Lineages of the Islamic State: An International Historical Sociology of State (De-)Formation in Iraq

Existing accounts of the Islamic State (IS) tend to rely on orientalist and technicist assumptions and hence insufficiently sensitive to the historical, sociological, and international conditions of the possibility of IS. The present article provides an alternative account through a conjunctural analysis that is anchored in an international historical sociology of modern Iraq informed by Leon Trotsky's idea of ‘uneven and combined development’. It foregrounds the concatenation of Iraq's contradictory (post-)colonial nation-state formation with the neoliberal conjuncture of 1990-2014. It shows that the former process involved the tension-prone fusion of governing institutions of the modern state and the intermittent but steady reproduction, valorization, and politicization of supra-national (religious-sectarian) and sub-national (ethno-tribal) collective identities, which subverted the emergence of an Iraqi nation. The international sanctions regime of the 1990s transformed sectarian and tribal difference into communitarian tension by fatally undermining the integrative efficacy of the Ba’ath party's authoritarian welfare-state. Concurrently, the neo-liberal demolition of the post-colonial authoritarian welfare states in the region gave rise to the Arab Spring revolutions. The Arab Spring however elicited a successful authoritarian counter-revolution that eliminated secular-nationalist forms of oppositional politics. This illiberal neoliberalisation of the region's political economy valorised the religionisation of the domestic effects of the 2003 US-led destruction of the Iraqi state and its reconstruction on a majoritarian basis favouring the Shi’as and hence transforming sectarian tension into sectarian conflict culminating in IS. Thus, IS is, the paper demonstrates, the result of neither an internal cultural pathology nor sheerly external forces. Rather, it is the novel product of an utterly historical congealment of the intrinsically interactive and multilinear dynamics of socio-political change.     

Optimising correlated QCHs in robust design using principal components analysis and DEA techniques

Taguchi method is found efficient for optimising process performance with a single quality characteristic (QCH) of a product or process. In practice, however, customers are concerned about multiple QCHs, which are usually correlated. This research proposes and implements an approach using principal components analysis (PCA) and two data envelopment analysis (DEA) models, including CCR and super efficiency, for optimising multiple correlated QCHs in robust design. The PCA is first utilised to obtain multiple uncorrelated linear combinations of principal components, which are the same number of QCHs and hence avoid the loss of information by ignoring some principal components. Then, these components are utilised in two DEA models to decide optimal factor levels. Three real case studies are provided for illustration; in all of which the proposed approach is found more efficient than some other techniques in literature, including engineering judgement, PCA, PCA and grey analysis, and utility concept. In conclusion, the proposed approach shall provide a great assistance to process/product engineers for obtaining robust design with multiple correlated QCHs.     

Sectarian terrorism in Pakistan: Causes,impact and remedies

Although terrorism has been widely studied for its impact and potential determinants in Pakistan, the answer to the policy question regarding the role of external factors in influencing specifically the sectarian terrorism is not empirically well researched. The study, particularly, analyses the role of Pakistan’s regional foreign policy towards neighbouring India, Afghan wars, and the relations with bi-polar fundamental Muslim Block, the Kingdom of Saudi Arabia (KSA) and Iran, on sectarian (religious) terrorist incidents for the period 1973–2017. The findings suggest that economic cooperation with India which drive peace-making relations increases the sectarian terrorism. Both the bilateral loans disbursed by the KSA and trade relations with Iran, significantly increase the chances of sectarian terrorism in Pakistan by activating extremist (proxy) groups. However, the Afghan Wars that call for Pak-US strategic partnership helps Pakistan to control the religious terrorism.     

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital-based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock-and-flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10-day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.     

Modeling the Factors Influencing the Evaluations of Items Through Mixture Models for Preference Datasets

In numerous situations, we use ranks dataset to exhibit preferences of a group of respondents towards a set of items. While assigning ranks, judges may consider several factors contributing to overall ranks of items. In this study, an attempt is made to model factors influencing the judges’ evaluations of items through mixture models for preference datasets. Both the probabilistic features of the mixture distribution and inferential as well as computational issues emerging out of the maximum likelihood estimation are addressed. Moreover, empirical evidences from observed dataset confirming the plausibility of the proposed model to preference dataset are provided.     

Compound-Estimator Based Cause-Selecting Control Chart for Monitoring Multistage Processes

Although there exists an increasing interest in monitoring and diagnosing multistage processes through the recent years, this issue has been overlooked to a large extent in cascade processes where the quality characteristics are liable to outliers. The presence of outliers has a debilitating effect on the detect-ability of the traditional cause selecting control charts and thus makes them unreliable. Therefore, the purpose of this article is to provide a robust approach to quality control in multistage processes. It is assumed that the process consists of two stages and the historical data with regard to both dependent quality characteristics contain outliers. A robust fitting procedure based on compound-estimator is employed to build the relationship between the quality variables and a robust monitoring approach is presented. Subsequently, simulation studies are undertaken to assess the performance of the robust scheme by means of the average run length (ARL) criterion. It is shown that the proposed robust procedure can much faster detect diverse types of shift.     

Testing fuzzy hypotheses based on vague observations: a <Emphasis Type=Italic>p</Emphasis>-value approach

This paper deals with the problem of testing statistical hypotheses when both the hypotheses and data are fuzzy. To this end, we first introduce the concept of fuzzy p-value and then develop an approach for testing fuzzy hypotheses by comparing a fuzzy p-value and a fuzzy significance level. Numerical examples are provided to illustrate the approach for different cases.     

Bayesian inference on P(X > Y) in bivariate Rayleigh model

In the literature, assuming independence of random variables X and Y, statistical estimation of the stress–strength parameter R = P(X > Y) is intensively investigated. However, in some real applications, the strength variable X could be highly dependent on the stress variable Y. In this paper, unlike the common practice in the literature, we discuss on estimation of the parameter R where more realistically X and Y are dependent random variables distributed as bivariate Rayleigh model. We derive the Bayes estimates and highest posterior density credible intervals of the parameters using suitable priors on the parameters. Because there are not closed forms for the Bayes estimates, we will use an approximation based on Laplace method and a Markov Chain Monte Carlo technique to obtain the Bayes estimate of R and unknown parameters. Finally, simulation studies are conducted in order to evaluate the performances of the proposed estimators and analysis of two data sets are provided.