Game theory has found successful applications in different areas to handle competitive situations among different persons or organizations. Several extensions of ordinary game theory have been ...studied by the researchers to accommodate the uncertainty and vagueness in terms of payoffs and goals. Matrix games with payoffs represented by interval numbers, fuzzy numbers, and intuitionistic fuzzy numbers have considered only the quantitative aspects of the problems. But in many situations, qualitative information plays a crucial role in representing the payoffs of a game problem. This work presents a valuable study on matrix games with payoff represented by linguistic intuitionistic fuzzy numbers (LIFNs). First, the paper defines some new operational-laws for LIFNs based on linguistic scale function (LSF) and studies their properties in detail. Next, we define a new aggregation operator called ‘generalized linguistic intuitionistic fuzzy weighted average (GLIFWA)’operator for aggregating LIFNs. Several properties and special cases of GLIFWA operator are also discussed. The LSF provides an ability to consider the different semantic situations in a single formulation during the aggregation process. Further, the paper introduces some basic results of matrix games with payoffs represented by LIFNs. We develop solution methods using a pair of auxiliary linear/nonlinear-programming models derived from a pair of nonlinear bi-objective programming models. Finally, a real-life numerical example is considered to demonstrate the validity and applicability of the developed methods.
•The efficiency of states and UTs of India is measured by using data envelopment analysis technique.•The efficiency score and Maximal Balance Index is used to completely rank the states and UTs of ...India.•The benchmarking for states and UTs were calculated for their future improvement.
Purpose: This article measured the performance of 32 states and union territories (UTs) of India against COVID-19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with the different models which are used in many articles to evaluate the efficiency of healthcare system. Here the input parameters are taken as public health expenditure in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected, and output parameters divided into good and bad categories such as the number of recovered are taken as good output. The number of death is taken as bad outputs. The modified undesirable output model is used to calculate efficiency score and compared the efficiency score with Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models. Finally, the states & UTs are completely ranked with the help of efficiency score and Maximal Balance Index, and evaluated benchmarking for each states & UTs.
Data Source: Secondary data were collected from Census 2011 and the Ministry of health & family welfare, Government of India on 32 stats & UTs (NHAC, 2018; NHP, 2019; COVID19India, 2021).
Results: According to Undesirable model results, 16 (50%) of 32 Indian states & UTs s were found to be efficient. Among the efficient DMUs, Chandigarh is the most efficient unit and Meghalaya is the most inefficient unit. Rajasthan was the most referenced state for inefficient states.
Limitation: The efficiency score is affected by changing the number of inputs and outputs. The lack of more effective parameters are used to evaluate performance and enable qualitative variable comparison.
Anaplastic thyroid cancer (ATC) is an aggressive malignancy without effective treatments. ATC cells demonstrate upregulated glycolysis (Warburg effect), generating lactate that is subsequently ...exported by monocarboxylate transporter 4 (MCT4). This study aims to determine whether MCT4 inhibition can suppress ATC growth.
ATC cell lines 8505C, JL30, and TCO1 were grown in low (3 mmol/L; LG) or high (25 mmol/L; HG) glucose medium containing the lactate shuttle inhibitors acriflavine (10-25 μmol/L; ACF), syrosingopine (100 µmol/L; SYR), or AZD3965 (20 µmol/L; AZD). Lactate level and cell proliferation were measured with standard assays. Seahorse analysis was performed to determine glycolytic response.
Compared with HG, addition of ACF to LG decreased lactate secretion for both 8505C (p < 10-5) and JL30 (p < 10-4) cells, whereas proliferation was also reduced (p < 10-4 and 10-5, respectively). During Seahorse analysis, addition of oligomycin increased acidification by 84 mpH/min in HG vs 10 mpH/min in LG containing ACF (p < 10-5). Treatment with LG and SYR drastically diminished 8505C and TCO1 growth vs HG (p < 0.01 for both). LG and AZD treatment also led to reduced proliferation in tested cell lines (p ≤ 0.01 for all) that was further decreased by addition of ACF (p < 10-4 vs HG, p ≤ 0.01 vs LG and AZD).
Inhibition of lactate shuttles significantly reduced proliferation and glycolytic capacity of ATC cells in a low-glucose environment. Targeting suppression of glycolytic and lactate processing pathways may represent an effective treatment strategy for ATC.
Data envelopment analysis (DEA) has come to be recognized as an important technique for evaluating the efficiency of decision-making units (DMUs) in recent years. In conventional DEA, it is often ...assumed that each DMU being assessed for efficiency utilizes the same number of inputs and produces the same number of outputs. In recent years, some academicians have attempted to address the non-homogeneity of the data in DEA; nevertheless, there is a lack of models that take into consideration the non-homogeneity of negative data. To address the issue of data heterogeneity in the presence of negative data, the research suggests a DEA model based on the range directional measure (RDM). More precisely, we are concerned in this study with the heterogeneity of output data generated by a homogeneous set of input data collections. Our objective is to determine the inefficiency of each subunit associated with an output that utilizes the optimal proportion of inputs instead of merely categorizing DMUs according to their output structure, as was the case in previous research. For empirical analysis of the proposed model first, we made a comparison between the proposed model and a representative set of data from earlier studies. We next used synthesized negative data generated uniformly using Matlab software version R2021b to provide an empirical illustration of the proposed model. In addition, we carried out an analysis to assess the research efficiency of 20 institutions.
Market volatility is becoming increasingly common as numerous factors are implemented in the financial system. As a result, portfolio managers and individual investors require reliable methods to ...assess stock performance. This study examines stock assessments using cross-efficiency evaluations in cases where negative data is present. An alternative approach to achieve this goal is to use an RDM DDF-based cross-efficiency model which oversees the negative data. We expand the RDM-based cross-efficiency analysis, which uses row and column average values to select portfolios and identify different groups for stock management. To explore the psychological factors that influence the choices made by stock market investors, we incorporate the cumulative prospect theory value for each stock as an output and the variance as an input to evaluate the overall efficiency of the assets. For the empirical analysis, our study focuses on a sample of 30 stocks listed on the Nifty-50 on India's National Stock Exchange. The results of our empirical study verify that the proposed method can serve as an effective tool for stock selection. This demonstrates how the chosen portfolio gives companies a more diversified and well-balanced approach for selecting stocks, thus improving logical decision-making.
The Neutrosophic set (NS), is a generalization of the fuzzy set and its extension sets, is a revolutionary type of fuzzy set that enables decision-makers (DMs) to express their level of uncertainty ...independently by assigned the truth, indeterminacy and falsity degrees of each element of NS. This article purposes a novel type of ranking function based on value and ambiguity index of a single value triangular neutrosophic number (SVTNN), which associated with DM’s preference level and risk factor that show the attitude of the DM towards taking risk. Also, this article purposes a novel technique for solving the Neutrosophic DEA (Neu-DEA) model having multiple input-outputs are the SVTNNs. The proposed ranking function is used to converts the Neu-DEA model into a corresponding crisp DEA model which is solved to measure the efficiency of the decision making units (DMUs) in Neutrosophic environment. The efficiency scores of the DMUs are calculated based on the DM’s preference level by taking a specific risk (λ ∈ 0, 1). A numerical example is provided to demonstrate the proposed model’s validity and existence, and to compare efficiency scores with Yang et al.’s ranking approach. Finally, How the DM’s preference level and risk factor affect the efficiency score of DMUs are discussed in details.
The evaluation of the performance of decision-making units (DMUs) that use comparable inputs to produce related outputs can be accomplished through a non-parametric linear programming (LP) technique ...called Data Envelopment Analysis (DEA). However, the observed data are occasionally imprecise, ambiguous, inadequate, and inconsistent which may result in incorrect decision-making when these criteria are ignored. Neutrosophic Set (NS) is an extension of fuzzy sets which is used to represent unclear, erroneous, missing, and wrong information. This paper proposes a neutrosophic version of the DEA model, and a novel solution technique for Neutrosophic DEA (Neu-DEA) model. The possibility mean for triangular neutrosophic number (TNN) is redefined and modified the Khatter’s approach to convert directly the Neu-DEA model into its crisp DEA model. As a result, the Neu-DEA model is simplified to a crisp LP problem with a risk parameter (δ ∈ 0, 1) that represents the attitude of the decision-maker towards taking risk. The efficiency score of the DMUs is computed by using various risk factors and divided into efficient and inefficient groups. The ranking of DMUs is determined by calculating the mean efficiency score of DMUs, which is based on various risk parameters. A numerical example is illustrated here to describe the suggested approach’s flexibility and authenticity and compared with some of the existing approaches.
Menstrual irregularities affect 2-5% of childbearing women, a number that is considerably higher among females under constant stress during a cycle.
To study the effect of perceived stress on cycle ...length, regularity and dysmenorrhoea.
A cross-sectional study was conducted on 100 female undergraduate students of a medical college. A questionnaire along with the Perceived Stress Scale (PSS) and Pictorial Blood Assessment Chart (PBAC) was provided to the students. The menstrual pattern was then correlated with the PSS using the chi- square test and the Fisher's Exact test for statistical analysis.
Out of the 100 undergraduate medical students, 30 students had a PSS score >20 while 70 had a score ≤20. An association was established between high stress levels (PSS >20) and menstrual irregularity. No association was found in students with PSS >20 with hypomenorrhoea, menorrhagia, dysmenorrhoea, long cycle length and short cycle length.
High stress levels (PSS >20) was associated with only menstrual irregularities and not with duration, amount of flow or dysmenorrhoea. Hence, other causes should be looked for in young women complaining of menstrual problems before stress is assumed to be the cause.
Establishing the appropriate hypothesis is one of the important steps for carrying out the statistical tests/analysis. Its understanding is important for interpreting the results of statistical ...analysis. The current communication attempts to provide the concept of testing of hypothesis in non inferiority and equivalence trials, where the null hypothesis is just reverse of what is set up for conventional superiority trials. It is similarly looked for rejection for establishing the fact the researcher is intending to prove. It is important to mention that equivalence or non inferiority cannot be proved by accepting the null hypothesis of no difference. Hence, establishing the appropriate statistical hypothesis is extremely important to arrive at meaningful conclusion for the set objectives in research.
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