Search Results (375)

Urban expansion is often tracked with annual land-cover data, yet year-to-year classification noise can masquerade as persistent urban growth. Previous work has usually treated detection, morphology, spatial structure, driver analysis, and forward-looking modelling separately. Here we follow urban growth in Tehran County from 2016 to 2025 within one linked workflow and then extend the analysis to a 2025–2030 growth-pressure screening under static covariates. Annual 10 m built-up composites from Dynamic World were passed through a temporal stability filter to retain persistent change. Stable growth was classified into four regimes, tested for clustering and interaction scale, and examined using a Spatial Durbin Model and multiscale geographically weighted regression. A two-stage machine-learning branch then produced a ranked growth-pressure surface. In this study, growth-pressure screening means identifying where recent spatial conditions are most compatible with continued growth under unchanged covariates; it is intended for relative spatial ranking under stated assumptions rather than deterministic estimation of future urbanization. Stable new built-up area totaled 107.64 km², most of it ribbon growth (57.60%) and edge expansion (32.77%). A stratified local validation of 300 samples returned a weighted overall accuracy of 97.30%, and a 27-scenario threshold test retained ribbon growth as the largest regime and edge expansion as the second largest in every case. Clustering was significant (Global Moran’s I = 0.326), with a dominant interaction range of about 8–10 km. Historical backtesting showed strong discrimination and ranking (ROC AUC = 0.979; PR AUC = 0.984; Spearman ρ = 0.902), while exact growth-magnitude performance was more moderate (R² = 0.341). The growth-pressure surface is therefore more useful for hotspot identification and relative ranking than for estimating exact future growth magnitude. Full article

Deep neural networks increasingly convert street-level imagery into quantitative measures of urban perception, but the cost of transformer backbones limits repeated inference over city-scale image collections. This study proposes an optimized Sparse Attention Regularized Transformer with a Low-Rank constraint (SART-LR), a Siamese vision transformer in which softmax is replaced by learnable $\alpha$-entmax attention and the attention and feed-forward projections are directly factorized. The evaluation assigns each Place Pulse 2.0 image to exactly one of the training, validation, or test partitions, thereby preventing the same image from entering multiple partitions through different comparisons. All models are tuned with an equal, architecture-specific validation budget and evaluated over five seeds. Under this image-disjoint evaluation, SART-LR reaches an average pairwise accuracy of 72.6%, 2.1 percentage points above ViT-B/16 and 1.2 points above Swin-T. The explicit layer-wise derivation gives 2.97 million trainable parameters, a 5.0-fold reduction relative to the depth- and width-matched dense backbone and a 29.1-fold difference from ViT-B/16; the latter comparison is reported only as an end-to-end model total because the architectures differ. Five repeated city-level folds give a cross-city accuracy of $67.0\pm 1.3$%, corresponding to a 5.6-point decrease from the image-disjoint result. Paired tests indicate that the advantage over Swin-T varies by attribute, and analyses stratified by rater agreement show lower accuracy for ambiguous comparisons. These findings support an accuracy–efficiency benefit on Place Pulse 2.0, while the absence of an independent urban-perception dataset and the geographic imbalance of the 56-city sample limit external-validity claims. Full article

Background/Objectives: Authentic assessment—the systematic observation of naturally occurring social–emotional behavior by familiar caregivers—is increasingly mandated by international early childhood frameworks, yet the field lacks a comprehensive tool inventory, conceptual clarity on “authenticity,” and synthesized evidence on contextual coverage. This scoping review maps authentic social–emotional development (SED) assessment tools for children aged 0–8, examines how authenticity is conceptualized, and identifies gaps in cultural adaptation, disability inclusion, and age-range coverage. Methods: Following the Arksey and O’Malley framework enhanced by Levac et al. and JBI methodology, six databases (PsycINFO, ERIC, Education Source, MEDLINE, Scopus, Web of Science) were searched for English-language peer-reviewed publications (2006–2026), supplemented by hand-searching and forward citation tracking. Inclusion criteria targeted children aged 0–8 assessed via authentic modalities (naturalistic observation, play-based assessment, portfolio, performance-based tasks, curriculum-embedded measurement) in any global setting. Data charting used an integrated sociocultural and interpretive assessment framework. Reporting followed PRISMA-ScR. Results: Thirty-three studies yielded 43 instruments, 29 meeting authentic assessment criteria. Naturalistic observation predominated (23 studies; 69.7%), followed by play-based assessment (10; 30.3%); portfolio and curriculum-embedded approaches were under-represented. Most studies originated from the United States (57.6%) and targeted preschoolers (3–5 years; 75.8%), with thin coverage of infants/toddlers and primary-grade children. “Authenticity” was operationalized across ecological, participatory, and cultural dimensions rarely integrated theoretically. Conclusions: This review provides the first systematic landscape map of authentic SED assessment for children aged 0–8, confirming feasibility and diversity while revealing gaps in age coverage, geographic reach, and cultural adaptation. The integrated framework offers a potentially transferable analytic model, and findings can inform equitable, contextually responsive assessment practice and policy. Full article

Black soil degradation poses critical threats to agricultural sustainability and global food security, yet systematic frameworks integrating ecological security assessment, driver identification, and forward-looking early warning remain underdeveloped for black soil watersheds. This study develops and implements a comprehensive assessment–interpretation–early warning framework for the Xingkai Lake Basin, a representative black soil region at the China–Russia border. We developed a multivariate ecological security index (MESI) to describe the spatiotemporal dynamics between 2000 and 2023. An XGBoost–SHAP framework was applied to quantify dominant drivers of ecological security spatial heterogeneity and examine synergistic effects among driving factors through geographical detector interaction analysis. A Bayesian network model was subsequently employed to simulate the probability of warning grade occurrence under multiple univariate and multivariate scenarios. Findings revealed the following: (1) Spatial analysis revealed persistent north–south differentiation, with high spatial association zones contracting from 25% to 21% despite strengthened global spatial auto correlation. (2) XGBoost–SHAP driver analysis quantified that land use intensity and landscape fragmentation collectively explained over 75% of spatial heterogeneity in MESI. (3) BN models demonstrated greater sensitivity in simulating no warning and severe warning levels. This study provides scientifically rigorous insights into the sustainable management of ecosystems in black soil river basins and offers a generalizable decision-support framework for conducting ecological safety early warning research in other regions facing similar agricultural pressures. Full article

Efficient data dissemination to mobile sink groups with heterogeneous spatial distributions that are globally sparse but locally dense remains a critical challenge in wireless sensor networks (WSNs). To address severe energy inefficiencies in conventional single-strategy approaches, we propose an energy-efficient, strictly lightweight hybrid mobile groupcasting protocol that dynamically integrates unicasting and partial flooding. The proposed protocol eliminates in-network computational overhead by shifting the entire subgrouping burden exclusively to the data source. The source formulates data dissemination as an analytical cost minimization problem and executes a highly scalable heuristic subgrouping algorithm that operates in linear time, $\mathcal{O}\left(\right|M\left|\right)$, relative to the number of member sinks. By embedding this optimal configuration directly into the data packet header, resource-constrained intermediate sensor nodes are completely relieved from heavy clustering calculations and only need to execute simple, predefined geographic forwarding or localized flooding rules. The simulation results using the QualNet 4.0 platform validate that our source-delegated architecture significantly reduces redundant transmissions and unnecessary flooding regions. The proposed protocol achieves up to 24% and 44.5% reductions in communication energy consumption compared to conventional unicasting-based and flooding-based protocols, respectively, while maintaining reliable data delivery under realistic network dynamics. Full article

Modern distribution networks increasingly face operational stress from variable demand and high penetration of distributed energy resources, challenging the adequacy of purely reactive protection schemes. This study addresses this challenge by enhancing a developed adaptive protection software platform with a Geographic Information System (GIS) driven predictive load forecasting capability to enable anticipatory protection coordination. The proposed framework integrates spatially resolved demand modeling, regulatory and planning constraints, and machine learning-based short- to medium-term load forecasting with a relay coordination and optimization engine. Forecasted load profiles are used as inputs to an optimization layer that proactively updates relay pickup and time delay settings to maintain selectivity and system security under predicted operating conditions. The approach is validated at laboratory scale using real Intelligent Electronic Devices (IEDs) interfaced with synthetic GIS-based network and load datasets. Experimental results indicate that incorporating forecast-informed settings improves coordination margins and reduces the risk of relay maloperation compared with reactive adaptive protection alone. The findings demonstrate that coupling GIS based constrained load forecasting with adaptive relay control can enhance protection performance in active distribution networks, supporting more resilient and forward-looking protection strategies. Full article

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

The circular economy (CE) can help businesses use resources more efficiently, but empirical evidence on CE adoption among non-European SMEs remains limited. This study examines CE practices, benefits, challenges, and future intentions in 220 Saudi Arabian SMIs. A structured survey collected data on four CE practice domains (resource efficiency, waste management, eco-design, and reverse logistics), four benefit dimensions (economic, environmental, operational, and reputational), four challenge dimensions (financial, organizational, technical, and regulatory), and six future intention items. CE adoption was moderate (M = 3.29 on a five-point scale) and balanced across all four practice domains, with resource efficiency scoring highest (M = 3.32). Benefit scores averaged 3.46, far outpacing challenges (M = 2.78). This benefit surplus of 0.68 points (on a five-point scale) indicates that Saudi SMIs perceive CE as worthwhile and view its barriers as manageable rather than prohibitive. Together, perceived benefits and perceived challenges explained 54.3% of the variance in CE adoption (R² = 0.543) in multiple regression analysis. Reducing perceived challenges may be a more effective lever for promoting CE adoption than amplifying perceived benefits, as challenges exerted a larger absolute standardised effect (β = −0.50) than perceived benefits (β = 0.39). Once perceptions were controlled, perceived benefits and challenges significantly predicted future CE intentions, but current CE practices did not. According to the Theory of Planned Behavior’s attitudinal pathway, firms without CE experience can develop strong forward-looking intentions if the business case is convincing and barriers are perceived as manageable. Technical and organizational barriers outweighed financial ones, indicating the need for capacity-building interventions over supplementary financing, unlike European findings. About 79% of respondents were neutral or positive about government-supported CE expansion. CE adoption did not differ significantly by firm size, geographic location, or ownership structure, suggesting that Vision 2030’s sustainability messaging has established a broad baseline of CE awareness across Saudi SMIs. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Urban travel intensity is a practical proxy for human mobility, but direct mobility data are often costly, geographically restricted, and privacy sensitive. UTScan uses continuous ambient seismic data to estimate urban travel intensity in a passive, non-intrusive manner. Model development used 10 cities in Hubei Province during January–April 2020, and external validation used 84 non-Hubei cities that satisfied the study’s data-quality criteria. From each hourly power spectral density (PSD) curve, we extracted 13 features in the 2–20 Hz anthropogenic band, applied a station-wise low-activity baseline subtraction, and then modeled daily travel intensity with a CatBoost–LSTM framework. Under the calendar-based forward-validation protocol, the final UTScan implementation (FusionB) achieved a mean RMSE of 0.537 ± 0.214 and a mean Pearson correlation of 0.768 ± 0.076 across the internal Hubei folds and a mean RMSE of 0.789 ± 0.229 and a mean Pearson correlation of 0.605 ± 0.370 across the 84-city external validation set. Additional sensitivity analyses using alternative validation windows and light-touch outlier handling indicated that the main conclusions were stable, while single-station representativeness remained the principal limitation. Ambient seismic noise is therefore a useful passive proxy for estimating city-scale mobility dynamics, especially for abrupt mobility disruptions, but its interpretation remains conditional on station siting, source mixture, and the proxy nature of the Baidu travel-intensity target. Full article

Small- and medium-sized islands struggle to secure reliable, affordable, low-carbon electricity due to their isolation, scarce land, and reliance on imported fossil fuels. Hybrid renewable energy systems (HRESs) offer a way forward, but research has focused overwhelmingly on solar–wind configuration. This review critically examines HRES configurations for islands (solar–wind, solar–marine current, and wind–wave), assessing how they match local resources, system needs, and constraints. The dominance of solar–wind hybrids is attributed to their mature technology and low costs, but marine-inclusive options can provide advantages such as better predictability, efficient land use, and multifunctionality in certain island settings. A cross-configuration analysis is conducted to compare the technology readiness, suitability, and deployment contexts of different hybrid configurations. The review also examines island-specific hurdles, including economic pressures, geographic remoteness, land limitation, environmental factors, and social issues, as well as the role of energy storage and diesel backup during the energy transition. Findings stress context-driven choices over technology biases, fostering resilient and locally tailored pathways for island energy transitions. Full article

Reliable estimation of extreme rainfall is essential for hydraulic design and flood risk mitigation, particularly in arid regions where rainfall exhibits strong temporal and spatial variability. This study presents a statistical framework for developing rainfall intensity-duration-frequency (IDF) curves, complemented by a machine learning-based assessment of model bias and performance. The analysis was conducted using data from ten rainfall stations located within or near the Wadi Al-Rummah Basin. Annual maximum series (AMS) from 1969 to 2024 were first reconstructed to address missing years using a modified normal ratio method (NRM) combined with nearest-station selection, ensuring spatial consistency while preserving station-specific rainfall characteristics. Six probability distributions (Weibull, Gumbel, gamma, lognormal, generalized extreme value (GEV), and generalized Pareto) were fitted to each station, and the best-fit distribution was identified using multiple goodness-of-fit (GOF) criteria, including the Kolmogorov–Smirnov (K-S) test, Anderson–Darling (A-D) test, root mean square error (RMSE), chi-square (χ²) statistic, Akaike information criterion (AIC), Bayesian information criterion (BIC), and the coefficient of determination (R²). Statistical IDF curves were then developed for durations ranging from 5 to 1440 min and return periods from 2 to 1000 years. To evaluate the robustness of the statistically derived IDF curves, three machine learning (ML) models, multiple linear regression (MLR), regression random forest (RRF), and multilayer feed-forward neural network (MFFNN), were trained as surrogate models using duration, return period, and station geographic attributes as predictor variables. Model performance was evaluated using RMSE, MAE, and mean bias metrics across stations and return periods. The lognormal distribution emerged as the best-fit model for four stations, while the Gumbel and gamma distributions were selected for two stations each. Overall, no single probability distribution consistently outperformed others, indicating station-dependent behavior. Among the machine learning models, the MFFNN achieved the closest agreement with statistical IDF estimates ($RMSE\approx 0.97$, $MAE\approx 0.65$, $bias\approx -0.02$), followed by RRF and MLR based on global average performance across all stations and return periods. The proposed framework offers a reliable approach for rainfall IDF development and evaluation in arid region watersheds. Full article

The paper presents a comprehensive methodological system for assessing the level of economic security of Russian regions, based on the synthesis of several complementary approaches and accounting for regional specifics. The central idea is a shift from static monitoring to dynamic analysis, which allows not only for capturing the current state but also for identifying the direction and stability of trends over time. The proposed methodology based on four stages: forming a set of indicators, normalizing their values, aggregating them into integral indices, and then visualizing them for operational decision-making. An important feature of sustainable development is the introduction of mechanisms to account for regional specifics through the clustering of regions and adjustment coefficients, which helps to mitigate the influence of geographical and structural differences on the results comparability. Together, they form an integrated system for diagnosing, planning, and monitoring the economic security of regions. The paper provides examples of threshold values for indicators such as the share of households with internet access, the length of the road network, birth rate, the volume of building commissioning, and innovation expenditures. A classification of regions into stability zones and recommendations for policy measures within each zone accompany the threshold analysis. In particular, for digitalization and transport infrastructure, measures are proposed to enhance monitoring, improve service accessibility, and invest in infrastructure; for the demographic component, measures are proposed to support families and improve quality of life. The practical significance of the research lies in creating a universal, yet flexible, toolkit for monitoring, ranking, and planning regional policy in the field of economic security. The proposed system was designed for application both at the federal level and for interregional analysis, including scenario planning and modeling the impact of management decisions. Thus, this study contributes to the literature by bridging the theory of economic security, the imperatives of sustainable regional development, and the practical potential of information technologies. It offers a concrete, scalable methodology for transforming regional economic security management into a data-driven, forward-looking, and context-sensitive process. In the future, the authors intend to further develop the methodology by considering the sectoral specialization of regions, integrating with medium- and long-term forecasting systems, and creating an automated monitoring platform. Full article

Sweeteners occupy a pivotal role in the global transition toward sustainable, health-aligned, and resource-efficient food systems. Conventional sucrose production carries significant environmental burdens, while escalating metabolic health concerns intensify demand for viable alternatives. This paper reframes sweeteners not as commodity ingredients, but as digitally engineered, biologically manufactured, and circularity-optimized materials within the emerging bioeconomy. Advances in artificial intelligence (AI), metabolic engineering, precision fermentation, and lignocellulosic valorization are fundamentally reshaping sweetener innovation. We introduce the Sweetener Innovation 4.0 framework, in which AI functions as the integrative engine linking molecular design, bioprocess optimization, and system-level sustainability. Across diverse sweetener classes, including steviol glycosides, mogrosides, rare sugars, sweet proteins, and forestry-derived polyols, AI accelerates discovery, improves metabolic flux control, optimizes downstream processing and enables more adaptive manufacturing systems. This digital–biological convergence is progressively decoupling sweetness production from land-intensive agriculture, reducing dependence on geographically constrained crops, and enabling resilient, low-carbon manufacturing pathways. Comparative life-cycle assessments highlight substantial sustainability gains, but also reveal persistent methodological gaps, particularly in accounting for downstream-processing energy and digital infrastructure emissions. Socioeconomic analysis further underscores the importance of equitable transitions, transparent labeling, and effective consumer communication as fermentation-derived sweeteners enter global markets. Looking forward, we identify key frontiers for Sweetener Innovation 4.0, including de novo AI-designed sweeteners, autonomous fermentation systems, carbon-negative feedstocks, personalized sweetness modulation, and integrated circular biorefineries. Together, these developments position sweeteners as a top domain for demonstrating how AI, biotechnology, and sustainability principles can jointly reshape ingredient development and industrial systems within the 21st-century circular-economy. Full article

Currently, our understanding of the fungi associated with Dracaena species is limited. There is a clear need for more comprehensive information, especially in the context of Thailand. In our study, we collected dead Dracaena leaves with fungal structures from limestone outcrops in seven Thai provinces: Chiang Mai, Kanchanaburi, Krabi, Nakhon Si Thammarat, Ratchaburi, Songkhla, and Tak. The fungi in these samples were isolated and identified using a combination of morphological characteristics and a multi-loci phylogeny (ACT, CHS-1, GAPDH, ITS, LSU, and TUB2). We are thrilled to introduce seven taxa belonging to four families within three orders (Chaetosphaeriales, Glomerellales, and Xylariales). Our detailed morphological descriptions and updated phylogenetic trees of two new species (Zygosporium dracaenae, and Z. dracaenicola) and five new host/geographical records (Colletotrichum dracaenophilum, C. gigasporum, C. truncatum, Malaysiasca phaii, and Neoleptosporella camporesiana) represent a significant step forward in our understanding of this field. Full article