Non-Compliance with COVID-19 Screening in Pakistan: A Cross-Sectional Survey

Objectives: To quantify the non-complaint portion of the general public – not wanting to be screened for COVID-19 and find the reason for this non-compliance, in the general public of Rawalpindi Pakistan. Study Design: Cross-sectional survey. Place and Duration of Study: General public of Rawalpindi, Pakistan. From June 19, 2020, to June 21, 2020. Methodology: A questionnaire was constructed based on a local study, it was injected to the accessible online population through Google Forms. Surveyors collected data from the illiterate population on printed proforma. A sample of 1108 was collected. IBM® SPSS® was used for data analysis. For categorical data, frequencies and percentages were calculated. A Chi-square test was applied for statistical significance. Results: 45.3% of participants were females, 54.7% were males. 37.9% of participants were married and 62.1% were unmarried. 3.8% were illiterate, 40.4% were matriculated and 47.1% had education higher than intermediate. 38.3% was non-compliant population – didn’t want to get screened for COVID-19. 30.7% were non-compliant because of ‘fear of isolation/ quarantine with other COVID-19 patients, leading to worsening of disease’ followed by 26.9% who ‘don’t trust the reliability of the test’. Gender and Education level variables were statistically significant in determining non-compliance. Marital status was found non-significant. Conclusion: A significant portion of the population i.e. 38.3% showed non-compliance with COVID-19 screening, which was statistically associated with gender and education level

Factors responsible for delay in provision of care to suspected COVID-19 patients presenting in surgical emergency and ways to combat it

Introduction: Health care workers are found to be at three times greater risk of getting infected as compared to the general public.  Scientists and doctors all over the world have agreed upon the use of PPE including gloves, masks, head covers, face shields, goggles, and jumpsuits in protection against COVID-19. Materials and Methods: This observational prospective study was conducted in the surgical emergency of Holy Family Hospital, Rawalpindi over a period of 2 months and 21 days. Patients included all those who presented to surgical emergency with suspicion of being positive for COVID-19 and time taken by first-line health care workers in attending them. 157 patients were observed for this purpose and 23 first-line surgeons including general, orthopedic, and neurosurgeons were interviewed regarding their fears and concerns about contracting COVID-19 and infecting their families. Results: It was observed that a surgeon took on an average of 10 minutes (+/-3 minutes) in wearing all the personal protective equipment and a total of 14minutes (+/- 5 minutes) in reaching a patient in the trauma room with symptoms suggestive of COVID-19. This was in contrast to a patient presenting to a trauma room who had no respiratory symptoms or fever, in which case, the patient was seen within 3 minutes (+/- 2 minutes) of presentation to a surgical emergency. Out of 23 surgeons, 15 had reasonably aware of the disease while 7 were knowledgeable up to the mark. 17 surgeons were extremely fearful about contracting the disease and infecting their friends and families. 7 surgeons confessed to avoiding COVID-19 patients and 9 surgeons confessed that they commanded their junior surgeons to see suspected COVID-19 patients in the emergency room. Conclusion: We concluded that delay in attending trauma patients suspected of being positive for COVID-19 was a worrisome problem that needed to be addressed. Numerous local and regional circumstances served as a factor for this delay, most important of which came out to be an inadequate provision of PPE, time consumed in collecting and wearing PPE, fear of the disease, and anxiety provoked due to this fear among surgeons

Compliance on the Use of Different Types of Face Mask by Healthcare Workers and General Public in Tertiary Care Hospital of RMU during COVID-19 Pandemic

Objective: To determine the compliance on the use of different types of facemask among HCWs, patients and the general public in different hospital settings of tertiary care hospital of RMU during COVID-19 pandemic Material and Methods: Cross-sectional descriptive study was done among 397 study subjects of either gender consisting of HCWs, patients, and the general public visiting the tertiary care hospital from 6th April-6th June 2020. Subjects were enrolled through convenience non-probability sampling. Data was gathered by a self-structured proforma. The study consisted of questioning the frequency of washing hands, using hand sanitizers, practicing physical distancing, using eye protection goggles or face shields, the practice of sterilizing or changing of shoes and clothes after coming back to home, frequent use of disposable gloves, use of caps or head covers and water-repellant aprons and gowns. Data were analyzed by SPSS version 25.0. Results: A total of 397 study subjects including 206 (52%) males and 190 (48%) females were enrolled in the study. 118 (29.7%) had an underlying disease, 93 (78.8%) of them showed regular use of masks. The mean age of participants was 34.7 ± 12.2 years. 90.4% of study subjects had good compliance with using face masks in hospital settings, 25.9% study subjects used respirator type of masks while the use of homemade cloth was 4.8%. A total of 57.8% of study subjects had use of single masks, 22.5% used double masks, and 10.4% people used triple masks remaining 9.3% uses no masks at all. Among 189 HCWs 54% were using respirator type of masks and 46% were using surgical masks. The general public preferred to use locally made surgical masks or homemade cloth because they are cheap and easily available. Conclusion: The selection and use of PPE especially facemasks vary among HCWs and non HCWs. Even among HCWs usage varies according to the type of healthcare workers and the working environment. Overall compliance with the use of face masks and other PPE was considerably low among non HCWs. Our study has provided preliminary data about the usage of masks among HCWs and non HCWs. Longitudinal studies must be conducted to collect better evidence about the use of the face mask as PPE and its associated factors

Axial Load-carrying Capacity of Steel Tubed Concrete Short Columns Confined with Advanced FRP Composites

Fiber Reinforced Polymers (FRPs) have wide applications in the field of concrete construction due to their superior performance over conventional materials. This research focuses on the structural behavior of steel tube FRP jacket–confined concrete (STFC) columns under axial concentric loading and proposes a new empirical equation for predicting the axial load-carrying capacity of STFC columns having thickness of FRP-fabric ranging from 0.09 mm to 5.9 mm. A large database of 700 FRP-confined concrete specimens is developed with the detailed information of critical parameters, i.e. elastic modulus of FRPs (Ef), compressive strength of unconfined concrete (fc’o), diameter of specimen (D), height of specimen (H), total thickness of FRPs (N.tf), and the ultimate strength of confined concrete (fc’c). After the preliminary evaluation of constructed database, a new empirical model is proposed for the prediction of axial compressive strength of FRP-confined specimens using general regression analysis by minimizing the error functions such as root mean squared error (RMSE) and coefficient of determination (R2). The proposed FRP-confinement strength model presented higher accuracy as compared with previously proposed models. Finally, an equation is proposed for the predictions of axial load carrying capacity of STFC columns. For the validation of proposed equation, an extensive parametric study is performed using the proposed nonlinear finite element model (FEM). The FEM is calibrated using the load-deflection results of STFC columns from literature. A close agreement was observed between the predictions of proposed finite element model and proposed capacity equation

Usability and acceptability of a mobile app for behavior change and to improve immunization coverage among children in Pakistan: A mixed-methods study

Background: Pakistan\u27s immunization uptake rates are still significantly lower than anticipated despite several initiatives. Lack of awareness, forgetting about vaccination schedule, and vaccine misconception/misinformation are a few of the major drivers that mitigate the rates of immunization. The current COVID-19 pandemic emphasizes the importance of immunization. The significant reductions in regular childhood vaccination during pandemic have increased the risk of outbreaks of vaccine-preventable diseases. Concerns among parents over possibly exposing their children to COVID-19 during child visits may have contributed to the reported declines. Innovative and cost-effective mHealth interventions must be implemented in order to address the problem of inadequate immunization rates. In addition, it is also critical to understand the end user needs in order to reflect on the highly relevant essence of the customized healthcare experience.Objective: The aim of this study was to learn about caregivers\u27 attitudes toward the usability and acceptability of behavior-change smartphone applications (mobile phones) for improving immunization coverage in Pakistan.Methods: A mixed-method design was employed for this study. The study was conducted at Aga Khan University, Hospital. Parents visiting the Community Health Center for 6-week vaccination of their children were recruited. The study was conducted in two stages. Stage 1 consisted of qualitative interviews that grasped the parent\u27s attitudes and challenges to immunization, as well as their acceptability and accessibility of the smartphone-based behavior-change application to increase vaccine uptake. Stage 1 was followed by stage 2, in which data were collected through a questionnaire designed by using data from qualitative interviews.Results: The majority of participants agreed that immunization serves an important role in protecting their child from illnesses that cause morbidity and mortality. Almost all of them emphasized the importance of using a pre-appointment method at vaccination center in order to reduce the waiting time. Furthermore, participants were also interested in AI-based behavior modification applications related to immunization. They also wanted to have applications in their native language for better understanding and communication of related information. In our study, approximately 95.2 percent of participants agreed to accept SMS immunization updates, which was also reasonably high. Lastly, the majority of them identified forgetfulness as a significant contributor to regular immunization.Conclusion: To enhance the uptake of childhood vaccines, overall vaccination rates, and overcome barriers related to vaccination coverage, cost-effective and user-friendly mHealth AI-based smart phone applications are required to raise awareness regarding the continuation of vaccination service and the importance of timely vaccination. Parents\u27 experiences and attitudes must be considered while designing and evaluating the efficacy of mHealth-based interventions

Predictive intelligence using ANFIS-induced OWAWA for complex stock market prediction

Traditional time series prediction methods are unable to handle the complex nonlinear relationship of a large data set. Most of the existing techniques are unable to manage multiple dimensions of a data set, due to which the computational complexity escalates with the increasing size of a data set. Many machine learning (ML) methods are unable to handle known unknown predictions. This paper presents a new forecasting method in the neural network structure based on the induced ordered weighted average (IOWA) weighted average (WA) and fuzzy time series. The proposed model is more efficient than existing complexity handling fuzzy time series prediction methods and other traditional time series prediction methods. The proposed model can accommodate the IOWA operator, weighted average, and relevance degree of each concept in a particular problem for a fuzzy nonlinear prediction. The contribution of this paper is twofold. First, it contributes to theory by proposing a new IOWAWA layer in the neural network to handle complex nonlinear prediction for a large data set. The second contribution is the application of the approach to predict nonlinear stock market data. The robustness of the approach is tested using Australian Securities Exchange (ASX) stock data by considering a case study of the housing and property sector. We further compare the prediction accuracy of the approach with sixteen existing methods. The experimental results demonstrate that the proposed model outperforms existing methods

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm

Utilization of Polymer Concrete Composites for a Circular Economy: A Comparative Review for Assessment of Recycling and Waste Utilization

Polymer composites have been identified as the most innovative and selective materials known in the 21st century. Presently, polymer concrete composites (PCC) made from industrial or agricultural waste are becoming more popular as the demand for high-strength concrete for various applications is increasing. Polymer concrete composites not only provide high strength properties but also provide specific characteristics, such as high durability, decreased drying shrinkage, reduced permeability, and chemical or heat resistance. This paper provides a detailed review of the utilization of polymer composites in the construction industry based on the circular economy model. This paper provides an updated and detailed report on the effects of polymer composites in concrete as supplementary cementitious materials and a comprehensive analysis of the existing literature on their utilization and the production of polymer composites. A detailed review of a variety of polymers, their qualities, performance, and classification, and various polymer composite production methods is given to select the best polymer composite materials for specific applications. PCCs have become a promising alternative for the reuse of waste materials due to their exceptional performance. Based on the findings of the studies evaluated, it can be concluded that more research is needed to provide a foundation for a regulatory structure for the acceptance of polymer composites.publishedVersio