Search Results (4,541)

Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the straight-line driving, longitudinal climbing, and lateral climbing processes of the chassis were theoretically analyzed, and the critical parameters that affect the normal climbing of the chassis were calculated. Meanwhile, the multi-body dynamics model of the tracked chassis was established by using the software SolidWorks 2020 and RecurDyn 2023, and its climbing and obstacle crossing performance were analyzed. The relevant motion parameters of the tracked chassis suitable for longitudinal and transverse slopes in hilly and mountainous areas were obtained, and field tests were conducted on the tracked chassis to verify the reliability of the simulation model. According to the simulation results, the tracked chassis achieves ultimate slope angles of 28° longitudinally and 23° laterally. It demonstrates the capability to navigate 140 mm high ridges and 250 mm wide trenches smoothly, while its straight-line driving offset rate conforms to prevailing agricultural machinery industry standards. Field test results indicated that the tracked chassis achieved a maximum longitudinal climbing angle of 26°. The relative error of less than 8% between the experimental and simulated data confirms a strong correlation. The maximum offset rate for straight-line travel is 1.95%, meeting the requirements of the agricultural machinery industry standards. The test verified the feasibility of the dynamic model of the crawler chassis of the silage harvester, providing a theoretical basis and technical support for the optimal design of the crawler chassis of the self-propelled silage harvester in hilly and mountainous areas. Full article

Existing technologies can achieve relative geometric correction and stabilization of geostationary satellite image sequences through fixed land scene matching or homonymous point adjustment. However, these methods heavily rely on fixed land areas, rendering them completely ineffective in vast ocean regions with only ship targets. Additionally, the trajectories of ship targets after processing still exhibit noticeable jitter, hindering motion information analysis. To address these issues, this paper proposes a joint image adjustment and stabilization method based on multi-target trajectories in marine environments: (1) An optimized target detection algorithm based on a multi-scale heterogeneous convolution module is introduced, which extracts background and target features through convolutions of different scales, enabling accurate detection and tracking of weak small targets in the image sequence frame by frame. (2) Curve fitting is performed on the detected positions of the same ship across multiple frames to simulate its motion trajectory under stabilized conditions. Combined with the prior assumption of uniform motion, an equal-division strategy is adopted to determine the corrected positions of the target in the image sequence. (3) The deviation correction values of multiple targets within the same frame are obtained, and based on the principle of intra-frame deviation consistency, precise image stabilization is achieved under multi-target constraints. Experiments based on Gaofen-4 satellite image sequences demonstrate that this method reduces the average position deviation of ship targets in the original images from 8.5 pixels (425 m) to 3.4 pixels (170 m), a decrease of approximately 59.41%, effectively improving the relative geometric accuracy of the image sequence and significantly eliminating target trajectory jitter. Full article

Net primary production (NPP) is a key indicator of the terrestrial carbon cycle, and its response to disturbance and subsequent recovery is important for understanding regional carbon sink dynamics. Conventional region-based statistical approaches have limitations in capturing localized heterogeneous changes. In this study, a typical ecologically fragile region on the northwestern Sichuan Plateau was selected as the study area. Using the Google Earth Engine (GEE) platform, Landsat time-series imagery (2001–2020) and MOD17A3HGF NPP data were integrated. The LandTrendr algorithm was applied to identify vegetation disturbance patches, and two representative disturbance years (2008 and 2014) were selected for long-term analysis. Trend analysis, coefficient of variation, and the Hurst exponent were used to characterize the spatiotemporal dynamics and stability of NPP in disturbed areas. The results show that: (1) NPP declined after disturbance and then exhibited a recovery trend, with significant spatial heterogeneity in recovery rates; (2) recovery trajectories differed between disturbance years, indicating combined effects of disturbance intensity and environmental conditions; and (3) Hurst exponent analysis suggests that although recovery trends are persistent in most areas, some disturbed patches show potential instability. This study establishes an analytical framework integrating disturbance detection and recovery tracking, which improves the representation of NPP dynamics in heterogeneous regions and provides a basis for assessing ecosystem recovery and carbon sink dynamics. Full article

Oceanfront relief varies along coastlines and serves as the first barrier to wave and surge damage. However, forecasted increases in storm frequency and sea levels are anticipated to enhance coastal erosion, potentially weakening this protection. The land–sea transition is variable along the New England coast, USA, and this variability has produced a range of coastal morphologies that can vary over short distances. It is important to track the beach transition zone to better understand transformations of the system and related hazard risks. A combination of field and computer-based methods was used to evaluate the beach transition zone of southwestern Rhode Island to determine alongshore variability and dynamics. More specifically, a decadal-scale study was conducted to examine changes in morphology from 2011 to 2022, and a short-term study at South Kingstown Town Beach examined changes from November 2023 to January 2024 using time-series drone-derived elevations. Classification of over 500 cross-shore transects illustrated the dominance of sedimentary shorelines, with smaller areas of rocky outcrops and hardening. Analysis of four different years (2011, 2014, 2018, and 2022) determined that beaches with dune morphology were the most common type of transition zone (41–47% of the transects) and transects with a high bank upland were the next most frequent class (34–41%). Following Hurricane Sandy in 2012, a 6% decrease in the number of dune-classified transects was measured; however, one-third of those recovered dune morphology by 2022. The greatest beach transformations over the short-term study occurred in response to strong storms in the 2023–2024 winter season, during which lateral beach movement (erosion) exceeded 15 m in portions of South Kingstown Town Beach. Dune erosion was accompanied by overwash flooding and deposition, and the area remained low-lying and thus vulnerable to future impacts. The beach transition zone classification and insights from this research will be informative for future planning by coastal communities by determining at-risk shorelines based on underlying geology and the stability of morphological features. Full article

Background: Monitoring skeletal muscle mass (SMM) during radiotherapy (RT) is important, as SMM loss is associated with poorer clinical outcomes. Cone-beam CT (CBCT), acquired before each RT fraction, offers the potential to track the lumbar skeletal muscle index (LSMI) over time. However, CBCT has lower image quality than conventional CT. This study assessed the agreement between CT and CBCT and evaluated the reliability of LSMI measurements in patients with head and neck squamous cell carcinoma. Methods: Patients who underwent both CT and CBCT on the same day during RT were included. The cross-sectional muscle area at C3 was measured, converted to L3, and used to calculate the LSMI. Two researchers analyzed all scans, with one repeating the measurements. Agreement and reliability were quantified using intraclass correlation coefficients (ICCs) and visualized with Bland–Altman plots. Results: LSMI measurements showed excellent agreement between CBCT and CT (ICC: 0.97–0.99; 95% CI: 0.95–0.99). The intrarater (ICC: 0.99; 95% CI 0.98–0.99) and interrater reliability (ICC: 0.97; 95% CI: 0.66–0.99) were high. Bland–Altman plots, however, revealed wide limits of agreement. Conclusion: CBCT provides reliable LSMI measurements and agrees well with CT, but the observed variability suggests cautious interpretation. When both modalities are available, CT remains the preferred standard for SMM assessment. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Path planning and formation control in leader–follower robotic systems are active areas of research, as both are highly relevant to the proper execution of the assigned task. In this work, a dual approach to the A* algorithm is applied to generate consistent trajectories for a multi-agent robotic system with a leader–follower scheme. The conventional A* algorithm aims to minimize the cost of finding the best path by minimizing distances. In this case, a modified A* algorithm is used because, although decision-making also involves choosing among eight options or cells, the goal is not to minimize distance; instead, the focus is on analyzing the direction of acceleration. The proposed algorithm is robust regarding the initial and relative pose of the leader with respect to the followers. The leader is tracked using a digital accelerometer. The algorithm is tested by simulating various patterns and implemented in two experimental test scenarios: the first with differential mobile robots, and the second with an Ackerman-type mobile robot. In both scenarios, the trajectories were achieved with deviations in x and y between the follower’s path and the leader’s path of less than 0.03, and the leader’s pose independence was maintained. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

Global aging is accelerating, with the proportion of the population aged 60 and above projected to reach 22% by 2050. In cold-region communities, the visual landscape environment is closely associated with the health of older adults, particularly showing associations with blood pressure (BP) and emotion states. However, associations between these factors across different landscape spaces and potential gender differences remain underexplored. This study utilized eye-tracking experiments to collect visual attention data from older adults in three types of cold-region community spaces: inter-building spaces, walkways and squares. The ground, buildings, trees, lawn, and the sky were identified as the primary Areas of Interest (AOIs). The Profile of Mood States (POMS) scale was used to assess emotion during walking experiments, revealing suggestive gender–environment interaction characteristics. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) were measured, and a Mann–Whitney U test indicated that DBP in community squares exhibited significant environmental dependency (U = 114.5, p = 0.004, r = 0.44). Hierarchical Linear Models (HLMs) revealed that, after controlling for individual differences, the number of fixation points on ground was independently associated (i.e., independent of measured individual characteristics) with elevated SBP ($\gamma =0.31, p=0.011$), while fixation on trees was associated with reduced SBP ($\gamma =-0.24, p=0.018$). Furthermore, gender moderation effects were observed: the association between ground fixation and SBP was stronger in females ($\gamma =0.18, p=0.022$), whereas the association between sports facilities and DBP was stronger in males ($\gamma =0.29, p=0.009$). Based on these findings, evidence-based design strategies are proposed, including the optimization of ground safety, gender-differentiated planting configurations, and targeted layouts for sports facilities. These results provide empirical support for age-friendly community design in cold regions. Full article

The use of Unmanned Aerial Vehicles (UAVs) has become quite popular in several applications during the last few years. Their spread is motivated by the flexibility of usage of UAVs and by their ability to automatically execute several tasks, mostly thanks to the availability of Global Navigation Satellite Systems (GNSSs), which usually allow reliable outdoor localization of aerial vehicles. However, the extension of task automatic execution indoors, and in other challenging working conditions for the GNSS, requires an alternative positioning system able to compensate for the unreliability or unavailability of GNSS in those cases. To this end, additional sensors are usually considered. Among them, cameras are probably the most popular ones. The most common case of a vision-based positioning system is a camera mounted on a moving platform used to determine its ego-motion in a dead-reckoning approach, i.e., visual odometry. Although this solution is affordable and does not require the installation of any infrastructure, it enables absolute positioning of the camera, i.e., of the UAV, only if certain landmarks, with known position, are visible in the flying area. In contrast, this work considers the use of external cameras installed in the flying area to track the UAV movements. This approach is similar to the one implemented in motion capture systems as well, where a set of static cameras is used to triangulate some target positions using calibrated cameras. Instead, this work investigates the use of vision and machine learning tools to (i) extract the UAV position from each video frame and (ii) estimate its 3D position. Estimation of the 3D UAV position is performed with a single camera, exploiting machine learning tools in order to avoid the need for camera calibration. Performance analysis is provided for a dataset collected at the Agripolis campus of the University of Padua. Full article

The growing reliance on virgin resources in construction, alongside accelerated urban development and the significant volumes of waste generated at the end-of-life phase of buildings, has intensified environmental impacts across the built environment. These challenges highlight the urgent need to transition towards a circular economy (CE) in the construction sector. At the same time, the sector’s ongoing digital transformation presents opportunities to enhance stakeholder collaboration and improve construction and demolition waste management (CDWM) practices. This paper aims to develop a conceptual framework for a Digital Information Management System (DIMS) to support CE implementation in construction through improved CDWM. Following the Design Science Research methodology, this paper addresses the first two stages: problem identification and solution proposition. A questionnaire survey with industry experts was conducted to validate the problem areas identified in the literature and assess the applicability of the proposed conceptual framework. The findings confirm critical gaps in CDWM, including limited stakeholder collaboration, fragmented processes, and the absence of lifecycle-spanning information systems, and validate the proposed conceptual framework solution, particularly the integration of BIM and IoT to support material and product flow tracking throughout the project lifecycle, supported by clearly defined stakeholder roles and engagements. However, respondents expressed reservations regarding Blockchain due to concerns about energy consumption and long-term data storage. Overall, the validated conceptual framework for DIMS provides a robust foundation for future studies, to focus on co-creating and developing a detailed conceptual model for DIMS for future real-world implementation. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

Children are among the most sensitive groups to air pollution. This study focuses on Chinese children aged 0–16 years, integrating six waves of tracking data from the China Family Panel Studies (CFPS, 2012–2022), the ChinaHighAirPollutants (CHAP) dataset, and MOD11A1 land surface temperature (LST) data, covering 20,241 samples across 25 provinces. Using the eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) framework, we quantified the relative contributions of fine particulate matter (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and climate factors to children’s respiratory disease risk. The overall area under curve (AUC) was 0.6765, with urban and rural sub-models achieving 0.6576 and 0.6864, respectively. SHAP analysis revealed that the temporal variable ranked first, reflecting population-level improvements from 2012 to 2022; age ranked second, with a 70.1% prevalence in the 0–6 age group. Rural PM_2.5 contribution was approximately 1.68 times that of urban areas; the O₃ effect showed opposite directions between urban (risk) and rural (protective association) settings; solid fuel contribution in rural areas was approximately 2.25 times the urban level. Regional clustering analysis identified differentiated environmental drivers across five geographic types. These findings provide a quantitative basis for differentiated regional prevention strategies. Full article

Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article