Nhat-Luong Nhieu

Fuzzy multi-criteria decision-making is important for technology assessment when expert judgments contain uncertainty, hesitation, and inconsistent evidence. This study develops a T-Spherical Fuzzy-Valued Neutrosophic Set (T-SFVNS)-based MEREC-EDAS framework for evaluating low-carbon cooling and energy-management technologies for data centers. Expert linguistic assessments are represented by T-Spherical Fuzzy-Valued Neutrosophic Numbers and aggregated before a score function is used at the explicit scalarization boundary. Standard MEREC then derives objective criterion weights from criterion-removal effects, and standard EDAS ranks alternatives by their positive and negative distances from the average score profile. The application evaluates nine technologies against ten criteria using assessments from thirty domain specialists. The corrected MEREC calculation assigns the greatest weights to carbon reduction potential (0.127), electricity demand reduction (0.125), maintenance complexity (0.124), operational cost efficiency (0.123), and cooling efficiency (0.123). The final ranking is Direct-to-Chip Liquid Cooling, Liquid Immersion Cooling, AI-Enabled Energy Management, Water-Side Free Cooling, Free-Air Cooling, Rear-Door Heat Exchanger Cooling, Hot/Cold Aisle Containment, Renewable-Powered Cooling, and Thermal Storage-Assisted Cooling. Weight perturbation, q-parameter, leave-one-expert-out, alternative-deletion, dominated-alternative, and multi-method comparisons show that the leading tier is robust, although the exact order of the two liquid-cooling technologies is sensitive in some scenarios. The findings provide a transparent and reproducible decision-support basis while explicitly acknowledging the information compression and rank-reversal limitations of score-based MCDM.

The global energy system is undergoing a structural transformation as countries pursue decarbonization while safeguarding energy security and economic resilience. However, renewable energy transition performance remains difficult to assess because existing evidence is often fragmented across single indicators, capacity-based measures, or isolated policy dimensions, while transition outcomes are shaped by multiple environmental, economic, institutional, technological, and cost-related factors. This study addresses this gap by benchmarking renewable energy transition performance across G7 economies through an integrated multi-criteria decision-making framework. The proposed methodology combines distance-based CRITIC (D-CRITIC) for objective criteria weighting and COBRA for compromise-based ranking to translate heterogeneous transition indicators into transparent cross-country comparisons. Using a secondary dataset covering fourteen indicators and seven G7 economies, the framework first derives objective criterion weights from cross-country dispersion and distance-based inter-criteria dependence, and then ranks countries by measuring their compromise distances from positive ideal, negative ideal, and average solutions. Using fourteen indicators spanning renewable energy penetration, environmental pressure, economic conditions, governance capacity, technology cost feasibility, and innovation capability, the D-CRITIC results identify CO₂ emissions per capita as the most informative criterion, followed by the share of electricity generated by bioenergy, the share of primary energy consumption from renewable sources, and solar LCOE. The COBRA-based assessment ranks Canada first, followed by the United Kingdom, Italy, Germany, Japan, the United States, and France. Robustness and sensitivity analyses show broad consistency in ranking patterns across alternative MCDM methods, while weight perturbation tests confirm that the ranking remains unchanged under ±5% and ±10% relative changes in criterion weights. These findings indicate that stronger renewable energy transition performance is associated with balanced progress across emissions reduction, renewable energy penetration, technology cost feasibility, institutional capacity, and innovation-related conditions, rather than superiority in a single indicator. The proposed framework offers a transparent and replicable tool for renewable energy transition benchmarking and evidence-based policy learning, while the results should be interpreted as cross-sectional comparative performance under the selected dataset and criteria rather than as a causal evaluation of specific policy instruments.

The rapid evolution of emerging technologies, the globalization of industrial networks, and the increasing complexity of production ecosystems have fundamentally reshaped the paradigm of modern manufacturing and industrial engineering

Sustainability assessment has emerged as a critical research area given the pressing challenges of balancing economic growth, environmental protection, and social equity. This study aims to develop an objective and reproducible framework to evaluate sustainability efficiency across countries by integrating multiple development dimensions into a unified decision model. Despite substantial prior research, inconsistencies often arise due to data heterogeneity and conflicting criteria. To address this gap, a hybrid multi-criteria decision-making (MCDM) framework was developed by combining the Double Normalization Method based on Removal Effects of Criteria (DNMEREC) for objective weighting and the Double Normalization Measurement of Alternatives and Ranking according to Compromise Solution (DNMARCOS) method for ranking alternatives. This integration ensures balanced consideration of beneficial and non-beneficial criteria while minimizing subjectivity. The model was empirically validated through a comparative assessment of G7 and BRICS countries using twelve sustainability indicators covering economic, environmental, and social dimensions. Results show significant variations in sustainability efficiency, with G7 countries generally demonstrating higher overall performance, while BRICS nations exhibit strong growth potential but face environmental and structural constraints. These findings confirm the robustness of the DNMEREC-DNMARCOS framework and highlight its adaptability to complex, multidimensional datasets. The study contributes a transparent methodological tool for researchers and policymakers seeking evidence-based strategies to enhance global sustainability performance and bridge development gaps.

This study illuminates the multifaceted influence of Chinese in Africa, driven by the imperative to understand the strategic and economic ramifications of this rapidly evolving relationship. Motivated by the critical role Africa plays in global geopolitics and resource dynamics, alongside Chinese expanding international influences, the research aims to quantitatively and psychologically assess the decision-making processes underpinning this engagement. Adopting a regret theory-based Multiple Criteria Decision Making (MCDM) framework, the study evaluates Chinese impact across 49 African countries from 2018 to 2022, employing six economic indicators to capture the breadth of Chinese activities. Through meticulous normalization, regret utility computation, and total gap analysis, the methodology affords a systematic ranking that reflects the varying degrees of Chinese economic influence. The findings uncover pronounced variances in the level of Chinese engagement across the continent, with countries like Nigeria and Egypt showcasing substantial influence convergence with the theoretical model of ideal economic partnership, whereas others like Cape Verde indicate minimal influence. Contributing to academic and practical discourse, this study not only provides a methodological blueprint for analyzing geopolitical influences but also offers insights that policymakers can leverage to optimize their engagement strategies with Chinese. It foregrounds the interplay between empirical economic data and behavioral economics within international relations research. The study acknowledges limitations, primarily in data availability, which may not capture the full scope of informal economic interactions. It proposes further research to enrich the understanding of the Chinese-Africa nexus through longitudinal studies, integration of qualitative data, and expansion of the analytical model to encompass broader socio-economic impacts and more diverse indicators.

In the context of increasingly strong digital transformation and production automation, choosing the right palletizing robot plays a key role in optimizing operational efficiency in industrial chains. However, the wide variety of robot types and specifications complicates decision-making and increases the risk of biased judgments. To overcome this challenge, this study develops an objective multi-criteria decision-making (MCDM) framework that integrates two complementary methods for selecting the optimal industrial pal-letizing robot in the context of modern manufacturing that is increasingly dependent on intelligent automation solutions. Specifically, an improved CRITIC approach is employed to determine objective criteria weights by refining the measurement of contrast intensity and inter-criteria conflict, while normalization ensures comparability of heterogeneous robot parameters. CRADIS is then applied to rank the alternatives based on their relative closeness to the ideal solution. The contributions of this study are twofold: methodological, enhancing the objectivity and robustness of weighting through refined CRITIC and normalization, and practical, offering a reproducible evaluation framework for managers when choosing industrial robots. Application to eight palletizing robots demonstrates that “repeatability” and “power consumption” significantly influence rankings. Sensitivity analysis further confirms the model’s stability and reliability. These findings not only support evidence-based investment decisions but also provide a foundation for extending the method to other industrial technology selection problems.