Search Results (7,014)

Dust generated during coal mining and transportation poses serious threats to miners’ health, operational safety, and the surrounding environment. However, comprehensive review studies on dust suppression in coal mines remain limited, particularly those integrating dust movement laws with numerical simulation approaches. This review presents a systematic and reproducible analysis of dust control methods in coal mines with a particular focus on numerical simulation. Current research progress and development trends are summarized from three aspects: structural optimization of dust suppression devices, optimization of operating conditions, and ventilation system design. Existing studies indicate that structural improvements mainly concentrate on nozzle geometry, diameter, installation position, and spraying distance, while operating condition optimization primarily involves pressure regulation. Due to the complexity and high cost of full-scale experimental platforms, ventilation system optimization is largely achieved through numerical simulation, supplemented by field measurements. Studies based purely on numerical simulations remain limited in addressing the chemical modification of dust removers; however, with the advancement of molecular dynamics techniques, this area may represent a promising direction for future research. Full article

Snow accumulation and ice formation can significantly reduce pavement friction, posing a serious threat to traffic safety during winter. Traditional snow-removal methods, including mechanical removal, chemical de-icing agents, and heated pavement systems, suffer from several limitations such as low efficiency, environmental impacts, and high operational costs. Electrically conductive asphalt concrete (ECAC) has therefore emerged as a promising active snow-melting technology. When an electric current passes through the conductive network formed within the asphalt mixture, heat is generated through the Joule heating effect. After incorporating conductive fillers, the electrical resistivity of ECAC mixtures can be reduced from approximately 10⁶–10⁸ Ω·cm for conventional asphalt mixtures to about 10⁻¹–10² Ω·cm. Under an applied voltage typically ranging from 30 to 60 V, ECAC pavements can increase the surface temperature by 10–30 °C within 10–30 min, thereby enabling rapid snow melting and ice removal. Meanwhile, an optimized conductive network can maintain sufficient mechanical performance, with dynamic stability generally exceeding 3000 cycles/mm. When the conductive filler content is reasonably controlled, only a limited reduction in fatigue resistance is observed. This paper presents a comprehensive review of electrically conductive asphalt concrete technologies for snow-melting pavements. The background, underlying mechanisms, material development, system configuration, and field applications of ECAC are systematically summarized. Finally, the current challenges are discussed, including the stability of conductive networks, the trade-off between electrical conductivity and pavement performance, and electrical safety. Future research directions focusing on material optimization, intelligent power control, and long-term field performance evaluation are proposed to support the practical application of ECAC pavements in sustainable winter road maintenance. Full article

Lithium is a critical mineral for traction batteries and a cornerstone of the sustainable transition toward low-carbon transportation. Understanding the supply–demand dynamics and resource-saving potential of lithium is essential for advancing circular economy goals and ensuring the long-term stability of the electric vehicle (EV) industry. This study develops an integrated lithium forecast framework by coupling a System Dynamics (SD) model with dynamic Material Flow Analysis (MFA) and multi-scenario pathways. To ensure robust conclusions, the model is validated against historical data, and a multi-level sensitivity analysis is conducted to address the inherent uncertainties of evolving socio-technical assumptions over a ten-year horizon. The simulation results reveal that under the baseline scenario, China’s EV stocks and annual lithium demand will grow by 8.3 and 4.7 times from 2024 to 2035, respectively. This rapid expansion poses a significant sustainability challenge, as cumulative demand will deplete 50–71% of China’s domestic lithium reserves by 2035. Despite a projected supply–demand gap of 110–120 kt/yr, the study identifies critical pathways for resource decoupling and circularity. Technology-driven interventions, such as enhancing energy density and extending battery lifespan, can reduce primary lithium demand by up to 18.9%. Furthermore, optimizing the closed-loop recycling system can contract the supply–demand gap by 31–39%, demonstrating the pivotal role of secondary resource recovery in building a resilient supply chain. Despite this reduction, a persistent reliance on international markets remains inevitable. These findings provide a quantified scientific foundation for policymakers, emphasizing that lithium security requires a synergistic transition from volume-based subsidies to resource efficiency mandates and standardized, formal closed-loop recycling systems. Full article

Urban heat islands (UHIs) pose escalating threats to public health and thermal comfort in dense urban environments. However, physics-based evaluations of material-specific cooling interventions and their integration into operational digital twin platforms remain limited. This study develops an integrated framework connecting computational fluid dynamics (CFD) modeling with digital twin visualization to evaluate UHI mitigation strategies. The objectives are to quantify the thermal mitigation effects of surface emissivity optimization on land surface temperature (LST) and pedestrian-level air temperature ($T_{air}$) to establish a data preprocessing pipeline converting CFD outputs into platform-independent visualization datasets, and to comparatively evaluate 2D GIS-based and 3D voxelization visualization approaches. Four emissivity scenarios were simulated using STAR-CCM+ for a 4 km² residential area in Gwacheon City, Republic of Korea. Comprehensive optimization (Case D) reduced the mean LST from 46.6 °C to 42.0 °C and T_air from 35.7 °C to 35.3 °C. Concrete-only optimization achieved 90.5% of the total thermal reduction while decreasing spatial variability (σ) from 7.1 to 5.8 during peak hours. The voxel-based 3D visualization provided a superior representation of vertical thermal stratification compared to 2D mapping. These findings establish a scalable foundation for climate-responsive urban management. Full article

Background/Objectives: Recombinant adeno-associated virus (rAAV) has become a leading vector in gene therapy. However, manufacturing limitations may result in replication-competent AAV (rcAAV) contamination of clinical rAAV products, posing safety risks. Rigorous testing is therefore essential, and the use of accurately calibrated rcAAV reference standard materials is critical for ensuring assay stability and reliability. A disadvantage of the widely used Tissue Culture Infectious Dose 50 (TCID₅₀) assay is its high variability. This study introduces an optimized TCID₅₀ assay for the precise quantification of infectious rcAAV particles. Methods: We developed a TCID₅₀ assay tailored to rep2-based rcAAV, optimizing key aspects such as viral infection conditions, qPCR reaction systems, and standard curve preparation. We employed an innovative strategy to prepare the standard curve using serial dilutions of rcAAV in cell lysate, ensuring alignment with the test sample matrices. Results: The rcAAV-derived standard curve demonstrated exceptional linearity (R² > 0.99), sensitivity (LOQ ≈ 38 copies), and reproducibility, enabling robust endpoint qPCR analysis. The optimized assay significantly improved the precision of the TCID₅₀ assay, as an inter-assay coefficient of variation (CV) of 11.4% was achieved. Conclusions: This refined TCID₅₀ assay is a reliable method for calibrating infectious titers of rcAAV reference standard materials, thereby enabling the standardization of rcAAV testing. Full article

Invasive alien plants (IAPs) pose a severe and escalating threat to biodiversity and ecosystem services in China. However, a systematic nationwide assessment that identifies invasion hotspots, quantifies their overlap with protected area networks, and pinpoints critical conservation gaps is still lacking. This hinders the development of spatially targeted management strategies. To address this, we developed an integrated analytical framework coupling the Maximum Entropy (MaxEnt) model with the InVEST habitat quality model. Using a high-resolution, county-level distribution database of 293 IAPs, we mapped potential species richness and habitat degradation across China. The geo-detector model was further employed to identify the primary environmental factors and their interactions. Spatial overlay analysis was conducted to delineate core invasion habitats (areas of high invasion suitability and high degradation) and assess their coverage within China’s national nature reserves. Nighttime light intensity (DMSP, 34.39%), annual precipitation (Bio12, 14.16%), and mean diurnal range (Bio2, 11.82%) were the factors with the highest contribution in the model, highlighting the statistical interaction between anthropogenic pressure and climatic conditions. The core invasion habitat spanned 20.10 × 10⁴ km², predominantly (66.04%) concentrated in high-intensity human disturbance zones. Notably, only 11.18% of this core habitat falls within existing national nature reserves, revealing a vast conservation gap of 17.85 × 10⁴ km². Our results indicate a profound spatial mismatch between invasion hotspots and the current protected area network in China. We prioritize southeastern coastal urban agglomerations-characterized by high anthropogenic pressure (DMSP), high precipitation (Bio12), and low diurnal temperature range (Bio2)-for immediate monitoring and intervention. This integrated assessment provides a national-scale, spatially explicit prediction of invasion risk for 293 plant species in China, and offers an evidence-based decision-support tool for optimizing invasive species management and biodiversity conservation. Full article

Mandatory lane-changing in complex driving scenarios poses significant challenges for autonomous driving systems due to complex vehicle interactions and strict safety requirements. Existing methods often rely on handcrafted rules or extensive expert demonstrations, which increase data collection costs and provide limited safety guarantees during learning. To address these issues, this paper proposes a rule-guided reinforcement learning framework for lane-changing policy optimization. A lightweight rule-based controller is employed to generate initial experience, guiding the training of an improved Distributional Soft Actor–Critic with Three Refinements (DSAC-T), while a safety-aware constraint controller filters high-risk actions to ensure stable and safe learning. The proposed method is evaluated in Regular Lane Change and Lane Merging scenarios under mixed traffic composed of aggressive and conservative vehicles within a simulation environment. Simulation results show that although lane-changing success rates decrease as traffic aggressiveness increases, the proposed method consistently outperforms SAC and TD3. Notably, under highly aggressive traffic conditions with an aggressiveness ratio of 0.7, the proposed approach improves the success rate by 17.13% compared to SAC and by 10.49% compared to TD3, demonstrating superior robustness and safety in complex, high-conflict lane-changing scenarios. The present study is conducted solely in simulation and requires further validation before application to real-world traffic environments. Full article

Environments with sparse or repetitive geometric structures, such as long corridors and narrow stairwells, remain challenging for LiDAR–inertial simultaneous localization and mapping (LiDAR–IMU SLAM) due to insufficient geometric observability and unreliable data associations. To address these issues, we propose VINA-SLAM, a novel LiDAR–IMU SLAM framework that constructs a unified global voxel map to explicitly exploit structural consistency. VINA-SLAM continuously tracks surface normals stored in the global voxel map using a normal-guided correspondence strategy, enabling stable scan-to-map alignment in degenerate scenes. Furthermore, a tangent-space metric is introduced to supplement missing rotational constraints around planar regions, providing reliable initial pose estimates for local optimization. A tightly coupled sliding-window bundle adjustment is then formulated by jointly incorporating IMU factors, voxel normal consistency factors, and planar regularization terms. In particular, the minimum eigenvalue of each voxel’s covariance is used as a statistically principled planar constraint, improving the Hessian conditioning and cross-view geometric consistency. The proposed system directly aligns raw LiDAR scans to the voxelized map without explicit feature extraction or loop closure. Experiments on 25 sequences from the HILTI and MARS-LVIG datasets show that VINA-SLAM reduces ATE by 25–40% on average while maintaining real-time performance at 10 Hz in the evaluated geometrically degenerate environments. Full article

Acidic mine drainage (AMD) poses a severe global environmental threat due to its high acidity and elevated levels of toxic hexavalent chromium (Cr(VI)), for which biogenic iron sulfide (FeS) nanoparticles have emerged as a promising remediation agent; however, their practical application is hindered by aggregation and oxidative deactivation. This research synthesized biogenic FeS nanoparticles via sulfate-reducing bacteria (SRB) and employed humic acid (HA) as a stabilizing agent to enhance Cr(VI) removal performance in simulated AMD conditions. Single-factor experiments combined with response surface methodology identified the optimal biosynthetic conditions for FeS: yeast extract powder dosage of 2.2 g/L, Fe/S molar ratio of 0.8, and NH₄Cl dosage of 3.1 g/L. Under these conditions, the material achieved 84.25% Cr(VI) removal, with the Fe/S molar ratio identified as the most influential parameter governing synthesis and performance. Introducing HA at an optimal dosage of 2 mg/L drove marked improvements in both nanoparticle yield and reactivity: FeS yield increased to 1096.26 mg/L, Cr(VI) removal efficiency reached 99.62%, and residual Cr(VI) dropped from 15.75 mg/L to just 0.38 mg/L. Kinetic and isotherm analyses, paired with SEM/TEM imaging and zeta potential measurements, revealed that HA stabilization improved particle dispersion and reduced lamellar stacking, resulting in a surface-controlled Cr(VI) removal process. FTIR and 2D-COS analyses demonstrated that HA-derived oxygen-containing functional groups, including O–H/N–H, C=O, and C–O moieties, played a central role in interfacial interactions during Cr(VI) sequestration. XRD results confirmed that Cr(VI) was reduced to Cr(III) and primarily immobilized as low-solubility CrOOH and Cr₂S₃, while the formation of Fe–Cr spinel-like phases remains tentative without X-ray Photoelectron Spectroscopy (XPS) validation. Further investigation via surface-sensitive spectroscopy and dynamic leaching tests is needed to fully assess the long-term stability of the reaction products. Full article

Proton exchange membrane fuel cells (PEMFCs) are promising energy conversion de-vices owing to high efficiency and zero local emissions. Accurate PEMFC performance assessment and control require well-posed models, whose predictive accuracy is largely determined by the correct calibration of key parameters. Metaheuristic algorithms (MHAs) have therefore been widely applied to PEMFC stack parameter estimation, but their rapid proliferation calls for a more systematic and fine-grained synthesis. This review refines the taxonomy of PEMFC mathematical modeling approaches and summarizes Zero-Dimensional PEMFC modeling methods, key parameters, and representative improvement directions aimed at reducing identification difficulty while retaining physical meaning. Newly developed MHAs and enhanced variants of existing methods are then surveyed, and over 40 distinctive optimization approaches are selected for systematic comparison. Modeling approaches and parameter identification methodologies are summarized. In addition, an algorithm selection guide and 26 representative algorithms with their variants are compiled and benchmarked across the five most widely used commercial PEMFC models to enable cross-model comparison. Full article

Laser wire directed energy deposition (LWDED) has garnered significant attention for the fabrication of large metallic components. However, the complex coupling effects among its process parameters pose challenges for porosity control. Optimizing parameter combinations to effectively minimize porosity is therefore critical to the broader adoption of this technology. In this study, systematic experiments and modeling were conducted to optimize the LWDED process parameters and predict porosity. First, single-factor and orthogonal experiments were performed to evaluate the individual effects of laser power, scanning speed, wire feeding speed, and air pressure on porosity. Subsequently, range analysis and analysis of variance were employed to determine the influence of each parameter and the significance of their interactions. Four machine learning models—SVR, RF, GPR, and XGBoost—were then trained and compared. Among them, the SVR model exhibited the best predictive performance, achieving an R² of 0.8960, an RMSE of 0.19, and an MAE of 0.15, outperforming the other three models. Based on this, the SVR model was further utilized to establish the mapping between process parameters and porosity. Contour maps and three-dimensional surface plots were generated to visualize porosity variation patterns under interacting parameters. Validation experiments showed that the maximum relative error between model predictions and experimental measurements was 0.514%, with an average error of 0.251%. This study provides a reliable reference for selecting low-porosity parameter combinations in the LWDED fabrication of 5356 aluminum alloy components. Full article

Background: The H1N1 influenza A virus evades host immunity through continuous antigenic drift, posing a significant challenge to broad-spectrum neutralizing antibody therapies. This study aims to systematically evaluate the neutralizing capacity of the broad-spectrum antibody C12H5 against H1N1 strains from different eras and identify key immune escape mutation sites. Methods: Three representative H1N1 virus strains from 2009, 2018, and 2023 were selected. An antigen–antibody binding prediction model based on the ESM-2 large language model was constructed by integrating 48,762 GISAID sequence data and deep mutation scanning data from the Bloom laboratory. Candidate escape sites were screened using SHAP (SHapley Additive exPlanations) value analysis. Mutant viruses were constructed via reverse genetics, and their neutralizing capacity and replication fitness were validated through hemagglutination inhibition assays, microneutralization assays, and viral growth kinetics analysis. Results: Machine learning scoring identified five potential escape sites, with K147 exhibiting the highest overall score (0.92). SHAP analysis revealed that the K147 site within the HA protein’s 130-loop region received the highest importance score (0.28), significantly surpassing other candidate sites. Experimental validation revealed that the K147N mutation reduced neutralizing potency against C12H5 by 8-fold (from 1:1024 to 1:128) and approximately 6-fold in microneutralization assays (from 8.3 log₂ to 5.7 log₂), while exhibiting a replication advantage in MDCK cells. Microneutralization assays further confirmed an approximately 6-fold reduction in neutralization sensitivity. Structural analysis indicated that K147 is located at the periphery of the HA receptor-binding domain, immediately adjacent to the receptor-binding site. Conclusions: K147N is identified as the critical mutation mediating C12H5 immune escape, and this mutation has emerged in 2023 circulating strains. This study provides important molecular targets and early warning mechanisms for broad-spectrum antibody optimization and influenza vaccine updates. Full article

As key power components in aviation machinery, airborne hydraulic systems exhibit significant coupling, nonlinearity, and strong noise interference, which pose enormous challenges for their mechanical fault diagnosis—an essential link in ensuring aviation mechanical system reliability. To address this issue, a policy gradient-based optimization method is proposed to autonomously tune network parameters, aiming to enhance the accuracy and robustness of mechanical fault diagnosis. Initially, a KAN (Kolmogorov–Arnold Network) convolution submodel is adopted to strengthen the extraction of weak mechanical fault features from complex hydraulic signals. Subsequently, the policy gradient methodology is employed to iteratively refine the overall network configuration, enabling adaptive optimization of fault diagnosis-related parameters. Extensive experiments on standard hydraulic system datasets demonstrate that the proposed approach outperforms other mainstream intelligent mechanical fault diagnosis methods in terms of diagnostic accuracy, anti-interference ability, and generalization performance. Full article

With the rapid development of the offshore wind power industry, the construction process of offshore wind power jackets faces numerous quality risks, particularly in welding, coating, and assembly operations. This paper aims to investigate the identification, assessment, and management of quality risks during the construction of offshore wind turbine foundation structures. By establishing a multidimensional quality risk assessment framework, key risk factors affecting quality were identified through expert interviews and brainstorming sessions. Comprehensive evaluations of these risk factors were conducted using the Entropy Weight Method (EWM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA). The findings indicate that welding and coating processes pose the highest risks during construction. Based on these assessments, corresponding risk mitigation measures are proposed, including process optimization, automation enhancement, environmental control, and management system refinement. This study provides theoretical foundations and practical guidance for improving construction quality and reducing costs in offshore wind turbine foundation manufacturing. It advances quality risk management by introducing an integrated evaluation model that addresses the limitations of single-method approaches in complex construction scenarios. Full article

Soil erosion poses a significant threat to slope stability and ecological functionality. The litter layer, with its complex physical structure, enhances surface roughness, mitigates direct rainfall impact, and improves rainwater interception and soil retention. A litter of three typical slope-protection plant species from Wuhan, Hubei Province, China (Cynodon dactylon, Indigofera amblyantha, and Cinnamomum camphora) was selected for this experiment. This study quantified the effects of litter mulch at four coverage levels (0, 500, 800, and 1000 g/m² based on dry mass) on slope runoff and sediment yield under simulated rainfall conditions at an intensity of 60 mm/h for a duration of one hour. The results indicated that (1) all litter types and coverage amounts effectively delayed the initiation of slope runoff, though their efficiencies in runoff and sediment reduction varied significantly. (2) Compared with the bare slope, the sediment yield in the plots covered with litter from Cynodon dactylon, Cinnamomum camphora, and Indigofera amblyantha decreased by 96.5%, 67.5%, and 9.4%, respectively, at a coverage of 800 g/m². Runoff yield decreased by 56.9% and 29.7% in the plots covered with Cynodon dactylon and Cinnamomum camphora litter, whereas Indigofera amblyantha litter cover instead increased runoff yield by 31.6%. (3) Furthermore, increasing litter coverage from 500 to 1000 g/m² progressively reduced runoff by 29% to 84% and sediment yield by 27.3% to 93.6% compared to the bare slope. These findings demonstrate the importance of litter cover in reducing runoff and soil erosion, offering quantitative support for optimizing vegetation-based slope management. Full article