Communication media as mediators of telework frequency and knowledge sharing in Japan under COVID-19

This paper examines how telework frequency has affected the usage of major communication media, and subsequently knowledge sharing, among a large sample of full-time Japanese employees with no prior telework experience during the country's fourth COVID-19 state of emergency. Results suggest that mandatory telework resulted in lower use of face-to-face meetings and phone calls;in higher use of instant messaging and virtual meetings, and that it had no effect on e-mail use. Moreover, phone call, instant messaging, and virtual meeting frequencies were found to mediate the relationship between telework frequency and knowledge sharing. These findings highlight the importance of both existing and newer communication media in offsetting the loss of face-to-face meeting opportunities. Government-mandated telework may have accelerated the adoption of new communication tools such as instant messaging and virtual meeting, which had not yet gained full acceptance before the pandemic.

Learning Complex Technology Online: Effect of Challenge and Hindrance Techno-stressors on Student Satisfaction and Retention

Despite increasing attention to online learning worldwide, learning complex technologies online has always been challenging and even hindersome to students, who are subjected to elevated levels of technostress. In contrast to most previous studies that focused on the negative side of technostress, this study investigated both the negative and positive sides of technostress. Based on the challenge hindrance framework (CHF), the holistic stress model (HSM), and the person-environment fit (P-E Fit) model, we examined how challenge and hindrance techno-stressors caused distress and eustress in online students and lead to associated outcomes. We empirically validated the research model by analyzing survey data collected from 565 online graduate business students enrolled at a university in the United States. The results revealed that some hindrance and challenge techno-stressors were associated with techno-distress and techno-eustress, which further impacted student satisfaction and student retention. We discussed the contributions and implications and provided future research directions.

Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

Barreiras para Adoção da Telessaúde em Instituições Militares na Amazônia

The objective of the research was to identify the barriers to telehealth adoption from the perspective of health professionals from the Military Police of the state of Pará in the Amazon. The analysis of evidence showed that of the seven aspects identified as barriers to the adoption of telehealth, the organizational issue was the most strongly pointed out. The main contribution of this research was the construction of a framework on the barriers to telehealth implementation in the view of health managers who serve regions with large territorial extensions. O Hospital Militar de Área de Sao Paulo (HMASP) já vem adotando essa prática realizando teleconsultas em campanha e até mesmo transmissao de cirurgias ao vivo.

Effectiveness of Health Communication Technology on Compliance Disposition of Covid-19 Guidelines

Recent research has mostly examined the role of health communication technology (HCT) in patient empowerment and in producing patient-focused outcomes. This study examines HCT in a larger context where it is used as a tool to improve public health. The objective is to examine how HCT is used to monitor Covid-19's spread, and how social factors affect individual assessment of HCT and individual compliance disposition of Covid-19 guidelines. Analyzing data collected from 360 HCT users suggests that the information and system quality of HCT indeed impact users' assessment of its effectiveness and their compliance disposition. However, such effects are strongly mediated by social factors: Peer influence and government trust can sway an individual's cognitive judgments of Covid-19 regardless of HCT's impacts. The findings highlight the importance of social factors in pandemic management and the need to socialize health informatics to make them more effective.

Cibermarketing político durante la pandemia COVID-19 en las elecciones presidenciales de Ecuador 2021

: This research aimed to analyze the use of cybermarketing in the political campaign of Andrés Arauz during the 2021 presidential elections, in contrast to that of Lenín Moreno in 2017, to identify the change in strategies in the COVID-19 pandemic. For the design, techniques such as interviews with experts in political communication and surveys of young centennials and millennials between 21 and 29 years old in various provinces of Ecuador were used. La política ha adoptado las nuevas herramientas digitales para sus objetivos, gracias a ellas, por un lado, se puede realizar una mejor segmentación, un mayor alcance de información, mayor contacto con el electorado, etc., todo ello forma parte de lo que se conoce como ciberpolítica, entendida como "nuevas formas de interacción entre el gobierno y los ciudadanos, entre éstos y los partidos políticos, entre las empresas y los órganos legislativos, entre ciudadanos de un régimen político y entre ciudadanos del mundo" (Caballero, 2016, p. 292). En el caso de WhatsApp se discute si la misma se puede considerar como una red social que sea parte del marketing político ya que es una herramienta personal y casi no invasiva.

Modelling Predictability of Airbnb Rental Prices in Post COVID-19 Regime: An Integrated Framework of Transfer Learning, PSO-Based Ensemble Machine Learning and Explainable AI

In this research, an effort has been put to develop an integrated predictive modeling framework to automatically estimate the rental price of Airbnb units based on listed descriptions and several accommodation-related utilities. This paper considers approximately 0.2 million listings of Airbnb units across seven European cities, Amsterdam, Barcelona, Brussels, Geneva, Istanbul, London, and Milan, after the COVID-19 pandemic for predictive analysis. RoBERTa, a transfer learning framework in conjunction with K-means-based unsupervised text clustering, was used to form a homogeneous grouping of Airbnb units across the cities. Subsequently, particle swarm optimization (PSO) driven advanced ensemble machine learning frameworks have been utilized for predicting rental prices across the formed clusters of respective cities using 32 offer-related features. Additionally, explainable artificial intelligence (AI), an emerging field of AI, has been utilized to interpret the high-end predictive modeling to infer deeper insights into the nature and direction of influence of explanatory features on rental prices at respective locations. The rental prices of Airbnb units in Geneva and Brussels have appeared to be highly predictable, while the units in London and Milan have been found to be less predictable. Different types of amenity offerings largely explain the variation in rental prices across the cities.

Entornos virtuales de aprendizaje como alternativa en la enseñanza de la materia de medicina crítica

: The general objective of this research work was to determine the feasibility of incorporating EVA as a didactic alternative in the teaching of critical medicine in postgraduate students. The study population was selected through an intentional sampling for convenience and was made up of 90 students and 23 teachers of the postgraduate degree in Critical Medicine from two private universities in Ecuador. The results obtained made it possible to determine the feasibility of incorporating EVAs in the academic training of critical medicine postgraduate students, thus also allowing the reduction of hospital absence times due to the academic training received virtually. Keywords: Learning, teaching, EVA, critical medicine, postgraduate. 1.Introducción El desarrollo de la tecnología en los últimos 5 años ha permitido a las personas contar con un mejor y mayor desarrollo en casi todas las áreas especialmente la educativa.

Revisión Sistemática: Influencia de los videojuegos educativos en el proceso de aprendizaje

: The article aimed to carry out a systematic review of the literature on the impact of educational video games on the learning process, 4 research questions were proposed and with the PRISMA methodology, 17 articles were selected, which met the inclusion and exclusion criteria. proposed. The results showed that video games are educational tools capable of emulating learning environments, favoring communication, social skills, problem solving and critical thinking;in addition, the best genres for educational video games are educational or serious. [...]the possibility of using educational video games in the learning process was determined and that these can be applied to improve this process. Introducción La pandemia de la COVID-19 provocó adaptarse mediante el uso de la tecnología a nuevos cambios educativos, se pasó de tener una educación presencial a virtual, pero distintos países no estaban preparados para esto, generando complicaciones al dar continuidad a las clases y evidenciando la incapacidad del sistema educativo respecto a cómo abordar la enseñanza remota, durante este periodo se empezó a usar y entender que el uso educativo de las TIC (Tecnologías de la Información y las Comunicaciones) fue necesaria de implementarse, lo que demandó una mayor capacitación en el uso de la tecnología por parte de docentes y alumnos;además, más allá de que las TIC sean un instrumento útil para el aprendizaje, se necesitó que se generen las condiciones administrativas y organizativas necesarias dentro del sistema educativo, para que la incorporación de esta nueva solución tecnológica sea positiva (González et al., 2021).

Sistema de recomendación integrado para el autocontrol emocional en adultos mayores con cuadro postcovid-19 en Panamá

: The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. Keywords: Covid-19, AI, IoT, Mobile apps, Machine learning. 1.Introducción La Covid-19 es, sin lugar a duda, la mayor catástrofe del siglo XXI, probablemente la crisis global más significativa después de la segunda guerra mundial. En este artículo, proponemos el diseño de una arquitectura altamente integral y flexible basada en diferentes elementos de TIC que permitirá extraer datos de un sensor, analizarlos y realizar recomendaciones a pacientes panameños adultos mayores con afecciones psicológicas o reacciones emocionales posteriores al contagio de la Covid-19 (post-covid-19), basado en la utilización de componentes como IA, IoT y aplicaciones móviles para lograr el autocontrol emocional y relajación mental.

¡Marketing Ágil! Tendencias en promoción y comercialización en redes sociales

Given the great technological gap driven by COVID-19, it is intended to analyze and explain the synergy between technology and marketing, through the implementation by the use of social networks. Where the application of fundamental digital tools is necesary in the creation of a content marketing plan and social media structures, as well as in digital positioning processes. (2020) "the situation in which we are living not only changed the lifestyles of humanity, but also the way in which the marketing of content, data, and the marketing and positioning processes is handled." Keywords: Agile Marketing;Social Networks;Technology;Content Marketing.

Uso de las herramientas digitales en la educación virtual en Ecuador

All these changes transformed several human paradigms, among the recent changes of the contemporary age, we found: secular education, and virtual education, the latter became potentialized thanks to a global event. Thanks to this event, digital tools were essential for the intellectual and economic development of the world, among those used was the e-learning platform that allowed teachers and students to interact with each other. [...]there coexisted countless homes in which buying a technological device or hiring internet service was not possible, either because of the economic situation or geographical location, they could not access virtual classes, which led them to make monetary sacrifices such as: loans or mobilizing to places where the signal could reach. Keyword: Digital tools;virtual education;digital platforms;social networks. 1.Introducción Desde el comienzo de la historia de la humanidad, nuestra especie se caracterizó por la capacidad del racionamiento, lo que nos diferencia de los otros animales por el hecho de saber interpretar y preguntamos constantemente el porqué de las cosas.

Aporte de los modelos matemáticos y la Big Data en sucesos epidemiológicos como el SARS Cov-2

The data unquestionably reflect the degree of impact it has had, not only on the mortality rate, but also on the economic indices of nations. In this context, the present study aims to analyze, from the theoretical mathematical models, what has been the contribution of this area of science to find and predict possible solutions to quell the effects of this global pandemic. For this purpose, statistical analyses based on three models will be used: non-linear phenomenological models, data modeling and the generalized logistic model, which are expected to contribute to a better evaluation and understanding of the measures taken to face this health crisis and, in the future, the importance of understanding the use of data and the technological tools available to mankind today in the face of any new virus. La modelación de datos es realizada por los modelos fenomenológicos más utilizados, para las enfermedades infecciosas;el modelo de Richards (RM) (1959), el modelo logístico generalizado (GLM) y el modelo de Richards generalizado (GRM) (1969), el primero de ellos es un modelo de crecimiento que considera cuatro variables, uno de sus parámetros permite modelar las curvas con mayor precisión, sin embargo, es criticado ya que este parámetro no tiene significado biológico.

Predictive model to determine the growth of mobile money transactions in Zambia using data mining techniques

Mobile money has been known to be a successful venture around the world especially so, for African countries due to the many limitations that traditional banks have like operations, expensive transaction costs and cumbersome process to open account to mention but a few. The presence of mobile money has not only allowed the unbanked population to have accounts but has also alleviated poverty for many rural communities. Zambia has seen an increase of mobile money accounts and COVID-19 has exacerbated this increase. Therefore, this paper sought to determine data mining algorithm best predicts mobile money transaction growth. This paper was quantitative in nature and used aggregated monthly mobile money data (from Zambian mobile network operators) from 2013 to 2020 as its sample which was collected from Bank of Zambia and Zambia Information Communications and Technology Authority. The paper further used WEKA data mining tool for data analysis following the Cross-Industrial Standard Process for data mining guidelines. The performance from best to least is K-nearest neighbour, random forest, support vector machines, multilayer perceptron and linear regression. The predictions from data mining techniques can be deployed to predict growth of mobile money and hence be used in financial inclusion policy formulation and other strategies that can further improve service delivery by mobile network operators.

A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade

Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.

Four Patterns of Digital Innovation in Times of Crisis

Exogenous shocks, such as COVID-19, significantly change fundamental premises on which economies and individual organizations operate. The light-asset nature of digital technologies provides the potential to not only facilitate an immediate crisis response, but also to catalyze novel innovation types to address the societal and economic changes caused by exogenous shocks. As digital innovation became a relevant part of organizations' COVID-19 responses, and given that a corresponding structured knowledge base did not exist, we found the need to better understand crisis-driven digital innovation. Drawing on prior knowledge from crisis management and organizational ambidexterity as a theoretical lens, we present four patterns of crisis-driven digital innovation, classified along two dimensions: (1) driven by a sense of urgency or ambition and (2) focusing on exploitative or explorative innovation. Based on a thorough analysis of digital innovation cases during the COVID-19 crisis, we illustrate and discuss these four patterns and their emerging properties to explain how and why they led to digital innovation in the context of the crisis. Our work contributes to the explanatory knowledge on digital innovation in times of crisis, helping researchers and practitioners to understand and develop digital innovation in response to exogenous shocks.

Effectiveness of Telemedicine in Diabetes Management: A Retrospective Study in an Urban Medically Underserved Population Area (UMUPA)

This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes mellitus (T2DM) were examined from 1 January 2019 to 30 June 2021. Multiple linear regression models indicated that T2DM patients with uncontrolled diabetes utilizing TM were similar to traditional visits in lowering hemoglobin (HbA1c) levels. The healthcare service type significantly predicted HbA1c % values, as the regression coefficient for TM (vs. F2F) showed a significant negative association (B = −0.339, p < 0.001), suggesting that patients using TM were likely to have 0.34 lower HbA1c % values on average when compared with F2F visits. The regression coefficient for female (vs. male) gender showed a positive association (B = 0.190, p < 0.034), with HbA1c % levels showing that female patients had 0.19 higher HbA1c levels than males. Age (B = −0.026, p < 0.001) was a significant predictor of HbA1c % levels, with 0.026 lower HbA1c % levels for each year's increase in age. Black adults (B = 0.888, p < 0.001), on average, were more likely to have 0.888 higher HbA1c % levels when compared with White adults.

Masked Face Recognition System Based on Attention Mechanism

With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recognition model and proposes an improvement of Facenet. We use ConvNeXt-T as the backbone of the network model and add the ECA (Efficient Channel Attention) mechanism. This enhances the feature extraction of the unobscured part of the face to obtain more useful information, while avoiding dimensionality reduction and not increasing the model complexity. We design new face recognition models by investigating the effects of different attention mechanisms on face mask recognition models and the effects of different data set ratios on experimental results. In addition, we construct a large set of faces wearing masks so that we can efficiently and quickly train the model. Through experiments, our model proved to be 99.76% accurate for real faces wearing masks. A combined accuracy of 99.48% for extreme environments such as too high or lousy contrast and brightness.

Research on Optimization of Public Opinion Supervision Model of Social Network Platform Based on Evolutionary Game

Network environments have recently emerged as a considerable research area. In addition to government supervision, platform supervision is also critical to improve network environments. So, we should improve the social network platform's single regulatory model. Against this background, we described public opinion supervision as a game between marketing accounts, netizens, and the platform. Unlike previous studies, we considered the influence of marketing accounts on online public opinion. Because of the players' bounded rationality, we built an evolutionary game model, and by solving it, we obtained the evolutionarily stable strategy (ESS). Then, we analyzed the influence of relevant parameters on the evolutionary stable state. Our research results show that if the authenticity of the information is not high, to not publish is the best choice for the marketing account, and to not participate is the best choice for the netizen. The platform penalty for the marketing account is also directly related to the marketing account's decision making. The platform penalty for the government, as well as the increase in the penalty risk coefficient, considerably affected the choice of platform. Finally, we put forward the "pre-event–in-the-event–post-event” three-stage supervision model, which provides countermeasures and suggestions for all parties to jointly maintain the network environment.

Learning Science through Cloud Gamification: A Framework for Remote Gamified Science Learning Activities Integrating Cloud Tool Sets and Three-Dimensional Scaffolding

In today's world where virtual interaction is becoming more and more important, remote collaborative problem solving has become a promising teaching strategy. The motivation to promote collaborative science learning and the depth of group discussions are key research issues. The use of gamification strategies has the potential to facilitate remote synchronous science instruction. In order to promote learners' collaborative science problem-solving skills, it is critical to design appropriate scaffolds and provide a guiding framework for teachers to integrate cloud-based interactive tools to design remote synchronous gamification activities. Based on years of research on scaffold-based gamified teaching, this study proposes a framework for gamified teaching activities that integrates a cloud-based toolset and a three-dimensional scaffolding (cognitive scaffolding, peer scaffolding, and metacognitive scaffolding). The framework can be used as a reference for science teachers to combine cloud tools for remote gamified teaching and for researchers in this field.