Search Results (4,879)

Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), weighted-sum scalarization, and ε-constraint methods. Performance assessment utilized three Pareto front quality metrics: Inverted Generational Distance (IGD) for convergence quality, hypervolume (HV) for objective-space coverage, and spacing for solution distribution uniformity. The algorithms were tested on PV-ESS design problems in three developing economies (Nigeria, South Africa, India) under identical problem formulations and computational resources. NSGA-II achieved superior performance across all metrics in all three case studies. For convergence quality, NSGA-II attained a mean IGD of 0.0083, outperforming MOPSO by 29%, ε-constraint by 64%, and weighted-sum by 131%. For objective-space coverage, NSGA-II achieved a mean HV of 0. 700, representing 10–16% better coverage than other methods. For solution distribution, NSGA-II showed a mean spacing of 0.076, indicating 30–117% more uniform Pareto fronts. Computational efficiency analysis revealed that NSGA-II’s runtime is between 5.5 and 7.8 h per case, providing better quality–time ratios compared to ε-constraint methods (which are 18 times slower), while avoiding MOPSO’s premature convergence. Statistical validation confirmed NSGA-II’s superiority, with p < 0.01 across all quality metrics. These results establish NSGA-II as the best algorithm for lifecycle-aware PV-ESS optimization, offering quantitative, evidence-based guidance for practitioners selecting optimization tools for renewable energy system design. The demonstrated performance leads to $ 45,000–$ 60,000 lifecycle cost savings per MW/MWh of system capacity through improved Pareto front identification. Full article

Background/Objectives: Spondylarthritis (SA) is characterized by high clinical heterogeneity, often resulting in a discrepancy between systemic inflammation and patient-reported symptoms. While hematologic indices are emerging as cost-effective biomarkers, their role in phenotypic differentiation remains unclear. We investigated the utility of traditional inflammatory markers, including the novel fibrinogen-to-platelet ratio (FPR), in differentiating SA subtypes and predicting patient-reported disease activity. Methods: This cross-sectional study included 64 patients with spondylarthritis: axial SA (n = 32), peripheral SA (n = 8), and psoriatic SA (n = 24). Clinical assessments included the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Functional Index (BASFI). Systemic inflammation was evaluated via C-reactive protein (CRP), fibrinogen, and calculated ratios (NLR, PLR, MLR, and FPR). Principal Component Analysis (PCA) was employed to map the inflammatory architecture, while Receiver Operating Characteristic (ROC) curves evaluated the predictive power for high disease activity (BASDAI ≥ 4). Results: Significant phenotypic differences were observed with the FPR demonstrating superior discriminative capacity (p = 0.003). Patients with peripheral SA exhibited significantly higher FPR (median 1.88) compared to axial (1.33) and psoriatic (1.32) subtypes, and the dedicated ROC analysis for phenotypic discrimination yielded an AUC of 0.866 (95% CI: 0.745–0.987) (1.36, p = 0.039). HLA-B27 prevalence was low overall (31.3%) and in psoriatic SA (4.2%, p = 0.012). Correlation analysis revealed strong associations between BASDAI and BASFI (ρ = 0.79), NLR and MLR (ρ = 0.78), and CRP and fibrinogen (ρ = 0.66). PCA identified two independent inflammatory dimensions explaining 74.8% of variance: neutrophil-hypercoagulable axis (41.4%, driven by NLR, PLR, and MLR), and an acute-phase/fibrinogen axis (33.4%, driven by CRP, fibrinogen, and FPR). Notably, FPR clustered with acute-phase reactants rather than leukocyte-derived ratios, supporting its role as a marker of systemic inflammatory burden. Although fibrinogen is involved in the coagulation cascade, the absence of direct coagulation markers precludes definitive characterization of this component as hypercoagulable. ROC analysis revealed that fibrinogen showed the highest discriminative ability for disease activity (BASDAI ≥ 4), with an AUC of 0.690 (95% CI: 0.519–0.861), followed by NLR (0.621), MLR (0.592), and FPR (0.583). However, overall discriminative performance remained modest, with most 95% confidence intervals crossing 0.5. Conclusions: FPR emerges as a robust phenotypic biomarker capable of discriminating against SA subtypes, particularly identifying peripheral SA with high accuracy and excellent negative predictive value. In contrast, its ability to predict patient-reported disease activity remains limited, reinforcing the distinction between trait and state biomarkers. Exploratory PCA revealed that FPR clusters with acute-phase reactants rather than leukocyte-derived ratios, supporting its biological link to systemic inflammatory burden. These findings advocate for a dual-purpose biomarker approach in SA: FPR for phenotypic stratification and fibrinogen for activity assessment, while clinical indices remain indispensable for symptom monitoring. Validation in larger, prospective cohorts is warranted. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Background and Objectives: The prioritization of intensive care unit (ICU) admission following surgery for cancer is controversial. There is an urgent need to develop an appropriate clinical predictive model to aid in making ICU admission decisions after surgery. Materials and Methods: Four model strategies were used to build post−operative ICU admission predictive models: SVM, Catboost, ANN, and KNN. Internal verification was used for model evaluation at a ratio of 7:3. The area under the curve (AUC) value, calibration plots, and decision curve analysis were employed to assess the performance and clinical usefulness of the model. Results: The ICU group of patients with cancer who underwent surgery showed better prognosis for disease−free survival (DFS, p = 0.0008) and overall survival (OS, p < 0.0001). Cox multivariate analyses validated that lower baseline RBC, LDH, and CRP; higher baseline ALB, LCR, and lower post−operative LDH; higher post−operative HCT and ApoA−I; and higher fluctuating MCH independently predicted better DFS and OS (all p < 0.05). The AUC of the Catboost model was superior to that of the other models in the training cohort and internal validation cohort. Calibration plot and decision curve analysis indicated that the Catboost model possessed the best performance, with higher clinical utility, compared with other models. Conclusions: ICU admission after surgery was associated with superior survival in patients with cancer. The cost−effective Catboost model has promising clinical application but requires further clinical evaluation. Unravelling the cellular and molecular foundation of ICU admission might enable the development of more practical life−support strategies. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

This study presents an investigation of the factors (driven by coupled multi-factor corrosion mechanisms) which contribute to the degradation of the spirally wound armored steel wires used to protect core-structured, unarmored optical-fiber submarine cables. The influences of the physical properties of deep-sea sediments on the durability of unarmored cables, as well as the impact of ionizing radiation on optical fibers, are also assessed. The objective of this paper is to establish a scientific basis for cable longevity by integrating theoretical insights with empirical evidence. Although the steel utilized in armored cables is cost-effective and durable, it remains vulnerable to corrosion. Since the inaugural practical deployment of submarine communication cables between the UK and France in the 1850s, only a small number of studies worldwide have examined the corrosion and durability of cable armor. There is also limited literature examining the physical characteristics of the deep-sea surface sediments that directly affect the service life of the cables’ mechanically fragile polyethylene sheathing. An in-depth analysis of the cable damage and environmental conditions observed during maintenance operations provides valuable insights into the key environmental factors that influence armor corrosion and cable longevity. This research aims to guide future design and support strategies to improve the sustainability and durability of cable systems in marine environments. Full article

The formulation of construction material unit price policies in areas with limited accessibility is a critical issue in ensuring effective and accountable government infrastructure planning. In such regions, construction costs are often highly volatile and difficult to predict, primarily due to transportation constraints, logistical inefficiencies, and geographical challenges. These conditions frequently result in budget overruns and inconsistencies between planned and actual project expenditures. Therefore, a rational and context-sensitive policy framework is required to support accurate cost estimation and sustainable infrastructure development. This study aims to develop a policy-oriented model for determining construction material unit prices in areas with limited accessibility based on influencing factors. A quantitative research approach was employed through a questionnaire survey involving 235 respondents, consisting of contractors, government representatives, consultants, and academics with experience in infrastructure development in remote or access-constrained regions. The collected data were analysed using Partial Least Squares–Structural Equation Modelling (PLS-SEM) to identify and validate the dominant factors affecting construction material unit prices. The results of the PLS-SEM analysis identified 33 influential factors that significantly contribute to the unpredictability of construction material unit prices in limited-accessibility areas. These factors encompass logistical costs, material price dynamics, government policies, geographical conditions, and local cultural aspects. The proposed model demonstrates that government policy plays a central role, both directly and indirectly through local cultural mediation, in influencing project performance and cost reliability. The findings of this study provide a structured and empirically grounded framework that can be utilized by local governments as a policy reference in establishing construction material unit prices for remote and access-constrained areas. By incorporating the identified influencing factors into unit price formulation, cost prediction accuracy can be improved, thereby supporting more effective budget allocation and ensuring that infrastructure quality is maintained without compromise due to unanticipated cost escalation. These improvements contribute to more sustainable infrastructure development by enhancing resource efficiency, minimizing cost overruns, and supporting equitable infrastructure provision in remote areas. Full article

Background/Objectives: Substance use disorder (SUD) in pregnancy is an increasingly complex public health challenge that is known to worsen maternal and neonatal outcomes. Rates of polysubstance use are steadily rising. The objective of this study was to assess the impact of co-occurring opioid and stimulant use disorder on adverse pregnancy outcomes (APOs) among inpatient pregnancy hospitalizations. Methods: We conducted a cross-sectional analysis of inpatient pregnancy hospitalizations for delivery admissions from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from 2016 to 2020. ICD-10 codes were used to identify patients with opioid and stimulant use disorder and with APOs. APO was defined as a composite to include hypertensive disorders of pregnancy, antepartum hemorrhage, postpartum hemorrhage, preterm birth, and fetal growth restriction. Multivariable logistic regression analyses were undertaken to predict the likelihood of APOs among pregnancy hospitalizations with opioid use, stimulant use, or co-occurring (opioid and stimulant) use disorders. Sociodemographic covariates, including age, race and/or ethnicity, insurance payor type, and income level, were accounted for. Results: From 2016 to 2020, 32,602 delivery hospitalizations complicated by stimulant or opioid use disorder were identified. Of these admissions, 21,049 (64.6%) had opioid use disorder, 9472 (29.1%) had stimulant use disorder, and 2081 (6.4%) had co-occurring opioid and stimulant use disorder. In the entire cohort, the prevalence of APOs was significantly highest among pregnancy delivery hospitalizations with co-occurring opioid use and stimulant use disorder (1136/2081—54.6%, p < 0.001), as compared with opioid use disorder (8923/21,049—42.4%) or stimulant use disorder alone (4654/9472—49.1%). Rates of APOs increased in subsequent years for all cohort groups. Adjusting for relevant sociodemographic covariates, co-occurring opioid and stimulant use disorder was an independent predictor of APO (aOR 3.65; CI 95%, 3.34–3.99). In comparison, opioid use disorder and stimulant use disorder were independent predictors of APOs with a less strong correlation, aOR 2.22 (CI 95%, 2.16–2.29) and aOR 2.89 (CI 95%, 2.77–3.02), respectively. Conclusions: Patients with co-occurring opioid and stimulant use disorder have the highest exposure risk for APOs, acting as an independent predictor for APOs when adjusting for sociodemographic covariates. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

As escalating energy prices challenge global efforts toward sustainable development, the intricate relationship between energy costs and industrial transformation stands at the forefront of economic and environmental policy debates. Against this backdrop, this study explores the impact of energy prices on sustainable industrialization in 32 OECD countries for the period of 2000–2021 by employing linear and non-linear models. Our findings indicate that energy prices are negatively associated with sustainable industrialization. Meanwhile, trade openness and economic development promote sustainable industrialization. Heterogeneity analysis indicates that developed and more open economies are better at utilizing and directing the resources towards industrial sustainability. Our findings further suggest that pursuing sustainable industrialization depends on a balanced policy strategy that incorporates energy prices in industrial and environmental settings. Policymakers should also promote the shift to renewable energy, use trade liberalization to support sustainable technology adoption, and redirect economic growth into innovation-based and sustainable industries. By addressing the challenges of rising energy prices while focusing on the favorable effects of trade and income, OECD countries can move toward a more stable and sustainable industrialization structure. Full article

Sewage sludge management remains a critical challenge in Greece, where increasing regulatory pressure, environmental constraints, and limited stakeholder participation complicate regional decision-making. In particular, the revision of regional Waste Management Plans requires decision-support approaches that are both technically robust and socially legitimate. This study develops and applies a participatory, data-driven multi-criteria decision analysis framework to evaluate sustainable sewage sludge management strategies in the Region of Eastern Macedonia and Thrace. The framework combines structured stakeholder participation with quantitative performance assessment, enabling transparent, reproducible, and systematic comparison of alternative sewage sludge management options. Four realistic sludge management alternatives—composting fr agriculture, forestry use, land restoration, and thermal drying with energy recovery were assessed against fifteen economic, environmental, and social sub-criteria. Data were collected through structured questionnaires administered to forty-four representatives from five stakeholder groups: utilities (water and sewerage service providers), local authorities, scientists/experts, end-users, and citizens. Group preferences were aggregated using equal group weighting to ensure balanced representation. The results show that environmental and economic criteria outweigh social aspects. The highest mean weights were assigned to compliance with environmental requirements for products derived from the disposal method (0.105) and compliance with stricter national environmental legislation (0.104), followed by energy intensity (0.097), installation cost (0.065), and operation and maintenance (O&M) cost (0.061). Overall rankings identified composting and thermal drying as the most preferred options, followed by land restoration and forestry use; sensitivity analysis (±10% variation in sub-criterion weights) confirmed ranking stability. The proposed framework enhances decision transparency by embedding measurable criteria and stakeholder inputs within a structured analytical process. From a policy perspective, it addresses participation gaps in Greek waste planning and offers a transferable decision-support tool for future regional planning. Further extensions may include integration with life cycle assessment and cost–benefit analysis to support adaptive updates under circular economy objectives. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency. Full article

This study examines the relationship between house prices and the factors driving their growth during the transition from a long-standing currency board regime to Eurozone membership. The main objective is to identify and quantify the key factors explaining the variation in house price growth in Bulgaria under conditions of prolonged currency convergence. The study applies a set of econometric techniques, including stationarity tests (ADF and KPSS), diagnostic checks for normality, serial correlation and heteroscedasticity, and robustness checks. The study is based on 40 quarterly observations covering the period 2015Q4–2025Q3 and 48 selected predictors of the General house price index. The final ARIMAX(0,2,1) model is estimated using second-differenced data. The model includes a first-order moving average component and three exogenous regressors: the owner-occupiers’ housing expenditures, the actual rentals for housing in Bulgaria and the homeowners’ utility expenses. The model explains 87% of the variation in house price acceleration, with a comparatively low mean squared error. The diagnostic analysis confirms model adequacy. The three exogenous regressors are statistically significant at the 1% level with strong and stable effects on house price dynamics. No statistically significant relationship is found for the set of traditional macroeconomic, demographic, financial, and sectoral factors. The results show that during Bulgaria’s transition from a currency board to the Eurozone, the sustained house price growth was driven by country-specific factors. The three statistically significant determinants of the house price acceleration in Bulgaria reflect, respectively, the active investment behaviour of homeowners in improving existing properties, the rational assessment by housing market participants of the balance between mortgage and rental payments, and the burden of utility and maintenance costs borne by owners and tenants, depending on property size and energy efficiency. The first factor is most influential for homeowners, the second for tenants, and the third has a similarly significant impact on both groups. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article