School Disruptions Exacerbated Inequality in High School Completion

Using school-month-level learning mode data and high school completion rates across three school years from 429 Wisconsin public high schools, this study examines the impact of disruptions to in-person instruction during the COVID-19 pandemic on high school completion rates, with a focus on socioeconomic disparities. Findings reveal that a longer time in virtual or hybrid learning mode in 2020-21 decreases overall school completion rates and increases the within-school gap in completion rates between economically disadvantaged and non-disadvantaged students. This study provides further evidence of the unequal impact of the pandemic and calls for initiatives to support disadvantaged students during school disruptions.

Experimental Analysis of Power Quality in a Power Substation Used in a Residential and Educational Sector

The article describes the experimental measurements made at a low-voltage residential and educational power substation, in a point of common coupling. Two groups of experiments were carried out, in normal conditions and during the COVID-19 pandemic. Measurements were made using a power quality analyzer and include phase RMS voltages and line currents, total harmonic distortion and unbalance of phase voltages and line currents, neutral current, active, reactive and apparent powers, power factors and displacement power factors, Fresnel diagrams, and harmonic spectra. Measurements indicate significant differences of power quality indicators between the two measurement groups. © 2023 IEEE.

A Bicentric Study to Investigate the Impact of COVID-19 on Urological Patients

In December 2019, SARS-CoV-2 broke out, which raised great attention worldwide. In fact, it was essential to reorganize the management of economic, infrastructural and medical resources to deal with the inadequate preparation of medical practitioners for this emergency. It was evident that the global health, medical and scientific communities were not adequately prepared for this emergency, so during the pandemic. In this paper, data extracted from hospital discharge records of the Department of Urology of the A.O.R.N "Cardarelli” in Naples, Italy, were used. This work is an extension of a previous work, whose goal concerned how admission procedure in the Urology department of the "San Giovanni di Dio and Ruggi d'Aragona” hospital has been affected by COVID-19 pandemic. In this work we compare the results obtained for the patients of the University Hospital "San Giovanni di Dio and Ruggi d'Aragona” of Salerno and the patients of the A.O.R.N. "Antonio Cardarelli” of Naples (Italy). Data have been extracted from both hospitals discharge records of the Departments of Urology. Experimental analysis performed comparing pre-pandemic data with those collected during the epidemic showed an in-crease in the number of emergency hospitalizations and a decrease in planned pre-admission hospitalizations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Computational design and experimental analysis of a novel visor for COVID-19 patients receiving high-flow nasal oxygen therapy

The Covid-19 global pandemic has reshaped the requirements of healthcare sectors worldwide. Following the exposure risks associated with Covid-19, this paper aims to design, optimise, and validate a wearable medical device that reduces the risk of transmission of contagious droplets from infected patients in a hospital setting. This study specifically focuses on those receiving high-flow nasal oxygen therapy. The design process consisted of optimising the geometry of the visor to ensure that the maximum possible percentage of harmful droplets exhaled by the patient can be successfully captured by a vacuum tube attached to the visor. This has been completed by deriving a number of concept designs and assessing their effectiveness, based on numerical analysis, computational fluid dynamics (CFD) simulations and experimental testing. The CFD results are validated using various experimental methods such as Schlieren imaging, particle measurement testing and laser sheet visualisation. Droplet capturing efficiency of the visor was measured through CFD and validated through experimental particle measurement testing. The results presented a 5% deviation between CFD and experimental results. Also, the modifications based on the validated CFD results improved the visor effectiveness by 47% and 38% for breathing and coughing events, respectively © 2022 The Author(s)

Research on the novel medical protective clothing for COVID-19.

With the outbreak of COVID-19, a large number of medical staffs have invested in the front line of anti-epidemic. Medical protective clothing (MPC) can provide a safe environment for the wearers to block bacteria and viruses. However, nowadays, the thermal performance of MPC on the market is very poor, resulting in the extremely low comfort of the wearer. Some improved MPCs were made of materials, which were not easy to obtain with high cost. Some improved MPCs were lack of thermal comfort experimental data based on real human body. Therefore, this paper proposed a novel MPC with reusable PCM and ventilation. Through simulation and experiment, human comfort of the novel MPC was compared with the other two kinds of MPCs. Five subjects were invited to carried out the comfort tests under three states of motion with three types of MPCs. The results showed the novel MPC with higher cost performance in the effective period had been proved that PPD decreased by 39.5% than the traditional MPC. Besides, the novel MPC could meet the comfort requirements of medical staffs for one shift. Furthermore, the work can provide theoretical methods and basic experimental data for the continuous improvement of the comfort and safety of MPC.

Applying LTOPSIS and SWOT to Evaluate Competitive Ability of Leisure Farm after COVID-19

Leisure farm tourism would not only allow tourists to experience agricultural activities but also can increase farmers' income and deeply root rural culture. However, during the recent period of COVID-19 leisure farm should consider and evaluate its competitive ability for attracting tourists to go to leisure farm again. In this research LTOPSIS and SWOT technology is applied to evaluate and analyze competitive ability for leisure farms. According to quasi-experimental analysis, case leisure farm should be operated its business carefully due to uncertainty of COVID-19. Leisure farm should retain some fund to avoid the possibility of collapse in the future. © 2022 ACM.

Covid-19 Detection based on Transfer Learning & LSTM Network using X-ray Images

COVID-19 is a virus which leads to infections in the upper respiratory system and lungs. On the scale of the global pandemic, cases and deaths are rising daily. X-ray is one test that can give a better picture of the severity of COVID-19. To monitor various lung diseases, chest X-ray imaging is helpful. This paper proposed techniques, viz. deep feature extraction and pre-trained neural networks (CNN) to distinguish COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, the pre-trained deep CNN model VGG-16 was used. An LSTM model is introduced in this study. The dataset contains 180 X-ray images of COVID 19 and 200 healthy ones used in the experimental analysis. The performance measurement of the research was based on categorizing accuracy. Experimental activities show that deep learning demonstrates the potentiality in detecting COVID-19 based upon chest X-ray images as examined;the introduced model accomplishes an average accuracy of 97.37%. Other strategies like Resnet50 give 82% accuracy, Inception gives 96% accuracy, and Xception provides 92% accuracy. This has shown deep mechanisms that work well compared to local descriptions of the method of curing COVID-19 based upon the chest X-ray images. These findings allow us to conclude that this article's proposed procedure may help clinicians determine COVID-19-related diagnoses. © 2022 IEEE.

Systematic Non-Invasive Experimental Analysis of Olfactory Dysfunction using EBG for COVID-19

SARS-CoV2 has encompassed showing symptoms from acute respiratory distress syndrome to minor symptoms like loss of smell, taste, fever, body ache. This paper, explains the brain activity observed, if loss of smell (Olfaction) persists. If the symptoms are treated as early biomarker will enable earlier diagnosis and preventative treatments of syndromes. The proposed framework suggests a portable, easy to deploy noninvasive method to detect olfactory dysfunctions at the COVID test center. The validation of the parameters under clinical expertise has laid a ground to predict and proper assess of olfactory deficits in a patient within 20 minutes. The selection of hyper parameters was done using RBF kernel. The test is steered using a simple neuro-imaging, non-invasive device gathering the EBG waves, essentially gamma waves received from the olfactory nerve present in the upper nostril. The results impress to establish a base, that a decreased sense of smell may be a pointer to patients in the initial stage of the syndrome. The statistical validator, Fisher's exact test is performed for data analyses taken from neuroimaging device. The statistical significance was defined as P <.05 for the anosmia (loss of smell). The P- value calculated through our experimental setup is 0.008 for anosmia proved as a significant factor for the detection of infection. © 2022 IEEE.

Tweet Credibility Detection for COVID-19 Tweets using Text and User Content Features

The deadly COVID-19 pandemic is currently sweeping the globe, and millions of people have been exposed to false information about the disease, its remedies, prevention, and origins. During such perilous times, the propagation of fake news and misinformation can have serious implications, causing widespread panic and exacerbating the pandemic's threat. This increasing threat factor has given rise to considerable research challenges. This article is mainly concerned about fake news identification and experimentation is specifically performed considering COVID-19 fake news as a case study. Fake news is spread intentionally to mislead the people and therefore we need to identify user's involvement and it's correlation with additional features. The aim of this research is to develop a model that can predict the essence of a tweet given as an input with the help of multiple features. Our strategy is to make use of the tweet's text as well as the user's metadata and develops a model using natural processing technique and deep learning method. In this process, we have analyzed the behavior of the accounts, observed the impact of the various factors that can lead to fake news. The experimental analysis shows that hybrid model with text and content features have generated a benchmark result than the existing state of art techniques. We have obtained a best F1-score of 0.976 during the experimentation. © 2022. All Rights Reserved.

Post Graduate Engineering Education from Home for Optical Networking Laboratory during COVID19 Second Wave in India

Effective Teaching learning process must be very difficult in this COVID19 era for engineering teachers where they need support of hardware to explain certain fundamental things. Optical networking is one subject where it is difficult to explain certain results without experimental analysis. This paper describes basic optical networking experiments like Wavelength Division Multiplexing types of Passive Optical Network (WDM-PON), Time Division Multiplexing types of Passive Optical Network (TDM-PON), Fiber to the Home Network (FTTH), Wireless passive Optical Network using free space optics (FSO) techniques. Optsim 5.0 software is used to simulate the applications. © 2022 IEEE.

Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset. Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach. Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose. Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach. The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently. We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time. © 2022 Tech Science Press. All rights reserved.

'Hypercycle' Learning System Based on 'Extension Classroom': Take Online Music Teaching During the COVID-19 Pandemic As An Example

The purpose of this paper is to provide an information interactive platform of teaching and learning, for self-learning diagnosis. We expand classroom teaching into an open teaching system consisting of 'before class' (online), 'in class' (online or offline) and 'after class' (online), which is called 'extension classroom'. This paper uses the method of complex system analysis to evaluate the students' learning state in 'before class' and 'after class' and concludes that the key to effective teaching and learning is that teachers clearly know how to teach and students know how to learn. This kind of teaching mechanism is realized in 'extension classroom' with supercycle. We applied the 'extension classroom' to the teaching of music performance during the COVID-19 pandemic, and the results showed that 65% of students improved their self-diagnosis ability, 46% had satisfactory communication with teachers, and 20% had a significant increase in learning performance. © 2021 IEEE.

Analysis of the Impact of COVID-19 on the U.S. Economy Based on Big Data Processing and Experimental Analyses

With the outbreak of COVID-19, the world has experienced unprecedented crises especially in economy. The United States is more seriously affected. In order to more clearly show the current situation of the U.S. economy affected by the epidemic from the data level, the author completes the paper by using the research method of big data processing and experimental analyses to show that how Coronavirus influences economy, that is, the impact on GDP and the exchange of volume of stock shares and the impact on unemployment rate which can be shown in specific data. The author also discusses the degree of influence on different industries. The result shows that COVID-19 has seriously affected the overall economy of America. The specific data performance is the decline of GDP (about 5%) and the rise of unemployment (about 15%). The stock price has dropped significantly, even affecting the overall stock trading volume (declined by 55%). The purpose of this paper is to clearly show the specific influence on the US economy from the data level. The result can provide a specific data reference for the formulation of American economic policy in the next few years and provide a data basis for the study of the economic situation after the epidemic in the United States. © 2021 IEEE.

Deepfake and Security of Video Conferences

Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing apphcations has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in onhne video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences. © 2021 IEEE