Estimating E-bayesian and hierarchical bayesian of scalar parameter of gompertz distribution under type II censoring schemes based on fuzzy data

In this study, the E-Bayesian and hierarchical Bayesian of the scalar parameter of a Gompertz distribution under Type II censoring schemes were estimated based on fuzzy data under the squared error (SE) loss function and the efficiency of the proposed methods was compared with each other and with the Bayesian estimator using Monte Carlo simulation.     

Algorithms for the maximum k-club problem in graphs

Given a simple undirected graph G, a k-club is a subset of vertices inducing a subgraph of diameter at most k. The maximum k-club problem (MkCP) is to find a k-club of maximum cardinality in G. These structures, originally introduced to model cohesive subgroups in social network analysis, are of interest in network-based data mining and clustering applications. The maximum k-club problem is NP-hard, moreover, determining whether a given k-club is maximal (by inclusion) is NP-hard as well. This paper first provides a sufficient condition for testing maximality of a given k-club. Then it proceeds to develop a variable neighborhood search (VNS) heuristic and an exact algorithm for MkCP that uses the VNS solution as a lower bound. Computational experiments with test instances available in the literature show that the proposed algorithms are very effective on sparse instances and outperform the existing methods on most dense graphs from the testbed.     

A Feasibility Study of Implementing the Policies on Increasing Birth Rate with an Emphasis on Socio-economic Status: A Case Study of Kermanshah Metropolis,Western Iran

Population and development have a mutual relationship, and more to the point, lack of sufficient knowledge on population development and fertility can lead to the presentation of inappropriate population plans in such a way that they may turn out to be useless and ineffective even in the event of implementation. So, the present study aimed to delve into the feasibility of implementing the new governmental policies on increasing birth rate with an emphasis on the socioeconomic status in Kermanshah Metropolis, Iran. To evaluate poverty and fertility in Kermanshah Metropolis, the present cross-sectional study used the latest statistics obtained from the Statistical Center of Iran (2011), and as for the data analysis, such tests in Arc/GIS and SPSS 20 software. The results of the present study showed that the spatial patterns of poverty and fertility were formed in clusters across the city, and the results of variance analysis of the relationship between the variables of fertility and poverty demonstrated that there was a significant difference between the selected neighborhoods in terms of the variables of poverty and total fertility (P < 0.001). Furthermore, the spatial autocorrelations of fertility and poverty in Kermanshah were positive and showed that the spatial means and standard distances of poverty and fertility were overlapping. Not all districts and urban neighborhoods were equal in the total fertility rate. So, given the societal classes’ different socioeconomic and cultural status across different neighborhoods, the government’s new approaches to increasing birthrate cannot be applied to all classes throughout Kermanshah Metropolis.

     

The Muslim Question in Australia: Islamophobia and Muslim Alienation

Islamophobia has become a significant problem across the Western world. Australia is no exception. The emergence of far right groups and a political environment that allows anti-Islamic discourse has created an increasingly unwelcome environment for Muslims, even though multiculturalism has long been a fundamental marker of Australian daily life. The rise of Islamophobia has been damaging to Australia. This paper explores the rise of anti-Islamic sentiments in Australia and the increasing marginalization of Muslim youth, showing that Islamophobia not only breaks the bond between Muslim youth and Australian society, it also polarizes relations within Australian Muslim communities.     

Reordering Public and Private in Iranian Cyberspace: Identity,Politics, and Mobilization

The capacity of cyberspace to bypass some of the spatial divisions that underpin social inequality endows it with political significance. This article examines some of the ways in which cyberspace has contributed to redrawing the boundaries between public and private and some of the consequences of this for people, things, and ideas in Iran and the Iranian diaspora. It shows how cyberspace influences a wide range of political phenomena including political mobilization and censorship, intergenerational communication, identity formation, sexuality, sense of belonging, and forms and location of symbolic capital. The relationship between net users in the diaspora and cyberspace involves a circuit of reorderings of one's understanding of the diaspora, one's contact with it in cyberspace, of one's own ideas on the basis of what one finds there, and of social relationships established and maintained in cyberspace. Paradoxically, the very richness and diversity of the ideas and opinions found in Iranian cyberspace may undermine the idea of there actually being a single Iranian diaspora.     

Promoting “Zest for Life”: A Systematic Literature Review of Resiliency Factors to Prevent Youth Suicide

Suicide is a leading cause of death among youth globally. In this critical interpretive synthesis, we examined literature on resiliency factors and suicidality. Systematic searches identified 474 articles, 37 of which were included. Results revealed internal (positive self‐appraisal, zest for life, personal traits, and coping skills) and external factors (social support system and inclusive environments) contribute to resilience among youth, with age, sex and gender, and Indigenous identity as important intersecting considerations. Findings validated fostering resilience as primary suicide prevention among youth, with little explanation for how these factors may work to protect youth from suicidality. Continued research in this area requires a focus on how to promote resilience at the community and systems levels.     

Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent‐Based Simulation

This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent‐based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg “leader follower” game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent‐based simulation. The evolutionary agent‐based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent‐based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent‐based approach results in a greater percentage of defender victories than does the PRA‐based approach.     

A doubly-inflated Poisson regression for correlated count data

Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.KEYWORDS: Count data, doubly-inflated, Poisson regression, zero-inflated, correlated data