Surveillance Rover to Maintain Social Distancing in Crowded Areas

COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.

Hand-powered centrifugal micropipette-tip with distance-based quantification for on-site testing of SARS-CoV-2 virus.

This paper proposed a hand-powered centrifugal micropipette-tip strategy, termed HCM, for all-in-one immunoassay combined with a distance-based readout for portable quantitative detection of SARS-CoV-2. The target SARS-CoV-2 virus antigen triggers the binding of multiple monoclonal antibody-coated red latex nanobeads, forming larger complexes. Following incubation and centrifugation, the formed aggregated complexes settle at the bottom of the tip, while free red nanobeads remain suspended in the solution. The HCM enables sensitive (1 ng/mL) and reliable quantification of SARS-CoV-2 within 25 min. With the advantages of free washing, free fabrication, free instrument, and without the optical device, the proposed low-cost and easy-to-use HCM immunoassay shows great potential for quantitative POC diagnostics for SARS-CoV-2.

Resilient sustainable investment in digital education technology: A stakeholder-centric decision support model under uncertainty

Investment in education technology (EdTech) is a complex decision problem for universities during the post-Covid era. With the objective to assess the quality and adoptability of education supply chain, a novel analytical evaluation model approach is proposed, based on quality function deployment and combinative distance-based assessment. To deal with uncertainty in the evaluation process, fuzzy theory is integrated into the model. To establish the house of quality matrix, technology-based stakeholders' requirements were identified and classified in four dimensions: economic and financial, technology adoption, sustainability, competencies. Moreover, nine supplier criteria were assumed. Based on expert evaluations, the results suggest that financial credit and supplier collaboration are the most prominent attributes to evaluate suppliers, while environmental commitment is sorted as the least important criterion. The results reveal that the three dominant suppliers, which provide the best response to the identified criteria, are providers of cloud service technology. © 2022

Resilient sustainable investment in digital education technology: A stakeholder-centric decision support model under uncertainty

Investment in education technology (EdTech) is a complex decision problem for universities during the post-Covid era. With the objective to assess the quality and adoptability of education supply chain, a novel analytical evaluation model approach is proposed, based on quality function deployment and combinative distance-based assessment. To deal with uncertainty in the evaluation process, fuzzy theory is integrated into the model. To establish the house of quality matrix, technology-based stakeholders' requirements were identified and classified in four dimensions: economic and financial, technology adoption, sustainability, competencies. Moreover, nine supplier criteria were assumed. Based on expert evaluations, the results suggest that financial credit and supplier collaboration are the most prominent attributes to evaluate suppliers, while environmental commitment is sorted as the least important criterion. The results reveal that the three dominant suppliers, which provide the best response to the identified criteria, are providers of cloud service technology.

Flexible Distance-Based TCR Analysis in Python with tcrdist3.

Paired- and single-chain T cell receptor (TCR) sequencing are now commonly used techniques for interrogating adaptive immune responses. TCRs targeting the same epitope frequently share motifs consisting of critical contact residues. Here we illustrate the key features of tcrdist3, a new Python package for distance-based TCR analysis through a series of three interactive examples. In the first example, we illustrate how tcrdist3 can integrate sequence similarity networks, gene-usage plots, and background-adjusted CDR3 logos to identify TCR sequence features conferring antigen specificity among sets of peptide-MHC-multimer sorted receptors. In the second example, we show how the TCRjoin feature in tcrdist3 can be used to flexibly query receptor sequences of interest against bulk repertoires or libraries of previously annotated TCRs based on matching of similar sequences. In the third example, we show how the TCRdist metric can be leveraged to identify candidate polyclonal receptors under antigenic selection in bulk repertoires based on sequence neighbor enrichment testing, a statistical approach similar to TCRNET and ALICE algorithms, but with added flexibility in how the neighborhood can be defined.

Integrating the Implications of Distance-Based Affective States in Cellular Automata Pedestrian Simulation

Cellular Automata have successfully been applied to the modeling and simulation of pedestrian dynamics. These simulations have often been focused on the evaluation of situations of medium-high density, in which the motivation of pedestrians overcomes natural proxemic tendencies. The COVID-19 outbreak has shown that in certain situations it is instead crucial to focus on situations in which proxemic is amplified by the particular affective state of the individuals involved in the studied scenario. We present the first steps in a research effort aimed at integrating results of quantitative analyses concerning effects of affective states on the perception of mutual distances by pedestrians of different type and the modeling of movement choices in a cellular automata framework. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Qualitative Case Study of Supervisors Experiences Related to Distance-Based Supervision

The COVID-19 pandemic created a rush to provide counseling and supervision services via distance-based technology. This study was conducted prior to the COVID-19 pandemic;however, it offers some insight into the process of providing distance-based supervision (DBS) to mental health trainees and professionals. Utilizing a multiple case study design, 10 counseling supervisors who had experience providing DBS were interviewed to understand their experiences. Five themes emerged from the data including reasons for providing DBS, benefits and challenges to DBS, and a desire for change to current training structures. Implications for supervision and suggestions for future research are provided.

Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm

The Covid-19 outbreak has an unprecedented effects on people's daily lives throughout the world, causing immense stress amongst individuals owing to enhanced psychological disorders like depression, stress, and anxiety. Researchers have used social media data to detect behaviour changes in individuals with depression, postpartum changes and stress detection since it reveals critical aspects of mental and emotional diseases. Considerable efforts have been made to examine the psychological health of people which have limited performance in accuracy and demand increased training time. To conquer such issues, this paper proposes an efficient depression detection framework named Improved Chimp Optimization Algorithm based Convolution Neural Network-Long Short Term Memory and Natural Language Processing for Covid-19 Twitter data. In the proposed method, the tweets are pre-processed, user's frequent tweet identification, and hash tag identification has been done. The processed tweets are then clustered through cluster head selection using Swap-Displacement-ReversionBull based Optimization Algorithm and cluster formation using the Bregman distance-based K-Means algorithm. Then, the psycholinguistic features are extracted from the clustered data and inputted to the Improved Chimp Optimization Algorithm-based-Convolution Neural Network-Long Short Term Memory network for depression classification. Preliminary results show that the proposed method provides greater performance with 97.7% efficiency and outperforms the existing methodologies.

Virtual auscultation course via video chat in times of COVID-19 improves cardiac auscultation skills compared to literature self-study in third-year medical students: a prospective randomized controlled cross-over study.

Background: Cardiac auscultation is a core clinical skill taught in medical school. Due to contact restrictions during the SARS-CoV-2 pandemic, interaction with patients was very limited. Therefore, a peer-to-peer virtual case-based auscultation course via video conference was established. Methods: A randomized controlled cross-over study was conducted to evaluate whether participation in a virtual auscultation course could improve heart auscultation skills in 3rd-year medical students. A total of sixty medical students were randomly assigned to either the experimental or control group after informed consent was obtained. Due to no-shows, 55 students participated. Depending on allocation, students attended three ninety-minute courses in intervals of one week in a different order: a virtual case-based auscultation course held via video chat, literature self-study, and an on-site course using a high-fidelity auscultation simulator (SAM II). The study's primary endpoint was the performance of the two groups at the simulator after participating in the virtual auscultation course or literature self-study. To evaluate their auscultation skills, students participated in five assessments using the same six pathologies: stenosis and regurgitation of the aortic and mitral valve, ventricular septal defect, and patent ductus arteriosus. Moreover, participants rated their satisfaction with each course and provided a self-assessment of competence. Results: Compared to literature self-study, participation in the virtual auscultation course led to a significantly improved description of heart murmurs at the auscultation simulator with regard to the presence in systole and diastole, low- and high-pitched sounds, and volume dynamics. There was no significant difference between the groups in diagnostic accuracy and identification of the point of maximal intensity. After the virtual course, students showed higher satisfaction rates and a higher increase in self-assessed competence compared to participants who engaged in literature self-study. Conclusions: For the first time, this study demonstrates that a case-based virtual auscultation course can improve aspects of cardiac auscultation skills on a simulator. This may facilitate the further acquisition of an essential clinical skill, even when contact restrictions will be lifted.

Medical students' perception of distance-based education during the COVID-19 pandemic in Slovenia: A qualitative study.

INTRODUCTION: The COVID-19 pandemic has had a broad direct impact on education, and at the same time it has significantly changed students' lives. This study examines how Slovenian medical students experienced the shift to distance-based education following multiple lockdowns. METHODS: The aim of this study is to examine experiences of medical students about distance-based education in the period of multiple lockdowns in 2020/2021. We used focused interviews to collect data. The questionnaire was developed in the following manner: the first set of questions was developed after studying the literature from Slovenia and abroad about distance-based education in higher education during COVID-19 lockdowns. The researchers then discussed this set to narrow the topics. In addition to preformulated questions, additional sub-questions also typical for focused interviews were asked as part of the research. We carried out a qualitative study using a qualitative content analysis method to analyze the data. RESULTS: Sixteen interviews were conducted. We defined four categories summarizing students' experiences with distance-based education during the COVID-19 pandemic: 1) technical issues, 2) organization of distance-based education, 3) social exclusion of students, and 4) suggestions for improvement. The categories are exclusive and represent individual topics for further analysis of students' experiences with DBE during the COVID-19 pandemic. The results are supported by quotes from the interviews. CONCLUSIONS: Medical students' experiences with DBE mainly revealed shortcomings in computer literacy. Technical issues were largely an indicator that significantly marked students' transition to DBE. Another important finding is that medical students emphasized problems related to social exclusion. Students made suggestions for improvements that broadly relate to the higher education system, and not only to the COVID-19 pandemic.

Advances in Nucleic Acid Amplification-Based Microfluidic Devices for Clinical Microbial Detection

Accurate and timely detection of infectious pathogens is urgently needed for disease treatment and control of possible outbreaks worldwide. Conventional methods for pathogen detection are usually time-consuming and labor-intensive. Novel strategies for the identification of pathogenic nucleic acids are necessary for practical application. The advent of microfluidic technology and microfluidic devices has offered advanced and miniaturized tools to rapidly screen microorganisms, improving many drawbacks of conventional nucleic acid amplification-based methods. In this review, we summarize advances in the microfluidic approach to detect pathogens based on nucleic acid amplification. We survey microfluidic platforms performing two major types of nucleic acid amplification strategies, namely, polymerase chain reaction (PCR) and isothermal nucleic acid amplification. We also provide an overview of nucleic acid amplification-based platforms including studies and commercialized products for SARS-CoV-2 detection. Technologically, we focus on the design of the microfluidic devices, the selected methods for sample preparation, nucleic acid amplification techniques, and endpoint analysis. We also compare features such as analysis time, sensitivity, and specificity of different platforms. The first section of the review discusses methods used in microfluidic devices for upstream clinical sample preparation. The second section covers the design, operation, and applications of PCR-based microfluidic devices. The third section reviews two common types of isothermal nucleic acid amplification methods (loop-mediated isothermal amplification and recombinase polymerase amplification) performed in microfluidic systems. The fourth section introduces microfluidic applications for nucleic acid amplification-based detection of SARS-CoV-2. Finally, the review concludes with the importance of full integration and quantitative analysis for clinical microbial identification.

Analysis of Imposition and Periodic Relaxation of Lockdown on the Spread of COVID-19 in India through Networked SEIR Model

In this paper, an investigation is carried out to analyse how periodic lockdown and unlocking have helped India to combat the first wave of COVID-19. To that end, a networked SEIR model is considered that captures the spreading dynamics of the disease in sixteen of the worst affected states of India. In this regard, a distance based contact matrix is constructed to reflect the connectivity between states. Various rate parameters of the model are estimated as well as the basic reproduction number (\mathscr{R}_{0}) of each of the sixteen states for each phase of lockdown is found out. Finally, a comparison is drawn between the simulated results of cumulative infected caseload using the estimated parameters and that with the real COVID-19 data of India till December 31, 2020, which establishes the effectiveness of the method. © 2021 IEEE.

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset.

The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I-XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5-89.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2-77.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.

A clustering-based approach for quality level verification of sanitation procedures in workplaces

With the COVID-19 pandemic outbreak, sanitizing procedures have become fundamental in work environments, where surfaces and objects are frequently touched by multiple people, enhancing the risk of exposure to the disease. To assure safe working conditions, it is of primary importance to assess the adherence of the sanitation activity to the recommended protocols with a certain level of accuracy. In this work, we propose a methodology able to estimate the accuracy level of sanitation procedures by applying clustering techniques on multiple features extracted from wrist-mounted accelerometric sensors measurements.

Geospatial analytics for COVID-19 active case detection

Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude, when would the outbreak likely to happen and the duration of the outbreak. The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia. The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19. Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases, next outbreak location, and the time interval between start dates of two similar sites. Such findings provided valuable insights for policymakers to perform Active Case Detection. © 2021 Tech Science Press. All rights reserved.

COVID-19 AND DENTAL DISTANCE-BASED EDUCATION: students' perceptions in an Italian University.

BACKGROUND: The aim of the study was to analyze the perception of dental faculties students regarding the complete transition to distance-based education (DE) and the adaptation of this educational strategy, due to Covid-19 pandemic. A questionnaire to be completed anonymously was submitted online to students attending the faculties of Dentistry and Oral Hygiene at Sapienza, University of Rome, after the end of distance lessons. The collected data were processed statistically, providing descriptive data and analysis of correlation of the most significant parameters, using Chi-squared test, Cramér V and Pearson φ2, Goodman and Kruskal's γ and λ and Kendall's τb. The level of statistical significance was p < 0.05. RESULTS: A total of 314 students participated in the survey. The overall level of satisfaction on a ten- point scale was 5.39 ± 2.59 for Oral Hygiene students and 6.15 ± 2.98 for Dentistry students. The most common complaints were the lack of a structured online curriculum, less interaction with professors and a lower level of attention. On the basis of the responses, scored using Likert-type Scale, oral Hygiene students reported statistically higher level of physical fatigue(p = 0.0189), a lower level of attention (p = 0.0136) and of the quality and quantity of acquired knowledge during distance education (p = 0.0392), compared to Dentistry students. Level of perceived stress and quality and quantity of acquired knowledge (γ = 0.81 and τb =0.56) and quality and quantity of acquired knowledge and fear of a decrease in knowledge (γ = 0.76 and τb =0.54) are associated variables. CONCLUSION: Students' feedback is essential to solve the key issues emerged from the questionnaire. New educational models should be define in order to ensure that distance education could be effective, meeting the learning needs of the students, and could not be a merely "online shift" of traditional methods, used as an alternative of live education.

Embracing Disruption: Measuring Effectiveness of Virtual Simulations in Advanced Practice Nurse Curriculum.

Changes in academia have occurred quickly in response to the COVID-19 pandemic. In-person simulation-based education has been adapted into a virtual format to meet course learning objectives. The methods and procedures leveraged to onboard faculty, staff, and graduate nurse practitioner students to virtual simulation-based education while ensuring simulation best practice standards and obtaining evaluation data using the Simulation Effectiveness Tool-Modified (SET-M) tool are described in this article.