Search Results (17,832)

Regional reassessments of agricultural carbon emission efficiency are essential for improving the sustainability of food production systems under climate constraints. This study evaluates agricultural carbon emission efficiency (ACEE) across China’s major grain-producing zone (GPZ), major grain-consuming zone (GSZ), and grain production–consumption balanced zone (GBZ) during 2003–2022, excluding Hong Kong, Macao, Taiwan, and Tibet due to data limitations. A super-efficient EBM–GML model incorporating both desirable and undesirable outputs is employed to measure ACEE at the provincial level, with comparisons conducted within each functional zone and nationally unified efficiency values used as a benchmark. Spatial dependence is examined using Moran’s I, and a spatial Durbin model is applied to identify driving factors and spatial spillover effects. The results indicate that the average efficiency levels differ systematically across functional grain zones, following the order GBZ > GPZ > GSZ, while several provinces experience notable changes in their relative rankings. Carbon emissions increase in the earlier period and decline in later years, whereas efficiency exhibits an opposite temporal pattern, reflecting a gradual transition of grain production systems from extensive input-driven growth toward more sustainability-oriented practices. Substantial regional disparities in ACEE are also observed. Rational industrial organization and efficient allocation of production resources contribute to positive spillover effects on neighboring regions, whereas natural disasters and inefficient resource distribution tend to weaken such effects. These findings suggest that functional grain zones provide an effective framework for capturing intra-regional heterogeneity and should be adopted as the basic unit for efficiency assessment and the formulation of differentiated governance strategies. Full article

Artificial levees along major rivers are critical for flood-risk mitigation, yet many aging structures have poorly constrained internal composition and material heterogeneity, limiting the reliability of conventional safety assessments. This study develops a quantitative, non-destructive framework for characterizing levee internal structure by integrating electrical resistivity tomography (ERT) with borehole (BH) observations. ERT profiles were combined with borehole measurements of grain size (D50) and water content to investigate subsurface compositional variability and to evaluate relationships between sedimentological and geophysical parameters. Grain-size data from borehole samples were modeled using four predictive approaches—random forest regression (RFR), artificial neural networks (ANN), linear regression (LR), and support vector regression (SVR)—based on ERT-derived resistivity and moisture information. The results reveal pronounced internal heterogeneity within the investigated levees and demonstrate consistent relationships between sediment composition, water content, and electrical resistivity. Among the tested models, the ensemble-based RFR provided the highest predictive performance (R² = 0.81). These findings indicate that D50 characteristics of levee materials can be reliably inferred from ERT data using machine learning, reducing the need for destructive sampling. The proposed approach offers a transferable methodology for levee assessment and supports future applications in non-destructive monitoring, spatially explicit flood-risk analysis, and climate-resilient flood-protection management. Full article

This study investigates the structural, chemical, and morphological characteristics of electron-beam–deposited GO/Ag nanocomposite thin films and establishes a compact continuum–spectral framework for quantifying their post-deposition roughness. Since atomic force microscope (AFM) measurements provide only the final, frozen morphology and no direct temporal information, distinguishing between transient and stationary spectra is not experimentally feasible within the limited AFM wavenumber band. In practice, the accessible power spectral densities (PSDs) show no resolvable deviation from the stationary form, and transient contributions cannot be uniquely identified. The stationary PSD is fitted directly to azimuthally averaged AFM spectra, allowing the smoothing coefficients, noise intensity, correlation length, and crossover scale to be extracted in a fully data-driven manner. The fitted model accurately reproduces the characteristic dual (k⁻²)/(k⁻⁴) spectral scaling and predicts the scan-size dependence of root-mean-square roughness, typically achieving logarithmic determination coefficients above 0.98. The close agreement among parameters obtained from spatially separated sampling points confirms the lateral uniformity of the deposited films and highlights the robustness of the continuum–spectral approach for data-guided roughness control in electron-beam-grown nanocomposite coatings. Full article

With the increasing constraints of resource and environmental factors and the prominent issues of regional development imbalance, how to scientifically measure the level of agricultural sustainable development and reveal its spatial-temporal differentiation patterns has become a key scientific question that urgently needs to be addressed in optimizing land use layout and promoting rural revitalization. This study takes the human-land spatial systems coupling theory as the core framework and constructs an evaluation index system for agricultural sustainable development covering five dimensions: economy, society, resources, ecology, and technology. Based on provincial panel data in China from 2001 to 2024, the entropy method is employed to measure agricultural sustainable development, while Dagum’s Gini coefficient, kernel density estimation, and convergence models are applied to analyze its spatial–temporal evolution. Furthermore, the fuzzy-set qualitative comparative analysis (fsQCA) method is introduced to identify multi-factor configurational driving pathways. The results indicate that the overall level of agricultural sustainable development in China shows a steady upward trend, exhibiting a regional gradient pattern characterized by “central region leading, eastern region steadily advancing, and western region gradually catching up”. The overall disparity presents a weak convergence trend, with inter-regional differences as the primary source, although their contribution is gradually declining. The development structure has evolved from regional fragmentation to a more complex spatial interaction pattern. The overall distribution shifts rightward with evident stage-based differentiation, accompanied by significant positive spatial dependence, with “high–high” and “low–low” clustering coexisting over the long term. Convergence analysis shows that σ-convergence exists at the national level. After accounting for spatial effects, significant absolute β-convergence is observed in the eastern and western regions, while the central region does not exhibit significant convergence. Conditional β-convergence further confirms the existence of regional convergence trends, although the convergence speeds vary. The fsQCA results indicate that agricultural sustainable development is not driven by a single factor but by multiple configurational pathways formed through the interaction of various conditions. These findings provide empirical evidence for optimizing agricultural spatial layout, strengthening land factor support, and promoting regionally coordinated agricultural sustainable development. Full article

Urban expansion in Europe is accelerating, increasing impermeable surfaces and intensifying climate-related pressures, while reducing the capacity of natural and semi-natural habitats to regulate climate. Despite growing interest in ecosystem service (ES), the assessment of resilience, and thus the stability of ES providers, as well as their integration into spatial planning tools, remain limited. This study develops and tests a comprehensive assessment framework that (i) evaluates the current performance of selected ecosystem functions underpinning key regulating ES important for climate adaptation using a look-up table method; (ii) assesses ecosystem resilience by quantification its preconditions; and (iii) applies spatial prioritization to identify and prioritize climate adaptation measures that enhance ecosystem functions and strengthen resilience. The framework was applied to the cadastral area of Liberec (Czech Republic). Results indicate that areas with the highest urgency for intervention were identified consistently across urban and peri-urban zones. However, proposed measures were more diverse and spatially differentiated in peri-urban and rural areas, whereas a single dominant measure prevailed in urban areas, suggesting higher practical applicability outside densely built environments. The approach supports evidence-based spatial planning and contributes to the implementation of the EU Adaptation Strategy by promoting resilient green infrastructure in urban and peri-urban landscapes. Full article

Measuring the service levels and spatial equity of urban parks constitutes a core research topic within the field of environmental justice. Against the backdrop of low-carbon urban transformation and sustainable development, this study constructs an ecological supply indicator calculation model for parks based on landscape ecology theory. Leveraging spatio-temporal big data such as Points of Interest (POI) and second-hand property transactions, it establishes a demand evaluation indicator system centered on human activity intensity. The study employs the Gini coefficient and location entropy to gauge the spatial equity of park supply–demand balance, utilizing the Z-score method to classify supply–demand matching types. An empirical case study is conducted in Shenzhen. Findings indicate that despite Shenzhen possessing abundant global-scale park resources, a Gini coefficient of 0.489 reveals significant deficiencies in the equitable provision of park services, with spatial distribution exhibiting pronounced social stratification. Specifically: (1) location entropy values exhibit an east-high, west-low spatial pattern; (2) areas with high location entropy are predominantly concentrated in Dapeng New District, rich in green space resources, where supply exceeds demand, creating an imbalance; and (3) areas with low locational entropy values are predominantly distributed in industrial clusters such as western Bao’an and western Longgang, exhibiting contradictory characteristics of low supply and high demand. Overall, the distribution of park and green space resources exhibits a polarized pattern. Full article

Background/Objectives: The incidence of pediatric pathological scars (PPS) has been gradually increasing due to various causes, highlighting the need for accurate scar assessment to monitor disease progression and therapeutic efficacy. Vancouver Scar Scale (VSS) and other scar evaluation systems are relatively subjective evaluation methods that rely on physicians’ or patients’ own judgment. By contrast, when comparing different scar scale evaluation methods, a three-dimensional (3D) camera and dermoscopy may provide relatively objective measurable parameters to avoid possible subjective bias created by the observers. This study aimed to compare the utility of traditional VSS evaluation with that of 3D cameras and dermoscopy in PPS evaluation. Methods: A total of 35 pediatric patients (aged 0–18 years) with PPS were involved, and their scars were assessed via the VSS, dermoscopy, and the Antera 3D^® system. In addition, a subset of 18 patients (36 scar regions) was also evaluated for therapeutic efficacy after 3–6 months of treatment. Briefly, VSS scores were blindly evaluated by two independent dermatologists under standardized conditions. Quantitative assessment was also performed using dermoscopy and the Antera 3D^® system. The former quantified chromatic parameters (pigmentation: L*, vascularity: a*, green value); the latter captured multispectral 3D images to analyze volume, pigmentation, and erythema. Data are presented as means ± standard deviation and analyzed using paired-sample t tests (one-tailed), the Wilcoxon signed-rank test, and standardized response means (SRMs) to assess therapeutic sensitivity, while baseline variability was evaluated using the standard deviation and coefficient of variation (CV). Results: The results showed that Antera 3D^® detected significant reductions in pigmentation (p < 0.01, SRM = −0.46), vascularity (p < 0.001, SRM = −0.59), and volume (p < 0.0001, SRM = −0.83), while dermoscopy indicated similar moderate improvements in vascularity (Green value: p < 0.001, SRM = 0.57; a*: p < 0.0001, SRM = −0.68) and pigmentation (L*: p < 0.0001, SRM = 0.48) after treatments. VSS showed significant gains in pliability (p < 0.0001, SRM = −1.13), height (p < 0.01, SRM = −0.54), and overall impression (p < 0.0001, SRM = −0.86), but minimal changes in pigmentation (p > 0.05, SRM = 0) or vascularity (p > 0.05, SRM = −0.21). At baseline, Antera 3D^® showed the greatest variability in pigmentation (CV 43.41%) and volume (CV 91.21%), followed by VSS in vascularity (CV 52.95%), pliability (CV 34.05%), and overall impression (CV 31.76%). Dermoscopy presented the lowest variability, indicating limited discriminative power. Conclusions: In conclusion, Antera 3D^® offers an objective, sensitive, and spatially precise approach for PPS assessment and may provide additional quantitative information for evaluating subtle and early changes alongside traditional scar assessment scales. Its integration into clinical practice will enhance treatment monitoring and support more accurate timing of therapeutic interventions. Full article

Urban green space (UGS) is a critical component of sustainable cities and a modifiable determinant of mental health (MH). This review synthesizes 93 empirical studies and 929 bibliometric records to map theoretical advances, methodological evolution, and governance implications in the UGS–MH field. We integrate the following six validated pathways into a unified socio-ecological framework: attention restoration, stress recovery, behavioral activation, physiological regulation, social cohesion, and environmental buffering. Methodological trends indicate a shift from static greenness proxies to street-view and multimodal exposure measures, and from cross-sectional correlations to models that address spatial heterogeneity, causal identification, and AI-enabled prediction. Bibliometric mapping reveals increasing interdisciplinarity, geographic diversification, and growing attention to dynamic exposure science. Persistent challenges include spatial and temporal misalignment between exposure and outcome measures, reliance on single-modality indicators, limited causal inference, and constrained cross-cultural generalizability. Building on these findings, we propose a governance-oriented framework to support sustainable and healthy cities through equitable green access, behavior-informed planning, nature-based interventions, and data-driven decision support. Overall, this review strengthens the bridge from evidence to action at the interface of urban sustainability and population mental health. Full article

This study assesses regional science and technology (S&T) innovation efficiency across 30 Chinese provinces from 2011 to 2022, incorporating a sustainable development perspective. Employing a non-oriented global frontier super-slack-based measure (SBM) model that accounts for undesirable outputs, along with kernel density estimation, cluster analysis, and Moran’s I, the research investigates the spatiotemporal evolution of innovation dynamics. The findings demonstrate a marked upward trend, with the national average efficiency score rising from 0.260 to 0.703. Temporally, efficiency advanced through three stages: an initial period of universally low efficiency, a phase of widening disparities, and a final stage of overall improvement and stabilization. Spatial analysis reveals a persistent “strong in the east, weak in the west” disequilibrium; however, absolute β-convergence tests indicate a significant reduction in regional disparities (p < 0.05). Kernel density estimation reveals a shift from a polarized “pyramid” shape to a more balanced “spindle-shaped” distribution. This is evidenced by a decrease in kurtosis and a rightward shift in the median. Spatial autocorrelation, as measured by the Global Moran’s I, evolved from a statistically insignificant distribution in 2011 to a strong positive correlation (0.223, p < 0.05) by 2022. This progression reflects a transition from isolated “unipolar” hubs to integrated “multi-center block linkages.” The results suggest that, although polarization is diminishing and the national innovation baseline is improving, policy efforts should prioritize the development of emerging innovation corridors to address the remaining east–west divide. Full article

Accurate prediction of wind direction is a critical requirement for coastal safety management, renewable energy optimization, and weather-driven risk mitigation, particularly in highly dynamic atmospheric environments where statistical and deep learning models often struggle to capture nonlinear interactions and temporal dependencies. Existing approaches typically rely on raw or weakly processed meteorological inputs and treat directional information implicitly, which limits their ability to exploit the underlying physical structure of wind evolution. To address these challenges, this research designs a novel Physics Vector Driven (PVD) data pre-processing framework that explicitly encodes physically meaningful gradients and directional dynamics from multivariate meteorological observations, transforming raw measurements into sequence-aware vector representations suitable for deep time-series learning. Building on this foundation, a novel Directional Temporal Gradient Vector Network (DTGVectorNet) is proposed, which fuses a Directional Gradient Attention ResNet (DGResNet 1D CNN) for spatial-directional feature extraction with a Temporal Gradient LSTM (TGLSTM) designed to model the temporal evolution of wind vectors. The tight integration of Directional Gradient Attention (DGA) and Temporal Gradient (TG) memory enables the network to jointly learn instantaneous directional cues and their temporal propagation, significantly enhancing predictive fidelity. An experimental evaluation of the Busan wind datasets demonstrates that the proposed DTGVectorNet achieves a wind direction prediction accuracy of 99.12%, substantially outperforming conventional state-of-the-art baselines. These results confirm that physics-aware vector preprocessing combined with directional-temporal gradient fusion provides a powerful and generalizable paradigm for high-precision wind direction forecasting. To ensure reproducibility and facilitate further research, the complete dataset and implementation details of DTGVectorNet are publicly available through an open-access repository, Zenodo. Full article

Community detection is a key task in the analysis of complex networks, particularly in social network analysis, where uncovering cohesive and well-separated groups is essential for understanding structural organization and interaction patterns. Many existing centroid-based community detection methods rely primarily on node degree for centroid selection, which often leads to centroid crowding and insufficient spatial separation among communities. To address these limitations, this paper proposes Degree–Distance Centroid–Community Detection with Influential Nodes (DDC-CDIN), a distance-driven and influence-aware community detection framework. In the proposed approach, nodes are first ranked according to an Enhanced Degree Centrality measure that incorporates degree information, neighbourhood structure, and local clustering characteristics to identify structurally influential nodes. Centroids are then selected iteratively from the top-ranked influential nodes by maximizing shortest-path distances, ensuring that the chosen centroids are both representative and well dispersed within the network. Once the centroids are determined, the remaining nodes are assigned to communities based on the minimum geodesic distance, yielding compact, clearly separated clusters. Extensive experiments across multiple real-world networks show that DDC-CDIN achieves competitive performance compared to traditional centroid-based and modularity-driven methods in terms of modularity, community cohesion, and boundary clarity. The results indicate that jointly incorporating influence-aware node ranking with distance-based centroid dispersion effectively mitigates centroid crowding and enhances overall community detection quality. These findings demonstrate the effectiveness and robustness of DDC-CDIN for detecting well-structured and topologically coherent communities in complex networks. Full article

We propose a coupling-aware equivalent source reconstruction framework for reproducing complex three-dimensional electromagnetic (EM) environments inside an anechoic chamber. A measured or simulated target field is represented by a finite set of physically realizable equivalent source antennas whose positions and complex excitations are identified by solving a nonlinear high-dimensional inverse problem. To ensure physical fidelity, the forward model explicitly accounts for mutual coupling through a full-wave Method-of-Moments (MoM) formulation, avoiding the inaccuracies of idealized uncoupled superposition. The inverse problem is efficiently solved using a hybrid evolutionary optimization scheme that combines an adaptive differential evolution strategy with stagnation-triggered CMA-ES refinement, augmented by a lightweight surrogate-based pre-screening to reduce expensive full-wave evaluations. The optimized source configuration is directly deployed in a microwave anechoic chamber, where the reconstructed field is measured on an observation plane and compared against the target field. The experimental results demonstrate close agreement in both amplitude and spatial distribution, while the proposed optimization pipeline substantially reduces the number of full-wave evaluations required for convergence. This work enables accurate repeatable chamber emulation of outdoor or in situ EM scenarios for robust system-level testing and evaluation. Full article

To mitigate the high measurement costs in electromagnetic compatibility (EMC) assessment, this paper proposes a robust active learning framework for fast 3D field reconstruction with sparse sampling. A novel “Four-Vote” query criterion is proposed to guide intelligent sample selection, which integrates Shannon entropy, committee variance, spatial density, and clustering-based representativeness, all derived from a heterogeneous radial basis function (RBF) committee. Furthermore, an adaptive polynomial degree adjustment mechanism is implemented to ensure stability in data-scarce 3D environments. Validated through full-wave HFSS simulations, the proposed method significantly outperforms traditional sampling strategies in both 2D and 3D scenarios, achieving high-fidelity field reconstruction with minimal sampling points. This framework provides an efficient solution for rapid spatial field mapping and EMC fault diagnosis in practical engineering scenarios. Full article

In recent years, the Mediterranean basin has been characterized as a climate change hotspot due to its rapid transition to warmer conditions and the strong agreement among most climate models predicting a significant decrease in precipitation by the end of the 21st century. These robust signals of climate change highlight the region’s high susceptibility to hydrometeorological extremes, such as droughts, which are expected to become more frequent, prolonged, and intense. In this context, the study focuses on Greece, where rising water scarcity threatens critical sectors such as food security, energy production, public health, and, more broadly, the resilience of ecosystems. Future drought conditions were assessed using the 12-month Standardized Precipitation Index (SPI-12) for 58 meteorological stations during 2071–2100, based on high-resolution regional climate simulations under RCP4.5 and RCP8.5. Spatial drought variability was examined using Principal Component Analysis, while drought severity and duration were quantified through Run Theory. The results indicate increasingly prolonged and severe droughts by the late 21st century, particularly in eastern Crete and southeastern Peloponnese, highlighting the urgent need for targeted adaptation measures. Full article

Background/Objectives: Performing presurgical functional magnetic resonance imaging (fMRI) mapping in young patients is considered a challenge for clinicians, as fMRI maps are the sole source of information about the functional organization of cognitive functions/areas, especially when an awake craniotomy is not possible, as is often the case for pediatric populations. The literature on the fMRI tasks used in pediatric populations with brain injuries shows a certain heterogeneity in the approaches (task-based or resting states) and tasks, with a preference for motor/language mapping: tasks assessing extra-language functions are lacking. Methods: We have designed fMRI tasks focused on language and extra-language functions, which can be easily be applied when clinicians need to perform presurgical mapping. We present a retrospective case series of 17 patients. Results: Seventeen young patients (13.4 ± 2.8 years; range 7–16) were included in the study, for whom fMRI was performed. All underwent successful fMRI mapping by completing fMRI tasks selected based on their lesion site. The number of tasks performed by each patient significantly correlated with their age (r(17) = 0.561, p = 0.019). The patients tolerated the assessment and had good motion control: their movement parameters were minimal (range of rotation of −0.015–0.01 degrees; range of translation of −0.8–0.2 mm). The most administered fMRI tasks were tongue motor localizer (60%) and object naming (70%), with some patients performing extra-language function mapping involving visuo-spatial processing, selective attention, memory, and inhibition. Conclusions: This is an exploratory study given the sample size. fMRI measurements were considered feasible, as patients were able to complete the tasks under clinically realistic conditions. We discuss the clinical implication/usefulness of administering tasks for a personalized functional assessment of the young patient before surgery. Full article