Search Results (7,258)

Urban passenger transportation contributes substantially to greenhouse gas emissions, yet the relative effectiveness of different decarbonization strategies remains difficult to assess due to inconsistent travel demand assumptions across studies. This study conducts a city-scale scenario analysis of daily passenger transportation CO₂ emissions for the Island of Montréal using a reconstructed representation of weekday passenger trips. An externally generated, survey-calibrated travel demand dataset is used as a fixed baseline, enabling consistent comparison across six decarbonization scenarios spanning vehicle electrification, modal shift, active travel substitution, and ride pooling. By holding daily travel demand constant, the analysis isolates the emissions impacts attributable to each intervention rather than to changes in mobility patterns. The scenario results represent upper-bound technical mitigation potential and provide system-level insight into how different strategies affect emissions across modes, vehicle categories, and network segments. The study demonstrates the value of city-scale scenario analysis for informing urban transport decarbonization under data-scarce conditions. Full article

Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems. Full article

Ensuring user safety while enabling independent mobility is crucial to autonomous healthcare and rehabilitation robots, such as active lower-limb orthoses and exoskeletons. A key requirement for these devices is to provide full assistance without supervision; however, existing designs do not simultaneously satisfy autonomous operation and inherent safety. To address this gap, a novel safety principle, Safety by Design, and a corresponding system architecture for a fully assistive active knee orthosis are introduced. The proposed architecture is based on a comprehensive risk analysis for the use of active orthoses and exoskeletons and integrates redundancies for all safety-critical components while minimizing additional weight. This redundancy enables the orthosis to remain operational at reduced power in the event of component failure, improving both user safety and system reliability. The design supports safe, unsupervised operation by ambulatory users, enhancing independent patient mobility and the performance of the gait activities of level walking, stair climbing and sitting down/standing up. The proposed architecture is scalable and adaptable to a wide range of robotic devices. By improving robustness, efficiency, and safety, this work contributes to the advancement of autonomous biomedical robotic systems and wearable assistive devices. Full article

Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based field positioning system (TerraHärp), drone-based laser scanning, and mobile laser scanning (MLS). The analysis was conducted in five long-term experimental forest sites in Lithuania, comprising pine- and spruce-dominated stands with varying stand densities. Tree locations derived from legacy maps and the TerraHärp system were compared to assess systematic and random positional discrepancies. TerraHärp-derived tree positions were subsequently used as a reference to evaluate the laser scanning-based methods. Positional accuracy was assessed using Hotelling’s T² test, root-mean-square error, and the National Standard for Spatial Data Accuracy (NSSDA), while spatial autocorrelation of deviations was examined using Moran’s I. The results indicated that discrepancies between TerraHärp and legacy maps were dominated by systematic horizontal shifts in the historical maps, whereas random positional variability was relatively small and consistent across stand types. Drone-based laser scanning showed a strong dependence of tree identification accuracy on stand density and mean tree diameter. Overall, CHM-based segmentation yielded more accurate tree identification than 3D point cloud segmentation, with mean F1-scores of 0.78 and 0.72, respectively. Positional accuracy varied by method, with the largest errors from CHM apexes and highest 3D point cloud points (mean NSSDA ≈ 1.8–2.0 m), improved accuracy using the lowest 3D cluster points (1.45–1.72 m), and the highest accuracy achieved using mobile laser scanning (mean NSSDA 0.76–0.90 m; >95% of trees within 1 m). These results demonstrate that pseudolite-based field mapping provides a reliable reference for high-precision tree location and for integrating field and laser scanning data in managed conifer stands. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

Precise modeling of narrow gorges is challenging due to extreme confinement, hindering visibility and accessibility. These environments often render Global Navigation Satellite Systems (GNSS)-based positioning unfeasible, a difficulty compounded by water and dense vegetation. Consequently, multi-technique data fusion is required. This study proposes a robust methodology to generate high-resolution 3D models of such complex environments by integrating multiple aerial (e.g., Unmanned Aerial Vehicles, UAVs) and terrestrial techniques. A multi-sensor approach combined UAV-Light Detection and Ranging (LiDAR) and UAV-photogrammetry for external areas with Terrestrial laser scanning (TLS), Mobile Mapping System (MMS), and Spherical Photogrammetry (SP) for the canyon floor. Furthermore, the representativeness of these 3D models was analyzed against standard Digital Terrain Models (DTMs) for determining water height levels during flood events. A one-dimensional hydraulic (1DH) model compared the 3D mesh approach with the traditional 2.5D perspective in a challenging, narrow canyon prone to flooding. Our results show that traditional 2.5D DTMs significantly over- or underestimate water levels in narrow sections—failing to account for overhangs and vertical wall irregularities—whereas high-resolution 3D meshes provide a more realistic representation of hydraulic behavior. This work demonstrates that multi-sensor data fusion is essential for accurate flood risk management and infrastructure planning in complex fluvial environments. Full article

This study investigates the spatial dimensions of labour market polarization in Hungary by examining the widening gap between developed agglomerations and lagging peripheral regions. It explores how educational inequality, technology-driven risks, and constrained mobility affect the spatial aspects of labour market polarization. It covers all 197 districts of Hungary on the LAU-1 level. Using cluster analysis and OLS regression models, we shall explore relationships between employment rates, educational attainment, automation exposure—as based on occupation-level data—and a composite mobility index. From the data, we detected distinct labour market zones, which are dynamic agglomerations, industrial transition zones, and peripheral lagging. The data confirms that the “triple trap” is clearly experienced by the peripheral regions, with lower educational attainment, high exposure to automation impacting nearly 50%, and mobility constraints keeping the workforce bound to local public works employment. These results provide evidence that labor market polarization is a self-reinforcing spatial process. It implies that successful policy interventions should be comprehensive, addressing the interrelated elements of transport infrastructure, skill development, and regional economic diversification in one stroke to break the vicious circle of immobility. Full article

Autonomous navigation in mobile robots operating in dynamic and partially known environments demands the coordinated integration of perception, decision-making, and control while ensuring stability, safety, and energy efficiency. This paper presents an integrated navigation framework for differential-drive mobile robots that combines deep learning-based visual perception, reinforcement learning (RL) for high-level decision-making, and a Lyapunov-based trajectory reference generator for low-level motion execution. A convolutional neural network processes RGB-D images to classify obstacle configurations in real time, enabling navigation without prior map information. Based on this perception layer, an RL policy generates adaptive navigation subgoals in response to environmental changes. To ensure stable motion execution, a Lyapunov-based control strategy is formulated at the kinematic level to generate smooth velocity references, which are subsequently tracked by embedded PID controllers, explicitly decoupling learning-based decision-making from stability-critical control tasks. The local stability of the trajectory-tracking error is analyzed using a quadratic Lyapunov candidate function, ensuring asymptotic convergence under ideal kinematic assumptions. Experimental results demonstrate that while higher control gains provide faster convergence in simulation, an intermediate gain value (K = 0.5I) achieves a favorable trade-off between responsiveness and robustness in real-world conditions, mitigating oscillations caused by actuator dynamics, delays, and sensor noise. Validation across multiple navigation scenarios shows average tracking errors below 1.2 cm, obstacle detection accuracies above 95% for human obstacles, and a significant reduction in energy consumption compared to classical A* planners, highlighting the effectiveness of integrating learning-based navigation with analytically grounded control. Full article

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules. Full article

The transition from energy sufficiency to deficiency triggers complex metabolic and immune adaptations that have traditionally been viewed through a reductionist pathological lens. During early lactation, coordinated mobilization of adipose tissue, muscle protein, and bone minerals supports milk synthesis, with ketogenesis specifically arising from hepatic oxidation of non–esterified fatty acids. This review introduces the Keto–Inflammatory Network (KIN), a novel framework positioning ketonemia as an evolutionarily conserved adaptive response rather than inherent metabolic dysfunction. The KIN integrates β–hydroxybutyrate (BHB) signaling with immune modulation, epigenetic regulation, circadian rhythms, and microbiota interactions. Through mechanisms including NLRP3 inflammasome inhibition, HDAC–mediated epigenetic modifications, and HCAR2 receptor activation, ketone bodies orchestrate anti–inflammatory responses while maintaining metabolic flexibility. Building upon important precedent work recognizing beneficial roles of ketones in ruminant metabolism, this review synthesizes recent advances in immunometabolism and systems biology into an integrated framework. The KIN encompasses calcium–ketone integration through the Calci–Keto–Inflammatory Code (CKIC), temporal regulation via the Ketoinflammatory Clock, and trans–kingdom signaling through microbiota interactions. In dairy cattle, this perspective reframes periparturient ketonemia as existing on a continuum from adaptive to pathological, with biological meaning determined by integrated metabolic–inflammatory patterns rather than absolute ketone concentrations. The CKIC paradigm, while requiring prospective validation, suggests novel therapeutic approaches leveraging ketone signaling for inflammatory diseases, autoimmune conditions, and metabolic disorders while challenging traditional threshold–based ketosis management strategies. This systems–level understanding opens new avenues for precision interventions that work with, rather than against, evolved adaptive mechanisms refined through millions of years of mammalian evolution. By distinguishing ketonemia (measurable ketone elevation) from pathological ketosis (dysregulated ketone accumulation), and by integrating evidence from both ruminant and monogastric models, this review provides a comprehensive framework for next–generation metabolic medicine. Full article

Space robots operate in unconventional environments, which places specific demands on their mechanical, actuation, and control systems. They need to address a variety of challenges in future space exploitation and exploration, such as in-orbit deployment, active debris removal, or servicing operations. Using robots for such applications presents a unique challenge, as a high level of autonomy is required, and the manipulator’s motion affects the position and orientation of the spacecraft. The article presents basic theoretical statements regarding redundancy in space robotics. Various methods for overcoming difficulties in designing, using, and controlling a space robot are considered. Specialized control algorithms based on the null space of the Jacobian matrix and zero reaction maneuvers (ZRMs) are discussed. The review is limited to space robots with one or more arms and does not include mobile and humanoid robots. Furthermore, the primary motion planning algorithms for these systems are evaluated. Redundant space robots are categorized based on their degrees of freedom, number of arms, operational efficiency, primary objectives, and application areas and the most commonly used algorithms for planning movements. The advantages and disadvantages of both redundant and hyper-redundant space robots are analyzed. The objective of this review is to provide a comprehensive overview of the current state and prospects for the development of redundant robots for operation in space conditions. Full article

Phosphogypsum (PG), a calcium sulfate-rich byproduct of phosphate fertilizer production, is generated in vast quantities worldwide and represents a major environmental management challenge. At the same time, its chemical composition makes PG a potentially valuable soil amendment, particularly for the reclamation of saline, sodic, and acidic soils. This review critically synthesizes current knowledge on PG generation processes, physicochemical properties, agronomic performance, and associated environmental and health risks. Evidence from peer-reviewed studies demonstrates that appropriately managed PG applications can improve soil structure, enhance water infiltration, reduce sodium toxicity, alleviate aluminum stress, and increase crop productivity. However, PG contains variable levels of impurities, including heavy metals and naturally occurring radionuclides, which raise concerns regarding soil contamination, groundwater pollution, food safety, and human health, especially under high or repeated application rates. Regulatory frameworks governing PG use differ substantially between regions, reflecting inconsistencies in waste classification, radiological thresholds, and leaching criteria. This review highlights key knowledge gaps related to contaminant mobility, bioavailability, and long-term ecological impacts and discusses mitigation strategies such as purification, controlled application rates, and integrated regulatory oversight. By balancing agronomic benefits against environmental risks, this work provides a comprehensive framework for the safe valorization of phosphogypsum in agriculture, supporting sustainable land management and circular economy objectives. Full article

Effective network screening methods play a significant role in highway safety management programs and contribute to sustainable mobility by facilitating the reduction in all crashes, including fatalities and injuries across the transportation system. This study presents a comprehensive analysis comparing the effectiveness of three new network screening techniques for pinpointing safety improvement locations on rural roads. The proposed methods are the Global Risk Scoring (GRS), the Crash Risk Index (CRI), and the Predicted Empirical Bayes (P-EB) methods. The analysis utilized 10 years of roadway geometry, traffic, and crash data from state-owned rural highways in Oregon, with the first five years (2011–2015) used for model development and the subsequent five years (2016–2020) for validation. Comparative tests assessed consistency with historical crash rankings and temporal stability across observation periods. The analysis revealed distinct strengths among the screening methods. The GRS method demonstrated a high level of consistency with historical crash data, while the P-EB method exhibited superior consistency across different time periods, suggesting its value for long-term safety planning. The CRI method demonstrated reasonable consistency in performance, irrespective of the test carried out. While no single method outperforms the others in all scenarios, each has unique advantages and data requirements that can better suit the agency’s needs, given available resources. This research provides actionable insights for improving safety management strategies and advancing sustainable mobility. Full article

In response to the problems of poor operating stability and easy tipping of small agricultural machinery under the complex terrain of hilly and mountainous areas, this study designed a tracked mobile platform suitable for hilly and mountainous areas and equipped with an omnidirectional leveling function. The omnidirectional leveling system adopted an innovative coordinated leveling scheme with four servo-electric cylinders of “dual lateral and dual longitudinal” structure. Integrated with dual-axis tilt sensors and a PLC control system, the system enabled decoupled leveling in both the lateral and longitudinal directions. Dynamic simulations of the platform’s leveling process under typical working conditions were performed using ADAMS. The simulation results verified the feasibility of the omnidirectional leveling system. Field tests on slopes in hilly and mountainous areas demonstrated that the omnidirectional leveling system achieves rapid leveling on steep slopes within 5–6 s. After leveling, the average fuselage inclination angle was stabilized within 2°, with a standard deviation of less than 3.4°. This study provided a reliable technical solution and design reference for agricultural machinery manufacturers, while offering users a safer and more efficient platform for operations in complex mountainous areas, significantly reducing the risk of overturning. Full article

Background: Missed nursing care, defined as essential patient care that is omitted or delayed, is a growing source of concern due to its effects on healthcare quality and patient safety. Our aims in this study were twofold: first, we examined the extent and types of missed nursing care, and second, we analyzed the relationship between the care missed by hospital nurses and the staffing and resource adequacy in an academic medical center. Methods: A descriptive cross-sectional study was conducted during the period between November 2022 and July 2023. Data were collected using a self-administered questionnaire that comprised items on socio-demographic and work-related characteristics, items on staffing and resource availability, and items from the ‘MISSCARE’ Survey. Results: The most frequently missed nursing care involved pressure-relieving interventions (Mean = 2.39) and ambulation/mobilization (Mean = 2.27), while medication administration (Mean = 1.60) and glucose monitoring (Mean = 1.56) were missed the least. Labor resource inadequacy (β = 0.315, p < 0.001) and communication and teamwork deficits (β = 0.285, p < 0.001) were positively associated with missed nursing care, whereas staffing and resource adequacy showed an inverse association (β = −0.164, p = 0.006). The model explained 49.8% of the variance in missed nursing care (R² = 0.498). Conclusions: These findings highlight that missed nursing care is a system-level issue primarily associated with staffing and resource constraints rather than individual characteristics. Improving staffing adequacy, resource availability, and interprofessional collaboration may reduce care omissions and enhance patient safety in Saudi Arabian academic medical centers. Full article