Search Results (1,810)

Microplastics (MPs) and nanoplastics (NPs) can affect microbial abundance and activity, likely by damaging cell membra75e components. While their effects on anaerobic digestion are known, less is understood about their impact on microbes involved in contaminant bioremediation. Chlorinated volatile organic contaminants (CVOCs) such as tetrachloroethene (PCE) and explosives like hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) are common in the environment, and their bioremediation is a promising cleanup strategy. This study examined how polystyrene (PS) and polyamide 6 (PA6) MPs and NPs influence CVOC and RDX biodegradation. PS particles did not inhibit the CVOC-degrading community SDC-9, but PA6 MPs impaired the reductive dechlorination of trichloroethene (TCE) to cis-1,2-dichloroethene (cis-DCE), causing a “cis-DCE stall” with no further conversion to vinyl chloride (VC) or ethene. Only 45% of TCE was dechlorinated to cis-DCE, and Dehalococcoides mccartyi abundance dropped 1000-fold in 35 days with PA6 MPs. In contrast, neither PA6 nor PS MPs and NPs affected RDX biotransformation. These results highlight the significant impact of PA6 MPs on CVOC biodegradation and the need to consider plastic pollution in environmental management. Full article

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults. Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

The dissociation of the C-NO₂ bond is the initial step in the process of the detonation of nitroaromatic explosives. The strength of the C-NO₂ bond is significantly influenced by the relative position of the nitro group with respect to the aromatic ring plane since the planar arrangement enables the delocalization of electron density, which strengthens this bond. In this study, we have combined a statistical analysis of geometrical parameters extracted from crystal structures of trinitroaromatic molecules with ab initio calculations of non-covalent index plots and Wiberg bond index values for selected trinitroaromatic molecules to elucidate the influence of nearby substituents on the relative position of nitro groups with respect to the aromatic ring plane. The results of the analysis showed that neighboring substituents have a significant impact on the geometry of nitro groups. The results also showed that this influence arises from the repulsive interaction of voluminous substituents, attractive non-covalent contacts, and the electronic effects of substituents. Full article

The emergence of electric vehicles (EVs) has brought specific risks, including the possibility of fires or explosions resulting from mechanical, thermal, or electrical failures, which can lead to thermal runaway (TR). There is a great lack of knowledge about how to act safely in this type of fire. This study carried out two full-scale fire experiments on electric vehicles to investigate response strategies to electric vehicle fires caused by thermal runaway. Centro Zaragoza provided technical advice for these tests, so that they could be carried out safely, controlling the risks. This advice has allowed Centro Zaragoza to analyze different response strategies to the fires in electric vehicles caused by thermal runaway. On the other hand, the propagation patterns of thermal runaway fires in electric vehicles were investigated. The early-phase effectiveness of fire blankets and other extinguishing measures was tested, and the temperature distributions inside the vehicle and the type of fire generated were measured. The results showed that fire blankets successfully extinguished flames by cutting off the oxygen supply. These findings contribute to the development of effective strategies for responding to electric vehicle fires, enabling the establishment of good practice for fire suppression in electric vehicles and their batteries. Full article

A model for the flow and thermal explosion of a micropolar gas is investigated, assuming the equation of state for a real gas. This model describes the dynamics of a gas mixture (fuel and oxidant) undergoing a one-step irreversible chemical reaction. The real gas model is particularly suitable in this context because it more accurately reflects reality under extreme conditions, such as high temperatures and high pressures. Micropolarity introduces local rotational dynamic effects of particles dispersed within the gas mixture. In this paper, we first derive the initial-boundary value system of partial differential equations (PDEs) under the assumption of spherical symmetry and homogeneous boundary conditions. We explain the underlying physical relationships and then construct a corresponding approximate system of ordinary differential equations (ODEs) using the Faedo–Galerkin projection. The existence of solutions for the full PDE model is established by analyzing the limit of the solutions of the ODE system using a priori estimates and compactness theory. Additionally, we propose a numerical scheme for the problem based on the same approximate system. Finally, numerical simulations are performed and discussed in both physical and mathematical contexts. Full article

Smokeless powders are a commonly used low explosive within the ammunition industry. Their ease of purchase has allowed criminals to use these products to build improvised explosive devices. Canines have become a vital tool in locating such improvised devices. With differing fabrication processes, one of the most difficult challenges for canine handlers is the optimal selection of training aids to choose as odor targets to allow for broad generalization. Several studies have been underway to understand the chemical odor characterization of smokeless powders, which can help provide canine teams with essential information to understand odor signatures from powder varieties. In this study, a SPME method optimization was conducted using unburned smokeless powders to provide a chemical odor profile assessment. Concurrently, statistical analysis using PCA and Spearman’s rank correlations was performed to explore whether odor volatile composition depicted associations between and within powder brands. The results showed that a longer extraction time (24 h) was optimal across all powders, as this yielded higher compound abundance and number of extracted odor volatiles. The optimal SPME fiber varied per powder, depicting the complexity of powder composition. There were 66 highly frequent compounds among the 18 powders, including 2-ethyl-1-hexanol, diphenylamine (DPA), and dibutyl phthalate. Principal component analysis (PCA) showed that while powders may be of the same type (single/double base), they can still portray clustering differences across and within brands. The Spearman’s rank correlation within powder type suggested that the double-base powders had a slightly higher similarity index when compared with the single-base powder types. Understanding the volatile odor profiles of various smokeless powders can enhance canine training by informing the selection of effective training aids and supporting odor generalization. Full article

The transition to sustainable energy sources has intensified interest in lignocellulosic biomass (LCB) as a feedstock for second-generation biofuels. However, the inherent structural recalcitrance of LCB requires the utilization of an effective pretreatment to enhance enzymatic hydrolysis and subsequent fermentation yields. This manuscript presents a novel, single-step, and optimized nitrogen explosive decompression system (NED 3.0) designed to address the critical limitations of earlier NED versions by enabling the in situ removal of inhibitory compounds from biomass slurry and fermentation inefficiency at elevated temperatures, thereby reducing or eliminating the need for post-treatment detoxification. Aspen wood (Populus tremula) was pretreated by NED 3.0 at 200 °C, followed by enzymatic hydrolysis and fermentation. The analytical results confirmed substantial reductions in common fermentation inhibitors, such as acetic acid (up to 2.18 g/100 g dry biomass) and furfural (0.18 g/100 g dry biomass), during early filtrate recovery. Hydrolysate analysis revealed a glucose yield of 26.41 g/100 g dry biomass, corresponding to a hydrolysis efficiency of 41.3%. Fermentation yielded up to 8.05 g ethanol/100 g dry biomass and achieved a fermentation efficiency of 59.8%. Inhibitor concentrations in both hydrolysate and fermentation broth remained within tolerable limits, allowing for effective glucose release and sustained fermentation performance. Compared with earlier NED configurations, the optimized system improved sugar recovery and ethanol production. These findings confirm the operational advantages of NED 3.0, including reduced inhibitory stress, simplified process integration, and chemical-free operation, underscoring its potential for scalability in line with the EU Green Deal for bioethanol production from woody biomass. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

Lightning represents a critical danger to facilities such as oil tank farms, with the potential to cause major explosive incidents. To address this risk, Indonesia’s oil and gas industry has adopted the NFPA 780 Standard for lightning protection systems. However, tank explosions and refinery disruptions caused by lightning strikes continue to occur annually, highlighting the need to reassess the standard’s self-protection criteria, particularly in Indonesia’s tropical climate. The NFPA 780 standard was primarily developed based on lightning characteristics in subtropical regions. This study evaluates its effectiveness in tropical environments, where lightning parameters such as peak currents, frequencies, and ground flash densities differ significantly. By analyzing specific incidents of tank explosions in Indonesia, the research reveals that compliance with the NFPA 780 standard alone may not be adequate to protect critical infrastructure. To address these challenges, this study proposes a novel approach to lightning protection by designing solutions tailored to the unique characteristics of tropical climates. By incorporating local lightning parameters, the proposed measures aim to enhance safety and resilience in oil and gas facilities. This research provides a framework for adapting international standards to regional needs, improving the effectiveness of lightning protection in tropical environments. Full article

Task-oriented medical dialogue systems face two fundamental challenges: the explosion of state-action space caused by numerous diseases and symptoms and the sparsity of informative signals during interactive diagnosis. These issues significantly hinder the accuracy and efficiency of automated clinical reasoning. To address these problems, we propose MA-HRL, a multi-agent hierarchical reinforcement learning framework that decomposes the diagnostic task into specialized agents. A high-level controller coordinates symptom inquiry via multiple worker agents, each targeting a specific disease group, while a two-tier disease classifier refines diagnostic decisions through hierarchical probability reasoning. To combat sparse rewards, we design an information entropy-based reward function that encourages agents to acquire maximally informative symptoms. Additionally, medical knowledge graphs are integrated to guide decision-making and improve dialogue coherence. Experiments on the SymCat-derived SD dataset demonstrate that MA-HRL achieves substantial improvements over state-of-the-art baselines, including +7.2% diagnosis accuracy, +0.91% symptom hit rate, and +15.94% symptom recognition rate. Ablation studies further verify the effectiveness of each module. This work highlights the potential of hierarchical, knowledge-aware multi-agent systems for interpretable and scalable medical diagnosis. Full article

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

This study suggests a method for analyzing the risk of methane explosions using the N-K model and Social Network Analysis (SNA) to understand how different risk factors related to coal-mine methane explosions are connected and change over time, aiming to prevent these accidents effectively. We identified 41 secondary risk factors and four fundamental risk factors—human, equipment, environment, and management—based on the 4M accident causation theory. The SNA model was utilized to determine the main risk factors and their evolutionary routes, while the N-K model was utilized to quantify the degree of risk coupling. The findings show that the number of risk variables engaged in the methane-explosion risk system closely correlates with the number of accidents that occur and the maximum coupling level among the four elements. The primary control factors in the methane-explosion risk system are poor equipment management, broken safety monitoring and control systems, inadequate safety education and training, safety regulation violations, and poor safety production responsibility system implementation. We utilized the primary evolution paths and key elements to propose risk control approaches. A reference for ensuring safety in coal-mine operations can be found in the research findings. Full article

The aim of this systematic review was to examine the effects of high-intensity interval training (HIIT) on physical performance and body composition in female basketball players. The review followed PRISMA guidelines, and the protocol was registered in the PROSPERO database (registration number: CRD420251006285). A comprehensive search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. Nine studies that met the inclusion criteria were analyzed, with intervention durations ranging from 4 to 12 weeks. Despite differences in protocols, a majority of studies reported improvements in VO_2max (6/9), explosive strength (7/9), agility (5/6), and speed (5/6) and reductions in body mass and fat percentage (3/3). These findings highlight HIIT as an effective method for enhancing both aerobic and anaerobic capacities, as well as optimizing body composition. Despite variations in study protocols, HIIT consistently offers improvements in performance, irrespective of training level. The results underscore the importance of HIIT in preparing athletes, not only during the preseason but also throughout the competition period. Coaches should consider integrating HIIT into training programs, adjusting intensity and volume based on the season to optimize performance and prevent overtraining. Full article