Search Results (1,706)

This work presents the structural analysis and validation of a sub-250 g FPV drone chassis, emphasizing both theoretical rigor and practical applicability. The novelty of this contribution lies in four complementary aspects. First, the structural philosophy introduces a screwless frame with interchangeable arms, joined through interlocking mechanisms inspired by traditional Japanese joinery. This approach mitigates stress concentrations, reduces weight by eliminating fasteners, and enables rapid arm replacement in the field. Second, validation relies on nonlinear static and transient FEM simulations, explicitly including crash scenarios at 5 m/s, systematically cross-checked with bench tests and instrumented flight trials. Third, unlike most structural studies, the framework integrates firmware (Betaflight), GPS, telemetry, and real flight performance, linking structural reliability with operational robustness. Finally, a practical materials pathway was implemented through a dual-track strategy: PETG for rapid, low-cost prototyping, and carbon fiber composites as the benchmark for production-level performance. Nonlinear transient FEM analyses were carried out using Inventor Nastran under multiple load cases, including maximum motor acceleration, pitch maneuvers, and lateral impact at 40 km/h, and were validated against simplified analytical models. Experimental validation included bench and in-flight trials with integrated telemetry and autonomous features such as Return-to-Home, demonstrating functional robustness. The results show that the prototype flies correctly and that the chassis withstands the loads experienced during flight, including accelerations up to 4.2 G (41.19 m/${\mathrm{s}}^2$), abrupt changes in direction, and high-speed maneuvers reaching approximately 116 km/h. Quantitatively, safety factors of approximately 5.3 under maximum thrust and 1.35 during impact confirm sufficient structural integrity for operational conditions. In comparison with prior works reviewed in this study, the key contribution of this work lies in unifying advanced, crash-resilient FEM simulations with firmware-linked flight validation and a scalable material strategy, establishing a distinctive and comprehensive workflow for the development of sub-250 g UAVs. Full article

Against the backdrop of intelligent manufacturing, the ball mill, as a key energy-consuming piece of equipment, requires an accurate perception of its load state, which is crucial for optimizing production efficiency and ensuring operational safety. However, its vibration signals exhibit typical nonlinear and non-stationary characteristics, intertwined with complex noise, posing significant challenges to high-precision identification. A core contradiction exists in existing diagnostic methods: convolution network-based methods excel at capturing local features but overlook global trends, while Transformer-type models, although capable of capturing long-range dependencies, tend to “average out” critical local transient information during modeling. To address this dilemma, this paper proposes a new paradigm for multi-scale feature collaborative perception. This paradigm is implemented through an innovative deep learning architecture—the Residual Block-Swin Transformer Network (RB-SwinT). This architecture subtly achieves hierarchical and in-depth integration of the powerful global context modeling capability of Swin Transformer and the excellent local detail refinement capability of the residual module (ResBlock), enabling synchronous and efficient representation of both the macro trends and micro mutations of signals. On the experimental dataset covering nine types of fine operating conditions, the overall recognition accuracy of the proposed method reaches as high as 96.20%, which is significantly superior to a variety of mainstream models. To further verify the model’s generalization ability, this study was tested on the CWRU public bearing fault dataset, achieving a recognition accuracy of 99.36%, which outperforms various comparative methods such as SAVMD-CNN. This study not only provides a reliable new technical approach for ball mill load identification but also demonstrates its practical application value in indicating critical operating conditions and optimizing production operations through an in-depth analysis of the physical connotations of each load level. More importantly, its “global-local” collaborative modeling concept opens up a promising technical path for processing a broader range of complex industrial time-series data. Full article

Hydropower is the primary source of electricity in several countries in Latin America. Hydropower provides approximately 80% of Ecuador’s electricity; however, it remains highly vulnerable to climate change, resulting in uncertainties in power generation due to altered precipitation patterns, runoff, and systematic failures. Consequently, Ecuadorians are becoming increasingly reliant on diesel generators during crises, resulting in public health, safety, and economic impacts, as well as social and political disruptions. This study evaluated noise pollution in the central urban area of the city of Loja for the first time during the 2024–2025 electricity crisis in Ecuador. A Type 1 integrating sound-level meter was used to monitor noise pollution (LAeq, 10min) at 20 locations during periods of generator operation and non-operation. At each location, the number of generators, the density of commercial activities along the streets, as well as traffic and other urban characteristics, were recorded. Results revealed that the presence of generators, street width, and the number of generators significantly increased the LAeq, 10min, often exceeding the limits set by the World Health Organization and Ecuador’s environmental regulations. Frequency spectrum analysis revealed that medium frequencies increased with A-weighting, while low frequencies rose with C-weighting, suggesting potential health risks to the local population. The thematic noise map during generator inactivity showed lower noise levels, averaging around 71.5 dBA. Conversely, when the generators were operational, noise levels exceeded 79.6 dBA, indicating a significant increase in environmental noise exposure associated with their use. This highlights an urgent need to implement and expand renewable energy sources, as existing options like wind power, photovoltaic energy, and biomass are insufficient to meet community demands. Full article

Antibiotic resistance genes (ARGs) have emerged as globally concerning environmental contaminants, posing serious threats to ecosystem health and public safety. This systematic review summarizes global research trends on ARGs across three key aspects: (i) identification and distribution in river and lake ecosystems, (ii) sources and environmental behaviors, and (iii) ecological and human health risks. Concentration data of ARGs in various rivers and lakes across China were compiled to reveal their spatial distribution patterns. The analysis of ARGs sources and environmental behaviors provides essential insights for designing effective mitigation strategies. Furthermore, this review highlights the potential ecological and human health hazards of ARGs and discusses limitations and improvement directions of current risk assessment methodologies. The main findings indicate that ARGs are widely present in rivers and lakes across China; higher abundances occur in eastern and southern regions compared with central–western and northern areas, such as 4.93 × 10²–8.10 × 10³ copies/mL in Qinghai Lake and 6.7 × 10⁷–1.76 × 10⁸ copies/mL in Taihu Lake. The environmental behaviors of ARGs are highly complex, involving multiple mechanisms and influenced by climatic conditions, nutrient levels, and additional environmental factors. Based on these findings, future efforts should prioritize long-term site-specific monitoring, evaluate their prolonged impacts on aquatic ecosystems, and develop integrated risk assessment models to support evidence-based environmental management. Full article

Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals. Full article

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams. Full article

A vast number of boreholes in underground mining operations are often plagued by deviation issues, which severely impact both production efficiency and safety. The accurate and rapid acquisition of borehole trajectories is fundamental for subsequent deviation control and correction. However, existing inclinometers are limited by their operational efficiency and estimation accuracy, making them inadequate for large-scale measurement demands. To address this, this paper proposes a novel method for the rapid and accurate reconstruction of underground mine borehole trajectories using a robotic system. We employ a custom-designed robot equipped with an Inertial Measurement Unit (IMU) and a displacement sensor, which travels stably while collecting real-time attitude and depth information. Algorithmically, a complementary filter is used to fuse data from the gyroscope with that from the accelerometer and magnetometer, overcoming both integration drift and environmental disturbances. A cubic spline interpolation algorithm is then utilized to time-register the low-sampling-rate displacement data with the high-frequency attitude data, creating a time-synchronized sequence of ‘attitude–displacement increment’ pairs. Finally, the 3D borehole trajectory is accurately reconstructed by mapping the attitude quaternions to direction vectors and recursively accumulating the displacement increments. Comparative experiments demonstrate that the proposed method significantly improves efficiency. On a complex trajectory, the maximum and mean errors were reduced to 0.38 m and 0.18 m, respectively. This level of accuracy is far superior to that of the conventional static point-by-point measurement mode and effectively suppresses the accumulation of dynamic errors. This work provides a new solution for routine borehole trajectory surveying in mining operations. Full article

This review systematically examined the transformative role of machine learning in predicting polymer aging lifetime, addressing critical limitations of conventional methods such as the Arrhenius model, time–temperature superposition principle, and numerical fitting approaches. The primary objective was to establish a comprehensive framework that integrates multi-mechanism coupling with dynamic data-driven modeling to enhance prediction accuracy across complex aging scenarios. Four key machine learning categories demonstrate distinct advantages: support vector machines effectively capture nonlinear interactions in multi-stress environments; neural networks enable cross-scale modeling from molecular dynamics to macroscopic failure; decision tree models provide interpretable feature importance quantification; and hybrid approaches synergistically combine complementary strengths. These methodologies have shown significant success in critical industrial applications, including building trades, photovoltaic systems, and aerospace composites, creating an integrated predictive system that bridges molecular-level dynamics with service-life performance. By transforming life prediction from empirical extrapolation to mechanism-based simulation, this machine-learning-driven paradigm offers robust methodological support for engineering safety design in diverse polymer applications through its capacity to model complex environmental interactions, adapt to real-time monitoring data, and elucidate underlying degradation mechanisms. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article

The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment. Full article

Akkermansia muciniphila—a mucus-resident commensal—has emerged as a promising target at the interface of metabolism, barrier function, and immunity. Observational human studies link higher intestinal abundance of A. muciniphila with healthier adiposity and glycemic profiles, while preclinical experiments demonstrate causal benefits on adiposity, insulin resistance, gut-barrier integrity, and inflammatory tone. These effects are attributed to mucus-layer reinforcement, reduced intestinal permeability and endotoxemia, production of short-chain fatty acids, and host signaling by defined bacterial components. In a randomized proof-of-concept trial in overweight/obese insulin-resistant adults, pasteurized A. muciniphila was safe and well-tolerated and improved insulin sensitivity and total cholesterol versus placebo; live cells showed directionally favorable but non-significant trends. A separate multicenter randomized trial of a five-strain consortium that included A. muciniphila improved post-prandial glucose and HbA1c in type 2 diabetes, supporting translational potential while underscoring the need for strain-resolved studies. Evidence for liver and cardiovascular benefits is strong in animals (e.g., MASLD and atherosclerosis models) but remains preliminary in humans. Inter-individual response heterogeneity—potentially influenced by baseline Akkermansia levels and gut-barrier status—highlights the value of personalized, microbiome-guided approaches. Larger, longer clinical studies are now warranted to define optimal dosing and formulation (live vs. pasteurized), durability, safety across populations, and impacts on hard outcomes (clinically meaningful weight change, glycemic endpoints, and cardiometabolic events). Overall, A. muciniphila represents a promising microbial adjunct for metabolic health with a plausible path from postbiotic concepts to clinical application, pending confirmatory trials. Full article

Under the drive of the “dual carbon” goals, the insufficient frequency regulation capability of nuclear power as a baseload source and the dynamic demand of integrating a high proportion of renewable energy into the grid have increasingly highlighted conflicts. The inherent minute-level regulation inertia of nuclear power units struggles to cope with second-level frequency fluctuations in the grid, leading to an increased risk of system instability. There is an urgent need for energy storage technologies to fill the millisecond-level power support gap for nuclear power frequency regulation. This paper, focusing on nuclear power frequency regulation scenarios, constructs a “Technology–Economy–Policy” multidimensional energy storage evaluation system for the first time. Through a systematic analysis of 11 key indicators, such as response time and safety, the paper selects energy storage technologies suitable for nuclear power frequency regulation scenarios and proposes a hybrid energy storage optimization strategy. The research provides a systematic evaluation framework and empirical support for the selection of energy storage for nuclear power frequency regulation, with significant practical value in improving grid dynamic stability and promoting the construction of new power systems under the “dual carbon” goals. Full article

The growing use of UASs in complex operations, including Beyond Visual Line of Sight (BVLOS) operations and missions over populated areas, has increased the need for robust navigation integrity. Within this framework, a GNSS is often used as the primary source for positioning, but its reliability can be affected by various degradation sources, particularly in urban or constrained environments. This paper explores the implications of using GNSSs as an external service in SAIL III operations, with a focus on Operational Safety Objective (OSO) #13, defined in Specific Operations Risk Assessment (SORA) 2.5. A review of SORA 2.5 requirements is provided, followed by experiments involving GNSS data acquisitions in different environments using both high-end and mid-range receivers. Various performance indicators available from the receivers, such as the Dilution of Precision (DOP), Carrier-to-Noise Density Ratio (C/N₀), estimated accuracy, and PLs, are examined to assess their ability to detect navigation degradations in real time. The results show that Protection Levels outperform the other indicators in detecting degradations under challenging conditions, highlighting the current limitations of GNSS-based navigation monitoring for specific category UAS operations. Full article

Occupational exposure to soil-borne pesticides remains a critical safety and process-management challenge in industrial and agro-industrial settings. This work proposes a process-integrated analytical workflow that couples comparative instrumental identification of soil xenobiotics with an occupational risk assessment framework. We comparatively evaluate GC-MS (gas chromatography–mass spectrometry), HPLC (high performance liquid chromatography), FTIR (Fourier-Transform Infrared Spectroscopy), LC-MS/MS (Liquid Chromatography coupled with tandem Mass Spectrometry), and ICP-MS (Inductively Coupled Plasma Mass Spectrometry) against matrix complexity, sensitivity, cost, and throughput, and implement the Quick, easy, cheap, effective, rugged, safe (QuEChERS) method-based sample preparation followed by GC-MS and LC-MS/MS to demonstrate applicability on representative soil and food-chain samples. Complementary risk tools (toxicity–probability matrices, exposure pathway diagrams) and an integrated monitoring scheme that combines environmental data with biomonitoring are used to link concentrations to exposure potential and control priorities. In a soil case sample, low-level organochlorines were detected with total DDT at 0.010 mg/kg and total HCH at 0.003 mg/kg, illustrating how analytical outputs feed decision matrices for prioritizing interventions. Case analyses from agricultural and industrial contexts indicate that targeted substitution, optimized application, ventilation and dust control, PPE (personal protective equipment) adherence, and worker training can measurably reduce symptoms and biomarkers of exposure. Overall, a complementary, process-analytical approach—integrating sensitive multi-technique detection with exposure assessment and continuous monitoring—supports proactive risk management and aligns with process systems and monitoring themes. Recommendations include standardizing workflows, coupling routine environmental monitoring with biomonitoring where feasible, and embedding preventive policies and training into industrial management systems. Full article

This paper presents a global performance-based seismic assessment of an existing reinforced concrete hospital building retrofitted with dissipative bracing systems. The study aims to evaluate the overall effectiveness of different dissipative configurations, two traditional systems and one innovative low-activation solution in enhancing the seismic performance of the structure in compliance with the Italian Building Code (NTC 2018). The analyses were carried out using nonlinear static (pushover) procedures to determine the global capacity, equivalent damping, and displacement demand at the Life Safety (SLV) and Near Collapse (SLC) limit states. The retrofitting interventions were modeled assuming elastic connections between the existing RC frames and the added steel members, consistent with standard design practice in which connections are dimensioned with overstrength to avoid premature failure. The results demonstrate that the integration of dissipative systems significantly increases stiffness and damping, effectively reducing lateral displacements and improving the seismic safety index above the 60% threshold required for strategic facilities. The study highlights the importance of global assessment methodologies in guiding the seismic upgrading of hospitals and other critical infrastructures, while local detailing and device-level optimization are identified as topics for future research. Full article