Research on mobile impulse purchase intention in the perspective of system users during COVID-19.

COVID-19 has caused a serious impact on the global economy. Effectively stimulating consumption has become a momentous mission in responding to the impact of the epidemic. The popularity of mobile shopping makes shopping behavior no longer limited by time and space, so impulse purchase is more commonly seen nowadays; it can effectively promote residents' consumption. However, consensus has not been reached regarding how impulse purchase emerges as a phenomenon, thus making it difficult to promote consumers' purchase behavior. This article aims to explore the generation process of consumers' impulsive purchase intention during the COVID-19 outbreak from the perspective of system users. For this purpose, the research proposes three mobile situation factors: personalized recommendation, visual appeal, and system usability. They have a positive impact on impulse purchase intention by influencing perceived arousal and perceived enjoyment. The experimental method is used for data collection and hypothesis testing. All the hypotheses are supported. And the theoretical value of the model of "mobile environment stimulation-consumer emotion-impulse purchase intention" is confirmed. Based on the conclusion, management suggestions are proposed for mobile shopping merchants from the perspective of improving consumers' shopping experience and expanding marketing.

Associations of Combustible Cigarette, Electronic Cigarette, and Dual Use With COVID Infection and Severity in the U.S.: A Cross-sectional Analysis of the 2021 National Health Information Survey.

Given the potential respiratory health risks, the association of COVID infection and the use of combustible cigarettes, electronic nicotine delivery systems (ENDS), and concurrent dual use is a priority for public health. Many published reports have not accounted for known covarying factors. This study sought to calculate adjusted odds ratios for self-reported COVID infection and disease severity as a function of smoking and ENDS use, while accounting for factors known to influence COVID infection and disease severity (i.e., age, sex, race and ethnicity, socioeconomic status and educational attainment, rural or urban environment, self-reported diabetes, COPD, coronary heart disease, and obesity status). Data from the 2021 U.S. National Health Interview Survey, a cross-sectional questionnaire design, were used to calculate both unadjusted and adjusted odds ratios for self-reported COVID infection and severity of symptoms. Results indicate that combustible cigarette use is associated with a lower likelihood of self-reported COVID infection relative to non-use of tobacco products (AOR = .64; 95% CI [.55, .74]), whereas ENDS use is associated with a higher likelihood of self-reported COVID infection (AOR = 1.30; 95% CI [1.04, 1.63]). There was no significant difference in COVID infection among dual users (ENDS and combustible use) when compared with non-users. Adjusting for covarying factors did not substantially change the results. There were no significant differences in COVID disease severity between those of varying smoking status. Future research should examine the relationship between smoking status and COVID infection and disease severity utilizing longitudinal study designs and non-self-report measures of smoking status (e.g., the biomarker cotinine), COVID infection (e.g., positive tests), and disease severity (e.g., hospitalizations, ventilator assistance, mortality, and ongoing symptoms of long COVID).

Development and use of a back to work symptom and risk assessment tool to reduce COVID-19 disease exposure

Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to guide behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy (e.g., work and school). This paper takes a design science approach to present the justification, design, development, and early assessment of a return-to-work COVID-19 symptom checker and risk assessor. The system was implemented across 34 institutions of health and education in the US State of Alabama, including over 174k users with >4 million total uses and >86k reports of exposure risk between July 2020 and April 2021. Users complied with use policies between 60-74% of the time, with k-12 schools showing higher compliance than colleges and universities. Using system use data and focus group discussions, findings indicate the system was generally accepted, used regularly, facilitated reduction of disease exposure, and enabled a path back to work and school. © 2022 IEEE Computer Society. All rights reserved.

Cryptocurrency Returns Clustering Using Japanese Candlesticks: Towards a Programmatic Trading System

Cryptocurrency trading drove the attention of individual traders during and after the lockdown period caused by COVID-19 restrictions. Trading systems use Japanese candlesticks-derived technical indicators to decide on behalf of traders. They offer insights into the market trend and help traders to decide how to manage their cryptocurrency portfolio. Japanese candlesticks help visualize the movement of cryptocurrencies' prices over a given period. This transformation is widely used to forecast the future trend, volatility, and prices of a cryptocurrency. Most of the research on forecasting returns suggests using three classes, uptrend, downtrend, and insignificant changes. Within this paper, we applied KMeans clustering to the Japanese candlesticks over a daily period of five of the highest capitalized non-stable coins (Bitcoin, Ethereum, BNB coin, Cardano, and Solana). Results indicates that the optimal number of clusters is ranging from 5 to 6 clusters using the elbow method for all the considered cryptocurrencies. The range of obtained results suggests that we should opt for a per cryptocurrency number of classes when dealing with cryptocurrencies classification tasks rather than using the three classes mentioned above. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Outdoor Social Distancing Violation System Detection Using YOLO Algorithm

The government issued orders to implement social distancing or physical distancing. Social distancing is a method of maintaining a distance of at least one meter from other people. This is useful for reducing/preventing disease transmission (virus) and reducing the chain of the spread of covid-19. So the hospital can provide optimal service. For this reason, this research is structured to create a system that can detect violations of social distancing in an open place. This system uses the You Only Look Once (YOLO) algorithm. The developed system uses a pre-Trained Yolov4 model to detect 80 object classes. Testing of this system is carried out based on several scenarios. The system is programmed using Python, with tools for coding Microsoft Visual Studio Code and Anaconda. The best result from creating the detection mode is obtained from a dataset ratio of 90% train data and 10% test data, with the mean average precision results obtained being 54.11%. © 2022 IEEE.

Circular Economy and Sustainable Development Goals 3 and 17: The Case of the PlastiCity Ecosystem

Plastic pollution is one of the most severe environmental and human health threats. Based on a linear model, our current economic system uses plastics as a primary resource to make products such as plastic bags and bottles. However, these products are not recycled into secondary resources. Instead, they are thrown away when they become unusable. In contrast, the circular economy considers plastic waste as an opportunity to create social, economic and environmental value. This model uses plastic waste as a raw material to produce new items. This research demonstrates that the circular economy contributes to Sustainable Development Goals 3 and 17 using the results of action and observatory research within the PlastiCity project. As part of PlastiCity, partners developed new products made from recycled plastic such as recycled face shields. This chapter describes our efforts in developing a business case for recycled face shields and deploying the PlastiCity ecosystem to improve collaboration and partnerships. This study suggests that the development of an ecosystem can facilitate collaboration between stakeholders in the plastic value chain and hence contribute to implementing circular business models. This research also demonstrates how the circular economy can respond rapidly to health-related societal challenges, such as the unavailability of personal protective equipment during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dormitory Food Delivery Smart Car

This paper designs a smart car that can automatically deliver meals in dormitories, isolated hotels and other scenarios. This system uses i.MX RT1064 as the main controller, and completes the route tracking and room number recognition of the smart car through the MT9V034 camera and the OpenART mini visual sensor module respectively. The target detection method is the SSD algorithm in the one-stage method. After optimization, the recognition rate is as high as 90%, which can successfully complete the meal delivery task. This system greatly reduces the risk of human-to-human contact, reduces the probability of contracting COVID-19, and contributes to epidemic prevention and control measures to minimize risks. © 2022 SPIE. All rights reserved.

Fine-tuning BERT to Classify COVID19 Tweets Containing Symptoms

Twitter provides a source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. Identifying personal mentions of COVID19 symptoms requires distinguishing personal mentions from other mentions such as symptoms reported by others and references to news articles or other sources. In this article, we describe our submission to Task 6 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2021. This task challenged participants to classify tweets where the target classes are - (1) self-reports, (2) non-personal reports, and (3) literature/news mentions. Our system uses a handcrafted preprocessing and word embeddings from BERT encoder model. We achieve. F1 score of 93%. © 2021 Association for Computational Linguistics.

Evaluation of the Bluetooth-based Proximity Estimation for Automatic Exposure Determination

A commonly used methodology to estimate the proximity of two individuals in an automatic exposure notification system uses the signal strength of the Bluetooth signal from their mobile phones. However, there is an underlying error in Bluetooth-based proximity detection that can result in wrong exposure decisions. A wrong decision in the exposure determination leads to two types of errors: false negatives and false positives. A false negative occurs when an exposed individual is incorrectly identified as not exposed. Similarly, a false positive occurs when a non-exposed individual is mistakenly identified as exposed. Both errors have costly implications and can ultimately determine the effectiveness of Bluetooth-based automatic exposure notification in containment of pandemics such as COVID-19. In this paper, we present a platform that allows for the analysis of the system performance under various parameters. This platform enables us to gain a better understanding on how the underlying technology error propagates through the contact tracing system. Preliminary results show the considerable impact of the Bluetooth-based proximity estimation error on false exposure determination. Alternatively, using this platform, analysis can be performed to determine the acceptable accuracy level of a proximity detection mechanism in order to have a more effective contact tracing solution. © 2022 IEEE.

Covid-19 Interior Security Tracking System Based on the Artificial Intelligence

This article presents an inexpensive artificial intelligence solution aimed at increasing indoor safety of COVID-19, including a number of important aspects: (1) breakdown of the process (2) Method for mask identification (3). Assessment methodology of social distancing The Arduino Uno sensor system uses an infrasound sensor or heat camera, whereas the Raspberry Pi is equipped with computer vision technologies for mask detection and social distance checks. Indoor measures are the most prevalent—people with a high body heat should stay at home, masks should be worn, and their distance should be at least 1.5–2 m. In the first case, the Arduino Uno temperature sensor board is utilized, while we utilize a single-board Pi Raspberry computer coupled with camera for two additional situations, using computer vision techniques. Due to their compact size and cost, we chose to utilize these devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A ROS-based Voice Controlled Robotic Arm for Automatic Segregation of Medical Waste Using YOLOv3

The outbreak of the Covid-19 pandemic has resulted in a surge in the generation of medical waste. Due to the transmissible nature of the Virus and the lack of effort at proper disposal, the safety of the front-line health workers, as well as the disposer, is at risk. Hence, to mitigate the spread of infectious diseases, a system is proposed that uses a robotic arm for segregating medical waste automatically. The robotic arm is operable through voice commands, and the segregating operation could function in automatic and manual mode. The system uses the YOLOv3 (You Only Look Once) algorithm to detect and classify the medical waste and then uses the Robot Operating System (ROS) platform to pick up and drop the waste object into color-coded bins. For this research, the medical waste has been categorized into 4 types, and for each type, a color-coded bin has been used for segregation. Our system has achieved 94% training accuracy for the YOLOv3 model on a custom dataset, whereas the system's overall accuracy in automated mode was 82.1%, derived after 30 trials. In the case of manual mode, an average accuracy of 82.5% has been achieved for the same number of trials. © 2022 IEEE.

Smart Infrared Temperature Measurement System Based On Elastic Compute Service Platform

In the fight against the novel coronavirus, this paper designs a smart infrared temperature measurement system based on the Elastic Compute Service platform. In the perceptual layer part of the Internet of Things(IoT), the system uses infrared temperature sensors to quickly collect the temperature of the forehead or arm of the human body and Uses the MCU to automatically transmits the measured data to the Elastic Compute Service through the WiFi module. In the network layer part of the Internet of Things,the data written into the database of the Elastic Compute Service through the program deployed on the Elastic Compute Service. In the application layer of the Internet of Things, the remote management terminal monitors the collected body temperature data in real-time and provides quick warnings. Finally, the User can log in to the applet on his mobile phone to quickly and easily obtain personal information. © 2021 IEEE

Music Recommendation Systum Based on Emotion

With the onset of lockdown in the COVID-19 scenario, people were forced to confine themselves within the four walls of their rooms which in the meantime invited mood disorders like depression, anxiety etc. Music has proven to be a potential empathetic companion in this tough time for all. The proposed emotion-based music recommendation system uses aser emotion as an input to recommend songs that are-ascertained using faciai expression or using direct inputs from the user. The model uses a Random Forest classifier and XGBoost algorithm to identify the song's emotion considering various features like instruineiitainess, energy, acoustics, liveness, etc, and lyrical similarity among songs with the help of Term-Frequency times Inverse Document-Frequency (TF-IBF). The results of comprehensive experiments on reai data confirm the accuracy of the proposed emotion classification system that can be integrated into any recommendation engine. © 2021 IEEE.

Machine Learning based Twitter Sentiment Analysis on COVID-19

Corona virus is a deadly disease that has been spreading around the world for many years. COVID-19 is a new variant of coronavirus which causes its least attack as cold, fever and headache to its severe attack as breathing problems and death. Twitter is a social network tool where a large number of people communicate and express their feelings through tweets and posts in everyday life. Our task is to analyze the perception of people from all over the world during this pandemic. So, analyzing the sentiment of COVID-19 tweets will be one way to find out how people's emotions and the situations are in the world. This system uses various vectorizers, NLP and NLTK tools, which can be used while building various machine learning models for n-grams, and, consequently, to classify tweets into bi-class and tri-class. This system shows a maximum accuracy of 96.7%. © 2021 IEEE.

Gapeau: Enhancing the Sense of Distance to Others with a Head-Mounted Sensor

Human perception lacks the capabilities to accurately assess distance. The recent Covid-19 pandemic outbreak rendered this ability particularly important. Augmenting our sense of distance can help maintain safe separation from others when required. To explore how systems can help users maintain physical distance, we designed, implemented and evaluated Gapeau - a head-mounted system for augmenting the sense of distance. Our system uses proximity sensors and thermal sensing to detect and measure the distance to other people. We conducted a validation protocol, an experiment, in which we compared different feedback modalities, and an in-the-wild study to evaluate Gapeau's performance and suitability for use in social contexts. We found that our system enabled users to more accurately determine whether they were maintaining a safe distance from others. Vibration and auditory feedback were found most effective and usable. Gapeau was perceived as socially acceptable. Our work contributes insights for augmented sensing systems with social relevance. © 2022 ACM.

Contactless IoT Doorbell for Covid-safe Household

The Covid-19 pandemic has proven to be the most disastrous pandemic in the history. Millions of people have lost their lives sending nations into lockdown and economic slowdowns. Given the fact that no specific anti-viral treatment is yet suggested for treating Covid-19 infection, 'Social distancing' is probably the most effective tool so far in stopping the virus spread. This paper has proposed an IoT based doorbell which alerts the house owner about arrival of a visitor having fever and who could be a Covid-19 patient. The system uses NodeMCU and MLX90614 non-contact infrared temperature sensor. FireBase online database is used to log all the readings of the system and a companion mobile App is also provided. The system was extensively tested using an experimental set up under various conditions. The system works with 99% average accuracy of body temperature measurement. © 2021 IEEE.

Voting Booth Helper System Using Machine Learning

Elections are the fundamental defining characteristics of any democracy that is being governed by the people, where in people express their choices or articulate opinions through voting. The existing voting system uses EVM system at polling booths for voting and its main drawback is the manual validation of the voter. In the polling booths, the voting process is organized by few organizers having a count from 5 to 10 or even above. These people are assigned to perform certain tasks, one of such tasks is to validate the voter. With the raising population this consumes a lot of time, which in turn increases the man power and the human error. This project aims to provide an efficient solution to overcome the drawbacks of the existing voting system. We have developed a module using face recognition algorithm, to validate the voter accurately and efficiently within no time. It even reduces the man power, as it alone, performs all the tasks performed by the several organizers at the voting booths. The algorithm made use of, is the Multi-Task Cascaded Convolutional Neural Networks which is known for its accuracy and speed. The reduction of man power helps to control the rapid increase of covid cases, which is the most prevailing problem and helps the voters to vote with ease. © 2021 IEEE.

Oxygen Availability Control and Monitoring System in Hospitals using IoT

Medical Gas is an important component in the treatment of patients with COVID-19 disease. Medical gas is used to help COVID-19 patients to reduce the effects of respiratory disorders by providing oxygen ventilators to patients. With the surge in typical COVID-19 sufferers as of July 2021, the need for Oxygen in hospitals is getting higher. This is when the control and monitoring of medical gases in hospitals are late because the integrated system is very dangerous. Therefore, a system that can be used to control and monitor medical gases in hospitals that are integrated and automated and can be monitored by the Government and Medical Gas Producers. This is useful for anticipating the lack of availability of Medical Gas in hospitals. In this study, the system used IoT systems as a base in delivery and control. This system uses Arduino as a minimum system that reads the press sensor on the Medical Gas tube and regulates the valve. The data obtained is then sent to the Local Server to be processed and delivered to the Hospital Officer. Local Servers also send the data to cloud servers to be monitored by the government funds of several medical gas producers. This design can help in the process of controlling and monitoring Medical Gases in hospitals in hopes of minimizing the risk of delays in supplying medical gases to hospitals. © 2021 Institute of Physics Publishing. All rights reserved.

Measuring e-learning systems success: Data from students of higher education institutions in Morocco.

The COVID-19 pandemic has forced Higher Education Institutions (HEI's) to rethink the teaching approach taken. In response to this emergency state, Moroccan universities switched to the e-learning approach as an alternative to face-to-face education. At this level the assessment of e-learning systems success becomes a necessity. This data article aims to identify e-learning systems success determinants during the COVID-19 pandemic. The data was collected from students of the Moroccan Higher Education Institutions. The research data are collected via an on a self-administered online questionnaire, from a sample of 264 university students. The responses are collected from students of 12 Moroccan universities and 31 Moroccan educational institutions. The data were analyzed using a structural equation modeling method under the Partial Least Squares approach (PLS-SEM). Data analysis was performed using SmartPLS 3 software. Universities managers can use the dataset to identify key factor to enhance e-learning system success.