Role of Multimedia in Education and its Impact on Academic Achievements

Multimedia is a diversified and powerful means to strengthen and aid the education methodology, by making learning interactive and interesting, this paper further justifies the same. The Internet acts as a gateway between multimedia producers and consumers. It is free from discipline boundaries. Several tools allow nontechnical educators to deal with diverse multimedia to create the learning material. These tools should stand fit some parameters, briefly discussed. Its employment is rapidly magnifying, enabling self-learning, which was boosted in the Covid era. This paper emphasizes on to prevent traditional teaching from being used as a supplement but should be primary. Various hiccups in its efficient use in education still prevail but are heading toward resolution. © 2023 Bharati Vidyapeeth, New Delhi.

Dynamics of Teleworking and Impact on Stakeholders in the Current Pandemic Context

The exponential development of information and communication technology (ICT) through computer networks, Wi-Fi systems, wireless signals, and information storage systems has contributed to the transition to the so-called new economy, which is becoming increasingly digital and global. Due to the COVID-19 pandemic, teleworking has begun to dramatically change the work dynamics for all stakeholders. The aim of this research was to identify the main impacting factors and their level of influence in relation to the macroeconomic context of teleworking, public policies, and the legal framework regarding quality of life, as well as the effects of teleworking on employees and employers. Through empirical research, we explored the perspectives of employees and employers as main stakeholders who had operated in the teleworking field within Suceava County in Romania during 2020. In this regard, we undertook exploratory research, the results of which were processed using SPSS v.20. The insightful results have practical implications for the labor market, where an obvious increase in the share of teleworking in the total forms of work has already occurred, and the relations between employer and employee are expected to become based more and more on cooperation and less on subordination. The results also revealed an important tendency of employees to appreciate the positive effects of teleworking on other aspects of life.

The Use of Triangular Norms in Epidemiological Models: A Comparative Study Using COVID-19 Data

In this manuscript we use triangular norms to model contact between susceptible and infected individuals in the susceptible-infected-recovered (SIR) epidemiological model. In the classical SIR model, the encounter between susceptible and infected individuals is traditionally modeled by the product of their densities ([Formula Omitted]). That is, the encounter is modeled by the product t-norm. We use the COVID-19 data and extended versions of the SIR model whose encounters are modeled by four triangular norms, namely, product, minimum, and Frank and Hamacher t-norms, to analyze the scenario in three countries: 1) Germany;2) Italy;3) Switzerland. We compare all versions of the SIR model based on these triangular norms, and we analyze their effectiveness in fitting data and determining important parameters for the pandemic, such as the basic and effective reproduction number. In addition, Frank and Hamacher triangular norms present an auxiliary parameter that can be interpreted as an indicator of control measure, which we show to be important in the current pandemic scenario.

Cyber Pandemics

The focus of this special issue is on studying the consequences of pandemics and cyber pandemics on privacy and trust both in the digital and the real worlds. In the aftermath of the recent COVID-19 pandemic that has shaken several aspects of our lives for almost two years of time, preliminary research indicates that the technological capabilities and the data that have been deployed and exploited to contain the spread of the disease may have affected people's privacy, sense of security, as well as their trust in their governing institutions. The enclosed articles explore both the effectiveness and the impacts of some of the deployed technologies in the handling of the COVID-19 pandemic on people's privacy as well as on their security and related risks. © 1997-2012 IEEE.

Torch Relay Commemorative Photography × Ultra-realistic Communication Technology Kirari!

Due to the spread of COVID-19, the number of people who experienced the exhibits was less than planned. Nevertheless, we were able to deliver surprises and smiles to the participants at the venues where we could provide the experiences. We believe that these interactions with the advanced technologies of our laboratories, which are not normally accessible to the public, enabled many people to experience firsthand NTT's innovativeness. Going forward, we will continue to work on creating new experiences using ICT and connect people with each other. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Harmonic Reproduction Technology between a Real Venue and Remote Audiences

NTT Human Informatics Laboratories has been researching and developing technology to reproduce the appearance of an audience enjoying a live-streamed event online remotely from home (remote audience) at the venue where the event is being held (real venue) while harmonizing it with the situation at the real venue. At the 34th Mynavi TOKYO GIRLS COLLECTION 2022 SPRING/SUMMER held on March 21, 2022, a demonstration experiment was conducted to support the excitement of the event by using low-latency video communication and cross-modal sound search to reproduce pseudo cheers at the real venue for both real-venue and remote audience members who could not cheer due to the COVID-19 pandemic. This article introduces the activities of this demonstration experiment. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Mass surveillance to fight COVID-19: acceptance of surveillance technologies depends on goal-directed motivation

Many governments use mass surveillance to fight COVID-19. Surveillance technologies can help in controlling the spread of the virus, but they may also raise privacy concerns among citizens. This research aims to explore the behavioural mechanisms underlying the acceptance of mass surveillance during COVID-19 and public health emergencies in the future. According to regulatory focus theory, goal-directed behaviour is regulated by two motivational systems – promotion and prevention. Promotion-focused individuals are motivated by growth and prefer to use eager strategies to achieve their goals. Prevention-focused individuals are motivated by safety and prefer to use vigilant strategies. Based on the theory, it is proposed that there is a relationship between regulatory focus and acceptance of mass surveillance. Study 1 tested this proposal with a questionnaire that measured chronic regulatory focus. Study 2 tested this proposal with an experimental priming technique. For both, chronic and experimentally manipulated regulatory focus, these studies showed that surveillance acceptance was associated with regulatory focus. In Study 1, surveillance acceptance was positively associated with prevention focus. Privacy concerns mediated this relationship. The higher the prevention focus, the lower were the concerns about privacy, and thus, the higher was the willingness to accept mass surveillance. Moreover, promotion focus moderated the relationship between privacy concerns and surveillance acceptance. The higher the concerns about privacy, the lower was the willingness to accept mass surveillance, and the higher the promotion focus, the stronger was the effect. Study 2 found that a situationally induced regulatory focus moderated the relationship between privacy concerns and surveillance acceptance. The higher the concerns over privacy, the lower was the willingness to accept mass surveillance, and the effect was stronger for an induced promotion (vs. prevention) focus. Models of surveillance acceptance are presented. Implications for public health and health communication are discussed.

Report on the 34th Asia-Pacific Telecommunity Standardization Program Meeting

The 34th Asia-Pacific Telecommunity Standardization Program meeting (ASTAP-34) was held from 18 to 22 April 2022. This meeting was held in an online format due to the impact of the spread of novel coronavirus infections and was attended by approximately 230 participants (registered) from 19 Member States. This article reports on ASTAP-34 and the Industry Workshop held in conjunction with ASTAP-34. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Pandemic politics and the rise of immigration: Online attitudes towards Westerners and the west in China

This article aims to unpack discourses of ‘race' and racializations associated with White Western foreign residents in China amid pandemic politics. China's proposal to loosen visa regulations for non-citizens during the pandemic (February 2020) sparked many racist and nationalistic sentiments online. Since then, exposés of the ‘special treatment' foreigners apparently demand during quarantine in China have met significant online backlash. Anti-foreigner sentiments are at a new high and not only against African migrants, who have been the focus of extant studies. COVID-19 hit the world at a time of vast international migration into China and China's growing power, and the revival of existing racializations and the ignition of new ones are intricately linked with these phenomena. This article proposes that understanding the conditioning contexts of Chinese postcoloniality and state-led patriotism can enable valuable insights into the emerging racialization of White Westerners in pandemic-era China. © 2023 The Authors. Global Networks published by John Wiley & Sons Ltd.

Teaching the Simple Network Management Protocol Using the Packet Tracer Anywhere Network Simulator

The new reality of the coronavirus lockdown has prohibited the students' physical presence in laboratories. Administrators, teachers, and students had to think of new alternatives to hold meetings by adopting a virtual format through the development of rapidly available and broadly accessible online resources. Online Open Educational Resources (OERs) can be used in the form of cloud applications to virtualize computers or other physical sciences laboratories, which are necessary for the realization of the objectives of the courses. OERs can efficiently and effectively prepare students to be able to practice their skills. In parallel, OERs offer a degree of flexibility to the teachers, as they allow them to manage information in multiple ways and at the same time accommodate the presentation of knowledge from multiple perspectives. In this article, we propose the use of computer network simulation software as a teaching method in the form of OERs. In this context, we support the teaching of the administrative perspective of a computer network management course utilizing OERs. We explore the effectiveness of the network simulation software Packet Tracer anywhere in online learning of both synchronous and asynchronous education environments. In particular, we examine its suitability and usability in light of group activities at the level of higher education. We investigate its functionality and the teaching benefits that arise through collaborative learning scenarios in a computer lab suitable for the course of network management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Transnational life and cross-border immobility in pandemic times

The COVID-19 pandemic dramatically disrupted and reconfigured the cross-border movements of people. Based on an anthropological study of the experiences of transnational migrants during the pandemic (May 2020–May 2021), this article explores stories of how cross-border immobility impacts transnational life and sense of belonging. The stories reveal the emotional toll of prolonged family separation across geographical distances when loved ones are no longer ‘just one flight away' and give voice to experiences of being ‘trapped', ‘stuck' or ‘stranded' in a state of transnational limbo. Running through the stories are intensified experiences of foreignness, non-belonging, precariousness and discrimination. Some also felt abandoned by their country of origin as border closures left them ‘locked out' and ‘blowing in the wind', fostering an experience akin to exile. © 2021 Global Networks Partnership & John Wiley & Sons Ltd.

Transnational lived citizenship turns local: Covid-19 and Eritrean and Ethiopian diaspora in Nairobi

This paper analyses how migrant community practices of transnational lived citizenship were altered by both, COVID-19 and the policy response from the Kenyan government. It is based on interviews with members of the Eritrean and Ethiopian diaspora residing in Nairobi. The paper demonstrates how policies introduced because of the pandemic caused migrant communities to lose local and remittance income. More than the loss of material resources, however, they were impacted by the elimination of social spaces that enable diaspora lives. These two dynamics have intensified a trend that may have been present before the pandemic, a local turn of transnational lived citizenship. By focusing on lived experiences and how they have been re-assessed during the pandemic, the paper argues that transnational lived citizenship is always in flux and can easily become reconfigured as more localized practices. The concept of transnational lived citizenship is demonstrated to be a useful lens for analysing shifting migrant livelihoods and belonging. © 2022 The Authors. Global Networks published by Global Networks Partnership and John Wiley & Sons Ltd.

Global value chains for medical gloves during the COVID-19 pandemic: Confronting forced labour through public procurement and crisis

This paper evaluates ways in which labour issues in global value chains for medical gloves have been affected by, and addressed through, the COVID-19 pandemic. It focuses on production in Malaysia and supply to the United Kingdom's National Health Service and draws on a large-scale survey with workers and interviews with UK government officials, suppliers and buyers. Adopting a Global Value Chain (GVC) framework, the paper shows how forced labour endemic in the sector was exacerbated during the pandemic in the context of increased demand for gloves. Attempts at remediation are shown to operate through both a reconfigured value chain in which power shifted dramatically to the manufacturers and a context where public procurement became higher in profile than ever before. It is argued that the purchasing power of governments must be leveraged in ways that more meaningfully address labour issues, and that this must be part of value chain resilience. © 2022 The Authors. Global Networks published by John Wiley & Sons Ltd.

Which mediated social interactions satisfy the need to belong?

This investigation uses the two-process model of needs to explore differences between face-To-face communication and interpersonal media in terms of belongingness need satisfaction. Using experience sampling methods, Study 1 (N = 117) explored change in feelings of loneliness and connection from interpersonal media use within a day. Study 2 (N = 1,747) examined the association between loneliness and life satisfaction as mediated by patterns of interpersonal media use in the year following the COVID-19 outbreak. Study 3 (N = 303) experimentally manipulated social belonging to examine changes in interpersonal media use and resulting feelings of connection and disconnection. Overall, when individuals were more connected, their behaviors were more consistent with the two-process model, compared to when they felt disconnected. Additionally, although some interpersonal media use was better than no social contact at all, interpersonal media are not equally capable of satisfying belongingness needs. © 2022 The Author(s). Published by Oxford University Press on behalf of International Communication Association.

A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called "deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.

Maintainability of Service-Oriented Architecture using Hybrid K-means Clustering Approach

The process of making changes to software after it has been delivered to the client is known as maintainability. Maintainability deals with new or changed client requirements. Service-oriented architecture (SOA) is a method for developing applications that helps services work on different environments. SOA works on patterns of distributed systems that help different applications communicate with each other using different protocols. To assess the maintainability of service-oriented architecture, different factors are required. Some of these factors are analyzability, changeability, stability, and testability. Modification is the process of upgrading the software functionality. After modification of service-oriented architecture, the module will go to the testing phase. The evaluation and verification of whether a software product or application performs as intended is known as testing. The testing phase is a combination of various stages, such as individual module testing and testing after collaborations between them. This testing stage is time-consuming in the maintenance process. The term "outlier" refers to a module in software systems that deviates significantly from the rest of the module. It represents the collection of data, variables, and methods. For instance, the program might have been coded mistakenly or an investigation might not have been run accurately. To detect the outlier module, test cases are needed. A methodology is proposed to reduce the predefined test cases. K-means clustering is the best approach to calculate the number of test cases, but the outlier is not automatically determined. In this paper, a hybrid clustering approach is applied to detect the outlier. This clustering method is used in software testing to count the number of comments in various software and in medical science to diagnose the disease of Covid patients. The experimental outcomes show that our strategy achieves better results.

Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

The COVID-19 pandemic has infected tens of millions of individuals around the world, and it is currently posing a worldwide health calamity. Wearing a face mask in public places is one of the most effective protection strategies, according to the World Health Organization (WHO). Moreover, their effectiveness has declined due to incorrect use of the face mask. In this scenario, effective recognition systems are anticipated to ensure that people's faces are covered with masks in public locations. Many people do not correctly wear the masks due to inadequate practices, undesirable behaviour, or individual vulnerabilities. As a result, there has been an increase in demand for automatic real-time face mask detection and mask position detection to substitute manual reminders. This proposed work classifies people into three categories such as with mask, without mask and mask with incorrect position. The dataset is tested using three different variants of object detection models, namely YOLOv4, Tiny YOLOv4, and YOLOv5. The experimental result shows that YOLOv5 model outperforms with the highest mAP value of 99.40% compared to the other two models.

Explainable Machine Learning Techniques in Medical Image Analysis Based on Classification with Feature Extraction

Animals are also afflicted by COVID-19, a virus that is quickly spreading and infects both humans and animals. This fatal viral disease has an impact on people's daily lives, health, and economy of a nation. Most effective machine learning method is deep learning, which offers insightful analysis for examining a significant number of chest x-ray pictures that have a significant bearing on COVID-19 screening. This research proposes novel technique in lung image analysis for detection of lung infection due to COVID using Explainable Machine learning techniques. Here the input has been collected as COVID patient's lung image dataset and it has been processed for noise removal and smoothening. This processed image features have been extracted using spatio transfer neural network integrated with DenseNet+ architecture. Extracted features has been classified using stacked auto Boltzmann encoder machine with VGG-19Net+. With the transfer learning method integrated into the binary classification process, the suggested algorithm achieves good classification accuracy. The experimental analysis has been carried out for various COVID dataset in terms of accuracy, precision, Recall, F-1score, RMSE, MAP. The proposed technique attained accuracy of 95%, precision of 91%, recall of 85%, F_1 score of 80%, RMSE of 61 % and MAP of 51%.

INTERNET OF THINGS-ASSISTED LORAWAN-ENABLED HAND HYGIENE MONITORING AND TRACKING SYSTEM FOR PREVENTING COMMUNITY-ACQUIRED PNEUMONIA AND HEALTHCARE-ASSOCIATED INFECTIONS

Healthcare-associated infections (HAI) are a primary concern in critical care units, dialysis centers, and nursing stations. Practicing Alcohol-Based Hand Hygiene (ABH) is predicted to reduce the risk of disease transmission. Recent technological breakthroughs in the Internet of Things (IoT) and Long Range communication (LoRa) protocols provide cutting-edge solutions to reduce HAIs, particularly community and hospital-acquired pneumonia. This research uses an IoT-enabled LoRa network to monitor and track hand hygiene practices to avoid pneumonia and other HAI infections. The ABH dispenser can recognize the subjects, activate the hand sanitizer, permit the subject admission, and record hand-washing activities to a server powered by an NVIDIA Jetson Nano Graphical Processing Unit computer. All the data, with a user ID and GPS location, is deployed in a cloud server and an application server for storage and display and relayed to a LoRa gateway using the ESP32 IoT platform equipped with a LoRaWAN and short-range Bluetooth Low Energy. A series of field tests was conducted in various hospital buildings and simulated scenarios. Real-world LoRa network situations have brought an overall success rate of 92%, whereas laboratory testing has an overall success rate of 98%. As individuals grew more conscious of the need for personal and institutional hygiene during the Covid-19 pandemic, the frequency of HAIs increased. The IoT-enabled intelligent ABH network is a cost-effective infection prevention and control mechanism, and it reduces pneumonia, HAI rate, and stress of healthcare workers and critical care unit patients. © 2022 Science Press. All rights reserved.