Search Results (369)

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

This entry paper explores the multifaceted oral health crisis in the Pacific island nation of Tuvalu, a remote archipelago of nine coral atolls. It delves into the severe burden of oral diseases, such as early childhood caries (ECC) and periodontitis, which are rampant within its population of just over 11,000. The analysis investigates the primary drivers of this crisis, including a significant dietary transition towards imported, ultra-processed foods, compounded by profound socioeconomic challenges and a lack of public health literacy. The paper critically examines the systemic failures of the national healthcare system, characterized by the absence of a formal oral health policy and a critically inadequate dental workforce, which forces residents to seek complex care abroad. Furthermore, it highlights how extreme geographic isolation and severely limited air connectivity function as direct barriers to accessing essential services, rendering specialized treatments like orthodontics and effective management of dental emergencies virtually impossible. In response to these challenges, the paper discusses innovative, forward-looking solutions, including the potential of teledentistry to bridge service gaps, the strategic development of regional medical or dental hubs in proximity to the biggest airports to centralize care, and the necessity of integrating oral health into broader strategies for economic development and climate resilience. Full article

Background/Objectives: The Fagopyrum dibotrys complex is a specialized high-altitude lineage in southwestern China with medicinal and breeding potential, but species delimitation remains unresolved. Methods: We sequenced 26 complete chloroplast genomes from the Hengduan Mountains to the Yunnan–Guizhou Plateau, analyzing genomic structures, variation patterns, and phylogenetic relationships. Results: All genomes exhibited typical quadripartite structures (152,213–160,302 bp), containing 133 genes (88 protein-coding, 8 rRNA, and 37 tRNA) with GC content of 37.9%. Collinearity analysis revealed highly conserved structures without structural rearrangements. Variations were concentrated in the large single-copy(LSC)/small single-copy(SSC) non-coding regions, with hotspots at ycf4–cemA and ndhF–rpl32. Codon usage showed an A/U bias, with leucine being most abundant and cysteine the least. Simple sequence repeats (SSRs) were predominantly mononucleotide repeats enriched in the LSC, while long repeats were mainly palindromic/forward. Maximum likelihood and Bayesian phylogenies consistently resolved three clades: Tibetan high-altitude specialists, limestone specialists, and a widespread Hengduan–Yunnan–Guizhou clade, with geographic clustering indicating isolation as the primary differentiation driver. Conclusions: This study refines the phylogenetic resolution of the F. dibotrys complex and identifies informative chloroplast markers, providing a genomic foundation for reliable species delimitation, evolutionary inference, and conservation management of this medicinal lineage. Full article

Historic waterfront streets are not only an important component of urban public spaces but also highlight the distinctive features and historical contexts of the city. High-quality streetscape visual perception plays a crucial role in advancing the cultural, social, environmental, and economic sustainability of the urban street space. This study was initiated to construct a multi-dimension and multi-scale comprehensive evaluation framework to assess the visual quality of waterfront streets, taking “Water City” Liaocheng as a typical case. Technical methods of semantic segmentation, sDNA (Spatial Design Network Analysis), GIS (Geographic Information System), and statistical analysis were utilized. Following the extraction and classification of street space elements, a multi-dimensional evaluation index system of natural coordination, artificial comfort, and historical culture for the visual assessment was established. Space syntax was performed on waterfront streets by sDNA to quantify macro-level scale spatial structure and meso-level scale pedestrian accessibility. The results of micro-scale visual perception, meso-scale behavioral walkability, and macro-scale spatial structure, were integrated to construct a multi-scale diagnostic framework for eight classifications. This framework provides a scientific basis to put forwards the refined and sustainable optimization strategies for historic waterfront streets. Full article

Common prosperity is an essential requirement of socialism with Chinese characteristics for a new era. Problems caused by the urban rural dual structure, such as resource misallocation, ecological-economic imbalance, and insufficient farmer income growth, not only hinder common prosperity but also conflict with the sustainable development strategy. As the core path to break the dual structure and narrow gaps, the multi-dimensional impact and mechanism of urban rural integrated development on common prosperity need systematic verification. Based on panel data of 31 Chinese provinces from 2014 to 2023, this paper uses fixed-effects and mechanism test models to examine its direct, indirect, and spatial spillover effects, focusing on transmission mechanisms of wage, property, and operating incomes. Findings show: First, it exerts significant positive direct and cross-regional spillover effects on common prosperity; Second, wage and property incomes are key transmission paths, while operating income’s mediating effect is unclear; Third, effects vary geographically, stronger in eastern/central China, weaker in northeast China and insignificant in west China; Fourth, economic and spatial integration play prominent roles, social service integration has inhibitory effect, and ecological integration’s effect is under-released. Accordingly, this paper puts forward countermeasures to optimize resource allocation, tackle the rural operating income dilemma, advance regional coordination, and enhance equal social services, providing references for improving common prosperity policies and rural sustainable development. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

Establishing quantitative causal relationships between drought indicators and vegetation degradation in the Chad Basin remained challenging due to statistical limitations of applying traditional Transfer Entropy to finite-length remote sensing time series. This study implemented a Machine Learning Enhanced Transfer Entropy structure to quantify directed information flow from primary drought drivers of precipitation and land surface temperature to vegetation dynamics from 2000 to 2023. A feed-forward neural network trained on 10,000 synthetic samples with known theoretical Transfer Entropies enabled causal inference from 24-year MODIS-derived NDVI, land surface temperature, and precipitation. The trained model was applied over 10 million pixels, producing Transfer Entropy maps. Results showed that precipitation and land surface temperature exerted comparable causal influences on NDVI, with mean Transfer Entropy values of 0.064 and 0.063, ranging from 0.041 to 0.388. Spatial analysis revealed distinct causal hotspots exceeding 75th percentile threshold of 0.069, indicating driver-specific vulnerability zones. The decline in mean annual NDVI from 0.225 in 2019 to 0.194 in 2023, together with spatially divergent hotspots, highlighted the need for geographically targeted land management. The study overcame finite-length time-series limitations and provided a replicable pathway for vulnerability assessment and climate adaptation planning in data-constrained drylands in the Chad Basin in Africa. Full article