Search Results (3,266)

The paper presents the results of experimental studies aimed at assessing thermal conductivity, compressive strength, water absorption and capillary action of samples in the form of ordinary concrete (reference sample—B1) and lightweight concrete with the addition of a biocomponent (C100) in the range of 3–31.2% porosity with varied morphology. Gas permeability studies were conducted for porous materials with an anisotropic structure. The measurement results indicate a significant effect of the type of material on thermal conductivity for B1, which is 3.05 W·(m·K)⁻¹ and C100 equal to 0.09 W·(m·K)⁻¹. On the other hand, the highest water absorption is demonstrated by C100, which is 99%, and the lowest by B1 equal to 2%. Tests were conducted for different gas permeability conditions using oxygen (O₂), nitrogen (N₂) and carbon dioxide (CO₂). The basis for assessing gas permeability through porous beds is the gas flow resulting from the overpressure forcing this flow. The highest gas permeability coefficient at a flow resistance of 6 kPa for B1 was 2.7·10⁻⁷ m², and for C100, 2.1·10⁻⁷ m² at CO₂ flow. The following issues were identified: scientific, identifying the lack of research on gas permeability testing for anisotropic concrete structures; application, identifying reports of premature failure of concrete structures in livestock buildings due to the effects of toxic substances. The novelty in the article is the indication of the gas permeability model and the performance of a comparative analysis (multi-criteria analysis) based on diagnostic features. In the hierarchical decision-making structure, gas permeability was used as one of the evaluation criteria, which can be assessed as a stimulant or destimulant depending on the climatic zone. The permeability of gas media is one of the features that allow for assessing the suitability of materials, among others, for small-sized prefabricated wall systems—the durability of both the element itself and any reinforcing inserts depends on permeability. The aim of this article was to compare the physical and functional properties of materials, such as thermal conductivity, water absorption, capillarity and gas permeability, in relation to the material composition. The research was of an application and engineering nature, focusing on macroscale functional parameters that are important from the point of view of the practical application of the tested building composites. The scientific problem is to indicate the lack of scientific research on the study of gas permeability in anisotropic concrete structures in livestock building conditions. The engineering use of hempcrete indicates its usefulness in various structural elements of livestock buildings. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

This paper proposes a robust multi-criteria decision-making (MCDM) framework for evaluating and selecting Point-of-Sale (POS) systems in the context of Retail 5.0, where decisions involve multiple criteria and inherent uncertainty. The approach integrates entropy-based objective weighting with fuzzy AHP for subjective assessment, while incorporating Z-number theory to explicitly account for decision-makers’ confidence. Unlike conventional methods that assume equal importance between subjective and objective components, the proposed framework introduces a confidence-adjusted integration mechanism, in which Z-numbers are used to dynamically modulate the influence of subjective judgments based on their reliability. This enables a more balanced and context-sensitive weighting process that better reflects both data characteristics and human uncertainty. The contribution of this study is twofold: methodologically, it develops a reliability-driven integration framework that enhances the robustness and credibility of criteria weighting; practically, it demonstrates the applicability of the approach through a real-world POS system selection case. The results confirm that the proposed method provides more stable and informative decision outcomes, highlighting its effectiveness in complex decision-making environments. Full article

In recent years, multi-criteria group decision-making (MCGDM) methods have attracted widespread attention in the academic community. However, most existing MCGDM approaches suffer from limitations in decision-makers’ expressive capacity and the loss of uncertain information. To address these issues, this study proposes a novel multi-criteria group decision-making (MCGDM) framework. First, we developed an evaluation information representation method called the hybrid probabilistic information set (HPIS), which allows DMs to fully express their opinions based on individual cognition using the most suitable form of representation. Second, the criteria importance through inter-criteria correlation (CRITIC) and the combined compromise solution (CoCoSo) methods are extended into the cloud model environment, ensuring that the rich uncertainty information is fully preserved and transmitted throughout the entire evaluation process. Finally, we apply the proposed MCGDM framework to a practical case study evaluating urban flood resilience within an urban agglomeration, to identify its vulnerable components. The results indicate that Baoding, Zhangjiakou, and Chengde are identified as the most vulnerable cities, necessitating immediate and targeted measures to bolster their flood defense capabilities. At the same time, decision-makers can select both qualitative and quantitative comments simultaneously and carry uncertainty information throughout the entire calculation process. Furthermore, the sensitivity and comparative analyses demonstrate the robustness and practical utility of the proposed method under the tested scenarios. Full article

This study examines the transformative role of digital and artificial intelligence (AI)-enabled public procurement systems in enhancing governance efficiency within smart city environments, with a specific focus on Yerevan, Armenia. As urban administrations increasingly adopt data-driven governance models and digital infrastructures, public procurement remains a critical yet underexplored domain for innovation in transition economies. Despite ongoing e-government reforms in Armenia, procurement systems continue to face challenges related to procedural inefficiencies, limited transparency, and institutional constraints. To address these challenges, the paper develops a Governance Efficiency Framework that integrates digitalization, AI capabilities, and multi-criteria decision-making principles to assess and optimize public procurement processes in urban settings. The framework incorporates key dimensions such as transparency, operational efficiency, accountability, and data integration, enabling a comprehensive evaluation of procurement performance. The empirical application of the framework to the case of Yerevan provides insights into the structural and technological determinants of procurement efficiency in a transition economy context. The findings indicate that while digitalization has contributed to improvements in transparency, significant limitations remain in efficiency and system integration. A scenario-based analysis further suggests that AI-enabled analytics, process automation, and digital procurement platforms have the potential to reduce administrative delays, enhance transparency, and support more strategic and evidence-based decision-making under assumed implementation conditions. By bridging the fields of public procurement, digital governance, and smart city research, this study contributes both theoretically and practically. It offers a structured and adaptable framework for policymakers and urban administrators seeking to modernize procurement systems and strengthen governance efficiency in evolving digital environments. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

Addressing building vulnerability to climate hazards requires advanced tools to support adaptation decisions. To this end, the current study presents an Agentic Artificial Intelligence (Agentic AI) Optimization framework to enhance the climate resilience of existing buildings, bridging policy guidelines and a practical tool for optimized and context-aware retrofit strategies. Aligned with EU Guidance, the framework operationalizes a Climate Vulnerability Assessment (CVA) within a Multi-Objective Optimization (MOO) engine through a multi-agent architecture. Specialized subagents, including Requirements, Cost, Strategy, and XAI Agents, collaborate to understand user goals, manage budget constraints, optimize strategies, and produce explainable reports. Two metaheuristic optimizers, such as Multi-Objective Invasive Weed (MO-IWO) and Grey Wolf (MO-GWO), were coupled with Multi-Criteria Decision Making (MCDM) models to minimize building vulnerability and adaptation costs against multiple climate hazards (e.g., heat waves and heavy precipitation). Results show that, despite MO-GWO’s lower computational burden, MO-IWO performed more robustly and is selected as the superior optimizer for integration into the Agentic AI system. Ultimately, the framework provides a scalable approach to asset management, significantly improving decision-making for building retrofits. Full article

The 6 February 2023 Kahramanmaraş earthquakes highlighted the importance of emergency assembly areas for disaster response, evacuation safety, and urban resilience in earthquake-prone cities. Although GIS-based multi-criteria decision-making approaches are widely used to assess spatial suitability, relatively few studies integrate suitability, capacity adequacy, and accessibility within a single framework, particularly in cities directly affected by the 2023 earthquakes. This study evaluates emergency assembly areas in Malatya, Türkiye, using an integrated GIS–Best–Worst Method (BWM) framework. Nine criteria—geology, population density, building density, elevation, slope, distance to roads, distance to rivers, distance to fault lines, and distance to buildings—were weighted based on the judgements of 15 experts involved in Provincial Disaster Risk Reduction Plan (İRAP) processes. The BWM results show that geology and distance to fault lines received the highest weights, whereas distance to roads had the lowest weight. The spatial analysis indicates that highly suitable areas are concentrated mainly in the city centre, while several peripheral neighbourhoods are constrained by geological, topographical, and accessibility-related factors. Existing official emergency assembly areas cover only 27.9% of the population and are located in 13 of 88 neighbourhoods. Estimated access times range from 0 to 5 min in central areas to 10–15 min, or beyond effective service coverage, in peripheral neighbourhoods. Although integrating parks and green spaces substantially increases potential capacity, it does not fully eliminate neighbourhood-level inequalities. The findings provide a spatial decision-support framework for emergency planning in earthquake-prone cities. Full article

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article

Improving pedestrian safety at urban intersections is a key challenge for achieving safer and more sustainable urban transport systems. This study develops a multi-criteria decision-making model (MCDM) for selecting the most appropriate traffic signal type at pedestrian crossings in different urban zones. Traffic conditions, illegal pedestrian crossings and the number of traffic accidents were taken into account during the modelling, as well as the characteristics of the urban environment. The research involved 66,616 pedestrians at 22 pedestrian crossings located in three urban zones: school zones, central zones, and non-central zones. The data were aggregated using Bayesian (beta-binomial) and classical statistical methods. The OPA-Group method was then used to develop the model. In the decision-making phase, the Ordinal Priority Approach (OPA) was applied as the core MCDM method. It was then extended to the OPA-Group framework to incorporate group-based evaluation in accordance with the model requirements. Additionally, a comprehensive sensitivity analysis was conducted, confirming the robustness and stability of the proposed model. The results show that traditional traffic signals are most suitable for school and non-central zones, whereas countdown traffic signals are recommended for central zones. Push-button traffic signals were identified as the least efficient solution for regulating pedestrian movement at pedestrian crossings. Full article

Financial stability and banking sector performance have become critical concerns for policymakers and regulators in the aftermath of global financial crises. This study aims to evaluate the financial soundness of banking sectors across OECD countries by employing an integrated multi-criteria evaluation framework based on Financial Soundness Indicators (FSIs) for the year 2024. The analysis focuses on key dimensions such as profitability, asset quality, capital adequacy, and liquidity conditions. To enhance methodological robustness, objective criterion weights are derived using the Modified Standard Deviation (MSD) and Modified Preference Selection Index (MPSI) methods and then combined within a unified weighting scheme. Country rankings are obtained through the MABAC method, and the stability of the results is further examined using sensitivity analysis. This integrated approach provides a more balanced evaluation by reducing the potential bias associated with relying on a single weighting method. The findings indicate that the ratio of non-performing loans to total gross loans plays a dominant role in differentiating banking sector soundness among OECD economies, highlighting the importance of credit risk and balance-sheet resilience in comparative macroprudential evaluations. In addition, the results reveal relatively distinct performance patterns between countries characterized by stronger capital structures and lower credit risk exposure and those exhibiting comparatively weaker resilience indicators. Overall, the study contributes to the literature by providing a structured and robust framework for comparative banking sector assessment and offers policy-relevant insights for comparative macroprudential monitoring and the assessment of banking sector resilience across OECD countries. Full article

Vermicomposting is an environmentally sustainable, economically viable, and agronomically valuable method for converting organic waste into nutrient-rich soil amendments, thereby supporting sustainable development. However, the fertilization efficiency of vermicompost can vary significantly depending on the physicochemical properties of the feedstock used. This study aims to compare different feedstocks on vermicompost and evaluate their performance on soil fertility and plant nutritional status. Organic matter (OM), pH, salinity (EC), total Kjeldahl nitrogen (TKN), total phosphorus (TP) and total potassium (TK) of various vermicompost samples were taken into consideration to evaluate their fertilization efficiency as performance determinants in terms of plant growth, plant nutritional status, yield, crop quality and cost with the aim of determining the weights of the specific parameters in the total performance using multi-criteria decision-making (MCDM) methods. The integrated ENTROPY-TOPSIS method was used. Twenty-one different vermicompost feedstock analyses were collected from the literature and compared in order to create an agronomic performance ranking based on the selected criteria. The ENTROPY method revealed that the TP was the most influential factor (21.6%), followed by the EC (20.7%) and the TK (18.5%), while the OM had the lowest impact (11.3%). Based on the TOPSIS ranking, vermicompost from brewer’s spent grain achieved the highest performance, followed by cow manure plus rice straw and olive pruning waste, whereas paper waste ranked at the bottom. A comparative analysis with other objective MCDM weighting methods proved strong correlations, particularly with WENSLO, MPSI and LODECI methods, confirming the robustness of the ENTROPY method. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

This manuscript creates a framework for decision support based on green urbanism to direct the sustainable development of Gamasa, an Egyptian seaside city. The paper aims to convert the concepts of green urbanism into a multi-criteria evaluation that can support strategic urban development and prioritize spatial interventions. Sustainable mobility, green and blue infrastructure, energy and resource efficiency, urban form and density, social livability and public space quality, and governance and implementation feasibility are the six dimensions that are defined. These dimensions are derived from international sustainability literature and tailored to Gamasa’s particular challenges. The study’s methodology combines a multi-criteria decision-making approach based on the AHP with spatial analysis of land use, street hierarchy, building shape, and green space distribution. Weights for these dimensions are determined by expert-based pairwise comparisons, which are backed by a SWOT analysis. To prioritize priority zones for green transformation, the weighted framework is applied to four important urban areas: residential districts, a large urban park, the waterfront, and the main urban corridor. The top priorities, according to the results, are climate-responsive coastal design, increased green and blue infrastructure, and sustainable transportation. For quickly urbanizing coastal cities, the method demonstrates how the AHP operationalizes green urbanism into quantifiable, context-sensitive goals. Full article