Search Results (54,569)

With the increasing demand for low-carbon energy, automated defect detection using unmanned aerial vehicle (UAV)-based thermal inspection has become essential for maintaining the reliability of photovoltaic systems. However, existing methods still suffer from low-contrast thermal imagery, large-scale variations of defects, and subtle thermal anomalies. To address these challenges, this study proposes Grouped-Hypergraph-Modulation DEIM (GHM-DEIM), a robust end-to-end detection framework based on an improved DEIM architecture. Specifically, a grouped multi-scale aggregation attention network is introduced to enhance global thermal perception and recover discriminative features from blurred backgrounds. In addition, an enhanced encoder incorporating a hypergraph-based context encoding mechanism is designed to model high-order non-local relationships and improve feature representation across different defect scales. Furthermore, a modulation fusion module is employed to adaptively refine multi-scale feature responses and suppress environmental noise interference. Extensive experiments conducted on the ThermoSolar-PV and PV-HSD-2025 datasets demonstrate that the proposed method consistently outperforms state-of-the-art detectors, achieving mAP@50 values of 88.6% and 74.2%, respectively, with improvements of 4.7% and 2.9% over the baseline. These results demonstrate the effectiveness and robustness of GHM-DEIM for UAV-based PV thermal defect inspection. Full article

Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

Autonomous navigation in unstructured off-road environments presents fundamental challenges due to terrain heterogeneity, the absence of structured road markings, and the necessity for real-time traversability reasoning from raw sensory observations. We present TerrainFormer, a hierarchical framework that integrates a world model for terrain dynamics prediction with a temporal decision transformer for action selection. Our methodology employs a two-phase training paradigm: (1) self-supervised world model pretraining on LiDAR point clouds to learn terrain representations encompassing traversability, elevation, and semantic segmentation; (2) behavioral cloning of the decision transformer conditioned on frozen world model features with temporally derived goal directions. The world model processes raw 3D LiDAR point clouds through a PointPillars encoder for real-time bird’s-eye-view (BEV) projection, followed by a Vision Transformer backbone that produces latent terrain representations. A principal contribution is our cross-dataset generalization paradigm: the world model is trained on separate datasets while the decision transformer is trained on separate sequences, ensuring zero data overlap between training phases. We introduce automatic goal direction computation from vehicle pose trajectories, enabling the model to learn directionally conditioned navigation policies. To address the class imbalance inherent in off-road driving data, we employ focal loss with inverse-frequency class weighting and action-chunk supervision. Experimental evaluation on the RELLIS-3D dataset achieves 87.31% test accuracy with 0.7948 macro F1 across all 12 action classes. The world model’s predicted future frames produce only a 0.79% accuracy drop versus ground-truth observations, with 98.82% action agreement, demonstrating effective cross-dataset generalization for real-time off-road navigation. Full article

Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) and switched to unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). However, there has been a lack of comparative studies on the energy and environmental assessment of these systems. This study aimed to evaluate the overall viability of different orchard spraying technologies in terms of energy efficiency, economic costs, and environmental impact. A life cycle assessment (LCA) of five sprayers was performed: a TMABS, a UGV, and three UAVs. The CML-IA methodology and SimaPro 9.5 software with the Ecoinvent v3 database were used to determine the environmental impact of the compared machines. Energy efficiency was calculated using fuel consumption data, human labor energy, and the energy embodied in the machinery. Economic viability was evaluated through capital depreciation, labor, energy consumption, consumable and maintenance cost per hectare calculation models. The results indicate that UAV systems, as compared to TMABS, can significantly reduce operational energy consumption, water use, and environmental impacts. The GWP of UAV systems was about 67% lower compared to the TMABS, while the UGV, due to lower performance efficiency, exhibited a 4% larger GWP (kg CO_2eq ha⁻¹). The findings of this study highlight that UAVs can produce the optimal results in comparison to other application methods. Full article

Navigating hilly orchards is challenging for autonomous agricultural vehicles due to the rugged terrain and dense canopy cover. Standard environmental modeling techniques are widely used, yet they often overlook how elevation uncertainty propagates during Digital Elevation Model (DEM) reconstruction. This oversight can directly affect terrain risk assessments and navigation planning. From an error-propagation perspective, this review examines how uncertainties originating from RTK-GNSS, LiDAR, and computer vision propagate through DEM reconstruction, terrain-feature extraction, cost map construction, and path planning. We further analyze how DEM elevation errors and vertical inaccuracies affect slope estimation, roughness representation, traversability assessment, vehicle stability, and navigation safety. Finally, we highlight practical bottlenecks in hilly orchard scenarios and suggest several research priorities, including multimodal fusion, uncertainty-aware modeling, lifelong map updating, and learning-based traversability assessment. Full article

High-performance thermal protection materials are urgently required in harsh thermal environments, such as hypersonic vehicles, the thermal runaway of energy batteries and high-temperature equipment. Conventional aerogels only exhibit passive thermal insulation and fail to resist instantaneous high-temperature attack. Herein, a cooling material of ammonium oxalate (AO) was introduced to achieve efficient, active endothermic protection. A cellular isolation effect induced by graphene nanosheets combined with anti-solvent crystallization was adopted to significantly decrease the size of AO crystals by over 93%. Based on superfine morphology and the constructed conduction network, the decomposition rate and heat absorption capacity of obtained graphene-doped AO powders (GdAPs) are improved by 41.2% and 30.4%, respectively. The mechanisms of morphology regulation and enhanced heat absorption are explored specifically in this study. Furthermore, GdAPs are embedded in phenolic resin to prepare thermal protection composite materials. Benefiting from their nearly complete thermal decomposition, GdAPs serve as a sacrificial template to generate discrete micropores in pyrolyzed resin. So, the as-prepared carbon aerogels (CAs) with a regulable microstructure exhibit an extremely low thermal conductivity of 0.056 W/(m·K), which is lower than those of reported CAs with the same density. Based on the above advantages, a synergistic endothermic-insulating thermal protection material is reported for the first time, and its heating rate is only 28.6% of that of commercial silica aerogel under identical high-temperature shock. Therefore, a new accessible strategy is demonstrated to provide high-efficiency thermal protection for resisting both abrupt and prolonged high temperature. Full article

Against the background of profound transformation within the construction industry, the construction industry ecosystem serves as a vital vehicle for regional economic development. Its resilience has become a key factor in influencing the sustainable development of industry. From an ecological perspective, this paper integrated the cusp catastrophe model and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method, using data from Shandong Province to investigate the evolutionary state of the construction industry ecosystem and the diverse concurrent paths driving the system toward high levels of functionality. This study found that: (1) the resilient development of the construction industry ecosystem in Shandong Province presented a differentiated pattern, characterized by dual-core leadership, relative strength in the east, and weakness in the west, and localized catch-up. (2) The results from the cusp catastrophe model indicated that construction industry ecosystems in different regions were primarily undergoing stable evolution, though some cities faced the risk of functional degradation due to the combined effects of insufficient resilience and severe shocks. (3) fsQCA identified three equivalent configuration paths for achieving high system functionality: the “resilience accumulation path”, “resilience synergy path”, and “resilience transition path”, as well as three equivalent configuration paths for low system functionality: the “low resilience-low shocks dependency path”, “low-resilience, fixed-type path”, and “low resilience-high shocks imbalance path”. (4) These paths demonstrated that recovery was the key factor determining a system’s level of functionality. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

In electric vehicle thermal management systems, direct measurement of the internal motor temperature is difficult. Therefore, the coolant pressure drop is an important indicator for estimating the motor thermal state. However, manufacturing and operating uncertainties in water-cooled interior permanent magnet synchronous motors (IPMSMs) can cause variability in cooling performance and pressure drop, requiring a reliability-based design approach. In this study, reliability-based design optimization (RBDO) is performed by considering manufacturing tolerances in the cooling channels and uncertainty in the inlet coolant flow rate. Based on coupled electromagnetic–thermal–fluid analysis and Kriging surrogate models, RBDO is applied to minimize the maximum temperature while satisfying the allowable pressure-drop limit at a target reliability level. The proposed RBDO improves the probability of satisfying the pressure-drop constraint from 54.1% in the baseline design to 99.9%, while increasing the mean maximum temperature by only 0.17 K. These results indicate that RBDO can improve the reliability of the pressure-drop constraint in IPMSM water-cooling systems under practical manufacturing and operating uncertainties, with only a limited change in thermal performance. Full article

The development of hydrogen fueling processes is an essential infrastructure component needed for the adoption of hydrogen-fueled vehicles as a transportation technology. This study provides techno-economic analysis (TEA) for two hydrogen fueling pathways (Case A, Case B), one of which (Case A) does not employ hydrogen liquefaction, while the other one (Case B) does. Both cases consider the same conditions as one another, of gaseous hydrogen inlet availability and gaseous hydrogen outlet dispensing. The TEA analysis carried out is based on data supported from the literature and process flowsheet UNISIM^® software simulations. The obtained TEA results indicate that the levelized cost of hydrogen (LCOH) of the gaseous hydrogen Case A is USD 4.20/kg H₂, which is lower than the LCOH of the liquefied hydrogen Case B, which is USD 10.14/kg H₂. Given the energy equivalence of a gallon of gasoline to kgH₂, and the higher efficiencies of hydrogen fuel cell vehicles over gasoline vehicles, the above conditions suggest that Case B fueling (with hydrogen liquefaction) involves high energy consumption and may delay the growth of hydrogen-fuel-based transportation technology, while Case A fueling (no hydrogen liquefaction) will likely become preferrable over both Case B hydrogen fueling and gasoline fueling, thus accelerating the growth of hydrogen-fuel-based transportation technology. Full article

Firms can gain a competitive advantage through a strategic patent portfolio, wherein patents elucidate technological advancements and establish legal barriers that keep competitors out. However, patents do not provide a perpetual monopoly within the prevailing open innovation paradigm, which means that firms should keep up with innovation input and patent applications to preserve their market dominance. Fostering technological breakthroughs in the patent network thus becomes a critical issue. Anchored in the theoretical views of open innovation, this study conducts an empirical analysis of patent data to examine how patent network structural features influence the technologies’ breakthrough tendency in the field of autonomous driving (AD). The findings indicate that centrality metrics such as degree centrality, harmonic centrality, and betweenness centrality within AD patent networks exert significant influence on technological breakthrough tendency, and the patent family size plays a moderating role in these relationships. Moreover, this research advances theoretical insights for patent strategy formulation in emerging firms of AD, with broader implications for other technology-intensive sectors. Full article

Grassland ecosystems are among the most threatened habitats in North America, and their degradation has contributed to widespread population declines of grassland-dependent birds. The Burrowing Owl (Athene cunicularia) is a grassland specialist whose populations have shown sustained declines at a continental scale; however, quantitative data on relative abundance remain limited in northern Mexico. We estimated a relative abundance index for the Burrowing Owl in the grasslands of the southern Chihuahuan Desert, Mexico, using vehicle-based line transects expressed as the number of individuals per linear kilometer (ind/km). Additionally, we evaluated the relationship between human disturbance and owl records using a standardized Human Disturbance Index (HDI) based on field indicators of grazing pressure and solid waste. A total of 18 transects (1 km each) yielded 83 detections, with a mean relative abundance of 4.61 ± 5.93 standard deviation (SD) ind/km. A Generalized Linear Model with a Negative Binomial distribution revealed a significant negative effect of the HDI on owl abundance (β = −1.27, z = −3.81, p = 0.0001; incidence rate ratio (IRR) = 0.28, 95% confidence interval (CI): 0.14–0.51). Our results provide a baseline abundance estimate for the Burrowing Owl in the southern Chihuahuan Desert and highlight the importance of habitat disturbance metrics to assess population status in fragmented and human-impacted grassland landscapes. Full article

The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article