Search Results (4,946)

The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate with localized consumption and cross-town employment mobility. (3) Higher road network density corresponds to a shrinking commuting radius near metropolises and intra-municipal intertown interconnection in distal towns, rather than mere external relation channels. This study empirically supports CFT at the small-town scale, explores plausible mechanisms, and informs differentiated planning strategies. Full article

Background: Conventional Herbst appliances are effective for the correction of skeletal Class II malocclusion, but they are frequently associated with dentoalveolar side effects, particularly lower incisor proclination. Skeletal anchorage systems may improve orthopedic outcomes; however, submucosal miniplates require invasive surgical procedures that may reduce patient acceptance. This pilot clinical study evaluated the feasibility, safety, and skeletal effects of a minimally invasive digitally guided protocol using supragingival miniplates for bone-supported Herbst therapy in late adolescents. Methods: Eleven late-adolescent patients (14–17 years; cervical vertebral maturation stages CS4–CS5) with skeletal Class II malocclusion due to mandibular retrusion were prospectively treated using a bone-supported Herbst appliance anchored to digitally planned supragingival stainless-steel miniplates fixed with bicortical miniscrews. Miniscrew placement was planned by merging CBCT and intraoral scan data and performed using 3D-printed surgical guides. Cephalometric variables, including SNA, SNB, Wits appraisal, mandibular plane angle, and incisor inclinations, were assessed before treatment and after a 10-month Herbst phase. Mandibular advancement was additionally explored using a complementary linear measurement (SeMndb-line). Results: All patients completed treatment without anchorage loss, appliance failure, or surgical complications. Significant skeletal improvements were observed, including an increase in SNB (+3.36°, p < 0.001) and a reduction in Wits appraisal (−2.65 mm, p < 0.001). The SeMndb-line increased by +3.49 mm (p < 0.001), supporting effective mandibular advancement. Lower incisor inclination remained stable (Δ = −0.18°, p = 0.909), indicating effective dentoalveolar control. No clinically relevant changes in vertical skeletal pattern were observed. Conclusions: Digitally guided supragingival miniplates for bone-supported Herbst therapy appear to be a feasible and minimally invasive approach for the treatment of skeletal Class II malocclusion in late adolescents. This protocol achieved clinically meaningful mandibular advancement while minimizing dentoalveolar side effects. Given the pilot design, small sample size, and lack of a control group, further controlled studies with larger samples and long-term follow-up are required. Full article

The Brazilian craft beer market has experienced continuous growth, increasing operational challenges for small- and medium-sized breweries that frequently rely on empirical and spreadsheet-based production routines. These practices often lead to inefficient resource allocation, production instability, and sustainability concerns. This study proposes an integrated analytical framework combining Machine Learning (ML) and Operations Research (OR) to improve demand forecasting and production planning. The methodology is based on a synthetic dataset calibrated to the operational conditions of a Brasília-based craft brewery, incorporating realistic demand patterns such as seasonality, trend, and intermittency across multiple SKUs over an 18-month horizon. Forecasting models—including Moving Average, Single Exponential Smoothing, and a global ML-based proxy—were evaluated using rolling-origin validation. The resulting probabilistic forecasts were integrated into a capacity-constrained optimization model based on linear programming, extended with risk-aware decision-making using Conditional Value-at-Risk (CVaR). The results indicate that the ML-based approach achieved competitive forecasting performance (sMAPE = 5.83% and MAE = 11.76) while enabling the generation of capacity-feasible and risk-aware production plans aligned with service-level targets. The integration of probabilistic forecasts into the optimization model allowed explicit trade-offs between cost, service level, and resource utilization. The main contribution of this study lies in demonstrating how the integration of predictive and prescriptive analytics can support more sustainable production planning in resource-constrained manufacturing environments. By replacing ad hoc spreadsheet routines with a closed-loop decision-support system, the proposed framework advances the literature on data-driven PPC and provides practical guidance for SMEs operating under uncertainty. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

This paper proposes a design-based framework for implementing competency-based education (CBE) in the Moodle Learning Management System (LMS) by integrating competency frameworks, learning plans (LPs), adaptive learning (AL) workflows, and visual learning path authoring. While existing research addresses these components separately, there is a lack of operational approaches that translate them into coherent and reproducible LMS-based implementations. The study adopts a design-based synthesis methodology, mapping pedagogical requirements of CBE and adaptive learning to concrete Moodle constructs and plugin-supported functionalities. Based on this mapping, a set of reusable implementation patterns is defined, including course-centric competency alignment, microlearning with branching logic, and adaptive assessment using computer-adaptive testing (CAT). The framework is further extended through visual authoring tools, including the Adele plugin ecosystem. The approach is informed by implementation experience within the TransLeader project (2023-2-PL01-KA220-HED-000179445), which integrates AI and IoT competencies with leadership training in higher education. This paper does not present empirical evaluation results; instead, it provides a structured implementation framework intended to support future empirical validation and institutional adoption. Full article

Coastal socio-ecological systems are increasingly exposed to the combined pressures of climate change, land-use intensification, hydrological alterations and expanding infrastructure networks. These pressures interact across the land–catchment–lagoon–sea continuum, generating complex feedbacks that challenge traditional planning instruments, which remain sectoral and fragmented. The Mar Menor (SE Spain), a semi-enclosed Mediterranean lagoon affected by intensive agriculture, urbanisation, hydrological modifications and recurrent extreme climatic events, exemplifies this systemic vulnerability. Existing planning frameworks—local urban plans, regional territorial plans, river basin management plans, maritime spatial plans and lagoon-specific strategies—operate independently, each addressing only a fragment of the system and none integrating climate change as a structuring axis. This article introduces Integrated Spatial Planning (ISP) as a novel territorial–climatic framework designed to overcome these limitations. ISP integrates climate forcing, land uses, catchment processes, lagoon dynamics, marine conditions, critical infrastructures, intermodal and energy corridors and multilevel governance into a single analytical structure. A central component of the methodology is a four-zone multilevel zoning system that connects municipal, regional, basin, marine and EEZ planning domains within a unified territorial–climatic logic. The ISP matrix is applied to the Mar Menor to produce the first holistic diagnosis of the system. Results reveal strong land–sea–catchment interactions, high climatic exposure, vulnerable infrastructures and structural governance fragmentation. The matrix exposes systemic incompatibilities and vulnerabilities that remain invisible in sectoral planning instruments. The discussion demonstrates how ISP clarifies the roles and responsibilities of each governance level, supports multilevel coherence and integrates critical infrastructures and intermodal corridors into climate-resilient planning. ISP reframes climate change as the organising principle of territorial planning and provides a replicable, scalable methodology for coastal socio-ecological systems facing accelerating climate pressures. The Mar Menor case illustrates the urgent need for integrated territorial–climatic governance and positions ISP as a scientifically robust and operationally viable pathway for long-term adaptation and resilience. Full article

Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article

The rapid expansion of digital assets has transformed contemporary financial systems, yet their role in inheritance planning remains underexplored, particularly in developing economies. Employing a mixed-methods design, this study examines the factors influencing individuals’ acceptance of digital assets as inheritance and explores their perceptions and readiness to adopt such assets within estate planning in Thailand. The quantitative phase analyzes survey data using descriptive statistics and binary logistic regression, focusing on investment experience, risk orientation, emotional responses to financial risk, financial capacity, and perceived suitability. The results indicate that investment orientation, discretionary financial capacity, familiarity with diverse digital asset types, and psychological resilience toward financial volatility significantly increase acceptance, with Preferred Investment Group emerging as the strongest predictor. In contrast, anxiety toward high-risk investments reduces acceptance. Qualitative findings, derived from content analysis of in-depth interviews, reveal persistent skepticism regarding asset stability, legal and institutional uncertainty, technological barriers, and subjective valuation. Despite these concerns, participants expressed conditional readiness to adopt digital assets in inheritance planning given clearer legal frameworks, professional guidance, and user-friendly technologies. This study contributes to the emerging literature on digital wealth transfer and offers practical implications for policymakers, financial advisors, and legal professionals seeking to develop regulatory frameworks, financial literacy initiatives, and technological infrastructures that support the secure intergenerational transfer of digital assets. Full article

To investigate the aging behavior of high-temperature-vulcanized silicone rubber (HTV-SiR) used in composite insulator sheds under coupled electrical, thermal, humidity, and mechanical stresses, accelerated aging tests were conducted to emulate the service conditions of ±800 kV ultra-high-voltage direct current (UHVDC) systems in Guangzhou, China. The physicochemical, mechanical, and electrical properties of the specimens were systematically characterized. The results show simultaneous degradation of both electrical and mechanical performance. In particular, the tensile strength exhibits a significant monotonic decrease and drops to 49.52% of its initial value under the most severe condition (0.5 kV·mm⁻¹ and 5% tensile strain) after 75 days. In contrast, the DC breakdown strength shows a non-monotonic “rise-then-fall” trend and decreases more markedly with increasing tensile strain. To address the one-shot and destructive nature of tensile testing and the associated statistical uncertainties, a lifetime prediction framework was developed by integrating a generalized Eyring acceleration relation with a stochastic degradation process. Under representative service conditions of 0.09 kV·mm⁻¹ and 0.2% tensile strain, the predicted lifetimes corresponding to failure probabilities of 10%, 75%, and 90% are 1.77, 9.08, and 17.90 years, respectively. The applicability of the model is supported by field-aged specimens. These findings provide a mechanistically grounded and reliability-oriented basis for condition assessment, lifetime-margin evaluation, material screening, and maintenance planning of UHVDC composite insulators operating in hot–humid environments. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article

To address climate change, reducing food loss along the production and supply chain is a global priority. Addressing this challenge requires a shift in agrifood systems toward greater sustainability, in which new technologies and novel foods appear as promising strategies. Among emerging novel foods, 3D-printed foods are an interesting new food technology for food loss reduction, resource optimization, and by-product valorization. However, to reach market success, it needs consumer acceptance, a topic far unexplored, particularly in the Italian context. To fill the literature gap, this article investigates Italian consumers’ intention toward 3D-printed savory snacks using an extended Theory of Planned Behavior, based upon the relevant literature. Survey data were collected, and partial least squares structural equation modeling was performed to test research hypotheses. Results show that attitude and subjective norms are the strongest predictors of purchase intention. In addition, perceived usefulness is shown to be a powerful construct, positively impacting attitude and subjective norms, while self-identity as a green consumer reinforces perceptions of the benefits of 3D-printed foods. Sensory appeal impacts consumer attitude. These insights have practical policy and micro-level applications, suggesting tailored strategies, educational campaigns, and supportive policies and marketing campaigns for fostering acceptance of 3D printing in the agrifood sector. Full article

Acute infectious diseases represent a persistent public health burden that exerts sustained pressure on hospital bed capacity, treatment resources, and the allocation of the healthcare workforce. Strengthening hospital-level preparedness and resource planning requires reliable early-risk stratification tools that remain robust to real-world temporal shifts. However, many existing clinical prediction studies simplify inherently ordered outcomes into binary categories and rely on random data splits, limiting their relevance for real-world health system decision-making. In this study, we developed and evaluated an ordinal machine learning framework using clinical data from 5066 patients hospitalized with acute infectious diseases between 2022 and 2024. Recovery trajectories were modeled as an ordinal outcome, reflecting changes in status between admission and discharge. Models were trained on 2022–2023 data and externally evaluated on a fully isolated 2024 cohort to assess temporal generalizability under realistic deployment conditions. Performance was evaluated using order-aware metrics, including Quadratic Weighted Kappa, Macro-F1, Balanced Accuracy, and ordinal mean absolute error, with explicit analysis of clinically meaningful error structures. Although predictive performance under future holdout validation was modest, misclassifications were predominantly concentrated between adjacent recovery levels, and no clinically critical extreme errors were observed. Model reliability was further assessed through calibration analysis, bootstrap-based uncertainty estimation, and temporal stability of explanatory patterns. Finally, ordinal predictions were translated into structured risk stratification categories aligned with hospital bed management, treatment prioritization, and workforce allocation logic. These findings demonstrate the methodological potential of temporally validated ordinal modeling as a proof-of-concept framework. Given the modest predictive performance and the absence of key clinical variables, the current model should not be regarded as a ready-made clinical decision-support tool, but rather as a foundation for further development with richer data in future research. monitoring prioritization. In practical terms, this framework demonstrates how ordinal predictions could, in principle, be structured for use at admission points. However, given the modest predictive performance observed, further development with richer clinical data is required before deployment. Full article