Search Results (4,082)

Rural maternity care in the United States faces an unprecedented crisis. As of 2022, more than half of rural hospitals lacked obstetric services, leaving many rural communities without local maternity care. This descriptive case study documents the implementation of maternity services at a 25-bed rural critical access hospital in North Carolina. We reviewed and coded data derived from the following sources from 2020–2025: institutional planning documents, clinical protocols, standard workflows, operational procedures, administrative records, meeting minutes, presentations and summaries, and published commentaries, manuscripts and articles related to the new maternity unit. We identified six critical domains for sustainability: (1) hospital and community engagement, (2) stable multidisciplinary staffing models, (3) emergency preparedness, (4) anesthesia service delivery, (5) financial sustainability, and (6) risk-appropriate scope of care. Successful implementation required integrated approaches across organizational dimensions facilitated by committed institutional leadership; community advocacy; innovative staffing models with family physicians, certified nurse-midwives, and certified registered nurse anesthetists; competency-based emergency training; Medicaid-based financial sustainability; and explicit risk stratification with clear interfacility communication protocols. Workforce sustainability emerged as the most significant ongoing challenge. Rural maternity service sustainability requires multifaceted, evidence-based approaches integrating workforce development, organizational infrastructure, financial mechanisms, and clear scope definitions. This case study demonstrates a model of perinatal regionalized and risk-appropriate care with replicable strategies that can inform efforts to address maternal health equity and strengthen rural health care systems. Full article

As a meta-heuristic inspired by human student learning behaviors, the original Student Psychology-Based Optimization (SPBO) suffers from insufficient exploitation of historical population records, simplistic individual interaction patterns and high risk of falling into local optima. This work develops an enhanced SPBO (ESPBO) embedding three dedicated learning mechanisms. The adaptive knowledge-accumulation learning component imports personal historical best, global elite and population-mean information into position update formulas to sustain coherent search trajectories and enhance convergence precision. The multi-level peer collaborative learning module categorizes agents into excellent, intermediate and under-performing groups based on fitness values. Customized learning rules are configured for each group to enable diverse information sharing: elite individuals expand promising search regions, medium-level agents learn from counterparts, and inferior individuals move toward high-quality candidates. The progressive examination feedback component dynamically modulates search intensity by measuring the fitness improvement of each individual, so as to better balance global exploration and local exploitation. Comparative numerical experiments are carried out on CEC2017 and CEC2022 benchmark test suites against multiple advanced meta-heuristic algorithms. Results indicate that ESPBO exhibits outstanding accuracy and robustness on unimodal, multimodal, hybrid and composite test functions. To explore its real-world applicability, ESPBO is adopted for mobile-robot path-planning simulations under multi-scale grid maps. Simulation results from 20 × 20, 40 × 40 and 60 × 60 environments illustrate that ESPBO stably produces collision-free trajectories, outperforming comparative algorithms in path length, smoothness and safety performance. It is demonstrated that the three embedded learning mechanisms substantially strengthen the optimization capacity of vanilla SPBO, and ESPBO possesses considerable application potential for numerical optimization as well as mobile robot path-planning scenarios. Full article

Loess mountainous regions in northern China suffer frequent landslides, collapses and debris flows controlled by complex geological settings, seasonal rainstorms, freeze–thaw cycles and large-scale human engineering activities. Multi-geohazard susceptibility evaluation can provide fundamental data support for regional disaster prevention and territorial planning. Taking Shanxi Province, a typical loess-mountain transition zone, as the study area, this paper establishes an evaluation framework for landslides, collapses and debris flows. Ten conditioning factors are selected, including lithology, terrain parameters, distance to faults, distance to rivers, NDVI and RX1day (annual maximum 1-day precipitation). A 30 m grid unit is adopted as the basic evaluation unit. A total of 2598 verified geohazard points are taken as positive samples. Negative samples with equal quantity are extracted from low and very low susceptibility areas of the preliminary zoning map generated by the Frequency Ratio (FR) method, with an 800 m minimum separation distance between sampling points to reduce spatial autocorrelation. Two models, Logistic Regression (LR) and Support Vector Machine (SVM), are constructed, and five-fold cross-validation is used to test model performance through five statistical indicators and AUC values. The results show that, under the specific model configurations and sampling strategy adopted in this study, the LR model achieved higher predictive performance (average test AUC = 0.995) than the SVM model (average test AUC = 0.752) in the comparative assessment. Statistical analysis of the final susceptibility map derived from the LR model indicates that high and very high susceptibility zones account for 80.94% of the total provincial area and contain 87.45% of all recorded geohazard points, which confirms the consistency and reasonableness of the zoning results. Spatially, high-susceptibility areas are concentrated in the western and northwestern loess tablelands, the Fenhe River fault basin, and fault-developed sections of the Lüliang and Taihang Mountains. Thick loess layers, river undercutting and coal mining activities jointly reduce slope stability in these zones. Compared with conventional susceptibility modeling workflows, this study incorporates the RX1day extreme precipitation index and implements Frequency-Ratio-constrained stratified negative-sample selection to reduce training-sample bias. The produced susceptibility maps can provide technical support for differentiated geological hazard risk management, ecological restoration and territorial spatial planning for loess-mountain transition regions in northern China, thereby directly contributing to regional sustainable development and disaster resilience. Full article

Environmental management and monitoring are essential for translating sustainability goals into operational practices in port systems. However, empirical evidence on how these processes are implemented in African ports remains limited, partly because comparable environmental management data are rarely available across ports. This exploratory study examines reported sustainability priorities, EMS-related organisational practices, and environmental monitoring arrangements in African ports using a structured practitioner-based self-diagnosis survey. Data were collected from 18 environmental managers, each representing one port, during a dedicated professional workshop. The analysis combines importance scoring of predefined sustainability items, binary self-reported information on EMS-related practices, and qualitative coding of open-ended responses on monitoring indicators, responsible entities, methodologies, and implementation challenges. The results should not be interpreted as statistically representative of African ports as a whole. Rather, they provide preliminary, practitioner-based evidence on recurring patterns and constraints reported by the participating ports. The findings suggest that core EMS-related elements, such as objectives, legal compliance, operational control, and monitoring, are more frequently reported than strategic and resource-intensive practices, including climate change integration, sustainability planning, and green services to shipping. Monitoring practices appear largely compliance-oriented, periodic, and dependent on external actors, reflecting reported constraints related to finance, technical expertise, equipment, and institutional coordination. The study contributes exploratory evidence from an under-researched regional context and develops tentative propositions for future research on environmental management capacity, monitoring integration, and sustainability transitions in African port systems. Full article

In Latin America, road planning and prioritization have focused on selecting roads based on traffic flow demand and population growth. Under this paradigm, rural roads are often underprioritized, despite having an impact on connectivity, access to services, territorial development, and spatial equity. This has led public programs to seek alternative funding sources to offset the road deficit in rural areas. However, road improvement programs lack support tools for the early formulation of efficient projects aimed at territorial sustainability. Thus, this research proposes an early planning tool for rural road improvement projects that contribute to territorial sustainability through a categorization method. To this end, a multi-criteria multi-objective evaluation model, the intelligent optimization algorithm Harmony Search, and discriminant analysis were used to categorize the sustainable contribution of rural roads within a territory. This study was applied to a case study of 101 rural road projects in the Araucanía region of Chile. The proposed categorization method identified four key criteria that influence the overall sustainability of the territory, as well as four differentiating categories by area. The proposed tool serves as an early planning guide for the design and location of rural roads, promoting a sustainable planning approach. Full article

Mountainous regions often experience a hydrological paradox where high monsoon rainfall coincides with severe dry-season water scarcity. The Chittagong Hill Tracts (CHT) of southeastern Bangladesh represent this challenge, as steep terrain, fractured geology, rapid runoff and limited monitoring constrain sustainable water-resource planning. This review critically evaluated water-resource characteristics, modeling approaches and decision-support strategies for the CHT. The literature shows that standalone models such as SWAT, MODFLOW and SWMM are useful for specific water-cycle components but do not adequately represent cross-domain processes including deep recharge, baseflow and stream–aquifer exchange in steep, complex terrain. International analogue studies indicate that coupled surface water–groundwater models can improve process representation when streamflow, groundwater-level, lithological and recharge data are available. However, their direct calibration and validation in the CHT remain limited by sparse groundwater monitoring and weak hydrogeological characterization. CHT-related and comparable Bangladesh studies indicate that machine-learning (ML) models can improve predictive suitability mapping when representative training data and independent validation are available; however, their relative advantage over the Analytic Hierarchy Process (AHP) is application-specific and depends on data quality, validation design, spatial autocorrelation, and model interpretability. The review identifies three key gaps: inadequate groundwater monitoring, absence of calibrated coupled modeling for fractured aquifers, and limited integration of socio-economic factors in rainwater-harvesting feasibility. Finally, a staged modeling pathway is proposed. Full article

Electric vehicles (EVs) are attracting choice and adoption as a travel mode in global transportation systems, driven by technological innovation, economic considerations, and advancing sustainable urban development. The planning, development, design, and location of EV charging stations are challenging tasks for underdeveloped countries such as Pakistan. To propose the site location for an EV charging station, this study comprises Multi-Criteria Decision Analysis and Geographic Information Systems. The study adopts a mixed-methods design, combining qualitative expert input with quantitative spatial analysis to investigate technical, social, and infrastructural criteria. The study considers indicators, including site suitability, population density, interaction between land use and transport, road network, transportation interactions, existing electricity grid infrastructure, and existing fueling and charging stations. This research presents pairwise expert comparisons, indicating that grid capacity (0.28) and demand (0.30) are important factors to consider. A composite suitability Index (CSI) through GIS-weighted overlay was used to classify the area into low, medium, and high suitability zones. According to the CSI, 26% of the area falls in highly suitable areas for EV infrastructure in Islamabad, and 41% falls in medium-to-high suitable areas. The remaining 33% area lies in low-suitability peripheral zones near the hilly region of Islamabad city, which is not considered suitable for EV infrastructure. A total of 67% of the city’s area is suitable for EV infrastructure. Full article

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Full article

Hybrid renewable microgrids are increasingly considered a practical solution for supplying reliable and sustainable electricity to remote regions. However, many planning studies do not consider the longer-term effects of battery degradation and the effect of changing operating conditions on system performance. This study develops an integrated techno-economic planning mathematical model for a self-sufficient PV/WT/DG/BESS microgrid using actual hourly meteorological and load-demand data from New Minia, Egypt. This framework integrates variability in renewable resources, battery degradation, reliability constraints, environmental factors, and uncertainty evaluation as part of a comprehensive assessment. The proposed approach identifies a system configuration that balances supply reliability, economic performance, renewable energy (RE) penetration, and long-term storage sustainability. The optimal configuration achieved a cost of energy (COE) of 0.1545 $/kWh and a net present cost of approximately 4.5 M$, while maintaining the RE contribution of 79.43%. The configuration achieved a low loss of power supply probability (LPSP) of 0.00593 and annual expected energy not served of 13,516.52 kWh. The resultant configuration reduced dependence on diesel generation and decreased yearly CO₂ emissions to 483.05 ton/year. A degradation-aware battery model was incorporated to represent long-term storage behavior, yielding an estimated battery service life of approximately 12.16 years under the adopted operating assumptions. Furthermore, deterministic sensitivity analysis was conducted to evaluate the influence of key economic and system parameters on the techno-economic performance of the proposed microgrid. Overall, the findings demonstrate the effectiveness and practical potential of the proposed degradation-aware and uncertainty-driven framework in supporting reliable and economically sustainable hybrid-microgrid planning. Full article

Reliable rooftop photovoltaic planning requires design-stage energy predictions to be verified against actual system performance. The novelty of this study is the integration of a multi-year assessment of agreement with PV*SOL design estimates with an independent assessment of normalized productivity, interannual variability, seasonality, and meteorological effects for several rooftop systems operating under the same regional conditions. PV*SOL, a commercial photovoltaic simulation software used to estimate system energy production during the design stage, was evaluated using three years (2023–2025) of monitored data from three rooftop photovoltaic (PV) systems (17.60–75.40 kWp) in Rzeszów, south-eastern Poland. The analysis comprised 108 installation-month observations and included final yield, capacity factor, annual prediction errors, seasonal variability, Pearson correlations, and hierarchical regression. Mean annual final yield ranged from 907.4 to 966.2 kWh/kWp, while annual deviations from PV*SOL design estimates ranged from −0.80% to +7.41%. Monthly final yield was strongly associated with solar irradiation, and the final hierarchical regression model explained 96.4% of its variability. The results indicate that PV*SOL provides a useful annual design reference, but operational monitoring and local benchmark data remain essential for reliable performance assessment. The study supports United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving the evidence base for rooftop photovoltaic planning and monitoring. Full article

Makkah faces substantial heat-stress challenges associated with extreme temperatures, dense urban form, and the large numbers of pilgrims present during Hajj and Umrah, creating important public health concerns. Recent heat-related fatalities highlight the need for complementary strategies that address outdoor as well as indoor heat exposure, alongside conventional cooling approaches such as air conditioning. This article examines the potential role of nature-based and complementary engineered interventions in mitigating urban heat stress in Makkah, focusing on afforestation, urban greening, and the possible use of artificial water bodies, contingent on sustainable water management. Drawing on case studies and published evidence from arid and heat-prone regions, including China, Pakistan, Saudi Arabia, and the wider Middle East, we summarize reported cooling effects, implementation experience, and feasibility considerations and assess their potential relevance to Makkah. The perspective highlights critical challenges related to water scarcity, spatial constraints, ecological impacts, and governance, while proposing phased implementation pathways that could be evaluated incrementally. If carefully designed and integrated with urban planning and climate-adaptation strategies, nature-based and complementary interventions could potentially reduce human heat stress, reduce cooling demand, and strengthen climate resilience in Makkah. However, their effectiveness, water requirements, environmental impacts, and scalability require evaluation under Makkah-specific environmental and operational conditions. Full article

Land-use change affects regional carbon accounting through ecological conversion and the concentration of energy-intensive economic activity. We combined 30 m China Land Cover Dataset maps for 2010, 2015, 2020, and 2025 with provincial socioeconomic and energy statistics to quantify land-use transitions and carbon emissions across nine Yellow River Basin provinces. Construction-land-associated emissions were decomposed using the logarithmic mean Divisia index, factors associated with land expansion were examined using random-forest models, and three 2030 scenarios were evaluated. Construction land expanded by 38.87% from 2010 to 2025, with 71.33% of new construction land converted from cropland and 17.75% from grassland. Net land-use carbon emissions increased by 69.82%, from 1139.06 to 1934.33 million t C. Economic-output density contributed 1144.84 million t C to the increase in construction-land-associated emissions, compared with 576.11 million t C from land expansion, whereas declining energy intensity offset 922.72 million t C. Projected 2030 emissions ranged from 2124.72 million t C under ecological protection to 2866.55 million t C under urban expansion. Construction-land expansion was substantial, but economic-output density made the larger positive contribution to historical emission growth. The projected 2030 estimates depended on the combined trajectories of construction-land demand, economic growth, and energy intensity. These findings highlight the importance of coordinating land-use planning, economic development, and energy-efficiency improvement for sustainable low-carbon transitions. Full article

Landslide and slope-instability hazards increasingly threaten residential areas in rapidly urbanizing Andean cities, where informal construction is common and formal geotechnical assessment is limited. Protection Motivation Theory and the Theory of Planned Behavior have been widely applied to explain protective intention toward natural hazards, yet few studies have jointly examined perceived geotechnical risk, trust in technical assistance, and willingness to pay for mitigation within a single model. This study assessed these constructs, together with mitigation adoption intention, among 420 residents and construction-related professionals in Cuenca, Ecuador, using a cross-sectional, descriptive–correlational design. Data were collected through a structured online questionnaire with seven Likert-scale constructs and a contingent-valuation item, analyzed using non-parametric correlation and group-comparison tests, followed by confirmatory factor analysis and structural equation modeling. Perceived risk, trust, and mitigation intention were all high and positively associated, while economic constraint correlated positively, rather than negatively, with intention, an effect that a structural model showed to be fully mediated through intention instead of acting directly on willingness to pay, which points to a recognized structural barrier and not an individually suppressive one; attitudinal willingness to pay did not consistently predict the declared monetary amount, and civil engineering professionals reported higher willingness to pay than other groups. These findings indicate that respondents in this sample reported high risk awareness and trust in technical assistance, while perceived economic constraint was the dimension most consistently associated with lower stated readiness for geotechnical mitigation in this Andean urban context. These findings contribute empirical evidence on the perceptual, trust-related, and economic drivers of household engagement with geotechnical mitigation, informing sustainable, resilience-oriented approaches to reducing disaster risk in informally built urban settlements across the Andean region. Full article

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Climate change has significantly influenced regional precipitation patterns, affecting agricultural productivity and water-resource sustainability in monsoon-dependent regions. Reliable climate projections are therefore essential for climate impact assessment and adaptation planning. However, the performance of Regional Climate Models (RCMs) varies considerably across regions and climatic conditions, necessitating rigorous evaluation before their application in local-scale studies. In this study, eight CORDEX-CORE South Asia (0.22°) RCMs were evaluated for their ability to reproduce historical precipitation over Thanjavur district, Tamil Nadu, India, during 1976–2005 using observed rainfall data from 13 rain gauge stations. Model performance was assessed using seven statistical metrics integrated through the Compromise Programming Index (CPI), together with evaluations of precipitation occurrence, rainfall intensity occurrence, and cumulative precipitation distribution. The results revealed considerable variability among the RCMs. RegCM-based models generally exhibited wet biases, whereas REMO and CCLIM tended to underestimate seasonal rainfall. Based on the selected metrics and CPI framework, NorESM–RegCM, NorESM-CCLIM, and NorESM-REMO demonstrated comparatively better overall performance. However, analyses of precipitation occurrence and distribution showed that model performance depended on the precipitation characteristic considered, with no single RCM consistently outperforming the others across all evaluation criteria. These findings demonstrate that a multi-criteria evaluation framework provides a more comprehensive assessment of RCM performance than conventional statistical metrics alone and offers a robust basis for selecting suitable RCMs for future climate projections and agro-hydrological applications in the Cauvery Delta region. Full article