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

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

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

A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning

Quality of Service (QoS) is one of the key indicators to measure the overall performance of cloud services. The quantitative measurement of the QoS enables the service provider to manage its Service Level Agreement (SLA) in a viable way. It also supports a consumer in service selection and allows measuring the received services to comply with agreed services. There is much existing literature that tries to predict the QoS and assist stakeholders in their decision-making process. However, it is tricky to deal with multidimensional data in time series prediction methods. The computational complexity increases with an increase in data dimension, and it is a challenging task to give precise weights to each time interval. Existing prediction methods could not deal with the intricate reordering of input weights. To address this problem, we propose a novel Clustered Induced Ordered Weighted Averaging (IOWA) Adaptive Neuro-Fuzzy Inference System (ANFIS), (CI-ANFIS) model. This fuzzy time series prediction model reduces data dimension and handles the nonlinear relationship of the cloud QoS dataset. The proposed method uses an intelligent sorting mechanism that regulates uncertainty in prediction while incorporating a fuzzy neural network structure for optimal prediction results. The proposed method employs the IOWA operator to sort input arguments based on associated order-inducing variables and assign customised weights accordingly. The inputs are further classified using three fuzzy clustering methods - fuzzy c-means (FCM), subtractive clustering and grid partitioning. The inputs further pass to the ANFIS structure that takes the benefits of both the fuzzy and neural networks. The fuzzy structure in ANFIS builds understandable rules for cloud stakeholders and deals with uncertain occurrences of data. The model uses a real cloud QoS dataset extracted from the Amazon Elastic Compute Cloud (EC2) US-West instance and predict its behaviour every five minutes for the next 24 h. The proposed method is further compared with the existing twelve methods. The comparative results show that the proposed CI-ANFIS model outperforms all current techniques. The proposed approach opens a new area of research in various complex prediction problems such as stock trading, big data, complex IoT sensors, and other social computing problems