Search Results (17,612)

Evaluating resource and environmental carrying capacity (RECC) serves as a fundamental approach for assessing regional environmental baselines and is widely applied in territorial spatial planning. Focusing on Daye City—a characteristic resource-exhausted city in Hubei Province—this study developed a comprehensive RECC evaluation system. By integrating the obstacle degree model, hotspot analysis, and Geodetector, we investigated the spatial differentiation mechanisms of RECC and the resulting production–living–ecological (PLE) spatial conflicts, ultimately proposing targeted optimization pathways. The core findings are as follows: (1) The RECC of Daye City exhibits pronounced spatial polarization and a distinct north–south gradient. (2) The spatial stress of industrial/mining land emerges as the primary obstacle (36.47%). Together with geological hazard risk and soil erosion sensitivity, it forms a core constraint chain. The highly significant hotspots of these factors strongly overlap in the north-central mining districts. (3) Geodetector analysis reveals robust bivariate and nonlinear enhancement effects among these core obstacle factors. This indicates that the cascading vicious cycle of mining disturbance, ecological degradation, and declining carrying capacity fundamentally underlies the constrained RECC in mining regions. (4) PLE spatial conflicts across the study area are dominated by production–ecological conflicts (47.73%), presenting a spatial pattern that heavily couples with the polarized obstacle zones. Based on these findings, this study proposes differentiated regulation strategies centered on mitigating mining-induced stress and interrupting the cascading transmission of disaster risks. These strategies aim to restructure and optimize the territorial spatial pattern, providing robust quantitative decision support for the sustainable transformation of similar resource-exhausted cities. Full article

Hyperspectral remote sensing enables precise identification of alteration mineral through spectral–image integration and high-resolution capabilities. However, vegetation interference significantly hinders the extraction of alteration information in vegetated areas, thereby posing challenges to the reliable identification of alteration minerals. This study employs GF-5B satellite AHSI imagery acquired in the Huzhou region of Zhejiang Province, China, to address this challenge via a novel Zonal Adaptive Vegetation Suppression Technique (ZAVST). By constructing segmented statistical models that links reflectance characteristics across multiple spectral bands to NDVI values, ZAVST demonstrates an enhanced capability to mitigate vegetation obscuration effects on subsurface lithological features while substantially improving the identification of subtle spectral signatures characteristic of mineralization. Results reveal distinct spatial patterns: Fe-bearing alteration minerals (hematite, pyrite) align along NE-trending faults and volcanic basin margins; Al-OH alterations (montmorillonite, kaolinite) cluster near intrusive contacts; Mg-OH alterations (chlorite, epidote) occur at interfaces between carbonate sequences and concealed intrusions. Composite alteration anomalies exhibiting stacked mineral signatures (up to four distinct types) were identified across the region, demonstrating a strong spatial correlation with known mineralization centers. By integrating alteration zonation, structural lineaments, stratigraphy, geochemical anomalies, and orebody records, this study delineated four priority targets: Lijiaxiang Town, eastern Meixi Town, Miaoxi Town, and the central Moganshan Volcanic Basin. Full article

A low-latency, low-complexity digital demodulator is presented for chirp spread spectrum (CSS)-modulated RF packets targeting low-power IoT wireless systems operating in spectrally congested environments. Conventional CSS receivers rely on fast-fourier transform (FFT)-based synchronization and long preamble sequences, resulting in increased latency and computational complexity. To address these limitations, the proposed receiver employs amplitude-domain synchronization using oversampled sub-chirp windows and maximum likelihood estimation without requiring FFT processing. A digital demodulator co-designed with receiver’s fractional-N phase-locked loop (PLL) architecture enables rapid sub-chirp generation and fast frequency settling, while compensation techniques mitigate symbol boundary offset (SBO) error due to PLL non-idealities during synchronization. The proposed system achieves packet synchronization within 17.5 preamble symbol cycles while maintaining symbol boundary offset estimation error below ±1%. Simulation results demonstrate a syncword misdetection probability below 10⁻³ at SNRs of 9 dB and 1 dB without and with 8× repetition, respectively. In the presence of interferences, the receiver tolerates worst-case in-band signal-to-noise ratio (SIR) levels down to −16.2 dB while consuming 877 µW and 830 µW average power at the digital demodulator, and fractional-N PLL, respectively. Implemented in 65 nm CMOS, the proposed architecture occupies 0.195 mm² active area. Full article

The development of biocompatible materials has gained traction due to the increasing clinical demands for customizable and functional medical devices. Chitosan, a deacetylated derivative of chitin, is a naturally occurring biopolymer with strong antimicrobial properties, immunocompatibility, and structural adaptability, making it a promising candidate for biomedical applications. Through mechanisms such as crosslinking, ionic bonding, gas formation, and UV radiation, the mechanical properties and stimulus responses of chitosan-based hydrogels can be tailored for drug delivery at specific sites or under specific pH, light, or electrical conditions. Beyond drug delivery, chitosan hydrogels have shown considerable potential for vascular tissue repair. The porous structure of chitosan allows patient specific vascular scaffolding to be created that promotes the recovery rate veins and stenting procedures. Thermally sensitive hydrogels can deliver drugs to target regions to further assist in vascular healing. Furthermore, recent developments with composite polymers and coatings engineered to self-assemble within veins provide scaffolds for vascular tissue growth. This manuscript reviews chitosan hydrogel fabrication methods and their corresponding materials properties, with particular emphasis on drug delivery to vascular tissues. Furthermore, relevant findings from clinical trials are summarized to support the potential of chitosan hydrogels for future clinical use. Challenges of chitosan hydrogels, such as insufficient mechanical strength, high degradation rates, and complex manufacturing, remain as areas for research break-through. Full article

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment. Full article

Multi-UAV cooperative search with static unknown targets requires both efficient regional allocation and responsive local maneuvering. However, single-level learning methods often suffer from redundant coverage, unclear division of labor, and unstable training. This paper proposes a high- and low-level decoupled reinforcement learning method for multi-UAV cooperative search. The high level periodically generates UAV-specific regional goals from visitation maps, target-existence belief maps, and UAV positions, while a spatial self-attention module enhances the representation of unvisited regions, high-belief target areas, and UAV distributions. The low level performs discrete steering actions based on local observations and high-level contexts, supported by a structured reward that encourages coverage, target discovery, goal-oriented progress, repeated-visit suppression, and boundary-safe motion. Simulation experiments are conducted in a two-dimensional grid environment with static targets and ideal sensing. Under this simplified simulation setting, the proposed method achieves higher training return and coverage rate than representative baseline algorithms while maintaining a high final target discovery rate and reaching the discovery threshold earlier. Ablation and visualization results further demonstrate the effectiveness and interpretability of the proposed hierarchical guidance mechanism within the considered simulation scenario. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

This study presents a compact controlled urban geophysics test site developed at the University of Malta to evaluate the response of complementary near-surface sensing methods under known shallow subsurface conditions. The experimental setup is designed to investigate buried target detection and the response to a simulated shallow leak, used here as a controlled water-release experiment in a shallow carbonate setting characterized by thin, laterally variable soil cover and anthropogenic disturbance. A preliminary passive seismic survey based on the horizontal-to-vertical spectral ratio (HVSR) method was used to compare candidate sectors and select the most suitable area for installation. The test site includes a buried iron plate and a perforated PVC pipe, the latter used to release water under controlled shallow conditions. Ground-penetrating radar (GPR), smartphone magnetometry, electrical resistivity tomography (ERT), and UAV-based thermal imaging were applied to assess target detectability and leak-related surface–subsurface responses. Results show that GPR provides the clearest response for static target detection, while smartphone magnetometry identifies the buried ferrous target under favourable conditions. For the simulated leak experiment, ERT provides the most robust subsurface evidence of moisture redistribution after water injection. UAV thermal imaging captures a complementary surface thermal response influenced by both moisture dynamics and local surface disturbance. The results show that a compact controlled test site can support the comparison of professional and low-cost sensing methods for shallow target detection and simulated leak assessment. In this configuration, the controlled water-release experiment provides a practical basis for evaluating leak-related surface–subsurface responses under known shallow conditions. The proposed setup has implications for methodological assessment, training, and near-surface environmental monitoring in heterogeneous urban settings. Full article

Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (C_a) separately from JDLink telematics, aligning model structure with each target’s response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere CH570 chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R²_CV = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; C_a was retained for MLR (R²_CV = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF’s univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure. Full article

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

This study conducts a multi-objective optimization of an impact pruner for coniferous plantation trees, using Prince Rupprecht’s larch (Larix principis-rupprechtii Mayr) in North China as a case study. The objective is to establish an impact cutting mechanics model and to construct an impact cutting platform. This study utilizes the Box–Behnken principle, with the cutting speed (v), cutter wedge angle (β), and cutting clearance (L) as influencing factors and the cutting energy consumption (Y₁), total equipment energy consumption (Y₂), and specific cutting area (S) as evaluation indexes. The cutting parameters were optimized using a mathematical model for multi-objective optimization. The experimental results indicate that the factors influencing target Y₁ were ranked as β, L, and v, while the factors influencing target Y₂ were ranked as β, v, and L, and the factors influencing target S were ranked as L, β, and v. Field tests demonstrated that the optimization reduced the cutting energy consumption by up to 16.90% and improved the cutting quality by up to 19.28%. These gains directly translate to improved operational efficiency and economic value in forestry management. The optimal parameters corresponding to these improvements are v = 2.15 m·s⁻¹, β = 20°, and L = 5 mm, resulting in Y₁ = 36.10 J, Y₂ = 3351.01 J, and S = 3.45. These results demonstrate the feasibility and efficiency of the impact pruning method for Larix principis-rupprechtii in coniferous plantation forests. By combing mechanism analysis with multi-objective optimization, this study proposes a solution that can improve the pruning quality of coniferous plantation trees, reduce the energy consumption of impact pruning machines, enhance tree health, and serve as a measure to prevent pests and diseases, contributing to the advancement of artificial forest plant protection technology. Full article

Background/Objectives: Health literacy (HL) is recognized as an important social determinant of health. It supports healthy behaviors and effective health management throughout one’s life. For children and adolescents, developing HL influences their well-being, development, and ability to make informed health decisions. Nurses are strategically positioned to promote HL from an early age. To our knowledge, no prior synthesis has specifically examined nurse-led HL interventions targeting pediatric populations, highlighting the originality and relevance of this scoping review. The purpose of this review was to map and characterize nursing interventions aimed at improving HL outcomes in children and adolescents. Methods: A scoping review was conducted according to the Joanna Briggs Institute methodology, using a three-step search strategy, and reported in accordance with the PRISMA-ScR guidelines. Searches were conducted in MEDLINE, CINAHL, Scopus, Web of Science, and ProQuest with no date restriction, including studies published in Portuguese, English, or Spanish. Studies involving children and adolescents (ages 0–18) in any healthcare or community setting were eligible. Data on intervention characteristics and HL outcomes were extracted and analyzed descriptively, and no critical appraisal of the included sources was conducted. Results: A total of 44 studies were included. Interventions were predominantly school-based and focused on adolescents (n = 26), with a clear gap in early childhood (n = 2). Studies of early childhood primarily used storytelling and reading activities, whereas interventions targeting older children and adolescents more often employed participatory educational strategies, group-based approaches and digital platforms. The most frequently addressed topics were chronic disease management (n = 12), mental health (n = 7), and nutrition (n = 5). HL domains mainly focused on healthcare and health promotion, with fewer studies addressing disease prevention. Most interventions were conducted in school settings (n = 24), highlighting this context over those in primary care, community, and hospital settings. Conclusions: The results revealed nursing interventions used to promote HL, particularly in the management of chronic diseases, mental health and nutrition. However, the existing body of research is still limited. Key gaps include the absence of standardized measurement tools and the scarcity of longitudinal studies evaluating long-term outcomes. These limitations constrain the comparability and generalizability of findings, highlighting the necessity of more rigorous, methodologically robust research to support evidence-based practices. This scoping review comprehensively maps nurse-led interventions that promote HL among children and adolescents, identifying key priorities to guide future research in this area. Full article

Rare genetic disorders of the central nervous system (CNS) remain some of the most complex and challenging diseases to treat for several reasons. Targeting the CNS, especially the brain, presents one of the greatest obstacles in gene therapy using adeno-associated virus (AAV) vectors. Although various AAVs have been identified for their ability to transduce different cells in the CNS, their effectiveness and efficiency are significantly limited by the presence of neutralising antibodies (NAbs) and restricted cargo capacity. Despite these challenges, our understanding of AAV structure and technological advances continue to enable researchers to develop innovative strategies that have resulted in groundbreaking, FDA-approved therapeutic products now available for Leber congenital amaurosis (LCA) (Luxturna^®), spinal muscular atrophy (SMA) (Zolgensma^®), and the two recent gene therapy products for aromatic L-amino acid decarboxylase (AADC) deficiency, Kebilidi^® and Upstaza^®, which currently hold FDA and EMA approval, respectively. This review aims to highlight recent advances in the field of AAV gene therapy for neurological disorders, identify research gaps, and suggest areas for future investigation to enable potential breakthroughs particularly in neurodegenerative, neurodevelopmental, and neuromuscular disorders. We foresee that more tissue- and cell-specific AAV vectors designed using AI-powered platforms will emerge to precisely and efficiently target specific brain regions, transforming how CNS disorders are treated. Full article

Online apparel size recommendation remains difficult because consumers cannot physically assess garment fit before purchase. It is a multi-dimensional fit evaluation problem, particularly for complex garments such as jackets, where multiple body areas jointly influence perceived fit. Existing methods often rely on limited anthropometric measures, heuristic rules, or behavioral data, restricting both accuracy and interpretability. To address this issue, this study proposes an interpretable multi-dimensional fit evaluation framework based on garment ease theory. The framework defines ideal ease as the target fit condition and quantifies deviations through a segment-based weighting mechanism. Section-level mappings between body and garment measurements are established, and differentiated penalties are assigned according to the semantic fit interval of each body area. Section-specific evaluations are aggregated into an overall fit score (OFS) for candidate size ranking and Top-K recommendation, while also providing detailed fit feedback. Experiments involving 270 female participants and two jacket styles show high recommendation accuracy, achieving Top-3 accuracies of 99.6% for the regular-fit jacket and 98.9% for the tight-fit jacket. Compared with traditional heuristic methods, the proposed approach demonstrates clear advantages in both performance and interpretability, offering a practical solution that balances accuracy, transparency, and deployability. Full article

Scent marking has been discussed as an important component of communication in brown bears (Ursus arctos Linnaeus, 1758). However, the environmental factors influencing the occurrence of rub trees and their value for non-invasive genetic sampling remain poorly understood. This study examined the patterns of rub tree occurrence in the eastern High Tatra Mountains (Slovakia) at two spatial scales. At the tree scale, paired-design generalized linear mixed models showed that rub trees were more frequently recorded on large-diameter coniferous trees, indicating an association with visually prominent and chemically suitable substrates. At the landscape scale, logistic regression models revealed that the probability of rub tree occurrence increased with elevation and distance from human settlements, identifying high-elevation forests as areas of higher predicted rub tree occurrence. The best-supported model was used to produce a predictive map of rub tree occurrence across the study area. We also evaluated whether rub trees are reliable sources of biological material for non-invasive sampling. Hair collected during repeated field visits provided DNA suitable for genotyping and individual identification. Overall, the results show that rub trees exhibit non-random spatial patterns and represent effective focal points for systematic genetic sampling, linking patterns of rub tree occurrence to the spatial targeting of non-invasive genetic sampling in mountain landscapes. Full article