Search Results (4,520)

Ultrafine particles (UFPs; <100 nm) can penetrate biological barriers and trigger inflammation, oxidative stress, and endothelial dysfunction, yet they remain largely unregulated and understudied relative to PM_2.5. This scoping review, conducted following PRISMA-ScR guidelines, systematically searched PubMed and Scopus for epidemiological studies examining ambient UFP exposure and mortality. Of 704 articles screened, 21 studies published between 2007 and 2025 met inclusion criteria. Fourteen assessed short-term exposure (≤1 month) using time-series or case-crossover designs with central-site particle number concentration monitors, and seven assessed long-term exposure using land-use regression, chemical transport, or machine learning models. A majority of both short-term (10 of 14) and long-term (6 of 7) studies reported positive associations between UFP exposure and mortality, particularly for respiratory outcomes. The strongest associations were observed in studies using higher-resolution or source-specific exposure methods, a pattern consistent with reduced misclassification. However, exposure assessment approaches varied widely in spatial resolution, instrument type, and particle size definitions, introducing substantial heterogeneity that limits comparability across studies. These findings underscore the need for standardized UFP measurement protocols, high-resolution exposure assessment, and further investigation of disparities in exposure and health outcomes to inform regulatory consideration. Full article

The increasing penetration of wind and photovoltaic (PV) generation introduces frequent operating-point variations into transmission networks, while missing supervisory control and data acquisition/phasor measurement unit (SCADA/PMU) measurements and bad data may further weaken the reliability of online state estimation. Conventional weighted least squares (WLS) estimators have a clear physical interpretation, but repeated online matrix solutions may become burdensome in large-scale rolling estimation. To address this issue, this paper proposes a graph–physics-constrained fast state estimation method with bad data detection (BDD) and filtering. Wind/PV-load operating scenarios are constructed on standard test systems, and mixed SCADA/PMU measurements are represented with missing masks and bad data perturbations. The filled measurements, measurement availability mask, and residual anomaly scores are used as input features, while the network topology is converted into a graph Laplacian prior. A regularized fast mapping, graph Laplacian smoothing, and threshold-calibrated residual screening are combined to obtain online state estimates and bad data labels. Five-seed case studies compare the proposed method with WLS and Huber robust WLS. In case300, the average online time is reduced from $66.063\pm 3.054$ ms for WLS to $1.922\pm 0.374$ ms for the proposed method, corresponding to a speedup of about 35.08 times. The results indicate that the proposed linearized prototype is most promising as a fast large-scale rolling estimator or abnormality screener, rather than as a full replacement for model-based estimators in all scenarios. Full article

With increasing renewable energy penetration, hydropower units face more frequent load rejection transients, which impose severe hydraulic excitation on Francis turbine runners. Although extensive studies have investigated flow dynamics and stress concentrations during transients, quantitative fatigue damage assessments for runners with pre-existing cracks remain scarce. To fill this gap, this study conducts CFD simulations coupled with one-way FSI to analyze a Francis turbine runner during load rejection, comparing uncracked and cracked configurations. Fatigue damage is evaluated using rain-flow counting, a modified S-N curve with Goodman mean stress correction, and the Palmgren–Miner linear damage rule. Results show that stress concentrations shift from the band-side to the crown-side T-junction during load rejection, with 4.5 times higher fatigue damage at the crown (D = 1.62 × 10⁻⁴) than at the band (D = 3.57 × 10⁻⁵). Pre-existing cracks increase local stress and reduce the allowable number of load rejection events from 6173 to 514 cycles. Reducing residual stress from 200 MPa to 100 MPa lowers fatigue damage by approximately 42%. This study provides a quantitative framework for transient fatigue assessments. Full article

The premature deterioration of reinforced concrete structures caused by steel reinforcement corrosion remains a major challenge to long-term structural durability. This study systematically investigates the effects of corrosion inhibitor dosage on the fresh properties, mechanical performance, chloride ion penetration resistance, and capillary water absorption of self-compacting concrete (SCC). The evolution of the pore structure is characterized using low-field nuclear magnetic resonance (LF-NMR) and X-ray computed tomography (X-CT), and the proportions of pores within different equivalent spherical diameter ranges are quantified. In addition, the microstructural characteristics are examined by scanning electron microscopy (SEM). The results show that the incorporation of the corrosion inhibitor increases the viscosity of fresh SCC, resulting in reductions in slump. In general, the corrosion inhibitor reduces both the compressive strength and splitting tensile strength of SCC, with the smallest strength reduction observed at a corrosion inhibitor dosage of 2 wt%. All mixtures containing the corrosion inhibitor exhibit lower electric flux and water absorption than the control mixture, indicating improved resistance to chloride ion penetration and capillary water ingress. The combined LF-NMR, X-CT, and SEM results indicate that an appropriate corrosion inhibitor dosage may optimize the spatial distribution of hydration products, refine the pore structure, reduce total porosity, and suppress the formation of macropores. Overall, a dosage of 2 wt% provides the most favorable balance among workability, mechanical properties, durability, and microstructural compactness. These findings provide experimental support and technical guidance for the mixture design of durable SCC used in aggressive environments, including marine and salt-lake regions. Full article

Efficient gas drainage in soft coal seams is commonly impeded by two coupled issues: coal-fines-induced clogging of screens and boreholes, and instability of the borehole wall. To overcome these limitations, an in situ grouted porous polyurethane system was developed for borehole completion. The polyurethane slurry, consisting of isocyanate, polyether polyol, catalyst, foam stabilizer, cell-opening agent, cross-linker, and water as a blowing agent, was formulated to coordinate foaming and gelation kinetics. By adjusting the type and dosage of catalyst, the gel time could be precisely controlled within 10–1500 s to suit different construction requirements. After curing, the material exhibited an interconnected open-cell structure with a porosity of approximately 83%, permeability greater than 4 D, and a uniaxial compressive strength of about 1.72 MPa. Mercury intrusion porosimetry revealed a highly connected, multiscale pore network, with an accessible porosity of 78.9%, a median pore size of 125 μm, and a dominant pore-size range of 1–301 μm, indicating favorable conditions for gas flow. Flow-through experiments under simulated methane drainage showed that coal-fine production is strongly dependent on flow rate: fines generation was negligible at flow rates ≤20 L/min and became noticeable at around 30 L/min. Scanning electron microscopy confirmed that coal fines were confined to the upper ~5 mm of the consolidation layer, where bridging and straining within small near-surface pores limited deeper penetration. Although near-surface fines deposition reduced permeability from the intrinsic polyurethane value (~4.0 D) to ~2.0 D, the permeability stabilized above ~1.5 D under dynamic conditions. Overall, these laboratory-scale results demonstrate that the porous polyurethane can effectively intercept coal fines within a shallow surface zone, provide sufficient mechanical support to stabilize the borehole, and maintain high permeability under the tested conditions, suggesting its potential as a candidate material for enhancing methane drainage performance in soft coal seams. Further field validation and comparative studies against conventional completion systems are needed to assess its true engineering viability. Full article

The growing penetration of distributed energy resources in distribution networks has highlighted the need to analyze power contribution characteristics under different operating and interconnection conditions. In particular, photovoltaic systems exhibit varying load-level power contribution patterns depending on their output levels and interconnection configurations, and machine-learning-based approaches have recently been investigated to efficiently estimate these contribution characteristics. However, conventional full-scale machine learning approaches require large datasets covering numerous PV operating and interconnection scenarios, resulting in substantial computational burden during dataset generation and model training. To address this issue, this paper proposes a screening-based machine learning algorithm for load-level PV power contribution estimation. A PV Impact-Range Index is developed by jointly considering active power contribution, voltage sensitivity, and electrical proximity to identify PV interconnection scenarios with meaningful contribution characteristics. Based on this index, non-informative scenarios are excluded, and a screening-scale dataset is constructed to train a computationally efficient learning model. Simulation results on a 19-bus distribution test system show that the proposed algorithm achieves prediction accuracy comparable to the full-scale learning approach while significantly reducing dataset generation and model training time. These results demonstrate that the proposed algorithm provides an efficient and practical solution for PV power contribution estimation in distribution networks. Full article

Imperatorin (IPT) is a naturally occurring coumarin with recognized antioxidant and anti-aging properties; unfortunately, its poor water solubility and low oral bioavailability severely limit its practical use. To get around these issues, we formulated IPT-loaded NLCs using grape seed oil and glyceryl monostearate—both food-grade excipients—with the goal of enhancing oral absorption. Optimized IPT@NLCs were prepared by high-pressure homogenization, featuring uniform spherical morphology, an average particle size of 186.63 ± 1.65 nm, a PDI of 0.188 ± 0.008, an encapsulation efficiency of 99.54 ± 0.10%, and a drug loading capacity of 9.08 ± 0.23%. IPT@NLCs remained stable in SGF, while their cumulative in vitro release over 48 h reached 90.56 ± 3.12% in SIF. We established a Caco-2/HT29-MTX-E12 co-culture monolayer to examine mucus penetration, cellular uptake, and transcellular transport routes. In parallel, oxidative stress experiments using 3T3-L1 cells, along with in vivo pharmacokinetic and gastrointestinal safety evaluations, were conducted to provide complementary evidence. Our results indicate that NLC encapsulation significantly improves both the dissolution and intestinal uptake of IPT, primarily by shifting the absorption mechanism from passive diffusion to energy-dependent active transport. In addition, IPT@NLCs effectively reduce intracellular oxidative damage through modulation of endogenous antioxidant enzyme activities. Animal studies further reveal an approximately 9-fold increase in relative oral bioavailability, with no notable irritation to gastrointestinal tissues. Overall, GSO-based NLCs offer safe and efficient oral delivery, enhancing IPT bioavailability and antioxidant activity, providing a strategy for developing natural-product-based formulations. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

To address the severe coverage blind spots and concurrent collision bottlenecks faced by LoRa networks in dense deployments and complex three-dimensional (3D) occlusion environments, this paper proposes a distributed cross-layer protocol framework for LoRa ad hoc networks supporting heterogeneous traffic. To break through the limitations of a single star architecture, this framework constructs a 3D penetration loss model at the physical layer and designs a distributed relay deployment algorithm based on hybrid simulated annealing, achieving blind-spot-free connectivity in complex spaces. At the MAC layer, a non-preemptive priority access mechanism based on symbol energy detection is introduced. Through differentiated backoff windows with time-domain isolation, it precisely guarantees the quality of service (QoS) requirements of heterogeneous traffic and significantly suppresses concurrent collisions. At the network layer, the CAM-AODV routing algorithm is proposed, which integrates hop count, link quality, MAC queue congestion, and nodal residual energy to achieve dynamic traffic diversion and network-wide energy balancing under bursty high loads. Simulation results demonstrate that this cross-layer framework effectively breaks the traditional network capacity bottlenecks. In a large-scale, high-density scenario with 300 nodes, CAM-AODV reduces the average end-to-end delay by 19.46% compared to the traditional AODV. Under high-concurrent loads, the packet delivery ratio (PDR) of the proposed framework improves by 16.32% over the traditional protocol, while the system delay is reduced by 13.66%. Furthermore, under the two aforementioned evaluation scenarios, the Energy Balancing Index (EBI) is significantly improved by 11.13% and 10.57%, respectively, compared to the traditional protocol. This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks. Full article

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links the German Federal Government energy transition pathway (2025–2045) with state estimation performance requirements, evaluated at five milestone years from 2025 to 2045 on two SimBench reference networks across three equipment size levels (large, medium, small) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer- and feeder-level instrumentation. Within this work’s analysis, congestion is caused exclusively by transformer overloading and voltage-band violations. No individual line exceeds its thermal rating (maximum: 98.6%). Equipment size governs congestion onset for a given deployment trajectory: under large equipment, congestion remains absent through 2045, under medium equipment it emerges from 2035 (4 of 10 scenarios), and under small equipment from 2025 (9 of 10). Without transformer instrumentation, median voltage estimation errors reach 6–42% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by an order of magnitude, achieving median errors of 0.5–1.4%. In urban networks, transformer-level instrumentation meets the VDE FNN voltage accuracy target (99th percentile voltage error below 2%) in all configurations. In rural networks under small equipment, the target is approached but not met. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption-driven metering rollout with risk-based deployment criteria linked to local congestion exposure. Full article

Red mud is a waste byproduct of alumina production. Its release into the environment poses risks, highlighting the need for strategies to limit pollution. Using red mud as a filler in polymer-matrix composites can reduce the leaching of heavy metals and metalloids. We fabricated composites with high red mud content (up to 60 wt.%) using two particle fractions (<125 μm and <500 μm). The study examined how filler concentration and particle size affected the composites’ microstructure and mechanical properties. Results showed that composites with smaller particles had better encapsulation and enhanced structural qualities, such as reduced porosity and fewer cracks. Among four filler mass loadings (20, 30, 40, and 60 wt.%), composites with 40 and 60 wt.% red mud exhibited greater epoxy penetration into agglomerates and partial deagglomeration, resulting in small, uniformly dispersed red mud particles within the matrix. Calorimetry analysis demonstrated that increasing the red mud concentration slows the curing process: for composites with 20 wt.% red mud, the curing time is approximately 10 h, whereas for composites with 60 wt.%, approximately 35 h. We performed a thorough environmental safety evaluation of high-loaded red mud–epoxy composites in accordance with the standard AS 4439.3:2019. The tests showed that epoxy resin significantly reduces the levels of potentially hazardous elements, such as Na and Al, in the leachates, demonstrating the safety of the composites. Composites with 40 wt.% red mud (particle size < 125 μm) showed the most effective epoxy impregnation into red mud agglomerates and demonstrated the best encapsulation behaviour, releasing the least amount of metals compared to red mud during both 20 h and 4-week, long-term leaching tests. Full article

High renewable penetration, distributed energy resources, and fast-varying electric loads are shifting smart-grid planning from energy-balance simulation toward quality-aware operational assessment. Full-unit benchmark models (FULL) preserve unit commitment, ramping memory, and reserve feasibility but are expensive for repeated annual studies, whereas conventional equivalent aggregation (EQ) can overstate the realizable flexibility of heterogeneous units. This paper proposes quality-aware flexibility-envelope aggregation (QFEA), which separates units by inherited boundary state, ranks them by renewable-following flexibility, constructs conservative cluster envelopes, and couples reduced optimization with feasible disaggregation and state write-back. The model coordinates renewable curtailment, reserve sufficiency, tie-line ramping, and a normalized quality-stress proxy without claiming to replace detailed power-flow, harmonic, or electromagnetic studies. In the nominal single-region case, QFEA reduces the number of commitment objects by 46.2% and computation time by 63.7%, while limiting total-cost deviation to 1.1% and renewable-curtailment deviation to 0.2 percentage points. In 20 matched 24-h stress scenarios, its mean quality-stress index is 2.56%, compared with 2.58% for FULL and 6.57% for EQ. A separate 13–104-unit simplified scaling test keeps inverse-mapping closure error below 5.2 × 10⁻⁹ MWh and disaggregation below 1.3% of measured end-to-end time. The results identify QFEA as a traceable intermediate model for renewable-integration screening when annual computational efficiency and implementable unit trajectories are both required. Full article

Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect investor behavioral biases and asset pricing efficiency and whether these effects exhibit nonlinear characteristics. Using investor-level trading records, survey data, and market data from the Chinese A-share market (N = 12,846 investors; 3876 questionnaires; 3 million+ transactions), we construct measures of machine learning adoption intensity, investor behavioral biases, and asset pricing efficiency. Employing fixed-effects models, instrumental-variable estimation (2SLS), and mediation analysis, we examine the behavioral and market consequences of machine learning adoption. The results reveal a significant U-shaped relationship between machine learning adoption intensity and investor behavioral biases (inflection point: AIDI* = 0.731), and an inverted U-shaped relationship between AI market penetration and asset pricing efficiency (threshold: AIPM* = 0.733). Investor behavioral bias mediates 26.34% of the total effect of AI adoption on pricing efficiency. Moderate adoption reduces behavioral biases by improving information processing and decision quality, whereas excessive reliance on algorithmic recommendations generates automation bias and weakens investors’ independent judgment. At the market level, machine learning adoption exhibits an inverted U-shaped relationship with asset pricing efficiency. While moderate adoption enhances information incorporation into prices and reduces pricing deviations, excessive market penetration may induce algorithmic homogeneity and diminish efficiency gains. Furthermore, investor behavioral bias serves as an important transmission mechanism linking machine learning adoption to asset pricing outcomes. Heterogeneity analyses indicate that institutional investors benefit more from machine learning tools than individual investors, and the effects are stronger during periods of high market uncertainty. These findings provide new evidence on the optimal adoption of machine learning in financial markets and offer practical implications for intelligent investment platforms, investor education, and financial regulation. Full article

This study primarily aimed to evaluate how the chemical form and carrier medium of radionuclide contamination influence the effectiveness of skin decontamination procedures. In addition, the study sought to identify decontamination strategies that align with recommended practices while reducing reliance on intensive mechanical cleaning methods. Although skin damage was not directly evaluated, the findings provide valuable information for improving the safety of decontamination procedures used by personnel handling radioactive materials and by emergency responders involved in radiological and CBRN (Chemical, Biological, Radiological, and Nuclear) incidents. Accidental spills of radiopharmaceuticals in laboratories and medical facilities, as well as contamination associated with uranium mining, fuel-cycle operations, spent fuel management, and the decommissioning of nuclear facilities, may involve radioactive isotopes present in a variety of chemical forms and solutions. Fresh porcine skin was used as an experimental model, and skin temperature was maintained at 37 °C to simulate physiological conditions. Europium-152 (¹⁵²Eu) was selected as the model radionuclide. Deionized water, concentrated nitric acid (HNO₃), saturated sodium hydroxide (NaOH) solution, and ethanol were used as carrier media representing different chemical environments of ¹⁵²Eu contamination. To simulate realistic contamination scenarios, contaminating solutions were allowed to dry on the skin surface before decontamination. The influence of contaminant chemistry, carrier medium, and drying conditions on radionuclide penetration and subsequent decontamination effectiveness was investigated. Particular attention was given to the extent to which different physicochemical forms of contamination affected radionuclide removal from the skin. The results demonstrated that the chemical form of the contaminant and the drying conditions were key factors determining decontamination efficiency. Among the tested methods, a decontamination kit consisting of a soap-based solution, the complexing agent DTPA, and an absorbent non-woven swab achieved the highest radionuclide removal efficiency. These findings indicate that successful radionuclide decontamination depends strongly on the physicochemical properties of the contaminant. The combined use of a complexing agent, detergent-based formulation, and absorbent material can significantly enhance radionuclide removal from contaminated skin surfaces. Furthermore, the study highlights the importance of considering contaminant chemistry when developing effective and safe decontamination protocols for radiological and CBRN incidents. Full article

High-quality renewable energy (RE) scenario generation is essential for secure, reliable, and economic power system operation under high RE penetration. To address the limitations of existing methods in preserving scenario fidelity, diversity, spatiotemporal dependence, and engineering consistency, this paper proposes a three-stage framework that combines a variational autoencoder (VAE) with a conditional latent diffusion model (CLDM), hereafter referred to as VAE-CLDM, for day-ahead renewable energy scenario generation. First, multi-source features are constructed by integrating renewable power outputs, meteorological variables, temporal lag information, and spatial correlation characteristics among wind farms and photovoltaic stations. Then, VAE compresses the high-dimensional features into a compact latent space while retaining key statistical and spatiotemporal information. Based on this latent representation, a CLDM generates realistic scenarios by progressively denoising random noise under meteorological conditions. A spatiotemporal feature modeling strategy is incorporated to better represent temporal fluctuations and inter-site correlations, while a diversity regulation factor is selected on the validation set to balance scenario fidelity and tail-event coverage. Finally, the generated scenarios are reconstructed into the physical space and checked using output-bound and ramp-consistency correction to improve their practical usability. Results on the open dataset released by the Chinese State Grid Renewable Energy Generation Forecasting Competition show that the proposed framework achieves the lowest root mean square error (RMSE), mean absolute error (MAE), and maximum mean discrepancy (MMD) among the tested models on the training-statistics-standardized renewable-power benchmark, with RMSE of $0.3013\pm 0.0022$, MAE of $0.3636\pm 0.0010$, and MMD of $0.04269\pm 0.00040$. After post-correction, lower- and upper-bound violations are reduced to 0.00%, and the ramp-violation rate is reduced to 0.08%, indicating that the proposed VAE-CLDM can provide useful scenario inputs for day-ahead dispatch and risk assessment in renewable-dominated power systems. Full article