Search Results (79)

The cause of chronic neurological effects associated with Lyme disease (LD) remains unclear. We propose that bacterial extracellular vesicles (BEVs) released by Borrelia burgdorferi, the causative agent of LD, exacerbate spirochete-induced damage and serve as a persistent source of antigenic stimulation. We showed that, over a 10-day period, in vitro cultures of B. burgdorferi B31 produced 38,000 BEVs per spirochete with a distinctive double-membrane structure and median diameter of 143.3 nm. BEVs contained known immunogenic and immunomodulatory molecules such as peptidoglycan, p66, flagellar filament protein (FlaB), basic membrane proteins A/B/D, BdrV, GroEL, CRASP-1, ErpA8, glycerophosphodiester phosphodiesterase, p37, OMS28, p13, OspA/B/C, VlsE, and outer membrane glycolipids (e.g., cholesteryl 6-O acyl beta D galactopyranoside). Chromosome-encoded 16S ribosomal RNA and cp32 plasmid-encoded OspE and terminase genes were also detected in the BEVs. Of the 45 Borrelia proteins identified in the urine of a C3H/HeJ murine model of Lyme disease, 14 were associated with BEVs. In human urine samples, 31 of 289 spirochete proteins detected in patients with either acute Lyme disease or persistent borreliosis post-treatment symptoms, including p66 and FlaB, were also BEV-associated. BEV treatment of HMC3 human microglial cells reduced phagocytic activity and triggered aberrant activation of inflammatory and immunometabolic pathways, including upregulation of interferon-alpha (IFN-α), aconitate decarboxylase 1 (Acod1), and Toll-like receptor 2 (TLR2) gene expression. BEVs also induced NRF2 nuclear translocation. In conclusion, these findings support that BEVs can amplify spirochete-induced damage and act as antigenic debris, driving dampened phagocytic activity and dysregulated inflammation, with implications for diagnostics and therapeutics targeting vesicle-mediated pathology. Full article

Choosing sites for solar photovoltaic (PV) power plants in developing countries like India is a crucial task while considering multiple conflicting factors and sub-factors simultaneously. Multi-criteria decision-making (MCDM) is an optimisation method that provides a framework for handling such situations in an intuitionistic fuzzy environment. The complexity and uncertainty associated with the site selection model are dealt with professionally. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the relative importance of the criteria, identifying airflow speed as the most influential factor, followed by humidity ratio, level of dust haze, availability of labour and resources, and ecological effects. This shows that airflow speed plays an important role in the power plant’s efficiency and performance. The Vlse Kriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method is then used to prioritise the alternatives as potential locations for setting up a solar PV power plant in India. A new de-i-fuzzification method based on the relative difference between two real numbers is also proposed. Sensitivity analyses and comparative studies are conducted to assess the robustness and effectiveness of the framework. Overall, the results demonstrate that the proposed framework is useful and effective for optimising site selection for solar power plants in India. 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

Urban floods increasingly threaten the mega-regions’ sustainable development, yet the pace and causes of change in urban flood resilience (UFR) remain elusive. This study proposes a new index system for UFR from three dimensions: resistance, recovery, and adaptability. The system includes 18 indicators across natural, economic, social, and infrastructure aspects. A comprehensive evaluation model combining entropy weighting, Criteria Importance Through Intercriteria Correlation (CRITIC), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods is developed and validated for the Beijing–Tianjin–Hebei (BTH) region of China, covering 2011–2022. Spatial dependence is diagnosed with global and local Moran’s I statistics, while an Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) isolates the contribution of each driver. The results indicate that UFR in the BTH region exhibited a generally increasing but fluctuating trend. Spatially, UFR displays a pronounced gradient, with higher levels concentrated in the northwest and lower levels in the southeast. Significant spatial autocorrelation is observed, spatial autocorrelation strength ranging from 0.330 to 0.404. Key drivers contributing to UFR include urban slope, hydrological station density, per capita park green space area, and population density, all with SHAP importance values exceeding 0.02 (ranging from 0.0012 to 0.1343). These indicators collectively play a dominant role in shaping the region’s resilience dynamics, highlighting their crucial influence on sustainable urban development. Full article

Borrelia garinii is a spirochete associated with Lyme borreliosis and is widely distributed across Eurasia. Although its genomic features have been well characterized in Europe, genomic data from East Asian isolates remain limited. Two B. garinii strains, HN13 and HN18, were isolated from a wild rodent (Apodemus agrarius) in South Korea and subjected to whole-genome sequencing and comparative genomic analysis. Their genomic features were compared with those of a tick-derived Korean strain 935 and additional global reference genomes. Phylogenetic analyses revealed that B. garinii strain HN18 clustered closely with French strains CIP103362 and 20047, whereas B. garinii strain HN13 showed high chromosomal similarity to the Korean strain 935. Both rodent-derived strains harbored plasmids carrying virulence-associated genes, including vlsE and vls silent cassettes, which were absent in B. garinii strain 935. This study provides new genomic insights into B. garinii circulating in East Asia and reveals host-associated plasmid variation linked to virulent potential. This study also suggests possible trans-Eurasian gene flow and underscores the need for continued genomic surveillance to better understand the evolution and epidemiology of Borrelia species. Full article

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective and objective weighting through a hybrid approach, where natural language processing (NLP) techniques extract perceptual preferences from user reviews, and the Interval-valued neutrosophic set analytic hierarchy process (IVNS-AHP) and entropy weight method (EWM) are employed to determine subjective and objective weights, respectively. The combined weights are used within the IVNS-VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) framework, enhanced by a possibility distribution (PD) to improve discriminative capability. Experiments were conducted using multiple performance metrics, including Precision, Recall, mean Average Precision at IoU = 0.5 (mAP@50), F1 Score, frames per second (FPS), and Parameters, to evaluate mainstream scalp detection algorithms. The results demonstrate that YOLOv8n achieves the highest comprehensive ranking with strong stability across different decision preferences. Comparative analyses with TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and fuzzy VIKOR variants confirm that the proposed PD-VIKOR method provides superior ranking stability and discriminative precision, offering a more reliable and robust evaluation under uncertainty. Full article

Decision-making theory has developed over many decades at the intersection of economics, mathematics, psychology, and engineering. Its classical foundations include Bernoulli’s expected utility theory, von Neumann and Morgenstern’s rational choice theory, and the criteria proposed by Savage, Wald, Hurwicz, and others. However, in real-world contexts, decisions are made under uncertainty, incompleteness, and unreliability of information, which classical approaches do not adequately address. To overcome these limitations, modern multi-criteria decision-making methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (Compromise solution approach) (VIKOR), and ELimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality) (ELECTRE), as well as their fuzzy and Z-number extensions, are widely applied to the modeling and evaluation of complex systems. These Z-number extensions are based on the concept of Z-numbers introduced by Lotfi Zadeh in 2011 to formalize higher-order uncertainty. This study introduces the Z-Regret principle, which extends Savage’s regret criterion through the use of Z-numbers. Supported by Rafik Aliev’s mathematical justifications concerning arithmetic operations on Z-numbers, the model evaluates regret not only as a loss relative to the best alternative but also by incorporating the degree of confidence and reliability of this evaluation. Calculations for the selection of digital advertising platforms in terms of performance assessment under various scenarios demonstrate that the Z-Regret principle enables more stable and well-founded decision-making under uncertainty. Full article

High uncertainty in new product development is primarily driven by multidimensional risks arising from dynamic interactions among factors including customer requirements (CRs), design characteristics (DCs), and solution decisions. To effectively address decision-making risks in uncertain environments, an integrative framework is proposed incorporating the Best–Worst Method (BWM), Interval-Valued Intuitionistic Fuzzy Quality Function Deployment (IVIF-QFD), and the IVIF-VlseKriterijumska Optimizacija I Kompromisno Resenje (IVIF-VIKOR) approach. Initially, CRs are identified through market research and focus group interviews, with weights determined by the BWM to enhance consensus and efficiency in judgment. Subsequently, an IVIF-QFD model is constructed. This model effectively addresses the fuzziness in expert judgments during the translation of CRs into DCs, strengthening its expressive capability in uncertain environments. Finally, candidate solutions are generated for critical DCs, and the IVIF-VIKOR method is employed to rank these solutions, identifying the Pareto-optimal solution. The framework’s effectiveness is validated by a steering wheel design, in addition, sensitivity analysis and comparative experiments are employed to quantify the robustness of the framework against parameter variations. This paper not only theoretically establishes a collaborative decision-making paradigm for uncertain environments but also provides an operational end-to-end decision support toolchain. 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

Against the backdrop of the energy revolution, global energy demands are rising. Solar–wind hybrid energy storage plants (SWHESPs) are undoubtedly a research hotspot in this field for enhancing energy efficiency. However, the primary challenge in constructing SWHESPs is site selection. This paper aims to comprehensively investigate the site selection process for SWHESPs and determine the optimal site scientifically and objectively by considering various aspects, including technology, society, environment, and economy. This study employs a literature review and the Delphi method to establish the site selection index system of SWHESPs. The triangular fuzzy number (TFN) is used in relative similarity as an objective weight, while the Decision-Making Test and Evaluation Laboratory (DEMATEL) is used as a subjective weight. The comprehensive weights are computed using the Lagrange optimization method. Additionally, the options are ranked and evaluated using Geographic Information System (GIS) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods based on prospect theory. The study also performs comparative and sensitivity analyses to confirm the effectiveness of the proposed methods. Proper siting can optimize the efficiency of resource use, which not only helps achieve more efficient use of clean energy but also promotes local economic development and job creation. Full article

To address potential risks during the construction process, improve construction quality and engineering safety, this paper constructs a construction safety risk analysis model for concrete gravity dams in cold regions based on fuzzy VIKOR-LEC. Firstly, an expert team employs linguistic variables to evaluate the likelihood of accidents (L), the frequency of personnel exposure to hazardous environments (E), and the consequences of accidents (C) for various risk factors in the LEC model. Secondly, the fuzzy analytic hierarchy process (FAHP) and maximum deviation method were used to construct a risk factor weight analysis matrix and find subjective and objective weights, respectively, to obtain the comprehensive weights of risk factors. Thirdly, VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) is introduced to improve the traditional LEC model and is used to calculate the risk priority number. Finally, in order to further verify the validity of the model, this paper selects the example of Linhai Reservoir dam in Heilongjiang Province to analyze the management of the construction safety risk. The research results may provide a scientific basis for the safety management of gravity dam construction projects in cold areas, and help to improve the level of project management and reduce construction risks. Full article

This paper proposes Linguistic Intuitionistic Fuzzy (LIF) aggregation operators, LIF-energies, LIF-correlation, and LIF-correlation coefficients. Supporting theorems are also proven for the proposed functions, which are utilized in the Linguistic Intuitionistic Fuzzy–Vlse Kriterijumska Optimizacija Kompromisno Resenje (LIF-VIKOR) method within Decision Support Systems (DSS). Additionally, numerical examples are presented to validate the method. The sensitivity analysis of weighting vectors is conducted, and the consistency of final rankings affirms the robustness of the proposed approaches. Arithmetic operations, specifically subtraction and division, are applied to LIF numbers (LIFNs) within the LIF-VIKOR algorithm. Furthermore, a function called the Linguistic Median Membership (LMM) function is introduced to convert LIFN values into crisp numbers. In the LIF-VIKOR algorithm, the proposed correlation coefficient is used for ranking alternatives, while the entropy method is applied to compute weights. Sensitivity analysis is performed to ensure the consistency of the proposed method. Finally, an Artificial Neural Network (ANN) is integrated into the VIKOR algorithm to enhance computational efficiency, reducing the time and manpower required to solve the model. Full article

The Tibet Autonomous Region presents immense potential for concentrated solar power (CSP) development, driven by its exceptional solar irradiance levels (e.g., a peak DNI exceeding 2100 kWh/m²/day). This positions it as a strategic contributor to China’s 2060 carbon neutrality target and aligns with global energy transition imperatives. However, CSP deployment in this region faces challenges stemming from unique high-altitude geographic characteristics, a complex terrain, and extreme climatic conditions—including pronounced diurnal temperature fluctuations, high wind speeds, and heavy winter snowfall. Additionally, traditional site-selection models inadequately address these region-specific environmental constraints. To address these limitations, an integrated framework combining geographic information systems (GIS) and multi-criteria decision-making (MCDM) is proposed in this study. A localized evaluation system is developed, incorporating four novel high-altitude-specific indicators: the average and maximum wind speed and the average and maximum snow depth. Criteria weights are determined through a hybrid approach integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), while candidate sites are prioritized using the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking method. The case study results demonstrate that region-specific environmental factors exert a significantly stronger influence on site suitability than traditional solar resource indicators (e.g., direct normal irradiance) under Tibet’s extreme climatic conditions, emphasizing the necessity of localized evaluation frameworks. The proposed methodology not only provides a robust scientific foundation for CSP site selection in high-altitude regions with environmental complexities but also establishes a replicable framework for optimizing multiple trade-offs in renewable energy systems under geographically complex conditions. Full article

Small- and medium-sized ports (SMSPs) face multifaceted challenges, including environmental pressures (pollution, resource depletion) as well as socio-economic strains (community impacts, trade dynamics). Governance complexities, regulatory fragmentation, limited funding, and stakeholder conflicts further exacerbate these challenges. This study identifies context-driven sustainability indicators for SMSPs and proposes best practices to enhance their resilience. Four small/medium-sized ports (SMSPs) in Crete, Greece—Souda (CHA), Heraklion (HER), Rethymnon (RET), and Sitia (SIT)—were evaluated to benchmark their sustainability performance using tailored indicators. The primary objectives were the following: (a) to identify sustainability indicators relevant to SMSPs, focusing on economic, environmental, and social aspects, and (b) to propose best practices for enhancing sustainability in these ports, considering their unique characteristics and constraints. A combination of the Best Worst Method (BWM) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis was employed. The findings revealed that “environmental monitoring and management” and “security of employment and social protection” are top priorities for medium-sized ports, while “competitiveness” was a crucial indicator for small ports, emphasizing the need for financial sustainability in the context of global trade. These results offer a strategic roadmap for policymakers, guiding interventions aimed at improving the sustainability and resilience of SMSPs, thereby supporting regional economies and international trade networks. Full article

This paper presents a method of choosing a single solution in the Pareto Optimal Front of the multi-objective problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple Input, Multiple Output) systems. It proposes the transformation of the group of non-dominated alternatives using the Box–Cox transformation with values of λ < 1 so that the graph with a complex shape is transformed into a concave graph. The Box–Cox transformation solves the selection bias shown by the decision-making algorithms in the non-concave part of the Pareto Front. After the transformation, four different MCDM (Multi-Criteria Decision-Making) algorithms were implemented and compared: SAW (Simple Additive Weighting), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PROMITHEE (Preference Ranking Organization Method for Enrichment Evaluations) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The simulations showed that the best value of the λ parameter is 0, and the MCDM algorithms which explore the Pareto Front completely for different values of weights of the objectives are VIKOR as well as SAW and TOPSIS when they include the Max–Min normalization technique. Full article