Search Results (461)

Electric vehicle (EV) charging presents both a challenge and an opportunity for modern power systems, particularly in workplace environments with grid constraints and dynamic energy pricing. This study presents a real-life implementation and experimental validation of a hierarchical distributed control system for smart EV charging. The proposed architecture combines upper-level receding horizon optimization with lower-level priority-based dispatch, enabling cost-efficient energy allocation and fair distribution among EVs. The system was deployed at the Risø campus of the Technical University of Denmark (DTU) and tested over two days under realistic operational conditions, including heterogeneous EV behavior and limited grid capacity. The control system demonstrated autonomous operation, responsiveness to price signals, and effective coordination between control layers. High energy delivery rates were achieved, nearly 100% on the first test day and close to 90% on the second, despite operating under a constrained energy budget. The study also documents practical challenges encountered during deployment, such as charger communication faults and EV-side issues, and proposes adaptation strategies. These results confirm the feasibility of distributed smart charging in real-world conditions and provide actionable insights for future implementations. Full article

In this paper, we tackle a bi-objective optimization problem in which we aim to maximize the portfolio diversification and, at the same time, minimize the portfolio volatility, where the ESG (Environmental, Social, and Governance) information is incorporated. More specifically, we extend the standard portfolio volatility framework based on the financial aspects to a new paradigm where the sustainable credits are taken into account. In the portfolio’s construction, we consider the classical constraints concerning budget and box requirements. To deal with these new asset allocation models, in this paper, we develop an improved Multi-Objective Particle Swarm Optimizer (MOPSO) embedded with ad hoc repair and projection operators to satisfy the constraints. Moreover, we implement a deep learning architecture to improve the quality of estimating the portfolio diversification objective. Finally, we conduct empirical tests on datasets from three different countries’ markets to illustrate the effectiveness of the proposed strategies, accounting for various levels of ESG volatility. Full article

Type 2 diabetes (T2D) constitutes a major health problem, reaching alarming rates over the last decades, especially due to contemporary lifestyle and associated obesogenic environments, as well as the aging population. Diabetes not only causes social consequences but also leads to increasing healthcare costs, posing a significant challenge for the health system. This paper applies a five-step approach for estimating T2D-related costs in Greece. The approach initially estimates the T2D-related ICD10 prevalence and the target population. Next it applies the appropriate therapeutic protocols to identify the most appropriate treatments. Subsequently, it calculates the total cost of medical treatments for each target population, based on the distribution of patients between the different treatments and treatment lines. Finally, based on the diagnostic and treatment protocols, it calculates the annual direct costs associated with the cost categories. Using the estimated future population of the country, the proposed methodology can also project the budget required, under certain conditions, to deal with T2D. The analysis estimated that T2D-related costs in 2021 under rational use of resources were EUR 1,397,871,172.55 billion and EUR 1,512,934,947.63 billion projected in the year 2030 considering the aging effect, per cost category, and in total, presenting an increase of approximately 115 million euros in 2030 compared to 2021. The term “rational use of resources” in this study refers to the use of internationally recognized, evidence-based diagnostic and therapeutic protocols, as adopted by the Greek Ministry of Health. This scenario represents an idealized standard of care rather than actual real-world adherence and is used to estimate the potential resource needs under optimal medical practice conditions. An inflation rate of 4.2% was applied to costs between 2021 and 2030. The analysis showed that the highest percentage (39%) of the total T2D-related healthcare expenditures is associated with complications that occur in T2D patients. Despite a comparatively modest prevalence of T2D in Greece relative to other European and Mediterranean countries, the economic burden associated with its management remains high. The aging of the population will lead to an increase in the total cost of T2D. The applied methodology of estimating budgets by aggregating categories of expenses under a specific disease (ICD10), instead of dividing budgets into categories of expenses, can successfully lead to the optimization and rationalization of expenses according to actual needs. The findings underline the significant economic burden of T2D in Greece, particularly due to complications and population aging. These results emphasize the urgent need for health policy strategies focusing on prevention, early intervention, and the efficient allocation of healthcare resources. The methodology applied can serve as a decision-making tool for forecasting healthcare budgets and optimizing expenditures under different population and treatment scenarios. Full article

The fragile karst ecosystem in Southwest China faces severe water scarcity. Since 2000, large-scale ecological restoration programs (e.g., the “Grain for Green” Program) have substantially increased vegetation coverage. Concurrently, climate change has manifested as a distinct warming trend and heightened drought risk in recent decades. Therefore, understanding the synergistic and competing effects of climate change and vegetation restoration on regional evapotranspiration (ET) is critical for projecting water budgets and ensuring the sustainability of ecosystems and water resources within this vital ecological barrier region. This study employs a dual-scenario PT-JPL model (simulating natural vegetation dynamics versus constant coverage) integrated with Sen + MK trend analysis to quantify the spatiotemporal patterns of ET and its components—canopy transpiration (ETc), interception evaporation (ETi), and soil evaporation (ETs)—in Southwest China’s karst region (2000–2018). Furthermore, multiple regression analysis and SEM were utilized to investigate the driving mechanisms of vegetation and climatic factors (temperature, precipitation, radiation, and relative humidity) on changes in ET and its components. The key results demonstrate the following: (1) Vegetation restoration exerted a net positive effect on total ET (+0.44 mm/a) through enhanced ETi (+0.22 mm/a) and ETs (+0.37 mm/a), despite reducing ETc (−0.08 mm/a), revealing trade-offs in water allocation. (2) Radiation dominated ET variability (66.45% of the area exhibiting >50% contribution), while temperature exhibited the most extensive spatial dominance (44.02% of the region), and relative humidity exhibited drought-mediated dual effects (promoting ETi while suppressing ETc). (3) Precipitation exhibited minimal direct influence. Vegetation restoration and climate change collectively drive ET dynamics, with ETc declines indicating potential water stress. These findings elucidate the synergistic regulation of vegetation restoration and climate change on karst ecohydrology, providing critical insights for water resource management in fragile ecosystems globally. Full article

To overcome the obstacles of maintaining covert transmissions in wireless networks employing collaborative wardens, we develop a reinforcement learning framework that jointly optimizes cooperative jamming strategies and relay selection mechanisms. The study focuses on a multi-relay-assisted two-hop network, where potential relays dynamically act as information relays or cooperative jammers to enhance covertness. A reinforcement learning-based relay selection scheme (RLRS) is employed to dynamically select optimal relays for signal forwarding and jamming; the framework simultaneously maximizes covert throughput and guarantees warden detection failure probability, subject to rigorous power budgets. Numerical simulations reveal that the developed reinforcement learning approach outperforms conventional random relay selection (RRS) across multiple performance metrics, achieving (i) higher peak covert transmission rates, (ii) lower outage probabilities, and (iii) superior adaptability to dynamic network parameters including relay density, power allocation variations, and additive white Gaussian noise (AWGN) fluctuations. These findings validate the effectiveness of reinforcement learning in optimizing relay and jammer selection for secure covert communications under colluding warden scenarios. Full article

Cybersecurity is increasingly recognized as a key enabler of Sustainable Development Goals (SDGs) and especially SDG 3 (Good Health and Well-being) as healthcare systems become more digitized. This study prioritizes cybersecurity control families from the NIST 800-53r5 framework using a structured framework combining the Analytic Hierarchy Process (AHP) and the Impact–Feasibility Matrix. From the impact–feasibility perspective, expert judgment reveals that while impact is the primary driver in selecting controls, feasibility—particularly budget and cost constraints—plays a decisive role in real-world implementation. A group of fifteen experts, including cybersecurity officers, health IT professionals, and public health advisors, has participated in structured surveys as per the methodological framework of this paper. Financial and budgetary limitations emerged as the top feasibility barrier, often determining whether high-impact controls are deployed or delayed. This underscores the need for strategic investments and phased implementation approaches, particularly in resource-constrained health systems. The results provide a practical roadmap for policymakers and healthcare administrators to allocate cybersecurity resources effectively, balancing technical necessity with economic feasibility to support resilient digital health infrastructures. Full article

Vaccination has resulted in substantial public health benefits for human populations worldwide since it was first introduced more than a century ago. This article presents an overview of the history of vaccine development, its implementation, and price setting, the latter mainly from a developed world perspective. It considers potential issues and challenges. Over time, vaccine development and production has evolved to a market-driven approach, conducted largely by private commercial entities. The complex processes of identifying potential vaccine targets and developing and producing vaccines at scale have now become more efficient. However, vaccine pricing is an emerging concern. The elements that maximize the overall health benefit of vaccination include high volume, high coverage, and rapid initial implementation to achieve the high coverage with the vaccine as quickly as possible. It therefore requires substantial initial investment. Consequently, the price set for the vaccine should be reasonable to avoid limiting the coverage given the available budget. Suboptimal coverage leads to suboptimal benefit if herd protection is not fully achieved. This may disappoint health authorities and may result in program discontinuation. Conventional cost-effectiveness analysis is therefore not ideally suited to vaccine price setting, as it is based on the concept of ‘more for more’, i.e., higher health gain achieved at a higher reimbursement cost that does not account for limited budgets. Constrained optimization (CO) combines value assessment with constrained budget allocation into one analysis method and may therefore be the better option for vaccine pricing. Full article

Educational institutions face complex challenges when allocating limited teaching resources to specialized seminars, where budget, capacity, and balanced disciplinary representation must all be satisfied simultaneously. We address this for the first time in the educational domain by formulating the teacher seminar selection problem as a multi-dimensional knapsack variant with category-specific benefit multipliers. To solve it, we design a constraint-aware genetic algorithm that incorporates smart initialization, category-sensitive operators, adaptive penalties, and targeted repair mechanisms. In experiments on a realistic dataset representing multiple academic categories, our method achieved an 11.5% improvement in solution quality compared to the best constraint-aware greedy baseline while maintaining perfect constraint satisfaction (100% feasibility) vs. 0–30% for baseline methods. Statistical tests confirmed significant and practically meaningful advantages. For comprehensive benchmarking, we also implemented binary particle swarm optimization (PSO) and Tabu Search (TS) solvers with standard parameterizations. While PSO consistently produced feasible solutions with high budget utilization, its optimization quality was substantially lower than that of the GA. Notably, Tabu Search achieved the highest performance, with a mean fitness of 1557.3 compared to GA’s 1533.2, demonstrating that memory-based local search can be highly competitive for this problem structure. These findings show that metaheuristic approaches, particularly those integrating constraint-awareness into evolutionary or memory-based search, provide effective, scalable decision-support frameworks for complex, multi-constraint educational resource allocation. Full article

Optimizing pavement maintenance and rehabilitation (M&R) strategies is essential, especially in developing countries with limited budgets. This study presents an integrated framework combining a deterioration prediction model and a genetic algorithm (GA)-based optimization model to plan cost-effective M&R strategies for flexible pavements, including asphalt concrete (AC) and double bituminous surface treatment (DBST). The GA schedules multi-year interventions by accounting for varied deterioration rates and budget constraints to maximize pavement performance. The optimization process involves generating a population of candidate solutions representing a set of selected road sections for maintenance, followed by fitness evaluation and solution evolution. A mixed Markov hazard (MMH) model is used to model uncertainty in pavement deterioration, simulating condition transitions influenced by pavement bearing capacity, traffic load, and environmental factors. The MMH model employs an exponential hazard function and Bayesian inference via Markov Chain Monte Carlo (MCMC) to estimate deterioration rates and life expectancies. A case study on Cambodia’s road network evaluates six budget scenarios (USD 12–27 million) over a 10-year period, identifying the USD 18 million budget as the most effective. The framework enables road agencies to access maintenance strategies under various financial and performance conditions, supporting data-driven, sustainable infrastructure management and optimal fund allocation. Full article

Due to the subjective nature of sensitive area radius, the effectiveness of current privacy protection strategies is often quite poor. To address this issue, a sensitive area radius design method of smart terminal is proposed by considering the total privacy budget and tolerance degree of adversary inference capabilities. With the increase in the tolerance degree of adversary’s inference capabilities, the sensitive area radius decreases. Based on the division results of the sensitive area, the privacy budgets of smart terminals are allocated separately. Then, a location data perturbation strategy is designed based on the allocated privacy budget and location encoding. Compared with the existing privacy protection strategies, the proposed method is more reasonable, which can improve the effectiveness of location privacy protection strategy. Finally, experimental results based on the real-world dataset show that the proposed algorithm can enhance data utility by 40%. Full article

Due to their vast number and heterogeneity, local public administrations can act as entry points (or attack surfaces) for adversaries targeting national infrastructure. The individual vulnerabilities of these entities function as entry points that can be exploited to compromise higher-level government assets. This study presents a nationwide risk analysis of the exposed perimeter of 7000 municipalities, achieved through the massive collection of 93 technological and contextual variables over three consecutive years and the application of supervised machine learning algorithms. The findings of this study demonstrate that geographical factors are a key predictor of external perimeter cyber risk, suggesting that supra-local entities providing unified, shared security services are better positioned in terms of risk exposure and therefore more resilient. Furthermore, the analysis confirms, contrary to conventional wisdom, that IT budget allocation lacks a significant statistical correlation with external perimeter risk mitigation. It is concluded that large-scale data collection frameworks, enhanced by Artificial Intelligence, provide policymakers with an objective and transparent tool to optimize cybersecurity investments and protection strategies. Full article

This case study evaluates the efficiency of STEM-based sustainability initiatives at the University of Brasilia (UnB) using a Bootstrap Data Envelopment Analysis (DEA) approach. Twenty projects were analyzed based on input variables—team size, budget, and workload—and output variables—number of beneficiaries and published papers. The results indicate higher efficiency in the Mathematics and Civil Engineering departments, while Energy Engineering showed the lowest performance. A strong correlation (r = 0.78) was observed between budget and publication volume, but no significant relationship was found between the inputs and number of beneficiaries. SDG 4 (Quality Education) was the most frequently addressed, whereas SDG 16 (Peace, Justice, and Strong Institutions) and SDG 14 (Life Below Water) received less attention. The study identifies key areas for improvement, emphasizing the need for more balanced resource allocation and contextual awareness over sustainability priorities. It also offers an adaptive and replicable framework to other faculties or institutions seeking to optimize sustainability efforts through the lens of resource allocation optimization. Full article

Urban flood damage is increasing globally, particularly in major cities. Factors contributing to flood risk include urban environmental changes, such as watershed development and precipitation variations caused by climate change. Rapid urbanization and weather anomalies further complicate flood management and damage mitigation. Additionally, detailed analyses at small spatial units (e.g., roads, buildings) remain insufficient. Hence, urban flood analysis considering such spatial variations is required. This study analyzed flood risk in Ulsan, Korea, under a severe flood scenario. Land cover changes from the 1980s to 2010s were examined in 10-year intervals, along with the frequency of heavy rainfall and high river water levels that trigger severe floods. Flood risk was structured as a matrix of likelihood and impact. The results revealed that land cover changes, influenced by development policies or regulations, had a minimal impact on urban flood risk, which is likely because effective drainage systems and stringent urban planning regulations mitigated their effects. However, the frequency and intensity of extreme precipitation events had a substantial effect. These findings were validated using a comparative analysis of an inundation damage trace map and flood range simulated by a physical model. The 10 m grid resolution and time-series likelihood-and-impact framework used in this study can inform budget allocation, resource mobilization, disaster prevention planning, and decision-making during disaster response efforts in major cities. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article

Quadratic programming (QP) formulations are widely used in optimal investment portfolio selection, a central problem in financial decision-making. In practice, asset allocation decisions operate at two interconnected levels: the strategic level, which allocates the budget across major asset classes, and the tactical level, which distributes the allocation within each class to individual securities or instruments. This study evaluates the Frank–Wolfe (FW) algorithm as a computationally alternative to a QP formulation implemented in CVXPY and solved using OSQP (CVXPY–OSQP solver) for tactical investment portfolio optimization. By iteratively solving a linear approximation of the convex objective function, FW offers a distinct approach to portfolio construction. A comparative analysis was conducted using a tactical portfolio model with a small number of stock assets, assessing solution similarity, computational running time, and memory usage. The results demonstrate a clear trade-off between the two methods. While FW can produce portfolio weights closely matching those of the CVXPY–OSQP solver at lower and feasible target returns, its solutions differ at higher returns near the limits of the feasible set. However, FW consistently achieved shorter execution times and lower memory consumption. This study quantifies the trade-offs between accuracy and efficiency and identifies opportunities to improve FW’s accuracy through adaptive iteration strategies under more challenging optimization conditions. Full article