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High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables' weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation. [ABSTRACT FROM AUTHOR]

To ensure the successful implementation of the old community renewal project (OCRP), it is essential for the participants to allocate the project risks reasonably. Firstly, this study comprehensively identifies the 20 key risk factors of the OCRP. Secondly, an index system is established from three dimensions to evaluate the risk allocation ability of participants, including a total of nine evaluation indexes. Furthermore, a risk-sharing model based on TOPSIS method and bargaining game model is proposed to determine the optimal risk bearer and risk-taking ratio between the government and the private sector in OCRP. Finally, an OCRP in Chongqing is taken as a case study to verify the applicability of the developed model. The results indicate that in OCRP under PPP mode, the government need to independently bear 7 risks related to politics, law, policy, while the private sector needs to independently bear 8 risks mainly from project financing, design, construction, operation, and maintenance stages. In addition, the risk-taking ratios of 5 risks that require both parties to share are divided. The research findings provide references for ensuring the smooth implementation of urban renewal and sustainable development. [ABSTRACT FROM AUTHOR]

The evaluation and selection of green suppliers, as an important part of the process of creating a green supply chain, has received attention from enterprises and scholars. However, the evaluation and selection of green suppliers is a complex multi-criteria decision-making problem, and the evaluation information provided by experts is often ambiguous, so it is difficult to obtain reasonable and accurate assessment results. Therefore, this paper proposes a green supplier evaluation model of Pythagorean fuzzy approximation of ideal solution ranking (Technology for Order Preference by Similarity to Ideal Solution, or TOPSIS). The model utilizes Pythagorean fuzzy sets to deal with fuzzy expert opinions and the TOPSIS method to obtain the ranking of alternative suppliers. In addition, the model calculates the criterion weights using the entropy weighting method in the fuzzy environment. Finally, the model proposed in this paper is used to help Company A determine the optimal green supplier selection object, and the effectiveness and superiority of the model are verified through a comparative analysis with existing green supplier evaluation models. [ABSTRACT FROM AUTHOR]

This research paper proposes a framework utilizing multicriteria tools for optimal site selection of photovoltaic solar farms. A comparative analysis was conducted using three quantitative methods—CRITIC (criteria importance through intercriteria correlation), PCA (principal component analysis), and entropy—to obtain the weights for the selection process. The evaluation considered environmental, demographic, financial, meteorological, and performance system criteria. TOPSIS (technique for order preference by similarity to ideal solution) was employed to rank the alternatives based on their proximity to the ideal positive solution and distance from the ideal negative solution. The capital cities of the seven departments in the Colombian Caribbean region were selected for the assessment, characterized by high annual solar radiation, to evaluate the suitability of the proposed decision-making framework. The results demonstrated that Barranquilla consistently ranked in the top two across all methods, indicating its strong performance. Cartagena, for instance, fluctuated between first and third place, showing some stability but still influenced by the method used. In contrast, Sincelejo consistently ranked among the lowest positions. A sensitivity analysis with equal weight distribution confirmed the top-performing cities, though it also highlighted that the weight assignment method impacted the final rankings. Choosing the appropriate method for weight calculation depended on factors such as the diversity and interdependence of criteria, the availability of reliable data, and the desired sensitivity of the results. For instance, CRITIC captured inter-criteria correlation, while PCA focused on reducing dimensionality, and entropy emphasized the variability of information. [ABSTRACT FROM AUTHOR]

Lean Six Sigma (LSS) implementation still shows a high failure rate. Lack of readiness is one of the causes. Readiness assessment requires special exploration of each sector, including the hotel sector, where there is still no development of an LSS readiness assessment model. This study aims to develop a readiness assessment model for LSS implementation in the hotel sector. Data processing in the readiness assessment model uses the fuzzy logic method, is translated again into linguistic terms using Euclidean distance, and is ranked using the TOPSIS approach. The literature review produced 34 Readiness Factors (RF) that were validated by eight credible hotel practitioners and used as guidelines in conducting LSS readiness assessments in the hotel sector. Case studies were conducted on Hotel A and Hotel B, which showed that Hotel A was “Very Ready” and Hotel B was “Ready” to implement LSS. This research is perhaps the first of its kind in the hotel sector. This research also uses the Delphi method to collect data on the importance level, which may not have been done in collecting data on LSS readiness in any sector. [ABSTRACT FROM AUTHOR]

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change Operator Weighting) and MAIRCA (Multi-Attributive Ideal-Real Comparative Assessment) to rank the suitability of different RES, including solar, wind, hydropower, and geothermal energy. Findings: The case study results reveal wind energy as the top-ranked alternative, supported by consistent findings across comparative methods such as COPRAS, MABAC, EDAS, and TOPSIS. Sensitivity analysis further confirms the stability of the proposed model under various scenarios. Originality: The originality of this study lies in the integration of T2NN, LOPCOW, and MAIRCA to address the limitations of traditional MCDM approaches in handling uncertainty and imprecision in data. The study demonstrates the efficacy of the proposed framework in providing a robust evaluation of RES, and its value lies in its potential to inform decision-making in the field of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This study integrated multiple objective decision-making approaches including data envelopment analysis (DEA), rough set theory and TOPSIS method for exploring the competitiveness of pharmaceutical multinational enterprises. Firstly, this study applied an advanced two-stage network DEA to measure the R&D efficiency and business performance of pharmaceutical multinational enterprises (PMNEs) listed in the Forbes Global 2000 and ranked these PMNEs by using rough set theory, DEA and TOPSIS method. In addition, differences in environmental, social, and governance (ESG) performance across three continents were investigated. Findings show that North America is significantly the best (worst) region in terms of business performance (R&D efficiency) while Europe is significantly the best (worst) region in terms of R&D efficiency (business performance). Alfresa Holdings Corporation, a PMNE from Japan, is the only PMNE that is efficient from both an R&D and business performance perspective. European PMNEs have significantly better environmental and social performance than other regions, however, they have the worst governance performance. Overall, this study provides insights to managers and investors into the application of various methods for accurately measuring performance and ranking multinational enterprises. [ABSTRACT FROM AUTHOR]

Concept selection is one of the most important activities in new product development processes in that it greatly influences the direction of subsequent design activities. As a complex multiple-criteria decision-making problem, it often requires iterations before reaching the final decision where each selection is based on previous selection results. Reusing key decision elements ensures decision consistency between iterations and improves decision efficiency. To support this reuse, this article proposes a fuzzy ontology-based decision tool for concept selection. It models the key decision elements and their relations in an ontological way and scores the concepts using weighted fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). By applying the tool to an example, this article demonstrates how the concepts, criteria, weights, and results generated for one decision can be reused in the next iteration. [ABSTRACT FROM AUTHOR]

• Heterogeneity in pedestrians' perceived importance of crossing facilities was studied. • TOPSIS was used to derive the relative importance and ranking of attributes. • Heterogeneity in perceived importance was investigated using non-parametric tests. • Mann-Whitney U and Kruskal-Wallis H tests were employed as non-parametric tests. • Heterogeneity was found to be significant w.r.t walking preference and age group. • There was no effect of gender on heterogeneity in perceived importance. Although several studies in the past discussed the effects of socio-demographic and trip characteristics on users' preferences towards crossing facilities in urban areas, they were mostly confined to metro cities or million-plus cities. Adequate investigation has not been performed for the same in the small-sized cities where pedestrian activities are expected to be more frequent because of the higher share of shorter and walkable trip lengths. Accordingly, the present study aimed to investigate the heterogeneity in the perceived importance of crossing facility attributes among different pedestrian groups based on gender, age group, and walking preference. The study was conducted in a small-sized city, Roorkee, in the state of Uttarakhand, India. A face-to-face questionnaire survey of 554 pedestrians was conducted to collect their perceived importance towards 12 crossing facility attributes under study using a 5-point Likert-type ordinal scale. Collected data was analyzed using the non-parametric tests to measure the heterogeneity in the perceived importance of crossing facility attributes among aforementioned pedestrian groups. Subsequently, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to derive relative importance scores of attributes found statistically significant in heterogeneity tests for different pedestrian groups. The findings of the present study reveal that perceived importance towards crossing facility attributes is heterogeneous based on the pedestrians' age group and walking preferences; however, it remains unaffected by gender. These findings would help policymakers, planners, and government authorities frame improvement strategies for the existing crossing facilities in the city. [ABSTRACT FROM AUTHOR]

According to survey data released by the National Bureau of Statistics, from 2011 to 2022, the total output value of China's construction industry showed an increasing trend year by year. From 2018 to 2021, infrastructure investment has always maintained a positive growth, indicating that the current construction industry is in a rapid development stage. In order to improve the development quality of the construction industry as much as possible, it is of great significance to develop the whole Process engineering consulting service model. The quality evaluation of whole process engineering consulting service modes is MAGDM. The single-valued neutrosophic sets (SVNSs) is useful tool to cope with uncertain information during the quality evaluation of whole process engineering consulting service modes. In this paper, the single-valued neutrosophic number combined TOPSIS (SVNN-CTOPSIS) model based on single-valued neutrosophic number Hamming distances (SVNNHD) and single-valued neutrosophic number Euclidean distance (SVNNED) is formed to cope with the MAGDM. The CRITIC model is utilized to obtain the weight numbers in light with the SVNNHD and SVNNED under SVNSs. Finally, numerical example and comparative analysis for quality evaluation of whole process engineering consulting service modes is utilized to verify SVNN-CTOPSIS model. The main contributions of this study are formed: (1) the CRITIC model is formed to obtain the weight numbers in light with SVNNHD and SVNNED; (2) the SVNN-CTOPSIS model is formed in light with SVNNHD and SVNNED under SVNNs; (3) Finally, numerical example and comparative analysis for quality evaluation of whole process engineering consulting service modes is employed to verify SVNN-CTOPSIS model. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic not only caused a global health crisis but also had a huge impact on household finances. This research aimed to examine the financial situation of rural and urban households in Poland between 2018 and 2022 from a multidimensional perspective. In a unidimensional approach, the financial situation of households was analyzed separately for their income, expenditures, savings rate, and shares of fixed expenses. In a multidimensional approach, the analysis used a synthetic index constructed for all these variables combined, employing the TOPSIS method and drawing on microdata from the Polish Central Statistical Office that totaled approximately 150,000 households. It was found that despite the pandemic outbreak, which led to major changes in the labor market, government-introduced organizational and legal measures protected the income of Polish households from decline. Indeed, income was increasing up until 2021, with a modest fall in 2022, which was less severe in rural households (by about 2%) than in urban ones (by 6%). A more immediate response to the outbreak was observed in consumer spending, which fell sharply, while the food share in consumption increased at similar levels in rural and urban areas. Regression analysis showed that compared to 2019, the pandemic worsened the financial situation of Polish households, but only in 2020 and 2022 were these changes statistically significant. In 2021, the financial situation improved slightly, but the change was not statistically significant. Before the pandemic, the synthetic assessment of the financial situation in rural areas was lower than in urban ones, and the pandemic period did not reduce these differences, showing no convergence between poorer rural areas and richer urban ones. Diagnosing and monitoring the household financial situation are crucial steps for achieving the social dimension goals of sustainable development. Research on the pandemic's effects on household finances provides valuable information that can help develop effective strategies to counter future crises while supporting the social dimension of the sustainable development strategy. [ABSTRACT FROM AUTHOR]

Signage functions as guidance and distribution assistance, directly affecting the operational efficiency of traffic in and around the comprehensive transportation hubs. Among the elements of signage, the visual guidance effect is the key factor affecting the information conveyance, which should be evaluated during the design and optimization process. This study conducted field investigations and developed panoramic videos for multiple transportation hubs in China and designed a survey accordingly. Human subjects were recruited to watch panoramic videos via virtual reality (VR) and respond to the surveys. The results show that the degree of visual attention of travelers significantly affects the evaluation results of guidance signage, with the influence being inversely proportional. Key factors affecting visual attention include accurate legibility, obstruction and defacement rates, informativeness, and whether signage is set up in a hierarchical manner. In unfamiliar environments, travelers focus on the overall context and closely observe the interaction between directional signs and their surroundings. The prominence and visibility of signage are influenced by interactions within the spatial environment. Notably, simple and clear signs are more likely to attract travelers' attention, and their directional information is more easily comprehended. Moreover, when the destination is clearly defined, visual attention significantly directs pedestrians' wayfinding behavior. [ABSTRACT FROM AUTHOR]

Achieving a sustainable dielectric medium for Electrical Discharge Machining (EDM) has now drawn the attention of researchers and industries alike. Increased awareness among the Government and NGOs paved the way for the manufacturing industries to move towards a green environment and sustainability in the production process. To search for sustainable dielectric, 5 different types of oils viz., Jatropha, coconut, groundnut, sunflower, and EDM 4 oil (conventional oil) are experimented in this paper and revealed its suitability of potential replacement for the conventional dielectric. The properties such as viscosity, density, flash point, electrical conductivity, and BDV (Break down Voltage) are tested as per the standards. Since the number of output response is more than, Multi Attribute Decision Making (MADM) algorithms namely PIV, TOPSIS, and MOORA are used for the ranking and optimization. Entropy weight method is adopted to endow criteria weight to each of the MADM techniques. Jatropha oil has been ascertained to be rank 1 followed by groundnut, coconut, sunflower, and EDM 4 oil. Subsequently, the Gas Chromatography & Mass Spectrometry (GC–MS) analysis has revealed the quantitative constituents present in Jatropha as well as remaining oils. Furthermore GC–MS analysis reports that the retention time of Jatropha (43.042 min) is relatively longer than other oils except coconut oil, to release Ricinoleic acids. Groundnut and EDM 4 oils took relatively less retention time to release the alkanes. Machining performance was also conducted with the obtained dielectric and compared with conventional type oil (EDM 4 oil). [ABSTRACT FROM AUTHOR]

The increasing need for harvests, resources, and facilities is associated with significant biowaste production from agricultural processes. Although the waste is nutrient-rich, it may become refinement lands for ailment-triggering bacteria when not managed correctly. The waste can be transformed into raw materials for valuable crops or sources of environmentally friendly energy. Therefore, this study examined agricultural waste management strategies as Indonesia's sustainable renewable energy source. SWOT and TOPSIS methods were used to identify the optimal approach for advancing renewable energy in Riau Province, while multiple respondents participated in identifying critical criteria and evaluating each option. The results showed that based on SWOT analysis, the Strength–Opportunity (SO) factor favored using agricultural waste for renewable energy in Indonesia. Furthermore, TOPSIS analysis indicated that Alternative A2 (Bioethanol) had the most significant distance among the alternatives, with a weight of 0.825. Future studies are needed to provide more accurate results and improve the current understanding regarding the evolution of renewable energy in Indonesia. Additionally, in-depth investigations should prioritize increased consumer awareness of renewable consumption, higher producer productivity, and strengthened policies. [ABSTRACT FROM AUTHOR]

The construction and operation of collaborative production networks based on multi-subject collaboration is an important path and means for enterprises to adapt to personalized, diversified, and differentiated market demand. It is of great practical significance to identify the key collaborative subjects in the collaborative network and protect and maintain them to ensure its normal operation. To identify the key collaborative subjects in the collaborative network of traditional manufacturing enterprises, this paper proposes a method for identifying and evaluating the importance of nodes in traditional manufacturing enterprise collaborative networks. Firstly, the method uses four parameters, degree centrality, betweenness centrality, closeness centrality, and subgraph centrality, as node importance evaluation indexes, based on complex network theory. Secondly, the coefficient of variation method (CVM) is used to calculate the weights of evaluation indexes. The Multiple Attribute Decision Making (MADM) based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is then used to comprehensively evaluate node importance and identify key nodes (key collaborative subjects) in the network. Finally, the proposed method's effectiveness, rationality, and scientific nature are verified by using the measurement index of network connectivity in combination with specific enterprise cases. The results show that the failure of key nodes has a more significant impact on network connectivity. Therefore, the node importance evaluation method based on Multiple Attribute Decision Making has better performance. It helps traditional manufacturing enterprises to focus on the protection and maintenance of the key collaborative subjects when coping with the competitive environment of the external market and provides a valuable reference for the normal operation of collaborative network organizations. [ABSTRACT FROM AUTHOR]

The analysis of the stock market performance ratios is crucial for investors and fund managers. The food and beverage industry in Turkey is the largest sector in the Istanbul Stock Exchange (ISE). It contributes over half of the country’s GDP and is a highly attractive sector. This study aims to rank the top food and beverage companies based on their stock market performance ratios. The criteria weights were determined by using DEMATEL and CRITIC methods, with the help of three experts for DEMATEL. The stock market performances of the companies were evaluated by using three MCDM methods; EDAS, WASPAS, and TOPSIS, with the weights obtained from both DEMATEL and CRITIC. The robustness of the results was tested by applying various combinations of weighting and evaluation methods. According to the DEMATEL, earnings per share had the highest weight while CRITIC found the market value to book value ratio as the most important criterion. The study concluded that the best-ranked companies are CCOLA and TBORG. Also, there is no significant stability in other companies’ rankings. To reveal which methods produced similar rankings, Spearman’s Rank Correlation analysis was conducted: while WASPAS combinations produced similar rankings, all EDAS and TOPSIS combinations gave similar findings. [ABSTRACT FROM AUTHOR]

Micro-Small and Medium Enterprises (MSMEs) play a key role in economic growth and employment generation. MSMEs are facing challenges related to Green House Gases (GHGs) emissions and compliance with other environmental regulations. Green Lean Six Sigma (GLSS) is a comprehensive approach that reduces negative environmental impacts and improves product quality. However, the current level of adoption of GLSS in MSMEs is limited due to various challenges faced by these organisations. This can be attributed to different barriers in the adoption of sustainable business practices like GLSS adopted by organizations. Therefore, the current research deals with prioritising and investigating GLSS barriers in Indian MSMEs. Thirty-five barriers of GLSS in MSMEs were identified through a systematic literature review and then prioritization of the same was performed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and validated through grey relational analysis. In this study, the authors used a purposive sampling technique to collect the responses for the analysis of GLSS barriers. The pilot-tested questionnaire was sent to 560 personnel (LSS personnel, academician and industry professionals) out of which 364 responses were received, and 14 responses were further neglected due to the incompleteness of the questionnaire. A final sample of 350 responses was considered for the analysis. The results of this study indicate that 'Lack of top leadership support for GLSS adoption', 'Involvement of inadequate employees that missing to implementing new strategies' and 'Lack of technology action plan for GLSS' are the top-ranked barriers with TOPSIS performance values of 0.8709, 0.869, and 0.8678 respectively. This research work will foster the implementation of GLSS within MSMEs by facilitating a comprehensive understanding of GLSS barriers and prioritization of the same. Further, the findings of this study will be beneficial for MSMEs to cope with their current level of emissions, and further formulate policies to mitigate challenges to pollution and climate change. [ABSTRACT FROM AUTHOR]

Purpose: PLGA nanoparticles are one of the most investigated drug delivery systems among all polymeric nanoparticles. Although there are several successfully developed and marketed microparticulate PLGA systems, unfortunately, there was little progress in the in vitro to clinic and marketing translation of PLGA nanoparticulate systems. One of the main reasons is the very low drug-loading capacity of PLGA nanoparticles. This situation becomes more problematic in some drugs such as doxorubicin. Doxorubicin is a very interesting molecule whose solubility and characteristics dramatically change depending on pH and the presence of various ions and compounds in the medium. This property of the doxorubicin probably directly influences the drug loading of the doxorubicin to PLGA nanoparticles. In this study, it was aimed to improve the doxorubicin loading to PLGA nanoparticles while the average particle size and polydispersity index are in the acceptable ranges. Method: TOPSIS-based Taguchi experimental design was adopted and the effect mainly water phase additive on drug loading, encapsulation efficiency, particle size, and polydispersity index were investigated. Result: Results show that generally using PBS and HEPES improves the overall results when compared with blank water as the water phase. Conclusion: Within the study, TOPSIS-based Taguchi design was successfully applied to the optimization of a PLGA nanoparticle formulation, and the optimum water phase additive was determined. These findings will be very beneficial to the researcher in the field of doxorubicin-loaded PLGA nanoparticles in their future studies. [ABSTRACT FROM AUTHOR]

The present study embarks on a strategic initiative to prioritize flood-prone watersheds, aiming for the optimal placement of watershed structures within the Siah Khor watershed, situated in the northeast of Islamabad West in Kermanshah province. Addressing the complexities of watershed management and flood control, the research employs an integrated methodological approach. This approach harmoniously combines multivariable decision systems, particularly the VIKOR(Vlsekriterijumska optimizacija I Kompromisno Resenkje) and TOPSIS(Technique for order preference by similarity to ideal solution) methods, with the advanced capabilities of the HEC-HMS hydrological model. The synergy of these methodologies facilitates a comprehensive analysis, leading to the precise identification and ranking of critical sub-basins. Such a targeted approach significantly enhances the allocation of resources, ensuring that flood mitigation efforts are both effective and efficient. Delving deeper into the nuances of watershed management, the study meticulously examines the pivotal criteria essential for informed decision-making in constructing watershed dams. It introduces an innovative, structured hierarchical model that effectively weights these criteria, thereby augmenting the clarity and precision of decisions. This comprehensive framework not only streamlines the decision-making process but also fosters a more systematic and principled approach to managing watershed regions. Through its meticulous analysis and strategic integration of sophisticated models, the study makes a substantial contribution to the field of watershed management. It presents a robust, multi-faceted strategy for flood prevention and control, demonstrating the potential for significant advancements in environmental management and sustainable development. Research Highlights: Multi-Variable Decision Systems in Flood Prioritization. Identifying Critical Sub-Basin for Flood Control. Model Validation: ROC Curve and AUC Analysis. Summary of Findings: According to the VIC method, Sub-basin 7 takes precedence for implementing watershed management structures in the Siahkhor watershed, followed by Sub-basins 1, 27, 13, and 9, respectively. The suitability assessment of locations for constructing stone and cement dams using TOPSIS method identified Sub-basin 7 in the Siahkhor watershed as having the highest priority for such construction. Flood susceptibility of sub-basins was prioritized using HEC-HMS model. Among the 32 proposed structures, approximately 15% were classified within the top six priority levels by HEC-HMS model, while around 40% were classified within the top fifteen priority levels. The flood susceptibility prioritization map of sub-basins based on the (f) index indicated that sub-basins located upstream have the highest importance in flood control. Therefore, implementing flood control operations in areas farther from the watershed outlet is more suitable. [ABSTRACT FROM AUTHOR]

Natural aquifers used for human consumption are among the most important resources in the world. The Locumba basin faces significant challenges due to its limited water availability for the local population. In this way, the search for possible aquifer recharge zones is crucial work for urban development in areas that have water scarcity. To evaluate this problem, this research proposes the use of the hybrid Fuzzy AHP methodology in conjunction with the TOPSIS algorithm to obtain a potential aquifer recharge map. Ten factors that influence productivity and capacity in an aquifer were implemented, which were subjected to Fuzzy AHP to obtain their weighting. Using the TOPSIS algorithm, the delineation of the most favorable areas with high recharge potential was established. The result shows that the most influential factors for recharge are precipitation, permeability, and slopes, which obtained the highest weights of 0.22, 0.19, and 0.17, respectively. In parallel, the TOPSIS result highlights the potential recharge zones distributed in the Locumba basin, which were classified into five categories: very high (13%), high (28%), moderate (15%), low (28%), and very low (16%). The adapted methodology in this research seeks to be the first step toward effective water resource management in the study area. [ABSTRACT FROM AUTHOR]

This study concentrates on the utility of the newly formulated machining fluid used for turning AISI 4340 steel under the near dry machining method. The preparation of the novel machining fluid involves a blending of vegetable oils and conventional lubricant in a 50:50 ratio. Three distinct compositions were investigated within this work. The biodegradability tests confirms that the novel lubricantaligns with the ethos of "Green Machining." The study's focal points include an analysis of the following crucial metrics – machining force, machining temperature, and surface finish. From the analysis, the best-formulated blend was identified. To evaluate its performance, the variance analysis on the dataset (ANOVA) and Weighted Entropy decision-making method that ranks the best option along with alternatives was executed. Further, the optimal process parameters were determined using Dragon Fly Algorithm (DFO) and Particle Swarm Algorithm (PSO) to enhance the efficacy and quality of machining. [ABSTRACT FROM AUTHOR]

AbstractBackground: Hospitals make up the main cost factor of health systems and also face increasing pressure to improve efficiency. Improving the performance of hospitals, which are vital for public health, is very important for developing countries such as Turkey. Aims: We conducted this study to assess the performance of 544 secondary-level public hospitals in Turkey by geographical region. Methods: In the study, we used the entropy weighting method to obtain the objective weights of indicators with the evaluation criteria, and then we utilized the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank the performance of hospitals by geographical region. Results: The comparison of hospital performances by geographical region indicated that the highest and the lowest mean scores belonged to the Mediterranean Region and the Eastern Anatolia Region, respectively. Conclusions: The socio-economic differences between geographical regions in Turkey were in parallel with the findings of the study. Although there is a need for professional management in resource allocation and use to improve the performance of public hospitals, it is necessary to focus on the origin of the problems first. [ABSTRACT FROM AUTHOR]

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future. [ABSTRACT FROM AUTHOR]

The interest in region-specific ethnic fermented foods and their functional microbiota is rising. The demands for functional foods are continuously rising, so research is going on to develop nutritious food with many beneficial attributes and low safety concerns. The present study was designed to isolate and characterize lactobacilli probiotic candidates from locally resourced fermented foods (dahi, lassi, and raabadi) to make ready-to-eat fermented functional products later. Cultures were isolated from 82 fermented food samples collected from different villages. The initial experiments of gram staining, catalase test, and carbohydrate fermentation were assessed for the morphology, purity, and primary characterization on the genus level, which was verified through molecular characterization using PCR. Seven lactobacilli strains (no. MS001-MS007) were then assessed for safety, probiotic candidacy, phytase degradation, and biofilm forming abilities. All seven bacterial cultures showed no hemolytic activity and antibiotic sensitivity against more than 14 antibiotics out of 20. All seven lactobacilli isolates were able to tolerate pH 3.0, 0.3% bile 0.5% pancreatin, lysozyme (100 mg/L to 300 mg/L) and also shown possessed phytase degradation ability. All the cultures showed antioxidative potential and biofilm formation ability. Culture MS007 showed considerably higher bile salt hydrolase activity among all the isolates, whereas MS005 possessed excellent phytate degradation ability among others. Bacterial strains were identified using 16S rRNA gene sequencing. Moreover, the order of preference of isolates was calculated using the multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on probiotic and other functional properties. The most promising attributes showing cultures were recognised as Limosilactobacillus fermentum MS005 and Lactiplantibacillus plantarum MS007, which could be further used for functional food product development. [ABSTRACT FROM AUTHOR]

In a Multi Criteria Decision Making (MCDM) problem, it is rarely possible to optimize all objectives simultaneously, since they can be contradictory, ambiguous or may involve other types of inconsistencies or uncertainties. Therefore, when trying to choose from a number of available alternatives, a decision maker is expected to assign weights to attributes whose values are utilized to evaluate the alternative under consideration for ranking. Attributes can be qualitative or quantitative, and their weights can be assigned by the decision maker in a somewhat subjective manner or algorithmically. In this paper, the impact of attribute weighting approaches on the ranking results across a number of widely used MCDM methods are discussed. That is, it examines how different weighting methods affect the results on the same multicriteria decision-making methods when making a rating. In doing so, consider five MCDM methods, namely, Evamix, Aras, Topsis, Vikor, Waspas, under three different objective attribute weight assignment procedures, namely, Critic, Entropy, and Standart Deviation (SD). Results indicate that, in some cases, the employed attribute weight-assignment mechanism influences the rating results more heavily than the MCDM method itself. In other words, different MCDM methods tend to yield similar results under the same weight assignment method whereas, the same method produces more distinguishable results under different weighting schemes. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the financial performance and annual stock returns of 25 companies from the food, construction, energy, transportation, and technology sectors traded on BIST. The study aims to observe how the COVID-19 period affected these relationships. Data from 2018-2019 before COVID-19 and 2021-2022 after COVID-19 were included in the analysis. The financial performances of the companies were analyzed using the TOPSIS method. The relationship between companies' stock returns and financial performance was examined using Spearman correlation analysis. According to the results of the analysis, it has been observed that the stock returns of the companies fluctuate constantly, regardless of the COVID-19 period. It was concluded that there is no significant relationship between companies' stock returns and financial performance. [ABSTRACT FROM AUTHOR]

Objective To simultaneously determine of shikimic acid, catechin, quercetin, kaempferol, isorhamnetin, 3'-hydroxypuerarin, puerarin, 3'-methoxypuerarin, daidzin and genistein in Songling Xuemaikang Capsules by quantitative analysis of multi-components by single-marker (QAMS), and to establish fusion model of weighted technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA) for evaluating the quality of 15 batches of samples. Methods HPLC method was used to calculate the relative correction factor and content of each component, using kaempferol as internal reference. The feasibility of QAMS method was verified by comparing with the measured value of external standard method. The content results were analyzed by chemo metrics to explore the quality difference markers of Songling Xuemaikang Capsules. Taking the VIP value of each component as the weight, we performed differential analysis on sample quality through fusion model of weighted TOPSIS and GRA. Results There was no significant difference between the results of QAMS and external standard method. The 15 batches of samples were clustered into three categories. Puerarin, 3'-hydroxypuerarin, quercetin, 3'-methoxypuerarin and shikimic acid were the quality difference markers of Songling Xuemaikang Capsules. The results of fusion model of weighted TOPSIS and GRA showed that there were some quality differences in Songling Xuemaikang Capsules produced at different times. Conclusion The hybrid model combining QAMS, weighted TOPSIS and GRA can be used for the comprehensive evaluation of the quality of Songling Xuemaikang Capsules. [ABSTRACT FROM AUTHOR]

To explore the evacuation situation of the underground space in the Starting Area of Guangzhou International Financial City under fire, personal evacuation was simulated with Pathfinder in three zones. Then, the visual animation and the human flow rate diagram were obtained and analyzed. A fire-risk assessment model based on a combined weighting and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to assess the fire risk of three zones. First, six second-level indices were determined from three aspects: regional safety evacuation, regional fire prevention, and regional fire extinguishment. The value of regional safety evacuation was determined by the results of the evacuation simulation. Second, the subjective and objective weights of the second-level indices were determined based on the analytic hierarchy process and entropy weight method, respectively, and a combined weighting method was adopted to reflect the subjective and objective weights. Next, TOPSIS was used to calculate the relative closeness between each scheme and the ideal scheme. The relative closeness is an evaluation index used to determine the fire-risk level of three zones. It was found that the fire-risk level of Zone Ⅰ is high, requiring large-scale rectification. The fire-risk level of Zone Ⅱ is low, and the fire toughness is strong. The fire-risk level of Zone Ⅲ is medium and needs local rectification. [ABSTRACT FROM AUTHOR]

In container transportation by sea, it is crucial to define the transshipment design. Liner operators aim not only to reduce operational costs but also to maximize customer satisfaction. Therefore, selecting a transshipment hub port becomes paramount for achieving efficient transshipment capabilities. The study aims to propose an alternative approach to determine the best transshipment hub port for a northbound container ship entering the Eastern Mediterranean basin through the Suez Channel. The proposed model evaluates the container ports with a novel quantitative method. The choice criteria were weighted with the AHP (Analytic Hierarchy Process) method and numerical data acquired with various scientific methods regarding each criterion was used to rank the alternatives based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions). Additionally, to generate data and utilize it as a criterion a cross-sectional efficiency snapshot regarding the evaluated container ports was also taken using the bootstrap DEA (Data Envelopment Analysis). Thus, as a decision-maker, the line operator can determine the transshipment port quickly using an easily applicable method upon determining the port calls and the schedules. The findings imply that connectivity is the most important criterion for transshipment hub port choice for containerized goods. The location was also considered important for this choice, but to improve transshipment function, the proper way may be to draw beneficial strategies on maritime connectivity. Conversely, capital investments such as superstructure improvement were found to be less critical for an attractive transshipment hub. [ABSTRACT FROM AUTHOR]

This experimental study investigates the effect of the cutting parameters of the Electrical Discharge Machining (EDM) process of Ti-6Al-4V alloy material on surface roughness (Ra), cutting time (t) and Material Removal Rate (MRR) then solve the Multiple Objective Optimization Problem using separate Multi-Criteria Decision-Making (MCDM) methods namely Entropy-Weighted TOPSIS (E-TOPSIS), MOORA, SAW, VIKOR, and WPM. Focusing on nine 3-levels variants including Operating Voltage (OV), Pulse-On Time (Ton), Pulse-Off Time (Toff), Short-Circuit Off Time (AFF), Secondary Voltage (SV), Feed Rate (WF), Tension (WT), Water Pressure (WL) and Material Cutting Speed (F). Due to the large number of variants studied, the Taguchi L27 experimental design was chosen to reduce the number of experiments while still ensuring reliability in assessing the impact of technological parameters on responses in the study. The optimization results from the different methods indicated two distinct optimal outcomes. According to the E-TOPSIS, MOORA, and SAW, the optimal result is a Ra of 3.27 μm, a t of 7.37 min, and an MRR of 7.45 mm3/min. This result suggests a balanced and harmonious optimization among all criteria. On the other hand, the figures VIKOR and WPM methods are 2.87 μm, 9.57 min, and 5.74 mm3/min, respectively. These results indicate a higher priority for certain criteria, reflected in the lower Ra, longer cutting time, and a smaller MRR in comparison to the figures for the remaining MCDM methods. The different optimal results achieved by various methods highlight that each method is suited to and excels with different sets of values. Therefore, in each specific research or production process, comparing and choosing results calculated by different methods provides a comprehensive view, aiding in making appropriate decisions. [ABSTRACT FROM AUTHOR]

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives' scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings. [ABSTRACT FROM AUTHOR]

Groundwater is crucial in mediating the interactions between the carbon and water cycles. Recently, groundwater storage depletion has been identified as a significant source of carbon dioxide (CO2) emissions. Here, we developed two data-driven models—XGBoost and convolutional neural network–long short-term memory (CNN-LSTM)—based on multi-satellite and reanalysis data to monitor CO2 emissions resulting from groundwater storage depletion in South Korea. The data-driven models developed in this study provided reasonably accurate predictions compared with in situ groundwater storage anomaly (GWSA) observations, identifying relatively high groundwater storage depletion levels in several regions over the past decade. For each administrative region exhibiting a decreasing groundwater storage trend, the corresponding CO2 emissions were quantified based on the predicted GWSA and respective bicarbonate concentrations. For 2008–2019, XGBoost and CNN-LSTM estimated CO2 emissions to be 0.216 and 0.202 MMTCO2/year, respectively. Furthermore, groundwater storage depletion vulnerability was assessed using the entropy weight method and technique for order of preference by similarity to ideal solution (TOPSIS) to identify hotspots with a heightened potential risk of CO2 emissions. Western South Korean regions were particularly classified as high or very high regions and susceptible to groundwater storage depletion-associated CO2 emissions. This study provides a foundation for developing countermeasures to mitigate accelerating groundwater storage depletion and the consequent rise in CO2 emissions. [ABSTRACT FROM AUTHOR]

Environmental issues are often neglected in previous studies on site layout evaluation problems. To address this research gap, a comprehensive evaluation model is developed to evaluate construction site layout planning in this paper. The proposed evaluation model includes four aspects: (1) land use; (2) safety; (3) logistics; and (4) sustainability evaluations. To make the results of the evaluation more accurate, the evaluation model includes the subjective factors that need to be scored by the experts and the objective factors that could be gained from the inspections of the projects and the related materials. Then, the entropy weight method-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is chosen to solve the proposed multicriteria evaluation model. In this paper, the construction site layout planning for four different typical and practical cases, which include two commercial mixed buildings, a residential building, and a sewage treatment plant, is studied. The evaluation results proved that the proposed model is suitable to evaluate common types of construction, such as residential and commercial buildings but not specific functional types, such as the sewage treatment plant. [ABSTRACT FROM AUTHOR]

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic. [ABSTRACT FROM AUTHOR]

The Yellow River Basin plays a crucial role as both an ecological barrier and an economic development area in China. However, achieving synergistic development between the environment and industry in the coastal provinces remains challenging. Conducting in-depth studies on the synergy between these two aspects can provide valuable guidance for the development of each province. This paper employs the Topsis method to assess the development level of the industrial and environmental systems in the Yellow River Basin. Additionally, it utilizes the Harken model to analyze the synergy between the environment and industry in the coastal provinces of the Yellow River Basin from 2011 to 2021. Furthermore, the overall distribution of the level of synergistic development between the industry and environment in the Yellow River Basin is analyzed using kernel density estimation. The empirical findings reveal the following: 1. The development level of both environmental and industrial systems in each province of the Yellow River Basin has exhibited a consistent increase over the study period. 2. The average synergy between environment and industry in the Yellow River Basin has shown a declining trend from 2011 to 2021. In 2021, the synergy level follows the pattern of "upstream

To enhance the effectiveness of supply chain talent education, higher education institutions and other organisations have started to develop and use Massive Open Online Courses (MOOCs) in their training programs. However, the problem is that the design and delivery of supply chain management MOOCs can be inappropriately presented and, thus, ineffective, especially for educational teams with fewer teaching experiences of MOOCs. This eventually makes it hard for the students' learning outcomes to meet the industrial requirements of supply chain experts. Motivated by such a problem, this paper aims to improve the design and delivery of supply chain management MOOCs to enhance student learning outcomes. To achieve this goal, the research method adopted in this paper is to analyse online reviews in a widely-used Chinese MOOC platform with a novel TOPSIS-CoCoSo approach, aiming to identify the barriers to supply chain management MOOCs and their potential solutions. The results of this study show that 16 barriers to MOOCs are identified from the online reviews and then ranked based on their severity of reducing learning outcomes. The perceptions of the severity of the barriers to students and lecturers are compared, and the solutions to the barriers are then discussed. In addition, our comparison indicates that although students and lecturers have similar perceptions of severity for the majority of the barriers, they have significant disagreements on certain barriers. The significance of this study is that it can inform lecturers in supply chain management or relevant disciplines to better design and deliver their MOOC content, as well as contribute to the existing literature by providing new methodological tools for educational analysis. Also, this study highlights the necessity of comparative study in the MOOC online review analysis. [ABSTRACT FROM AUTHOR]

Copyright of Accounting & Auditing Perspective Magazine / Muhasebe ve Denetime Bakış is the property of Union of Chambers of Certified Public Accountants of Turkey (TURMOB) 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.)

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xihua University (Natural Science Edition) is the property of Xihua University 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.)

Recently, a few Multi-Criteria Decision Making (MCDM)-based charging scheduling schemes have been proposed. However, these schemes have still connoted the problems from the viewpoint of assigning weights to multi-criteria and exploiting redundant capability of a Mobile Charger (MC). In this paper, we propose an efficient charging scheduling scheme using an integrated FCNP-TOPSIS to solve the above-mentioned problems. The proposed scheme firstly divides the whole network into sub-areas by using the Fuzzy C-Means (FCM) algorithm so as to evenly distribute charging request load into multiple MCs and assign a MC to each sub-area. Next, each MC draws up a charging schedule into on-demand or semi-on-demand charging scheduling scheme according to the MC's charging capability and the number of charging Request Nodes (cRNs). In charging scheduling, first the Fuzzy Cognitive Network Process (FCNP) assigns the relative weights to multi-criteria to characterize the cRNs and predict the potential-to-be-Bottlenecked Nodes (pBNs). Then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) selects the most suitable next charging location for on-demand charging scheduling and the proactive charging nodes among the predicted pBNs for semi-on-demand charging scheduling. While drawing up the on-demand charging schedule, the partial charging time at each charging location is calculated considering the weights of multi-criteria by FCNP. Extensive simulation experiments have been conducted to show that the proposed scheme greatly improves the charging and network performance at various performance metrics compared to existing schemes. In special, if the number of nodes is 650, the network lifetime of the proposed scheme is 129.4%, 239.8%, 282.5%, 283.2% and 293.6% longer compared to the FAHP-VWA-TOPSIS, FLCSD, AHP-TOPSIS, OPPC, and NJNP schemes, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Erciyes Akademi is the property of Erciyes Universitesi Sosyal Bilimler 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.)

Promoting bicycling and making it attractive requires appropriate infrastructure. Sociodemographic characteristics, frequency and experiences of bike use, and purpose of bicycle trips can affect preferences towards bicycle infrastructure facilities in urban areas. Hence, this study aims to explore the heterogeneity in the perceived importance of bicycle infrastructure facility attributes in various cyclist groups based on gender, age, weekly biking frequency, daily cycling distance, cycling experience, and bicycle trip purpose. Data were collected from bicycle users through a questionnaire disseminated via social media platforms and QR code brochures distributed in Hasselt, Belgium. A 5-point Likert-type ordinal scale was used to collect data on the perceived importance of bicycle infrastructure facility indicators. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank the indicators. At the same time, Mann–Whitney U and Kruskal–Wallis tests were utilized to verify the heterogeneity among the groups. The findings reveal that bicycle infrastructure, i.e., bicycle lanes or paths, is the most critical variable, while the slope was considered the least important. No heterogeneity was found regarding the importance of bicycle infrastructure indicators based on gender. However, heterogeneity was observed based on age, daily bicycle use, cycling experience, weekly bicycle use, and bicycle trip purpose. The findings of this research help urban and transport planners develop improvement strategies for the city's existing bicycling facilities and prioritize future developments by considering various cyclist groups' preferences. [ABSTRACT FROM AUTHOR]

This study develops an integrated methodology to incorporate public perspectives into the establishment and development of public transportation infrastructure systems. The approach involves surveying citizens to collect data, performing demographic analyses to identify differences between cities, and applying Multi-Criteria Decision-Making (MCDM) techniques to weight, scale, and integrate evaluation criteria in order to determine the optimal transportation option. The primary aim of this research is to incorporate public perspectives into transportation planning in developing countries and to promote stakeholder engagement for transportation initiatives in cities such as Baghdad, Iraq, and Tehran, Iran. First, an initial survey was conducted to identify the top three preferred criteria among 200 participants from both cities. The survey results revealed that the three most important criteria were safety, travel time, and reliability. Subsequently, a larger survey utilizing the Saaty scale was administered to capture citizens' preferences, with a total sample size of 550 from Baghdad and 345 from Tehran. The weights of the criteria were then calculated using the Group Analytical Hierarchy Process (GAHP). Three transportation alternatives—monorail, Light Rapid Transit (LRT), and metrobus—were suggested by transportation experts to be evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the weighted citizen preferences. The results indicate that for Baghdad residents, transportation safety is the most important priority, followed by reliability and travel time. However, LRT is rated as the most optimal transportation solution (0.721), followed by monorail (0.596) and metrobus (0.078). In Tehran, travel time represents the most preferred transportation attribute, followed by reliability and safety. The residents of Tehran are shown to prefer LRT (0.843), followed by monorail (0.370) and metrobus (0.143). Despite the similar ranking of transportation alternatives in the two cities, the performance scores differ between them, highlighting the importance of tailoring transportation planning to the unique preferences and needs of local communities. The validation of the results was conducted through sensitivity analysis to determine how variations in the criteria weights and input parameters affected the final rankings. Additionally, a stated preference survey was employed as a practical method to evaluate the robustness of the final ranking of the alternatives. [ABSTRACT FROM AUTHOR]

Every new storage tank in the oil sector is required to be calibrated before using them for oil storage and also to be re-calibrated as statutorily required. Depending on the prevailing regulatory guidelines in the country, either a wet or geometrical method of calibration is adopted. This study examines various geometrical methods of tank calibration vis–a–vis their strengths and weaknesses. Tank farm owners (operators) are always faced with the challenge of selecting the best geometrical method of tank calibration while considering some number of factors. To address this aforementioned issue, this study was embarked upon to rank the known four (4) geometrical methods of tank calibration using Fuzzy TOPSIS (F-TOPSIS) approach. Three different experts were drawn from reputable calibration companies to respond to the questionnaire based on the following criteria: Accuracy; Hazard involved; Time consumed; Drudgery involved; and Cost. The interdependencies among the criteria were considered, and a triangular fuzzy set was adopted. The results revealed that the Electro-Optical Distance Ranging (EODR) is the best alternative with a closeness coefficient of 0.974, while the Optical Reference Line Method was ranked least with a closeness coefficient of 0.197. To validate the result of rating by F-TOPSIS, another hybrid MCDM, Fuzzy Analytic Hierarchy Process (FAHP) was used to rank the alternatives, and EODR was also ranked as the best alternative. Sensitivity analysis was carried out for five different scenarios to validate the robustness of the decision-making tool used in this study. All the scenarios considered for the sensitivity analysis ranked EODR and OTM (Optical Triangulation Method) first and second, respectively. So, it can be concluded that EODR is the best geometrical method of tank calibration. Though the cost of using EODR might be higher than other methods, this is being compensated for by higher accuracy, less time with less exposure to hazards. It can also be confirmed that F-TOPSIS is a formidable MCDM tool that finds its usage in every facet of life for a robust decision-making process. [ABSTRACT FROM AUTHOR]

Increased waste generation and disposal have become serious concerns in terms of landfill burden and environmental pollution. Transforming the waste materials into useful ones by recycling or reprocessing is the right solution to address the challenges. Eggshells are one sort of bio-waste that has had a significant impact on the material market, which is in desperate need of lightweight, wear-resistant materials. Present research uses Habesha eggshells derived from Ethiopia to process the novel epoxy composite with improved tribo-properties. The open-cast mold technique is employed to fabricate epoxy composites with varying vol.% of Habesha eggshell particles in 5, 7.5, and 10. A pin-on-disc apparatus is pressed into service to conduct the wear test on the composite specimen. The results manifest that the wear rate of composite and frictional index of 10 vol.% of Habesha egg shell particles are 2.14 and 2.46 times better than 5 vol.% respectively. A decrement in frictional properties with an increase in eggshell content is observed along with an increment in the wear rate with the increase in sliding velocity and applied load. The L9 orthogonal array approach is followed to conduct the experiments, and a hybrid statistical approach of GRA-TOPSIS is applied to analyze the data and minimize wear objectives. [ABSTRACT FROM AUTHOR]

The success of the turning process is dependent on the turner's ability to choose cutting parameters such as cutting fluid, cutting speed, feed rate, and depth of cut. In addition, the workpiece material and machine tool play critical roles in the turning procedure. This work investigates the effect of the cutting parameters namely Cutting speed (V), Feed (f), and Depth of cut on the output responses Material removal rate (MRR), Tool wear (TW), and Surface roughness (Ra). The design of experiments was planned using the Taguchi method of experiment. While turning the INCONEL X750 alloy; experiments were planned under wet conditions. ANOVA analysis was performed to determine the most influencing factor on the behavioral change of the output responses. Optimum evaluation of the output responses was performed using the TOPSIS method. TOPSIS method assigns the ranking of the output responses which were further validated using the Taguchi analysis. [ABSTRACT FROM AUTHOR]

Effective management of flood risks requires the prioritization of appropriate flood control solutions. This study aims to prioritize structural flood control options using multi‐criteria decision‐making (MCDM) methods. Four MCDM methods, namely analytic hierarchy process, technique for order preference by similarity to ideal solution, multi‐criteria optimization and compromise solution, and Fuzzy‐VIKOR are employed to assess and rank the flood control options based on multiple criteria. Field surveys, interviews with local authorities and experts, and on‐site assessments of existing flood control structures constituted the primary data collection methods. The findings demonstrate the effectiveness of reservoir dams, retention basins, and levees as viable solutions. Conversely, flood control gates and the no‐project options were assigned lower priorities. The findings highlight the importance of considering multiple MCDM methods to account for variations in rankings. The study provides valuable insights into the decision‐making process for prioritizing flood control options in the study area. These findings can assist policymakers and stakeholders in effectively allocating resources and implementing appropriate structural flood control measures to mitigate flood risks. [ABSTRACT FROM AUTHOR]