14 research outputs found
Diagnostic Accuracy of Body Mass Index (BMI) to Identify Obesity in Saudi Adult Population in a Community Based Setting
No full textStudies on Burnout among Doctors in Saudi Arabia: A Scoping Review
No full textPhysician burnout, a global issue, negatively impacts healthcare systems, patient outcomes, and clinical judgment. It is particularly prevalent in high-stress specialties like emergency medicine. In Saudi Arabia, the rapid healthcare system expansion and unique cultural factors contribute to burnout. This scoping review aims to synthesize existing literature on physician burnout in Saudi Arabia, aiming to inform policy and administrative decision-making
Understanding suicide and its prevention in the Indian context: Mental Health Perspective
No full textIn India, suicide research has largely concentrated on the prevalence, method, psychological, and demographic risk factors. Suicide processes, paradigms, prevention strategies, and other features of suicide that are common in the West may not be applicable in India. It is vital to study potential underlying processes, various suicide prevention methods, and suicide prevention in general, as well as what more work has to be done in the Indian context. Suicide, on the other hand, is a cross-sectoral public health issue that demands collaboration across all key sectors, and its prevention should engage all stakeholders in India
Stark contrast in prevalence and correlates of mental disorder in the Arabic and Indian populations.
No full textThe Indian and Saudi Arabian (a prototypical Arab nation) national mental health surveys were compared. In comparison to Saudi Arabia, India had a 2.5-fold lower lifetime prevalence of mental illnesses, a 3.8-fold lower current prevalence, and a 7-fold lower prevalence of serious mental disorders. All mental disorders, except drug use disorder, were less common in India. Being over 40 years old and having a better education level had a greater rate of mental illness in India; conversely, being a woman increases the risk of mental illness in Saudi Arabia, particularly anxiety and eating disorders. Besides substance abuse disorders, the treatment gap for mental illnesses is larger in Saudi Arabia. Overall, the comparison suggests a contrasting difference in the prevalence of psychiatric disorders, and their demographic correlation varies between the Indian and Saudi Arabian populations. There is a need to understand as to why such discrepancies exist
Relationships of academic expectation stress & self-efficacy, efficacy for self-regulated learning with academic performance during Covid pandemic
No full textBackground: Academic expectation stress & self-efficacy and efficacy for self-regulated learning may affect academic performance. The Covid pandemic has affected the physical and psychological well-being of all, including students. However, there is a paucity of studies examining these variables in college students in Saudi Arabia. Objectives: This study was conducted to explore the levels and relationships of academic expectation stress & self-efficacy, self-regulated learning, and its relationships with academic performance in college students of ---- University during the Covid pandemic. Material and Methods: A total of 302 students were recruited in this cross-sectional study. They were assessed with sociodemographic and academic proforma designed for this study, the Academic expectation stress inventory (ASE), the Academic self-efficacy scale (AES), and the efficacy of the self-regulated learning scale (SRL). Results: Results revealed that the mean score of AES, SRL, and ASE was 29.18, 35.38, and 41.11, respectively. On linear regression analysis exam score was statistically significant positively predicted by the score on SRL, and the Score of AES was statistically significantly predicted by the score of SRL (+ve) and the score of ASE (-ve). Conclusions: It may be concluded that efficacy for self-regulated learning may mediate academic performance and academic self-efficacy during the Covid pandemic. Enhancing self-regulated learning may improve academic performance during the pandemic
Circuit Design for Memristor based In-Memory Computing
No full textModern computing systems suffer due to inability of CMOS-device technology and conventional Von-Neumann architectures to support today's ever-increasing demand of high performance, reliability, cost and energy-efficiency. While CMOS device suffers from high static leakage, reduced reliability and manufacturing complexity; conventional computing architecture suffers from high power consumption with memory access and performance bottlenecks. Non-volatile and CMOS-compatible emerging memristive device technology with extremely compact memory structures offers in-memory computing solutions. However, research lacks quantitative benchmarking of memristor-based primitive logic designs. Moreover, the arithmetic and functional circuit design solutions are inefficient and hence incompetent to replace the state-of-the-art.The thesis first covers device level physics of different memristive devices, elaborating their basic structures, working principles and behavioural analyses using Verilog-A models. Building on single device behavioural analyses, a comprehensive exploration and quantitative benchmarking of all existing primitive gates is provided, thereby concluding that scouting logic design technique is the optimal logic gate to perform in-memory computing. Going forward, using scouting logic as the building block, the work presents efficient arithmetic and functional circuit designs that outperform previously proposed in-memory computing solutions and attempts to make a strong case to challenge the current CMOS-based state-of-the-art computing paradigm.Different flavours of a novel circuit design are proposed to tackle limitations common to circuits implementing primitive arithmetic operations and complex multiply-accumulate (dot-product) operations supporting data-intensive applications. The proposed circuit deploys in-built sample-and-hold and two bit-wise weighting techniques to enable pipelining and self-timing-path to improve accuracy against variations. As compared to 4-bit adder utilising integrate and fire circuit (IFC) that is optimised for area/power, the proposed design improves the speed, area, and energy consumption by 4X, 2.5X, and 11X, respectively. Incorporating additional components such as high-gain differential amplifier and modified IFC provides a highly accurate, linear, power efficient dot product engine with significant improvement in memristor endurance. To perform 64_4 dot 64_1, the proposed dot product engine improves the speed, area and energy consumption by 2X, 3.5X and 54X, respectively, as compared to area-efficient IFC-based engine, while also extending the range of operands operated in parallel by >3X. Compared to highly accurate SAR-ADC(current sense amplifier) based dot product engine, the proposed design improves the speed, area and energy by a factor of 0.4X(1.2X), 200X(6X) and 260X(108X), respectively, with comparable accuracy. Read endurance is significantly improved as < 0.1V is maintained across the memristors during the dot-product operation, as opposed to > 1V endured using prior proposed designs. To showcase the scalability and versatility of the proposed circuit designs, design prepositions of multi-operand 4-bit adder, 4x4 multiplier and 4-bit comparator are also presented. Supporting equations, graphs, figures and tables have been included to justify the choices made as part of this work and to enhance the understanding of novel non-volatile memristor based in-memory computing.MNEMOSENEElectrical Engineering | Microelectronic
Impact of Baseline Characteristics on Stroke Outcomes in Pakistan: A Longitudinal Study Using the Modified Rankin Scale
No full textIntroduction. Stroke is a leading cause of disability and mortality globally, with a significant impact on healthcare systems. Various factors, including age, gender, comorbidities, and the type of stroke, influence the burden of stroke and its outcomes.
The study was conducted with an objective to determine the impact of baseline characteristics on the long-term functional outcome of stroke patients.
Methods. This prospective observational study was conducted between April 6, 2022 - December 31, 2023, at a tertiary hospital. The study included patients with radiologically confirmed stroke, selected through convenience sampling. Stroke patients of any gender and all age groups, with any comorbidity, were included. The Modified Rankin Scale (mRS) assessed disability on admission and three months post-stroke.
Results. Of the 213 patients, 122 (57.3%) were males and the majority, 199 (93.4%) individuals, had acute ischemic stroke. The median age of the participants was 60 years (range: 13-97 years; IQR=18 years). The mRS score on admission was poor (5.0; IQR=1.0) for patients ≥ 60 years. In 74 (34.74%) participants, the left middle cerebral artery was a frequently involved site. Age of ≥ 60 years (mRS=4.0; IQR=4.0; p=0.001) and the presence of ≥ 3 comorbidities (mRS=5.0; IQR=1.0; p=0.001) were significantly associated with poor outcomes three months post-stroke. Ordinal logistic regression revealed that a mRS score of 4 (OR=14.20; 95% CI=1.70-145.25; p=0.02) and a mRS score of 5 (OR=78.84; 95% CI=9.35-820.25; p < 0.001) on admission were associated with poor outcomes. In addition, the presence of ≥ 3 comorbidities (OR=4.59; 95% CI=14.65; p < 0.01) and increasing age (OR=1.04; 95% CI=1.01-1.07; p=0.02) were predictors of poor outcomes three months post-stroke.
Conclusions. The study underscores the importance of early intervention and effective management of comorbidities to improve functional outcomes in stroke patients. It highlights the need for targeted stroke care and rehabilitation strategies
A Classification of Memory-Centric Computing
No full textsponsorship: The results presented in this article have been obtained in the framework of the project "Computation-in-memory architecture based on resistive devices" (MNEMOSENE), which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 780215. (European Union|780215)status: Publishe
Improving Automated Arabic Essay Questions Grading Based on Microsoft Word Dictionary
No full textThere are three main types of questions: true/false, multiple choice, and essay questions; it is easy to implement automatic grading system (AGS) for multiple choice and true/false questions because the answers are specific compared with essay question answers. Automatic grading system (AGS) was developed to evaluate essay answers using a computer program that solves manual grading process problems like high cost, time-consuming task, increasing number of students, and pressure on teachers. This chapter presents Arabic essay question grading techniques using inner product similarity. The reason behind this is to retrieve students’ answers that more relevance to teachers’ answers. NB (naive Bayes) classifier is used because it is simple to implement and fast. The process starts by preprocessing phase, where tokenization step divides answers for small pieces of tokens. For normalization step, it is used to replace special letter shapes and remove diacritics. Then, stop word removal step removes meaningless and useless words. Finally, stemming process is used to get the stem and root of the words. All the preprocessing phase is meant to be implemented for both student answer and dataset. Then, classifying by naive Bayes classifier to get accurate result also for both students’ answers among with dataset. After that, using Microsoft Word dictionary to compare and get enough synonyms for both students’ answers and model answers in order to have exceptional results. Finally, showing results with the use of inner product similarity then compare the results showed by inner product similarity with human score results so the evaluation among with the efficiency of the proposed technique can be measured using mean absolute error (MAE) and Pearson correlation results (PCR). According to the experimental results, the approach leads to positive results when using MS dictionary and improvement Automated Arabic essay questions grading, where experiment results showed improvement in MAE is 0.041 with enhanced accuracy is 4.65% and PCR is 0.8250. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG
SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
No full textBackground: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population. © The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd
