Project description:Due to increasing urbanization and population growth, municipal solid waste management (MSWM) is a significant environmental concern in developing countries. Inadequate waste management systems lead to environmental pollution, health hazards, and economic losses. While considering the challenges and limitations, policymakers and authorities need to opt for such waste management scenarios that are environmentally friendly and resolve energy issues. Ten MSWM scenarios were developed and evaluated using seven different criteria. Four multi-criteria decision-making (MCDM) techniques, namely fuzzy logic, AHP, TOPSIS, and PROMETHEE II, were employed to rank the scenarios and identify the most appropriate option for solid waste management in Lahore. This study highlights that the optimal waste management approach comprises a composition of 54% anaerobic digestion, 37% gasification, and 9% landfill technologies. These percentages collectively represent the most suitable and effective strategies for the city's waste management needs. All the MCDM techniques consistently produce similar results. These scenarios have broader applicability across cities in Central Asia and beyond. The study's findings are aligned to promote sustainable and environmentally friendly MSWM practices. These findings endorse implementing strategies and measures aimed at fostering environmental sustainability and the responsible handling of waste, serving as a valuable reference for various regions.
Project description:This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979-2004 and 2081-2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.
Project description:Asthma is a medical condition characterized by inflammation, narrowing, and swelling of a person's airways, leading to increased mucus production and difficulties in breathing. Topological indices are instrumental in assessing the physical and chemical attributes of these asthma drugs. As resistance to current treatments continues to emerge and undesirable side effects are linked to certain medications, the search for novel and enhanced drugs becomes a top priority. In this study, the examination of 19 distinct asthma medications was focused. In this study, quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modeling, in combination with multicriteria decision-making (MCDM) technique VIKOR (VIekriterijumsko KOmpromisno Rangiranje) were employed on asthma drugs, to achieve the most favorable rankings for each asthma drug, taking into account their distinct properties. The topological indices employed for QSPR modeling were Randic index, reciprocal Randic index, Zagreb indices, hyper-Zagreb index, harmonic index, geometric arithmetic index, and forgotten index.
Project description:We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20th-century baseline), but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.
Project description:BackgroundDiabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques.ObjectiveThe central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field.MethodsThe literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined.ResultsThe findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications).ConclusionsOur review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.
Project description:As a generalization of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets have been developed to represent uncertain, imprecise, incomplete, and inconsistent information existing in the real world. And interval neutrosophic sets (INSs) have been proposed exactly to address issues with a set of numbers in the real unit interval, not just a specific number. However, there are fewer reliable operations for INSs, as well as the INS aggregation operators and decision making method. For this purpose, the operations for INSs are defined and a comparison approach is put forward based on the related research of interval valued intuitionistic fuzzy sets (IVIFSs) in this paper. On the basis of the operations and comparison approach, two interval neutrosophic number aggregation operators are developed. Then, a method for multicriteria decision making problems is explored applying the aggregation operators. In addition, an example is provided to illustrate the application of the proposed method.
Project description:BackgroundCurrent US colorectal cancer screening guidelines that call for shared decision making regarding the choice among several recommended screening options are difficult to implement. Multicriteria decision analysis (MCDA) is an established method well suited for supporting shared decision making. Our study goal was to determine whether a streamlined form of MCDA using rank-order-based judgments can accurately assess patients' colorectal cancer screening priorities.MethodsWe converted priorities for 4 decision criteria and 3 subcriteria regarding colorectal cancer screening obtained from 484 average-risk patients using the analytic hierarchy process (AHP) in a prior study into rank-order-based priorities using rank order centroids. We compared the 2 sets of priorities using Spearman rank correlation and nonparametric Bland-Altman limits of agreement analysis. We assessed the differential impact of using the rank-order-based versus the AHP-based priorities on the results of a full MCDA comparing 3 currently recommended colorectal cancer screening strategies. Generalizability of the results was assessed using Monte Carlo simulation.ResultsCorrelations between the 2 sets of priorities for the 7 criteria ranged from 0.55 to 0.92. The proportions of differences between rank-order-based and AHP-based priorities that were more than ±0.15 ranged from 1% to 16%. Differences in the full MCDA results were minimal, and the relative rankings of the 3 screening options were identical more than 88% of the time. The Monte Carlo simulation results were similar.ConclusionsRank-order-based MCDA could be a simple, practical way to guide individual decisions and assess population decision priorities regarding colorectal cancer screening strategies. Additional research is warranted to further explore the use of these methods for promoting shared decision making.
Project description:In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one. Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts' opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination.
Project description:As an energy-saving and environmentally friendly means of transportation, electric vehicles have been advocated and promoted by various countries, resulting in an increase in the number of electric vehicles. The improvement of public charging infrastructure not only drives the development of the electric vehicle industry but also solves the problems of user difficulty in charging and the low utilization rate of charging piles. From the perspective of electric vehicle (EV) user experience, this research establishes a framework of indicators, including the reputation level, service quality, convenience, economy and safety. Second, the objective entropy weight method and the subjective decision-making trial and evaluation laboratory (DEMATEL) method are combined to weight the indicators. Among the indicators, the comprehensive weights of market share (C2), app operation interface (C3), and charging mode (C5) are 0.107, 0.088, and 0.090, respectively, ranking in the top three. These three indicators should be given more attention by public charging infrastructure operators. Finally, three alternative public charging infrastructures are sorted by using the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method. Since the positive ideal solution Si of h1 (state grid) is 0.084, the negative ideal solution Ri is 0.248, and the comprehensive index Qi is 0.000. All ranking first, this finding indicates that the public charging infrastructure of this operator has strong competitiveness in the market. In addition, the results are consistent with actual news reports, which also proves the effectiveness of the index system and model.
Project description:BackgroundMultiple technologies, procedures and programs call for fairly-based decisions for prioritization of healthcare interventions. There is a diversity of perspectives of what constitutes a legitimate decision, which depends on both the process and the reasoning applied. Current approaches focus on technical aspects while methods to support alignment of decisions with the compassionate impetus of healthcare systems is lacking.MethodsThe framework was developed based on an analysis of the foundations of healthcare systems, the reasoning underlying decisions and fair processes. The concept of reflective multicriteria was created: it assumes that decisionmakers guided by a generic interpretative frame rooted in the compassionate impetus of healthcare systems, can sharpen their reasoning, raise awareness of their motivation and increase legitimacy of decisions. The initial framework was made available through a not for profit organization (the EVIDEM Collaboration, 2006-2017) to stimulate its development with thought leaders and stakeholders in an open source philosophy. Development was tailored to the real-life needs of decisionmakers and drew on several domains of knowledge including healthcare ethics, evidenced-based medicine, health economics, health technology assessment and multicriteria approaches.ResultsThe 10th edition framework builds on four dimensions: (1) the universal impetus of healthcare systems, (2) reasoning, values and ethics, (3) evidence and knowledge on interventions, and (4) a transformative process. Mathematical aspects of the framework are designed to help clarify, express and share individual reasoning; this non-conventional use of numbers requires a cultural change and needs to be phased in slowly. The framework includes four tools for easy adaptation and operationalization: (a) concepts and operationalization, (b) adapt and pilot, (c) evidence matrix, (d) mathematical representation of reasoning. Application is useful throughout all types of healthcare interventions, for all levels of decision, and across the globe.ConclusionBy clarifying their reasoning while keeping decisionmakers aware of the impetus of healthcare systems, reflective multicriteria provides an effective approach to increase the legitimacy of decisions. Beyond a tool, reflective multicriteria pioneered by EVIDEM is geared to transform our vision of the value of healthcare interventions and how they might contribute to relevant, equitable and sustainable healthcare systems.