ABSTRACT
The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.
ABSTRACT
The COVID-19 pandemic, caused by the novel coronavirus, has had a significant impact on daily life, education, business, and trade. The virus spreads quickly through direct contact with droplets, fecal-oral transmission, and water contamination. The consequences of the pandemic can be classified into three categories: health, economic, and social. The physical, mental, and psychological behaviors of individuals have also changed due to the pandemic. This study aimed to assess the impact of COVID-19 on the general population. A survey questionnaire with ten questions was distributed through an online portal, and the responses were analyzed using SPSS software. The results showed that healthcare workers were among the most affected, with the primary impact on their social and psychological well-being. Although previous research suggested that all fields were equally affected, this study found that healthcare workers were the most impacted group. The study concluded that the COVID-19 pandemic had a significant impact on the social and psychological well-being of the general population, with healthcare workers being the most affected. © 2023 IEEE.
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Social virtual reality (VR) platforms have increased in popularity with many people turning to these platforms to experience social connection, including a rapid influx of users during the COVID-19 pandemic. However, there is limited understanding of how people appropriate and use emerging social VR applications to actively support their mental health and wellbeing in daily life. Through an online questionnaire and exploratory interviews conducted within the social VR app VRChat during the COVID-19 pandemic, we document how social VR is being used explicitly as a mental health support tool. Participants reported positive wellbeing benefits, mostly attributed to the anonymity provided by avatars and perceived safety within digital worlds and communities of practice. We also report how people use social VR to practice social interaction, reduce negative thoughts and form strong social bonds and connections with others. © 2023 ACM.
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The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Wearable sensor technologies have improved people's daily lives through their applications in almost every field. Sensor technologies of inventive kinds are used in an extensive variety of applications in lifestyle, healthcare, fitness, manufacturing, etc. There have also been crucial issues in making significant improvements to the actual mechanical, electrical, and optical sensing methods mainly in upgrading the precision of identification of wearable sensors to various stimuli. With an extensive study of the basic demands in wearable device technology as of now, the road map becomes clearer for creating greater innovations in the future. This is a review that gives an outline of types of wearable sensors by the score that is utilized in daily life. © 2023 IEEE.
ABSTRACT
Context: Nowadays, mobile applications (or apps) have become vital in our daily life, particularly within education. Many institutions increasingly rely on mobile apps to provide access to all their students. However, many education mobile apps remain inaccessible to users with disabilities who need to utilize accessibility features like talkback or screen reader features. Accessibility features have to be considered in mobile apps to foster equity and inclusion in the educational environment allowing to use of such apps without limitations. Gaps in the accessibility to educational systems persist. Objective: In this paper, we focus on the accessibility of the Blackboard mobile app, which is one of the most common Learning Management Systems (LMS) used by many universities, especially during the current COVID-19 pandemic. Method: This study is divided into two-fold. First, we conduct a survey using questionnaires and interviews to explore the extent to which students consider the Blackboard mobile app usability. A Total of 1,308 hearing students and 65 deaf and hard-of-hearing students participated in the study. Second, we collected 15,478 user reviews from the Google Play Store and analyzed the reviews to extract accessibility issues. Result: We observed that most deaf and hard-of-hearing students found difficulty in the Blackboard mobile app, compared to hearing students. Also, our app store analysis showed that only 31% of the reviews reported violations of accessibility principles that apps like Blackboard must comply with. This study highlights these violations and their corresponding implications to support LMS frameworks in becoming more inclusive for all users. © 2023 ACM.
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The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3-layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images;edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that has no correlation with each other. The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset, and the findings that we have obtained are quite encouraging. © 2023 Tech Science Press. All rights reserved.
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Deep learning has stretched out its roots even more in our daily lives. As a society, we are witnessing small changes in lifestyle such as self-driving cars, Google Assistant, Netflix recommendations, and spam email detection. Similarly, deep learning is also evolving in healthcare, and today many doctors often use it more comfortably. Using deep learning models we can detect severe brain tumors with the help of MRI scans, in fact in the Covid era, deep learning evolved majorly to detect the disease with the help of Lung X-Rays. Magnetic Resonance Imaging (MRI) is used when a person has a brain tumor to detect it. Brain tumors can fall into any category, and MRI scans of these millions of people are needed to determine if they have the disease and if so, which category they belong to. Determining the type of brain tumor can be a rigid task and deep learning models play an important role here. For the proposed deep learning model, we have implemented convolution neural networks (CNN) through which our model has achieved a testing accuracy of 96.5%. Also, along with this, the libraries of Keras and Tensorflow have been explored by the authors in this research. © 2022 IEEE.
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In a constantly evolving society where radical socio-cultural changes have been introduced in the last year, as a consequence of the world health emergency linked to the spread of the Covid 19 virus, the centrality of images in the daily life of individuals has been reconfirmed. In situations of risk for the community, such as in the case of a health emergency due to the spread of a highly contagious virus, the role of communication is particularly important for public institutions, to obtain the cooperation of the population in procedures aimed at preventing the spread or slowing down the contagion, using images that describe the recommendations to be followed. The image confirms its social role in contemporary culture, as a means of communication and a channel for the rapid and direct dissemination of information, regardless of cultural level, language, or age. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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We use many kinds of digital technologies in our daily life and they lead to radical changes. These technologies are recently being adopted by agriculture and food industry and their use in various applications is tested. The results of the studies conducted with the use of these technologies, especially IoT (internet of things)-based systems, are quite promising for the solution of the chronic problems of agriculture and food industry such as food-borne diseases, mycotoxin contaminations, pesticide residues, increasing waste, etc. Under extraordinary conditions, just like the ones we have recently experienced due to COVID-19 pandemic, IoT-based systems are crucial to ensure the sustainability of agriculture and food supply chain. In this review, the fundamentals of IoT-based systems and recent developments in their use in agriculture and food supply chain are explained. Based on the literature, examples of successful applications of IoT-based systems for irrigation efficiency, pesticide treatments, supply chain management etc. are given. Nowadays, there is a great demand for the integration of IoT-based systems into the present agricultural practices and supply chains and it seems to increase exponentially. Experts in electronics and computer sciences have achieved noteworthy success in the simulations. On the other hand, only a few studies have been conducted in real agricultural and food systems. However, IoT-based systems should be tested on-site and their success in practical applications should be proved. It is obvious that new era will be one in which IoT-based technologies and their tools will be more commonly used in agriculture and food supply. © 2023 Elsevier Ltd
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Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Starting in early 2020, with the spread of the Covid 19 virus, the home environment has been designated an elective shelter, marking the boundary against potentially threatening outdoor space. Life inside the domestic enclosure, necessary to prevent the risk of contagion and to protect everyone's health, inevitably coincided with the exclusion from participation in a series of public meeting occasions, clearly affecting daily life and also urging the world of art, design and architecture to offer solutions aimed at encouraging adaptation to the emergency situation. Into this scenario comes the experience of The House Floats, in Louisiana. In February 2020, celebrations for Mardi Gras, the New Orleans carnival, contributed to the accelerated spread of coronavirus disease. Faced with the prescription to stop the 2021 carnival festivities, the answer comes in the initiative of a series of artists and designers: the facades of entire neighborhoods where the traditional parade takes place are temporarily transformed into staged masks inspired by floats. The occasion of interaction between the facade and the "mask” takes the form of the insertion of installations, with results that from time to time deny or amplify the compositional characteristics of the facade. In any case, the dynamic character of the parades of allegorical floats is replaced by the fixity of a prepared representation. Starting from the analysis of the above-mentioned case study, the paper proposes a new interpretation focused on the topic of the "allegorical facade”, interpreted as a design strategy capable of educating to social respect and to the community well-being. Through the methods of architectural survey and drawing, this paper focuses on the analysis of the relationship between the refuge and the sign, attributing to the latter a clear value in the processes of reconstruction of the city, both from a physical and an identity point of view. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The COVID-19 outbreak turned the world upside down by infecting hundred million people, killing more than five million and disrupting everyday life across the planet. The Wuhan virus shattered the global economy and brought daily life to a grinding halt in much of the world. The second largest populated country India had no escape as well. Since the very beginning of 20th century, machine learning based methodologies have been largely applied in epidemiological data analysis in order to control diseases and other health issues. In this regard, researchers have come up with various predictor models to forecast the future impact of the Wuhan virus, so that further spreading of virus can be controlled by implementing precautionary measures. The purpose behind this work is to investigate the prediction capability of Legendre Polynomial Neural Network (LEPNN) trained using the very popular bio-inspired Flower Pollination Algorithm on the real data set of three categories of COVID cases in India as well as Odisha. The three types are the confirmed, deceased and recovery cases of daily basis. The prediction performance of the LEPNN-FPA model has been assessed with respect to the performance of two other models. © 2022 IEEE.
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It's been over two years that the world has been dealing with the novel Coronavirus Disease 2019 (COVID-19). It has rocked the world in the face of another major outbreak. Countries have undergone various lockdowns curfews in their own ways, which certainly has impacted our daily lives. COVID-19 has undergone various mutations till now. It is responsible for the spikes in COVID-19 cases across the world. The latest variant 'Omicron'., labeled as B.1.1.529, has been marked as a Variant of Concern by the World Health Organization (WHO). It has been proven to be the most infectious, but less deadly as of now. This paper attempts to propose an analysis and prediction of Omicron daily cases in India using SARIMA Exponential Smoothing Machine Learning models. Both of these machine learning models are based on the time series forecasting concept and rely on previous data to predict future outcomes. © 2022 IEEE.
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Accelerated by the COVID-19 pandemic, anthropomorphic service robots are continuously penetrating various domains of our daily lives. With this development, the urge for an interdisciplinary approach to responsibly design human-robot interaction (HRI), with particular attention to human dignity, privacy, compliance, and transparency, increases. This paper contributes to design science, in developing a new artifact, i.e., an interdisciplinary framework for designing responsible HRI with anthropomorphic service robots, which covers the three design science research cycles. Furthermore, we propose a multi-method approach by applying this interdisciplinary framework. Thereby, our finding offer implications for designing HRI in a responsible manner. © 2022 IEEE Computer Society. All rights reserved.
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Globally, COVID-19 pandemic has influenced and changed norms and common health cultures. Different countries have implemented risk management and dealt with the condition based on the applicability of the international measures and some uniquely to their situations. As technology has become a key tool in daily lives and smart phones and connectivity has become a common necessity for most of the world's population, these can be used to help face the pandemic and the new normal it brings. Using one of the widely used software platforms, the research intends to design a framework for a health monitoring application for private institutions. © 2022 IEEE.
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Coronavirus pandemic started spreading in 2019 and is still spreading until now in 2021 all over the world. Due to this the healthcare sectors are going on crisis all over the world. One basic protective measure that we can implement in our daily life is wearing a face mask. Wearing a mask properly can control the spread of this virus to a great extent. Various regions have made wearing face mask mandatory to prevent spread of this virus. In this paper we have proposed a deep learning-based model to detect face mask using python, OpenCV, TensorFlow and it can be used in our health care sectors. © 2022 IEEE.
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Autistic young adults are at a higher risk of experiencing elevated mental and psychological distress during times of isolation, such as the COVID-19 pandemic, due to the challenges related to uncertainty and abrupt changes in every aspect of daily life. In this research, we aim to develop participant-centric interventions for assisting autistic young adults in addressing their anxiety and stress during times of isolation. We first conducted an exploratory literature review to gather the design requirements for an effective stress management technology. Based on our findings, we designed our initial high-fidelity prototype, MindBot, a mindfulness and AI-based chatbot application. We conducted an in-depth qualitative study (semi-structured interviews with 15 autistic young adults and a cognitive walkthrough with 20 participants who have training in HCI and usability evaluation techniques) to identify the design and usability issues to improve the effectiveness of MindBot. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.
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Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author