Multi-criteria decision making to validate performance of RBC-based formulae to screen [Formula: see text]-thalassemia trait in heterogeneous haemoglobinopathies.

BACKGROUND: India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies. METHODS: We compared the performance of a recently developed formula SCS[Formula: see text] and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden's Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures. RESULTS: MCDM methods revealed that the Shine & Lal and SCS[Formula: see text] were the best-performing formulae. Further, a modification of the SCS[Formula: see text] formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS[Formula: see text] along with the condition MCV[Formula: see text] 80 fl was recommended for a higher heterogeneous population set. It was found that SCS[Formula: see text] can classify all BTT samples with 100% sensitivity when MCV[Formula: see text] 80 fl. CONCLUSIONS: We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS[Formula: see text] and its web application SUSOKA can provide 100% sensitivity when MCV[Formula: see text] 80 fl.

Identifying the most critical side effects of antidepressant drugs: a new model proposal with quantum spherical fuzzy M-SWARA and DEMATEL techniques.

Identifying and managing the most critical side effects encourages patients to take medications regularly and adhere to the course of treatment. Therefore, priority should be given to the more important ones, among these side effects. However, the number of studies that make a priority examination is limited. There is a need for a new study that determines which of these effects are more priority to increase the quality of the treatment. Accordingly, this study aims to define the most important side effects of antidepressant drugs with a novel model. Quantum Spherical fuzzy M-SWARA technique is considered to compute the importance weights of the items. The main contribution of this study is that the most critical side effects can be understood for antidepressant drugs by establishing a novel decision-making model. The findings demonstrate that psychological side effects are defined as the most critical side effects of antidepressant drugs. Furthermore, physical side effects also play a key role in this condition. Side effects in antidepressant treatment have a great impact on the effectiveness of treatment and patient compliance.

A data-driven and cost-oriented FMEA-MCDM approach to risk assessment and ranking in a fuzzy environment: A hydraulic pump factory case study.

In today's highly competitive business environment, firms strive to maximize profitability by minimizing or eliminating disruptions and failures to maintain a competitive edge. This study focuses on evaluating risks in a hydraulic pump factory as a means to achieve sustainable growth. To accomplish this, a team of experts was formed to identify potential errors, utilizing a combination of risk priority number criteria weighted by Fuzzy Shannon's entropy and a fusion of multi-criteria decision-making and failure mode and effects analysis for evaluating and ranking failures. Moreover, the study emphasizes the importance of considering the interaction among risk assessment indicators, the inclusion of cost of failure, and modeling under fuzzy uncertainty circumstances, as they have a notable impact on the final ranking of failures to be processed for risk mitigation action planning. This research brings a new dimension to enhance the overall effectiveness of risk assessment by aggregation, as evidenced by a novel use of data classification in machine learning and correlation in statistics. The findings indicate that the aggregated ranking data series is best matched and most influenced by the weighted aggregated sum product assessment method, with the highest rate of recall and precision accomplished.

Accelerating depression intervention: identifying critical psychological factors using MCDM-MOORA technique for early therapy initiation.

BACKGROUND: A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient's condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration. METHODS: The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis. RESULTS: The integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33-35% reduction (χ2 = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34-39% reduction (χ2 = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37-43% reduction (χ2 = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively. CONCLUSION: The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman's rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

A priory allocation of ecosystem services to forest stands in a forest management context considering scientific suitability, stakeholder engagement and sustainability concept with multi-criteria decision analysis (MCDA) technique: A case study in Turkey.

Various research endeavours are designed to identify ecosystem services, assess their spatial distribution, and prioritize them in a given forest landscape. The Turkish State Forest Organization has introduced an ecosystem-based multiple-use forest management philosophy since 2008, which emphasizes the need for identifying and allocating ecosystem services to each forest planning unit. This paper aims to investigate the use of Multiple Criteria Decision Analysis (MCDA) techniques and explores their effectiveness and suitability in identifying and allocating ecosystem services to forest units, considering scientific suitability, stakeholder engagement and the sustainability concept in the context of ecosystem-based forest management decision-making processes in a case study area of Turkey. We propose a framework that entails an iterative process comprising various stages, starting from identifying ecosystem services (ES) to allocating them to forest stands with a participatory approach. We employed the Analytical Hierarchy Process (AHP) and the Delphi method to determine stakeholder preferences and allocate ecosystem services to forest stands. This was achieved through an equation newly developed using scientific suitability, stakeholder preferences, and the sustainability concept. The landscape percentage allocated primarily to ES was as follows: water regulation (55.44%), soil protection (16.47%), biodiversity conservation (14.03%), wood production (13.08%), and aesthetic-recreation (0.84%). Notably, no allocations were made for national defence and climate regulation services. In conclusion, the stratification of Posof forests into zones was efficiently achieved a priori, considering both scientific-technical and socio-cultural criteria through MCDA techniques based on stakeholder preferences. This study streamlines the decision-making process involved in spatially allocating ecosystem services and provides crucial information instrumental in determining management objectives and optimal forest activities.

Evaluating challenges of circular economy and Internet of Things in renewable energy supply chain through a hybrid decision-making framework.

Due to industrial development, expansion of communities, and attention to sustainable development, sustainable energy supply has become a big challenge for communities. In this regard, the development and use of Renewable Energy (RE) are considered due to reducing the harmful environmental effects of fossil fuels. Improving the efficiency of the Renewable Energy Supply Chain (RESC) is important for using RE. To improve the performance and efficiency of RESC, it is necessary to use emerging technologies such as the Internet of Things (IoT) and its integration with the principles of the Circular Economy (CE). Therefore, this study proposes integrating IoT and CE for sustainable development and resource management in RESC. Also, this research provides a hybrid decision framework to assess the challenges of IoT and CE in the RESC of Iran. The CRiteria Importance Through Intercriteria Correlation (CRITIC) technique is used to specify the importance of the criteria. The Fuzzy Evaluation Based on Distance from Average Solution (FEDAS) technique ranks the challenges. The findings indicated that considering the cost of investment, the rate of return on investment, and the productivity rate were the most important sub-criteria with values of 0.149, 0.129, and 0.106 respectively. Then, the sensitivity of the results is examined and the validation of the findings is analyzed with decision-making methods. The results indicate the high priority of the challenge related to transparency in the implementation procedures of IoT and RE projects and information dissemination protocols, the development of guidelines for the integration of IoT in other systems in the information network, and the amount of investment and lack of access to financial resources. This study provided practical insights for RE development based on IoT and CE capabilities for energy planning.

Multi-criteria sustainability assessment of solid waste management in Jordan.

This study comprehensively evaluates Jordan's municipal solid waste (MSW) management sector from 2022 to 2030, in alignment with Jordan Vision 2030. This study introduces new sustainability indicators and innovative waste management alternatives to address the challenges of rapid industrialization and population growth. Four strategic scenarios-1) recycling, composting, and sanitary landfilling; 2) recycling, anaerobic digestion, and sanitary landfilling; 3) incineration and sanitary landfilling; and 4) sanitary landfilling alone-were assessed against the business-as-usual scenario. Using multi-criteria decision analysis (MCDA) and sensitivity analysis, this study evaluates net greenhouse gas emissions, annual operating expenses, revenue streams, and employment rates to measure environmental, economic, and social sustainability. The results indicate that Scenario 1 is the optimal scenario for integrating a material recovery facility (MRF) with a composting plant and sanitary landfill, achieving the lowest greenhouse gas emissions, annual costs, and employment opportunities. This study offers practical and sustainable solutions to Jordan's waste management challenges, provides novel insights through the developed MCDA and sensitivity analysis, and significantly contributes to sustainability research.

Urban greening in Dhaka: Assessing rooftop agriculture suitability using GIS and MCDM techniques.

Dhaka ranks among the world's most densely populated cities, with built-up areas expanding to accommodate the demands of a growing population. The rapid urbanization has reduced green space and exacerbated urban heat and pollution in the city. In the quest for a greener and healthier urban environment, rooftop agriculture has emerged as a promising solution, offering opportunities for the restoration of the environment and safe food production. Despite its potential, limited studies have explored the viability of this alternative greening solution for Dhaka. Therefore, this study aims to assess the suitability of rooftops for agricultural activities employing Geographic Information System (GIS) and Multi-Criteria Decision Making (MCDM) techniques. First, seven criteria were selected based on the literature, such as building age, height, rooftop size, building utility, property value, sunlight, and water availability. Second, an expert opinion survey was conducted using the Best Worst Method (BWM) to calculate the criteria's weights. Finally, the suitability map for Dhaka was derived by combining the criteria layers and was subsequently validated. Rooftop area and property value were identified as the most and least important criteria. Approximately 9% (6.27 km2), 68% (46.59 km2), 22% (15.15 km2), and a negligible portion (0.1 km2) of Dhaka city has been classified as highly suitable, suitable, moderately suitable, and not suitable, respectively, for rooftop agriculture. By identifying and promoting the most suitable locations for rooftop agriculture and highlighting existing opportunities, this research will help to initiate and expand sustainable agriculture practices that can contribute to climate change adaptation and urban resilience.

Multi-Criteria Decision-Making Approach for Pre-Synthesis Selection of the Optimal Physicochemical Properties of TiO2 Photocatalytic Nanoparticles for Biomedical and Environmental Applications.

Nanomaterials are widely used in several biomedical and environmental applications, due to their ideal properties. However, the synthetic and characterization procedure requires significant costs and has a negative environmental impact. Various methods are available in order to control the pre-synthesis design of the produced materials, predicting their behavior and minimizing the series of experiments. Multi-Criteria Decision-Making is proposed in this study in order to determine the best combination of the physicochemical parameters and to define the best alternative among fifteen different samples of nanostructured titanium dioxide. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to achieve a final ranking of the available alternatives by avoiding several of the trials that would follow testing the biological effect and the photocatalytic degradation of organic pollutants. Thus, this approach helps us to stay environmentally and ethically correct, saving time, money, and energy and also providing an optimization of the nanomaterials that are developed.

Improve the satisfaction of medical staff on the use of home nursing mobile APP by using a hybrid multi-standard decision model.

OBJECTIVE: To identify critical satisfaction gaps in a home nursing mobile application (APP) using a systematic decision-making model. METHODS: Initially, the decision-making trial and evaluation laboratory method was used to analyze the relationship structure and corresponding weights among the indicators. The Importance-Performance Analysis (IPA) method was used to identify the categories of all indicators and their corresponding strategic directions. Twenty-six home nursing specialists currently providing home nursing services were recruited for this study. RESULTS: The IPA results revealed that "Assurance," "Reliability," and "Personal security protection" are critical satisfaction gaps. From the influence network and weight results, "information quality" and "system quality" were the critical quality factors in the home nursing mobile APP. The influence of the network relationship structure and weight demonstrated a 98.12% significance level, indicating good stability. CONCLUSION: Continuous improvement in information and system quality is recommended to optimize the overall quality of the home nursing mobile APP. Additionally, user demands should be considered, and personal safety guarantee functions should be developed and integrated into the system to ensure the safety of home nursing workers.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Application of multi-criteria decision-making on low-impact development practice selection for the Kinyerezi River sub-catchments in Dar es Salaam, Tanzania.

The impermeable areas in catchments are proportional to peak flows that result in floods in river reaches where the flow-carrying capacity is inadequate. The high rate of urbanization witnessed in the Kinyerezi River catchment in Dar es Salaam city has been noted to contribute to floods and siltation in the Msimbazi River. The Low-Impact Development (LID) practices that includes bio-retention (BR) ponds, rain barrels (RBs), green roofs (GRs), etc. can be utilized to mitigate portion of the surface runoff. This study aims to propose suitable LID practices and their sizes for mitigating runoff floods in the Kinyerezi River catchment using the Multi-Criteria Decision-Making (MCDM) approach. The results indicated that the BR and RBs were ranked high in capturing the surface runoff while the sediment control fences were observed to be the best in reducing sediments flowing into the BR. The proposed BR ponds were greater than 800 m2 with 1.2 m depth while RB sizes for Kinyerezi and Kisungu secondary schools and Kinyerezi and Kifuru primary schools were 2,730; 2,748; 1,385; and 1,020 m3, respectively. The BR ponds and RBs are capable of promoting water-demanding economic activities such as horticulture, gardening, car washing while reducing the school expenses and runoff generation.

Extended Hellwig's Method Utilizing Entropy-Based Weights and Mahalanobis Distance: Applications in Evaluating Sustainable Development in the Education Area.

One of the crucial steps in the multi-criteria decision analysis involves establishing the importance of criteria and determining the relationship between them. This paper proposes an extended Hellwig's method (H_EM) that utilizes entropy-based weights and Mahalanobis distance to address this issue. By incorporating the concept of entropy, weights are determined based on their information content represented by the matrix data. The Mahalanobis distance is employed to address interdependencies among criteria, contributing to the improved performance of the proposed framework. To illustrate the relevance and effectiveness of the extended H_EM method, this study utilizes it to assess the progress toward achieving Sustainable Development Goal 4 of the 2030 Agenda within the European Union countries for education in the year 2021. Performance comparison is conducted between results obtained by the extended Hellwig's method and its other variants. The results reveal a significant impact on the ranking of the EU countries in the education area, depending on the choice of distance measure (Euclidean or Mahalanobis) and the system of weights (equal or entropy-based). Overall, this study highlights the potential of the proposed method in addressing complex decision-making scenarios with interdependent criteria.

Approach Based on the Ordered Fuzzy Decision Making System Dedicated to Supplier Evaluation in Supply Chain Management.

The selection of suppliers represents a pivotal aspect of supply chain management and has a considerable impact on the success and competitiveness of the organization in question. The selection of a suitable supplier is a multi-criteria decision making (MCDM) problem based on a number of qualitative, quantitative, and even conflicting criteria. The aim of this paper is to propose a novel MCDM approach dedicated to the supplier evaluation problem using an ordered fuzzy decision making system. This study uses a fuzzy inference system based on IF-THEN rules with ordered fuzzy numbers (OFNs). The approach employs the concept of OFNs to account for potential uncertainty and subjectivity in the decision making process, and it also takes into account the trends of changes in assessment values and entropy in the final supplier evaluation. This paper's principal contribution is the development of a knowledge base and the demonstration of its application in an ordered fuzzy expert system for multi-criteria supplier evaluation in a dynamic and uncertain environment. The proposed system takes into account the dynamic changes in the value of assessment parameters in the overall supplier assessment, allowing for the differentiation of suppliers based on current and historical data. The utilization of OFNs in a fuzzy model then allows for a reduction in the complexity of the knowledge base in comparison to a classical fuzzy system and makes it more accessible to users, as it requires only basic arithmetic operations in the inference process. This paper presents a comprehensive framework for the assessment of suppliers against a range of criteria, including local hiring, completeness, and defect factors. Furthermore, the potential to integrate sustainability and ESG (environmental, social, and corporate governance) criteria in the assessment process adds value to the decision making framework by adapting to current trends in supply chain management.

Managing surgical waiting lists through dynamic priority scoring.

Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.

Identification of critical quality factors and critical satisfaction gaps in emergency training courses for new nurses by the multi-criteria network structure model.

OBJECTIVE: To identify critical quality factors and critical satisfaction gaps in emergency training courses for new nurses through a systematic decision-making model. METHODS: Firstly, the service quality (SERVQUAL) was used in the evaluation index system of this study. Then, the decision-making trial and evaluation laboratory (DEMATEL) method was used to analyze the relationship structure and the corresponding weights between the indicators. Finally, the importance-performance analysis (IPA) method was used to identify the categories of all indicators and the corresponding strategic directions. Fifteen new nurses in Taizhou Hospital of Zhejiang Province were selected as participants in this study. RESULTS: The IPA results showed that "(C13)," "(C22)," "(C52)," "(C53)," "(C54)," "(C55)," "(C56),"and "(C57)" are critical satisfaction gaps. From the results of influence network and weight, empathy (C5) was the critical quality factor of the entire training course. The influence network relationship structure and weight had a 98.1% significant confidence level, indicating good stability. CONCLUSION: Teachers' empathy is key to the learning outcomes of new nurses in emergency nursing training courses. Hence, teachers should be attentive to the empathetic quality of their teaching methods to help new nurses gain knowledge and experience in emergency care, especially when they come from different professions and departments.

Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases.

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis.

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors' Communication Optimization in an IoT-Fog Ecosystem.

With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

Promoting sustainable management of hazardous waste-to-wealth practices: An innovative integrated DPSIR and decision-making framework.

Sustainable valorization of tannery sludge (TS) is vital for achieving several sustainable development goals (SDGs) in the tannery industry. TS is considered a hazardous waste by-product posing a significant environmental challenge. However, TS can be utilized for energy or resource recovery by considering it as biomass and implementing the circular economy (CE) concept. Therefore, this study aims to develop an innovative DPSIR (Driver, Pressure, State, Impact, and Response) framework for promoting sustainable valorization of TS. Further, the study extends to quantify the importance of subjective DPSIR factors by offering interval-valued intuitionistic fuzzy number-based best worst method (IVIFN-BWM), which is relatively new in the literature and able to deal with the uncertainty, inconsistency, imprecise, and vagueness in the decision-making process. The study also investigates the most appropriate TS valorization technologies concerning identified DPSIR factors using a novel IVIFN-combined compromise solution (CoCoSo) approach. This research contributes to the literature by developing a comprehensive solution approach that combines the DPSIR framework, IVIFN-BWM, and IVIFN-CoCoSo method in addressing sustainability and resource recovery challenges for the tannery industry. The research findings highlight the potential of sustainable valorization of TS in reducing the waste amount and promoting sustainability and CE practices in the tannery industry. The findings indicated that response factors 'creation of national-level policies and awareness campaign' and 'facilitating financial support to adopt waste valorization technologies' received the highest priority among other DPSIR factors for managing and fostering sustainable valorization of TS. The IVIFN-CoCoSo analysis confirmed that the most promising TS valorization technology is 'gasification', which is followed by pyrolysis, anaerobic digestion, and incineration. The study's implications extend to policymakers, industrial practitioners, and researchers, who can leverage the research findings to develop more sustainable TS management practices in the tannery industry.

Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model.

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.