Impact of Modality on Workload among Engineering Undergraduates during the COVID-19 Pandemic

The COVID-19 pandemic created unprecedented disruptions in models for engineering student training. At The Citadel, an undergraduate-focused college in the Southeastern United States, a variety of modalities were implemented following the onset of the pandemic, including emergency online and Hyflex learning. We conducted a longitudinal study to analyze the cognitive load among our undergraduate engineering students throughout changing modalities. Using data from the NASA Task Load Index (TLX) and open-ended reflections on student challenges, we found that total workload (a surrogate for cognitive load) was generally highest during emergency online learning in the second half of Spring 2020 semester, with experiences possibly varying across different demographic and academic groups. Emergency online challenges were often related to time management, personal organization, and responsibility for learning. In contrast, HyFlex challenges were often related to technology and communication challenges. While emergency online learning was a cognitive load disruption, that disruption was often associated with personal and/or academic development. HyFlex learning mediated cognitive load disruption;although, student challenges may have been simple nuisances rather than mediators of developmental change. © 2023 American Society of Civil Engineers.

Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

An Investigation into Teachers' Perceptions towards Digital Transformation in Teaching and Learning

Industry 4.0 has occurred and impacted many industries. Along with that are the heavy effects of the Covid-19 pandemic taking place globally. The dual impact on education is so great that the shift to compulsory online instruction has already taken place. And on that basis, universities and colleges promote their own educational digital transformation programs. The context of this study is a vocational college in which digital transformation has been applied for several years. This research aims to survey teachers' opinions about the implementation of digital transformation as well as their intention to continue teaching online in the future. The research method used in this study is a simple statistical method of data through an online survey via Google Form. The survey results show that the initial digital transformation process has received a lot of positivity and satisfaction from the teachers and students at the institution and the intention to continue implementing digital transformation in the future. © 2022 ACM.

A Simple, Pipette-free, and Power-free Device for Virus Nucleic Acid Detection

A portable, inexpensive, and easy-to-manufacture microfluidic device is developed for the detection of SARS-CoV-2 dsDNA fragments. In this device, four reaction chambers separated by carbon fiber rods are pre-loaded with isothermal amplification and CRISPR-Cas12a reagents. The reaction is carried out by simply pulling the rods, without the need for manual pipetting. To facilitate power-free pathogen detection, the entire detection is designed to be heated with a disposable hand warmer. After the CRISPR reaction, the fluorescence signal generated by positive samples is identified by naked eye, using an inexpensive flashlight. This simple and sensitive device will serve as a new model for the next-generation viral diagnostics in either hospital or resource-limited settings. © 2023 SPIE.

Survey on Healing Herbs Detection using Machine Learning

Nowadays health is very important. All need to take care of their health so that they can prevent diseases and improve their quality of life. The Sanskrit word Ayurveda comprises Science and Knowledge. In simple words, we can say that Ayurveda is the art of living. Medicines can cause hazards to our bodies as well but Ayurveda uses all the natural things for treatment so it is not harmful or dangerous for our bodies. The precise identification of medicinal plants is critical in Ayurvedic medicine. Human specialists use visual characteristics and fragrances to identify plants. Along with leaves flowers and spices are also a vital component in curing diseases. Flowers like lavender, marigold, hibiscus and many more, spices like clove, ginger, cumin, turmeric and so on play crucial role along with their leaves. Covid -19 had very terrible impact on lives of many people. Along with medicines;Ayurveda also played a very important role in curing people. Ayurvedic kadas and many more vanaspatis were used to get rid of this virus, many of the people got rid of this virus at home using home remedies. So, our main aim is to predict the ayurvedic plants that can cure various diseases using machine learning models. © 2023 IEEE.

Predicting Stock Price Movement for the Inefficient Market: Case of Hanoi Stock Exchange (HNX)

If the market is efficient, with stock prices accurately reflecting the true risk of an investment, then the issue becomes simpler. While this is true, investors may have a window of opportunity to discover a successful investing strategy if the market is inefficient. The primary goal of this research is to use the Support Vector Machine (SVM) algorithm to predict daily cycles of price increases for the ten largest-cap companies trading on the Hanoi Stock Exchange (HNX) over the Covid-19 timeframe (January 1st, 2019, to December 1st, 2022). Study how the model performs when trained and tested with a moving window. The outcome was an impressive average accuracy of 81.68 percent for the predicting model. © 2023 IEEE.

Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

A simple dilution model of the expired jet

Short-range exposure to expired aerosols or droplet nuclei has been considered as the predominant route for SARS-CoV-2. The observed effect of mask wearing, and social distancing suggests the importance of expired jet in the spread of COVID-19. The well-known steady-state dilution model is no longer valid for the interrupted expiratory jet. We reanalysed the existing interrupted jet data and proposed a simple dilution model of expired jet using the two-stage jet model. The interrupted jet consists of two stages, i.e., the jet-like and puff-like stage. Results show dilution factor grows linearly with the distance at the jet-like stage but increases with the cubic of the increasing distance in the puff-like stage. Dilution factor at any distance for the puff-like stage decreases as the activity intensifies, which is still much larger than that estimated via the steady jet model. The findings can be further applied into the short-range airborne exposure assessment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Data Visualization for Developing Effective Performance Dashboard with Power BI

Data visualization is a very important step in data analysis as it provides insight into the data in a more effective manner that is interesting, simple, and understandable to every-one without any language barrier. It can also represent a huge amount of data in a small space very easily. In the previous two years, the whole world has suffered from a very terrifying nightmare known as COVID-19. Known to be starting from the country of China, the pandemic affected not only the health and well-being of mankind, but also had serious impacts on the economies of various countries. Hence, a visualization of the data set of the pandemic might provide beneficial insights for finding a possible solution and can help in overcoming the impacts of the pandemic. Microsoft Power BI is a very famous tool for analyzing data. Power BI provides a different way to visualize the data. This paper has been analyzed the covid-19 data by using Power BI to understand the trends and patterns of the Pandemic. With the help of visualizing the data, it can be represented in stacked column charts, tables, and maps. These three ways are easy and simple to understand the patterns of the pandemic. It also helps to understand how covid impact the world. This research with power BI dashboard by using a dashboard feature that connects different pieces of visual graphs. © 2023 IEEE.

Evaluation of Early Diagnosis of COVID-19 Algorithms

The coronavirus disease 2019 (COVID-19) has been stated as a global pandemic, and the BA.4 and BA.5 variants are anticipated to drive the next wave of COVID-19 infection. Early diagnosis of this infection reduces its viral excretion. In this paper, after a large study of existing algorithms for pre-symptomatic COVID-19 detection in the state-of-the-art, we discovered a notable flaw in most models related to the choice of the evaluation function, such that, all the tested algorithms perform worse (from the evaluation function perspective) than an algorithm that generates alarms randomly from a binomial distribution. Therefore, we propose a simple and less biased evaluation function to better compare the quality of different algorithms. Comprehensive experimental evaluations of the state-of-the-art algorithms over the real-world dataset published by Nature Medicine journal contains 84 COVID-19 patients and 2,000 healthy participants show the effectiveness and the relevance of our evaluation method. Moreover, the proposed framework is released as an open-source library. © 2022 IEEE.

Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.

Predicting the COVID-19 Outbreak

This paper focuses on the use of mathematical modelling of propagation dynamics of infectious diseases. We use the discrete logistic model to propose a simple method to determine the start of coronavirus outbreak. Further, we apply the proposed method on real data of confirmed coronavirus cases from the Kingdom of Saudi Arabia. Our results suggested that the proposed method can be used for raising an alarm of coronavirus outbreak. © 2022 IEEE.

Attention to the Fads and Fashions in the Indian Stock Markets During COVID-19

Do people show fads and fashions in their attention searches? With the Google online search data during COVID-19, particularly from January to May 2020 for the socio-economic keywords, this study examines if online searches show short-run and long-run attention dynamics leading to fads and fashions in attention to the NSE Nifty and BSE Sensex indices. This study employs the methodology of cointegrating relationship with autoregressive distributed lag (ARDL) model and explains investors' attention search dynamics at the 'NSE Nifty Index' and 'BSE Sensex Index' caused by socio-economic attention searches. It also examines if the dynamics of attention coordination are parsimonious in nature and it explores the same with the generalized autoregressive conditional heteroskedastic (GARCH-X) model. With the ARDL models, this study finds robust and unbiased cointegrating impacts of socio-economic attention searches on the attention search for the NSE Nifty index but these are not the best linear unbiased and efficient (BLUE) ones, while the same on the BSE Sensex Index are BLUE. For the NSE Nifty index, the attention dynamics at the GARCH-X specification are BLUE while for the BSE Sensex index, the GARCH-X specification also has some additional information in terms of the ARCH effect only.

The impact of lockdown on symptoms and health status of patients with chronic airway diseases: An appraisal of patient perceived impressions.

Background: The pandemic-specific lockdown may influence the health status of patients with chronic airflow obstruction (CAO) as COPD, COPD-PH, and chronic asthma. Objectives: To find the impact of the lockdown on symptoms, and the degree of perceived change in physical activity and emotional health with possible reasons including the indicators of ambient air pollution. Methods: A cohort of CAO patients was telephonically enquired regarding their perceived well-being in symptom status, physical activity, and emotional health with the perceived contribution from plausible reasons (regular medication, simple food, no pollution, and family attention) for the change; all being expressed in percentages. The change in symptom scores as 0-39, 40-79, and 80-100 were regarded as 'low', 'medium', and 'high' respectively. The impact of the individual contributing factor was calculated statistically. The assessment of the CAT (COPD assessment test) score and the ambient air pollution (PM2.5 and PM10) was also done for their association with well-being. Results: There was a universal improvement (p < 0.5) in COPD (n = 113), COPD-PH, (n = 40), and chronic asthma, (n = 19) as regards symptoms, physical activity, and emotional health that tallies to overall and individual change in CAT score. There were concomitant reductions in PM10 and PM2.5 levels during the lockdown compared to the same period of the previous year. All the four listed factors contributed with the 'no/low pollution' and 'simple food being the most important; on acting together, they reduced the moderate and severe symptoms impressively. Conclusion: Reduced air pollution and simple food appear most important for the improvement of CAO patients during the lockdown period.

The Language of Smell: Code Failure

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid‐19 Pandemic

This study presents forecasting methods using time series analysis for confirmed cases, the number of deaths and recovery cases, and individual vaccination status in different states of India. It aims to forecast the confirmed cases and mortality rate and develop an artificial intelligence method and different statistical methodologies that can help predict the future of Covid‐19 cases. Various forecasting methods in time series analysis such as ARIMA, Holt's trend, naive, simple exponential smoothing, TBATS, and MAPE are extended for the study. It also involved the case fatality rate for the number of deaths and confirmed cases for respective states in India. This study includes the forecast values for the number of positive cases, cured patients, mortality rate, and case fatality rate for Covid‐19 cases. Among all forecast methods involved in this study, the naive and simple exponential smoothing method shows an increased number of positive instances and cured patients.Alternate :Este estudio presenta métodos de pronóstico que utilizan el análisis de series temporales para los casos confirmados, el número de muertes y casos recuperados, y el estado de vacunación individual en diferentes estados de la India. Su objetivo es pronosticar los casos confirmados y la tasa de mortalidad y desarrollar un método de inteligencia artificial y diferentes metodologías estadísticas que puedan ayudar a predecir el futuro de los casos de Covid‐19. Para el estudio se adaptaron varios métodos de pronóstico para el análisis de series temporales como ARIMA, la tendencia de Holt, el ingenuo, el suavizado exponencial simple, TBATS y MAPE. También se incluyó la tasa de fatalidades para el número de muertes y casos confirmados para los respectivos estados de la India. Este estudio incluye los valores de pronóstico para el número de casos positivos, los pacientes curados, la tasa de mortalidad y la tasa de fatalidades para los casos de Covid‐19. Entre todos los métodos de pronóstico utilizados en este estudio, el método ingenuo y el de suavización exponencial simple muestran un mayor número de casos positivos y de pacientes curados.Alternate :抄録本研究は、インドの州における確定症例、死亡数及び回復例、および個人のワクチン接種状況に関する時系列分析を用いた予測方法を提示する。確定症例と死亡率を予測し、人工知能を用いた方法とCOVID‐19の症例の将来を予測するのに役立ついくつかの統計学的方法論を開発することを目指す。ARIMA、Holtのトレンド、単純法、単純指数平滑化法、TBATS、MAPEなどの時系列解析における各種予測法を拡張した。また、インドの各州の死亡者数と確定症例数の致死率も含んだ。本研究は、COVID‐19症例に対する、陽性症例数、治癒患者数、死亡率、および致死率に対する予測値を含む。この研究に含まれるすべての予測法の中で、単純法と単純指数平滑法は、陽性者数と治癒患者数の増加を予測した。

The aerolayer: airborne filtration by aerodynamic focusing and growth

In this work, a novel approach for airborne filtration with particular reference to medical (non-oil) medical mask is discussed. Here, and contrariwise to current approaches, filtration is attained neither by reducing the hydraulic diameter of the pore nor by increasing the fibre layers thickness-both of them with a strong penalty in the breathability of the mask, but rather by aerodynamic focussing and growth of the particles themselves. Aerodynamic focussing of particles is achieved by a proper simple parallel rearrangement of the traditional crisscrossing fibres-a configuration which we called the aerolayer;and the growth by coalescence. Utilizing a simplified geometrical and physical model, an expression for the required length of the aerolayer was derived. It is shown that the aerolayer is not only able to increase the probability of capture for small particles but also can potentially improve the breathability by reduction of the total thickness of the current layers required. Additional R &D is required in order to arrive to the most optimized practical design of the aerolayer. © 2023, Indian Academy of Sciences.

Trash to treasure: Sulfonation-assisted transformation of waste masks into high-performance carbon anode for sodium-ion batteries

The global pandemic of COVID-19 poses significant challenge to the recycling of disposable polypropylene (PP)-based waste masks. Herein, a simple but effective sulfonation route has been proposed to transform PP-based waste masks into value-added hard carbon (CM) anode materials for advanced sodium-ion batteries. The sulfonation treatment improves the thermal stability of the PP molecule, preventing their complete decomposition and the release of massive gas molecules during the carbonization process. Meanwhile, the oxygen functional groups introduced during sulfonation effectively facilitates the cross-linking between the PP chains, hindering the rearrangement of carbon microcrystalline structures and enhancing its structural disorder. As a result, the prepared hard carbon anode (CM-180) with a high disorder degree and minimal surface defects realizes a high sodium storage capacity of 327.4 mAh g−1 with excellent cycle and rate capability. In addition, when coupled with O3–NaNi1/3Fe1/3Mn1/3O2 cathode, the fabricated sodium-ion full cell delivers a high energy density of 238 Wh kg−1 and achieves an outstanding rate capability with a retained capacity of 75 mAh g−1 even at an ultrahigh current rate of 50 C. This work offers a novel insight into transforming the waste masks to value-added hard carbons with promising prospects for sodium-ion batteries. © 2023

Gadgets Made for Preventing the Spread of COVID-19 infection in a Small University

Development of gadgets, which are an easy input system in the health survey and a simple carbon dioxide (CO2) alarm, for preventing infection of COVID-19 in a university's campus is discussed. Cluster infection did not occur in the rooms where a gadget of them was installed, until summer of 2022. © 2023 Owner/Author.

An Efficient and Intelligent Systems for Internet of Things Based Health Observance System for Covid 19 Patients

This paper describes how an IoT -based health monitoring system was conceived and built (IoT). With the proliferation of new technologies, doctors nowadays are constantly on the lookout for cutting-edge electronic tools that will make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, non-intrusive healthcare assistance systems. In this article, we introduce the Comprehensive Health Monitoring System, or CHMS. Normal people can't afford to buy separate devices or make frequent trips to hospitals. Our CHMS will monitor a patient's vitals, including temperature, heart rate, and oxygen saturation (OS), and relay that information to a portable device. To make sense of the information gathered by the physical layer's sensors, the logical layer must analyses it. The application layer then makes judgments based on the processed data from the logical layer. The primary goal is to reduce costs for average consumers. Patients will have simple access to individual healthcare, in addition to financial sustainability. This study introduces an IoT -based system that would streamline the operation of a complex medical gadget while reducing its associated cost, allowing its users to do so from the comfort of home. The public's adoption of these gadgets as aids in a given setting might have significant effects on their own lives. © 2023 IEEE.