Search Results (2,811)

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article

The impact of invasive alien species (IAS) is one of the direct factors causing global biodiversity decline and economic losses, and predicting the potential invasion risks of invasive species is crucial for developing prevention and control strategies. In recent years, an increasing number of studies have shown that invasive species undergo rapid shifts in climate niche in invaded areas. Accurately quantifying the dynamic shifts in the climate niche of invasive species in invaded areas is crucial for developing a more accurate framework for early warning of invasive species risks. Pomacea canaliculata is a freshwater snail found in South America and has become one of the most aggressive aquatic species in the world. Since its introduction to China in 1981, it has rapidly spread and caused multiple serious damages to agriculture, ecology, and public health. Therefore, based on multi-source distribution data of P. canaliculata, this study calculated the climate niche overlap by Schoener’ s D, quantified the niche shifts between the P. canaliculata in native and invaded areas (China) via the COUE scheme (a unified terminology representing niche centroid shift, overlap, unfilling, and expansion), and analyzed their changes on a time scale. The results revealed that there have been significant climate niche shifts (Schoener’s D < 0.2, niche similarity tests p > 0.01, niche equivalence tests p < 0.01) between the native and invaded areas (China) of P. canaliculata, which does not support the climate niche conservation hypothesis. The minimum temperature of the coldest month (Bio 6) and precipitation seasonality (Bio 15) were the key climate variables driving the climatic niche shift, and P. canaliculata can survive in colder and more arid regions than their native counterparts. The changes in the niche shifts in P. canaliculata on a time scale show significant temporal heterogeneity, and its invasion behavior in China presents a discontinuous and phased expansion pattern, with strong adaptability to new environments. The results are of great significance for the future development of more accurate ecological niche model (ENM), the formulation of more targeted prevention and control strategies, and the study of adaptive evolution mechanisms of invasive species. Full article

(1) Background: Virtual Reality (VR) films challenge traditional visual cognition by offering novel perceptual experiences. This study investigates the applicability of Gestalt grouping principles in dynamic VR scenes, the influence of VR environments on grouping efficiency, and the relationship between viewer experience and grouping effects. (2) Methods: Eye-tracking experiments were conducted with 42 participants using the HTC Vive Pro Eye and Tobii Pro Lab. Participants watched a non-narrative VR film with fixed camera positions to eliminate narrative and auditory confounds. Eye-tracking metrics were analyzed using SPSS version 29.0.1, and data were visualized through heat maps and gaze trajectory plots. (3) Results: Viewers tended to focus on spatial nodes and continuous structures. Initial fixations were anchored near the body but shifted rapidly thereafter. Heat maps revealed a consistent concentration of fixations on the dock area. (4) Conclusions: VR reshapes visual organization, where proximity, continuity, and closure outweigh traditional saliency. Dynamic elements draw attention only when linked to user goals. Designers should prioritize spatial logic, using functional nodes as cognitive anchors and continuous paths as embodied guides. Future work should test these mechanisms in narrative VR and explore neural correlates via fNIRS or EEG. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

Controlled thermal stratification in water is crucial for applications such as testing the thermal stealth of underwater vehicles and studying aquatic ecosystems. However, existing laboratory methods for generating such stratified environments often lack stability and uniformity. This study systematically investigates the influence of various hot water injection methods on the stability of thermal stratification. A computational fluid dynamics model, incorporating the overlapping dynamic mesh technique and the Volume of Fluid free-surface capturing method, was used to compare four generation strategies: single-side fixed discharge, towed horizontal discharge, towed vertical upward discharge, and multi-nozzle towed vertical upward discharge. The results indicate that towed discharge methods produce more stable and uniform thermal stratification compared to the fixed discharge method, achieving a 10.1% increase in the water body’s vertical stability coefficient and a 4.5% increase in the Richardson number, while the maximum surface temperature difference was significantly reduced from 0.98 K to 0.37 K. Among the towed methods, vertical upward discharge demonstrated superior stability over horizontal discharge, as it directly transports the high-temperature plume to the upper layer, minimizing thermal exchange with the lower layer, with its vertical stability coefficient and Richardson number being 17.9% and 23% higher, respectively. While maintaining a constant heat input per unit volume, the multi-nozzle configuration yielded ${\mathrm{N}}^2$ and Ri values comparable to the single-nozzle version, while further improving the temperature uniformity at the free surface. Consequently, the towed vertical upward discharge emerges as a highly efficient method for establishing stable and uniform thermal stratification, with the multi-nozzle configuration showing significant promise for large-scale applications. This study provides a valuable reference for creating stratified fluid environments and for related engineering fields. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

The coupled factors of high temperature, high humidity, and high salinity in tropical marine atmospheres severely threaten the long-term service performance of power transmission and transformation infrastructure. This paper establishes an accelerated cyclic testing protocol (salt spray → drying → damp heat → drying) to evaluate performance and elucidate the dynamic corrosion failure mechanisms of the organic Zn14Al1.4 composite coating. By integrating multiphysical characterization techniques (SEM, EDS, XPS) with electrochemical analysis, this study for the first time elucidates the dynamic transformation of corrosion products: initially dominated by Zn(OH)₂, progressing to complex passive phases such as Zn₅(OH)₈Cl₂·H₂O, Zn₅(OH)₆(CO₃)₂, and Zn₆Al₂(OH)₁₆CO₃ in the mid-term, and ultimately dominated by Fe-based products (FeO, Fe₂O₃, Fe₃O₄, FeOOH) that drive interfacial failure. And a four-stage corrosion evolution model was defined: incubation period, accelerated degradation phase, substrate nucleation stage, and catastrophic failure phase. The investigation reveals a shift in the coating/substrate interface failure mechanism from purely physical barrier effects to electrochemical synergy, providing a theoretical framework for the optimized design and service-life prediction of anticorrosive coatings for transmission and transformation equipment in tropical environments. Full article

Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehicles, and swinging doors—often degrade SLAM performance by introducing unreliable features that cause localization errors. In this paper, we define dynamic regions as areas in the scene containing moving objects, and dynamic features as the visual features extracted from these regions that may adversely affect localization accuracy. Inspired by biological perception strategies that integrate semantic awareness and geometric cues, we propose Instance-level Point-Line SLAM (IPL-SLAM), a robust visual SLAM framework for dynamic environments. The system employs YOLOv8-based instance segmentation to detect potential dynamic regions and construct semantic priors, while simultaneously extracting point and line features using Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features), collectively known as ORB, and Line Segment Detector (LSD) algorithms. Motion consistency checks and angular deviation analysis are applied to filter dynamic features, and pose optimization is conducted using an adaptive-weight error function. A static semantic point cloud map is further constructed to enhance scene understanding. Experimental results on the TUM RGB-D dataset demonstrate that IPL-SLAM significantly outperforms existing dynamic SLAM systems—including DS-SLAM and ORB-SLAM2—in terms of trajectory accuracy and robustness in complex indoor environments. Full article

High strength and high ductility concrete (HSHDC) exhibit exceptional compressive strength (up to 90 MPa) and remarkable tensile ductility (ultimate tensile strain reaching 6%), making them highly resilient under impact loading. To elucidate the influence of strain rate and wet–dry cycling of salt spray on the dynamic compressive response of HSHDC, a series of tests was conducted using a 75 mm split Hopkinson pressure bar (SHPB) system on specimens exposed to cyclic corrosion for periods ranging from 0 to 180 days. The alternating seasonal corrosion environment was reproduced by using a programmable walk-in environmental chamber. Subsequently, both uniaxial compression and SHPB tests were employed to evaluate the post-corrosion dynamic compressive properties of HSHDC. Experimental findings reveal that corrosive exposure significantly alters both the static and dynamic compressive mechanical behavior and constitutive characteristics of HSHDC, warranting careful consideration in long-term structural integrity assessments. As corrosion duration increases, the quasi-static and dynamic compressive strengths of HSHDC exhibit an initial enhancement followed by a gradual decline, with stress reaching its peak at 120 days of corrosion under all strain rates. All specimens demonstrated pronounced strain-rate sensitivity, with the dynamic increase factor (DIF) being minimally influenced by the extent of corrosion under dynamic strain rates (112.6–272.0 s⁻¹). Furthermore, the peak energy-consumption capacity of HSHDC was modulated by both the duration of corrosion and the applied strain rate. Full article

Location-based games offer innovative approaches for health promotion among older adults, but their effectiveness depends on understanding complex contextual factors beyond technological design. In our study, we aimed to adapt a location-based game in the form of a smartphone application which originally targeted younger people. We employed ethnographic observations in a field test under real-world conditions for identifying the needs and preferences of older adults in this regard. Field notes of one co-creative workshop were analyzed using thematic analysis. Four key contextual factor categories emerged that significantly influenced user engagement: (1) temporal/spatial factors including weather conditions, topography, and traffic safety that impacted screen visibility and cognitive function; (2) virtual-physical orientation challenges requiring high cognitive load to transfer abstract digital maps to real environments; (3) individual factors such as technical competence, mobility levels, and prior accessibility experiences that shaped usage patterns; and (4) social dynamics that provided motivation and peer support while potentially creating exclusionary practices. Successful digital health innovations for older adults require a socio-technical systems approach that addresses environmental conditions, reduces cognitive transfer demands between virtual and physical navigation, leverages social elements while preventing exclusion, and accounts for heterogeneity among older adults as contextually interactive factors rather than merely individual differences. Full article

In response to the instability fluctuations and erosion characteristic changes in pantograph-catenary system (PCS) arcs induced by humidity variations in an open environment, a single-variable controlled experimental approach based on multi-source data fusion is proposed. This study innovatively establishes a humidity-controlled reciprocating current-carrying arc initiation test platform, integrating digital image processing with the dynamic analysis of multi-physics sensor signals (current, voltage, temperature). The study quantitatively evaluates the arc motion characteristics and the erosion effects on the frictional contact pair under different relative humidity levels (30%, 50%, 70%, and 90%) with a DC power supply (120 V/25 A). The experimental data and analysis reveal that increasing humidity results in higher contact resistance and accumulated arc energy, with arc stability first improving and then decreasing. At low humidity, arc behavior is more intense, and the erosion rate is faster. As humidity increases, the electrode wear transitions from adhesive wear to electrochemical wear, accompanied by copper transfer. The results suggest that the arc stability is optimal at moderate humidity (50% RH), with a peak current-carrying efficiency of 66% and a minimum loss rate of 14.5%. This threshold offers a vital theoretical framework for the optimization and risk assessments of PCS design. Full article

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data. Full article

Natural zeolites have gained attention as low-cost adsorbents for the removal of heavy metals (HMs) from wastewater. However, their performance can be compromised by the presence of competing cations such as ammonium (NH₄⁺). This study investigated the competitive adsorption and desorption dynamics of NH₄⁺ and six HMs (Cd, Cr, Cu, Ni, Pb, and Zn) on two natural zeolites. Batch and column experiments using synthetic wastewater were conducted to evaluate the effects of different NH₄⁺ concentrations, pH, and particle size on HM removal efficiency and desorption effects. Results showed that increasing NH₄⁺ concentrations significantly reduce HM adsorption, with total capacity decreasing by ~45% at 100 mg/L NH₄-N in kinetic tests. Adsorption isotherms of the HM mixture for both zeolite types followed a clear sigmoidal trend, which was captured well by the Hill model (R² = 0.99), with loading rates up to 56.14 mg/g. Pb consistently exhibited the highest affinity for zeolites, while Cd, Cr, Ni, and Zn were most affected by NH₄⁺ competition in the column tests. Desorption tests confirmed that NH₄⁺ rapidly re-mobilises adsorbed metals, in particular Cd, Cu, and Zn. Slightly acidic to neutral pH conditions were optimal for minimising HM remobilisation. These findings underscore the need to consider competitive interactions and operational conditions when applying natural zeolites for HM removal, especially in ammonium-rich environments such constructed wetlands, soil filters, or other decentralised applications. Full article