Motivational factors, job satisfaction and job stress among Omani medical laboratory scientists

Job satisfaction is a quality indicator that measures the cognitive and behavioral aspects of workers’ attitudes toward their job. A World Health Organization (WHO; 2013) report predicts that a world-wide shortfall of 12.9 million healthcare professionals (nurses, midwives, and doctors) will be faced by 2035. In order to promote the retention of healthcare workforce governments, health authorities and healthcare providers need to work on increasing job satisfaction, a strategy that also will increase the attractiveness of healthcare workplaces and support the recruitment of staff. No previous studies have been reported on Oman in addressing the factors behind job satisfaction among medical laboratory scientists. The lack of research on job satisfaction of this group of health professionals in Oman, together with the personal interest of the researcher, has prompted this study to explore sources of satisfaction and dissatisfaction in the demanding fields of medical laboratories. This research aimed to study the job satisfaction, motivation, and stress of medical laboratory scientists. Content and process theories of job satisfaction and motivation were used to develop the theoretical framework of the study. The study was conducted at eight hospitals in Oman to assess motivational factors, job satisfaction and job stress and the association between these factors. The overall aim of this thesis is to explore factors affecting job satisfaction among medical laboratory scientists. A qualitative approach was used to investigate working conditions, conceptualized as job satisfaction (and dissatisfaction) and job stress among the target group (sub-studies I and II), and followed up by a survey study, based on the results of the qualitative studies, to explore measures of job satisfaction and stress and analyze possible associations between components of satisfaction and job stress (sub-study III). Results showed that the major factors affecting job satisfaction and causing dissatisfaction among medical laboratory professionals were workload, promotion, health and safety, relationships with supervisors, professional status (recognition and appreciation), and the hospital appraisal and job description system. Job stress is the de facto outcome of these dissatisfaction factors, and there is a negative correlation between job satisfaction and job stress. The most important dissatisfaction factors, which are related to stress, were insufficient support for professional development, poor relations with supervisors and coworkers, and heavy workload in the laboratories. Omanis were found to have higher job stress scores than non-Omanis, and younger laboratory personnel were more stressed and dissatisfied than older colleagues. Important differences between the hospital types were observed. At the Sultan Qaboos University Hospital, there was less satisfaction at work and, therefore, more stress than at the equivalent Ministry of Health hospitals. The findings of the sub-studies give a picture of what affects job satisfaction, dissatisfaction, motivation, demotivation, and stress among medical laboratory professionals, to be used to design human resource management strategies to deal with problems of dissatisfaction and job stress and thus support the recruitment and retention of this important group of healthcare staff. To provide intrinsic rewards and improving working conditions will be important actions. These insights are useful not only to healthcare managers but also to policymakers, government officials and health authorities

Job Satisfaction Among Employees in Government Sector: Case of Ministry of Manpower Inspectors

Purpose of the study: The objective of the research is to examine the relationship between the factors – nature of the job, working condition, motivation, career growth and job satisfaction of labour inspectors in the Ministry of manpower in the Sultanate of Oman. Design/Methodology: The study includes all the inspectors working in the different Human Resources Department branches of the ministry of manpower in Oman. 147 respondents from all the 269 inspectors working in the ministry were selected and the data was collected using a questionnaire. Statistical Package for Social Sciences (SPSS) was used to analyse the data. Non-probability sampling technique was used for sample selection based on their job position, knowledge and the effective relationship of the inspectors.  Findings: The result of the research was that there was a statistically significant relationship between the nature of work and job satisfaction; the significant relationship between working conditions and job satisfaction and significant relationship between career growth and job satisfaction. But there was no statistically significant relationship between motivations and job satisfaction.   It was also observed that the Ministry of Manpower fully insures the risks of these jobs. Further, increased attention is being paid to the working condition needs of inspectors, including rental cars and clothing appropriate to the sites, providing incentives, free scholarships and training inside and outside the country to ensure inspectors are satisfied with their work.   Research Implications: This research reinforces the theoretical framework of the importance of job satisfaction between work and inspectors of the Ministry of Manpower through studying the dependent variables that affect the level of job performance to enable inspectors to increase their efficiency and productivity of work. Practical Implications: Through this research, job satisfaction has been identified with the labour inspectors at the Ministry of Manpower, and the proposals that have been made that serve the Ministry of Manpower in improving the level of job satisfaction through taking study recommendations. Originality: There was no study was conducted before in the sultanate of Oman in relates to satisfaction of Labour Inspectors

Improving shared access to Cloud of Things resources.

Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be significantly underutilised, and 3) the adoption of CoT is slowed. Improving shared access to CoT resources can provide efficient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants. The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability

New insights into the age and palaeoenvironments of the Upper Cretaceous Fiqa formation of Oman using calcareous nannofossil biostratigraphy and inorganic geochemistry

Planktonic microfossils recovered from the deep marine shales and marls of the Fiqa Formation, Oman, deposited through ∼15 Ma, from the late Coniacian to earliest Maastrichtian provide a window into Late Cretaceous tropical, pelagic ecosystems. In this thesis I present new records based on well-preserved calcareous nannofossil assemblages and inorganic geochemistry to provide new information on the chronostratigraphic subdivision, palaeoenvironments, high-resolution correlation and sequence stratigraphy of the Fiqa Formation. This study is based on the examination of 341 cuttings and side core samples from 11 hydrocarbon exploration wells distributed across a palaeobathymetric gradient within the Upper Cretaceous Aruma Basin. Results include the first detailed taxonomic analysis of Late Cretaceous nannofossils from the Fiqa Formation including the identification of two new species; a new biozonation scheme for the Fiqa Formation that is correlated into the global UC biozonation scheme of Burnett et al. (1998); new biostratigraphic correlations across the basin and integration with regional sequence stratigraphy; and finally, integration of nannofossil assemblage data with detailed bulk rock carbon isotope and element composition analysis and the reconstruction of long-term palaeoenvironmental change and basin evolution, both in space across Oman and through time

The creation of public value through e-government in the Sultanate of Oman

Public value (PV) is a debatable topic with different definitions across the public administration literature and e-government literature. Public value is seen as the last paradigm of both public administration and e-government studies, redefining the definition of e-government, its aims, success indicators and evaluation frameworks. Existing implementations are typically biased towards the realisation of efficiency and service effectiveness, with far less attention being paid to the delivery of PV. In addition, PV-related e-government studies have not presented a comprehensive and holistic framework to investigate e-government PV. This study seeks to address this gap by investigating how e-government facilitates the creation of PV. [Continues.

The Effect of Using WebQuests in the Development of the Skills of Statistical Thinking for the Tenth Grade Students in Oman

The study sought to investigate the effect of WebQuest in the development of the skills of statistical thinking for the tenth grade students in the Sultanate of Oman. To achieve this objective, the study was applied to two groups: one is experimental and the other is control and regulator. The two groups consisted of (30) students from the tenth grade. Both groups sat for test of statistical thinking about the content of the statistical approach of the tenth grade before and after the experiment. The results showed statistically significant differences (p < 0.05) in favor of the experimental group that was using the WebQuest. The study recommended the WebQuest be used in the development of the mathematical thinking skills in general, and statistical thinking, in particular, based on its effectiveness. Training courses and workshops are recommended for those in charge of teaching math statistics

Performance modelling and optimization for video-analytic algorithms in a cloud-like environment using machine learning

CCTV cameras produce a large amount of video surveillance data per day, and analysing them require the use of significant computing resources that often need to be scalable. The emergence of the Hadoop distributed processing framework has had a significant impact on various data intensive applications as the distributed computed based processing enables an increase of the processing capability of applications it serves. Hadoop is an open source implementation of the MapReduce programming model. It automates the operation of creating tasks for each function, distribute data, parallelize executions and handles machine failures that reliefs users from the complexity of having to manage the underlying processing and only focus on building their application. It is noted that in a practical deployment the challenge of Hadoop based architecture is that it requires several scalable machines for effective processing, which in turn adds hardware investment cost to the infrastructure. Although using a cloud infrastructure offers scalable and elastic utilization of resources where users can scale up or scale down the number of Virtual Machines (VM) upon requirements, a user such as a CCTV system operator intending to use a public cloud would aspire to know what cloud resources (i.e. number of VMs) need to be deployed so that the processing can be done in the fastest (or within a known time constraint) and the most cost effective manner. Often such resources will also have to satisfy practical, procedural and legal requirements. The capability to model a distributed processing architecture where the resource requirements can be effectively and optimally predicted will thus be a useful tool, if available. In literature there is no clear and comprehensive modelling framework that provides proactive resource allocation mechanisms to satisfy a user's target requirements, especially for a processing intensive application such as video analytic. In this thesis, with the hope of closing the above research gap, novel research is first initiated by understanding the current legal practices and requirements of implementing video surveillance system within a distributed processing and data storage environment, since the legal validity of data gathered or processed within such a system is vital for a distributed system's applicability in such domains. Subsequently the thesis presents a comprehensive framework for the performance ii modelling and optimization of resource allocation in deploying a scalable distributed video analytic application in a Hadoop based framework, running on virtualized cluster of machines. The proposed modelling framework investigates the use of several machine learning algorithms such as, decision trees (M5P, RepTree), Linear Regression, Multi Layer Perceptron(MLP) and the Ensemble Classifier Bagging model, to model and predict the execution time of video analytic jobs, based on infrastructure level as well as job level parameters. Further in order to propose a novel framework for the allocate resources under constraints to obtain optimal performance in terms of job execution time, we propose a Genetic Algorithms (GAs) based optimization technique. Experimental results are provided to demonstrate the proposed framework's capability to successfully predict the job execution time of a given video analytic task based on infrastructure and input data related parameters and its ability determine the minimum job execution time, given constraints of these parameters. Given the above, the thesis contributes to the state-of-art in distributed video analytics, design, implementation, performance analysis and optimisation

Video Forensics in Cloud Computing: The Challenges & Recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system