Search Results (911)

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

The stability of shield tunnel segmental linings is highly sensitive to the lateral pressure coefficient, especially under weak, heterogeneous, and variable geological conditions. However, the mechanical behavior of steel plate-reinforced linings under such conditions remains insufficiently characterized. This study aims to investigate the effects of varying lateral pressures on the structural performance of reinforced tunnel linings. To achieve this, a custom-designed full-circumference loading and unloading self-balancing apparatus was developed for scaled-model testing of shield tunnels. The experimental methodology allowed for precise control of loading paths, enabling the simulation of realistic ground stress states and the assessment of internal force distribution, joint response, and load transfer mechanisms during the elastic stage of the structure. Results reveal that increased lateral pressure enhances the stiffness and bearing capacity of the reinforced lining. The presence and orientation of segment joints, as well as the bonding performance between epoxy resin and expansion bolts at the reinforcement interface, significantly influence stress redistribution in steel plate-reinforced zones. These findings not only deepen the understanding of tunnel behavior in complex geological environments but also offer practical guidance for optimizing reinforcement design and improving the durability and safety of shield tunnels. Full article

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article

The 2020 southern Puerto Rico earthquake sequence highlighted the severe seismic vulnerability of informally constructed two-story reinforced concrete (RC) houses. This study examines the failure mechanisms of these structures and assesses the effectiveness of first-floor RC shear-wall retrofitting. Nonlinear pushover and dynamic time–history analyses were performed using fiber-based distributed plasticity models for RC frames and nonlinear macro-elements for second-floor masonry infills, which introduced a significant inter-story stiffness imbalance. A bi-directional seismic input was applied using spectrally matched, near-fault pulse-like ground motions. The findings for the as-built structures showed that stiffness mismatches between stories, along with substantial strength and stiffness differences between orthogonal axes, resulted in concentrated plastic deformations and displacement-driven failures in the first story—consistent with damage observed during the 2020 earthquakes. Retrofitting the first floor with RC shear walls notably improved the performance, doubling the lateral load capacity and enhancing the overall stiffness. However, the retrofitted structures still exhibited a concentration of inelastic action—albeit with lower demands—shifted to the second floor, indicating potential for further optimization. Full article

This study presents a simulation-based damage modeling and fatigue risk assessment of a reusable ceramic matrix composite thruster designed for short-duration, green bipropellant propulsion systems. The thruster is constructed from a fiber-reinforced ultra-high temperature ceramic matrix composite composed of zirconium diboride, silicon carbide, and carbon fibers. Time-resolved thermal and structural simulations are conducted on a validated thruster geometry to characterize the severity of early-stage thermal shock, stress buildup, and potential degradation pathways. Unlike traditional fatigue studies that rely on empirical fatigue constants or Paris-law-based crack-growth models, this work introduces a simulation-derived stress-margin envelope methodology that incorporates ±20% variability in temperature-dependent material strength, offering a physically grounded yet conservative risk estimate. From this, a normalized risk index is derived to evaluate the likelihood of damage initiation in critical regions over the 0–10 s firing window. The results indicate that the convergent throat region experiences a peak thermal gradient rate of approximately 380 K/s, with the normalized thermal shock index exceeding 43. Stress margins in this region collapse by 2.3 s, while margin loss in the flange curvature appears near 8 s. These findings are mapped into green, yellow, and red risk bands to classify operational safety zones. All the results assume no active cooling, representing conservative operating limits. If regenerative or ablative cooling is implemented, these margins would improve significantly. The framework established here enables a transparent, reproducible methodology for evaluating lifetime safety in ceramic propulsion nozzles and serves as a foundational tool for fatigue-resilient component design in green space engines. Full article

LiDAR-based Simultaneous Localization and Mapping (SLAM) serves as a fundamental technology for autonomous navigation. However, in complex environments, LiDAR odometry often experience degraded localization accuracy and robustness. This paper proposes a computationally efficient enhancement strategy for LiDAR odometry, which improves system performance by reinforcing high-quality features throughout the optimization process. For non-ground features, the method employs statistical geometric analysis to identify stable points and incorporates a contribution-weighted optimization scheme to strengthen their impact in point-to-plane and point-to-line constraints. In parallel, for ground features, locally stable planar surfaces are fitted to replace discrete point correspondences, enabling more consistent point-to-plane constraint formulation during ground registration. Experimental results on the KITTI and M2DGR datasets demonstrated that the proposed method significantly improves localization accuracy and system robustness, while preserving real-time performance with minimal computational overhead. The performance gains were particularly notable in scenarios dominated by unstructured environments. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

This study presents the fabrication and characterization of sustainable polylactic acid (PLA)-based biocomposites reinforced with bio-origin fillers derived from food waste: seashells, eggshells, walnut shells, and spent coffee grounds. All fillers were introduced at 15 wt% into a commercial PLA matrix modified with a compatibilizer to improve interfacial adhesion. Mechanical properties (tensile, flexural, and impact strength), morphological characteristics (via SEM), and hydrolytic aging behavior were evaluated. Among the tested systems, PLA reinforced with seashells (PLA15S) and coffee grounds (PLA15C) demonstrated the most balanced mechanical performance, with PLA15S achieving a tensile strength increase of 72% compared to neat PLA. Notably, PLA15C exhibited the highest stability after 28 days of hydrothermal aging, retaining ~36% of its initial tensile strength, outperforming other systems. In contrast, walnut-shell-filled composites showed the most severe degradation, losing over 98% of their mechanical strength after aging. The results indicate that both the physicochemical nature and morphology of the biofiller play critical roles in determining mechanical reinforcement and degradation resistance. This research underlines the feasibility of valorizing agri-food residues into biodegradable, semi-structural PLA composites for potential use in sustainable packaging or non-load-bearing structural applications. Full article

This study aimed to develop novel oleogels using whey protein (WP) and bacterial cellulose nanowhiskers (BCNW) to expand the potential applications of spent coffee grounds oil (SCGO). An emulsion-templated approach was employed to structure SCGO with varying WP:SCGO ratios, while the incorporation of BCNW was evaluated as a potential stabilizing and reinforcing agent. All oleogels behaved as “true” gels (tan δ < 0.1). Rheological analysis revealed that higher WP content significantly increased gel strength, indicating enhanced structural integrity and deformation resistance. The addition of BCNW had a significant reinforcing effect in oleogels with a higher oil content (WP:SCGO 1:5), while its influence was less evident in formulations with lower oil content (WP:SCGO 1:2.5). Notably, depending on the WP:SCGO ratio, the storage modulus (G′) data showed that the oleogels resembled both hard (WP:SCGO 1:2.5) and soft (WP:SCGO 1:5) solid fats, highlighting their potential as fat replacers in a wide range of food applications. Consequently, this study presents a sustainable approach to structuring SCGO while tailoring its rheological behavior, aligning with global efforts to reduce food waste and develop sustainable food products. Full article

In the 2021 summit, ASEAN leaders acknowledged the ocean as an essential driver of economic recovery post pandemic, leading to the ASEAN Declaration on the Blue Economy for the responsible management of marine resources. As an ASEAN nation with a long history in the fishing sector, Indonesia then actively spread this concept across the region. The hegemony theory of Gramsci, which considers the interaction of a nation’s material resources, ideational influence, and institutional strategy, is further used to assess Indonesia’s leadership dynamics in the ASEAN to obtain consensus-based power. In this study, Joko Widodo’s speeches from 2023 are taken out and coded to determine the narrative that Indonesia constantly reinforces. With thematic analysis, speech data is processed to generate keywords such as unity, cooperation, and shared responsibilities, which Indonesia often uses to advance its regional agenda. By aligning member states’ interests with regional goals, Indonesian governance creates common ground for a blue economy and emphasizes how the sea is an integral source of opportunity for the region’s position as the Epicentrum Of Growth. Instead of pushing countries to agree with directives, Indonesia effectively advocates for regional agreements and ASEAN-led structures through the blue economy framework, with the ABEF emerging at its 2023 ASEAN chairmanship deliberations. Full article

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

The disposal of agro-industrial waste is a pressing environmental issue. At the same time, due to the high silica content in specific agricultural residues, their processed products can be utilised in various industrial sectors as substitutes for commercial materials. This study investigates the key technological, physico-mechanical, and viscoelastic properties of industrial elastomeric compounds based on synthetic styrene–butadiene rubber, intended for the tread of summer passenger car tyres, when replacing the commercially used highly reinforcing silica filler (SF), Extrasil 150VD brand (white carbon black), with a carbon–silica filler (CSF). The CSF is produced by carbonising a finely ground mixture of rice production waste (rice husks and stems) in a pyrolysis furnace at 550–600 °C without oxygen. It was found that replacing 20 wt.pts. of silica filler with CSF in industrial tread formulations improves processing parameters (Mooney viscosity increases by up to 5.3%, optimal vulcanisation time by up to 9.2%), resistance to plastic deformation (by up to 7.7%), and tackiness of the rubber compounds (by 31.3–34.4%). Viscoelastic properties also improved: the loss modulus and mechanical loss tangent decreased by up to 24.0% and 14.3%, respectively; the rebound elasticity increased by up to 6.3% and fatigue resistance by up to 2.7 thousand cycles; and the internal temperature of samples decreased by 7 °C. However, a decrease in tensile strength (by 10.7–27.0%) and an increase in wear rate (up to 43.3% before and up to 22.5% after thermal ageing) were observed. Nevertheless, the overall results of this study indicate that the CSF derived from the carbonisation of rice production waste—containing both silica and carbon components—can effectively be used as a partial replacement for the commercially utilised reinforcing silica filler in the production of tread rubber for summer passenger car tyres. Full article

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article