional cloud-based or gateway-centric intrusion detection systems (IDSs) offer essential security, they are still characterized by high detection latency, considerable bandwidth demand, and a lack of precise monitoring of single device actions. This study proposes the Edge AI Bridge, a novel micro-computing security layer positioned between IoT devices and the gateway to enable early-stage threat interception. The architecture integrates embedded AI hardware with a hybrid pipeline, utilizing unsupervised anomaly detection for behavioral profiling and a lightweight signature-matching module to minimize false positives. System operations—including localized traffic inspection, protocol parsing, and feature extraction—are performed before data aggregation, which preserves device-level privacy and reduces the computational burden on the IoT gateway. The contemporary CIC-IoT-2023 dataset, which captures a wide range of smart-city protocols and attack vectors, is used to evaluate the architecture. The Edge AI Bridge leads to a significant reduction in detection latency—≈50 ms on average as opposed to the 500 ms of cloud-based solutions—while the resource footprint is kept low to about 20% CPU utilization. The Edge AI Bridge demonstrates a potential solution that is scalable, modular, and can preserve privacy while improving the cyber resilience of the smart-city infrastructures that are large, heterogeneous, and difficult to manage. Full article

Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF). To address this critical gap, this paper proposes the Trustworthy Visual Privacy Auditing (TVPA) system, which transitions conventional static detection models into a dynamic and secure governance ecosystem. We first establish system resilience against adversarial threats by proposing an active auditing mechanism called Resilient Federated Protection (RFP) to embed unique model parameter watermarks within client-side updates. The RFP mechanism enables the federated aggregator to verify node legitimacy and automatically isolate malicious clients attempting poisoning attacks. Then, to ensure strict accountability, we design an immutable audit log mechanism in the RFP mechanism that utilizes a Cryptographic Hash Chain (CHC) to record and verify the provenance of every model update, creating a transparent chain of custody. Furthermore, the prediction mechanism is enhanced by Causal Governance (CG) that integrates causal inference to provide counterfactual reasoning for explaining the root causes of privacy risks rather than merely flagging associations. Experiments on the VISPR dataset demonstrate that our TVPA system can synthesize high-performance recognition with robust security, auditability, and causal explainability to provide trustworthy AI governance. Full article

The increase in utility-scale PV generation and the displacement of synchronous machines reduce system strength, reactive power headroom, voltage resilience, and overall power system stability, motivating a robust comparison of various mitigation technologies beyond static load-flow or PV assessments. RMS time-domain simulations are performed for balanced and unbalanced contingencies, and performance is quantified using post-fault voltage dip depth, undervoltage area (V < 0.9 pu.), recovery time to nominal, and RoCoF. These metrics are aggregated into a single weighted composite severity score, which is then normalised to the baseline to form the dynamic voltage resilience index (DVRI) and the Frequency Disturbance Relative Index (FDRI). The results show that the converter-based reactive power support devices deliver the fastest and most controllable post-fault voltage restoration, with the STATCOM achieving the lowest composite penalty and best DVRI under severe fault conditions but the poorest FDRI during PV plant trip/reconnection events. The synchronous condenser (SC) improves post-fault recovery through excitation driven reactive capability and increased short-circuit contribution, but its recovery to nominal voltage levels is slower and can produce negative-sequence current under unbalanced fault conditions whilst producing the smallest frequency disturbance and best FDRI. The SVC provides effective steady-state regulation but becomes less effective during extremely low voltages due to the voltage-dependent reactive power output, and its FDRI remains close to baseline. The BESS-GFM is dependent on the inverter current limits and the control priorities, which influence both voltage recovery and response times, achieving an FDRI scoring second to the SC. These metrics are combined into baseline normalised composite indices (DVRI and FDRI) using explicitly dimensionless sub-metrics (dip magnitude, exposure area, and recovery delay for voltage and deviation magnitude, windowed RoCoF, and exposure for frequency). Equal weights are used as a neutral baseline, and a weight sensitivity study is included to confirm that technology rankings are robust to plausible variations in weighting choice. Full article

Parasites are integral components of marine ecosystems, providing insights into host biology, trophic interactions, and environmental variability. This study presents the first systematic characterization of the metazoan parasite community of the Green Jack (Caranx caballus) in the northern Colombian Pacific, a region designated as an Exclusive Artisanal Fishing Zone (ZEPA) but with limited parasitological research. Specimens were collected from the Cupica Gulf across wet and dry seasons, and parasitological analyses were conducted to evaluate parasite load, community structure, spatial distribution, and seasonal variation. Of 46 fish examined, 20 were parasitized (overall prevalence: 43.5%), with low infection intensities (1–3 parasites per fish). Nine parasite species were identified, including monogeneans, digeneans, and copepods. Copepods (Caligus sp.) represented the most species-rich and dominant group, while Allopyragraphorus caballeroi exhibited aggregated distribution. Parasite communities showed low richness and diversity, seasonal stability, and strong trophic linkages to crustacean prey, particularly brachyuran megalopa. Host condition was unaffected by parasitism, suggesting resilience under current infection levels. These findings provide the first reference on parasite richness and diversity for C. caballus in Colombia, extending the known distribution of several species within the Eastern Tropical Pacific and underscoring the role of parasites as biological markers for fisheries monitoring and ecosystem change. Full article

Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint-governed inference architecture and synthesizes advances in rule-based adjudication, reliability-aware aggregation, post-fusion projection, dual-track adaptation, and hierarchical backoff control. By establishing a taxonomy of boundary constraints—specifically mass conservation, reaction kinetics, hydraulic transport, and ecological tipping points—an admissible prediction manifold identifies key structural limitations in existing paradigms, particularly their vulnerability to physical inconsistency and diminished reliability during non-stationary distribution shifts. A unified end-to-end robust framework is proposed in which candidate predictions are separated from admissibility validation, uncertainty is directly coupled to aggregation logic, and degradation pathways are explicitly defined under distribution shift. Furthermore, a multidimensional robustness evaluation matrix is introduced, incorporating structural consistency, ecological compliance, calibration quality, and adaptive stability alongside conventional accuracy metrics. The study advances water quality forecasting from model-centric optimization toward architecture-level governance, demonstrating that constraint-aware designs improve structural consistency, robustness under distribution shifts, and early warning reliability, providing a systematic reference for developing resilient, transparent, and operationally deployable environmental prediction systems. Full article

Traditional concrete faces challenges such as low energy absorption, brittleness and major environmental impacts, attributed to its dependence on natural resources. Integrating natural fibers with recycled coarse aggregates into concrete presents a promising method of enhancing concrete’s sustainability and mechanical performance. Still, accurately predicting the mechanical properties of these innovative concrete mixes remains complex. This research investigates the predictive abilities of two machine learning (ML) models, classification and regression trees (CART) and stepwise polynomial regression (SPR), for estimating the compressive and splitting tensile strengths of NF-reinforced concrete containing recycled coarse aggregates. The CART model showed greater predictive accuracy, reaching R² = 0.91 for compressive strength and R² = 0.89 for splitting tensile strength. Additionally, the model demonstrated consistently lower error metrics (RMSE, MAD, MAPE, MSE) than comparable approaches. For compressive strength, CART achieved R² = 0.91, RMSE = 5.5686, MSE = 31.0098, MAD = 4.1076, and MAPE = 0.1055, while for splitting tensile strength, it achieved R² = 0.89, RMSE = 0.3954, MSE = 0.1563, MAD = 0.2996, and MAPE = 0.0939. These results emphasize the significant potential of ML, particularly CART, to optimize the design of sustainable concrete mixtures, enabling more accurate and effective strength predictions and finally contributing to more resilient and sustainable infrastructure. Full article

Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature–classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect different feature combinations, classifiers, and subjects. In this work, we provide, to our knowledge, the first systematic robustness map of a conventional sEMG pipeline under controlledclipping and single-sensor failure. sEMG from nine subjects performing a multi-session, multi-gesture protocol is windowed (250 ms, 50 ms hop) and represented using four common time-domain features (Root Mean Square, Variance, Zero Crossing, and Waveform Length). We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (${10}^{-6}$–${10}^{-1}$) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This design enables explicit characterization of the following: (1) accuracy recovery as clipping weakens for each feature pair; (2) dependency of robustness on which channel fails; and (3) differences among Support Vector Machine, Linear Discriminant Analysis, and Random Forest under identical degradations. The results show that lightweight feature pairs (Root Mean Square + Waveform Length, Variance + Zero Crossing, and Waveform Length + Zero Crossing) coupled with Random Forest form a consistently robust operating point, with performance recovering as clipping weakens and remaining resilient under single-channel dropout. Beyond robustness, the conventional pipeline trains substantially faster than representative deep learning baselines under a unified end-to-end timing definition, supporting real-time recalibration and repeated robustness sweeps in wearable deployments. Full article

Multi-station sensing telemetry typically arrives out-of-order at the Open RAN (O-RAN) Near-RT RIC due to non-deterministic jitter in cloud-native protocol stacks, inducing a “temporal scrambling” effect that invalidates traditional spatial fusion. To bridge this gap, we introduce Age-of-Sensing (AoS) as a dynamic reliability metric for asynchronous sensing reports and establish an AoS-aware graph neural network (GNN) paradigm for asynchronous sensing fusion. This paradigm shifts the focus from conventional spatial-only aggregation to time-aware inference by explicitly incorporating sensing freshness into graph-based fusion. As a physics-informed realization of this paradigm, we present Time-Aware Fusion (TA-Fusion), which introduces a TA-Gate mechanism to recalibrate node trust prior to graph aggregation. Unlike passive feature concatenation, the TA-Gate serves as an active gating signal to prioritize fresh telemetry while adaptively suppressing stale outliers. On a standardized O-RAN benchmark, TA-Fusion achieves a root mean square error (RMSE) of $12.22$ m, delivering a $21.7\%$ reduction in Mean absolute error (MAE) over the AoS-aware GNN baseline and maintaining robustness in extreme jitter scenarios where traditional linear methods suffer from severe accuracy degradation due to their static weighting logic. Extensive Monte Carlo simulations confirm that the framework preserves consistent error bounds across diverse base station geometries without manual recalibration. These findings support the real-time feasibility of the proposed paradigm for delay-critical Integrated Sensing and Communication (ISAC) services, providing a resilient spatial foundation for 6G orchestration under substantial network-layer jitter. Full article