Systematic Ranking Research Articles

Evaluation of energy economic optimization models using multi-criteria decision-making approach

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

Advancing Sustainable Urban Development: Navigating Complexity with Spherical Fuzzy Decision Making

This study explores the complexities of urban planning and addresses major issues by carefully weighing four options for smart city technology, community-based development, green infrastructure investment, and transit-oriented development. Unlike traditional evaluations, our study applies the novel SWARA-WASPAS model to spherical fuzzy sets (SFSs), thus identifying and navigating the uncertainty present in decision making. This methodological approach improves the accuracy of our assessment by providing detailed information about the advantages and disadvantages of each option. Our study offers useful insights for urban policymakers and planners using carefully weighted criteria and employing a methodical ranking procedure. The aim is to provide insights for decisions that promote equity, environmental consciousness, resilience, and symmetry in urban environments. The application of the SWARA-WASPAS approach not only advances the field but also provides a strong basis for informed decision making. This improves the accuracy of our evaluations and provides detailed insights into each option’s pros and cons. Our study uses weighted criteria and systematic ranking to advise urban policymakers and planners. Our main goal is to help urban populations make resilient, environmentally responsible, equitable, and symmetrical decisions. Our research aims to further the conversation on sustainable urban development by offering a framework based on data that addresses the difficulties associated with dynamic urban environments. In the end, we want our humanized viewpoint to speak to a wider audience and inspire a shared dedication to creating cities that flourish in the face of changing urban environments.

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed.

Ranking of Tourism Web Sites According to Service Performance Criteria with CRITIC and MAIRCA Methods: The Case of Turkey

Multi-Criteria Decision-Making (MCDM) methods enable the systematic ranking of decision alternatives by evaluating them with more than one maximized or minimized decision criterion. Multi-Criteria Decision-Making methods provide decision-makers with forms that include specified steps for calculating the importance weights of decision criteria and ranking decision alternatives. Weighting methods for calculating criterion weights are divided into subjective and objective. CRITIC method is one of the most widely used objective weighting methods in which criterion weights are determined according to the structure of the data. The MAIRCA method, a Multi-Criteria Decision Making method based on linear vector optimization, is widely preferred in the literature, especially in recent years, as it is also suitable for criteria with zero and negative values. This study aims to rank tourism websites, which play an essential role in terms of national tourism, according to nine service performance criteria. The CRITIC method determined the importance and weight values of the criteria, and the ranking of the websites was carried out by the MAIRCA method. The most decisive decision criterion in the website ranking was the live support and number of complaints. As a result of the ranking of the websites with the MAIRCA method, tripadvisor.com and seyahatyanımda.com took the first two places, while etstur.com and setur.com took the last two.

Quality of service attributes based hybrid decision-making framework for ranking cloud service providers under Fermatean fuzzy environment

Cloud Computing has gained substantial popularity due to its ability to offer diverse and dependable computing services suited to clients demands. Given the rapid expansion of this technology, an increasing number of IT service providers are competing to deliver cloud services that are both of excellent quality and cost-efficient, in order to best meet the requirements of their clients. With the extensive range of options available, selecting the best Cloud Service Provider (CSP) has become a challenging dilemma for the majority of cloud clients. When evaluating services offered by many CSPs, it is important to consider multiple attributes. Efficiently addressing the selection of the best CSP involves tackling a challenging Multi-Attribute Decision Making (MADM) problem. Several MADM techniques have been proposed in academic literature for evaluating CSPs. However, the persisting problems of inconsistency, uncertainty, and rank reversal remain unresolved. In this paper the authors present a hybrid MADM framework to rank eight CSPs using nine Quality of Service (QoS) attributes. In order to achieve this objective, Fermatean fuzzy sets-full consistency method (FFS-FUCOM) is combined with Grey-Relational-Analysis and the Technique-for-Order-Preference-by-Similarity-to-Ideal-Solution (Grey-TOPSIS) technique. The framework successfully resolved the aforementioned problems. Sensitivity analysis is conducted to assess the stability and robustness of the results produced by the proposed framework. The sensitivity analysis results indicate that the proposed framework offers an accurate and robust solution. A systematic ranking test is undertaken to ensure that the results are ranked in a systematic manner. Additionally, a comparative analysis is carried out with the most relevant study.

Winner-Take-All? Visibility, Availability, and Heterogeneity on Webcam Sex Platforms

Online platforms have profoundly changed the organization of work in many economic sectors, and the sex industry is no exception. Webcam sex platforms, in particular, host large and heterogeneous populations of workers who are not formally employed and rely heavily on algorithmic systems to manage this workforce. These systems are often said to produce or reinforce unpredictable and unequal winner-take-all effects, contributing to economic pressure and precarity. Most research trying to empirically assess these claims has focused on single platforms and on the experiences of limited samples of regular workers, excluding more sporadic performers that nonetheless compete for visibility within the same ranking systems. In this article, we seek to address these limitations through a multi-platform study based on systematic ranking data collected by scraping the complete homepages of five webcam platforms over 11 weeks. The article proceeds in four steps. We first discuss existing work on algorithmic workplace management and webcam sex platforms. We then introduce the case studies, present our empirical approach, and discuss ethical considerations. The findings section is organized around two complementary lines of inquiry: an examination of visibility distributions across our sample of websites, as well as their connection with viewer numbers, and an exploration of the relationship between visibility and labor practices, which allows us to link performer availability to ranking outcomes. We conclude by highlighting the substantial differences between these designed marketplaces and discuss repercussions for both webcam sex research and the broader field of platform studies.

Evaluation of autonomous underwater vehicle motion trajectory optimization algorithms

The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selection. In all three motion types – “depth”, “yaw”, and “theta” – Kp_θ had the highest weight value, with respective weights of 0.143, 0.149, and 0.142. In contrast, Ki_depth consistently received the lowest weight value across all three motion types, with respective weights of 0.057, 0.0598, and 0.057. Regarding the “depth” motion, the Archimedes optimization algorithm (AOA) was the highest-performing alternative with a score of 0.077, while the eagle strategy–particle swarm optimization algorithm was the worst alternative with a score of 0.022. The Cuckoo optimization algorithm was identified as the best alternative for the “yaw” motion with a score of 0.072, whereas black hole optimization had the lowest score of 0.036. The best alternative for the “theta” motion was AOA with a score of 0.067, and the worst algorithm was sunflower optimization with a score of 0.028. This developed approach was evaluated based on systematic ranking and sensitivity analysis, which confirmed the validity of the proposed work.

A comprehensive evaluation approach for efficient countermeasure techniques against timing side-channel attack on MPSoC-based IoT using multi-criteria decision-making methods

ContextTiming side-channel attack countermeasure techniques (TSCA-CTs) evaluation is a multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform architectures, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against timing side-channel attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weigh the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. ObjectivesThis research proposes a Fermatean-FDOSM framework for evaluating TSCA-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. MethodsThe methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included seven defense approaches (e.g., Gossip and SER router) based on ten criteria (e.g., number of malicious IP cores, number of routers, power, latency… etc.). Then, the decision matrix was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results(1) CRITIC weighting shows that MPSoC NoC throughput (packet/clock) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Adaptive routing countermeasure technique is the highest score alternative compared to the Combined with separate interface hybrid approach. (3) The TSCA-CTs priority ranks were subjected to a systematic ranking that was confirmed by strong correlation results throughout ten criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. ConclusionThe outcomes of this study are anticipated to give particular knowledge and direction to those who want to do decision theory-based MPSoCs-based IoT and NoC communication security research.

A decision modeling approach for smart e-tourism data management applications based on spherical fuzzy rough environment

The technology deployment in smart e-tourism brings high potential in terms of customer data, events, reservations, and others. It acts as an effective and personalized guide to aid travelers. There is an increasing variety of smart e-tourism apps with multiple categories and criteria, but in terms of decision making, this presents a multicriteria complex problem to determine the best app from a group of available options with high criteria subjectivity. Literature reviews have evaluated and modeled the existing smart e-tourism apps alternatives, but informational uncertainty remains. The fuzzy sets and Multi-Attribute Decision Analysis (MADA) were used to handle the subjectivity issue. However, this process includes levels of uncertainty, which affects the decisions made and still an open issues. Spherical fuzzy rough sets (SFRSs) environment are useful in this situation for resolving fuzziness and ambiguity. This paper proposed a decision modeling approach for smart E-Tourism data management applications based on SFRSs environment. For methodology: firstly, a decision matrix is adopted for 5 different categories of Smart E-tourism’s system applications on the basis of the integrated 12 evaluation criteria. Secondly, a new formulation and development formulating a new extension of FWZIC, called a Spherical Fuzzy Rough-Weighted Zero-Inconsistency (SFR-WZIC), for weighting the smart key concept attributes involved in modeling smart e-tourism, whereas a new formulation and development for a new extension of FDOSM, called a Spherical Fuzzy Rough Decision by Opinion Score Method (SFR-DOSM), for modeling the applications of smart e-tourism per each e-tourism category; then, the new developments are integrated. The proposed methods were evaluated using systematic ranking and sensitivity analysis.

A Shortlisting Framework for Crop Diversification in the United Kingdom

We present a systematic framework for nationwide crop suitability assessment within the UK to improve the resilience in cropping systems and nutrition security of the UK population. An initial suitability analysis was performed using data from 1842 crops at 2862 grid locations within the UK, using climate (temperature and rainfall) and soil (pH, depth, and texture) data from the UK Met Office and British Geological Survey. In the second phase, additional qualitative and quantitative data are collected on 56 crops with the highest pedoclimatic suitability and coverage across the UK. An exercise was conducted on crops within each category using a systematic ranking methodology that shortlists crops with high value across a multitude of traits. Crops were ranked based on their nutritional value (macronutrients, vitamins, and minerals) and on adaptive (resistance to waterlogging/flood, frost, shade, pest, weed, and diseases and suitability in poor soils) and physiological traits (water-use efficiency and yield). Other characteristics such as the number of special uses, available germplasm through the number of institutions working on the crops, and production knowledge were considered in shortlisting. The shortlisted crops in each category are bulbous barley (cereal), colonial bentgrass (fodder), Russian wildrye (forage), sea buckthorn (fruit), blue lupin (legume), shoestring acacia (nut), ochrus vetch (vegetable), spear wattle (industrial), scallion (medicinal), and velvet bentgrass (ornamental/landscape). These crops were identified as suitable crops that can be adopted in the UK. We further discuss steps in mainstreaming these and other potential crops based on a systematic framework that takes into account local farming system issues, land suitability, and crop performance modelling at the field scale across the UK.

A comparative study of evaluating and benchmarking sign language recognition system-based wearable sensory devices using a single fuzzy set

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.

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.

Federated Learning for IoMT Applications: A Standardisation and Benchmarking Framework of Intrusion Detection Systems.

Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the standardisation and multicriteria decision-making (MCDM) problems. Thus, this study is developing an MCDM framework for standardising and benchmarking the ML-based IDSs used in the FL architecture of IoMT applications. In the methodology, firstly, the evaluation criteria of ML-based IDSs are standardised using the fuzzy Delphi method (FDM). Secondly, the evaluation decision matrix (DM) is formulated based on the intersection of standardised evaluation criteria and a list of ML-based IDSs. Such formulation is achieved using a dataset with 125,973 records, and each record comprises 41 features. Thirdly, the integration of MCDM methods is formulated to determine the importance weights of the main and sub standardised security and performance criteria, followed by benchmarking and selecting the optimal ML-based IDSs. In this phase, the Borda voting method is used to unify the different ranks and perform a group benchmarking context. The following results are confirmed. (1) Using FDM, 17 out of 20 evaluation criteria (14 for security and 3 for performance) reach the consensus of experts. (2) The area under curve criterion has the lowest set of weights, whilst the CPU time criterion has the highest one. (3) VIKOR group ranking shows that the BayesNet is a best classifier, whilst SVM is the last choice. For evaluation, three assessments, namely, systematic ranking, computational cost and comparative analysis, are used.

Evaluation Approach for Efficient Countermeasure Techniques Against Denial-of-Service Attack on MPSoC-Based IoT Using Multi-Criteria Decision-Making

Context: Denial-of-Service Attack countermeasure techniques (DoS A-CTs) evaluation is a Multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform design, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against Denial-of-Service (DoS) attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weight the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. Objectives: This research proposes a Fermatean-FDOSM framework for evaluating DoS A-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. Methods: The methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included eighteen defense approaches (e.g., Sniffer, SeRA, and RLAN) based on thirteen criteria (e.g., size, power, latency, and effectiveness…etc.). Then, the Decision matrix (DM) was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results: (1) CRITIC weighting shows that MPSoC NoC Routing Algorithm (XY and YX) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Collision Point Router Detection (CPRD) countermeasure technique is the first-ranked alternative compared to the Secure Model Checkers (SMCs) approach. (3) The DoS A-CTs priority ranks were subjected to a systematic ranking that was confirmed by solid correlation results throughout thirteen criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. Conclusion: The results of this research are expected to provide a specific understanding and guide for those who want to engage in MPSoCs-based IoT and NoC communication security research with decision theory.

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment.

This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.

Integration of FDOSM and FWZIC Under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-attribute decision-analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist.

Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents

Sulfur dioxide (SO 2 ) is one of the main atmospheric pollutants and an active threat to human health. SO2 separation from industrial flue gases improves air quality, decreases human health problems, and reserves sulfur resources. The capturing and recycling processes demand a green solvent with an effective, selective, and reversible SO 2 absorption. Deep eutectic solvents (DESs) have recently been engaged in SO 2 capture/recycle processes. The SO 2 removal capacity of DESs depends on their ingredients (the hydrogen bond donor, hydrogen bond acceptor, water content), temperature, and pressure. Despite comprehensive experimental investigations, literature presents no clue to compare the SO 2 absorption capacity of DESs considering their compositions and operating conditions. Therefore, this work deploys an efficient machine learning model to estimate the SO 2 absorption capacity of DESs as a function of their molecular weight, water content, pressure, and temperature. All the laboratory-measured datasets reported in the literature have been included to ensure that the deployed model is reliable and generalized. Furthermore, the proposed model has been selected among five different classes of artificial neural networks . A single hidden-layer neural network with only eleven neurons optimized by the Levenberg-Marquardt is the most precise model predicting 480 DES-SO 2 phase equilibria with the mean squared error , mean absolute percentage error, and coefficient of determination of 1.13 × 10 −3 , 4.76%, and 0.97936, respectively. This research is the first step toward constructing a reliable model for screening available DESs based on their SO 2 absorption capacity and finding the best candidate. • Two scenarios have been followed for estimating the SO 2 solubility in 29 DESs. • Various machine learning techniques were designed/compared to find the best model. • Systematic ranking analyses confirm that the MLP-LM is the highest accurate model. • The model shows excellent performance, i.e., R 2 = 0.97936, MSE = 1.13 × 10 −3 , MAPE = 4.76%. • Trend analyses confirm that the MLP-LM has compatibility with the system behavior.

Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance

Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 1011 gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.