Search Results (14,910)

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

As a vital component of urban tourism, urban theme parks increasingly face experience homogenization and intensifying competition. Accordingly, the implementation of refined digital marketing and operational strategies based on visitor digital behavior has become increasingly essential. In this context, tourists’ social media sharing has become a crucial link between destination marketing and visitors’ experience construction. Within the SOBC (Stimulus–Organism–Behavior–Consequence) framework, this study examines how theme park servicescapes (S) shape sharing motivations (O), which, in turn, influence multimodal sharing intentions (B—text, image + text, video) and subsequently contribute to memorable theme park experience (C). A two-stage, mixed-method design was employed, and the study considered visitors to Beijing Universal Studios and Shanghai Disney Resort. Semi-structured interviews and grounded analysis identified five motivations: altruism, self-presentation, affective expression, hedonic motivation, and community identification. Testing was performed using a survey (N = 604), along with structural equation modeling. The findings indicate that the staff-related social environment exerts significant positive effects on all five motivations, whereas the effects of the physical environment are more selective. Motivations differentially predict modal intentions: text aligns with altruism and affective expression; image + text aligns with altruism, community identification, and self-presentation; and video aligns with self-presentation, hedonism, community identification, and affective expression. All three intentions positively affect memorable theme park experience. These results clarify how motivations map onto content forms and validate a support SOBC framework from servicescapes to memorable experience, offering actionable implications for experience design and digital marketing. Full article

Active and healthy ageing has become a strategic objective in European and national policy agendas, grounded in grounded in internationally recognised definitions and policy frameworks such as the Madrid International Plan of Action on Ageing (MIPAA) and the European Innovation Partnership on Active and Healthy Ageing (EIPAHA). In Italy, the translation of this paradigm has taken place within a fragmented welfare system characterised by strong regional autonomy and persistent social and territorial inequalities, particularly along regional and gender lines. This scoping review has a twofold aim: (1) to map the Italian scientific and grey literature on active and healthy ageing, identifying dominant dimensions, priorities and gaps, and (2) to examine how policies and interventions frame, address or overlook social, territorial and gender inequalities across the life course Following established scoping review methodological frameworks and PRISMA-ScR guidelines, the review systematically identified, selected and synthesised Italian scientific studies and institutional documents published between 2012 and 2024. An inductive thematic analysis was conducted across four main areas—health and wellbeing; social inclusion and participation; indicators and measurement tools; and governance and public policies—with specific attention to the explicit and implicit treatment of inequalities. The analysis reveals a heterogeneous and regionally unbalanced policy landscape. While some territories have developed more integrated approaches linking prevention, participation and social inclusion, others remain largely confined to sectoral and fragmented interventions. Gendered patterns of unpaid care, differential access to programmes and services, and uneven territorial distribution of resources emerge as key dimensions of inequality shaping opportunities for active ageing. A partial discontinuity can be observed after 2019, with the introduction of national coordination mechanisms, although substantial differences in regional implementation capacity persist. The findings highlight the need for more coherent and equity-oriented strategies capable of integrating health, social and educational dimensions through a life-course and intersectional perspective. Strengthening multi-level governance and explicitly addressing social, territorial and gender inequalities as structural determinants—rather than residual variables—appears crucial to enhancing both the effectiveness and the fairness of active and healthy ageing policies in Italy. Full article

Forest biometrics has emerged as one of the fastest-growing scientific disciplines within environmental sciences. Machine learning (ML), an increasingly essential approach that uses effective algorithms, has proven to be an accurate and cost-efficient solution to forest-related problems. Recently, ML methods have evolved, from traditional machine learning (TML) algorithms to more sophisticated approaches, such as deep learning (DL) and ensemble (ENS) methods. To uncover these developments, a structured review and analysis of 150 peer-reviewed studies was conducted, following a standardized workflow. The analysis reveals clear shifts in methodological adoption. During the most recent five-year period (2021–2025), DL and shallow neural network (SNN) methods dominated the literature, accounting for 37.5% of published studies, followed by ENS and TML methods, contributing 29.2% and 27.1%, respectively, presenting a marked increase in the utilization of artificial neural networks (ANNs) and related algorithms across the domains of forest biometrics. Nevertheless, overall trends indicate that the benefits of TML methods still need further exploration for ground-based received data. Advances in remote sensing and satellite data have brought large-scale remotely sensed data into environmental research, further boosting ML utilization. However, each field could be strengthened by implementing standardized evaluation metrics and broader geographic representation. In this way, robust and widely transferable modeling frameworks for forest ecosystems can be developed. At the same time, further research on algorithms and their applicability to natural resources proves a key component for comprehensive and sustainable forest management. Full article

Non-Pneumatic Tires (NPTs) have been recognized for their advantages, such as low rolling resistance, burst resistance, and lightweight design, which make them highly suitable for application in electric vehicles under complex conditions, including high-frequency starts and stops and high torque. However, the discontinuous spoke support structure has resulted in a significantly higher ground contact pressure distribution compared to traditional pneumatic tires, leading to more severe wear, especially in the contact area where complex stress concentrations have occurred. Currently, the wear behavior mechanisms of NPTs have not been fully clarified, and wear simulation methods that take temperature effects into account are lacking. In this study, a temperature-modified Archard wear equation was integrated into the UMESHMOTION subroutine to achieve real-time updates of the tire surface geometry and simulate the evolution of wear. The modeling approach was validated through experimental testing. The simulation results showed that as the load increased from 100 N to 700 N, the peak ground contact pressure significantly increased, and the contact area gradually expanded, resulting in a notable increase in wear. Additionally, as the slip ratio increased from 2% to 5%, the contact stress and wear area were significantly amplified, leading to an increase in surface roughness and evident local damage. Comparative results indicated that the slip ratio had a more significant impact on wear volume than the load. The study has been conducted from a physical mechanism perspective to verify the dominant role of the slip ratio in the short-term rolling distance of tires, providing a theoretical basis for the structural optimization and wear-resistant design of non-pneumatic tires under complex operating conditions. Full article

Background/Objectives: This study investigated the biomechanics of a hip exoskeleton during sit-to-stand transitions. Methods: Eleven participants performed the task under three conditions: without the exoskeleton (No Exo), wearing the exoskeleton without assistance (Exo Off), and wearing it with hip extension assistance (Exo On). Results: The analyses revealed that joint angles (hip, knee, and ankle) and vertical ground reaction forces were comparable across all conditions. However, Exo Off significantly increased transition time, whereas Exo On did not differ significantly from No Exo. Additionally, both exoskeleton conditions led to increased integrated EMG (iEMG) activity in the rectus femoris, vastus medialis, and gluteus maximus—likely due to the added device mass. Notably, iEMG analysis revealed a significant reduction in gluteus maximus activity in Exo On compared to Exo Off. Conclusions: Despite providing only moderate torque assistance (0.12 Nm/kg), the results suggest that well-timed exoskeleton support can partially reduce the physical demands of sit-to-stand transitions. However, the observed reduction in gluteus maximus activity was limited, likely reflecting the combined effects of the assistance strategy, including its magnitude and timing, user adaptation and training, postural demands due to device weight and external torques, and mechanical constraints such as potential joint misalignment. Further research is needed to optimize hip exoskeleton support for daily activities. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

In emergency rescue operations, coordinating ground users (GUs) efficiently to handle dispersed tasks is crucial for saving lives and property. However, challenges such as task assignment and channel access hinder effective performance. The heterogeneity of GU abilities and the multiple ability requirements of tasks often lead to mismatched assignments, reducing rescue efficiency. Furthermore, channel access is complicated by the lack of channel state information (CSI) in disaster environments, which increases resource consumption if all channels are explored exhaustively. To address these challenges, this paper proposes a two-stage optimization framework that combines task assignment and channel access under unknown environments. First, a clustering-based method groups GUs according to multiple ability requirements. The task assignment problem is formulated as a transferable utility coalition formation game (CFG) with defined utility and preference relations. Second, a channel access mechanism is designed and modeled as an optimal stopping problem to optimize exploration time and select the optimal channel from the explored set. A task assignment and channel access optimization algorithm for cooperative rescue is proposed, where a multi-round matching preprocessing step supports coalition formation, and a one-stage look-ahead (1-SLA) rule balances exploration and data reception. Simulation results show that the proposed algorithm effectively satisfies task ability requirements, accelerates channel access, and improves the actual total utility. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article

To date, searching for bionematicidals is essential. In the absence of nematicides, “Basic Substances” are gaining ground since they are cost-effective, do not mandate an expiration date and have no inherent capacity to cause endocrine-disrupting neurotoxic or immunotoxic effects. Most “Basic Substances” are authorized for the control of phytoparasitic fungi and insects, whereas nematicidals are yet to be available. In this study, we employed “Basic Substances” and in particular, beer, sodium bicarbonate, and sodium chloride, together with rose aromatotherapy by-products, on nematicidal bioassays against Meloidogyne incognita. We report that chemical composition analysis of the nematicidal rose extracts correlates with bioactivity. Paralysis-based bioassays were used as primary criteria to assess efficacy, specifically targeting second-stage juveniles of Meloidogyne incognita. The evaluated treatments were assessed after one day, two days, and three days of J2 immersion in test solutions. According to our results, the “Basic Substances” demonstrated a significant paralysis effect on J2, thus indicating, for the first time, the considerable significance of their authorization to the root knot nematodes. Similarly, the rose extracts were found to be nematicidal, and since they are foodstuffs, and thus nonconcern compounds, “Basic Substances” can be developed as aromatherapy by-products in the frame of a circular economy. Full article

To overcome the limitations of ground-based observations, this study aims to identify optimal color indices for detecting tree phenological events using time-series Unmanned Aerial Vehicle(UAV) orthomosaics. We monitored 37 woody taxa at the Korea National Arboretum from April to November 2025. By extracting Red, Green, and Blue (RGB) values from canopy polygons, we calculated four indices: Brightness, Green Chromatic Coordinate (GCC), Red Chromatic Coordinate (RCC), and Green-Red Vegetation Index (GRVI). We then evaluated signal detectability using Z-score standardization. The analysis confirmed that 74.6% of phenological events were detectable. Specifically, flowering and autumn coloration showed high detection rates (88.9% and 100%, respectively), identifying Brightness, RCC, and GRVI as key indicators for capturing these distinct visual changes. Conversely, gradual transitions like leaf-out showed lower detectability. These findings demonstrate that selecting specific color indices based on the visual characteristics of each event enables effective quantitative monitoring. This study provides a methodological basis for utilizing UAV-based indices as a complementary tool in long-term ecological monitoring. Full article

Background/Objectives: Noncontact knee ligament injuries, including anterior cruciate ligament (ACL) ruptures and medial collateral ligament (MCL) sprains, are prevalent in sports that involve frequent cutting and pivoting. Conventional rigid knee braces can offer stability but often compromise comfort and performance, whereas soft sleeve-type supports provide minimal mechanical protection. The purpose of this study was to evaluate the acute biomechanical effects of a tensioned cable knee bracing system on peak knee valgus angle and external knee abduction moment during a controlled lateral shuffle task. Methods: Ten physically active adults (mean age 21.7 ± 3.8 years) performed submaximal lateral shuffle movements under three conditions: unbraced, sleeve-only (zero-tension), and a novel tensioned cable brace. Three-dimensional knee kinematics and ground reaction forces were collected, and peak knee valgus angle and external abduction moment were calculated during the eccentric phase of each movement. Results: Wearing the knee brace under tension significantly reduced knee valgus angle (4.5° vs. 7.9°) and peak external knee abduction moment (1.6 vs. 2.0–2.1 Nm/kg) compared to the unbraced condition. Conclusions: These findings indicate that the tensioned cable brace effectively reduced frontal plane knee loading during a lateral shuffle task, indicating its potential as an effective bracing approach. Full article

The joint optimization of computing resources and network routing constitutes a central challenge in Computing First Networks (CFNs). However, existing research has predominantly focused on computation offloading decisions, whereas the cooperative optimization of computing power and network routing remains underexplored. Therefore, this study investigates the joint routing optimization problem within the CFN framework. We first propose a computing resource scheduling architecture for CFN, termed SICRSA, which integrates Software-Defined Networking (SDN) and Information-Centric Networking (ICN). Building upon this architecture, we further introduce an ICN-based hierarchical naming scheme for computing services, design a computing service request packet format that extends the IP header, and detail the corresponding service request identification process and workflow. Furthermore, we propose Computing-Aware Routing via Graph and Long-term Dependency Learning (CRGLD), a Graph Neural Network (GNN), and Long Short-Term Memory (LSTM)-based routing optimization algorithm, within the SICRSA framework to address the computing-aware routing (CAR) problem. The algorithm incorporates a decision-making framework grounded in spatiotemporal feature learning, thereby enabling the joint and coordinated selection of computing nodes and transmission paths. Simulation experiments conducted on real-world network topologies demonstrate that CRGLD enhances both the quality of service and the intelligence of routing decisions in dynamic network environments. Moreover, CRGLD exhibits strong generalization capability when confronted with unfamiliar topologies and topological changes, effectively mitigating the poor generalization performance typical of traditional Deep Reinforcement Learning (DRL)-based routing models in dynamic settings. Full article

The sustainable management of marine resources requires accurate knowledge of fishing activity patterns and their interaction with coastal infrastructure. Intelligent Transportation Systems (ITS) are increasingly applied in the maritime domain, where data-driven approaches enhance safety, efficiency, and sustainability. In this context, Automatic Identification System (AIS) data provide valuable insights into vessel behavior and fisheries management. This study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify fishing grounds, and a density map-based approach to recognize port locations. By integrating AIS data with machine learning techniques, the study detects and analyzes fishing vessel activities, providing deeper insights into behaviors such as fishing ground visit times, durations, and transitions between fishing grounds and ports. A case study in the Aalesund area of Norway demonstrates that DBSCAN effectively reveals fishing activity patterns relevant to regulatory oversight and spatial planning, while density mapping accurately identifies fishing ports. The findings highlight the potential of AIS-based analytics and clustering methods within maritime ITS frameworks to enhance situational awareness, support compliance with fisheries regulations, and contribute to sustainable marine resource management. Using 2023 AIS data from the Aalesund region, 6 recurrent fishing grounds and 15 port locations are identified, and size-stratified visit frequency and residence-time distributions are quantified together with monthly seasonality in ground usage. Full article

Marine ports located in regions of moderate seismicity can face high Natech (natural hazard-triggered technological) risk because large inventories of hazardous materials are stored near dense urban populations. This study proposes and applies a Natech risk framework to a representative port on the Saint-Laurence River in Quebec, Canada. Site-specific peak ground accelerations (PGA) are first estimated for 12 earthquake scenarios using regional ground motion prediction equations adjusted for local site conditions. These hazard levels are combined with a damage probability matrix to estimate Hazardous Release Likelihood Index (HRLi) scores for atmospheric steel storage tanks. Offsite consequences are then evaluated to obtain Maximum Distances of Effect (MDEs) for different types of hazardous materials. MDE footprints are intersected with block-level demographic data and complemented by a domino-effect based on inter-tank spacing, yielding a tank-level Natech Risk Index $NR{\mathrm{I}}_{\mathrm{i},\mathrm{s}}$ for each storage tank ($\mathrm{i}$) and seismic scenario ($\mathrm{s}$). These values are then averaged over all tanks to obtain a scenario-level mean Natech Risk Index ($\overline{NRI}$) for each tank substance. Regression equations relating $\overline{NRI}$ to PGA are provided as a practical tool for defining critical intensity thresholds for seismic Natech risk management in marine ports. Full article