Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets. © 2023 IEEE.

Perspectives of Key Stakeholders on Ecotourism Sustainability and Stakeholder Management: A Case Study of Penang Hill

Penang Hill, one of Malaysia's most well-known natural and cultural tourist sites, is a popular destination for both domestic and foreign visitors. The study's objective was to explore the perspectives of key stakeholders regarding ecotourism sustainability and stakeholder management. In-depth semi-structured interviews were conducted with three key stakeholders, and content analysis was done to explore the key findings. It was observed that PHC engages stakeholders through meetings, workshops, and seminars and tries to understand their interests for better management. Keen interests of stakeholders were observed towards activities that generate monetary rewards. It was also noted that all stakeholders showed great interest in the environmental conservation of the destination. The study concludes by stating that even though Penang Hill management is on the right path and trying hard to achieve ecotourism sustainability, stakeholders still need more support from the management authority to work towards the destination's sustainability, especially in this Covid-19 pandemic. The findings have ramifications for stakeholder management to implement long-term ecotourism in the destination © 2022, e-Review of Tourism Research.All Rights Reserved.

Effects of COVID-19 vaccination on chronic urticaria patients: First results of the UCARE COVAC-CU study

Background: Chronic urticaria (CU) is a common chronic inflammatory disease. Vaccination against viral infections including COVID-19 can induce increased CU disease activity. As of now, it is unclear how often CU exacerbations occur after COVID-19 vaccination. Method(s): COVAC-CU is an international, multicenter, observational, cross-sectional study of the global network of urticaria centers of reference and excellence (UCAREs). COVAC-CU evaluates the effects of COVID-19 vaccination in patients with CU including rates and risk factors of CU exacerbation. Here, we analyzed 1857 patients with CU who had received at least one COVID-19 vaccination. Data were collected via a questionnaire and retrieved from patient charts. Result(s): Of 1857 patients with CU (median age: 42 years;range: 18-91 years), 72.1% were female and 71.2%, 14.4% and 14.4% had chronic spontaneous urticaria, chronic inducible urticaria, or both, respectively. Most patients had received two doses of COVID-19 vaccine (79.1%), compared to one (9.7%), three (11%), or four (0.3%). Vaccine type included: BTN162b2 (58.4%;BioNTech/Pfizer), ChAdOx1 nCOV-19 (13.8%;AstraZeneca), BBIBP-CorV (8.2%;Sinopharm), Gam-COVID- Vac (8%;Sputnik), mRNA-1273 (5.3%;Moderna), and Ad26.COV 2.5 (4.7%;Janssen/J&J). Less than 10% of patients used premedication, and less than half of patients (44.4%) reported one or more adverse reactions after vaccination. The most common adverse reactions were local injection site reactions (29.6%), fatigue (19.7%), fever (19%), muscle pain (17.9%), headache (14%), and exacerbation of CU (15%). Severe allergic reactions/anaphylaxis were reported by 0.4% of CU patients. In almost all patients who experienced exacerbation of their CU, this occurred within one week after receiving the vaccine, i.e. after 1 to 12 hours (25.8 %), after 12 hours to 48 hours (31.1%) or after 2-7 days (37.9%). Conclusion(s): Most CU patients tolerate COVID-19 vaccination well;severe allergic reaction (anaphylaxis) rates were similar or lower than the self-reported rates reported in the general population. Exacerbation of urticaria was reported in one in five patients, mostly in a week after receiving the vaccine.

Clinical Outcomes of Patients with COVID-19 Infection and Cardiac Implantable Electronic Devices

BACKGROUND: Existing cardiac disease contributes to poor outcome in patients with coronavirus disease 2019 (COVID-19). Little information exists regarding COVID-19 infection in patients with a cardiac implantable electronic device (CIED). OBJECTIVE(S): To assess the association between CIEDs and severity of COVID-19 infection. METHOD(S): We performed a retrospective analysis including 13,000 patients > 18 years old with COVID-19 infection between January and December 2020. Patients with COVID-19 who had a permanent pacemaker or defibrillator were matched 1:4 based on age and sex followed by univariate and multivariate analyses. Baseline characteristics and clinical outcomes were assessed. RESULT(S): Forty patients with CIED and 160 patients without CIED were included in the current analysis. Mean age was 72.6 +/- 13 years, and approximately 50% were females. Majority of the patients in the study arm had a pacemaker (63%), whereas only 15 patients (37%) had a defibrillator. Patients with COVID-19 and CIED presented more often with atrial fibrillation, coronary artery disease, heart failure, hypertension, diabetes, and chronic kidney disease. They were more likely to be hospitalized in the intensive care unit (ICU) and required more ventilatory support (35% vs. 18.3%). Thirty-day mortality (22.5% vs. 13.8%) and 1-year mortality (25% vs. 15%) were higher among patients with COVID-19 and CIED. CONCLUSION(S): Patients with COVID-19 and CIED had a significantly higher prevalence of co-morbidities that were associated with increased mortality. Although, CIED by itself was not found as an independent risk factor for morbidity and mortality, it may serve as a warning for severe illness with COVID-19.

Post COVID-19: Risk Factors, Prevention, and Management of Black Fungus

Background: Mucormycosis is a genuine, however uncommon shrewd fungal contamination that spreads quickly, and subsequently brief analysis and treatment are important to stay away from the high pace of mortality and morbidity rates. Mucormycosis is brought about by the inward breath of its filamentous (hyphal structure) fungus, particularly in immunosup-pressed patients. The pandemic of COVID-19 stays on an upsurge pattern. The second rush of this infection has prompted alarm in numerous nations, including India and a few pieces of the world experiencing the third wave. As there could be no appropriate treatment choices or cures accessible for this lethal contamination, steady consideration gears like oxygen chambers, venti-lators and substantial utilization of steroids assume an imperative part in the management of COVID-19. Amidst this pandemic, the COVID-19 patients are procuring optional contamina-tions, for example, mucormycosis otherwise called black fungus infection. Objective(s): It is important to lead research on COVID-19 patients to even more likely to forestall and oversee pioneering contaminations to diminish their occurrence and bleakness. So, this paper is to foster preventive treatment plans and use corticosteroids sanely. Method(s): Using search engines like Embase, PubMed, Google Scholar, etc., various research, review articles, and some book chapters, also various articles on websites and guidelines on the ICMR portal were accessed, after which we prepared this review article. In this review, we have included reported cases and case series of mucormycosis among patients with and without COVID-19 spanning the period of 1969 to 2021 and have described the clinical features and out-comes. Result(s): In gentle instances of COVID-19 (without hypoxemia) or when utilizing higher dosages of glucocorticoids, glucocorticoids ought to be kept away from ongoing investigations which have archived a disturbing number of COVID-19 patients with mucormycosis contamination. A large portion of these patients had diabetes which was controlled with the use of steroids. Conclusion(s): Thus, the current article underlines mucormycosis and its connected conditions, its component in typical and COVID-19 influenced people, affecting variables and difficulties to defeat this disease. Early distinguishing proof and further examination of its growth will alto-gether diminish the seriousness of the sickness and death rate in COVID-19-influenced patients. Copyright © 2023 Bentham Science Publishers.

The staff's perception of level of clinical competence of graduating dental students amid COVID-19: A qualitative research in one Saudi dental school La percepción del personal sobre el nivel de competencia clínica de los estudiantes de odontología que se gradúan en medio de COVID-19: una investigación cualitativa en una escuela de odontología de Arabia Saudita

Introduction: The COVID-19 pandemic had a huge impact on dental education and assessment. This study focuses on the changes made to the delivery of teaching, assessment, and clinical competence of the final year students enrolled on the dentistry programme of the Faculty of Dental Medicine, Qassim University during the COVID-19 pandemic. Material and Methods: This is a full qualitative study that uses semi-structured interviews to focus on the faculty members of one dental school in Saudi Arabia. The online interviews were performed using Zoom platform. The interviews were transcribed verbatim and analysed using thematic analysis. Themes were gathered and grouped into emerging topics. Purposive sampling method was adopted and continued until data saturation was achieved. Results: The change to online delivery of teaching proved to be a good solution for the cohort immediately affected by COVID, however, the online delivery of teaching placed significant amount of added pressure on faculty members. The work-life boundaries blurred and most staff complaint of long hours of working, mostly into the late evening. Despite the fact that the COVID graduating cohort achieved less clinical experience compared to typical graduates, a useful agreement between the University and college allowed newly graduated dentists to focus on the areas of low experience during their first rotation of their internship. Conclusion: Despite COVID's challenges and lockdowns and its effect on teaching and assessment, staff believed that students were graduating at a safe beginner level. With mitigating strategies in place, the newly graduated dentists achieved the same amount of experience as a typical graduate but over a slightly prolonged span of time.

The staff's perception of level of clinical competence of graduating dental students amid COVID-19: A qualitative research in one Saudi dental school

Introduction: The COVID-19 pandemic had a huge impact on dental education and assessment. This study focuses on the changes made to the delivery of teaching, assessment, and clinical competence of the final year students enrolled on the dentistry programme of the Faculty of Dental Medicine, Qassim University during the COVID-19 pandemic. Material and Methods: This is a full qualitative study that uses semi-structured interviews to focus on the faculty members of one dental school in Saudi Arabia. The online interviews were performed using Zoom platform. The interviews were transcribed verbatim and analysed using thematic analysis. Themes were gathered and grouped into emerging topics. Purposive sampling method was adopted and continued until data saturation was achieved. Results: The change to online delivery of teaching proved to be a good solution for the cohort immediately affected by COVID, however, the online delivery of teaching placed significant amount of added pressure on faculty members. The work-life boundaries blurred and most staff complaint of long hours of working, mostly into the late evening. Despite the fact that the COVID graduating cohort achieved less clinical experience compared to typical graduates, a useful agreement between the University and college allowed newly graduated dentists to focus on the areas of low experience during their first rotation of their internship. Conclusion: Despite COVID's challenges and lockdowns and its effect on teaching and assessment, staff believed that students were graduating at a safe beginner level. With mitigating strategies in place, the newly graduated dentists achieved the same amount of experience as a typical graduate but over a slightly prolonged span of time. Resumen Introducción: La pandemia de COVID-19 tuvo un gran impacto en la educación y evaluación dental. Este estudio se centra en los cambios realizados en la entrega de la enseñanza, la evaluación y la competencia clínica de los estudiantes de último año matriculados en el programa de odontología de la Facultad de Medicina Dental de la Universidad Qassim durante la pandemia de COVID-19. Material y métodos: Este es un estudio cualitativo completo que utiliza entrevistas semiestructuradas para centrarse en los miembros de la facultad de una escuela de odontología en Arabia Saudita. Las entrevistas en línea se realizaron utilizando la plataforma Zoom. Las entrevistas se transcribieron literalmente y se analizaron utilizando un análisis temático. Los temas se reunieron y agruparon en temas emergentes. Se adoptó y continuó el método de muestreo intencional hasta que se logró la saturación de datos. Resultados: El cambio a la entrega en línea de la enseñanza resultó ser una buena solución para la cohorte inmediatamente afectada por el COVID, sin embargo, la entrega en línea de la enseñanza ejerció una cantidad significativa de presión adicional sobre los miembros de la facultad. Los límites de la vida laboral se difuminaron y la mayoría del personal se queja de largas horas de trabajo, sobre todo hasta altas horas de la noche. A pesar del hecho de que la cohorte de graduados de COVID logró menos experiencia clínica en comparación con los graduados típicos, un acuerdo útil entre la Universidad y la facultad permitió a los dentistas recién graduados centrarse en las áreas de baja experiencia durante su primera rotación de su pasantía. Conclusión: A pesar de los desafíos y confinamientos de COVID y su efecto en la enseñanza y la evaluación, el personal creía que los estudiantes se estaban graduando a un nivel principiante seguro. Con las estrategias de mitigación en vigor, los dentistas recién graduados lograron la misma cantidad de experiencia que un graduado típico, pero durante un lapso de tiempo ligeramente prolongado.

Learning Mathematical Model of Multi-Objective Functions for Coronavirus Optimization Algorithm

The purpose of this paper is to study mathematical formulas that explain how the coronavirus is transmitted from a primitive person equal zero(no pantion) to an uninfected person at known rates. And then proposing a mathematical formula using the multiple obj-function as well as creating a mathematical formula that aims to reduce the cost of transporting the vaccine from the United States of America to the world. Important concepts in The Coronavirus Optimization Algorithm so that the reader can have a broad knowledge and awareness of idealism, In addition to that we bring the reader closer to know How to create mathematical formulas based on the classification of society into three categories, healthy, injured, and survivors.And the last part dealt with the transfer of the vaccine manufactured in the United States of America and the world, and we proposed a formula for it. © 2022 American Institute of Physics Inc.. All rights reserved.

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets.

In a financial system, entities (e.g., companies or markets) face systemic risk that could lead to financial instability. To prevent this impact, we require quantitative systemic risk management we can carry out using conditional value-at-risk (CoVaR) and a network model. The former measures any targeted entity's tail risk conditional on another entity being financially distressed; the latter represents the financial system through a set of nodes and a set of edges. In this study, we modify CoVaR along with its multivariate extension (MCoVaR) considering the joint conditioning events of multiple entities. We accomplish this by first employing a multivariate Johnson's SU risk model to capture the asymmetry and leptokurticity of the entities' asset returns. We then adopt the Cornish-Fisher expansion to account for the analytic higher-order conditional moments in modifying (M)CoVaR. In addition, we attempt to construct a conditional tail risk network. We identify its edges using a corresponding Delta (M)CoVaR reflecting the systemic risk contribution and further compute the strength and clustering coefficient of its nodes. When applying the financial system to global foreign exchange (forex) markets before and during COVID-19, we revealed that the resulting expanded (M)CoVaR forecast exhibited a better conditional coverage performance than its unexpanded version. Its superior performance appeared to be more evident over the COVID-19 period. Furthermore, our network analysis shows that advanced and emerging forex markets generally play roles as net transmitters and net receivers of systemic risk, respectively. The former (respectively, the latter) also possessed a high tendency to cluster with their neighbors in the network during (respectively, before) COVID-19. Overall, the interconnectedness and clustering tendency of the examined global forex markets substantially increased as the pandemic progressed.

Explaining COVID-19 diagnosis with Taylor decompositions.

The COVID-19 pandemic has devastated the entire globe since its first appearance at the end of 2019. Although vaccines are now in production, the number of contaminations remains high, thus increasing the number of specialized personnel that can analyze clinical exams and points out the final diagnosis. Computed tomography and X-ray images are the primary sources for computer-aided COVID-19 diagnosis, but we still lack better interpretability of such automated decision-making mechanisms. This manuscript presents an insightful comparison of three approaches based on explainable artificial intelligence (XAI) to light up interpretability in the context of COVID-19 diagnosis using deep networks: Composite Layer-wise Propagation, Single Taylor Decomposition, and Deep Taylor Decomposition. Two deep networks have been used as the backbones to assess the explanation skills of the XAI approaches mentioned above: VGG11 and VGG16. We hope that such work can be used as a basis for further research on XAI and COVID-19 diagnosis for each approach figures its own positive and negative points.

Simultaneous Effect of Seismic Intensity and Shear Wall Design on the Cost of High-Rise Buildings in Saudi Arabia

This research study intends to investigate the influence of shear wall design and seismic intensities on the potential cost of Reinforced Concrete (RC) high-rise buildings in Saudi Arabia. The seismic intensities of the proposed locations vary from relatively low to relatively high. Conventional and ductile design methods are considered for designing the shear walls in different locations. The influence of these selected locations and employed design methods associated with the anticipated cost is numerically computed and quantified. The three-dimensional finite element models are created using the available structural software package ETABS. Cost analyses are performed and obtained for the conventionally designed buildings in the considered different locations. The corresponding cost analyses considering the ductile design of shear walls are also computed for all the considered models. Reinforcement steel material and labor costs are utilized to determine the total cost of the shear walls. Other cost elements such as concrete, formwork, machinery, finishing, etc. are not included. The relation between spectral accelerations and required reinforcement for each shear wall design is made. Sensitivity analysis, using @Risk software, with respect to the labor cost is performed to cover the expected risk during the COVID-19 Pandemic. Copyright © 2022 Praise Worthy Prize S.r.l.-All rights reserved. © 2022 Praise Worthy Prize S.r.l.

Investigating the Impact of Educational Space Design in Fostering Social Distancing: A Case Study of the University of Technology Buildings, Iraq

In the light of the ongoing outbreak of epidemics, the Iraqi government ordered the temporary closure of university buildings and explicit compliance with social distancing, fearing increased infection rates among the large numbers of students. This closure, and the fear of infection, acted as an obstacle for users of educational spaces. To overcome this challenge, the study aimed to investigate the impact of educational space design on the effectiveness of social distancing to reduce the spread of epidemics. The shape, area, and furniture arrangement pattern were determined in the study of educational spaces design as a spatial configuration through the level of wayfinding and permeability. To better understand the relationship between the design of the current educational spaces and the effectiveness of social distancing, this study used a visual survey, field visits, and a quantitative method using a space syntax analysis. And the analysis was carried out on various models of educational spaces design in three elected samples of the buildings of the Technological University Baghdad, Iraq. The analysis values were represented in quantitative tables to illustrate the values of the space syntax attributes and charts showing measures of permeability and wayfinding in all the analysed models. The study results show an impact and a close relationship between the elements of educational space design as a spatial composition and the effectiveness of social distancing. This relationship is formed by the effect of the shape, space, and furniture arrangement pattern in changing the values of spatial space relationships. Furthermore, permeability and wayfinding as spatial characteristics depend on those relationships and control users’ circulation within the educational space, which is essential in determining the effectiveness of social distancing. © 2022, Kauno Technologijos Universitetas. All rights reserved.

Examining the Impact of COVID-19 on Ridership for an Agency Running a Demand Response System

COVID-19 emerged in December 2019 and changed transportation across the United States, especially for people dependent on transit buses to make trips. Most transit agencies saw a significant decrease in ridership;however, the impact on travel for agencies that offer demand-response transit hasn't been examined completely. This paper examines ridership for a demand-response rural transit system from three perspectives: Pre-pandemic, mid-pandemic, and pandemic-recovery. The paper examines total trips, origin locations, and destination locations at the three time intervals. In addition, the paper develops a transit social vulnerability index (SVI) to evaluate which riders continued to use the service and what factor or factors lead to those decisions. The results of the paper show that transit trip making and passengers of the system were not all affected uniformly. © ASCE. All rights reserved.

An Optimal Control of SIRS Model with Limited Medical Resources and Reinfection Problems

COVID-19 is a global public health problem that causes severe acute respiratory syndrome (SARS). It is also extremely contagious with rapidly increasing death rates. In this paper, we propose an optimal control model with SIRS (Susceptible-Infected-Recovered-Susceptible) kinetics to examine the effects of several intervention measures (e.g., vaccination and treatment) under the limited medical resources scenarios. This model is also employed to investigate the possibility of reinfection because of the fading of immunity problem. As a case study, the modeling framework is parametrised using COVID-19 daily confirmed and recovered cases in Malaysia. The parameters have been approximated by relying on the model's best fit to actual data published by the Malaysian Ministry of Health (MOH). Our numerical simulation results show that the inclusion of optimal control components with vaccination and treatment strategies would dramatically reduce the number of active cases even in the presence of reinfection forces. Regardless of the relative weightage (or costs) of vaccination and treatment, as well as the possibility of reinfection, it is critical to plan effective COVID-19 control measures by vaccinating as many people as possible (and as early as possible). Overall, these insights help explore the importance of intervention measures and the allocation of medical resources to control the severity of this pandemic.

The correlation between the concentration of ovarian steroid and the severity of COVID-19 in females

Background: Coronaviruses are positive single-stranded RNA viruses that have a spherical shape and some spike-shaped proteins (spike proteins) that project from their surface, from which they derive their name because they resemble a solar corona. The RNA sequence of SARS-CoV-2 is approximately thirty thousand nucleotides in length and encodes both structural and non-structural proteins. Objective: To assess the relation between the concentration of ovarian steroid and the severity of covid-19 in females. Patients and method: A cross sectional study carried at Consulting clinic for chest and respiratory diseases in Babylon Governorate at the period from 1st of January 2021 to the 1st of June 2021. Patients were female infected with COVID-19 with in age between 25-65 years old. Results: mean age in the studied group was (44.75±13.87) years and the main age group between 40-49 years (29.6%), the majority of the patients (96.3%) were married women, and (71.6%) were obese. fever was found in 68 (84.0%) of patients, diarrhea in 40 (49.4%), loss of smell in 48 (59.3%) of patients and loss of taste were found in 59 (72.8%) of them, and 46 (56.8%) of patients with COVID-19 presented with cough, the level of progesterone and estrogen were decreased with increase of lobe involvement. Conclusion: Ovarian steroid play an important role to decrease the severity of COVID-19. © 2022 International Journal of Health Sciences.All rights reserved.

SAWBRID: SmArt WhiteBoard Replacement Interactive Device

The idea of attending a professor's office hours seems very basic to the average college student. The beginning of each semester brings about a wave of invitations to visit each professor in their office, at the allotted time for the section of their class. Recent developments such as the growing prevalence of texting and email, as well as specific events such as the Covid-19 pandemic have brought the norm away from these in person meetings between students and professors, to the detriment of the students' education. The SmArt WhiteBoard Replacement Interactive Device (SAWBRID) is an innovative solution composed of an interactive device with a Low-power screen that, through a user friendly mobile application, makes the facilitation of office hours and the student/professor interactions outside of the classroom far more flexible and simple. In whole, the project is centered around the individual professor, their schedule, and how that schedule is communicated. The SAWBRID sits in an accessible casing outside of the professor's office, relaying information about their schedule, available time slots to be scheduled through the mobile application, and personalized messages. The device is self-updating whenever a change is detected in the professor's schedule, or when they decide to update their personalized message. The student can access a professor's schedule through the mobile application and schedule an appointment, which will place their initials in the selected time slot on both the mobile application and the SAWBRID. The professor has a different interface to interact with their SAWBRID from the mobile application giving them more control over their schedule, the personalized messages they want to display and other features. We use security services such as confidentiality and authentication throughout the system to protect user credentials, user data, and to ensure the privacy of the users. Our solution effectiveness and performance are evaluated through power measurements to determine the device's ability to self-sustain for long periods of time and the ease of use.

The Outcome of Psychological traumatic situation experienced by medical students in the COVID-19 pandemic

Objective: We aimed to investigate the knowledge of medical students about COVID-19, the effects of the traumatic situation they experienced, the stress they perceived and the factors affecting them. In addition, we aimed to learn the thoughts of the students about the virus due to the uncertainties. Methods: The study was carried out online between May 30, June 5, 2020 with a questionnaire prepared with google forms. For the study, all students studying at the Faculty of Medicine of University of Health Sciences, Lahore were called through class representatives and WhatsApp class groups. The questionnaire included sociodemographic information, knowledge and sources of information about the disease, to agreement degree the proposition whether covid 19 is produced as a biological weapon. The Perceived Stress Scale (PSS) and Impact of Events Scale-Revised (IES-R) were applied. Results: The total number of participants was 275 students. No student was infected with COVID-19 at the time of the survey. The presence of chronic disease in the participants was found to be a factor that increased anxiety (p = 0.01). Majority of participants (60.40%) stated that they agree with COVID-19 is a biological weapon. The mean scores of women 's total PSS and IES-R were higher than men. It was found that the families of the students had a lower monthly income than the minimum monthly wage is increasing the anxiety about getting COVID-19 infection and perceived stress. One third of the students reported that sleep and appetite were impaired than the before pandemic. The announcements and website of Ministry of Health and the social media was the main source of information of the participants. Conclusions: It was found that medical students were highly worried about being infected with COVID-19. The scores obtained from the pre-clinic students' anxiety to become infected with COVID-19, PSS and IES-R total scores were found to be significantly higher than their clinical students.

Employing deep learning for lung sounds classification

Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the World Health Organization (WHO). Recently, with coronavirus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Determinants of hydroxychloroquine-mediated hemolytic anemia in G6PD-deficient COVID-19 patients (preprint)

Aims To identify the factors contributing to hydroxychloroquine (HCQS)-mediated hemolytic anemia in G6PD deficient COVID-19 patients.Materials and Methods We have developed Multiple linear regression (MLR) and classification and regression tree (CART) models based on the published data (n = 13) and evaluated the impact of G6PD variants in exerting hemolysis.Results African Ancestry subjects had significant HCQS-mediated drop in hemoglobin (pre- vs. post-therapy Hb, g/dl: 12.75 ± 1.57 vs. 6.78 ± 0.62, p = 0.0008) than those from non-African ancestry (pre- vs. post-therapy Hb, g/dl: 13.27 ± 1.51 vs. 9.97 ± 2.34, p = 0.04). Diabetics had significant HCQS-mediated drop in hemoglobin (pre- vs. post-therapy Hb, g/dl: 12.96 ± 0.98 vs. 6.82 ± 0.76, p = 0.0007) than non-diabetics (pre- vs. post-therapy Hb, g/dl: 12.88 ± 2.13 vs. 9.13 ± 2.51, p = 0.05). MLR model explained 53.75% variability in HQCS-mediated hemoglobin drop when African Ancestry, diabetes, hypertension and azithromycin used as input variables. CART model efficiently explained HQCS-mediated hemolytic anemia by projecting African Ancestry and diabetes as the key predictors (R2 = 1.00). Although, African Ancestry G6PD A (-) variant had lesser ligand propensity than G6PD Mediterranean, G6PD Y70H and G6PD Union, it is thermolabile similar to G6PD Orissa, G6PD Y70H and G6PD L235F.Conclusions African Ancestry diabetic patients are more prone for HQCS-mediated hemolytic anemia in G6PD deficient COVID-19 patients. MLR and CART models explain 53.75% and 100% variability in HQCS-mediated hemolytic anemia. In view of similar in silico profile of Indian G6PD variants to G6PD A (-), HCQS should be used with caution in diabetics during COVID-19 therapy.

The Effect of COVID-19 on the Transit System in Two Regions: Japan and USA

The communication revolution that happened in the last ten years has increased the use of technology in the transportation world. Intelligent Transportation Systems wish to predict how many buses are needed in a transit system. With the pandemic effect that the world has faced since early 2020, it is essential to study the impact of the pandemic on the transit system. This paper proposes the leverage of Internet of Things (IoT) devices to predict the number of bus ridership before and during the pandemic. We compare the collected data from Kobe city, Hyogo, Japan, with data gathered from a college city in Virginia, USA. Our goal is to show the effect of the pandemic on ridership through the year 2020 in two different countries. The ultimate goal is to help transit system managers predict how many buses are needed if another pandemic hits. © 2021 IEEE.