Search Results (9,421)

In this paper, we propose a cooperative security method for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to address the scenario of unauthorized rogue drones (RDs) intruding into an airport’s restricted airspace. The proposed method integrates artificial intelligence techniques with engineering solutions to enhance the autonomy and effectiveness of air–ground cooperation in airport security. Specifically, the MADDPG algorithm enables the Security Interception UAVs (SI-UAVs) to autonomously detect and counteract RDs by optimizing their decision-making processes in a multi-agent environment. Additionally, Particle Swarm Optimization (PSO) is employed for distance-based target assignment, allowing each SI-UAV to autonomously select intruder targets based on proximity. To address the challenge of limited SI-UAV flight range, a power replenishment mechanism is introduced, where each SI-UAV automatically returns to the nearest UGV for recharging after reaching a predetermined distance. Meanwhile, UGVs perform ground patrols across different airport critical zones (e.g., runways and terminal perimeters) according to pre-designed patrol paths. The simulation results demonstrate the feasibility and effectiveness of the proposed security strategy, showing improvements in the reward function and the number of successful interceptions. This approach effectively solves the problems of target allocation and limited SI-UAV range in multi-SI-UAV-to-multi-RD scenarios, further enhancing the autonomy and efficiency of air–ground cooperation in ensuring airport security. Full article

Integrated circuits have become indispensable in modern society owing to their formidable computational power and high integration, finding extensive applications in critical fields such as artificial intelligence and new energy vehicles. However, continued increases in integration density and reductions in physical size lead to a significantly higher heat flux density, thereby posing major challenges for thermal management and overall chip reliability. To address these thermal challenges, this study introduces an optimized manifold microchannel design. A three-dimensional conjugate heat transfer model was developed, and computational fluid dynamics simulations were performed to analyze the thermal–hydraulic performance. To mitigate temperature non-uniformity, several strategies were implemented: adjusting channel widths, employing uneven inlet gaps, and incorporating micro-fins. Results demonstrate that the optimized configuration achieves a maximum temperature reduction of 7.7 K, with peak thermal stress decreasing from 55.29 MPa to 47 MPa, effectively improving temperature uniformity. This study confirms that the proposed optimized design significantly enhances overall thermal performance, thereby offering a reliable and effective strategy for advanced chip thermal management. Full article

The problem of source-code analysis using machine-learning techniques has gained much attention recently, as several powerful code-embedding methods have been created. Having different embedding methods available for source code has opened the way to tackling many practical problems in source-code analysis. This paper addresses the problem of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions to a competitive programming problem. To achieve this, we employ a novel unsupervised algorithm that uses a multiview interpretation of data based on different embedding and clustering methods, a multidimensional assignment problem (MAP) to determine a subset of a higher probability of correctness, and a self-training method based on voting to determine the correct clusters of the remaining set. We investigate the following two aspects: (1) whether the proposed unsupervised approach outperforms existing methods when the number K of distinct algorithmic strategies is known and (2) Whether the approach can also be applied to determine the optimal value of K. We have addressed these using seven embedding methods with three clustering strategies in a data-analysis pipeline that tackles the previously described issues on a newly created dataset consisting of 15 algorithmic problems. According to the results, for the first aspect, the proposed unsupervised voting algorithm significantly improves the baseline clustering approach for a known K. This improvement was observed across all problems in the dataset, except one. In the case of the second one, we prove that the proposed method has a negative impact on determining the optimal number K. Scale-up of the data-analysis pipeline to datasets of thousands of problems may yield the ability to profoundly understand and learn about the innovative process of correctly designing and writing code in the context of competitive programming or even industry code. Full article

The production of concrete strongly influences the environment. It is a versatile and sustainable construction material capable of creating a wide range of structures. It has always been indispensable as a material for the engineering and construction industry, including applications in hydraulic structures (e.g., dams, underwater tunnels, sluices, and other concrete structures), where mass concrete is a fundamental material in the construction industry. Developing sustainable concrete as an alternative construction material to the traditional one provides a reduction in the carbon dioxide footprint with regard to cement use and waste material disposal in landfills. This paper provides a comprehensive review of current trends and opportunities in sustainable construction using mass concrete. It underscores the importance of incorporating eco-friendly practices to mitigate environmental impact by using by-products as green materials. The review highlights how optimizing clinker content, supplementary cementitious materials (SCMs), and aggregates can improve the strength, durability, and thermal stability of mass concrete. Strategic material selection helps minimize thermal cracking, extend service life, and reduce environmental impact. Future research should focus on developing advanced mix design strategies and standardized practices for sustainable infrastructure. Full article

To address the problem of the proton exchange membrane fuel cell (PEMFC) temperature management under stochastic disturbances, this paper integrates a PEMFC thermal model with a water pump model and establishes a nonlinear stochastic model for temperature regulation. The objective is to maintain the stack temperature at its optimal value. Due to the inherent complexity of the PEMFC electrochemical reactions, the thermal dynamics exhibit strong nonlinear characteristics. To tackle this issue, a control strategy based on the stochastic backstepping method is proposed. Furthermore, to cope with variations in membrane water content and ambient temperature during operation, we design stochastic estimator-based adaptive laws. Simulation results, considering both stochastic disturbances driven by tracking error and those driven by stack temperature and load current, indicate that the proposed control strategy effectively maintains the stack temperature at 343 K under various operating conditions, with a maximum deviation of 0.2 K, thereby confirming its effectiveness and robustness. Full article

Recent advances in microbial bioremediation have significantly enhanced the effectiveness of wastewater management, offering innovative and sustainable alternatives to conventional treatment methods. Microorganisms, including bacteria, fungi, and algae, are increasingly recognized for their remarkable ability to degrade, transform, and remove a broad spectrum of pollutants such as organic compounds, heavy metals, and emerging contaminants from wastewater. Cutting-edge research has led to the development of novel approaches such as bioaugmentation, bio-stimulation, and the use of genetically engineered microbes, which have improved the efficiency, specificity, and resilience of bioremediation processes. The application of microbial consortia and advanced bioreactor designs further optimizes pollutant removal under diverse environmental conditions. Additionally, omics technologies and systems biology are providing deeper insights into microbial community dynamics and metabolic pathways, enabling the fine-tuning of bioremediation strategies for targeted outcomes. Despite ongoing challenges related to scalability, environmental variability, and regulatory considerations, these advances are paving the way for more robust, cost-effective, and eco-friendly wastewater management solutions. Overall, the integration of innovative microbial technologies holds great promise for addressing global water quality challenges and promoting environmental sustainability. Full article

This study investigates the displacement behavior of excavations under asymmetric loading conditions and proposes optimized support design strategies from the perspective of displacement control. Physical model tests reveal that, in excavation projects under eccentric loading conditions, the retaining structure as a whole tends to deform toward the non-surcharge side rather than following the conventional symmetric deformation pattern. Displacement increases nonlinearly with surcharge intensity, but the growth rate diminishes as the load further increases due to localized surcharge effects and structural restraints. Numerical analyses further demonstrate that increasing embedment depth and wall thickness effectively mitigates lateral displacement, although a marginal effect is observed beyond critical thresholds. For instance, at an embedment depth of 12 m (twice the excavation depth), maximum lateral displacement decreases by nearly 50%, and when combined with a wall thickness of 13 cm and a depth of 14 m, the reduction reaches approximately 90%. These findings establish a quantitative basis for deformation control in excavations subjected to asymmetric loading and guide the efficient optimization of retaining systems. They enhance design reliability and construction efficiency, offering practical value for improving safety, performance, and overall project economy. Full article

The present paper provides a general overview of the state of the art of steam generating heat pumps (SGHPs) technology employed in the industrial field. Recommended best practices and optimization procedures for overall performance improvement of compression closed-loop-based systems are described in detail, as well as the main modifications of the thermodynamic heat pump cycle. Once the main configurations of SGHPs are described, the different concepts are compared in terms of supply temperature ranges; cases of comparison among different concepts are reviewed, and techno-economic barriers are also discussed. The working fluids (including natural refrigerants) commonly selected for these steam generating systems are presented along with their uses. Moreover, zeotropic refrigerant mixtures and new potentially usable mixtures are mentioned. After that, refrigerant selection criteria for high temperature heat pumps are also discussed. Then, using an internal heat exchanger, refrigerant injection technique and super heating in lubricated compressors are herein presented as best practices for general performance improvement. Regarding thermodynamic cycle modifications, basic auto-cascade and quasi-two-stage compression cycles are discussed along with further improvements suggested in the specialized literature. Lastly, optimization strategies useful to enhance the heat pumps’ design and based on TOPSIS method, advanced exergy analysis, exergy-based cost minimization and combined design are analyzed. Full article

Background/Objectives: Due to the rising problem of antimicrobial resistance, there is increasing attention in the scientific community towards alternative approaches to combat Antimicrobial-Resistant (AMR) pathogens that do not involve the use of antibiotics. In this regard, the European Medicines Agency (EMA) and the European Food Safety Authority (EFSA) have promoted experimentation with predatory bacteria to fight antibiotic resistance. With the aim of identifying predatory bacteria suitable for the control of antibiotic-resistant bacteria, in this work we isolated a strain of Halobacteriovorax from an estuarine aquatic environment using a CTX-M-producing E. coli strain as prey and characterized it with respect to optimal physico-chemical parameters for growth and predation. Furthermore, we studied its predatory capacity against other E. coli strains and Multi-Drug-Resistant (MDR) Salmonella. Finally, we conducted challenge experiments to evaluate the growth of predator and prey over time. Methods: The Halobacteriovorax strain, designated HE7, was identified by 16S rRNA analysis. To isolate Halobacteriovorax and to evaluate its predatory ability towards different preys, the double-layer agar plating technique was applied. Results: HE7 showed in vitro predatory activity against all MDR strains of E. coli and Salmonella tested. In the 10⁷ predator/10³ prey and 10⁷ predator/10⁷ prey challenges, HE7 after 6 h achieved the total killing and a reduction of about 6 logs in the prey, respectively, maintaining this effect for up to 24 h. Conclusions: The results of this study highlight that HE7, but more generally Halobacteriovorax, could find application both alone and in an integrated context of antimicrobial strategies as an alternative to antibiotics. Full article

Element doping technology is widely recognized as an effective strategy for high-performance MnO₂-based cathode materials. While this approach improves the electronic and ionic conductivity of MnO₂, it is often accompanied by the introduction of oxygen vacancies. This synergistic effect poses challenges for precisely investigating the effect of doping elements on the d-electron configuration of the Mn site and establishing atomic-level structure-activity relationships for high-energy aqueous zinc-MnO₂ batteries. In this paper, the rational design of d-electron configurations in the Mn site has been achieved through simple Co doping in α-MnO₂ (CMO). Experimental results confirm that the introduction of Co can delocalize the d-electrons of the Mn site and increase the ratio of e_g (d_z² and d_x²_−y²) occupancy. Consequently, the charge transfer resistance, electrode polarization, and Zn²⁺ diffusion coefficient of the CMO-2 cathode have been greatly optimized. Thus, the as-prepared electrode delivers a high specific capacity of 287.4 mAh g⁻¹ at 1 A g⁻¹, with a capacity retention rate of 92.8% and a corresponding remaining capacity of 199.7 mAh g⁻¹ after 700 cycles. This study paves the road for the designation and construction of high-energy MnO₂ cathodes with optimized electronic structures for advanced aqueous zinc ion batteries. Full article

Cross-view geo-localization allows an agent to determine its own position by retrieving the same scene from images taken from dramatically different perspectives. However, image matching and retrieval face significant challenges due to substantial viewpoint differences, unknown orientations, and considerable geometric distribution disparities between cross-view images. To this end, we propose a cross-view geo-localization framework based on novel view synthesis that generates pseudo aerial-view images from given street-view scenes to reduce the view discrepancies, thereby improving the performance of cross-view geo-localization. Specifically, we first employ 3D Gaussian splatting to generate new aerial images from the street-view image sequence, where COLMAP is used to obtain initial camera poses and sparse point clouds. To identify optimal matching viewpoints from reconstructed 3D scenes, we design an effective camera pose estimation strategy. By increasing the tilt angle between the photographic axis and the horizontal plane, the geometric consistency between the newly generated aerial images and the real ones can be improved. After that, the DINOv2 is employed to design a simple yet efficient mixed feature enhancement module, followed by the InfoNCE loss for cross-view geo-localization. Experimental results on the KITTI dataset demonstrate that our approach can significantly improve cross-view matching accuracy under large viewpoint disparities and achieve state-of-the-art localization performance. Full article

In continuous strata engineering, such as foundations and underground caverns, the differences in shear strength between sandstone and mudstone rock mass–structural plane–structural block systems critically affect design and safety. However, the underlying mechanisms and controlling factors of these shear strength parameters remain poorly understood, leading to challenges in optimizing engineering strategies. This study investigates the differences in shear strength parameter correlations between sandstone and mudstone and develops an intelligent model for predicting rock mass displacement. We constructed multi-parameter correlation models using laboratory and field shear test data combined with a random forest algorithm. The results show that the model achieved high prediction accuracy (R² = 0.997–0.998, RMSE = 1.649–3.898, MAE = 1.110–2.991). For instance, the peak shear strength of sandstone structural planes was approximately 54% higher than that of mudstone. Sensitivity analysis revealed that for sandstone, structural plane shear stress (27.80%) and structural block stress (25.50%) are the most sensitive factors, while for mudstone, structural plane shear displacement (35.20%) and structural block strain (34.20%) dominate. These correlations are model-predicted based on empirical data from shear tests. These findings provide a mechanistic understanding of plastic instability in sandstone and slip-strain-induced fissure extension in mudstone, and they can guide shear strength prediction and stability assessment in mixed sandstone–mudstone strata. The study contributes to the field by offering a quantitative basis for stratified adaptive design in continuous strata engineering, enhancing the efficiency and safety of foundation treatment and cavern support. Full article

To address the pronounced asymmetry and strong nonlinearity exhibited by the tractor electro-hydraulic hitch system during lifting and lowering operations, this study proposes a distributed parameter identification method based on a dual-mode grey-box modelling approach. Following a mode decomposition strategy, the lifting and lowering processes are regarded as two independent subsystems. Benchmark transfer function models are established for each subsystem through theoretical derivation. Considering the nonlinear characteristics and unmodeled dynamics that cannot be accurately captured by the benchmark model, a long short-term memory (LSTM) neural network compensator is introduced to enhance the model performance. Ultimately, a series-compensated dual-channel grey-box model is established, which effectively integrates mechanistic interpretability with high modelling accuracy. Then, to cope with the high-dimensional and heterogeneous parameter space of the constructed grey-box structure, a distributed parameter identification framework is proposed. This framework employs a staged optimization process that combines the whale optimization algorithm (WOA) with the gradient descent (GD) method to efficiently identify the hybrid parameter set. The identified models are validated through bench experiments. The results show that the proposed grey-box models achieve root mean square errors (RMSEs) of 0.33 mm and 0.48 mm, and mean absolute errors (MAEs) of 0.24 mm and 0.40 mm for the lifting and lowering processes, respectively. Compared with a single transfer function model, the RMSE is reduced by 57.6% and 87.3%, and the MAE is reduced by 59.2% and 87.9%, respectively. The proposed method substantially improves the modelling accuracy of the electro-hydraulic hitch system, providing a reliable foundation for system characterization and the design of high-performance control strategies for tractor electro-hydraulic hitch systems. Full article

Underground coal mine conveying systems operate in unstructured environments. Influenced by geological and operational factors, coal conveyors are frequently contaminated by foreign objects such as coal gangue and anchor bolts. These contaminants disrupt conveying stability and pose challenges to safe mining operations, making their effective removal critical. Given the significant heterogeneity and unpredictability of these objects in shape, size, and orientation, precise manipulation requires dual-arm cooperative control. Traditional control algorithms rely on precise dynamic models and fixed parameters, lacking robustness in such unstructured environments. To address these challenges, this paper proposes a cooperative grasping method tailored for heterogeneous objects in unstructured environments. The MATD3 algorithm is employed to cooperatively perform dual-arm trajectory planning and grasping tasks. A multi-factor reward function is designed to accelerate convergence in continuous action spaces, optimize real-time grasping trajectories for foreign objects, and ensure stable robotic arm positioning. Furthermore, priority experience replay (PER) is integrated into the MATD3 framework to enhance experience utilization and accelerate convergence toward optimal policies. For slender objects, a sequential cooperative optimization strategy is developed to improve the stability and reliability of grasping and placement. Experimental results demonstrate that the P-MATD3 algorithm significantly improves grasping success rates and efficiency in unstructured environments. In single-arm tasks, compared to MATD3 and MADDPG, P-MATD3 increases grasping success rates by 7.1% and 9.94%, respectively, while reducing the number of steps required to reach the pre-grasping point by 11.44% and 12.77%. In dual-arm tasks, success rates increased by 5.58% and 9.84%, respectively, while step counts decreased by 11.6% and 18.92%. Robustness testing under Gaussian noise demonstrated that P-MATD3 maintains high stability even with varying noise intensities. Finally, ablation and comparative experiments comprehensively validated the proposed method’s effectiveness in simulated environments. Full article

Efficient ventilation systems are of paramount importance for maintaining optimal air quality in indoor and enclosed environments, both on Earth and in space. Such environments include buildings, space habitats, international space station crew quarters, tunnels, underground mines and other structures. However, conventional ventilation systems encounter various challenges, including uneven air distribution, energy inefficiency, noise, and limited adaptability to fluctuating environmental conditions. Concurrently, a multitude of organisms in nature have demonstrated the capacity to construct structures that can facilitate efficient air exchange and heat regulation. Illustrative examples of such structures include ant nests, termite mounds and prairie dog burrows. The present study explores, analyses and summarizes the mechanisms, structures and strategies found in nature that can inspire the design of efficient and effective ventilation systems. The purpose of this paper is to highlight the practical implications of the aforementioned designs. To this end, it reviews the progress of research into bio-inspired ventilation, focusing on the following three areas: air regulation, component optimization and environmentally adaptive strategies. A bibliometric analysis and research trend is presented to illustrate the key developments in this field over the past 25 years. The potential of integrating the bio-inspired strategies into ventilation systems, with a particular emphasis on off-Earth habitats and underground mines, is discussed. This study provides a comprehensive overview of the development of bio-inspired ventilation systems, thereby establishing the foundation for the creation of innovative and efficient design solutions. Full article