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Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in practice, helping optimize energy efficiency and reduce costs. Integrating digital twin technology with the Multi-Criteria Decision-Making (MCDM) methods offers a novel approach to managing and optimizing energy systems in smart cities. The paper aims to create a consistent and robust approach to determining the best digital twin solution for energy systems in smart cities. The paper identifies critical factors for decision-making and establishes a method for assessing the significance of criteria using Triangular Neutrosophic Sets (TNS) through the MEthod based on Removal Effects of Criteria (MEREC) and the Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) approach. These methods are used to evaluate and prioritize multiple criteria in decision-making processes. Furthermore, the methods are combined with Triangular Neutrosophic Sets (TNS) to support decision-making for smart cities' energy systems, better accounting for the complex and uncertain nature of energy systems. A case study is conducted to apply and validate the developed methodology and perform a sensitivity analysis of the experimental results. The research outcomes indicated that the proposed methodology is robust and effective in handling the uncertainty and complexity inherent in smart cities' energy systems. The sensitivity analysis further confirms the stability and adaptability of the proposed methodology across different scenarios, making it a valuable tool for policymakers and stakeholders in the energy sector. [ABSTRACT FROM AUTHOR]

Economic performance can be measured using different macroeconomic indicators and different analysis methods. In this context, it is aimed to compare the economic performances of BRICS countries and Turkey for the 2000-2022 period by using macroeconomic variables and to analyze the performances of the countries discussed separately during his period. In the study, GDP per capita, growth rate, exports, current account balance (percent of GDP), inflation, unemployment, exchange rate and import were determined as performance evaluation criteria. Within the scope of the study, an integrated model was used, which deals with the economic performances of BRICS-T countries, ENTROPY and WEDBA (Weighted Euclidean Distance Based Approximation) methods. In the ENTROPY method, the weight coefficients obtained separately for each year and for each country were calculated, and in the WEDBA method, the performance ranking was made by using the obtained weights. According to the results of the study, the country with the best economic performance in the ranking of economic performance among the countries was China in all years, while India had the poorest overall performance compared to other countries on average. In addition, while Turkey was in the second place in 2011, it took the third place when the average of all years was taken. [ABSTRACT FROM AUTHOR]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

In the aviation industry, customer satisfaction plays a critical role in business success. As consumer expectations change post-pandemic, monitoring and improving the performance of airlines is becoming increasingly important. As such, this study aims to examine the performance of airlines in terms of passenger satisfaction, focusing on the Star Alliance strategic network. To this end, secondary data obtained from online passenger reviews by Skytrax was used to measure passenger satisfaction. In the study, Multi Criteria Decision-Making methods (MCDM) were used, namely the LOPCOW method to determine the importance levels of satisfaction criteria and the AROMAN method to rank the performance of airline alternatives. The findings revealed that the most important satisfaction criteria are foods and beverages, value for money, and cabin staff service, respectively. In addition, Air New Zealand was identified as the airline with the highest passenger satisfaction. Finally, a rigorous two-stage sensitivity analysis was conducted to test the consistency of the ranking, and it was concluded that the ranking was largely consistent. This study provides airlines with a robust and consistent model for evaluating their performance in the post-pandemic period. [ABSTRACT FROM AUTHOR]

A low‐cost, high‐performance alternative fuel to traditional fossil fuels is required due to rising energy demand, fossil fuel depletion, and rising prices. In this case, one of the green fuels could be synthesized using transesterification from waste‐fried edible oil (WFEO). The current study deals with neat diesel (D100) and waste‐fried edible oil biodiesel (B100) as the baseline fuels. By volume, a binary blend of 50% diesel and 50% WFEO biodiesel was prepared. The biodiesel/methanol/diesel mixture was considered a ternary blend under two different ternary ratios, such as B40M10D50 and B30M20D50, respectively. The fuel characterization and the rheological study were performed for all the fuel blends as per the ASTM standards. According to engine experimental results, the B30M20D50 blend has 25.42% higher brake thermal efficiency (BTE) than B100 but 3.4% lower than D100 at full load due to the higher methanol percentage. When compared with D100, adding 20% methanol to the ternary blend reduced brake‐specific fuel consumption (BSFC) by 33.34%. Both ternary blends increased NOx emissions by 27.38% and 22.97% compared to D100 but decreased them by 14.23% and 18.68% compared to B100. Both ternary blends produced 0.4 and 0.36 kg/kWh at lower loads, while the D100 and B100 produced 0.46 and 0.42 kg/kWh. Finally, the entropy‐weighted technique for order preference by similarity to the ideal solution (TOPSIS) multi‐criteria decision‐making method was used to determine the best blend for engine performance and emissions. The entropy‐weighted TOPSIS technique likewise found that a B30M20D50 ternary blend with 75% loading had the lowest emissions and best performance. [ABSTRACT FROM AUTHOR]

The Solar energy is a non-exhaustible type of energy that serve to the survivability of mankind and all the living beings on the earth. In recent time due to increase in population density and rapid urbanization, the entire world is facing enormous environmental challenges such as global warming, air pollution, imbalance of ecology, and many more. There is a necessity to sustain natural endowments with the help of advanced materials and technologies. In the research domain, the majority of them are looking for a mechanism that can be used for harnessing solar energy. Many types of solar devices such as solar panels, solar heaters, solar concentrators, etc. are in practice. The Scheffler reflector is used for the harness of solar energy at an advanced stage. Addition of a thermal storage unit in the Scheffler system, heating performance can be enhanced by extending the heat storage time. In this study, a comparative analysis of the thermo-physical properties of nine Latent Heat Storage (LHS) storage materials (generally used in solar applications) is carried out using three MCDM techniques: analytical hierarchy process, TOPSIS, and weighted sum model. These techniques are very useful in solving such problems, where we need to select the best option amongst the group having similar characteristics. Materials taken for the study are as: Di-Sodium Hydrogen Phosphate Do-decahydrate (Na2HPO412H2O), Erythritol, Stearic Acid, Acetamide, Calcium Chloride Hexahydrate (CaCl26H2O), Magnesium chloride hexahydrate (MgCl26H2O), Malic Acid, Palmitic acid, and Lauric Acid. Results show that the three techniques are nearly yielding the same (slight deviation) ranks of the top materials (rank 1; Erythritol, rank 2; Stearic acid and rank 3; Na2HPO412H2O) which can be used in Scheffler reflector for using the heat up to a longer period of time. This research work is novel in the sense that the selection of LHS material for Scheffler reflector using MCDM hybrid model is carried out for the first time. LHS material demand is expected to increase over time due to its more consciousness and stronger regulatory frameworks. Other MCDM techniques can be applied to select the best thermo-chemical substances which improve the heat-carrying capacity of the Scheffler reflector. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of the material selection, manufacturing, and Electro-Mechanical testing of radomes made from Polymer Matrix Sandwich Composite materials. The objective is to achieve optimal electromagnetic (EM) and mechanical performance using a combination of PMI foam core and hybrid face sheets (Kevlar & S Glass) sandwich composite structures. The selection of the material configuration was based on a Multi Criteria Decision Matrix (MCDM) method to meet the requirements of Electromagnetic Interference (EMI) transparency and aerodynamic/inertial loads during flights. A full-scale nose radome was manufactured using a hand-layup technique assisted by vacuum bagging, specifically designed for flight vehicles with speeds up to <0.8 Mach. The EMI behavior of the radome was experimentally tested using the free space measurement method and verified through numerical modeling in Altair FEKO software, which showed minimal EM losses of 3% and <1% respectively. Multiple specimens were prepared to determine the mechanical properties and were tested according to ASTM standards for tensile and flexural properties. Additionally, finite element modeling (FE) was employed to validate the mechanical properties of the radome, ensuring that it meets the required constraints. The combined results from the EMI testing, FE modeling, and mechanical testing provided an optimum combination of materials and structure that satisfies the functional requirements of the radome. [ABSTRACT FROM AUTHOR]

The identification of areas prone to soil erosion in ungauged river basins is crucial for timely preventive measures, as erosion causes significant damage by lowering soil productivity and filling reservoirs with sedimentation. This study proposes a novel approach to prioritize sub-watersheds (SWs) in Ponnaniyar river basin. It utilizes different combinations of five objective-based weighting methods and seven Multi-criteria Decision Making (MCDM) techniques under outranking and synthesis methods with soil loss, morphometry, land use/land cover (LULC), and topography parameters. The results obtained from different hybrid models are validated using metrics like percentage and intensity of change. The findings reveal that MW-PROMETHEE (53.85%) and CRITIC-WASPAS (8.31) perform best in prioritizing areas based on morphometry, while CRITIC-TOPSIS (48.35% and 7.58) is more effective in prioritizing areas based on land use/land cover (LULC) and topography. The grade average method is used to integrate the rankings from 71 models: 35 based on morphometry, 35 based on LULC, and 1 based on the RUSLE model. The analysis identifies SW2 with a grade value of 4.34 as severely affected by soil erosion, followed by SW11 (5.45), SW5 (5.56), and SW9 (5.68), all falling within the very high priority level. This study recommends implementing appropriate water harvesting structures, which might be helpful in mitigating soil degradation, promoting soil conservation, and ensuring sustainable agricultural productivity. [ABSTRACT FROM AUTHOR]

In the context of intensive urban development, urban complexes have emerged as crucial public spaces that address the needs of urban populations. However, current research on urban complexes is predominantly qualitative and lacks a rigorous scientific and quantitative analysis. Therefore, this study employs the analytic hierarchy process (AHP) to construct a standardized system encompassing five dimensions: spatial function, spatial perception, architectural style, surrounding environment, and energy-saving technology. The objective is to determine the weights of the indices that influence people's use of urban complexes under the goal of "humanization". Additionally, the study quantitatively analyzes key indices using spatial syntax and other analytical methods. Subsequently, we employ multi-criteria decision making (MCDM) analysis to examine three real-world cases in China, aiming to validate further the importance of the AHP + MCDM approach, which incorporates the TOPSIS method based on grey correlation. This methodology considers both the subjective factors of crowd evaluations of urban complex usage and the interrelationships among indicators, ensuring that the statistical calculations of the indicators remain objective and scientifically robust. The results indicate that (1) the degree of facility improvement has the greatest impact on the crowd's use of urban complexes; (2) there is a discrepancy between the results of the TOPSIS method and the MCDM evaluation model, with the MCDM evaluation method aligning more closely with real-world scenarios; and (3) the Shanghai MOSCHINO received the highest evaluation score, while the Nanjing Central Emporium received the lowest. Finally, we discuss the experimental results and propose targeted strategies for optimizing the design of urban complexes to achieve the goal of "humanization". [ABSTRACT FROM AUTHOR]

Copyright of Verimlilik Dergisi is the property of Verimlilik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Verimlilik Dergisi is the property of Verimlilik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Anatolian Environmental & Animal Science is the property of Bulent Verep and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Benchmarking brain–computer interface (BCI) applications, considering all available smart training environment (STE) criteria, is a challenging task due to the following issues: inconsistent weights, static ranks and ranking stability measurements. Therefore, this study aims to develop a dynamic decision-making framework for benchmarking BCI applications based on STE criteria through three integrated phases. In the first phase, the adaptivity of the decision matrix is identified concerning two dimensions: 27 BCI applications as alternatives and 10 STE criteria. In the second phase, the consistency of weights is evaluated and constructed to each STE criterion via the fuzzy-weighted zero-inconsistency (FWZIC) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method for benchmarking the BCI applications. In the third phase, four sensitive scenarios are developed for measuring the consistency of the STE criteria's weights and the ranking performance of the BCI applications. The experimental result shows that the 'ease of use' STE criterion obtains a high-affected weight with a value of 0.13, while other criteria, augmented reality, hybrid and desktop use (stationary), obtain less weight with a 0.075 value. Additionally, BCI applications A5 and A6 are robust and stable among the others based on the consistency of weights concerning the four scenarios, and they are further candidates to be deployed in real-life applications. The overall ranking results are stable and less affected when applied to the four sensitive scenarios due to the robustness of the integrated FWZIC-VIKOR method of the proposed dynamic framework. The outcome of this framework is objectively validated in terms of five groups, and the ranking results are reliable and the closest to the decision-makers' viewpoints. The proposed framework considers a good solution for choosing a dependable application to support the user and community of BCI systems with a stable STE environment. [ABSTRACT FROM AUTHOR]

The demand for effective e-learning platforms requires prioritizing pedagogical excellence in online educational websites. Current approaches struggle with uncertainties, hindering optimal e-learning environments due to a lack of comprehensive evaluation in traditional methods. An integrated approach is crucial to avoid inefficiencies and incomplete understanding of learner needs. This research introduces a pioneering methodology integrating Inter-Valued Fuzzy (IVF) COPRAS-EDAS-PIV hybrid Multiple Criteria Decision-Making (MCDM) techniques, addressing existing limitations. Leveraging the IVF concept allows a holistic assessment of pedagogical parameters, ensuring a thorough understanding of the decision-making landscape. The study involves an extensive literature review, parameter identification, and data acquisition through group decision-making. The selection of a suitable e-learning website is based on seven conflicting parameters, and preference ranking orders are prescribed using EDAS, COPRAS, and PIV MCDM model. Rigorous analysis using these techniques facilitates precise ranking and informed decision-making. The findings underscore the efficacy of the proposed IVF-MCDM approach for the design of a pedagogical e-learning website. Final results reveal that alternative 5 as the most preferable, followed by alternative 2, while alternative 3 is the least favored option among the group. Comparative and sensitivity analyses validate the approach's superiority, enabling stakeholders to make well-informed decisions for optimal e-learning websites that cater to diverse learner needs, thus enhancing the overall online learning experience. [ABSTRACT FROM AUTHOR]

This study presents an in‐depth inquiry into estimating ADME properties for promising anticancer drugs, particularly amino acid‐based alkylating agents, through ev‐ve degree topological indices and QSPR analysis. The aim of the study is to compare multigraph modeling to simple graph modeling in estimating six ADME properties. Results demonstrate that multigraph modeling's superior performance, with notable high correlations such as r=0.926$$ r=0.926 $$ for maximum passive absorption (MPA) using the M‐ev index, compared to simple graph modeling's r=0.68$$ r=0.68 $$ with the M2$$ {M}_2 $$‐ev index. This emphasizes the need for sophisticated modeling techniques in drug development. The primary objective is to compare multigraph and simple graph modeling using topological structure descriptors, followed by QSPR analysis to determine the better approach in estimating ADME properties. MCDM weight allocation techniques validate correlation values, enhancing understanding of estimators and identifying potential drugs. This underscores the importance of considering various MCDM methods and weight allocation approaches for reliable decision‐making in healthcare contexts. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) approaches have been developed to support a wider range of application areas. For example, channel allocation, among the vehicles in infrastructure-based/hybrid Vehicular Ad hoc NETworks (VANETs), is done by exploiting MCDMs to minimize the waiting time and maximize the request service rate by the Road Side Unit (RSU). The Analytic Hierarchy Process (AHP) developed by Saaty, however, has gained enormous popularity and been used all over the world since its invention in 1977. AHP is capable of prior weight calculation of individual criteria using Criteria Preference Matrix (CPM) and rank computation of alternatives as well. Another MCDM technique, termed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was created by Hwang and Yoon in 1981. It is a ranking technique that requires inputs of the weights of the criteria. This paper aims to analyze the impact on ranks of various alternatives for different CPMs using the AHP-AHP method and the AHP-TOPSIS method. For both cases weights of criteria have been calculated using AHP before rank computation. In addition to that, this paper deals with the performance evaluation of infrastructure-based VANETs for priority-based channel allocation using the AHP-AHP and the AHP-TOPSIS method in terms of Request Service Rate and Average Waiting Time for various CPMs. It is observed that AHP-TOPSIS performs better in terms of Request Service Rate with the increasing alternatives. Furthermore, the AHP-AHP method shows frequent rank change while increasing the number of alternatives for various CPMs. [ABSTRACT FROM AUTHOR]

With global shifts towards sustainable energy models, the urgency to address rising fossil fuel prices, military conflicts, and climate change concerns has become evident. The article aims to identify the development of wind energy in Poland. This study introduces an integrated methodology for enhancing renewable energy capacities by selecting new construction sites for onshore wind farms across Poland. The proposed methodology utilises a hybrid model incorporating multiple criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), alongside the semiautomated spatial analysis method using QGiS software (v. 3.32 Lima). The model considers economic, social, and environmental criteria and limitations, offering a comprehensive approach to the decision-making process. It was found that wind farms occupy 460.7 km2 in Poland, with a 250 m buffer around each turbine and a total power capacity of 5818 MW. The results show that an additional 7555.91 km2 of selected areas, 2.34% of the country's area, theoretically offer significant opportunities for wind energy development. The spatial analysis identifies potential sites with promising opportunities for domestic and international renewable energy investors. The study's findings contribute towards achieving national and EU renewable energy targets while offering a replicable framework for informed spatial planning decisions in other regions. [ABSTRACT FROM AUTHOR]

This study aims to effectively support decision-makers in evaluating leading e-commerce companies in Vietnam from a sustainability perspective. In addition, this study evaluates and analyzes criteria that affect the performance of e-commerce companies in Vietnam to find the best practices for e-commerce implementation of managers. In this way, companies can save costs and increase marketing and promotion efficiency, helping to reach more customers. In doing so, an integrated framework based on MCDM is proposed for the first time in e-commerce industry assessment. In the first stage, the fuzzy decision making trial and evaluation Laboratory method is applied to determine the weights of 9 criteria based on collected data. This study lists prominent quantitative evaluation criteria, taking into account the sustainability aspect. In the second phase, ranking the top 12 e-commerce companies in Vietnam according to their performance based on these nine criteria was done by applying the neutrosophic fuzzy axiomatic design method. According to an analysis of the data, the external financial assistance coefficient (12.7%) is the most crucial factor determining how competitive international e-commerce businesses are. The results of this study can help underperforming companies make strategic decisions to improve their performance. Integrating these two techniques provides a new method for evaluating global trading companies that have hitherto remained unaddressed in the trading industry and, therefore, leave a gap that needs to be investigated. [ABSTRACT FROM AUTHOR]

The crop-land suitability assessment (LSA) is important for managing land and water resources and developing sustainable agricultural systems. In the field of determining the suitability of cropland, the multi-criteria evaluation approach (MCEA) or multi-criteria decision-making (MCDM) is becoming more popular as a tool for analyzing difficult situations and obtaining logical conclusions. The 75 relevant publications from 2010 to 2023 were reviewed and summarized in this paper to precisely explain and identify MCEA application areas and decision-making problems resolved within the crop-land suitability assessment. The findings indicated that the MCDM or MCEA techniques have been applied with or without remote sensing, Agricultural Land Use Evaluation System, parametric methods, etc. for land suitability evaluation. This study also contributes to the classification of criteria used for this purpose. A total of 117 parameters from six groups: soil properties, climate, topography, hydro-geomorphology, land use compatibility, and hazard are necessary for LSA. [ABSTRACT FROM AUTHOR]

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case. [ABSTRACT FROM AUTHOR]

Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on that basis, to explore their effects on purchase intentions regarding advertised products or services. A total of 376 filled-in questionnaires from an online survey were analysed. The main characteristics of digital influencers' behaviour that affect consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers' messages. The obtained results provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is an individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure the continuous improvement of this e-marketing channel. [ABSTRACT FROM AUTHOR]

The expansion of renewable energy (RE) technology could be assisted by energy policies that tackle significant barriers. Several obstacles have slowed the RE sector’s growth in developing nations, leading to less-than-ideal development in this area. Moreover, exploring potential alternate strategies to surmount these constraints has received limited attention. It is essential to identify the barriers preventing the use of RE technologies before proposing an adequate replacement to tackle these issues appropriately. Hence, the present research aspires to identify and prioritize the obstacles currently impeding the advancement of RE technologies in developing countries, using the spherical fuzzy analytical hierarchy approach. The results suggest that the “Policy and political” category (0.1769) has the highest ranking among the main groups, followed by “Economic” (0.1647), “Administration & regulatory” (0.1640), and “Technology” (0.1438), respectively. The market barriers, with a weight of (0.1333), exhibit the lowest weight among the various factors considered. Similarly, the geographical and ecological barriers, with a weight of (0.1170), and the social and cultural barriers, with a weight of (0.1003), all have relatively lower weights. In contrast, the comprehensive global rankings of sub-obstacles indicate that the lack of a sufficient market base (0.05419), transportation problems (0.04375), and a lack of local entrepreneurship (0.04243) are the three most notable sub-obstacles among the remaining thirty-five obstacles across diverse categories. Moreover, recommendations are provided for the eradication of the obstacles. Furthermore, it is essential to consider the possible advantages of devising approaches to indorse the effective execution of RE projects in developing nations. [ABSTRACT FROM AUTHOR]

Increasingly, software organizations are implementing DevOps culture to benefit from it in terms of continuous testing, delivery, improvement, and so forth. Implementing DevOps is difficult due to a lack of understanding about the practices and their effective application for its effective implementation. This paper aims to explore different DevOps practices that can be implemented in software organizations. The study involves conducting a systematic literature review (SLR) to identify DevOps implementation practices, followed by the utilization of the fuzzy best–worst method (FBWM) to establish a taxonomy or classification of software practices. We have used an SLR to investigate the practices, and subsequently, the survey method was followed to validate the identified practices. Moreover, the best–worst method (BWM) was considered to evaluate the significance and develop the taxonomy of the practices. The results of this study extracted 19 practices that have been identified in the SLR process. The identified factors are further classified into six core DevOps lifecycle phases. The results of the BWM approach are shown. The outcomes of the study conclude that the proposed taxonomy of the practices could help DevOps practitioners and researchers effectively implement them in software development organizations. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between posthoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Realworld Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank. [ABSTRACT FROM AUTHOR]

Cloud computing using hosts that are accessed online, data is maintained und managed using the storage arrangement for storing information. This means that instead of storing data on local storage devices like hard drives, Data is kept on servers that cloud vendors maintain. Cloud computing storage offers many benefits over traditional storage methods. Firstly, it provides easy and convenient access to access. This makes it an ideal storage solution for businesses and individuals who need to access data on the go. Secondly, cloud computing storage is highly scalable, allowing users to without worrying about running out of storage space. A variety of storing choices are often provided by cloud service companies to suit the needs of different users, from small businesses to large enterprises. Finally, cloud computing storage offers robust security features to protect data from unauthorized access, theft, and data loss. Cloud service providers employ advanced security protocols, encryption, and access controls to ensure data is kept safe and secure. Overall, cloud computing storage provides a flexible, scalable, and secure way to store and manage data in today's digital world. Cloud computing storage has significant implications for research, particularly in the areas of data management and collaboration. Here are some ways in which cloud storage is impacting research: Data management: Cloud storage enables researchers to store and manage large volumes of data, including structured and unstructured data, in a secure and accessible manner. This is particularly important in fields such as genomics, where researchers need to store and process massive amounts of data. Collaboration: Cloud storage makes it easier for researchers to collaborate and share data and resources with colleagues around the world. Cloud storage platforms can provide tools for sharing and collaboration, such as version control, commenting, and real-time collaboration. Accessibility: Cloud storage can help researchers to save money on data storage and management. Rather than investing in on-premise storage infrastructure, researchers can use cloud storage on a pay-as-you-go basis, which can be more cost-effective in the long run. Scalability: Cloud storage can scale up or down as needed, which is important for research projects with fluctuating data storage needs. This enables researchers to easily add or remove resources as needed, without having to worry about hardware limitations. Overall, cloud computing storage has significant implications for research. By enabling researchers to store and manage large volumes of data, collaborate with colleagues, access resources remotely, and save money on data storage, cloud storage is helping to accelerate research and advance scientific knowledge. DEMATEL (Decision Making Trial and Evaluation Laboratory) They are divided into analysis using the Nonmetal mineral product It is the interaction between the factors Visualized and assesses dependent relationships Through the structural model Also deals with identifying important. Cloud Computing, Cloud Storage Forensics, Digital Forensics, Mobile Forensics and Incident Handling. the Rank using the DEMATEL for Cloud Computing Storage in Cloud Computing is got the first rank whereas is the Digital Forensics is having the Lowest rank. [ABSTRACT FROM AUTHOR]

The rough set (RS) and multi-granulation rough set (MGRS) theories have been successfully extended to accommodate preference analysis by substituting the equivalence relation (ER) with the dominance relation (DR). On the other hand, bipolarity refers to the explicit handling of positive and negative aspects of data. In this paper, with the help of bipolar fuzzy preference relation (BFPR) and bipolar fuzzy preference δ -covering (BFP δ C), we put forward the idea of BFP δ C based optimistic multi-granulation bipolar fuzzy rough set (BFP δ C-OMG-BFRS) model and BFP δ C based pessimistic multi-granulation bipolar fuzzy rough set (BFP δ C-PMG-BFRS) model. We examine several significant structural properties of BFP δ C-OMG-BFRS and BFP δ C-PMG-BFRS models in detail. Moreover, we discuss the relationship between BFP δ C-OMG-BFRS and BFP δ C-PMG-BFRS models. Eventually, we apply the BFP δ C-OMG-BFRS model for solving multi-criteria decision-making (MCDM). Furthermore, we demonstrate the effectiveness and feasibility of our designed approach by solving a numerical example. We further conduct a detailed comparison with certain existing methods. Last but not least, theoretical studies and practical examples reveals that our suggested approach dramatically enriches the MGRS theory and offers a novel strategy for knowledge discovery, which is practical in real-world circumstances. [ABSTRACT FROM AUTHOR]

To solve difficulties involving various groups' decision-making problems, this work has been proposed to develop a logical aggregation approach to aggregate decision-makers' crisp data into Pythagorean fuzzy numbers. By combining the established strategy with the Pythagorean fuzzy TOPSIS method, a hybrid Pythagorean fuzzy multiple criteria group decision-making methodology is presented. Based on fuzzy rules inference and the Takagi–Sugeno technique, a novel function is created to represent the degrees of uncertainty in decision-makers' data. As an example, the material selection process in practical additive manufacturing designs is provided to show how the proposed methodology may be applied to actual applications. Sensitivity analysis is used to evaluate the effectiveness of the suggested methodology. The outcomes demonstrate that the plan was successful in producing a PFN that accurately reflects the decision-maker's knowledge. [ABSTRACT FROM AUTHOR]

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The optimization of gas pipeline networks plays a pivotal role in ensuring the efficient and economically viable transportation of natural gas. In this research, we have developed a comprehensive mathematical model capable of analyzing diverse network configurations, encompassing both linear and branched topologies. Our scientific investigation aims to explore the optimization potential of gas pipeline networks, employing a sophisticated and systematic approach to enhance network design and operation. The overarching objective is to achieve maximum efficiency and reliability in gas delivery to customers. The optimization process focuses on minimizing power requirements, maximizing gas flow rate, minimizing the fuel consumption, and maximizing line pack to ensure the optimal utilization of the pipeline infrastructure. To accomplish these objectives, our study employs advanced mathematical models that accurately depict network behavior, cutting-edge simulation tools to explore various operational scenarios, and state-of-the-art optimization algorithms to identify the most favorable network configuration and operating conditions. To facilitate this optimization process, we have incorporated the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method, a potent multi-criteria decision-making technique. Through the application of this approach to two case studies, we have demonstrated its effectiveness in identifying optimal network configurations. Furthermore, we have conducted an analysis to determine the total cost and fuel consumption associated with different network configurations, offering valuable insights for decision-making purposes. The results of our study underscore the superiority of our approach in identifying more economical networks compared to existing methods. By embracing the proposed approach, gas transportation networks can be optimized to achieve superior cost-efficiency and reduced fuel consumption. [ABSTRACT FROM AUTHOR]

Attracting foreign investment is essential for the competitiveness and prosperity of nations. When deciding where to invest, an investor may be interested in considering specific criteria for investing or doing business and preferences for those criteria. In the same way, when evaluating the situation of a country to attract investment, consider different aspects to determine its ease of doing business. The MultiCriteria Decision-Making (MCDM) methodology is suitable for evaluating nations according to their ease of doing business due to the multifactorial elements of each nation. This work applied the ELECTRE-III method; it evaluated 190 nations based on the decision-maker's preferences, giving different importance to the ten criteria considered in the World Bank's Doing Business 2020 study. The results with this methodology show better-positioned nations in the ranking compared to the report presented by the World Bank Group. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) is one of the technologies of Industry 4.0 that has been contributing to the development of different manufacturing industries. The integration of sustainability concepts into additive manufacturing has been gaining attention among researchers. This integration is essential in the development of AM technologies and can be a significant asset in terms of decision making for organizations. This work aims to present a concise literature review on the integration of decision making, especially multi-criteria decision making, and sustainability into the AM environment. The literature on this topic currently possesses a total of fifteen documents, which were analyzed in this work. Some developments on this topic have been achieved in domains such as material selection, process selection and challenges, and drivers' analysis of sustainable AM. This review shows that even though there has been an effort in recent years to integrate sustainability into additive manufacturing, there is still a long road to the development of this topic for the future, and so some recommendations for future research paths are presented. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kafkas University, Journal of Economics & Administrative Sciences Faculty / Kafkas Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of University of Kafkas, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to alleviate the problem of water resources shortage and unequal distribution in time and space in Guyuan City, this paper firstly analyzes the basic situation of water resources in Guyuan City, and constructs a multi-objective optimal allocation model of water resources with economic benefits, social benefits and ecological benefits as the objectives. Then, NSGA-III algorithm is proposed to solve the optimal allocation scheme of water resources. Based on the original NSGA-III algorithm, multi-point crossover operator and multi-point mutation operator, namely non-dominated sorting genetic algorithm-III with Random multipoint crossover mutation(RNSGA-III), are used. In the selection of the next generation, SAW and MCDM schemes were used to evaluate the chromosomes and set the weights to achieve better and more ideal results. Finally, the proposed scheme is compared with the existing configuration scheme based on NSGA-III and NSGA-II algorithms. Simulation experiments show that the proposed scheme is better than the allocation scheme based on NSGA-III algorithm and NSGA-II algorithm in the comprehensive cost of economic benefits, water shortage and ecological benefits, which can provide a basis for the rational allocation of water resources in Guyuan City, achieve unified management and sustainable utilization of water resources and support the sustainable development of economy and society. [ABSTRACT FROM AUTHOR]

Presence of numerous competing suppliers along with conflicting evaluation criteria, increased participation and expectations of the stakeholders having varying interest, uncertainty in demand and supply, and lack and vagueness of information make healthcare supplier selection a challenging task which can be effectively solved with the application of multi-criteria decision making (MCDM) methods in fuzzy environment. This paper proposes a two-stage methodology for solving a supplier selection problem in the Indian healthcare scenario. Four efficient suppliers are first shortlisted from a pool of 25 candidate pharmaceutical suppliers using data envelopment analysis based on nine pivotal financial metrics. In the second stage, three MCDM techniques, in the form of weighted aggregated sum product assessment (WASPAS), combinative distance-based assessment (CODAS) and combined compromise solution (CoCoSo) are individually integrated with intuitionistic fuzzy (IF) set to identify the most apposite healthcare supplier on the basis of five qualitative criteria (cost, delivery time, service, quality and flexibility). It is interestingly noticed that all the adopted MCDM techniques behave similarly in selecting the best supplier in IF environment. A sensitivity analysis with respect to changing criteria weights is also carried out to search out the most robust IF-MCDM method. [ABSTRACT FROM AUTHOR]

Data standardization is considered as mandatory work when applying most Multi-Criteria Decision Making (MCDM) methods. This study was conducted to identify the appropriate data standardization methods in combining with the RAM (Root Assessment Method) method. Thirteen different data normalization methods were applied in combining with the RAM method to solve the different problems. Among the thirteen tested data normalization methods, there is an available data normalization method from RAM method. In addition to this one, this study proposed another data normalization method that is determined to be suitable when combining with the RAM method. [ABSTRACT FROM AUTHOR]

This study focuses on evaluating modular house construction projects, which is a critical segment within sustainable building practices. Despite the significant advantages of modular construction, such as enhanced resource efficiency and reduced environmental impact, existing research often overlooks its unique attributes and constraints. Our objectives were to identify crucial parameters for a comprehensive evaluation of modular construction, particularly emphasizing sustainability, and to explore how an advanced conversational AI tool, ChatGPT, can assist in modular building assessments. We employed the Delphi method to define these parameters and integrated ChatGPT to develop a robust assessment methodology. This approach allowed us to harness AI-driven insights to enrich the evaluation process. Our findings suggest that ChatGPT delivers high-quality results comparable to those produced by experts in modular building assessments. ChatGPT formulated a detailed description of the evaluation scale for each criterion, effectively outlining the guidelines for evaluating modular house projects. To illustrate the effectiveness of our proposed methodology, we applied it to a real-world modular house project in Lithuania, demonstrating how this approach can significantly contribute to advancing sustainable construction practices. [ABSTRACT FROM AUTHOR]

The purpose of this research paper is to present a framework for selecting a biogas plant for the Indian rural community, considering performability factors such as reliability, quality, maintainability, safety, and sustainability. This will ensure that the plant operates reliably, efficiently, and safely over its entire life cycle and can play a significant role as a decision-support tool for decision-makers (e.g., managers, engineers, stakeholders). The proposed framework integrates the Analytic Hierarchy Process (AHP), and the Weighted Aggregated Sum Product Assessment (WASPAS) to optimally evaluate and prioritize the best alternative based on performability factors. The findings show that the suitable biogas plant in the context of the Indian rural population is a fixed-dome-type plant. The decision-making process in selecting the best biogas plant can be effectively aided by using this suggested tool. Currently, there are no proper tools or methods for selecting biogas plants for rural areas due to a lack of data or relevant literature on operational issues. The proposed method uses performability factors for the selection, which has not been researched so far. Moreover, the AHP–WASPAS approach offers a robust method for selecting biogas plants, ensuring efficient and sustainable energy production. The proposed method will help policymakers and stakeholders to choose the best biogas plant in the context of Indian rural application. [ABSTRACT FROM AUTHOR]

Enterprise resource planning (ERP) is widely used to boost the total market power of businesses. The wrong selection is one of the key reasons why ERP installations fail. Due to the complexity of the business environment and the range of ERP systems, choosing an ERP system is a complex and time-consuming procedure. ERP alternatives may be assessed using several criteria, so the ERP selection process may be considered a multicriteria decision-making (MCDM) problem. In this study, the rough best worst method (BWM) was used to determine criteria weights, while the newly developed rough integrated simple weighted sum product (WISP) was used to rank ERP alternatives. Results suggest that the SFT-4 coded software is regarded as the best option, followed by SFT-5, SFT-6, SFT-2, SFT-3, and SFT-1. Results of the newly developed rough WISP method are compared to those of existing rough techniques in the sensitivity analysis. The differences between them have been found to be negligible. The outcomes show how effectively developed rough BWM and WISP integrated method performs in terms of ERP selection with usability, accuracy, ease of use, and consistency. This study will help decision-makers in a context where ERP is implemented choose the best ERP software for different sectors. [ABSTRACT FROM AUTHOR]

As climate change intensifies, urban transportation infrastructure faces unprecedented challenges from extreme weather events, such as floods. This study investigates the resilience and vulnerability of such infrastructure under extreme rainfall conditions in Changchun City. Utilizing Multi-Criteria Decision-Making Analysis (MCDM) and Geographic Information System (GIS) techniques, we comprehensively assess the physical, functional, and service vulnerabilities of the transportation network. Our analysis reveals that only 3.57% of the area is classified as highly resilient, demonstrating effective flood management capabilities. In contrast, a significant 61.73% of the area exhibits very low resilience, highlighting substantial vulnerabilities that could impact urban operations. Based on our findings, we propose specific strategies to enhance resilience, including optimizing drainage systems, upgrading infrastructure standards, implementing green infrastructure initiatives, and integrating disaster risk factors into urban planning. These strategies and insights provide valuable references for global cities facing similar climatic challenges. [ABSTRACT FROM AUTHOR]

Purpose: Identifying and prioritizing supply chain risk is significant from any product's quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment. Design/methodology/approach: The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the "Technique for Order Performance by Similarity to Ideal Solution." The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment. Findings: The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a "better quality in risk management." Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control. Research limitations/implications: In a company's supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk. Practical implications: The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018). Originality/value: This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model's outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality. [ABSTRACT FROM AUTHOR]

The goal of this research is to optimize the input parameters utilized in dry CNC drilling of forging steel to attain sustainable machining. Particular emphasis will be placed on achieving high productivity while minimizing the impact on surface quality. To achieve the aforementioned goal, three Taguchi-based multi-criteria decision-making (MCDM) approaches, such as traditional gray relational analysis (GRA), weighted gray relational analysis (WGRA), and data envelopment analysis ranking (DEAR), were used for simultaneous optimization of the MRR and Ra. In Taguchi's L12 (24) orthogonal array design, the cutting mode parameters—such as cutting speed, depth of cut, feed rate, and point angle—have been chosen as the input parameters for the modeling and analysis of the drilling process characteristics. The process of determining the effect of the input parameters on the output parameters was carried out with the use of analysis of variance (ANOVA). The best results from the studies were Ra = 2.19 and MRR = 375 mm3/s, which corresponded to Taguchi's single optimization levels, S2F1D1A2 and S2F2D2A1, respectively. In the next step, the performance values obtained for each MCDM technique were reoptimized using the Taguchi method, and the optimal levels were obtained: for traditional GRA, the level S2F1D2A1 (Ra = 2.52 µm, MRR = 125 mm3/s); for WGRA, the level S2F1D1A1 (Ra = 2.31 µm, MRR = 83 mm3/s); and for DEAR, the level S2F2D2A1 (Ra = 4.42 µm, MRR = 375 mm3/s), respectively. Lastly, in order to compare the experiments' performance, validation tests were carried out. The results of the experiments using multi-objective optimization show that traditional GRA improved the overall quality response characteristics by 29.86% compared to the initial setup parameters, while weighted GRA improved them by 34.48%, with the DEAR method providing an improvement of 96%. Based on the findings of this investigation, the DEAR optimization method outperforms the GRA method. As a result, the proposed methods are useful tools for multi-objective optimization of cutting parameters. [ABSTRACT FROM AUTHOR]

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper's main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items' order, but it remained within the same two sustainable manufacturing pillars: economic and environmental. [ABSTRACT FROM AUTHOR]

A nation's development depends on its transport networks, particularly the road network, which plays a crucial role in the country's economic and social advancement and well-being. However, roads are subject to deterioration due to weather conditions, traffic loading, and construction quality. If they are not maintained properly, they will quickly worsen over time, resulting in reduced mobility and accessibility. To develop and maintain a good road network, careful planning is needed, which covers all aspects of road maintenance, funding, construction, quality, and other criteria. However, due to limited budgets, not all roads can be maintained and rehabilitated at the same time. Road maintenance priority and optimal use of insufficient funding are the most challenging problems the authorities face. The development of a systematic approach is essential to formulate appropriate maintenance policies. This is why the concept of road maintenance prioritization is essential. Additionally, industry experts have also identified a lack of a Multi-Criteria Decision Making (MCDM) technique that can incorporate the views of all Decision Makers (DMs) in the road maintenance prioritization process. This study aims to propose a framework for prioritizing road maintenance using MCDM techniques in a fuzzy environment. A case study that considers 20 criteria was conducted. The study integrated two MCDM techniques, namely the Fuzzy Best-Worst Method (BWM) and VIKOR, to help DMs evaluate and rank the alternatives, on the basis of the selected maintenance criteria. The aim of this framework is to enhance the decision-making process with impartiality and reliability and to assist in reaching an optimal decision. By comparing the Q values for each alternative, A5 was revealed to have higher priority over the other roads in terms of maintenance and rehabilitation activities. [ABSTRACT FROM AUTHOR]

Globalization may cause companies to broaden their customer bases, operate internationally, and serve more customers. However, it also entails dangers, including shifting circumstances, disruptions, and elevated prices. To address these issues, outsourcing has evolved, lowering outputs and service costs. To mitigate these risks, businesses employ strategies such as interventionism, protectionism, and regionalization. By investigating and simulating the interplay of outsourcing approaches to equip businesses with supply chain resilience (SCRES), this study aims to close the knowledge gap between the existing knowledge and global supply chain (GSC) risk management practices. To choose the best alternative for our case study company, we analyzed four distinct outsourcing techniques, namely offshoring, reshoring, nearshoring, and farshoring; furthermore, we investigated the outsourcing elements typically taken into account to achieve resilience. The results indicated that the reshoring method would be the most promising one for this company, under specific circumstances, in terms of attaining resilience in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Geosites are significant geological heritage areas that serve as field laboratories for researchers. They offer insights into geological processes that have occurred from the past to the present and hold high potential for geotourism. To preserve and make geosites sustainable, their accessibility is a prerequisite. For this purpose, a model consisting of various parameters was developed in accordance with the Multi-Criteria Decision Making (MCDM) method, identifying 183 geosite areas that have been determined by experts and are subjects of literature. As a result, four evaluation criteria were established, including accessibility, access to service/attractiveness areas, scientific importance, and geomorphological features. Taking these parameters into account, a collective evaluation map was obtained using GIS-based AHP, drawing upon literature and expert opinions. The study concluded that the most critical parameter for accessibility to geosites is accessibility itself, accounting for 50% of the importance. Other parameters have influenced the accessibility of geosites in the following order of importance: 25% land, 15% scientific importance, and 10% service/attractiveness. Consequently, the areas where accessibility to geosites in Türkiye is most convenient are those with the highest road density. Road density, in turn, exhibits a pattern based on the characteristics of topography, population, and trade factors. Additionally, a parallel outcome emerged between areas that are more suitable in terms of these factors and the density of geosites. As a validation of the newly created model, the accessibility of Türkiye's areas recognized by UNESCO was found to be very high, indicating that the model is a suitable and convenient method that can be applied to different areas. [ABSTRACT FROM AUTHOR]

The Circular Economy of the Automotive Industry’s Sustainable Supply Chain in the Case of Lithium-Ion Batteries is pioneering in environmental protection and ecological resource utilization. In addition to solving environmental problems, this method provides economic benefits by reducing the need for raw materials and lowering manufacturing costs. However, introducing a circular economy approach in the lithium-ion battery supply chain has numerous risks and challenges. This study addresses these challenges by crafting a framework that encapsulates the risks involved. It identifies the risks that evolving circular economy strategies might bring to the lithium-ion battery supply chain through an integrated Gray Delphi–DEMATEL–ANP method. Furthermore, this work introduces the Gray Degree of Possibility to unveil worst-case scenarios in risk analysis and extends it into zero-sum Game Theory. The study then formulates an improved zero-sum game model to determine optimal strategies for mitigating these risks. The numerical analysis reveals that, according to the proposed methodology, Environmental Pollution Risk emerges as the most critical, with a definite weight of 0.1525. This is followed by the Support Program Deficiency Risk at 0.1452 and the Improper Waste Management Risk at 0.1372.

Lung disorders involve swelling, inflammation, and muscle tightening around the airways, with symptoms such as coughing, wheezing, shortness of breath, and abnormal fluid build-up. The global prevalence of these conditions is rising, highlighting the need for extensive research to alleviate their severity and symptoms. Due to the chronic nature and recurrence of these disorders, the human body often develops immunity and side effects to certain medications. Therefore, developing novel and appropriate drug combinations is crucial. This study analyzes a dataset of lung disorder drugs, characterized by various topological indices. The structures of 16 drugs used to treat lung disorders are correlated with their physical properties using degree-based graph invariants. When considering specific attributes, the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method provides an optimal ranking for each drug. The QSPR results highlight the effectiveness of this approach in drug prioritization, offering valuable insights for clinical decision-making and drug development. This methodology can enhance the strategic selection of treatments for lung disorders, leading to improved patient care and better resource allocation.

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts’ fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.