Set Of Weights Research Articles

The establishment of evaluation systems and an index for energy superpower

Building an energy superpower is a major development strategy for China at present and long-term goal for other countries. This study aims to propose an ideal evaluation system, and construct a quantifiable index for energy superpower. 7 weight allocation schemes and 16 weight sets were employed to test the impacts of different weights on energy superpower index (ESPI), ensuring authenticity, rationality, and accuracy, and finally the subjective entropy weight method was adopted to determine the weights. The ESPI of 11 economies, including China, the United States, Canada, Russia, Australia, Germany, Japan, the United Kingdom, South Korea, Argentina, and the European Union, was computed for a period of 21 years (2000−2020). China's index demonstrated a remarkable increase from the second-to-last place in 2000 to second place in 2020, surpassing the other 10 countries. The United States and Argentina's energy superpower indices consistently ranked first and last respectively from 2000 to 2020, while the early European Union's index ranked second, but was later surpassed by China, falling to third. The energy superpower indices of Canada, Russia, Australia, Germany, the United Kingdom, Japan, and South Korea share similar values with slow growth rates, roughly ranking in between the European Union and Argentina.

A Structured Graph Neural Network for Improving the Numerical Weather Prediction of Rainfall

AbstractThe current challenges of numerical weather prediction (NWP) of rainfall mainly stem from the complex and multiscale nature of rainfall. In recent years, as observation capability improved worldwide, there has been an increased feasibility to use data‐driven models to enhance forecasting performance with rainfall observation. Compared to traditional statistical and machine learning models, deep‐learning models show considerable promise in capturing the spatial‐temporal features of weather processes from multiple predictors, but the convolution‐based feature extractor is suboptimal due to the linear nature of convolution kernels. In this study, a multilevel forecasting model is proposed to forecast each rainfall level, in which each submodel adopts a graph neural network for feature extraction. Spatial and temporal propagation functions based on grid structure are designed to explicitly represent feature fusion and propagation of multiple predictors across multiple scales. On model training, a weight setting strategy that balances the impact of samples with different rainfall values on the total training loss is proposed, and a soft classification label is designed to convert observed rainfall into the probability of rainfall above each threshold. The proposed model was trained and validated on NWP data provided by European Center for Medium‐Range Weather Forecast, and results show significant improvement over the NWP in terms of threat score (TS) and Heidke Skill Score (HSS) scores. Analysis of the forecast results for two typical rainfall processes also illustrates that the proposed method can predict rainfall with more reasonable location and intensity.

Computing well-covered vector spaces of graphs using modular decomposition

A graph is well-covered if all its maximal independent sets have the same cardinality. This concept was introduced by Plummer in 1970 and naturally generalizes to the weighted case. Given a graph G, a real-valued vertex weight function w is said to be a well-covered weighting of G if all its maximal independent sets are of the same weight with respect to w. The set of all well-covered weightings of a graph G forms a vector space over the field of real numbers, called the well-covered vector space of G. Since the problem of recognizing well-covered graphs is co\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{co}$$\\end{document}-NP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{NP}$$\\end{document}-complete, the problem of computing the well-covered vector space of a given graph is co\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{co}$$\\end{document}-NP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{NP}$$\\end{document}-hard. Levit and Tankus showed in 2015 that the problem admits a polynomial-time algorithm in the class of claw-free graphs. In this paper, we give two general reductions for the problem, one based on anti-neighborhoods and one based on modular decomposition, combined with Gaussian elimination. Building on these results, we develop a polynomial-time algorithm for computing the well-covered vector space of a given fork-free graph, generalizing the result of Levit and Tankus. Our approach implies a polynomial-time recognition algorithm for the class of well-covered fork-free graphs and also generalizes some known results on cographs.

Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries

One of the most critical metrics for evaluating countries’ logistics performance is the Logistics Performance Index (LPI). Although the LPI is an effective tool, it is also an index with some concerns regarding performance evaluation. The two main concerns with LPI are assuming equal criterion weights and ignoring operational logistics performance and macroeconomic indicators. To overcome these problems, this study presents a logistics performance evaluation model in which the criteria weights are determined by GA. This study used 11 techniques to determine the logistics performances of the EU countries across 33 indicators. Employing more than one MCDM techniques enabled robustness and consistency. Median, Mean, Linear and K nearest neighbor techniques are implemented to impute the missing values in the dataset. The Linear Regression method is found as the best-performing imputation technique among other techniques for the data of this research. The performance of the GA is compared with other well-known criteria weight determination procedures namely CRITIC, Entropy and equal weight. The ranking results of the MCDM tools were integrated with the Copeland method. The genetic algorithm assigned the highest three weights to the Goods, Value of Exports, Quality of roads, and GDP per capita, indicating that these criteria play an essential role in the countries' logistics performance. The importance of these criteria is not captured by other weight sets. The correlation with the actual LPI scores is higher with Genetic algorithm compared with other weight determination techniques (CRITIC, Entropy, equal weight). Moreover, random weights are created and the results are examined. Weight simulation indicates that, some of the countries ranking are higher than the other countries, regardless of the weights of the criteria. The paper's novelty is to use the Genetic Algorithm (GA) as a criteria weight determination tool for MCDM methods. The proposed system can be used as a decision support system for evaluating the logistics performances of countries. The LPI index is published every two years, but if an automatic or semi-automatic system can be developed using the proposed approach, the logistics performance ranking of countries can be examined more frequently. This can help countries that want to rank high on the LPI ranking determine which criteria to focus on to improve their logistics performance.

Why higher copayments for opioids did not reduce use among Medicare beneficiaries.

To examine whether higher cost-sharing deterred prescription opioid use. Medicare Part D claims from 2007 to 2016 for a 20% random sample of Medicare enrollees. We obtain estimates of the effect of cost-sharing on prescription opioid use using ordinary least squares and instrumental variables methods. In both, we exploit the variation (change) in cost-sharing within plans over time for a sample of beneficiaries who remain in the same plan. Focusing on changes in cost-sharing within a plan for a constant sample of beneficiaries mitigates potential bias from plan selection and using a constant set of weights derived from use in year (t) eliminates changes in the cost-sharing indexes due to (endogenous) consumer choice in year (t+1). Part D plans adopted benefit changes designed to reduce opioid use, including moving opioids to higher cost-sharing tiers. Increasing plan copayments for hydrocodone or oxycodone was associated with reductions in plan-paid claims and offsetting increases in cash claims. Widespread availability of low-cost generics combined with the anti-clawback provision in Part D mediated the effect of higher cost sharing to curb opioid use. As plans moved generic opioids to higher cost-sharing tiers, beneficiaries simply paid cash prices and aggregate use remained largely unchanged. The anti-clawback provision in Part D, intended to protect beneficiaries from price gouging, limited plans' ability to constrain opioid use through typical demand-side measures such as increased cost-sharing.

Definition of Compliance Criterion Weights for Bridge Construction Method Selection and Their Application in Real Projects

The main research purpose of the present paper is the establishment of certain compliance criteria, applied for the selection of the most appropriate, per case, bridge construction method, as well as the definition of the weights of these criteria. The five basic concrete bridge construction methods considered in this study are: Cast-in-place, Precast I-Girder, Incremental Launching, Advanced Shoring, and Balanced Cantilever. In this context, the choice of construction method in a concrete road bridge project is proposed based on seven compliance criteria which are: safety, economy, durability, construction speed, serviceability, aesthetics, and environmental harmonization. The inclusion of all these criteria is achieved via the decision-making tool of multi-criteria analysis. A notable innovation of the current study is that road bridges are divided into three categories (bridges for highways, national roads, and provincial roads), in accordance with the importance of the road that contains them. Thus, three different sets of weights of criteria are calculated, corresponding to each bridge category. The research method used for this purpose was a structured questionnaire that was distributed to a large number of selected experts in the field of bridges, who come either from academia or the construction industry. The research results showed that the criteria of safety and economy are the most significant according to the experts, while aspects such as the correlations between experts’ profile and their weights were also considered. Finally, the derived criterion weights were applied to two case studies of real bridge projects in Greece.

Assessing potential of high temperature reactor facilities of Russian design for near-term deployment of hydrogen production

In frames of the work, a pilot comparative multi-criteria evaluation of several options for the production of hydrogen using the energy of nuclear reactors was carried out. Technical and economic models of hydrogen production by methods of steam methane reforming, high-temperature and low-temperature electrolysis using nuclear reactors as a source of thermal and electrical energy have been developed. Russian projects of high-temperature gas-cooled reactors and existing VVER-type NPPs are considered. An approach to a comparative multi-criteria evaluation of options for hydrogen production using the energy of nuclear reactors is proposed. The proposed approach has been tested by comparing several variants of hydrogen production. When evaluating the values of the criteria, data obtained from open sources, with the help of developed economic models and expert assessments, were used. As a result, the considered variants of hydrogen production were ranked using several metrics and sets of criteria weights. The sensitivity analysis of metric values and option ratings to changes in the main criteria showed a sufficient sustainability of the multi-criteria analysis carried out.

Exploring an innovative method for objectively assessing the social value of university-community engagement and research

There is increasing recognition regarding the potential of Higher Education Institutions (HEIs) to be significant Anchor institutions capable of impacting the wellbeing of the local communities where they are situated. Subsequently, more universities are prioritising their social and civic responsibility by embedding a research ethos based on closer engagement with their locality, within their key institutional processes. However, demonstrating the social value of community engagement in a meaningful way is difficult, particularly the type of intangible benefits such as relationship building and co-production of knowledge that are forged through collaboration. We present results of a pilot study which applied a Socially Modified Economic Valuation (SMEV) approach to three very different case study projects within a University-Community partnership initiative situated in Belfast, Northern Ireland, to help evaluate how well the university’s civic and social obligations are being fulfilled. Two sets of potential social weights were derived. The first was based on the official Deprivation Index and the second on a combination of desirable ‘social’ and ‘strategy’ goals. Findings showed that the social valuation element of SMEV may potentially be crucially important in assisting with nuanced interpretation of social value across different communities in different places.

Resetting Priorities of Near-Zero Emission Energy Projects: Multi-Criteria Analysis

ABSTRACT Egypt’s national development plan aims to diversity energy resources and expand near-zero emission energy (NZEE) generation technologies. The target for NZEE’s share in the Egyptian energy basket is 20% by year 2030. There is a set of NZEE projects on the agenda of the decision-makers that needs reconsideration. This study goal is to assist decision-makers and stakeholders in reprioritising the implementation of these projects based on technical, economic, environmental, and social criteria. Multi-criteria analysis (MCA) was employed to rank five specific projects categorised under pumped storage hydropower, nuclear, clean coal, solar, and wind. The results showed that, for the non-biased set of weights, El Dabaa Nuclear Power Plant took the first rank. It has been also given the first priority according to environment activists’ biased weights. For the social biased weights, Benban Solar Park outranked all the other projects.

Multiplicities of maximal weights of the sℓ ̂(n)-module V (kΛ0)

Consider the affine Lie algebra [Formula: see text] with null root [Formula: see text], weight lattice [Formula: see text] and set of dominant weights [Formula: see text]. Let [Formula: see text] [Formula: see text] denote the integrable highest weight [Formula: see text]-module with level [Formula: see text] highest weight [Formula: see text]. Let [Formula: see text] denote the set of weights of [Formula: see text]. A weight [Formula: see text] is a maximal weight if [Formula: see text]. Let [Formula: see text] denote the set of maximal dominant weights which is known to be a finite set. The explicit description of the weights in the set [Formula: see text] is known [R. L. Jayne and K. C. Misra, On multiplicities of maximal dominant weights of [Formula: see text]-modules, Algebr. Represent. Theory 17 (2014) 1303–1321]. In papers [R. L. Jayne and K. C. Misra, Lattice paths, Young tableaux, and weight multiplicities, Ann. Comb. 22 (2018) 147–156; R. L. Jayne and K. C. Misra, Multiplicities of some maximal dominant weights of the [Formula: see text]-modules [Formula: see text], Algebr. Represent. Theory 25 (2022) 477–490], the multiplicities of certain subsets of [Formula: see text] were given in terms of some pattern-avoiding permutations using the associated crystal base theory. In this paper the multiplicity of all the maximal dominant weights of the [Formula: see text]-module [Formula: see text] are given generalizing the results in [R. L. Jayne and K. C. Misra, Lattice paths, Young tableaux, and weight multiplicities, Ann. Comb. 22 (2018) 147–156; R. L. Jayne and K. C. Misra, Multiplicities of some maximal dominant weights of the [Formula: see text]-modules [Formula: see text], Algebr. Represent. Theory 25 (2022) 477–490].

Analyzing the influencing factors of informatization teaching quality in higher vocational education using fuzzy comprehensive evaluation model

Abstract This paper compares the characteristics and methods of informatization teaching in higher vocational education with traditional teaching modes, extracts the factors affecting the quality of higher vocational informatization teaching, and constructs a preliminary evaluation index system of higher vocational informatization teaching quality based on evaluation principles and evaluation strategies. At the same time, the qualitative evaluation was transformed into a quantitative evaluation based on the affiliation theory of fuzzy mathematics, the index weight set was established, and the fuzzy comprehensive evaluation model was designed comprehensively. In the evaluation results, the ratios of teachers’ factor, students’ factor, teaching content influence factor, and teaching effect on the quality of information technology education were 0.39, 0.33, 0.34, and 0.36.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

Correlation of hepatic venous saturation and mixed venous saturation: pooled analyses.

In critical care, monitoring adequate tissue oxygenation is essential. Mixed venous oxygen saturation has traditionally been considered the gold standard for measuring cardiac output, which represents systemic oxygen delivery. Studies have shown that hepatic vein saturation is correlated with mixed venous oxygen saturation and mortality. The primary aim of this study was to determine the correlation between hepatic vein saturation and mixed venous saturation, and the impact of clinical characteristics on this correlation. A systematic review of the literature was performed to identify manuscripts. They must have included patients who received simultaneous mixed venous saturations and hepatic vein saturations, and the data for both must have been explicitly shared. Data were pooled from these studies to analyze the correlation between mixed venous saturation and the corresponding hepatic vein saturation. A total of 13 studies with 333 patients were included in the final analyses. The average age across these studies was 60.3±5.2. The pooled correlation between the mixed venous saturation and hepatic vein saturation was 0.88, demonstrating a strong correlation between the two. The average mixed venous saturation was 73.3±5.0 while the average hepatic vein saturation was 59.5±11.1. In these pooled analyses, hepatic vein saturation has a strong correlation with mixed venous saturation. This correlation is not significantly impacted by patient age, weight, or clinical setting. Nonetheless, further prospective studies are needed for confirmation.

Sarcopenic Dysphagia and Simplified Rehabilitation Nutrition Care Process: An Update.

Sarcopenic dysphagia is characterized by weakness of swallowing-related muscles associated with whole-body sarcopenia. As the number of patients with sarcopenia increases with the aging of the world, the number of patients with sarcopenic dysphagia is also increasing. The prevalence of sarcopenic dysphagia is high in the institutionalized older people and in patients hospitalized for pneumonia with dysphagia in acute care hospitals. Prevention, early detection and intervention of sarcopenic dysphagia with rehabilitation nutrition are essential. The diagnosis of sarcopenic dysphagia is based on skeletal and swallowing muscle strength and muscle mass. A reliable and validated diagnostic algorithm for sarcopenic dysphagia is used. Sarcopenic dysphagia is associated with malnutrition, which leads to mortality and Activities of Daily Living (ADL) decline. The rehabilitation nutrition approach improves swallowing function, nutrition status, and ADL. A combination of aggressive nutrition therapy to improve nutrition status, dysphagia rehabilitation, physical therapy, and other interventions can be effective for sarcopenic dysphagia. The rehabilitation nutrition care process is used to assess and problem solve the patient's pathology, sarcopenia, and nutrition status. The simplified rehabilitation nutrition care process consists of a nutrition cycle and a rehabilitation cycle, each with five steps: assessment, diagnosis, goal setting, intervention, and monitoring. Nutrition professionals and teams implement the nutrition cycle. Rehabilitation professionals and teams implement the rehabilitation cycle. Both cycles should be done simultaneously. The nutrition diagnosis of undernutrition, overnutrition/obesity, sarcopenia, and goal setting of rehabilitation and body weight are implemented collaboratively.

A novel comorbidity index in Italy based on diseases detected by the surveillance system PASSI and the Global Burden of Diseases disability weights

BackgroundUnderstanding comorbidity and its burden characteristics is essential for policymakers and healthcare providers to allocate resources accordingly. However, several definitions of comorbidity burden can be found in the literature. The main reason for these differences lies in the available information about the analyzed diseases (i.e., the target population studied), how to define the burden of diseases, and how to aggregate the occurrence of the detected health conditions.MethodsIn this manuscript, we focus on data from the Italian surveillance system PASSI, proposing an index of comorbidity burden based on the disability weights from the Global Burden of Disease (GBD) project. We then analyzed the co-presence of ten non-communicable diseases, weighting their burden thanks to the GBD disability weights extracted by a multi-step procedure. The first step selects a set of GBD weights for each disease detected in PASSI using text mining. The second step utilizes an additional variable from PASSI (i.e., the perceived health variable) to associate a single disability weight for each disease detected in PASSI. Finally, the disability weights are combined to form the comorbidity burden index using three approaches common in the literature.ResultsThe comorbidity index (i.e., combined disability weights) proposed allows an exploration of the magnitude of the comorbidity burden in several Italian sub-populations characterized by different socioeconomic characteristics. Thanks to that, we noted that the level of comorbidity burden is greater in the sub-population characterized by low educational qualifications and economic difficulties than in the rich sub-population characterized by a high level of education. In addition, we found no substantial differences in terms of predictive values of comorbidity burden adopting different approaches in combining the disability weights (i.e., additive, maximum, and multiplicative approaches), making the Italian comorbidity index proposed quite robust and general.

Aggregating faculty members’ research effectiveness to the department or university level: Exact versus approximate solutions

Using the Benefit-of-the-Doubt (BoD) model, we estimate research effectiveness at the university level based on the research effectiveness scores of its faculty members with three different set of aggregation weights: an exact and two approximates. Our empirical results for the university of Macedonia, Greece indicate that both set of approximate weights tend to overestimate considerably the research effectiveness at the university level as the faculty level research effectiveness scores are highly positively correlated with both set of the approximate aggregation weights.

New Hybrid EC-Promethee Method with Multiple Iterations of Random Weight Ranges: Applied to the Choice of Policing Strategies

The decision-making process is part of everyday life for people and organizations. When modeling the solutions to problems, just as important as the choice of criteria and alternatives is the definition of the weights of the criteria. This study will present a new hybrid method for weighting criteria. The technique combines the ENTROPY and CRITIC methods with the PROMETHE method to create EC-PROMETHEE. The innovation consists of using a weight range per criterion. The construction of a weight range per criterion preserves the characteristics of each technique. Each weight range includes lower and upper limits, which combine to generate random numbers, producing “t” sets of weights per criterion, allowing “t” final rankings to be obtained. The alternatives receive a value corresponding to their position with each ranking generated. At the end of the process, they are ranked in descending order, thus obtaining the final ranking. The method was applied to the decision support problem of choosing policing strategies to reduce crime. The model used a decision matrix with twenty criteria and fourteen alternatives evaluated in seven different scenarios. The results obtained after 10,000 iterations proved consistent, allowing the decision maker to see how each alternative behaved according to the weights used. The practical implication observed concerning traditional models, where a single final ranking is generated for a single set of weights, is the reversal of positions after “t” iterations compared to a single iteration. The method allows managers to make decisions with reduced uncertainty, improving the quality of their decisions. In future research, we propose creating a web tool to make this method easier to use, and propose other tools are produced in Python and R.

Multi-Objective Order Scheduling via Reinforcement Learning

Order scheduling is of a great significance in the internet and communication industries. With the rapid development of the communication industry and the increasing variety of user demands, the number of work orders for communication operators has grown exponentially. Most of the research that tries to solve the order scheduling problem has focused on improving assignment rules based on real-time performance. However, these traditional methods face challenges such as poor real-time performance, high human resource consumption, and low efficiency. Therefore, it is crucial to solve multi-objective problems in order to obtain a robust order scheduling policy to meet the multiple requirements of order scheduling in real problems. The priority dispatching rule (PDR) is a heuristic method that is widely used in real-world scheduling systems In this paper, we propose an approach to automatically optimize the Priority Dispatching Rule (PDR) using a deep multiple-objective reinforcement learning agent and to optimize the weighted vector with a convex hull to obtain the most objective and efficient weights. The convex hull method is employed to calculate the maximal linearly scalarized value, enabling us to determine the optimal weight vector objectively and achieve a balanced optimization of each objective rather than relying on subjective weight settings based on personal experience. Experimental results on multiple datasets demonstrate that our proposed algorithm achieves competitive performance compared to existing state-of-the-art order scheduling algorithms.

A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection

Healthcare waste (HCW) management is a complex issue influenced by many factors, including technological, economic, environmental, and social factors. It is possible to regard the evaluation of the best treatment technique for HCW management as a challenging case of MCDM (multi-criteria decision-making), where various alternatives and evaluation criteria must be considered. The presentation and handling of the shaky data are crucial to choosing the HCW treatment technology. In order to address the issue of MCDM issues with Fermatean fuzzy (FF) data, we first build a consensus-based WASPAS approach in this study. In the suggested integrated methodology, the rank of the alternatives is determined using the WASPAS method in an FF environment, and the attribute weights are estimated using the entropy measure technique. In the preceding, an HCW treatment technology assessment issue is considered to make the proposed structure's applicability more transparent. In this study, four HCW treatment methods—chemical disinfection, microwave disinfection, cremation, and autoclaving—are considered options. According to the study's findings, autoclaving is the most effective HCW treatment method. Additionally, we demonstrate a sensitivity assessment using several criteria weight sets to test the stability of our intriguing proposed approach. We also call attention to a contrast between our suggested approach to decision-making and the practices now in use.

Bridging machine learning and weighted residual methods for delay differential equations of fractional order

In this research, an attempt has been made to build a bridge between a machine learning model called Least-Squares Support Vector Regression (LS-SVR) and Weighted Residual Methods (WRMs) to solve delay differential equations in fractional derivative order. Our machine learning algorithm, which is influenced by the collocation method, uses Legendre polynomials as a kernel function to approximate the solution of linear and nonlinear delay differential equations, including the Lane–Emden pantograph equation and some fractional cases. In the proposed algorithm, the estimated solution of delay differential equations is approximated as a linear combination of M degrees of Legendre polynomials and a set of weights that are learned during the fitting process. The roots of the Legendre functions are utilized as training data to develop the algorithm. The effectiveness of our method is demonstrated through the use of numerical graphs and tables. A comparative analysis of the numerical results has shown that the proposed method yields better accuracy and convergence compared to other numerical techniques.