Search Results (3,966)

The increasing penetration of renewable energy sources (RES) introduces significant variability and instability in modern power systems, creating a growing need for advanced and coordinated energy storage solutions. However, a key unresolved challenge remains the integrated modeling and optimal sizing of hybrid energy storage systems (ESS) that combine technologies with different temporal characteristics under high RES penetration. This study addresses this challenge by developing a unified techno-economic and physical–mathematical framework for hybrid ESS integrating lithium-ion (Li-ion), vanadium redox flow batteries (VRFB), and hydrogen (H₂) technologies. Unlike conventional approaches that treat storage technologies independently or use simplified hybrid representations, the proposed framework jointly considers dynamic energy balance, degradation-aware lifecycle behavior, and multi-criteria cost optimization. The model was implemented using Python 3.10-based simulation tools and evaluated under renewable penetration scenarios of 30%, 50%, and 70%. The results indicate that increasing RES penetration leads to higher power fluctuations, reaching ±15–20% at 50% RES and ±20–25% at 70% RES. The optimized hybrid system achieves an overall efficiency of up to 92%, reduces total system cost to approximately 450 USD/kWh, and extends operational lifetime to 25 years, demonstrating a balanced techno-economic performance compared to standalone storage technologies. The proposed framework addresses this gap by coupling dynamic energy balance analysis with degradation-aware techno-economic optimization, enabling coordinated allocation of storage functions across short-, medium-, and long-duration timescales. In this way, the study not only evaluates hybrid storage performance, but also provides a practical decision-support framework for renewable-dominated power systems, particularly in the context of Kazakhstan’s energy transition. Full article

Functionally graded multi-material thermoplastic architectures provide a promising route for tailoring viscoelastic energy dissipation through controlled phase contrast and interfacial interactions. However, rational selection of optimal material compositions remains challenging due to competing requirements among stiffness, damping efficiency, thermal stability, and processability. The absence of a quantitative decision framework often limits systematic design of architected polymer systems. This study proposes an Analytic Hierarchy Process (AHP)-based multi-criteria decision model to identify the optimal rigid–elastic thermoplastic composition for enhanced damping performance. Nine performance criteria were considered, including storage modulus, loss factor, damping bandwidth, interfacial adhesion strength, elongation at break, impact resistance, glass transition temperature, thermal stability, and printability. Fourteen alternative material configurations combining different rigid phases, elastomeric interlayers, filler contents, and layer thickness ratios were evaluated. Pairwise comparison matrices were constructed based on experimentally measured thermomechanical data and literature-reported values, and consistency ratios were maintained below 0.1 to ensure decision reliability. Numerical results indicate that a graded PLA/soft-TPU/PLA architecture with optimized layer thickness ratio achieved the highest global priority weight (0.431), outperforming the baseline PLA/TPU system by approximately ~25–30% in overall performance index. Sensitivity analysis confirmed ranking robustness across variations in damping and stiffness weighting factors. The proposed framework establishes a systematic methodology for polymer material selection and multi-material architectural optimization, enabling data-driven design of thermoplastic systems with tunable viscoelastic performance. Full article

The study analyzes flood susceptibility in the Banat Hydrographic Area (Romania) using an integrated GIS framework based on MCDA–AHP multicriteria analysis and the multitemporal evaluation of static and dynamic factors for two scenarios (2005 and 2023). The results highlight differences between the two scenarios, mainly driven by variations in precipitation: although the moderate class remains dominant (~56% of the area), the share of high and very high susceptibility classes is lower in 2023 (~6%) compared to 2005 (~17%), accompanied by an expansion of the low susceptibility class (~26% to ~37%). Validation using flood extent data from April 2005 shows that approximately 99% of the affected area falls within the moderate, high, and very high susceptibility classes (χ² = 9475, p < 0.001). The multitemporal analysis indicates high stability (75% of the territory), while 25.35% exhibits transitions toward lower susceptibility classes. Dynamic factors show differentiated roles: precipitation exerts a dominant regional control (95.44% of the area), while LULC changes contribute locally. The differences between scenarios should be interpreted as a model response to climatic variability rather than as structural changes in intrinsic susceptibility. The approach provides a reproducible framework for susceptibility assessment and supports spatial planning and risk management. Full article

Children’s scooters, as products integrating mobility, safety, and developmental functions, require systematic and reliable design decision-making approaches. However, existing processes often suffer from unsystematic user demand extraction, strong subjectivity in weight determination, and insufficient quantitative support for evaluating alternative schemes. To address these issues, this study proposes an integrated AHP–CRITIC–VIKOR framework for engineering-oriented design optimization. User requirements are identified through field investigation, questionnaires, and affinity diagram analysis, and a multi-level evaluation indicator system is constructed. AHP is applied to determine subjective weights, while CRITIC incorporates objective data characteristics, enabling balanced weighting. VIKOR is then used to evaluate design schemes and obtain compromise solutions under multi-criteria conflicts. The results show that safety-related factors, including material safety, braking performance, and load-bearing capacity, dominate the decision process. The optimal scheme demonstrates the closest proximity to the ideal solution. Sensitivity analysis confirms the robustness of the model, and comparison with TOPSIS shows consistent results and improved compromise decision capability. The proposed framework enhances decision reliability and provides an effective quantitative tool for multi-criteria product design optimization. Full article

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments. Full article

Amid Europe’s demographic decline and the intensifying global “war for talent,” migration is increasingly viewed as a critical source of human capital capable of sustaining economic growth and welfare systems. Nevertheless, the literature on Macro-Talent Management (MTM) has primarily focused on the attraction of highly skilled expatriates, paying limited attention to how national integration systems shape the broader capacity of countries to attract and retain migrant talent. Addressing this gap, the present study conceptualizes migrant integration as a strategic component of macro-level talent management and evaluates the “talent attractiveness” of different European welfare and migration regimes. Methodologically, the study develops a multi-criteria evaluation framework based on the PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations) outranking method, enabling the simultaneous assessment of institutional, socio-economic, and administrative dimensions of migration governance. The model integrates nine indicators combining policy inclusiveness (e.g., Migrant Integration Policy Index—MIPEX (Migrant Integration Policy Index), citizenship accessibility), labor market outcomes (employment and gender gaps), and systemic pressures on migration management (asylum applications). By integrating policy indicators with real-world labor market performance and administrative capacity, the proposed framework offers a novel analytical tool for comparative migration policy evaluation and decision support. The empirical application covers six European countries representing distinct migration regimes: Portugal, Sweden, France, Poland, Greece, and Germany. The results challenge the conventional assumption that economic strength alone determines migrant attractiveness. Portugal emerges as the most attractive destination, demonstrating that inclusive rights-based integration policies can offset lower GDP levels. In contrast, Germany ranks last in the sample, revealing signs of systemic overextension due to extreme administrative pressure, while Greece occupies the fifth position characterized by structural integration deficits. The study contributes to the literature by linking migration governance, integration policy effectiveness, and macro-level talent management and by introducing a multi-criteria decision-analytic approach for evaluating national migration systems in Europe. The study offers a reassessment of the ‘talent attractiveness’ of European welfare models in a post-pandemic context (2023). Full article

Population aging requires residential environments that go beyond basic accessibility. This study theorizes and validates the Accessibility Gap (the divergence between regulatory compliance and the functional lived experience of older adults) using a Multi-Criteria Decision Analysis (MCDA) tool. The research uses a weighted linear aggregation model based on user-centered design and the International Classification of Functioning, Disability, and Health (ICF). Thirty dwellings—apartments, single-story, and two-story houses—were evaluated in Chile’s Metropolitan Region. The model applies 40 indicators, normalized on a 0–100% scale across six dimensions, and weighted by older adults and caregivers. Results reveal fragmented accessibility gap: basic features often meet standards; yet important deficits remain in highly prioritized areas—autonomy, safety, and communication. The Global Performance Index (GPI) identifies “accessibility gaps” that traditional assessments miss. By combining objective metrics with subjective experiences, this study delivers a replicable, evidence-based framework. It shows that specific design choices, rather than architectural configuration, better support functional independence. The MCDA approach provides a robust tool for guiding housing rehabilitation and public policies that support aging in place and ensure homes meet the needs of an aging population. Full article

Municipal solid waste (MSW) landfill site selection is a complex multi-criteria decision-making (MCDM) problem involving uncertainty and conflicting criteria. Although spherical fuzzy extensions of the Technique for Order Preference by Similarity to Ideal Solution (SF-TOPSIS) are widely used, existing studies rely on conventional distance measures that do not fully capture the geometric structure of spherical fuzzy sets. To address this limitation, this study proposes an enhanced SF-TOPSIS framework incorporating a novel spherical distance measure to improve consistency, discrimination capability, and structural compatibility. The framework integrates Spherical Fuzzy Weighted Arithmetic Mean (SWAM) and Spherical Fuzzy Weighted Geometric Mean (SWGM) operators and evaluates robustness using Spearman rank correlation. Additionally, a coefficient of variation (CV)-based analysis is conducted to examine the dispersion of closeness coefficients. The applicability of the approach is demonstrated through a landfill site selection case; however, the main contribution lies in a generalized distance-based formulation applicable to various MCDM problems. Results show that the proposed distance improves agreement between aggregation operators, increasing correlation values from 0.905 to 0.976, while producing a more stable distribution of closeness coefficients. Overall, the study advances spherical fuzzy MCDM by introducing a geometrically consistent distance formulation. Full article

The sustainable renovation of ageing industrial buildings presents both a challenge and an opportunity to enhance energy efficiency while preserving architectural and structural integrity. This study develops an integrated methodological framework for assessing and optimising multilayer wall systems in such conversions, combining thermal, environmental, and durability analyses. Six composite wall configurations were designed and numerically evaluated using steady-state 2D heat conduction and vapour-diffusion models. The results reveal substantial thermal improvement compared to the reference uninsulated brick wall (U = 1.41 W/m²·K). The proposed systems achieved U-values between 0.351 and 0.172 W/m²·K, meeting or surpassing European energy standards. The BP–EPS wall exhibited the lowest U-value (0.172 W/m²·K), while the FC–EPSR configuration achieved superior corner performance with a 2D surface temperature (Tsi) of 17.99 °C and the highest surface temperature factor (fRsi = 0.943), along with a reduced condensation risk, indicating more balanced overall performance. Weight and thickness reductions of up to 80.5% and 52%, respectively, were observed, enhancing retrofit feasibility and space efficiency. Life Cycle Assessment results indicated that optimised wall configurations reduced embodied carbon (A1–A3) by up to 78% and total life cycle emissions (A1–A3 + B6) by over 86% relative to the reference case. Vapour-diffusion analysis confirmed the FC–EPSR wall’s lowest condensation fraction, indicating excellent hygrothermal durability. Multi-criteria evaluation using the simple additive weighting method and Monte Carlo robustness analysis verified FC–EPSR as the most balanced and reliable system. Overall, the findings present a validated and replicable pathway for the sustainable renovation of industrial buildings, supporting the goals of European carbon neutrality and the circular economy. Full article

Quality evaluation in production systems represents a significant challenge in the manufacturing industry, particularly in environments where expert judgment plays a key role in managing the inherent uncertainty of the production system. This study proposes a fuzzy multicriteria decision-making control chart, termed Fuzzy Decision-Making Control Chart based on AHP-Extent and Triangular Fuzzy Numbers (FDMCC-AHPE). The method integrates expert knowledge through triangular fuzzy numbers and a Fuzzy Analytic Hierarchy Process supported by Extent Analysis, to define fuzzy decision intervals for quality assessment and subsequently perform a structured analysis to classify the product within a control chart framework. In this framework, expert judgments expressed through linguistic evaluations are systematically translated into triangular fuzzy numbers and processed using FAHP–Extent Analysis, allowing the aggregation of subjective assessments within a structured mathematical decision model. The proposed method was validated in a tannery company, specifically in the retanning process. The industrial case study considers both qualitative criteria, such as surface defects and color uniformity, and quantitative process variables that include bath pH, treatment duration, and processing temperature. The results were compared with an empirical expert-based evaluation and a structured expert assessment supported by a multicriteria decision-making method. The findings demonstrate that the FDMCC-AHPE exhibits greater sensitivity in discriminating between quality states under uncertain evaluation conditions, particularly when samples involve complex evaluation conditions. Full article

This study addresses the increasing need to structure knowledge in the field of circular resource management, with a focus on energy efficiency and sustainable resilience. Previous studies have examined various taxonomies for the circular economy, yet a clear gap remains in understanding how energy efficiency and resilience serve as the main pillars for operational stability. This study is designed as a bibliometric analysis based on a selection of relevant scientific articles. The identified factors were extracted based on their frequency of occurrence in the literature and processed using statistical clustering techniques to group them into coherent categories. The results show that the field is defined by a set of interconnected factors that can be structured into distinct clusters, reflecting key dimensions such as operational performance, environmental impact, and system resilience. Specifically, the analysis demonstrates how energy-related attributes and resilience attributes act as stabilizing factors within closed-loop systems. Based on these findings, this study proposes a structured framework that organizes the identified factors into a clear configuration. This framework provides a reference point for researchers who aim to develop models in this area and for practitioners involved in the design and optimization of circular systems. This study contributes by offering a structured view of the field and by supporting the development of consistent analytical and decision-making approaches grounded in the necessity of balancing resource recovery with system stability. Full article

This study proposes a novel data-driven machine learning (ML) framework for multi-criteria environmental, social, and governance (ESG) evaluation. The framework aims to address the transparency, consistency, and subjectivity limitations of existing ESG evaluation systems by employing a fully data-driven, modular, and ML-supported architecture. It comprises three main modules: (i) ESG data preprocessing with missing-data imputation by the MissForest algorithm; (ii) a three-plane ESG feature selection workflow that integrates clustering, feature importance, and classification algorithms to identify representative ESG indicators; and (iii) a hybrid weighting and ranking procedure that combines unsupervised principal component analysis (PCA), criteria importance through inter-criteria correlation (CRITIC), and technique for order preference by similarity to ideal solution (TOPSIS) methods. A recent 2024 real-world application involving 57 listed Chinese pharmaceutical and biotechnology companies and 70 ESG indicators demonstrates the framework’s practical utility in producing transparent and objective ESG rankings. The main contributions of this work are fourfold: (1) the development of an end-to-end, entirely data-driven ML framework for ESG evaluation; (2) the introduction of an innovative three-plane ESG feature selection workflow within the framework; (3) the first study for designing a hybrid PCA-CRITIC-TOPSIS approach in ESG weighting and ranking; (4) the validation of the framework through a real-world industry application using recent and authentic ESG data. Full article

This study reconceptualises marginality in China’s inter-provincial border regions as a dynamic, scale-sensitive spatial relationship rather than a static condition of underdevelopment. Using the Hubei–Henan–Anhui border area as a case study, we quantitatively assess marginality across three dimensions—production, livelihood, and ecology—based on panel data from 61 counties for 2000, 2010, and 2021. The entropy-weighted TOPSIS method is used to calculate comprehensive development indices, and geographic detector models identify key driving factors. The results show that production marginality is persistently shaped by economic level and industrial structure. Livelihood marginality exhibits a clear temporal shift: dominant drivers move from healthcare security to cultural amenities and finally to transport accessibility. Ecological marginality remains primarily determined by natural endowments such as habitat quality and ecosystem services. Theoretically, the study advances marginality analysis by integrating spatial, temporal and dimensional perspectives. Practically, it offers a diagnostic framework to support differentiated, cross-administrative governance strategies that can transform peripheral border regions into cooperative hubs. Full article

Translating diverse stakeholders’ needs for tourism into precise technical requirements for mobile resilience applications is a significant challenge, especially for at-risk coastal communities. Therefore, we developed a structured decision-support framework that uses the Analytic Hierarchy Process (AHP) combined with Multi-Criteria Decision Analysis (MCDA) to systematically identify and prioritize functional features for a disaster-resilient tourism application called AuTour. The framework was validated through a case study in Aurora Province, Philippines, involving 152 diverse stakeholders, including government officials, tourism operators, and technology students. The AHP analysis results revealed that safety infrastructure (a mean weight of 0.5256) was the dominant design criterion, far outweighing environmental sustainability (0.2480) and community preparedness (0.1241). The MCDA ranked key functional modules using these criteria to determine an optimal system architecture. The highest-priority features identified were a real-time Disaster Preparedness Alert module, a geospatial Smart Tourism Guide, and a participatory Health Surveillance module. The analysis results confirmed high utility for features incorporating AI-powered chatbots (a mean score of 4.1921) and multi-dialect communication capabilities (4.1513). The developed scalable, data-driven framework can be used for user-centered design in the critical domain of disaster-resilient technology. By translating stakeholder priorities into a ranked set of technical specifications, the framework contributes to the development of resilient mobile systems, supporting the achievement of Sustainable Development Goals for innovation (SDG 9) and resilient infrastructure (SDG 11). Full article

Ship collision accidents remain a critical concern in maritime safety because of their potential to cause operational disruption as well as environmental and economic damage in areas with dense shipping activity. Complex traffic interactions, differences in vessel characteristics, and dynamic environmental conditions make collision risk increasingly difficult to manage using traditional navigation measures alone. This paper presents a structured review of ship collision research, focusing on collision impacts, collision avoidance strategies, risk assessment methodologies, and safety index development. The review synthesizes reported collision cases and their environmental consequences, examines commonly used analytical frameworks including probabilistic, data-driven, and multicriteria approaches, and discusses recent developments in AIS-based analysis, sensor-based monitoring, and intelligent prediction techniques. The analysis identifies several methodological gaps in existing studies. Collision avoidance methods and risk assessment models are often developed independently, while their integration with safety index frameworks remains limited. In addition, safety index formulations differ considerably in terms of indicator selection and modeling approaches, which reduces comparability between studies conducted in different waterways. The findings highlight how different analytical approaches contribute to maritime safety evaluation at strategic, operational, and real-time levels and provide insights for developing more integrated safety assessment frameworks to support navigation risk monitoring in high-traffic maritime environments. Full article