Assessment of Knowledge of Cardiopulmonary Resuscitation among Pharmacy Students of Mirpur, Azad Jammu & Kashmir

Introduction: Cardiopulmonary resuscitation (CPR) is the most important lifesaving technique in several emergency situations such as cardiac arrest. In future, being part of health care professionals, pharmacy students are deemed to possess basic skills and expertise which are required to perform CPR. Objective: To access the knowledge of cardiopulmonary among pharmacy students of Mirpur Azad Jammu & Kashmir. Methods: It was a questionnaire-based, descriptive cross-sectional study, conducted among 4th and 5th year students of two Pharmacy institutes of Mirpur AJ&K from November 2018 to January 2019. A pre-tested questionnaire from previous study was used to collect data. It comprised of 13 dichotomous questions with “Yes/No” options, regarding knowledge of CPR. Descriptive statistics was used to unfold the demographic characteristics. Inferential statistics (Kruskal Wallis and Man Whitney) tests were used for evaluating difference between dependent continuous variables and independent variables and Chi-square was applied to determine difference between grouped variables. P-value of less than 0.05 is considered significant. The data was analyzed using SPSS version 20. Result: Overall, 131 out of 150(response rate= 92%) students participated in current study. Gender distribution among the participants was almost equal with 66 males (50.4%) males and 65 (49.6%) females took part in current study. Participants of the age group 21-23 years (n=88, 67.2%) were dominant. Only few students (n=23, 17.6%) reported to have taken training in CPR previously. Eighty-eight (66.7%) had average knowledge of CPR. No significant differences among male and female, 4th and 5th year students of both the institutes were found. Conclusion: The study revealed that knowledge level of CPR is adequate in most of the students of pharmacy. However, further improvements are required to perform CPR in an efficient manner. Thus, training in CPR should be mandatory in the pharmacy curriculum

زوار حسین [مصور] ۔ پاکستان کے ابتدائی نثری نظم نگار

Zawar Hussain is a well known painter, calligraphist and sculptor. Lahore museum owns four of his marvelous paintings. Tree is the basic symbol of his art work. Besides art work he is a well versed poet (prose poetry, Ghazal and free verse) and prose writer. There are 11 books (Poetry+Prose) at his credit. Prose poetry is a new form of expression in Urdu literature. Zawar Hussain was one of the pioneers of prose poetry. His book "Haroof" is one of the early works done in this filed

Radar sensor based machine learning approach for precise vehicle position estimation

Abstract Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art

Epidemiology of Toxoplasmosis among the Pakistani Population: A Systematic Review and Meta-Analysis

Toxoplasma gondii is an intracellular obligate parasite that causes toxoplasmosis, a zoonotic infection that affects warm-blooded animals and humans worldwide. To comprehensively characterize the disease condition in Pakistan for future reference, we ascertained the prevalence of Toxoplasma infection and predisposing factors in the Pakistani population over a 20-year period. We systematically reviewed research articles published in English (2000–2020) from PubMed and Google Scholar. The search results 26 publications involving 10,924 people and 2611 seropositive cases. The toxoplasmosis seropositivity rate was higher in women (25.44%) as compared to men (21.48%) and were statistically significant (p T. gondii infection. Toxoplasma infection was significantly more prevalent in Khyber Pakhtunkhwa province (25.87%) than in Punjab (20.42%) (p T. gondii infection epidemiology in Pakistan. It reveals a high frequency of infection among women. We strongly encourage further research to aid patient care and the development of more efficient diagnostic tests and preventative techniques

Deep Learning Based Multi Pose Human Face Matching System

Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm