Search Results (64)

In the context of large-scale renewable integration and increasing demand for power-system flexibility, energy-storage systems are indispensable components of modern grids, and their safe, reliable operation is a decisive factor in investment decisions. To mitigate lifecycle degradation and cost increases caused by frequent charge–discharge cycles, this study puts forward a two-layer energy storage capacity configuration optimization approach with explicit integration of cycle life restrictions. The upper-level model uses time-of-use pricing to economically dispatch storage, balancing power shortfalls while maximizing daily operational revenue. Based on the upper-level dispatch schedule, the lower-level model computes storage degradation and optimizes storage capacity as the decision variable to minimize degradation costs. Joint optimization of the two levels thus enhances overall storage operating efficiency. To overcome limitations of the conventional Whale Optimization Algorithm (WOA)—notably slow convergence, limited accuracy, and susceptibility to local optima—an Improved WOA (IWOA) is developed. IWOA integrates circular chaotic mapping for population initialization, a golden-sine search mechanism to improve the exploration–exploitation trade-off, and a Cauchy-mutation strategy to increase population diversity. Comparative tests against WOA, Gray Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO) show IWOA’s superior convergence speed and solution quality. A case study using measured load data from an industrial park in Zhuzhou City validates that the proposed approach significantly improves economic returns and alleviates capacity degradation. Full article

Portfolio optimization is a multifaceted process aimed at achieving a balance between investors’ risk tolerance and expected returns. However, the inherent uncertainty and unpredictability of financial markets significantly hinder the attainment of this balance. Therefore, there is an increasing need for models capable of representing these uncertainties in a more realistic manner. In this study, novel intuitionistic fuzzy mathematical models are proposed to provide alternative portfolio options that align with diverse investor expectations and risk perceptions. By utilizing mathematical programming formulations incorporating intuitionistic fuzzy parameters, the study contributes to the theoretical framework and enables the analysis of portfolio structures that vary in response to imprecisely defined return levels. The intuitionistic fuzzy parameters are modeled using appropriate membership and non-membership functions, and mean absolute deviation is employed as the risk measure within the proposed models. Various alternative solutions are generated by considering different lower and upper bound constraints, thereby allowing for the construction of flexible investment strategies suitable for different investor profiles. The practical applicability of the proposed models is demonstrated using real-world stock data obtained from Borsa Istanbul. The empirical results reveal that the models provide solutions that are sensitive to individual risk preferences and adaptable to changing market conditions. Accordingly, the developed intuitionistic fuzzy models serve as effective tools for determining optimal portfolio allocations and developing resilient investment strategies. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Sustainable agricultural production systems are a global consensus. Their life-cycle economic feasibility is essential for long-term sustainable goals. This study integrates life-cycle costing with building energy simulation to assess the cost performance of conventional and innovative greenhouse tomato production systems in China’s Hexi Corridor, using dynamic thermal load modeling to overcome empirical-data limitations in traditional life-cycle costing. Under the facility-lease farming model, construction companies incur life-cycle costs of CNY 10.53·m⁻²·yr⁻¹ for the conventional concrete-walled Gobi solar greenhouse and CNY 10.45·m⁻²·yr⁻¹ for the innovative flexible insulation-walled Gobi solar greenhouses. However, farmer greenhouse contractors achieve 10.5% lower life-cycle costs for tomato cultivation in the conventional structure (CNY 2.87·kg⁻¹·yr⁻¹) than in the innovative one (CNY 3.21·kg⁻¹·yr⁻¹) due to 52.6% heating energy savings from the integrated active solar thermal systems. Furthermore, life-cycle cash flow analysis confirms construction companies incur non-viable returns, while farmers achieve substantial profits, with 52.5% higher cumulative profits obtained in the conventional greenhouse than the innovative greenhouse. This profit allocation imbalance threatens sustainability. Our pioneering stakeholder-perspective assessment provides evidence-based strategies for government, investors, and farmers to optimize resource allocation and promote sustainable Gobi agriculture. Full article

This study develops an integrated drought assessment framework based on trivariate copula modeling to simultaneously evaluate three key drought characteristics: duration, severity, and peak intensity. Meteorological data from stations across 23 meteorological stations in Northeastern Thailand, covering the period of 2007–2025, were analyzed. The Standardized Precipitation–Evapotranspiration Index (SPEI) was employed to characterize multidimensional drought conditions. The trivariate copula approach provides a flexible and robust statistical framework, enabling the separation of marginal distributions from dependence structures, capturing nonlinear and tail dependencies more effectively than traditional methods. Results demonstrate that this modeling framework significantly improves the accuracy of drought risk estimation and enables the calculation of joint return periods for extreme drought events. These findings offer valuable insights with respect to designing adaptive water resource management strategies, enhancing agricultural resilience, and strengthening early warning systems under future climate variability. Full article

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

This study examines the effects of capital lease arrangements on the operating performance of shipping firms as proxied by Return on Assets (ROA). The maritime industry is highly capital-intensive, often requiring substantial investments in fleet acquisition and maintenance, making ROA particularly relevant as it captures the effectiveness of firms in utilizing their leased and owned assets to generate operating income. As such, many firms rely on lease arrangements to access necessary resources while preserving liquidity and financial flexibility. Using an international sample of 209 shipping firms, we estimate fixed effects regressions to assess the relationship between lease intensity and performance of the shipping firms. The findings reveal that capital lease intensity is positively associated with operating performance, indicating that leasing can be a value-enhancing financing strategy in this sector. However, the performance benefits of capital leases diminish under IFRS 16 reporting, particularly for firms with higher leverage. These findings offer important implications for investors, regulators, and managers evaluating capital structure decisions and financial reporting strategies in capital-intensive industries post-IFRS 16 implementation. Full article

The relationship between firm size and the financial performance of advertising and marketing companies remains understudied in the academic literature, including in the regional context. Using a panel data methodology, this study analyzes the impact of three proxies for firm size (total assets, number of employees, and sales) on the financial performance (return on assets and profit margin) of the 500 most profitable advertising and marketing companies from 16 Western European countries over the period 2019–2023. Weighted least squares regression analysis revealed statistically significant negative effects of all three proxies for firm size on financial performance, with the strongest negative effects on total assets on return on assets and sales on profit margin, which is similar to return on sales. Empirical data confirm the inverse relationship between total assets and their profitability; this indicates the advantages of resource-optimized business models with high management flexibility and effective use of intellectual capital compared to material-intensive structures. The inverse relationship between the number of employees and financial performance is due to higher operating personnel costs and the difficulty of effectively managing human resources as the number of employees increases. Increased sales negatively affect profit margins, demonstrating a decrease in the efficiency of converting revenue into profits as operations expand. These findings are important for developing effective financial management strategies and making investment decisions in the industry under study. The research contributes to SDGs 8, 9, and 12 by demonstrating how resource-optimized structures with higher management flexibility and effective use of intellectual capital can outperform material-intensive structures in the advertising and marketing industry. Full article

The construction industry is perceived as an industry that is not viable for females to progress their careers in. Existing research studies focused on retaining senior female construction professionals are lacking. Particularly, the existing challenges and strategies found through a critical literature review were scattered and not specific to the retention of senior construction professionals. Identifying this gap, this study led to an empirical research phase to gather the firsthand experiences of 14 senior female professionals in Australia through semi-structured interviews. Subsequently, the gathered data was analysed through content analysis using NVivo software (2020). This study revealed several barriers, which were categorised into three clusters: culture in construction, disrupted career progression, and difficult working conditions. The results revealed that some barriers were consistent with the broader literature findings, while some were interesting context-specific barriers such as a lack of recognition and respect, a lack of confidence in decision-making, misalignment of childcare and construction hours, and lack of on-site feeding facilities. Similarly, the strategies were also discussed under three categories: to support females to adapt in construction, support them in their return to work from leave, and increase flexible work. Finally, recommendations were provided for individuals, organisations, and the industry to retain female employees in construction. Theoretically, this study advances understanding by identifying barriers and retention strategies specific to senior female construction professionals, framing retention as a multi-level challenge, while practically, the findings inform targeted policies to address gaps in the Australian context and improve gender equity. Full article

Water use strategies reflect the ability of plants to adapt to drought caused by climate change. However, how these strategies change with stand development and seasonal drought is not fully understood. This study used stable isotope techniques (δD, δ¹⁸O, and δ¹³C) combined with the MixSIAR model to quantify the seasonal changes in water use sources and water use efficiency (WUE) of Eucalyptus urophylla S.T.Blake × E. grandis (E. urophylla × E. grandis) at four stand ages (2-, 4-, 9- and 14-year-old) and to identify their influencing factors. Our results showed that the young (2-year-old) and middle-aged (4-year-old) stands primarily relied on shallow soil water throughout the growing season due to the limitations of a shallow root system. In contrast, the mature (9-year-old) and overmature (14-year-old) stands, influenced by the synergistic effects of larger and deeper root systems and relative extractable water (REW), exhibited more flexibility in water use, mainly relying on shallow soil water in wet months, but shifting to using middle and deep soil layer water in dry months, and quickly returning to mainly using shallow soil water in the episodic wet month of the dry season. The WUE of E. urophylla × E. grandis was affected by the combined effect of air temperature (T), vapor pressure deficit (VPD), and REW. WUE was consistent across the stand ages in the wet season but decreased significantly with stand age in the dry season. This suggests that mature and overmature stands depend more on shifting their water source, while young and middle-aged stands rely more on enhanced WUE to cope with seasonal drought stress, resulting in young and middle-aged stands being more vulnerable to drought stress. These findings offer valuable insights for managing water resources in eucalyptus plantations, particularly as drought frequency and intensity continue to rise. Full article

This paper presents a joint optimization framework for multi-period planning in a Manufacturing–Remanufacturing–Transport Supply Chain (MRTSC), focusing on carbon emission reduction and economic efficiency. A novel Mixed Integer Linear Programming (MILP) model is developed to coordinate procurement, production, remanufacturing, transportation, and returns under environmental constraints, aligned with carbon tax policies and the Paris Agreement. To address uncertainty in future demand and the number of returned used products (NRUP), a two-stage approach combining forecasting and optimization is applied. Among several predictive methods evaluated, a hybrid SARIMA/VAR model is selected for its accuracy. The MILP model, implemented in CPLEX, generates optimal decisions based on these forecasts. A case study demonstrates notable improvements in cost efficiency and emission reduction over traditional approaches. The results show that the proposed model consistently maintained strong service levels through flexible planning and responsive transport scheduling, minimizing both unmet demand and inventory excesses throughout the planning horizon. Additionally, the findings indicate that carbon taxation caused a sharp drop in profit with only limited emission reductions, highlighting the need for parallel support for cleaner technologies and more integrated sustainability strategies. The analysis further reveals a clear trade-off between emission reduction and operational performance, as stricter carbon limits lead to lower profitability and service levels despite environmental gains. Full article

With the rapid development of a new power system under the “dual carbon” goal, pumped storage has gained increasing attention for its role in integrating renewable energy and enhancing power system flexibility and security. This study proposes a dynamic benefit evaluation method for pumped storage projects, addressing the limitations of static analyses in capturing the evolving benefit trends. In this paper, the multi-stage dynamic benefit evaluation model was constructed by introducing time-of-use tariffs, periodic capacity pricing mechanism, and ancillary service revenue prediction based on machine learning and the multiple regression method. Sensitivity analysis was applied to explore the impact of key parameter variations on economic indicators. The results show that the benefit structure differs significantly across stages, and with electricity market development, a diversified pattern supported by electricity, capacity, and ancillary service revenues will emerge. The application of the model to an actual operating pumped storage power station yielded an internal rate of return of 8.18%, a payback period of 16.4 years, and a 26% increase in net present value compared with traditional methods. The proposed model expands the theoretical framework for pumped storage benefit evaluation and provides strong support for investment decisions, policy design, and operational strategy optimization. Full article