Universal Principles of Bioethics and Patient Rights in Saudi Arabia

In order to keep pace with international bioethical practices, and with international bioethical declarations, this dissertation will investigate challenges facing patients’ rights discourse in Saudi Arabia, and the adaptation of universal bioethics standards in the Saudi healthcare system. The role of religion and issues of human rights will be discussed further, given that religion and human rights affect patient’s rights profoundly. Specifically, the divergence between religious dictations and the secular language of human rights principles will provide a distinctive perspective on patients’ rights discourse, especially in a country such as Saudi Arabia where religion is integral to the national foundations, and were customs are vividly alive. This dissertation will examine patients’ rights as practiced in an international context in order to compare Saudi bioethics practices to other bioethics systems, while pinpointing the strengths and limitations. In addition, Saudi practices concerning patient’s rights are compared to the universal principles of bioethics, to show the variation between the existing and the desired ideal practices. Furthermore, this dissertation will highlight the organizational and cultural challenges that decrease the possibility for the full adoption of patients’ rights in Saudi hospitals in order to analyze the problems and formulate recommendations for future action. This study carries with it presumed significance as one of a few analyses of patients’ rights in one of the least studied countries in the field of bioethics, Saudi Arabia

Online network intrusion detection system using temporal logic and stream data processing

These days, the world are becoming more interconnected, and the Internet has dominated the ways to communicate or to do business. Network security measures must be taken to protect the organization environment. Among these security measures are the intrusion detection systems. These systems aim to detect the actions that attempt to compromise the confidentiality, availability, and integrity of a resource by monitoring the events occurring in computer systems and/or networks. The increasing amounts of data that are transmitted at higher and higher speed networks created a challenging problem for the current intrusion detection systems. Once the traffic exceeds the operational boundaries of these systems, packets are dropped. This means that some attacks will not be detected. In this thesis, we propose developing an online network based intrusion detection system by the combined use of temporal logic and stream data processing. Temporal Logic formalisms allow us to represent attack patterns or normal behaviour. Stream data processing is a recent database technology applied to flows of data. It is designed with high performance features for data intensive applications processing. In this work we develop a system where temporal logic specifications are automatically translated into stream queries that run on the stream database server and are continuously evaluated against the traffic to detect intrusions. The experimental results show that this combination was efficient in using the resources of the running machines and was able to detect all the attacks in the test data. Additionally, the proposed solution provides a concise and unambiguous way to formally represent attack signatures and it is extensible allowing attacks to be added. Also, it is scalable as the system can benefit from using more CPUs and additional memory on the same machine, or using distributed servers

An overview of Wall Envelope Thermal Performance in Arid Climate Buildings

Energy efficiency is an important issue that has been considered by many construction sectors. Recently, the research attention is focused on the thermal performance of the wall envelope, in particular, for its high energy consumption. This paper conducted a literature review highlighting the recent studies and research approach and methodology on the building wall envelope. Results indicate that climate, insulation and orientation are the key factors that should be considered on wall envelope design and installation. Keywords: Wall Envelope, Orientation, Thermal Performance, Insulation Thickness eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.250

Religion as a barrier to the use of student loans for higher education:a community‐based participatory study with Somalis living in England

The unwillingness of the Somali community to finance higher education has largely gone unnoticed within the academic literature and government policy documents. This study explores the role of religion and the influence of Shari'ah scholars on the use of interest‐bearing student loans within the Somali community. In the absence of any theoretical framework on this topic, we explore the multiple socioeconomic factors that may influence the attitude, perception of need, motivation and action of using student loans for higher education, by proposing the UK Somali Muslims Acceptance of Interest‐bearing Student Loan Model. This is also a community‐based participatory study that actively involved Somali community members in exploring and interpreting the results. This was achieved through regular consultations with the sampled Somali Muslim communities within the UK. Our results contribute to the broader debate on the effect of cultural, religious and social values of marginalised communities on inclusion and widening access policies for higher education. The findings reemphasise that people sharing the same location do not necessarily share the same level of opportunities for higher education because of the intersectionality of race, religion, gender and class. The results also show the complexity of the issue of exclusion and the atheoretical nature of student loans as a financial instrument for improving financial inclusion and widening access to higher education among Somali residents in England

Thermal Performance of a High-Rise Residential Building with Internal Courtyard in Tropical Climate

Natural ventilation is an effectual passive design approach to create a better indoor thermal condition as well as energy efficiency. The primary goal of building design is providing a healthy and comfortable indoor environment titled as sustainable architecture.  Literature suggests that the significant feature that alteration has to take place on for better energy performance is the envelope design. This paper aims to augment the Window to Wall Ratio (WWR), orientation and courtyard corridor size for improving the design of naturally ventilated courtyard high-rise residential buildings. Briefly, the findings indicate that contending with WWR, orientation and courtyard corridor size could increase the potential of improving its natural ventilation and thus, thermal performance

An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method

Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset

A multimedia courseware for human heart anatomical and functional illustration

Advances in computer science have provided unique opportunities to apply Interactive Multimedia (IMM) courseware to a wide variety of medical and health care functions. Courseware can be called an easy to learn, teachable and course materials which is an important in Information Communication Technology world today. It helps the learners/students to improve their knowledge, skills and creativity. One area which holds the high ability for using computer systems is medical and health science education. This paper describes the design of an IMM courseware for learning about Human Heart. It proposes a Human Heart Anatomical and Functional Illustration (HHAFI) courseware for students, health officials and everyone interested in having a healthy heart. The HHAFI courseware is implemented by Toolbook Instructor and presented on Windows platform. The courseware includes an introduction that describes the heart and recall such as mechanisms of the heart, heart diseases, healthy tips and living a healthy lifestyle. The HHAFI courseware is tested with the student to identify the improvement in their knowledge and measure the level of interest in the topic. The HHAFI courseware provides learning and interactive training functions for interested individuals

Human gait identification using Kinect sensor

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

open access articlePrediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption