A Business User Model-Driven Engineering Method for Developing Information Systems

This thesis is all about raising the level of abstraction at which information systems are built, using business end-users knowledge and MDE to achieve the result. The work intro- duces, first, Micro-Modelling Language (μML), a lightweight modelling language that is used to express basic structural and behavioural aspects of information systems using effectivily business-users knowledge of their desired system. Throughout the work, graphical notation and semantics for the language concepts are identified, providing a simpler and semantically cleaned modelling language than standard UML and other UML-based languages. The work also proposes BUILD (Business-User Information-Led Development), an End- User MDE approach to support the construction of information systems using high-level specifications and accelerate the development process using layered model transformation and code generation. Throughout the thesis, a number of development phases and model transformation steps are identified to allow the low-level technical detail be introduced and developed automatically by rules, with less end-users engagement. Domain-Specific code generators, for generating executable Java Swing Applications code and MySQL script, are used to demonstrate the validity of the research

Effectiveness and Needs Assessment of Faculty Development Programme for Medical Education: Experience from Saudi Arabia

Objectives: Faculty members are the most important resource in any institution of higher education as medical education has been, and continues to be, a priority for medical colleges in Saudi Arabia. This study aimed to assess faculty members’ perceptions of faculty development programmes (FDPs) in supporting important goals in medical education. In addition, this study aimed to assess faculty members’ perceived needs. Methods: This crosssectional study was conducted between August 2016 and August 2017 and involved participants from six universities in Saudi Arabia’s Western Province. The survey consisted of 31 items designed to assess FDP effectiveness and 49 items designed to assess needs in FDPs. Results: A total of 210 faculty members participated in the study (response rate = 52.5%) and identified 49 needs. Faculty members perceived personal improvement in delivering medical education and the provision of greater educational involvement as the most effective considerations in an FDP. The respondents considered 13 needs to be of utmost importance; the remaining were considered important. Conclusion: This study assessed and identified faculty needs and important skills to consider when establishing an FDP. Furthermore, it provided information addressing the needs of, or gaps between, current and desired conditions in medical education in Saudi Arabia. The study also identified the most important elements (i.e. personal improvement) of faculty-perceived effectiveness for a successful FDP in medical education.Keywords: Faculty; Program Development; Needs Assessment; Perception; Medical Education; Saudi Arabia

English proficiency test as a predictor of academic achievement in a health sciences program

The present study aims to investigate possible correlations between academic achievement of freshman students based on English Proficiency Test (EPT) results and pre-admission criteria, i.e., High School Grade point average (GPA), the General Aptitude Test (GAT), and the Scholastic Achievement Admission Test (SAAT) at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) in Saudi Arabia. The study involved 528 first-year students enrolled in a pre-professional program in two campuses of the university. Pre- and post-tests of the EPT were conducted along with a demographic survey to gather details about the participants. Descriptive and inferential statistics analysis were applied to test the correlations between variables. The results showed a significant improvement in all components of the EPT in the two campuses. The differences in the scores among the GAT, SAAT, and EPT were significantly and positively correlated with the higher GPA. The analysis also revealed a strong correlation with higher GAT and SAAT results to scoring better in EPT and gaining a higher GPA. The improvement in the EPT results could indicate that the objectives of the English program were achieved. Finally, our study shows that the pre-admission criteria could predict students’ academic performance in an English program

Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks

BERT-Based Approach for Greening Software Requirements Engineering Through Non-Functional Requirements

The incorporation of sustainability principles during the requirements engineering phase of the development life cycle constitutes greening software requirements. This incorporation can have a variety of effects on the software design employed in modern and cutting-edge information technology (IT) systems. When sustainability principles are incorporated into requirements engineering, software design priorities can change and address current design issues such as energy and resource consumption, modularity, maintainability, and adaptability. In contrast to other green approaches that consider sustainable development, there is a further need to investigate the relationship between software development and the relevant green principles of sustainability during the requirements engineering phase. We present a new mechanism for mapping software nonfunctional requirements (NFRs) to defined dimensions of green software sustainability, consisting of two mapping steps: 1) between NFRs and sustainability dimensions; and 2) between sustainability dimensions and two clusters of green IT aspects defined in this work. The overall architecture of the promising approach is based on the use of the Bidirectional Encoder Representations from Transformers (BERT) language model with an expanded dataset. We consider transfer learning and domain-specific fine-tuning capabilities for constructing and evaluating a model specifically tailored for developing a proof of concept of the greening software requirements engineering task, as language models have recently emerged as a potent technique in the field of software engineering, with numerous applications in code analysis, automated documentation, and code generation. In addition, we test the model’s performance using an extended version of the PROMISE_exp dataset after adding a new binary classification column for categorizing sustainability dimensions into two defined clusters: Eco-technical and Socioeconomic, and having a selected domain expert label the raw data. The model’s efficiency is evaluated using four matrices—1) accuracy; 2) precision; 3) recall; and 4) F1 score—across a variety of epoch and batch sizes. Our numerical results demonstrate the viability of the approach in text classification tasks via performing well in mapping NFRs to software sustainability dimensions. This acts as a proof of concept for automating the sustainability measurement of software awareness at the early development stage. In addition, the results emphasize the importance of domain-specific fine-tuning and transfer learning for obtaining high performance in classification tasks in requirements engineering

Neurological Complications of Middle East Respiratory Syndrome Coronavirus: A Report of Two Cases and Review of the Literature

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was first discovered in September 2012 in Saudi Arabia. Since then, it caused more than 1600 laboratory-confirmed cases and more than 580 deaths among them. The clinical course of the disease ranges from asymptomatic infection to severe lower respiratory tract illness with multiorgan involvement and death. The disease can cause pulmonary, renal, hematological, and gastrointestinal complications. In this paper, we report neurological complications of MERS-CoV in two adult patients, and we hypothesize the pathophysiology. The first patient had an intracerebral hemorrhage as a result of thrombocytopenia, disseminated intravascular coagulation, and platelet dysfunction. The second case was a case of critical illness polyneuropathy complicating a long ICU stay. In these cases, the neurological complications were secondary to systemic complications and long ICU stay. Autopsy studies are needed to further understand the pathological mechanism

Nosocomial herpes simplex encephalitis: A challenging diagnosis

Summary: Herpes simplex encephalitis (HSE) is a rare disease, but it is the most common form of sporadic encephalitis. HSE is transmitted through direct contact and developing nosocomial HSE is rarely reported in the literature. Nosocomial HSE is difficult to diagnose due to its non-specific clinical features. In this article, we present a case of nosocomial HSE that was responsible for grave consequence. We also explore its causes, outcome, and give recommendations to avoid such fatal occurrence. We stress on strict adherence to the standard precautions and preventive control measures. Keywords: Encephalitis, Herpes simplex virus, Nosocomial infection, Hospital-acquired infectio

A Monte Carlo study of arms effect in myocardial perfusion of normal and abnormal cases utilizing STL heart shape

Arms influence in myocardial perfusion single photon emission computed tomography (SPECT) imaging has been studied for the last two decades. These studies suggested that arms positioning next to the patient would not show cardiac abnormalities or perfusion defects in SPECT imaging while it would not affect the scan during positron emission tomography (PET) scans. As a recent improvement in Geant4 Application for Tomographic Emission (GATE), -a Monte Carlo simulation toolkit- a new feature was added to enable the use of STereoLithography (STL). STL files are implanted as an input geometry for most human organs, which would give superior advantages in details compared to analytical geometry shapes. This study is adopting this recent improvement in GATE to study arms effect in SPECT imaging with the consideration of four scenarios; normal heart perfusion imaging with and without arms positioned next to the patient and two of the same scenarios with a perfusion myocardial defected. The results showed that perfusion defect could be observed with arms next to the patient. For image reconstruction, both filtered backprojection (FBP) and iterative technique – maximum likelihood expectation maximization (MLEM) were used. The MLEM was performed to analyse the four different patient scenarios. The difference in counts between arms-up and arms-down position for the abnormal case was shown to be less than 6%. The conclusion from this paper is that arm influence during abnormal heart SPECT imaging can be measured and has a minimal contribution to the reconstructed images. Keywords: GATE, Reconstruction, SPECT, Arms, Heart, Myocardial, Perfusion, ST

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data