Search Results (14,984)

This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration. Full article

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Background: This study develops a transparent, decision-focused framework that integrates the World Health Organization’s defined daily dose (DDD) standard with the planned-backorder economic order quantity (EOQ) model to manage nursing and pharmacy workflows within a unified economic and operational scale. Method: Demand was expressed in DDD per year, and process-based costs were monetized according to National Health Service (NHS) workflow steps, where the holding cost was computed as H = r × cu and the delay cost B was derived from the target fill rate via a closed-form shadow-price relationship. The model was calibrated for a typical NHS acute-care hospital with 600 beds (D ≈ 130,305 DDD/year). Results: Calibration resulted in an ideal order quantity of 7554 DDD, an inter-order interval of 21 days, and a minimum annual total cost of £451. In the national conceptual scenario, the fill rate is about 99.4%, and the minimum annual total cost is £26,366. At this optimum, cost components are symmetrically balanced, with order cost and combined holding–delay cost contributing equally. Conclusions: This repeatable framework, based on the DDD scale, enhances management visibility regarding the cost–service balance, thereby confirming the policy’s robustness. Full article

Urban plastic waste management in large metropolitan regions remains constrained by low recovery rates despite growing policy attention. This study adopts a sustainability-oriented innovation (SOI) perspective to examine how policy–technology integration reshapes system-level performance in urban plastic waste systems. Using Bangkok as a representative case, a system-level model integrates plastic waste generation growth, time-dependent behavioural adoption of separation at source, contamination-sensitive sorting efficiency, and mass-balance material flows. Three scenarios are assessed: Business-as-Usual, separation-at-source policy only, and an integrated policy–technology system with advanced sorting. Results show that the baseline system remains stagnant at approximately 3.1% recovery. Policy intervention alone increases recovery gradually, reaching around 20% by 2045 despite participation approaching an 85% ceiling. In contrast, integrating policy with advanced sorting generates non-linear gains, surpassing 20% recovery within two years and reaching approximately 47% by 2045, driven by substantial contamination reduction. A Monte Carlo sensitivity analysis extends the integrated pathway to 2060. The median recovery trajectory stabilises at 68%, while the probability of achieving more than 70% recovery rises to 28% by 2040 and plateaus at 33% thereafter. The findings demonstrate that circular economy performance is probabilistic and depends on system-level alignment between behavioural participation, material quality, and technological capability. Full article

Healthcare waste management is a growing environmental and economic challenge due to increasing waste volumes, hazardous materials, and continued reliance on linear disposal methods such as incineration and landfilling. This review aims to examine how circular economy and zero-waste approaches can be applied to healthcare waste management to improve sustainability, resource efficiency, and system performance. A structured narrative review was conducted using peer-reviewed literature obtained from prominent scientific databases, concentrating on circular strategies, zero-waste initiatives, digital technologies, and policy frameworks relevant to healthcare waste systems. The reviewed studies indicate that practices such as improved waste segregation, recycling and material recovery, reusable product design, digital waste tracking, and Extended Producer Responsibility can significantly reduce waste generation, lower environmental impacts, and achieve cost savings, while maintaining infection control and patient safety. However, the review also identifies key barriers to implementation, including regulatory complexity, limited infrastructure, financial constraints, and weak coordination among stakeholders. The novelty of this review lies in its integrated analysis of circular economy and zero-waste strategies through the lens of digital enablement, offering a systems-based framework for transforming healthcare waste management beyond incremental improvements. The findings highlight that successful circular healthcare waste management requires strong institutional leadership, supportive policies, and the integration of digital technologies to enable monitoring, traceability, and decision-making. This review enhances the comprehension of how circular economy principles can facilitate the transition from linear to sustainable healthcare waste systems and provides guidance for policymakers, healthcare managers, and researchers. Future research should focus on evaluating real-world implementation, advancing recyclable and reusable medical materials, and developing standardised indicators to measure circular performance in healthcare settings. Full article

Coastal aquifers are essential freshwater sources for domestic, agricultural, and industrial use, particularly in regions where surface water is limited. However, these systems face growing stress from saltwater intrusion, climate-driven reductions in recharge, sea level rise, and intensified groundwater extraction. This review synthesizes recent research on coastal aquifer responses to these pressures, highlighting the interplay between natural hydrogeologic conditions and human-induced demand. Across deltaic and sedimentary systems, studies consistently show declining groundwater levels, the landward migration of saline interfaces, and reduced aquifer buffering capacity, especially in areas with high evaporation and limited recharge. The review also evaluates emerging strategies to preserve coastal groundwater security. Integrated hydrological models, managed aquifer recharge (MAR), optimized abstraction schemes, and remote sensing-based monitoring are advancing adaptive management capabilities. In parallel, policy and nature-based interventions—such as aquifer protection zoning, wetland rehabilitation, and dune system restoration—support long-term resilience by enhancing natural recharge and reducing vulnerability. The overall findings reveal the need for climate-informed and locally tailored groundwater management. Future efforts should prioritize coupling high-resolution climate projections with aquifer system models, evaluating MAR viability in saline-prone environments, and strengthening collaborative governance frameworks to ensure sustainable and equitable use of coastal aquifers. Full article

Land use transition and its effects on farmers’ well-being are central to the transformation and sustainable development of resource-exhausted areas (REAs). While extensive research has emerged in recent years, there remains a critical lack of systematic synthesis and clarity regarding key scientific issues in this domain. To bridge this research gap, an R-based bibliometric analysis was conducted on an extensive corpus encompassing 8245 papers on land use transition and 931 papers on farmers’ well-being published between 2001 and 2024, systematically investigating the mechanisms of transition, regional transformation dynamics, and the multi-dimensional determinants of well-being. The findings indicate that: (1) land use transition research has evolved from spatial patterns to management strategies, yet it lacks comprehensive regional and multi-scale characterization; (2) although land use is recognized as central to REA studies, the underlying theoretical frameworks require significant refinement; and (3) research on farmers’ well-being has shifted from broad ecosystem services to multidimensional micro-analyses, though the explicit correlation mechanisms with land use remain unclear. Based on these insights, four pivotal directions are identified for future research in REAs: establishing theoretical and analytical frameworks that link land use transitions to well-being under regional development logic; uncovering the spatiotemporal processes and multi-scale driving mechanisms of these transitions; quantitatively measuring their impacts on multidimensional well-being; and developing regulatory policies that balance regional coordination with well-being enhancement. This review provides a robust scientific foundation for optimizing land resources and promoting sustainable human–environment interactions in REAs. Full article

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches. Full article

The textile industry faces machinery maintenance challenges due to reactive practices, lack of real-time monitoring, and absent integrated management systems, resulting in unplanned downtime, elevated costs, and quality variability. This study addresses these limitations by proposing a hybrid predictive–prescriptive framework integrating XGBoost 3.2.0 and LSTM models with a multi-objective optimization engine to generate data-driven maintenance recommendations. The framework was validated on four critical components, needles, hooks, needle guides, and thread tensioners, using operational data from a textile plant (November 2024–January 2026). Plant-wide Mean Time Between Failures increased by 38% (15–21 to 24–28 h), while Mean Time To Repair decreased by 15% (5.31 to 4.6 h). These improvements yielded 5.5% lower maintenance costs, 9% less fabric waste, and reduced cost per operating hour from $25 to $23.5. The prescriptive module transformed imperfect predictions into robust decisions by evaluating interventions against production constraints, spare parts availability, and risk criteria. Beyond quantitative gains, the framework enabled sustainable practices including data-driven spare parts policies and condition-based inspections. This work demonstrates that integrating prediction with prescription effectively overcomes structural maintenance challenges in textile manufacturing, providing a replicable methodology for broader industrial adoption. Full article

Traffic congestion in urban areas poses substantial challenges to transportation management, urban planning, and environmental sustainability. This study introduces an explainable artificial intelligence (XAI) framework for predicting traffic congestion in Casablanca, Morocco, by integrating gradient boosting models with lightweight XAI techniques that are suitable for edge deployment. Employing SUMO-simulated traffic data comprising 30,000 data points across 30 scenarios, we implemented a GradientBoostingRegressor (scikit-learn) enhanced with native feature importance analysis, permutation importance, and partial dependence plots, achieving $R^2=0.9939$, MAE = 0.015, and RMSE = 0.019. The XAI analysis reveals that lag features (32.0%), temporal patterns (35.0%), and infrastructure features (15.0%) are the primary contributors to congestion prediction, with culturally relevant factors, such as Friday prayers, accounting for 8.7% of the total feature importance. The model was deployed through a knowledge-distillation TinyML pipeline, achieving 31× compression (2.4 MB → 76 KB) on ESP32 microcontrollers with 2.1 ms inference latency and a 667× reduction in per-inference energy consumption compared to cloud-based deployment. This lightweight XAI approach directly addresses the gap between interpretability requirements and edge deployment constraints, facilitating sustainable intelligent transportation systems in developing countries with limited infrastructure and energy resources. The proposed framework is transferable to other rapidly urbanizing cities in the Global South, offering a replicable template for data-driven interpretable traffic management that can directly inform infrastructure investment prioritization, adaptive signal-control policy design, and culturally aware urban mobility planning strategies. Full article

Equity in gifted education remains a persistent international challenge, particularly regarding the “excellence gap” in advanced achievement and long-term attainment. This study investigates the supports and barriers shaping the talent development of socio-economically disadvantaged gifted students in Taiwan. Using a retrospective narrative inquiry, we analyzed the life stories of 25 alumni from the “Bright Minds Award Program,” a long-term initiative providing financial aid, mentorship, and enrichment opportunities for high-ability learners from low-income households. Findings indicate that participants often displayed early academic promise, yet their developmental trajectories were continuously negotiated under structural constraints (limited material and cultural resources, restricted access to domain-specific cultivation, and opportunity gaps across educational transitions) and the psychosocial burden of poverty (shame, stigma management, and identity strain). Drawing on the Actiotope Model of Giftedness, we identify how exogenous educational capital (e.g., scholarships, information brokerage, mentoring networks) and endogenous learning capital (e.g., resilience, self-regulation, goal persistence) interact to stabilize—or destabilize—developmental pathways. A novel contribution is the emergence of “Acting Middle Class” as a coping mechanism through which participants navigated social stigma and the hidden curriculum of elite educational settings. We argue that effective intervention requires not only resource provision but sustained “educational scaffolding” that is psychologically safe and institutionally stigma-sensitive. Implications are discussed for talent development research, school practice, and equity-oriented policy designs aimed at preventing talent attrition and promoting developmental justice. Full article

Electric two-wheelers (E2Ws) are promoted as lower-emission options in emerging economies. Their long-term cost competitiveness depends mainly on battery durability and how batteries are managed at the end of their life. This research examines Li-ion and nickel-cobalt-manganese (NCM)-type batteries versus the previously common lead-acid batteries in these markets. The study uses a 12-year total cost of ownership (TCO) framework that includes battery degradation, estimated first-life duration, and alternative lifecycle pathways. It covers three sensitivity analysis cases: conservative, base case, and optimistic. Three scenarios are evaluated: (1) no lifecycle management, (2) refurbishment for first-life extension, and (3) integrated lifecycle management with refurbishment, second-life utilisation, and recycling. Results show that managing the battery lifecycle can reduce TCO. The amount of reduction depends on first-life duration, ownership horizon, refurbishment cost, downstream residual value, and use intensity. The greatest TCO gains are found in battery categories with short first-life duration, allowing substantial residual value recovery during ownership. Batteries with first-life durations of 12 years or more provide smaller benefits. These findings support optimising lifecycle pathways for maximum residual value. Improved TCO performance, along with supportive infrastructure, policies, and market development, is critical for broader E2W adoption. Full article

Backgrounds: Ports are critical nodes in global logistics and supply chains, yet their operations generate substantial environmental and social externalities. Existing evaluation frameworks have limited capability to address uncertainty, ambiguity, and expert hesitation. Moreover, prior studies frequently examine isolated performance dimensions, overlooking the interconnected roles of port authorities as landlords, regulators, operators, and community stakeholders. Methods: This study proposes an integrated evaluation framework using the Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) to assess environmentally sustainable port performance under uncertain decision environments. By incorporating membership, non-membership, and hesitation degrees, the approach improves the robustness of expert judgments and applies a dual consistency check to reduce bias. Empirical data are obtained from Malaysian port management professionals, enabling the development of a comprehensive framework that includes four main functions and twenty sub-functions. Results: Results reveal that the landlord function holds the highest priority, while operational sustainability dimensions receive the greatest emphasis, with a global weight of approximately 0.105. In contrast, community engagement and social initiatives are assigned relatively lower importance. Conclusions: The IF-AHP framework offers an uncertainty-aware tool that prioritizes sustainability functions, especially environmental mitigation and energy efficiency, enabling informed resource allocation, strategic planning, and policy formulation for balanced, sustainable port overall performance. Full article

The degradation of forest landscapes in West Africa, particularly in Togo, threatens ecological and socio-economic sustainability. This study analyzes the spatio-temporal dynamics of land use in the central plains of Togo between 1991 and 2022, and projects its evolution for 2030 and 2050 to guide restoration strategies. The methodology integrates the interpretation of Landsat images (1991, 2005, 2022) and the analysis of indicators, including conversion rates and the anthropization index. Prospective modeling (Markov chains and neural networks) follows a trend scenario. The results reveal a sharp decline in natural forest formations: dense semi-deciduous and dense dry forests (−50.55%) and woodlands (−62.06%), converted mainly to cropland, plantations, and built-up areas. Shrub/tree savannas, the dominant class, represent a transitional stage resulting from forest degradation. The average annual deforestation rate is 0.75%. The ecological disturbance index increased from 0.24 (1991) to 0.45 (2005), and then to 0.56 (2022), reflecting increased human impact and fragmentation. Projections indicate that these trends will continue, highlighting the growing vulnerability of ecosystems and the need to integrate this dynamic into sustainable management and restoration policies. Full article

Background: Mucopolysaccharidosis type II (MPS II; Hunter syndrome) is a rare X-linked lysosomal storage disorder caused by pathogenic variants in the iduronate-2-sulfatase gene, leading to progressive multisystem involvement. Although international management guidelines exist, challenges in their implementation across different healthcare systems remain insufficiently addressed. This study aimed to establish a national expert consensus in Türkiye on the treatment and management of MPS II, aligning local practice with international standards. Methods: A modified Delphi methodology was conducted using two rounds of online surveys supported by three steering committee meetings. The process involved 10 experienced clinicians and a scientific committee of six professors. Based on international guidelines and country-specific clinical challenges, 72 consensus statements and 84 exploratory questions were developed. Statements achieving ≥ 80% agreement were accepted as consensus. Results: Consensus supported initiating enzyme replacement therapy (ERT) in both severe and attenuated MPS II, guided by functional and cognitive status. Severe cognitive impairment was not considered an exclusion criterion for ERT, given its somatic benefits. Experts agreed on continuing ERT into adulthood with individualized discontinuation decisions. Routine evaluations every 6–12 months, including respiratory, cardiac, and neurocognitive assessments, were recommended. Additional consensus areas included individualized premedication strategies, structured transition to adult care, selective home infusion, annual patient-reported outcome assessments, and the establishment of a national MPS II registry. Hematopoietic stem cell transplantation was not endorsed. Conclusions: This Delphi study demonstrates strong expert consensus on MPS II management in Türkiye, providing a practical framework to guide clinical practice, support alignment with international recommendations, and inform future policy and research priorities. Full article