Search Results (3,006)

Biogas production from agricultural residues is a promising solution for renewable energy production, improved waste management, and beneficial impact on climate change mitigation. The aim of this study is to assess the actual use and theoretical potential of agricultural biogas produced from animal manure in Poland at the local level. The potential and actual use of agricultural biogas are presented regionally (16 voivodeships) and locally (314 districts). The theoretical potential of agricultural biogas was estimated based on data from the Agricultural Census conducted by the Central Statistical Office in Poland in 2020. Actual biogas production is based on data from the Register of Agricultural Biogas Producers maintained by the National Support Center for Agriculture. The study shows that Poland is only tapping into the existing potential for agricultural biogas production to a limited extent. Furthermore, both actual agricultural biogas production and the identified theoretical potential vary spatially (greater potential in the northern part of the country, significantly lower in the southern part). This situation is attributed to existing barriers that hinder the utilization of existing potential. Therefore, it is crucial to seek new solutions to reduce existing barriers of an organizational, legal, technical, economic, environmental, spatial, and social nature. Full article

Urban heat islands are among the most intense and unequal climate impacts in Mediterranean cities, with direct effects on health, thermal comfort, and habitability. This reality calls for the incorporation of binding and verifiable climate criteria into spatial planning and urban planning law. This article examines the extent to which the Spanish legal framework—at national, regional, and municipal levels—incorporates measurable standards to mitigate urban heat islands and how it might evolve towards operational climate-responsive urbanism. A legal–analytical and comparative methodology is applied, based on multilevel normative content analysis and a comparison of four autonomous communities, four Spanish cities, and four international reference cases with consolidated metrics. The results show that, despite progress in recognising adaptation, territorial asymmetries persist, enforceable parameters remain scarce, and there is a prevailing reliance on strategic or voluntary instruments. In response to these gaps, the study proposes a coherent set of urban climate standards (urban vegetation, functional soil permeability, roof albedo/cool roofs, green roofs and façades, plot-scale performance indices, urban ventilation, and thermal diagnostics) and a multilevel integration model aimed at guiding legislative reforms and strengthening cities’ adaptive capacity and thermal equity. Full article

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

High-mountain areas with sensitive ecosystems are experiencing a steady increase in visitation, with visitors progressively moving outside designated trails, generating pressures on the natural environment. In extensive areas with numerous access points, it is difficult to monitor visitors’ movement and resulting impacts. This article describes a method for combining various data sources and approaches to determine affected areas, including their locations and extent. The method combines (1) field-mapping, (2) remote-sensing data display analysis, and (3) processing of publicly available GNSS tracks from sports applications, using 46 test plots along a selected trail to Mount Triglav in Slovenia. Affected-area surfaces and their spatial overlap were compared across the three approaches. The usefulness of remote-sensing displays and GNSS tracks for determining and predicting affected areas was assessed by reference to field measurements. A linear regression model showed that the display-analysis approach can explain 52.7% of the variability in field-mapping approach, while GNSS tracks do not provide enough information nor the accuracy comparable to field surveys. This study can help other researchers and nature-protection managers in selecting most suitable data derived from non-traditional sources to improve delineation of hiking trails and estimation of potential pressures on fragile environments. Full article

With the rapid development of Massive Open Online Courses (MOOCs), high dropout rates have become a major challenge, limiting the quality of online education and the effectiveness of targeted interventions. Although existing MOOC dropout prediction methods have incorporated deep learning and attention mechanisms to improve predictive performance to some extent, they still face limitations in modeling differences in course difficulty and learning engagement, capturing multi-scale temporal learning behaviors, and controlling model complexity. To address these issues, this paper proposes a MOOC dropout prediction model that integrates multi-scale convolution with a symmetric dual-attention mechanism, termed SDA-Net. In the feature modeling stage, the model constructs a time allocation ratio matrix (MRatio), a resource utilization ratio matrix (SRatio), and a relative group-level ranking matrix (Rank) to characterize learners’ behavioral differences in terms of time investment, resource usage structure, and relative performance, thereby mitigating the impact of course difficulty and individual effort disparities on prediction outcomes. Structurally, SDA-Net extracts learning behavior features at different temporal scales through multi-scale convolution and incorporates a symmetric dual-attention mechanism composed of spatial and channel attention to adaptively focus on information highly correlated with dropout risk, enhancing feature representation while maintaining a relatively lightweight architecture. Experimental results on the KDD Cup 2015 and XuetangX public datasets demonstrate that SDA-Net achieves more competitive performance than traditional machine learning methods, mainstream deep learning models, and attention-based approaches on major evaluation metrics; in particular, it attains an accuracy of 93.7% on the KDD Cup 2015 dataset and achieves an absolute improvement of 0.2 percentage points in Accuracy and 0.4 percentage points in F1-Score on the XuetangX dataset, confirming that the proposed model effectively balances predictive performance and model complexity. Full article

Floodplain vegetation is an essential part of riverine wetland ecosystems. Hydrological fluctuations significantly influence its survival and distribution. This study examines the floodplain vegetation of the Jingjiang section of the Yangtze River. This study uses annual mean NDVI data over six time periods from 2000 to 2023 to represent the changes in floodplain vegetation. The driving factors include inundation frequency, annual mean temperature, annual mean precipitation, elevation, and slope gradient. To analyze the data, this study employs multiple analytical methods, including threshold segmentation, pixel-by-pixel linear regression (using the least squares method), Geodetector, and Pearson’s correlation analysis. This study clarifies the spatiotemporal evolution of the NDVI and the distribution of vegetation in these floodplain. It also quantitatively assesses the influence of multiple drivers and reveals the areas and extent of vegetation distribution affected by different inundation frequencies. The findings indicate: (1) Over six time periods from 2000 to 2023, NDVI values and the area covered by vegetation in the Jingjiang section of the Yangtze River floodplain exhibited fluctuating growth trends. The area covered by vegetation increased by 66.94 km² in 2023 compared with that in 2000. (2) NDVI values were influenced by multiple interacting drivers, with inundation frequency being the dominant factor affecting vegetation change in the Jingjiang section (q-value: 0.79–0.86), followed by slope (q-value: 0.46–0.56). Interactions between different drivers amplify their impact on the annual average NDVI value. (3) Areas with inundation frequencies of 20%–40% exhibit positive spatial correlation with NDVI values. The maximum area of positive correlation is 112.51 km², which is predominantly distributed across the central and marginal bars of the Jingjiang section. Within this range, inundation frequency has the strongest positive effect on vegetation growth. Full article

Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance accessibility in underserved areas, limited research has examined the extent of their individual contributions. To better capture the local dynamics that shape healthcare accessibility, this study decomposes spatial accessibility to primary healthcare services using the chain substitution method (CSM), which quantifies the impact of each component by substituting them one by one. By examining how the order of factor substitution affects the relative impact of each factor on spatial accessibility, we analyzed the importance of substitution order in the CSM. This study found that the order of factor substitution plays a significant role in measuring the relative contribution of each factor. To mitigate the effects of substitution order, we proposed an averaging CSM that uses the average value across all possible substitution combinations. Based on the averaging CSM, our findings offer insight for healthcare policymakers and urban planners by clarifying how demand, supply, and traffic speed interact in determining accessibility, ultimately supporting targeted interventions in underserved areas. Full article

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach. Full article

Utilizing saline aquifers for carbon mineralization has proven to be a reliable approach for CO₂ storage. However, less attention has been given to CO₂ mineralization and geomechanical response at engineering durations and spatial scales. The objective of the study is to evaluate the feasibility of a potential CO₂ sequestration site in the Ordos Basin, located at a depth of approximately 1100 m, using the CMG-GEM numerical simulator. A coupled hydraulic–mechanical–chemical model was formulated, accounting for multiphase fluid flow, geochemical reactions, and geomechanical response. The simulation results indicated the following: (1) When CO₂ is injected into a saline formation, it can react with minerals. These chemical reactions may lead to the precipitation of certain minerals (e.g., calcite, kaolinite) and the dissolution of others (e.g., anorthite), potentially affecting the porosity and permeability of the storage formation; however, the study found that the effect on porosity is negligible, with only a 1.2% reduction observed. (2) The extent of ground uplift caused by CO₂ injection is strongly influenced by the injection rate. The maximum vertical ground displacements after 25 years is 6.1 cm at an injection rate of 16,000 kg/day; when the rate is increased to 24,000 kg/day, the maximum displacement rises to 9.4 cm, indicating a 54% increase. Full article

Urban street greening structure plays a crucial role in promoting environmental justice and enhancing residents’ daily well-being, yet existing studies have primarily focused on vegetation quantity while neglecting how perception and governance interact to shape fairness. This study develops an integrated analytical framework that combines deep learning, machine learning, and spatial analysis to examine the impact of perceptual experience and socio-economic indicators on the equity of greening structure distribution in urban streets, and to reveal the underlying mechanisms driving this equity. Using DeepLabV3+ semantic segmentation, perception indices derived from street-view imagery, and population-weighted Gini coefficients, the study quantifies both the structural and perceptual dimensions of greening equity. XGBoost regression, SHAP interpretation, and Partial Dependence Plot analysis were applied to reveal the influence mechanism of the “Matthew effect” of perception and the Site governance responsiveness on the fairness of the green structure. The results identify two key findings: (1) perception has a positive driving effect and a negative vicious cycle effect on the formation of fairness, where positive perceptions such as beauty and safety gradually enhance fairness, while negative perceptions such as depression and boredom rapidly intensify inequality; (2) Site management with environmental sensitivity and dynamic mutual feedback to a certain extent determines whether the fairness of urban green structure can persist under pressure, as diverse Tree–Bush–Grass configurations reflect coordinated management and lead to more balanced outcomes. Policy strategies should therefore emphasize perceptual monitoring, flexible maintenance systems, and transparent public participation to achieve resilient and equitable urban street greening structures. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article

Hand representation maps of the primate primary motor (M1) and somatosensory (SI) cortices exhibit plasticity, with their spatial extent modifiable through training. While activation and map enlargement during tapping tasks are well documented, the directionality of information flow between these regions remains unclear. We applied Information Imbalance Gain Causality (IIG) to examine the propagation and temporal dynamic of BOLD activity among Area 4 (precentral gyrus), Area 3a (fundus of the central sulcus), and SI areas (postcentral gyrus). Data were collected from both hemispheres of nine participants performing alternating right–left hand finger tapping inside a 1.5T fMRI scan. The results revealed strong information flow from both the precentral and postcentral gyri toward the sulcus during tapping task, with weaker bidirectional exchange between the gyri. When not engaged in tapping, both gyri communicated with each other and the sulcus. During active tapping, flow bypassed the sulcus, favoring a more direct postcentral to precentral way. Overtime, postcentral to sulcus influence strengthened during non task periods, but diminished during tapping. These findings suggest that M1, Area 3a, and SI areas form a dynamic network that supports rapid learning processing, where Area 3a of the sulcus may contribute to maintaining representational plasticity during complex tapping tasks. Full article

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

Based on the collected mobile phone location data, this paper analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the M_S6.8 Dingri earthquake. By comparing natural neighbor interpolation and Thiessen polygon interpolation methods, we explore novel rapid assessment approaches for earthquake perception ranges, combined with actual seismic intensity maps. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake. Full article