Search Results (62)

The diffusion of intelligent construction technologies has improved construction efficiency and information integration, while also increasing the complexity and uncertainty of governance decisions in megaprojects. In particular, selecting an appropriate Engineering Transaction Mode (ETM) under intelligent construction involves multiple conflicting criteria, expert judgments, and loss-averse risk preferences, which are not fully captured by conventional multi-criteria decision-making methods. This study proposes a decision-making model that combines Pythagorean fuzzy sets (PFSs) and prospect theory to support ETM selection for megaprojects under intelligent construction. The model constructs an ETM evaluation system grounded in a systematic literature review and questionnaire evidence, encodes expert judgments using PFSs, determines expert and criterion weights via information-utility and fuzzy-entropy measures, and aggregates perceived gains and losses relative to positive and negative ideal solutions through prospect theory. A mega-pumping station project with four ETM alternatives is used for validation. Results indicate that “Self-management + Network-based integrated application + Consultant assistance” achieves the highest prospect value and is consistently ranked first; the same ordering is obtained using TOPSIS and a fuzzy comprehensive evaluation method, demonstrating robustness. The study contributes to theory by coupling hybrid fuzzy representation with loss-aversion-based behavioral aggregation for ETM governance under intelligent construction and provides practitioners with a transparent, replicable decision tool to support ETM selection in complex, uncertainty-laden megaprojects. Full article

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

Greenhouse gas emissions from the energy sector are the main driver of global warming, which has led to an increase in the average surface temperature of the Earth by more than 1 °C above pre-industrial levels. Responding to the urgent need for energy transition, the countries of the European Union have set themselves the goal of achieving climate neutrality by 2050. The main objective of this article is to comprehensively assess the progress of decarbonization in the 27 European Union countries between 2004 and 2024, using an advanced multi-criteria model. The study used the quantitative Analytical Hierarchy Process (AHP) method to construct a multidimensional decision-making model. Eight energy technologies were evaluated through the prism of 13 criteria grouped into three pillars of sustainable development: economic (including technical), environmental, and social. Based on the weights of each criterion, estimated by a group of experts, a synthetic decarbonization index (DI) was calculated for each technology. In the next stage, a cumulative decarbonization index (CDI) was formulated for each country, reflecting the structure of its energy mix. The analysis revealed a fundamental divergence between conventional and zero-emission technologies. Renewable sources and nuclear energy have the highest positive impact on decarbonization (highest DI): hydropower (27.5), nuclear (20.7), wind (20.3). The lowest, unfavorable values of the index are characteristic of fossil fuels: oil (3.6), coal (3.9), and gas (4.8). The average cumulative decarbonization index (CDI) for the EU-27 rose from 14.0 in 2004 to 26.4 in 2024, demonstrating the effectiveness of the EU’s common policy. The leaders of the transition are countries with diversified, green mixes, such as Luxembourg (CDI = 40.4), Lithuania (CDI = 39.6), Portugal (38.5), Austria (36.9), and Spain (33.6). Despite starting from the lowest level in 2004 (CDI = 5.2), Poland recorded one of the most dynamic increases in 2024 (CDI = 17.7), mainly due to a reduction in the share of coal from 93% to 53.5%. The analysis confirms the effectiveness of the EU’s common climate and energy policy and demonstrates the usefulness of the methodology presented for a comprehensive assessment of the decarbonization process. The results indicate the need to further increase the share of zero-emission energy sources in the energy mix in order to achieve the objectives of the European Green Deal. The varying pace of transformation among Member States requires an individualized approach and support for countries with a historical dependence on fossil fuels. Full article

Current research on disaster-adaptive resilience predominantly focuses on urban systems, with insufficient attention paid to the unique scale of traditional settlements and their formation mechanisms and pathways to systemic realization remain significantly understudied. There is also a lack of multi-dimensional coupling analysis and innovative methods tailored to the specific contexts of rural areas. To address this, this study innovatively introduces ant colony bionic intelligence, drawing on its characteristics of swarm intelligence, positive feedback, path optimization, and dynamic adaptation to reframe emergency decision-making logic in human societies. An evaluation model for disaster-adaptive resilience is constructed based on these four dimensions as the criterion layer. The weights of dimensions and indicators are determined using a combined AHP–entropy weight method, enabling a comprehensive assessment of settlement resilience. Taking Fenghuang Ancient Town as an empirical case, the research utilizes methods such as field surveys, questionnaire surveys, and GIS data analysis. The results indicate that (1) the overall resilience evaluation score of Fenghuang Ancient Town is 3.408 (based on a 5-point scale); (2) the path optimization dimension contributes the most to the overall resilience, with road redundancy design (C21) being the core driving factor; within the positive feedback mechanism dimension, soil and water conservation projects (C15) provide the fundamental guarantee for village safety; (3) based on these findings, hierarchical planning strategies encompassing infrastructure reinforcement, community capacity enhancement, and ecological risk management are proposed. This study verifies the applicability of the evaluation model based on ant colony bionic intelligence in assessing the disaster resilience of traditional settlements, revealing a new paradigm of “bio-intelligence-driven” resilience planning. It successfully translates ant colony behavioral principles into actionable planning and design guidelines and governance tools, providing a replicable method for resilience evaluation and enhancement for traditional settlements in ecological barrier areas such as the Qinling Mountains. Full article

This study introduces a comprehensive three-dimensional micromechanical framework to capture the nonlinear mechanical behavior of diverse composite materials, including coupled elastic degradation, inelastic strain evolution, and phenomenological failure in their constituents. The primary objective is to integrate a generalized elastic degradation–inelasticity (EDI) model into the parametric high-fidelity generalized method of cells (PHFGMC) micromechanical approach, enabling accurate prediction of nonlinear responses and failure mechanisms in multi-phase composites. To achieve this, a unified three-dimensional orthotropic EDI modeling formulation is developed and implemented in the PHFGMC. Grounded in continuum mechanics, the EDI employs scalar field variables to quantify material damage and defines an energy potential function. Thermodynamic forces are specified along three principal directions, decomposed into tensile and compressive components, with shear failure accounted for across the respective planes. Inelastic strain evolution is modeled using incremental anisotropic plasticity theory, coupling damage and inelasticity to maintain generality and flexibility for diverse phase behaviors. The proposed model offers a general, unified framework for modeling damage and inelasticity, which can be calibrated to operate in either coupled or decoupled modes. The PHFGMC micromechanics framework then derives the overall (macroscopic) nonlinear and damage responses of the multi-phase composite. A failure criterion can be applied for ultimate strength evaluation, and a crack-band type theory can be used for post-ultimate degradation. The method is applicable to different types of composites, including polymer matrix composites (PMCs) and ceramic matrix composites (CMCs). Applications demonstrate predictions of monotonic and cyclic loading responses for PMCs and CMCs, incorporating inelasticity and coupled damage mechanisms (such as crack closure and tension–compression asymmetry). The proposed framework is validated through comparisons with experimental and numerical results from the literature. Full article

The slab system can account for a substantial portion of the structural cost; an optimized choice is essential for the financial success of a project. Despite its importance, existing research often relies on limited pairwise comparisons or single-criterion analyses (e.g., cost only), failing to provide a holistic framework. A significant gap exists in the application of a formal, quantitative Value Engineering (VE) approach that systematically balances function against cost. This study aims to fill this gap by developing a robust multi-criteria decision-making (MCDM) model to determine the optimal structural slab system for high-rise buildings based on the principles of Value Engineering. Unlike previous studies limited to pairwise comparisons or single-criterion analyses, this research simultaneously evaluates eight diverse slab alternatives across eight weighted performance criteria, providing a comprehensive value-based framework for systematic slab selection. First, eight key evaluation criteria were identified and weighted using the Step-wise Weight Assessment Ratio Analysis (SWARA) method, based on input from a panel of industry experts. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to evaluate the performance of eight distinct slab alternatives, including conventional, voided, and precast systems. The TOPSIS ranking scores were then integrated with normalized cost data to calculate a Value Engineering index, enabling quantitative comparison and final ranking of alternatives. The main finding revealed that the Post-Tension Slab offers the highest value (VE score = 2.467), achieving a superior balance of high performance—particularly in speed and structural efficiency—and low normalized cost. Interestingly, the traditional Solid Slab ranked a close second (VE score = 2.418). Practically, this study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond mere cost-cutting, ensuring an optimal balance between cost, schedule, and functional performance. The study provides project managers, developers, and engineers with a transparent, data-driven decision-making tool to justify slab selection beyond cost considerations. Full article

The ecological protection and high-quality development of the Yellow River Basin is a major national strategy in China. The Yellow River “Ji” Bay is an important part of the basin. This study evaluates the comprehensive risk of the water–energy–food–ecology–carbon (WEFEC) complex system within the “Ji” Bay. Using 2004–2023 panel data from nineteen regional cities, this study develops a 24-indicator WEFEC index system that assesses reliability, synergy, and resilience. A comprehensive evaluation method based on the game theory–cloud model is employed to determine the risk levels. The study results show the following: (1) the multi-year average comprehensive risk of the WEFEC complex system in the “Ji” Bay from 2004 to 2023 was at a high alert level; (2) the overall synergy of the “Ji” Bay was moderate; (3) spatially, the number of cities in extreme and high alert states decreased, whereas the number of cities in no alert and light alert states increased; and (4) indicators such as per capita water resources, water production modulus, and water area ratio are the main factors restricting the comprehensive risk of the WEFEC complex system. Based on these findings, this paper proposes policy recommendations using the following three aspects: criterion layers, risk factors, and different regions. Full article

Assessing energy security in the context of sustainable development, as well as the current geopolitical climate, is a highly important, timely, and complex challenge. Addressing this issue, this paper introduces a new multi-barrier methodological approach to evaluation based on the Multi-Barrier Energy Security System (MBEES) model. This model incorporates five barriers (dimensions) influencing energy security. The MBEES model, along with the developed methodology, was applied to assess the energy security of the EU-27 countries for the period of 2014–2023, in line with EU policy objectives such as Fit for 55 and the Green Deal. The Criteria Importance Through Intercriteria Correlation and Entropy methods, combined with the Laplace criterion, were employed to determine the weights of the model’s sub-indicators. This multi-criteria decision-making (MCDM) approach enabled a synthetic overall evaluation of both the general energy security status of the EU-27 countries and the performance of each barrier examined. The study also identified the weakest elements (barriers) within national energy systems that could potentially threaten their stability and resilience. This identification is essential for effective energy risk management and for enhancing the resilience of energy systems against disruptions. Due to its broad scope—covering availability, self-sufficiency, diversification, energy efficiency, energy costs, as well as environmental and social aspects—the study delivered a comprehensive evaluation of energy security in the EU-27 during the examined period. The findings reveal significant spatial and temporal variations in energy security levels among the EU-27 countries. Scandinavian and Western European nations achieved the highest scores, whereas Central, Eastern, and Southern European countries showed lower MBEES index values, reflecting persistent structural, social, and environmental vulnerabilities. The results hold strong potential for practical application, offering guidance for EU policymakers in aligning national strategies with overarching policy frameworks such as REPowerEU and the European Green Deal. Full article

This study presents a comprehensive evaluation of agricultural performance across 38 Organization for Economic Co-Operation and Development countries using an integrated Multi-Criteria Decision-Making framework that combines Technique for Order Preference by Similarity to Ideal Solution, VlseKriterijumska Optimizacija I Kompromisno Resenje, Analytical Hierarchy Process-based weighting, and equal-weighting strategies. The analysis reveals that the VlseKriterijumska Optimizacija I Kompromisno Resenje method exhibited greater sensitivity to changes in criterion weights, as confirmed by Spearman’s rank correlation ($P_v = 0.507$ < $P_t = 0.938$), while Technique for Order Preference by Similarity to Ideal Solution produced more stable rankings. To confirm the differing outcomes, the Borda count technique is applied, yielding a highly consistent final ranking ($P_{rank} = 0.819$). Remarkably, according to the integrated ranking results, Norway (total Borda score: 73) emerges as the top-performing country in terms of agricultural sustainability, whereas Ireland (total Borda score: 0) is positioned at the bottom. These findings offer a critical reference point for policymakers and stakeholders, highlighting both methodological rigor and practical relevance. By combining subjective and neutral weighting approaches, this study provides a balanced decision-support model and also underscores the potential of hybrid Multi-Criteria Decision-Making structures in generating nuanced and actionable insights in agricultural strategy development. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

Background: Acute kidney injury (AKI) in critically ill patients is often complicated by arrhythmias, potentially affecting outcomes. This study aimed to develop predictive models for arrhythmias in AKI patients and assess the impact of antiarrhythmic drugs on in-hospital mortality. Methods: We conducted a multi-database retrospective cohort study using MIMIC-IV and eICU databases. XGBoost and Bayesian Information Criterion (BIC) models were employed to identify key predictors of arrhythmias. Weighted log-rank and Cox analysis evaluated the effect of amiodarone and metoprolol on in-hospital mortality. Results: Among 14,035 critically ill AKI patients, 5614 individuals (40%) developed arrhythmias. Both XGBoost and BIC showed predictive power for arrhythmias. The XGBoost model identified HR_max, HR_min, and heart failure as the most important features, while the BIC model highlighted heart failure had the highest odds ratio (OR 1.18, 95% CI 1.16–1.20) as a significant predictor. Patients experiencing arrhythmia is associated with in-hospital mortality (arrhythmia group: 636 (11.3%) vs. non-arrhythmia group: 587 (7.0%), p < 0.01). Antiarrhythmic medications showed a statistically significant effect on in-hospital mortality (amiodarone: HR 0.28, 95% CI 0.19–0.41, p < 0.01). Conclusions: Our predictive models demonstrated a robust discriminatory ability for identifying arrhythmia occurrence in critically ill AKI patients, with identified risk factors showing strong clinical relevance. The significant association between arrhythmia occurrence and increased in-hospital mortality underscores the clinical importance of early identification and management. Furthermore, amiodarone therapy effectively reduced the risk of in-hospital mortality in these patients, even after accounting for time-dependent biases. The findings highlight the necessity of precise arrhythmia definition, careful consideration of time-dependent covariates, and comprehensive model validation for clinically actionable insights. Full article

As technological progress and population growth continue to drive rising energy demand, renewable energy has emerged as a key focus of the global energy transition due to its environmental sustainability. However, in suitability assessments and site selection for green energy projects such as photovoltaic (PV) power generation, key criteria such as supply–demand balance and land price are often inadequately considered, despite their direct impact on decision outcomes. Moreover, excessive reliance on expert judgment for weighting, along with the neglect of inter-criterion relationships, introduces uncertainty. Combined with the presence of ill-posed problems, these issues limit the practical value of the evaluation results. This study integrates economic cost–benefit analysis into the evaluation criteria system alongside climatic and geographical criteria, constructing a set of 11 spatial indicators, including global horizontal irradiation (GHI), land prices, and regional power demand, to support PV site selection. Furthermore, a comprehensive evaluation framework is proposed that combines geographic information systems (GIS), multi-criteria decision analysis (MCDA), fuzzy comprehensive evaluation (FCE), and machine learning (ML). The framework enables the collaborative optimization of expert-constrained and data-driven criteria weighting. A national suitability zoning map for PV power plants was developed and validated against actual construction cases. The results demonstrate that the proposed methodology outperforms traditional approaches, achieving a 0.1178 improvement in weight determination compared to expert-based methods, producing a photovoltaic suitability map that more accurately reflects actual construction trends, thereby providing better and more effective support for PV site planning. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

Silicone structural glazing (SSG) sealants are crucial sealing materials in modern building curtain walls, whose performance degradation may lead to functional and safety issues, posing significant challenges to building safety maintenance. This study comprehensively investigated the effects of temperature, humidity, stress, and ultraviolet (UV) irradiance on the durability of SSG sealants through multi-gradient matrix aging tests, revealing the influence patterns of these four aging factors on tensile bond strength (TBS). Based on aging test data and degradation patterns, a novel degradation model for TBS aging was established by incorporating all four aging factors as variables, enabling the model to reflect their combined effects on TBS degradation. The unknown parameters in the model were calculated using the Markov chain Monte Carlo (MCMC) algorithm and validated against experimental data. A recursive algorithm was developed to predict TBS degradation under actual service conditions based on the degradation model and environmental records, with verification through outdoor aging tests. This study established a service life prediction methodology that combines the degradation model with environmental data through recursive computation and standard-specified strength limits. The results demonstrate that increasing temperature, humidity, stress, and UV irradiation accelerates TBS changes, with influence intensity ranking as UV irradiation > temperature > humidity > stress. Synergistic effects exist among all four factors, where UV irradiation shows the most significant coupling effect by amplifying other factors’ combined impacts, while UV’s primary influence manifests through such synergies rather than independent action. Among temperature, humidity, and stress combined effects, temperature contributes approximately 50%, temperature–humidity interaction about 35%, with temperature-related terms collectively accounting for 90%. The degradation model calculation results show excellent agreement with experimental data (R² > 0.9, MAE = 0.019 MPa, RMSE = 0.0245 MPa). The characteristic TBS minimum value considering material discreteness and strength assurance rate serves as a reliable criterion for service life evaluation. The proposed prediction method provides essential theoretical and methodological foundations for ensuring long-term safety and maintenance strategies for glass curtain walls. Full article