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Market efficiency has been a hot pursuit of researchers in the area of financial markets. In both developed and developing countries inquiries have been made to test the existence of the three forms of market efficiency. Moreover, the results are different for developing countries as compared to developed countries. For example, in developed countries markers have been consistently found to exhibit weak form of efficiency, however, they have only occasionally been found to exhibit semistrong form of efficiency, and have hardly been found to exhibit strong form of efficiency. The case of developing is difference as they have only occasionally been found to exhibit Market efficiency weak-form of efficiency only. This study has, therefore, been conducted to test the existence of weak-form of efficiency in Pakistan stock market. For that purpose, statistical tests such as runs, test, auto-correlation test, and the Box-Ljung Statistics were employed. It was found that none of the tests confirmed the existence of weak-form of efficiency in Pakistan stock market. [ABSTRACT FROM AUTHOR]

Cardiogenic shock (CS) portends an extremely high mortality of nearly 50% during index hospitalization. Prompt diagnoses of CS, its underlying etiology, and efficient implementation of treatment modalities, including mechanical circulatory support (MCS), are critical especially in light of such high predicted mortality. Venoarterial extracorporeal membrane oxygenation (VA-ECMO) provides the most comprehensive cardiopulmonary support in critically ill patients and hence has seen a steady increase in its utilization over the past decade. Hence, a good understanding of VA-ECMO, its role in treatment of CS, especially when compared with other temporary MCS devices, and its complications are vital for any critical care cardiologist. Our review of VA-ECMO aims to provide the same. [ABSTRACT FROM AUTHOR]

Background: Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, which can be mitigated by adequate anticoagulation. Direct oral anticoagulants (DOACs) are increasingly being used as alternatives to warfarin for anticoagulation, but their efficacy and safety profile has been debated. We aim to compare the therapeutic efficacy and safety of DOACs versus warfarin for the treatment of LVT. Methodology: We systematically searched PubMed/Medline, Google Scholar, Cochrane library, and LILCAS databases from inception to 14th August 2020 to identify relevant studies comparing warfarin and DOACs for LVT treatment and used the pooled data extracted from retrieved studies to perform a meta-analysis. Results: We report pooled data on 1955 patients from 8 studies, with a mean age of 61 years and 59.7 years in warfarin and DOACs group, respectively. The pooled odds ratio for thrombus resolution was 1.11 (95% CI 0.51–2.39) on comparing warfarin to DOAC, but it did not reach a statistical significance (p = 0.76). The pooled risk ratio (RR) of stroke or systemic embolization and bleeding in patients treated with warfarin vs DOACs was 1.04 (95% CI 0.64–1.68; p = 0.85), and 1.15 (95% CI 0.62–2.13; p = 0.57), respectively; with an overall RR of 1.09 (95% CI 0.70–1.70; p = 0.48) for mortality. Conclusions: DOACs appears to be non-inferior or at least as effective as warfarin in the treatment of left ventricular thrombus without any statistical difference in stroke or bleeding complications. [ABSTRACT FROM AUTHOR]

Training optimization plays a vital role in the development of convolution neural network (CNN). CNNs are hard to train because of the presence of multiple local minima. The optimization problem for a CNN is non-convex, hence, has multiple local minima. If any of the chosen hyper-parameters are not appropriate, it will end up at bad local minima, which leads to poor performance. Hence, proper optimization of the training algorithm for CNN is the key to converge to a good local minimum. Therefore, in this paper, we introduce an evolutionary convolution neural network (ModPSO-CNN) algorithm. The proposed algorithm results in the fusion of modified particle swarm optimization (ModPSO) along with backpropagation (BP) and convolution neural network (CNN). The training of CNN involves ModPSO along with backpropagation (BP) algorithm to encourage performance improvement by avoiding premature convergence and local minima. The ModPSO have adaptive, dynamic and improved parameters, to handle the issues in training CNN. The adaptive and dynamic parameters bring a proper balance between the global and local search ability, while an improved parameter keeps the diversity of the swarm. The proposed ModPSO algorithm is validated on three standard mathematical test functions and compared with three variants of the benchmark PSO algorithm. Furthermore, the performance of the proposed ModPSO-CNN is also compared with other training algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications, such as CIFAR-10 dataset and face and skin detection dataset. [ABSTRACT FROM AUTHOR]

Although several studies have focused on conflict resolution, none of them has considered the effect of intragroup conflicts on self-determination and self-confidence of university students. The nature of study was descriptive type. The data were collected through multistage random sampling technique. The sample consisted of 386 students of 6 universities of Punjab. Structural Equation Model was developed by applying AMOS 21. Findings revealed that intragroup conflict had a positive effect on students' selfconfidence and self-determination. Results of the study have a suggestion to implement strategies to create harmony within the group to promote self-determination and selfconfidence. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients. [ABSTRACT FROM AUTHOR]

Qatar's digital health initiatives are growing through the development of research capacity, innovative technologies, and academic programs to serve the national health needs of the country. The digital health community of Qatar lacks the adequate representation of a professional body to serve the needs of the community. The purpose of this paper is to develop a strategic framework for the Health Informatics Association of Qatar (HIAQ) and present the preliminary findings of this effort. We utilized a multi-stage mixed methods approach in the development of HIAQ's strategic framework. We first reviewed the relevant literature, interviewed key stakeholders within the region, validated our findings with local stakeholders, and adapted the strategic directions to conform with Qatari laws. We present the purpose, organizational structure, vision, mission, and aims of HIAQ. Future work will engage the digital health community to further refine and adapt HIAQ's strategic direction to serve the local needs of Qatar. [ABSTRACT FROM AUTHOR]

Clinical Decision Support Systems (CDSSs) are used in a clinical setting to help physicians make decisions to improve clinical performance and patient care. There are many benefits to the implementation and adoption of CDSSs, such as reducing the rate of misdiagnosis, improving efficiency and patient care, and reducing the risk of medication errors. On the other hand, CDSSs can have several disadvantages. For example, physicians can see CDSSs as a threat to their clinical autonomy. CDSSs can also be very costly to adopt, maintain, and support. These advantages and disadvantages can have both positive and negative impacts on physicians. We conducted a scoping review to explore the impact of CDSSs on physicians. We searched the following electronic databases: CINAHL, PubMed, and Google Scholar. Two reviewers independently selected the retrieved studies and extracted data from the included studies. A narrative approach was used to synthesize the extracted data. We included 14 studies of the 300 retrieved studies. We identified the following positive impacts: work efficiency, providing more personalized care, improving care and knowledge, increasing confidence in making decisions, improving prescribing behavior, and reducing the number of ordered laboratory and medical imaging tests. Several negative impacts were also reported by the studies, namely: inefficient documentation, interruption in the patient-physician communication, and an increase in unnecessary referrals. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) trigger a high number of deaths across the world. In this study, we investigate the food, drinking, smoking, and lifestylerelated habits for a Qatari CVD cohort to understand the implication of these factors on CVD. Statistical analysis shows that the CVD group is consuming a lower amount of fast foods, soft drinks, snacks, and meats compared to the control group. Alarmingly, the level of smoking is still higher in the CVD group, and the consumption level of healthy items (e.g., cereal, cornflakes) in breakfast is relatively lower compared to the control group. Interestingly, the CVD cohort is spending more time walking and avoiding heavy sports, compared to the control group, but their involvement in moderate physical activities is lower than the control group. Overall, we conclude that the Qatari CVD cohort is following most of the standard guidelines related to food items and heavy sports; however, the cohort should reduce smoking habits, and may modify the moderate level of physical activity based on physician guidelines. [ABSTRACT FROM AUTHOR]

The heterogeneous localized concepts of various hospitals reduce interoperability among localized data models of Hospital Information Systems (HIS) and the knowledge bases of clinical decision support systems (CDSS). The leading solution to overcome the interoperability barrier is the reconciliation of standard medical terminologies with localized data models. In this paper, we extend the semantic reconciliation model (SRM) to provide mappings among diverse concepts of localized domain clinical models (DCM) and concepts of standard medical terminologies such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). In the extended SRM, we insert the explicit semantics only into the word vector of the localized DCM concepts instead of the implicit semantics, which enhances the system's accuracy with a lower computational cost. The extended SRM performed well on the datasets of localized DCM and SNOMED CT with a precision of 0.95, a recall of 0.92, and an F-measure of 0.93. [ABSTRACT FROM AUTHOR]

Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CNN) based on VGG16 architecture consisting of 16 fully connected convolutional layers. A total of 5856 high-resolution frontal view chest X-ray images were used for training, validating, and testing the model. The model achieved an accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4%, precision of 97.2%, and a F1 Score of 97.6%. This indicates that the model has an excellent performance in classifying pneumonia cases and normal cases. We believe, the proposed model will reduce physician workload, expand the performance of pneumonia screening programs, and improve healthcare service. [ABSTRACT FROM AUTHOR]

In this study, we analyze the food and lifestyle-related factors for a Diabetic cohort from Qatar, where the prevalence of diabetes is among the top in the Middle East region. Statistical analysis shows that the diabetic group is consuming a lower amount of fast foods, soft drinks and meats as a meal but a higher amount of vegetables and fruits compared to the control group. Though the diabetic cohort consumes a lower number of snacks and desserts, they consume a higher amount of sugar for tea. Interestingly, we find the diabetes cohort is spending a lower amount of time in sedentary life but their involvement in different physical activities is lower than the control group. Overall, we conclude that the Qatari diabetic cohort, considered in this study, is following standard guidelines for food and drinks but they may need to improve the physical activity level following physician guidelines. [ABSTRACT FROM AUTHOR]

Rapidly increasing of the energy crises, depletion of fossil fuels and its severe environmental hazards, demanded to establish the green processes. The best available solution is to convert low-grade waste heat into useful work by using the organic Rankine cycle (ORC), which reduces not only the energy shortage but also environmental problems simultaneously. This work presents the theoretical and numerical methodology using Engineering Equation Solver (EES) software to evaluate both energy and exergy analysis of the basic and three modified organic Rankine cycles. R-113 was used as a working fluid. The basic ORC system was modified by integrating internal heat exchanger, regeneration, and the combination of both. Among these four cycles, the ORC system integrating with both the internal heat exchanger and regeneration proved the best cycle, which gave the highest thermal efficiency (22.43%), exergy efficiency (33.82%), and lowest exergy destruction (44.5kW) at evaporator pressure of 2.5MPa. Furthermore, it has been concluded that the evaporator played a significant role in ORC system performance because its exergy destruction was maximum, which was enhanced by increasing its pressure. [ABSTRACT FROM AUTHOR]

Quality of science education is under the consideration of educationists and researchers. Achievement in science subjects, which is one of the indicators of quality of science education, determines not only the students' performance but it also helps the teachers to evaluate their teaching strategies. This study is an effort to explore the factors related to science achievement in Pakistani perspective. In this survey, science achievement scores of the students of 8th grade decide their entrance in science subjects at 9thgrade. Sample of 1149 students of 8th and 9th grade students of high and elementary government schools were taken randomly for data collection. Major results were that parental qualification and maternal job status effect significantly students' science achievement. Female students were significantly higher than male students, and 8thgrade students were significantly higher than 9th grade students in their science achievement scores. It is recommended that teachers of secondary schools and rural schools during parent teacher meeting may motivate the parents to supervise their children during studies at home and they themselves study books at home. [ABSTRACT FROM AUTHOR]

Massive Online Open Courses (MOOCs) are a form of online learning that has become increasingly popular across the globe, yet they often suffer from low completion rates. This study aims to advance understanding of the Dropout Phenomenon in MOOCs through a qualitative case study conducted in Pakistan. The purpose of this research is to understand why learners drop out of MOOCs by identifying the factors that influence the decision of learners. Semi-structured interviews with MOOC participants were conducted and analysed who had enrolled in two MOOCs that had a high dropout rate. The responses from the interviews were categorised under three overarching themes: lack of motivation, perception of course content, and inability to manage time. Within those themes, several factors that affect course dropout rates were identified, including perceived rewards, difficulties in comprehension, academic and family commitments, and duration of the course. [ABSTRACT FROM AUTHOR]