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The stochastic block model (SBM) is a random graph model that focuses on partitioning the nodes into blocks or communities. A degree‐corrected stochastic block model (DCSBM) considers degree heterogeneity within nodes. Investigation of the type of edge label can be useful for studying networks. We have proposed a labeled degree‐corrected stochastic block model (LDCSBM), added the probability of the occurrence of each edge label, and monitored the behavior of this network. The LDCSBM is a dynamic network that varies over time; thus, we applied the monitoring process to both the US Senate voting network and simulated networks by defining structural changes. We used the Shewhart control chart for detecting changes and studied the effect of Phase I parameter estimation on Phase II performance. The efficiency of the model for surveillance has been evaluated using the average run length for estimated parameters. [ABSTRACT FROM AUTHOR]

Social networks have an important role in today's lifestyle and detecting any changes in their structure could be vital for social systems. Change detection in a social process could be complicated because of stochastic and complex manners of its component's which are humans. In this article, a novel approach is proposed which model and analyze social networks' structure. For this purpose, the proposed method combines the nodal attributes with structural tendencies of ERGMs to find the best fitting model which can properly define humans' characteristics in the observed network. Then, to detect any changes in the proposed model Hotelling T2 and MEWMA control charts are employed. Experimental simulation study and data analysis demonstrated the efficiency of the proposed technique to detect changes and its sensitivity in finding anomalies. [ABSTRACT FROM AUTHOR]

In the real world, monitoring the number of transactions between nodes in many networks such as transportation, sales, financial communications, etc. is very important, from which network stakeholders can enjoy significant benefits, as well. The present paper attempts to show a significant reduction in the performance of the statistical methods in detecting network anomalies resulting from losing information due to disregarding the weights of edges in the case of modeling and monitoring weighted networks through using binary models. This paper focuses on and applies normal distribution in a real non social network because the statistical distribution of edges in most weighted networks is normal. The performance of the statistical model in the form of a case study, monitoring electronic components exchange network in a repair company, is described. Using simulation, the ability of the model in detecting network anomalies is compared with the binary model. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the relationship between the critical factors of quality management and customer outcomes in the subject of work units in organisations. The ways in which critical total quality management factors can improve the performance of a management system in work units within organisations is assessed. For this purpose, a conceptual model with specific hypotheses is suggested to test the data obtained from 86 respondents. Using structural equation models and partial least squares techniques (PLS) software, these hypotheses were tested. The findings of this research suggest that the proposed model, can be used as a reference framework for the implementation, assessment and improvement of quality in the field of work units. In this paper, we want to improve the gap in empirical research in the management of work units, and aids unit leaders to discover the variables that improve the results from client and stakeholder satisfaction. [ABSTRACT FROM AUTHOR]

Monitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T2 control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out‐of‐control condition. Finally, some real‐world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision‐maker's preferences. [ABSTRACT FROM AUTHOR]

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters. [ABSTRACT FROM AUTHOR]

Controlling damages in a short production run is very subtle. Because the samples for each part are small, the control charts for each part can be obscure therefore the deviation of the actual from the nominal size of each part in a unique control chart will be applied. Differences between the control limits of various deviations can generate a heavy-tailed distribution. We will discuss it with an example of Cauchy distribution as a member of this family. From Phase (I) we will propose the Corrected Numbers and these numbers will be applied in Phase (II). [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a nonparametric data-based approach that is used to evaluate the performance of a set of homogeneous units; hence, inputs and outputs play a crucial role in this evaluation. In many technologies, the inputs and outputs are so volatile and complex that even other stochastic events can have an influence in the measurement accuracy. A suitable estimation method can be selected according to the type of stochastic model used. If the model has stochastic noises, then in the first step, the noise must be identified by its stochastic behaviour as well as a probability distribution. In DEA, it is important to transform the probabilistic form of a model to its equivalent deterministic model. The chance-constrained programming input (output) oriented model with a normal distribution assumption is most often applied in stochastic cases. However, this study improves the assumption by using a family of heavy-tailed distributions, which is called Stable Distributions. It is assumed that the inputs are deterministic and that the outputs include stochastic noise with a stable distribution. The interesting properties of the selected distribution family help us to solve the probability constraints. A numerical example is provided to illustratively validate the proposed approach. [ABSTRACT FROM AUTHOR]