Search Results (290)

The increasing penetration of renewable energy presents substantial challenges to frequency stability, particularly in low-inertia microgrids. This study introduces an agent-based microgrid model that integrates generators, loads, an energy storage system (ESS), and renewable sources, mathematically formalized through the discrete-event system specification (DEVS) to ensure both structural clarity and extensibility. To dynamically simulate power system behavior, the model incorporates multiple control strategies—including ESS scheduling, automatic generation control (AGC), predictive AGC, and grid-forming (GFM) inverter control—each posed as an mathematically defined control problem. Simulations on the IEEE 13-bus system demonstrates that the coordinated operation of ESS, GFM, and the proposed strategies markedly enhances frequency stability, reducing frequency peaks by 1.14, 1.14, and 0.72 Hz, and shortening the average recovery time by 9.05, 0.15, and 2.58 min, respectively. Collectively, the model provides a systematic representation of grid behavior and frequency regulation mechanisms under high renewable penetration, and establishes a rigorous mathematical framework for advancing microgrid research. Full article

Sludge is a common engineering byproduct that poses environmental and land-use challenges when disposed of directly. Converting sludge into high-quality subgrade filling material through solidification is therefore of both engineering and ecological significance. In this study, dynamic triaxial tests were conducted on sludge soils stabilized with mineral-based cementitious binders to investigate the effects of binder content, loading frequency, and curing age on the backbone curve, dynamic shear modulus, maximum shear modulus, ultimate stress amplitude, shear modulus ratio, and damping ratio. Scanning electron microscopy (SEM) was further employed to examine the microstructural evolution of the stabilized soils. The results indicate that increasing binder content and curing age significantly enhance the dynamic shear modulus while reducing the damping ratio, and the modulus exhibits a frequency-dependent behavior within the tested loading range. The modified Hardin-Drnevich constitutive model was successfully applied to fit the experimental data, accurately characterizing the dynamic response of stabilized sludge soils and enabling the development of a normalized model for the dynamic shear modulus ratio. SEM observations confirm that hydration reactions between the binder and soil produce gel products that fill interparticle pores, leading to a denser structure and explaining the observed macroscopic improvements in mechanical behavior. Overall, this work elucidates the dynamic response mechanisms of sludge stabilized with mineral-based cementitious materials and provides theoretical and experimental support for its resource utilization in road engineering applications. Full article

Heavy metal contamination in soil poses significant ecological risks, particularly within agricultural and forest ecosystems. This study evaluates the bioaccumulation of heavy metals (Cr, Co, Ni, Cu, Zn, As, Mo, Pb) by the ground beetle Carabus coriaceus Linnaeus, 1758, across contrasting Croatian ecosystems, with a focus on the role of soil pH in shaping metal dynamics. Concentrations in soils (0–30 and 30–60 cm) and beetle tissues were measured using portable X-ray fluorescence (pXRF), which provides total concentrations; inferences on bioavailability were based on soil properties such as pH and organic matter. Orchard soils showed higher Cu (49.9 mg/kg), Mo (10.3 mg/kg), and Ni (32.5 mg/kg), whereas forest soils contained elevated Zn (105.6 mg/kg), Pb (84.5 mg/kg), As (29.7 mg/kg), and Co (16.3 mg/kg). Beetles accumulated up to 481.0 mg/kg Zn at the orchard and 90.0 mg/kg Cu at the forest site. Bioaccumulation factors exceeded 1.0 for Co, Cu, and Zn, with particularly high values for Zn (2.20–5.75) suggesting both site-specific availability and possible physiological regulation. Soil and beetle analyses were complementary rather than equivalent: soils indicated total load, while beetles reflected biologically relevant fractions. C. coriaceus, therefore, represents a sensitive bioindicator, suitable for biodiversity-based soil contamination monitoring. Full article

Wire-rope isolators (WRIs) are widely used in vibration and seismic protection due to their multidirectional flexibility and amplitude-dependent hysteretic damping. However, their complex nonlinear behavior, especially under inclined and combined-mode loading, poses challenges for predictive modeling. This study presents a simplified finite-element modeling framework using constant-property Timoshenko beam elements with tuned Rayleigh damping to simulate WRI behavior across various configurations. Benchmark validation against analytical ring deformation confirmed the model’s ability to capture geometric nonlinearities. The framework was extended to five WRI types, with effective cross-sectional properties calibrated against vendor-supplied quasi-static data. Dynamic simulations under sinusoidal excitation demonstrated strong agreement with experimental force-displacement loops in pure modes and showed moderate accuracy (within 29%) in inclined configurations. System-level validation using a rocking-control platform with four inclined WRIs showed that the model reliably predicts global stiffness and energy dissipation under base accelerations. While the model does not capture localized nonlinearities such as pinched hysteresis due to interstrand friction, it offers a computationally efficient tool for engineering design. The proposed method enables rapid evaluation of WRI performance in complex scenarios, supporting broader integration into performance-based seismic mitigation strategies. Full article

Background: Respiratory viral infections pose a major global health burden, and molecular diagnostics such as Real-Time RT-PCR have revealed frequent co-infections. However, precise quantification of viral RNA remains challenging. Digital PCR (dPCR) offers absolute quantification without standard curves and may improve diagnostic accuracy. This study compares dPCR and Real-Time RT-PCR in detecting and quantifying influenza A, influenza B, respiratory syncytial virus (RSV), and SARS-CoV-2 during the 2023–2024 tripledemic. Methods: A total of 123 respiratory samples were analysed and stratified by cycle threshold (Ct) values into high, medium, and low viral load categories. Both dPCR and Real-Time RT-PCR were used to quantify and compare viral loads across these categories. Results: dPCR demonstrated superior accuracy, particularly for high viral loads of influenza A, influenza B, and SARS-CoV-2, and for medium loads of RSV. It showed greater consistency and precision than Real-Time RT-PCR, especially in quantifying intermediate viral levels. Conclusions: These findings highlight the potential of dPCR to enhance respiratory virus diagnostics and support a better understanding of co-infection dynamics. Nonetheless, its routine implementation is currently limited by higher costs and reduced automation compared to Real-Time RT-PCR. Full article

The simultaneous circulation of multiple arboviruses, often driven by (re)emergence events, poses challenges to public health systems. In Brazil, the co-circulation of Dengue virus (DENV), Zika virus (ZIKV), Chikungunya virus (CHIKV), and Oropouche virus (OROV), together with the potential urban emergence of Mayaro virus (MAYV), underscores the importance of understanding interactions among these pathogens within their vectors. This study investigated the effects of CHIKV and MAYV coinfection and superinfection on replication dynamics in Aedes aegypti. Mosquitoes were experimentally exposed to CHIKV and MAYV through artificial blood meals under coinfection and superinfection conditions. Infection (IR), dissemination (DR), and transmission (TR) rates, as well as viral loads, were quantified by quantitative reverse transcription PCR (qRT-PCR). To confirm viral replication and assess cytopathic effects, positive saliva samples were inoculated in Vero cells, followed by serial passages and plaque assays for viral titration. The results showed that Ae. aegypti is capable of transmitting both CHIKV and MAYV concurrently during coinfection. However, in superinfection scenarios, prior infection with either virus significantly reduced the transmission efficiency of the subsequently acquired virus, indicating viral interference at the replication level. These findings underscore the complexity of arboviral interactions within vectors and highlight their potential implications for transmission dynamics. Continuous entomo-virological surveillance and targeted research are essential for anticipating and mitigating the impact of arboviral co-circulation in endemic regions. Full article

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety. Full article

The dynamic environment of coastal regions subjects infrastructure to multiple interacting natural hazards, with the simultaneous occurrence of windstorms and earthquakes posing a particularly critical challenge. Unlike inland hazards, these coastal threats frequently exhibit irregular statistical behavior and terrain-induced anomalies. This study proposes a novel probabilistic framework to assess compound hazard effects, advancing beyond traditional single-hazard analyses. By integrating maximum entropy theory with bivariate Copula models, a unified return period analysis is developed to capture the joint probability structure of seismic and wind events. The model is calibrated using long-term observational data collected from a representative coastal zone since 2000. For the PGA marginal distribution, our sixth-moment maximum-entropy model achieved an R² of 0.90, compared with 0.57 for a conventional GEV fit—reflecting a 58% increase in explained variance. Analysis shows the progressive evolution of damage from slight damaged through moderate damaged and severe damaged to collapse for an 18-story reinforced concrete frame structure, and shows that the combined effect of seismic and wind loads results in risk probabilities of aforementioned damage state of approximately 2 × 10⁻³, 6 × 10⁻⁴, 2 × 10⁻⁴, and 3 × 10⁻⁵, respectively, under a 0.4 g ground motion and a concurrent wind speed of 15 m/s. Furthermore, when both the uncertainty of loss ratios and structural parameters are incorporated, the standard deviation of the economic loss ratio reaches up to 0.015 in the transition region (PGA 0.2–0.4 g), highlighting considerable variability in economic loss assessment, whereas the mean economic loss ratio rapidly saturates above 0.8 with increasing PGA. These findings demonstrate that uncertainty in economic loss is most pronounced within the transition region, while remaining much lower outside this zone. Overall, this study provides a robust framework and quantitative basis for comprehensive risk assessment and resilient design of coastal infrastructure under compound wind and seismic hazards. Full article

To address stability and safety issues in hydraulic support design under inclined–declined and large-dip-angle working conditions, this paper proposes a design-driven dynamic pose–load co-evolution solution method based on the physical entity of the ZFY12000/21/36D hydraulic support. The feasibility of the proposed method is demonstrated through theoretical analysis, spatial modeling, and experimental verification. First, a spatial coordinate system describing hydraulic support pose is established based on Denavit–Hartenberg (DH) theory, constructing a “physical space-geometric coordinate system-DH parameter space” pose mapping model via DH principles, matrix iteration, and kinematic simulation. Second, a load-bearing characteristic analytical method is developed through systematic coupling analysis of dip angle, pose, and load distribution. Finally, coal mine field data collection and hydraulic support test platform experiments analyze load-bearing characteristics under varying poses and loads. Results show Root Mean Square Error (RMSE) values of 0.836° for the front link inclination, 0.756° for the rear link, 0.114° for the balance ram, and 0.372° for the column; load-bearing state evolution under pose–load synergy aligns with theoretical models, confirming method feasibility. This approach fills a domain gap in hydraulic support dip–pose–load co-solving and provides critical references for designing hydraulic support products under extreme dip-angle operations. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Background/Objectives: Hepatitis B virus (HBV) infection poses a major global health challenge, with current therapies like nucleos(t)ide analogs and pegylated interferon alpha offering limited functional cure rates due to persistent HBsAg-driven immune tolerance. This study aimed to develop a targeted immunoadsorption system using a high-affinity humanized anti-HBsAg monoclonal antibody for efficient HBsAg and viral particle clearance, providing a novel approach to overcome therapeutic bottlenecks in chronic hepatitis B (CHB). Methods: A murine anti-HBsAg monoclonal antibody was humanized via complementarity-determining region grafting, resulting in HmAb-12 (equilibrium dissociation constant, K_D = 0.36 nM). A stable Chinese Hamster Ovary K1 (CHO-K1) cell line was established for high-yield expression (fed-batch yield: 8.31 g/L). The antibody was covalently coupled to agarose microspheres (coupling efficiency > 95%) to prepare the immunoadsorbent. Efficacy was evaluated through in vitro dynamic circulation assays with artificial sera and preclinical trials using an integrated blood purification system in two CHB participants. Clearance rates for HBsAg and HBV DNA were quantified, with safety assessed via blood component monitoring. Results: In vitro, a single treatment cycle achieved HBsAg clearance rates of 70.14% (high antigen load, >10⁵ IU/mL) and 92.10% (low antigen load, ~3000 IU/mL). Preclinically, one treatment session resulted in acute HBsAg reductions of 78.30% and 74.31% in participants with high and moderate antigen loads, respectively, alongside HBV DNA decreases of 65.66% and 73.55%. Minimal fluctuations in total protein and albumin levels (<15%) confirmed favorable safety profiles, with no serious adverse events observed. Conclusions: Preliminary findings from this study indicate that the HBsAg-specific immunoadsorption system can achieve efficient HBV antigen clearance with an initial favorable safety profile in a small cohort. These results support its further investigation as a potential therapeutic strategy for functional cure in CHB. Future work will focus on validating these findings in larger studies and exploring the system’s combinatory potential with existing blood purification platforms. Full article

The Qinghai-Tibet Plateau (QTP) is uniquely characterized by widespread permafrost and desertification due to its distinctive natural environment and geographic setting. The current lack of understanding regarding the mechanisms by which the number of freeze-thaw cycles (N) exacerbates soil erosion poses a significant challenge to accurately assessing regional erosion dynamics. Here, we simulate realistic freeze-thaw conditions using an optimized cryogenic simulator and systematically quantify changes in soil physical properties, surface microstructure, and frost heave deformation. Research shows that as the number of freeze-thaw cycles rises, the surface soil moisture content decreases by 54.3%. Total porosity and bulk density display opposite trends. These changes in soil properties are mainly driven by frost heave forces disrupting soil cohesion. In particular, repeated water-ice phase transitions lead to continuous accumulation of axial frost heave stress, which rearranges soil particles. This significantly raises surface porosity with a growth rate as high as 60.3% and greatly reduces the soil’s resistance to external erosion. At the same time, the aggregate size distribution shifts toward finer particles, accompanied by a continued decrease in the mean weight diameter (MWD), which declines by approximately 8%. Notably, this degradation persists even when external loading partially suppresses frost heave. Therefore, the progressive physical degradation induced by frost heave-manifested through as moisture loss, porosity changes, aggregate breakdown, and compromised stability even under load-establishes the core mechanistic pathway through which freeze-thaw cycles intensify erosion in QTP soils. Full article

High-definition (HD) maps are essential for safe and reliable autonomous driving, but their growing size and the need for real-time updates pose significant challenges for in-vehicle storage and communication efficiency. This study proposes a lightweight and scalable HD map provisioning system based on Geohash spatial indexing and Smart Roadside Units (Smart RSUs). The system divides HD map data into Geohash-based spatial blocks and enables vehicles to request only the map segments corresponding to their current location, reducing storage burden and communication load. To validate the system’s effectiveness, we constructed a simulation environment where multiple vehicle clients simultaneously request map data from a Smart RSU. Experimental results showed that the proposed Geohash-based approach achieved an average response time (RTT) of 1244.82 ms—approximately 296.3% faster than the conventional GPS-based spatial query method—and improved database query performance by 1072.6%. Additionally, we demonstrate the system’s scalability by adjusting Geohash levels according to road density, using finer blocks in urban areas and coarser blocks in rural areas. The hierarchical nature of Geohash also enables consistent integration of blocks with different resolutions. These results confirm that the proposed method provides an efficient and real-time HD map delivery framework suitable for dynamic and dense traffic environments. Full article

Small targets in drone imagery are often difficult to accurately locate and identify due to scale imbalance and limitations, such as pixel representation and dynamic environmental interference, and the balance between detection accuracy and resource consumption of the model also poses challenges. Therefore, we propose an interpretable computer vision framework based on YOLOv12m, called LCW-YOLO. First, we adopt multi-scale heterogeneous convolutional kernels to improve the lightweight channel-level and spatial attention combined context (LA2C2f) structure, enhancing spatial perception capabilities while reducing model computational load. Second, to enhance feature fusion capabilities, we propose the Convolutional Attention Integration Module (CAIM), enabling the fusion of original features across channels, spatial dimensions, and layers, thereby strengthening contextual attention. Finally, the model incorporates Wise-IoU (WIoU) v3, which dynamically allocates loss weights for detected objects. This allows the model to adjust its focus on samples of average quality during training based on object difficulty, thereby improving the model’s generalization capabilities. According to experimental results, LCW-YOLO eliminates 0.4 M parameters and improves mAP@0.5 by 3.3% on the VisDrone2019 dataset when compared to YOLOv12m. And the model improves mAP@0.5 by 1.9% on the UAVVaste dataset. In the task of identifying small objects with drones, LCW-YOLO, as an explainable AI (XAI) model, provides visual detection results and effectively balances accuracy, lightweight design, and generalization capabilities. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article