Workplace Psychosocial Factors and Mental Health among a Sample of Expatriates and the Country’s Nationals Who Working at King Khalid University, Saudi Arabia

Worplace stress is the harmful physical and emotional response that occurs when there is a poor match between job demands and the capabilities, resources, or needs of the worker. Mental health is an important issue in the workplace, particularly in developing countries. This study was aimed to explore the workplace psychosocial factors and mental health among expatriates and the country’s nationals; examine the relationship between workplace psychosocial factors and mental health. . The sample was composed of 518 country's nationals and 554 expatriates teaching staff on job at King Khalid University. Two tools were used for data collection: Socio-demographic data sheet, and Copenhagen Psychosocial Questionnaire (COPSOQ). The study results indicated that Poor mental health had a strongly statistically significant positive correlation with “quantitative  demands”,” Work-family conflict”, and “Emotional demands “ in total country’s nationals and expatriates   (p<0.01). While it was a strongly statistically significant negative  correlation with “Work pace”, “Influence at work”, “Social support from colleague”, “Social support from supervisors”, “Meaning of work”, “Commitment to the workplace” “Predictability”, and  “Recognition” (p<0.01). It was concluded that country's nationals experience worse mental health than expatriates and  this  to some extent caused by exposure to psychosocial factors at work. It has indicated the importance of taking action to reduce psychosocial factors, as this would benefit both country’s nationals and expatriate workers. Key words: Expatriates , country's nationals , workplace psychosocial factors, mental health

Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems

Big Data is an emerging area and concerns managing datasets whose size is beyond commonly used software tools ability to capture, process, and perform analyses in a timely way. The Big Data software market is growing at 32% compound annual rate, almost four times more than the whole ICT market, and the quantity of data to be analyzed is expected to double every two years. Security and privacy are becoming very urgent Big Data aspects that need to be tackled. Indeed, users share more and more personal data and user-generated content through their mobile devices and computers to social networks and cloud services, losing data and content control with a serious impact on their own privacy. Privacy is one area that had a serious debate recently, and many governments require data providers and companies to protect users’ sensitive data. To mitigate these problems, many solutions have been developed to provide data privacy but, unfortunately, they introduce some computational overhead when data is processed. The goal of this paper is to quantitatively evaluate the performance and cost impact of multiple privacy protection mechanisms. A real industry case study concerning tax fraud detection has been considered. Many experiments have been performed to analyze the performance degradation and additional cost (required to provide a given service level) for running applications in a cloud system

Exhaled Nitric Oxide is Not a Biomarker for Pulmonary Tuberculosis.

To reduce transmission of tuberculosis (TB) in resource-limited countries where TB remains a major cause of mortality, novel diagnostic tools are urgently needed. We evaluated the fractional concentration of exhaled nitric oxide (FeNO) as an easily measured, noninvasive potential biomarker for diagnosis and monitoring of treatment response in participants with pulmonary TB including multidrug resistant-TB in Lima, Peru. In a longitudinal study however, we found no differences in baseline median FeNO levels between 38 TB participants and 93 age-matched controls (13 parts per billion [ppb] [interquartile range (IQR) = 8-26] versus 15 ppb [IQR = 12-24]), and there was no change over 60 days of treatment (15 ppb [IQR = 10-19] at day 60). Taking this and previous evidence together, we conclude FeNO is not of value in either the diagnosis of pulmonary TB or as a marker of treatment response

Decreased functional connectivity within a language subnetwork in benign epilepsy with centrotemporal spikes.

OBJECTIVE: Benign epilepsy with centrotemporal spikes (BECTS, also known as Rolandic epilepsy) is a common epilepsy syndrome that is associated with literacy and language impairments. The neural mechanisms of the syndrome are not known. The primary objective of this study was to test the hypothesis that functional connectivity within the language network is decreased in children with BECTS. We also tested the hypothesis that siblings of children with BECTS have similar abnormalities. METHODS: Echo planar magnetic resonance (MR) imaging data were acquired from 25 children with BECTS, 12 siblings, and 20 healthy controls, at rest. After preprocessing with particular attention to intrascan motion, the mean signal was extracted from each of 90 regions of interest. Sparse, undirected graphs were constructed from adjacency matrices consisting of Spearman's rank correlation coefficients. Global and nodal graph metrics and subnetwork and pairwise connectivity were compared between groups. RESULTS: There were no significant differences in graph metrics between groups. Children with BECTS had decreased functional connectivity relative to controls within a four-node subnetwork, which consisted of the left inferior frontal gyrus, the left superior frontal gyrus, the left supramarginal gyrus, and the right inferior parietal lobe (p = 0.04). A similar but nonsignificant decrease was also observed for the siblings. The BECTS groups had significant increases in connectivity within a five-node, five-edge frontal subnetwork. SIGNIFICANCE: The results provide further evidence of decreased functional connectivity between key mediators of speech processing, language, and reading in children with BECTS. We hypothesize that these decreases reflect delayed lateralization of the language network and contribute to specific cognitive impairments

Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction