In this paper, by conditioning on the matrix variate normal distribution (MVND) the construction of the matrix t-type family is considered, thus providing a new perspective of this family. Some important statistical characteristics are given. The presented t-type family is an extension to the work of Dickey [8]. A Bayes estimator for the column covariance matrix S of MVND is derived under Kullback Leibler divergence loss (KLDL). Further an application of the proposed result is given in the Bayesian context of the multivariate linear model. It is illustrated that the Bayes estimators of coefficient matrix under both SEL and KLDL are identical.

A double ambiguity has been charged against Rawls’s difference principle (DP). Is it Maximin, Leximin, or something else? Usually, following A. Sen, scholars identify DP with the so-called Leximin. One argues here that one has to distinguish 1° the Leximin, 2° the Maximin (as rule of justice formally analogous to the maximin rule of decision), represented by the figure in L of the perfectly substitutable goods, and 3° the genuine DP. When the augmentation of inequality benefits the worse off, only Pareto-strong improvements are permitted. Leximin would also permit Pareto-weak improvements too (after the first maximum D), where only the richest improves: from (2, 3) to (2, 5), say. This is forbidden by DP. With two classes, unlike Maximin, DP has no curve of indifference and is always decisive, as Leximin is. For undecisive Rules of Justice, which admit indifferent curves, I propose to add a lexically secondary rule, to break ties. That move is able to clarify the links and the differences between on the one hand Maximin alone, with its typical indifference curves in L, and on the other hand, the DP properly understood and the Leximin, which both have no indifferent curves. With two classes of persons (best off/worse off), DP appears more egalitarian than Leximin, because it's secondary rule is MinIn (Minimization of Inequality). But the intuition behind the distinction is that it cannot possible “fair” that only the best off improves in a productive social cooperation.

Profitability has always been a major goal of banks operating as businesses and suppliers of financial resources. However, their probability is constantly affected by specific and structural factors as well as periods of prosperity and recession. Accordingly, the panel data quantile regression method was employed in this study to estimate cyclic, structural, and specific factors affecting profitability at 20 banks and credit institutes listed in the Tehran Stock Exchange (i.e., Bank Mellat, Bank Saderat Iran, Tejarat Bank, Tourism Bank, Karafarin Bank, Shahr Bank, Sina Bank, Sarmayeh Bank, Saman Bank, Refah Bank, Resalat Bank, Bank Day, Middle East Bank, Melal Bank, Post Bank of Iran, Pasargad Bank, Parsian Bank, Ayandeh Bank, Iran Zamin Bank, and Eghtesad Novin Bank) during 2009–2022. The results indicate that the conditional distribution of profitability of banks was affected by cyclic, structural, and specific factors. In fact, the shocks of these factors had symmetric and asymmetric effects on the conditional distribution of profitability. Generally, the ratio of noncurrent liabilities (i.e., a specific banking factor) had the greatest effect on the conditional distribution of profitability at the banks. The second most effective factor was the centralization degree (i.e., a structural factor), whereas liquidity growth and economic growth were the cyclic factors classified as the third and fourth most effective factors, respectively.

Background: Legionella species are ubiquitous in natural aquatic environments, capable of existing in waters with varied temperatures, PH levels, and nutrient and oxygen contents. Of 49 known legionella species, 20 species have been linked to pneumonia in humans. Contamination by legionella has occurred in the distribution systems of many hospitals. Aerosol-generating systems such as faucets, showerheads, cooling towers, and nebulizers are responsible for their transmission from water to air.Methods: A total of 113 water samples were gathered from different wards of 32 hospitals in different geographical regions of Tehran city. These samples were concentrated by filtration, treated with the acid and temperature buffers, and isolated on a BCYE agar culture medium.Results: A total of 22 hospitals out of 33 (26.5%) were contaminated by legionella species, and 30 samples (26.5%) out of 113 were positive. Chlorine concentration and pH level of the water samples were 0.18-2.2 mg/l and 6.6-7.6, respectively.Conclusion: The high rate of waste water contamination in Tehran hospitals with Legionella indicates the resistance of this microorganism to chlorine and other disinfectants, or inadequate disinfection process, representing the insufficiency of the current decontamination of hospital water distribution system. Thus identifying legionella species and their controlling in water distribution system of hospitals is of great importance.

Background: Lung cancer is one of the most common cancers and most frequent cause of cancer-related death worldwide. In Iran, this cancer ranks second in cancer related deaths for men and third for women. The aim of this study is to discover the geographic distribution of the age-standardized incidence rate for lung cancer in both genders in Iran.Methods: This ecological study used re-analysis medical records aggregated to provinces from the National Registry of Cancer and Disease Control and Prevention Report of the Ministry of Health and Medical Education for lung cancer in 2008. For each province, we calculated the average annual age-standardized incidence rate.Results: Our study showed that squamous cell carcinoma and adenocarcinoma were the most common histological types of lung cancer in males (28.6%) and females (28.8%). The central and southern Iranian provinces had the highest age-standardized incidence rates for lung cancer. The highest age-standardized incidence rates in both genders related to the 80-84 year age group for both males (131.51) and females (38.82).Conclusion: The central and southern Iranian provinces are lung cancer hot zones.Thus, implementation of prevention programs and increased access to screening services should be considered.

Domain adaptation can transfer knowledge from a training set (source domain) to a test set (target domain), promoting the performance of the model learned from the training set. In addition, sparse coding makes the learned model more succinct and easy to manipulate. However, the existence of the distribution mismatch across the source and target domains reduce the performance of model. In this paper, we propose an unsupervised domain adaptation model to minimize the prediction error of image classification. Sample reweighting is utilized to handle redundant and useless information of source data in the new representation. Moreover, the difference of the conditional distributions across the source and target domains is reduced along with the subspace alignment. Our proposed approach learns a sparse domain-invariant classifier in a latent subspace with preserving the structure of the input data. Extensive experiments demonstrate that our proposed approach shows 4. 49% improvement in classification accuracy on real-world datasets compared to state-of-the-art machine learning and domain adaptation methods.

Selecting the most accurate method of risk measurement is the main challenge in risk estimation. This study aims to measure value at risk (VaR) and conditional value at risk (CoVar) in Tehran Stock Exchange (TSE) using the Frechet distribution (FD) and the generalized Pareto distribution (GPD). It used the data from 21 and 63-day time series of TEPIX, free-float, and the indices of the top 50 TSE companies between 2012-3-20 and 2020-3-19. It used COPIC post-test and Christoffersen’ s conditional coverage test for models statistical confirmation. It applied Lopez and Blanco-Ihle’ s second loss functions for model comparison. The CoVaR models were ranked by two loss functions, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results indicated according to the statistics measured in Lopez’ s second loss function, GPD performed better than FD in measuring VaR of TEPIX and the return index of the selected 50 companies, however, FD performed better for the free-float index. Blanco-Ihle’ s loss function results contradicted the ones derived from Lopez’ s second loss function. MAE and RMSE results indicated FD is better in estimating CoVaR.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.