A new DEA approach to rank alternatives in MCDA

One of the principal subjects in multiple criteria decision analysis is ranking alternatives. Here, we present a new method to rank alternatives by using data envelopment analysis. In this paper, one ranking method is proposed based on applying an artificial alternative called aggregate alternative. The method is based on the fact that one efficient alternative with a better performance has stronger effects on the group of other alternatives. That means its deletion forces the remaining alternatives to get smaller efficiency. The described idea in this paper is inspired of Lotfi and et al. (2011). One feature of the proposed method is that it does not need to determine the weight of the prior. Two examples are used to illustrate how the proposed method works in actual practices, and the results are compared with those obtained from the TOPSIS method

‎Fuzzy Logistic Regression Analysis Using the Least Squares Method

One of the most efficient statistical tools for modeling the relationship between a dependent variable and several independent variables is regression‎. ‎In practice‎, ‎observations relating to one or more variables‎, ‎or the relationship between variables‎, ‎may be vague or non-specific‎. ‎In such cases‎, ‎classic regression methods will not have enough capability to model data‎, ‎and one of the alternative methods is regression in a fuzzy environment‎. ‎The fuzzy logistic regression model provides a framework in the fuzzy environment to investigate the relationship between a binary response variable and a set of covariates‎. ‎The purpose of this paper is to attempt to develop a fuzzy model that is based on the idea of the possibility of success‎. ‎These possibilities are characterized {by several} linguistic phrases‎, ‎including low‎, ‎medium‎, ‎and high‎, ‎among others‎. ‎Next‎, ‎we {use a set of precise explanatory variable observations to model the logarithm transformation of‎ "‎possibilistic odds.‎" ‎We assume that the model's parameters are triangular fuzzy numbers.} We use the least squares method in fuzzy linear regression to estimate the parameters of the provided model‎. ‎We compute three types of goodness-of-fit criteria to evaluate the model‎. ‎Ultimately‎, ‎we model suspected cases of Systemic Lupus Erythematosus (SLE) disease based on significant risk factors to identify the model's application‎. ‎We do this due to the widespread use of logistic regression in clinical studies and the prevalence of ambiguous observations in clinical diagnosis‎. ‎Furthermore‎, ‎to assess the prevalence of diabetes in the community‎, ‎we will collect a sample of plasma glucose levels‎, ‎measured two hours after a meal‎, ‎from each participant in a clinical survey‎. ‎The proposed model has the potential to rationally replace an ordinary model in modeling the clinically ambiguous condition‎, ‎according to the findings‎

Maximum Entropy and the Entropy of Mixing for Income Distributions

Over the last 100 years, a large number of distributions has been proposed for the modeling of size phenomena, notably the size distribution of personal incomes. The most widely known of these models are the Pareto, log-normal, generalized log-normal, generalized Gamma, generalized Beta of the first and of the second kind, the Dagum, and the Singh-Madala distributions. They are discussed as a group in this note, as general forms of income distributions. Several well-known models are derived from them as sub-families with interesting applications in economics. The behaviour of their entropy is what is here under study. Maximum entropy formalism chooses certain forms of entropy and derives an exponential family of distributions under certain constraints. Finding constraints that income distributions have maximum entropy is another direction of this note. In economics and social statistics, the size distribution of income is the basis of concentration on the Lorenz curve. The difference between the tail of the Lorenz function and the Lorenz function itself determines the entropy of mixing. In the final section of this note, theoretical properties of well-known income distributions are also derived in view of the entropy of mixing.</jats:p

HNMR-Based Metabolomics Survey in Breast Cancer Cell Line Treated by Chimera Alpha – Fetoprotein (AFP) Peptide

Abstract Purpose: Breast cancer is one of the most common cancer among. Chemotherapy and radiation along with surgery are common methods for treating cancer, but they exhibit side effects on normal cells in the body. We used peptides, as new anti-cancer agents, seem to have fewer reactions on the body's natural cells and are specialized markers for targeting cancer cells. Materials and methods: Metabolomics data are obtained by 1HNMR, LC/MS and GC/MS spectrometry and analyzed by chemometrics techniques and the affected metabolic cycles identified using different databases. ZR-75-1 cells were collected with estradiol (positive control), without estradiol (negative control) and estradiol with peptide (treatment group) and the metabolites of these 3 groups were collected by chloroform/methanol or water extraction method. Spectra were analyzed by 1HNMR and chemometric methods using PLS-DA techniques by which differentiating chemical shifts and their respective metabolites were identified using the Human Metabolome Database. The differentiating metabolic pathways were detected using Metaboanalyst.ca website. Results: A concentration of 10-9 M estradiol induced the growth of estradiol-dependent ZR-75-1 cells compared to the control group. A concentration of 10-10 M MH-I peptide inhibited the growth of estradiol-induced growth in this ER + breast cancer cell line. Altered metabolites and metabolic pathway were distinguished. Conclusion: Changes were observed in different amino acids and carbohydrates. The pathways of aminoacyl-t-RNA, glycolysis. Gluconeogenesis and biosynthesis of biotin and amino acids showed the most changes. The results from this study introduces a new peptide drug lead for the treatment of breast cancer with estrogen positive receptor.</jats:p

Longitudinally extensive transverse myelitis after Covid-19 vaccination: case report and review of literature

Mass vaccination has been the main policy to overcome the Covid-19 pandemic. Several vaccines have been approved by the World Health Organization. With growing vaccination, safety concerns and adverse events that need prompt evaluation are also emerging. Herein, we report a case of a healthy woman with longitudinally extensive transverse myelitis after vaccination with the AstraZeneca vaccine. The patient was successfully treated after ruling out all the possible causes