The hijab between men and women in Saudi Society

Many studies by international organisations and non-Saudi researchers conclude that all Saudi women are forced to wear hijabs. This can now be questioned, particularly with the lifting of the requirement to wear hijab. Therefore, a comprehensive understanding of the hijab in Saudi Arabia is needed, especially in light of the monumental social reforms implemented in recent years. This study explores the concept of the hijab in Saudi thought, the social norms underpinning the wearing of the hijab within collectivist Saudi culture and the consequences of challenging these norms. The study also explores the views of Saudis on several stigmas linked to the hijab and the impact of specific declarations following the announcement of Saudi Vision 2030 on women’s hijab. This was achieved using a qualitative approach and semi-structured interviews to collect data on the studied topic. The findings of this study indicate that, according to Saudi thought, the hijab should cover a woman’s face and conceal her identity. These findings also reveal that there are three types of hijabs: the Sahwa hijab, the current generation’s hijab, and the hijab of new generation. These hijabs have varying degrees of modesty, and the study participants expressed different levels of acceptance for each type. Changing the rules around hijab is difficult, and it impacts the reputations of both men and women. The study findings show that Saudis reject the notion that the hijab is associated with oppression, thus invalidating the perceived stigma of oppression linked to the hijab in the West. Saudis have varying views on female freedom, with most supporting men’s prerogative to prescribe women’s behaviour regarding the hijab. Finally, participants expressed varying opinions on the recent changes in Saudi society, with most participants being aware of the impact of these changes on women’s wearing of the hijab

Neural Correlates of Gain Control in Drosophila and Humans

Previous research has shown that visual sensitivity in human epilepsy patients is abnormal – characterized by increased responses at high contrast levels. These abnormalities have been linked to changes in neuronal gain control. Using animal models to study these changes is a useful approach. In this thesis, we used a steady-state visually evoked potential (SSVEP) technique similar to that used in humans to study photoreceptor-level and neuronal gain control in wild type (w-) Drosophila across a range of ages. We then compared these responses to those obtained from Drosophila carrying the kcc potassium channel mutation that renders young flies susceptible to light and shock-induced seizures. By taking into account the age and temperature dependence of the mutant (kccDHS1) flies, we were able to identify increased neural activity that recovers to the normal profile as they get older. We also found that these kccDHS1 flies are hypersensitive to light, particularly when young. These two findings are consistent with the fact that the level of the KCC protein increases with age. In addition, we found that kccDHS1 flies generate high frequency oscillations in their ERGs in response (50 – 100 Hz) to abrupt light onsets and offsets – a phenomenon that might be linked to abnormal changes in the gain control of neuronal feedback circuits. Studying visual abnormalities in Drosophila can reveal important information but eventually we need to link any visual abnormalities observed in animal models to humans. We therefore, attempted to measure subtle changes in gain control in humans due to adaptation, and at the same time make use of the human mental ability to measure another measure of gain control, attention, using an fMRI technique. Although our data did not show any interaction between adaptation and attention, it suggests that attention in early visual pathways largely affects the level of suppression in non-stimulated regions around the adaptor rather than responses to the probe itself. This is a manipulation that links to our work on adaptation in Drosophila in Chapter 6. Overall, the results presented in this thesis showed that fly models of epilepsy can be useful for studying changes in visual gain control, and showed that this work might be extended to humans

Spectrophotometric flow injection method for the determination of Bendiocarb insecticide in water samples using chromogenic reagent 2,4-dinitrophenylhydrazine

Objective: Bendiocarb (BEN) is an acutely toxic carbamate insecticide which is used in public places and agriculture. The present study describes a new, sensitive, and accurate flow injection analysis method for the determination of BEN in its pesticide formulations and water samples.Methods: The developed method is based on an alkaline hydrolysis of BEN in NaOH, and the resultant product was coupled with 2,4-dinitrophenylhdrazin in the presence of sodium periodate to form red-colored product which measured at 515 nm.Results: Under the optimum conditions established (sample volume 150 μL, flow rate 2 mL/min, with 75 cm reaction coil length) for spectrophotometric determination of Bendiocarb. Beer's law is obeyed in the range of 1–150 μg/mL with a detection limit of 0.738 μg/mL, with average recovery of 100.737 and relative standard deviation % of 1.331%.Conclusion: The established method was successfully applied for the determination of BEN in pesticide formulation and real spiked water sample

Scaling up stochastic gradient descent for non-convex optimisation

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arising when using both shared and distributed memory. Typically, the former is bounded by limited computation resources and bandwidth whereas the latter suffers from communication overheads. We propose a unified distributed and parallel implementation of SGD (named DPSGD) that relies on both asynchronous distribution and lock-free parallelism. By combining two strategies into a unified framework, DPSGD is able to strike a better trade-off between local computation and communication. The convergence properties of DPSGD are studied for non-convex problems such as those arising in statistical modelling and machine learning. Our theoretical analysis shows that DPSGD leads to speed-up with respect to the number of cores and number of workers while guaranteeing an asymptotic convergence rate of O(1/T) given that the number of cores is bounded by T1 / 4 and the number of workers is bounded by T1 / 2 where T is the number of iterations. The potential gains that can be achieved by DPSGD are demonstrated empirically on a stochastic variational inference problem (Latent Dirichlet Allocation) and on a deep reinforcement learning (DRL) problem (advantage actor critic - A2C) resulting in two algorithms: DPSVI and HSA2C. Empirical results validate our theoretical findings. Comparative studies are conducted to show the performance of the proposed DPSGD against the state-of-the-art DRL algorithms

Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums

In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is difficult, as truly urgent comments are rare. From a data analytics perspective, this represents a highly unbalanced (sparse) dataset. Here, we aim to automate the urgent comments identification process, based on fine-grained learner modelling—to be used for automatic recommendations to instructors. To showcase and compare these models, we apply them to the first gold standard dataset for Urgent iNstructor InTErvention (UNITE), which we created by labelling FutureLearn MOOC data. We implement both benchmark shallow classifiers and deep learning. Importantly, we not only compare, for the first time for the unbalanced problem, several data balancing techniques, comprising text augmentation, text augmentation with undersampling, and undersampling, but also propose several new pipelines for combining different augmenters for text augmentation. Results show that models with undersampling can predict most urgent cases; and 3X augmentation + undersampling usually attains the best performance. We additionally validate the best models via a generic benchmark dataset (Stanford). As a case study, we showcase how the naïve Bayes with count vector can adaptively support instructors in answering learner questions/comments, potentially saving time or increasing efficiency in supporting learners. Finally, we show that the errors from the classifier mirrors the disagreements between annotators. Thus, our proposed algorithms perform at least as well as a ‘super-diligent’ human instructor (with the time to consider all comments)

Predicting the porosity in selective laser melting parts using hybrid regression convolutional neural network

Assessing the porosity in Selective Laser Melting (SLM) parts is a challenging issue, and the drawback of using the existing gray value analysis method to assess the porosity is the difficulty and subjectivity in selecting a uniform grayscale threshold to convert a single slice to binary image to highlight the porosity. This paper proposes a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert a single slice to a binary image. In order to test the algorithm, as the training of the RCNN would require a large amount of experimental data, this paper proposed a new efficient approach of creating artificial porosity images mimicking the real CT scan slices of the finished SLM part with a similarity index of 0.9976. Applying RCNN improved porosity prediction accuracy from 68.60% for image binarization method to 75.50% using the RCNN. The algorithm was then further developed by optimizing its parameters using Bees Algorithm (BA), which is known to mimic the behavior of honeybees, and the hybrid Bees Regression Convolutional Neural Network (BA-RCNN) produced better prediction accuracy with a value of 85.33%

The Role of Vitamin D in the Incidence of Metabolic Syndrome in Undergraduate Female Students in Saudi Arabia

Abstract: Background: Vitamin D insufficiency/deficiency prevalent in all age groups across the world is common in obesity and may play an important role in the risk factors of metabolic syndrome (MS). Objectives: This cross-sectional study is to evaluate the relationship between levels of adiponectin and circulating 25(OH)D, and its effect on metabolic biomarker among overweight/obese female students. Methods: Three hundred female students; with mean age 20.9 ± 3.2 years were attending the Aljouf University, Sakaka, Saudi Arabia. They were randomly selected from university during the studying year 2015 – 2016. Anthropometric and biochemical indices were determined. Results: The study showed 19% of the female’s student were either overweight or obese (15% and 4%, respectively). The frequency of MS diagnosis among the students was 17%, with 13% and 4% had either three or four risk factors, respectively. Overweight/obese subjects had significantly worse anthropometric and biochemical characteristics, including waist/hip ratio, blood pressure (BP), fasting blood glucose (BG), insulin, insulin resistance (HOMA-IR), triglyceride levels (TG), low-density lipoprotein cholesterol levels (LDL-C), leptin, adiponectin, leptin/adiponectin ratio and high-density lipoprotein cholesterol levels (HDL-C) compared to normal weight. Of the subjects diagnosed with MS, 59% had mild and 8.6% had severe 25(OH)D deficiency. There was negative association between 25(OH)D and both FBG and HOMA-IR among young women obese/overweight. Conclusion: In our study, it suggested that low level of adiponectin was strongly correlated with low 25(OH)D levels. Also, the prevalence of MS tends to increase with high occurrence rate of low circulating 25(OH)D levels that is, known cause poor glycemic control and prediction of cardiovascular outcomes

CO sensing characteristics of In-doped ZnO semiconductor nanoparticles

Abstract A study on the CO sensing characteristics of In-doped ZnO semiconductor nanoparticles (IZO NPs) prepared by a modified sol–gel technique is reported. The morphological and microstructural features of IZO NPs with various dopant concentrations (1 at.%, 2 at.%, 3 at.%, and 5 at.% In) were investigated by scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). The influence of indium doping on defect characteristics of ZnO was also investigated by photoluminescence (PL). A thick film of IZO NPs was deposited by screen printing on an alumina substrate provided with a pair of Pt interdigitated electrodes to fabricate a simple conductometric sensor platform. The as fabricated In-doped ZnO sensors showed enhanced sensitivity to CO gas with respect to pure ZnO one. Sensors with low dopant loading (1 at.% and 2 at.% In) were found to be more sensitive with shorter response and recovery times than those with high dopant loading