Search Results (96)

This paper presents a comparative mathematical analysis of electrode configurations used in active fog water harvesting systems based on electrostatic ionization. The study begins with a brief overview of fog formation and typology. It also addresses the global relevance of fog as a decentralized water resource. It also outlines the main methods and collector designs currently employed for fog water capture, both passive and active. The core of the work involves solving the Laplace equation for various electrode geometries to compute electrostatic field distributions and analyze field line density patterns as a proxy for potential water collection efficiency. The evaluated configurations include centered rod–cylinder, symmetric parallel multi-rod, and asymmetric wire–plate layouts, with emphasis on identifying spatial regions of high field line convergence. These regions are interpreted as likely trajectories of charged droplets under Coulombic force influence. The modeling approach enables preliminary assessment of design efficiency without relying on time-consuming droplet-level simulations. The results serve as a theoretical foundation prior to the construction of electrode layouts in the portable HygroCatch experimental harvester and provide insight into how field structure correlates with fog water harvesting performance. Full article

Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets. Full article

The Qinghai–Tibet Plateau (QTP), a globally significant tourist destination and critical ecological barrier, faces an intrinsic conflict between development and conservation. The scientific identification of suitable tourism zones is therefore crucial for formulating sustainable development policies. Conventional suitability assessments, however, which typically rely on subjective, expert-based weighting and static, supply-side data, often fail to capture the complex, non-linear dynamics of actual tourist–environment interactions. To overcome these limitations, an innovative analytical framework is presented, integrating massive tourist trajectory big data (66.7 million GPS points) as an objective, demand-driven suitability proxy, a Geo-detector model to identify key drivers and their interactions, and a Random Forest algorithm for spatial prediction. The framework achieves high predictive accuracy (AUC = 0.827). The results reveal significant spatial heterogeneity: over 85% of the QTP is unsuitable for tourism, while suitable zones are intensely concentrated in southeastern river valleys, forming distinct agglomerations around core cities and along primary transport arteries. Analysis demonstrates that supporting conditions—particularly transport accessibility and service facility density—are the dominant drivers, their influence substantially surpassing that of natural resource endowment. Furthermore, the formation of high-suitability zones is not attributable to any single factor but rather to the synergistic coupling of multiple conditions. This research establishes a replicable, data-driven paradigm for tourism planning in environmentally sensitive regions, offering a robust scientific basis to guide the sustainable development of the QTP. Full article

Since the 18th National Congress of the Communist Party of China in 2012, green and low-carbon development has become a national strategic priority. This study constructs a 39-indicator evaluation system grounded in the DPSIRM framework, which includes six interlinked subsystems. A key innovation lies in incorporating the Digital Inclusive Finance Index as a driver of green transitions and using Baidu search indices for “environmental protection” and “carbon dioxide” as proxies for public awareness. Using a projection pursuit model optimized by simulated annealing, we assess green low-carbon development across 30 Chinese provinces from 2011 to 2021. Temporal and spatial patterns are analyzed via kernel density estimation and Moran’s I, while Theil Index decomposition quantifies regional disparities. Results: First, substantial variations exist among Chinese provinces in both subsystem performance and integrated green low-carbon development levels, and response subsystems have the greatest influence on the overall development level. Second, over time, the gaps in green, low-carbon development between provinces have become more pronounced. Third, geographically, a distinct east-to-west declining gradient characterizes the regional clustering patterns of green low-carbon development. Fourth, the Theil Index for green, low-carbon development exhibits an overall trend of fluctuating increase, indicating that the overall gap in green, low-carbon development is gradually widening, with within-group disparities as the primary cause. This research enhances understanding of China’s green and low-carbon development, actively promoting global sustainable development and environmental improvement. Full article

Accurate estimation of tree diameter at breast height (DBH) from LiDAR point clouds is essential for forest inventory, biomass assessment, and ecological monitoring. This paper presents a perimeter-based DBH estimation framework that achieves competitive accuracy against geometric fitting methods across three datasets. The proposed approach partitions the trunk cross-section into angular sectors and employs Gaussian Mixture Models (GMMs) to identify representative boundary points in each sector, weighted by radial proximity and statistical confidence. To handle occlusion and partial scans, missing sectors are reconstructed using symmetry-aware proxy generation. The final perimeter is modeled via either convex hull or B-spline interpolation, from which DBH is derived. Extensive experiments were conducted on two public TreeScope datasets and a custom mobile LiDAR dataset. Compared to the Density-Based Clustering Ring Extraction (DBCRE) baseline, our method reduced RMSE by 22.7% on UCM-0523M (from 2.60 to 2.01 cm), 34.3% on VAT-0723M (from 3.50 to 2.30 cm), and 29.6% on the Custom Dataset (from 2.16 to 1.52 cm). Ablation studies confirmed the individual and synergistic contributions of GMM clustering, radial consistency filtering, and proxy synthesis. Overall, the method provides a flexible alternative that reduces dependence on strict geometric assumptions, offering improved DBH estimation performance with moderate occlusion and incomplete, uneven boundary coverage. Full article

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Full article

We adapt previously developed conceptual and numerical models of ciguateric food chains on the Great Barrier Reef, Australia, to model the bioaccumulation of ciguatoxins (CTXs) in parrotfish, the simplest food chain with only two trophic levels. Our model indicates that relatively low (1 cell/cm²) densities of Gambierdiscus/Fukuyoa species (hereafter collectively referred to as Gambierdiscus) producing known concentrations of CTX are unlikely to be a risk of producing ciguateric fishes on the Great Barrier Reef unless CTX can accumulate and be retained in parrotfish over many months. Cell densities on turf algae equivalent to 10 Gambierdiscus/cm² producing known maximum concentrations of Pacific-CTX-4 (0.6 pg P-CTX-4/cell) are more difficult to assess but could be a risk. This cell density may be a higher risk for parrotfish than we previously suggested for production of ciguateric groupers (third-trophic-level predators) since second-trophic-level fishes can accumulate CTX loads without the subsequent losses that occur between trophic levels. Our analysis suggests that the ratios of parrotfish length-to-area grazed and weight-to-area grazed scale differently (allometrically), where the area grazed is a proxy for the number of Gambierdiscus consumed and hence proportional to toxin accumulation. Such scaling can help explain fish size–toxicity relationships within and between trophic levels for ciguateric fishes. Our modelling reveals that CTX bioaccumulates but does not necessarily biomagnify in food chains, with the relative enrichment and depletion rates of CTX varying with fish size and/or trophic level through an interplay of local and regional food chain influences. Our numerical model for the bioaccumulation and transfer of CTX across food chains helps conceptualize the development of ciguateric fishes by comparing scenarios that reveal limiting steps in producing ciguateric fish and focuses attention on the relative contributions from each part of the food chain rather than only on single components, such as CTX production. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

As a catalyst for sustainable urbanization, urban vibrancy drives human interactions, economic agglomeration, and resilient development through its spatial manifestation of diverse activities. While previous studies have emphasized the connection between built environment features—especially street network centrality—and urban vibrancy, the broader mechanisms through which the full spectrum of street configuration dimensions shape vibrancy patterns remain insufficiently examined. To address this gap, this study applies a GeoXAI approach that synergizes random forest modeling and GeoShapley interpretation to reveal the influence of street configuration on urban vibrancy. Leveraging multi-source geospatial data from Xiamen Island, China, we operationalize urban vibrancy through a composite index derived from three-dimensional proxies: life service review density, social media check-in intensity, and mobile device user concentration. Street configuration is quantified through a tripartite measurement system encompassing network centrality, detour ratio, and shape index. Our findings indicate that (1) street network centrality and shape index, as well as their interactions with location, emerge as the dominant influencing factors; (2) The relationships between street configuration and urban vibrancy are predominantly nonlinear, exhibiting clear threshold effects; (3) The impact of street configuration is spatially heterogeneous, as evidenced by geographically varying coefficients. The findings can enlighten urban planning and design by providing a basis for the development of nuanced criteria and context-sensitive interventions to foster vibrant urban environments. Full article

Increased CT-derived fat tissue radiodensity has been indicated as a poor prognostic factor in oncological settings, although the reasons are not clear. One hypothesis is that increased radiodensity may reflect the loss of fat droplets within adipocytes, being a proxy of recent weight loss. This study aims to test this hypothesis by evaluating the association between longitudinal variations in fat tissue radiodensity and area in a cohort of COVID-19 patients. Baseline and 2–3-month follow-up chest CT scans of severe COVID-19 pneumonia survivors were retrospectively reviewed to measure subcutaneous, visceral, and intermuscular adipose tissue (SAT, VAT, and IMAT) areas and densities at the T7–T8 vertebrae, and longitudinal variations were computed for each variable. The associations between each compartment area and radiodensity variations (standardized values) were evaluated in univariate linear models and models adjusted by age and sex. A total of 196 COVID-19 survivors with suitable baseline and follow-up CT scans were included (mean age 65 ± 11 years, 62 (31.6%) females, 25% with diabetes and 2.6% with morbid obesity). Longitudinal variation in SAT area was inversely associated with longitudinal variation in SAT radiodensity in univariate models (coeff −0.91, 95%CI = −1.70/−0.12, p = 0.02) and after adjustment by age and sex (coeff −0.89, 95%CI = −1.7/−0.09, p = 0.03). The effect was similar and stronger for IMAT (coeff −2.1, 95%CI = −3.06/−1.19, p < 0.01 in adjusted models), and absent for VAT. Longitudinal variations in subcutaneous and intermuscular adipose tissue areas and densities are inversely associated. Higher adipose tissue radiodensity may be due to decrease in fat area (i.e., weight loss), explaining the poor prognostic effect found in cancer patients. Full article

The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article

Urbanization causes significant environmental and structural changes in habitats, one of them being increased fragmentation. Traits associated with increased locomotory capacity may be advantageous in such situations, as individuals with those traits may expand their home range or have a chance to escape the patch where conditions threaten their survival. Individuals of the forest specialist, flightless ground beetle Carabus convexus in urban habitats may respond to urbanization by increasing their locomotory capacity (increased muscle mass) with respect to their conspecifics in rural habitats. In order to test this hypothesis, morphological traits standardized for body size were assessed using linear mixed-effects models. Pronotum volume (as a proxy for muscle mass) showed no significant difference between urban and rural individuals. The size of the tibia and femur of the front, middle, and hind legs (a proxy for leg muscle mass) significantly differed between sexes, with males having significantly larger tibiae and femora than females. Furthermore, urban males had significantly larger hind tibiae than rural conspecifics. Sex-specific differences in tibia and femur size is expected because males usually have higher locomotory activity than females. Larger tibiae of urban males can be advantageous to extend their home range, ensuring that males find mating partners even in low-density urban populations. Full article

Uranium–niobium (U-Nb) alloys, used in a variety of industrial and energy applications that require high density, ductility, and good corrosion resistance, comprise a highly complex, multiphasic system with a phase diagram well established through decades of extensive experimental and theoretical research. They are also one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. The key factor determining the performance and safety of a metallic fuel such as U-Nb is its operational limits in the application environment, which are closely related to material’s structure and thermodynamic stability. They are in turn closely related to the ambient (zero-pressure) melting point $\left(T_m\right);$ thus, $T_m$ is an important engineering parameter. However, the current knowledge of $T_m$ of the U-Nb system is limited, as the only experimental study of its Nb-rich portion dates back to 1958. In addition, it has not yet been adequately modeled based on general thermodynamics principles or using an equation-of-state approach. In this study, we present a theoretical model for the melting curve (liquidus) of a mixture, and apply it to U-Nb, which is considered as a mixture of pure U and pure Nb. The model uses the known melting curves of pure constituents as an input and predicts the melting curve of their mixture. It has only one free parameter, which must be determined independently. The ambient liquidus of U-Nb predicted by the model appears to be in good agreement with the available experimental data. We calculate the melting curve (the pressure dependence of $T_m$) of pure U using ab initio quantum molecular dynamics (QMD), the knowledge of which is required for obtaining the model parameters for U. We also generalize the new model to nonzero pressure and consider the melting curve of U-6 wt.% Nb (U-6Nb) alloy as an example. The melting curve of U-6Nb alloy predicted by the model appears to be in good agreement with the ab initio melting curve obtained from our QMD simulations. We suggest that the U-18Nb alloy can be considered as a proxy for protactinium (Pa) and demonstrate that the melting curves of U-18Nb and Pa are in good agreement with each other. Full article

Environmental heterogeneity is especially important in determining the distribution and spatial management of marine mammals. Intra-annual changes in distribution exhibited by highly mobile species such as baleen whales, however, present a challenge to traditional area-based management measures which should be accounted for in the designations, but these data are typically lacking. In the present study, we investigated the seasonal variables influencing the spatio-temporal distribution of feeding/foraging minke whales in the Southern Trench MPA in northeast Scotland. A presence–absence model was selected to determine the associations of feeding/foraging whales with areas of high prey density and other environmental determinants. Whale presence was strongly correlated with high burrowed sandeel density (BSD) in May and June and offshore thermal fronts (derived from the standard deviation of sea-surface temperature (SST SD)) from June to September. Both were concluded to be valuable proxies for the distribution of available prey and provided a compelling explanation for observed spatio-temporal shifts and high intraannual variability of whales from our long-term data. The present findings illustrate the value of prey data inclusion in habitat models for baleen whales on their feeding grounds, and advocate for a more dynamic, ecosystem-based approach to management for these highly mobile protected whales. Full article