Administration in private higher education institutions through virtual platforms in Honduras

The main goal of this research was to analyze the education in universities through virtual platforms in Honduras caused by COVID.19. A quantitative approach was used, with an exploratory-descriptive research level;in addition, questionnaires were used with a reliability level of 0.847 Cronbach's Alpha. The sample consisted of five private universities represented by sixty teachers. It was found that a fast adaptation to virtuality was achieved, which reflected a high level of satisfaction of the teachers. It is concluded that private institutions at the higher education develop new strategies to maintain the fluency of classes by having programs and platforms for virtual teaching

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

The Cloud-powered Hybrid Learning Process to Enhance Digital Natives' Analytical Reading Skills

Analytical reading is a necessary cognitive skill for advancing to other skills required in the digital age. Thailand is focused on the instructional development and use of digital media to enhance the digital natives' analytical reading skills, which will assist learners of all ages in effectively and quickly adapting to changes in the digital environment. After the COVID-19 pandemic situation, educational institutions in Thailand have begun to embrace a hybrid learning approach like never before. The limitations of the existing learning process for boosting digital natives' analytical reading skills are the lack of integration between reading techniques, hybrid pedagogies, and emerging learning technologies to enhance learners' seamless learning experiences. Thus, this study aims to propose the Cloud-powered Hybrid Learning process (Cp-HL process) to enhance digital natives' analytical reading skills. This study consisted of two main stages in the research methodology: 1) learning process development;and 2) learning process evaluation. The developed Cp-HL process had four main learning phases: (1) preparation for hybrid learning;(2) presentation for interactive learning;(3) practice with analytical reading;and (4) progress reports on analytical reading skills. All the experts agreed that the newly developed Cp-HL process performed extremely well in terms of overall suitability. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools

Affected by COVID-19, the maintenance process of machine tools is significantly hindered, while unmanned maintenance becomes an emerging trend in such background. So far, three challenges, namely, the dependence on maintenance experts, the dynamic maintenance environments, and unsynchronized interactions between physical and information sides, exist as the main obstacles in its widespread applications. In order to fill this gap, a bio-inspired LIDA cognitive-based Digital Twin architecture is proposed, so as to achieve unmanned maintenance of machine tools through a self-constructed, self-evaluated, and self-optimized manner. A three phases process in the architecture, including the physical phase, virtual phase, and service phase, is further introduced to support the cognitive cycle for unmanned maintenance of machine tools. An illustrative example is depicted in the unmanned fault diagnosis on the rolling bearing of a drilling platform, which validates the feasibility and advantages of the proposed architecture. As an explorative study, it is wished that this work provides useful insights for unmanned maintenance of machine tools in a dynamic production environment. © 2022

Digital Tools in Post-pandemic Education: Useful Lessons

COVID-19 pandemic gave rise to new didactics—new ways of teaching were actively introduced due to global closures of schools. Teachers faced a new challenge connected with the quality of distance learning. Having access to a multitude of digital tools, it was quite confusing for them to choose the most efficient ways of teaching which could satisfy the learning needs of modern students. The experience of distance learning during the pandemic showed that not all digital techniques are worth incorporating into post-pandemic education. Having analyzed the experience of various countries including Russia, the authors single out the digital tools which, in their opinion, can be combined with traditional teaching methods to provide high quality education to cater for the needs of modern labour market. On the example of a lesson plan of an English class the authors show what digital resources and what kinds of activities involving them can be used in the classroom. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The ELF Tribe: Redefining Primary Education in a Post-COVID Era

Coronavirus brought the entire world to a grinding halt, and it has still not recovered from the vandalization caused. Along with the dramatic loss of lives worldwide, the situation also proved to be disruptive in every sector of the economy. Education system also faced immense turbulence. It has impacted the younger children the most as the effectiveness of online classes is highly doubtful among them. Even the teachers are not able to deliver classes to their best ability. Keeping in mind the present circumstances and the return to state of normalcy, ‘The ELF Tribe' is designed as an educational tool for both teachers and students. There is substantial evidence in science to prove that kids learn better with hands-on experience but nothing can replace a good teacher. This application assists them in non-teaching tasks which, when blended with traditional schooling, has the potential to redefine meaningful schooling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Use of Digital Tools in French as a Second Language Teacher Education in Ontario

This article discusses a 2021 survey of French as a second language (FSL) teacher candidates (TCs) in faculties of education in Ontario whose practice teaching experiences were affected by the COVID-19 pandemic, pivoting them into remote FSL teaching and learning. The survey, which formed a component of a larger mixed method SSHRC-funded research project, was designed to capture the varied practice-teaching experiences of FSL teacher candidates in order to ascertain symmetries and asymmetries in their preferred digital practices, devices, and tools for both social communication and French language teaching and learning. Survey respondents from different teacher education programs in universities across Ontario provided a picture of scattered and fragmented approaches to FSL digital pedagogies and hinted at a persistent reliance on traditional FSL pedagogies in the classroom. Digital preferences for teaching and learning tended to be anchored in common educational tools and platforms that reaffirmed teacher-centred approaches to FSL rather than more innovative, learner-centred, and agentive language teaching and learning. The survey results raise an important question: Has FSL teacher education adequately moved with the communicative changes wrought by wider socio-technical transformations and related pedagogical innovations?

Far UV-C radiation: An emerging tool for pandemic control

Far UV-C, informally defined as electromagnetic radiation with wavelengths between 200 and 230 nm, has characteristics that are well-suited to control of airborne pathogens. Specifically, Far UV-C has been shown to be highly effective for inactivation of airborne pathogens;yet this same radiation has minimal potential to cause damage to human skin and eye tissues. Critically, unlike UV-B, Far UV-C radiation does not substantially penetrate the dead cell layer of skin (stratum corneum) and does not reach germinative cells in the basal layer. Similarly, Far UV-C radiation does not substantially penetrate through corneal epithelium of the eye, thereby preventing exposure of germinative cells within the eye. The most common source of Far UV-C radiation is the krypton chloride excimer (KrCl*) lamp, which has a primary emission centered at 222 nm. Ozone production from KrCl* lamps is modest, such that control of indoor ozone from these systems can be accomplished easily using conventional ventilation systems. This set of characteristics offers the potential for Far UV-C devices to be used in occupied spaces, thereby allowing for improved effectiveness for inactivation of airborne pathogens, including those that are responsible for COVID-19. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Management Tools for Process Mapping and Modeling in Assembly Industry

In a highly globalized world affected by economic policies and a latent COVID-19 pandemic, companies need to organize or reorganize to achieve efficient operations. The correct management of organizations has led their administrators to adopt resourceful management models that guide their activities towards fulfilling their objectives. Thus, management tools have been presented as an effective alternative to control processes and activities. According to a previous study, the main tools that allow process mapping and modeling are Business Process Management (BPM), Business Process Reengineering (BPR), and Event Process Chain (EPC). This article analyzes their characteristics based on compatibility and affinity with four case studies dedicated to the assembly industry using this previous study. In addition, the advantages and disadvantages of the three tools related to the possible adaptability in the four companies are studied. This work uses qualitative qualification matrices, which answer specific questions, determine the potential improvement of organizational management, and create a methodology for mapping and modeling processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Introductory BIM Course for Engineering Students

Building Information Modelling is being adopted worldwide and universities are thus expected to provide the market with new professionals with BIM knowledge and skills. However, introduction of this theme into the curriculum can be challenging to teaching staff. Having successful implementation examples can help carrying on this task. This paper presents the structure, syllabus, adopted tools and activities of an introductory BIM course offered to first-year engineering students. Implemented with only 2 credits, it covers BIM fundamental concepts and develops collaboration skills and abilities with BIM software tools. It was effectively deployed on big classes and successfully offered both in face-to-face and remote modes, adopting a practice focus. An innovative organization for student group projects was adopted, enabling student participation on several projects, performing a different role in each one. Perceived benefits to students' development are reported. The covid-19 pandemics impact is discussed. Future improvements in the course are suggested. Overall results achieved were considered very good. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Innovative Methods Used in Monitoring COVID-19 in Europe: A Multinational Study.

Several innovative methods have been deployed worldwide to curb the COVID-19 pandemic. The aim of the study is to investigate which innovative methods are used to monitor COVID-19 health issues in Europe and related legislative and ethical aspects. An online questionnaire was administered to European countries' representatives of the project Population Health Information Research Infrastructure. Additional information was obtained from websites and documents provided by the respondents; an overview of the literature was also performed. Respondents from 14 countries participated in the study. Digital tools are used to monitor the spread of COVID-19 (13/14 countries) and vaccination coverage (12/14); for research, diagnostics, telehealth (14/14); to fight disinformation (11/14) and forecast the pandemic spread (4/14). The level of implementation of telehealth applications was mostly low/medium. Legislative and ethical issues were encountered in many countries, leading to institutional distrust. The COVID-19 pandemic has highlighted the need for timely and accurate health data for research purposes and policy planning. However, the use of innovative methods for population health monitoring and timely data collection has posed challenges to privacy and online security globally. Adequate regulatory oversight, targeted public health interventions, and fight against disinformation could improve the uptake rate and enhance countries' emergency preparedness.

SunFA - An open-source application for behavior analysis in online video-conferencing

Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied;however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa © 2022 IEEE.

Secure E-health Framework in Cloud-Based Environment

Since December 2019, the world still fighting to beat coronavirus (COVID-19). However, coronavirus is continuing its spread in many countries and claimed the lives people. It is not easy to differentiate between COVID-19 symptoms and simple flu symptoms, especially at the first stage of the infection. This is the main challenge where we have to run many tests as possible and isolate any suspicious people 14 days at least to make sure that they are not carrying the virus. This will increase the cost and people may lose their jobs. Therefore, the economy has to continue. Companies and organization start running their business using online tools, this will draw different future and employee need to gain special task to continue their work. In order go back to the normal life, we have track the virus and stay away from infected area or people. In this paper, we propose a secure cloud-based health framework to record patients' readings, give initial diagnose to identify infected areas and control the spread of the virus. The proposed framework will be running in a secure environment to protect patient's records. © 2022 IEEE.

A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.

Viral Pneumonia and Covid Screening on Lung Ultrasound

The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model's accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM). © 2022 IEEE.

Improved Method of X-Ray Image Classification Using Deep Learning for Covid-19 Detection

COVID-2019, which popped up in December 2019 in Wuhan, China. It quickly spread around the world and turned into a pandemic.It has wreaked havoc on people's daily lives and public health. It has wreaked havoc on people's everyday lives, health, and the economic growth. Positive cases should be found as early as possible in order to control the disease outbreak and treat those who have been infected as fast as possible. Because there are no precise toolkits available, the demand for additional diagnostic tools has increased significantly. Current findings from radiology imaging techniques suggest that such images can reveal a lot about the COVID virus. The use of modern AI technologies (Artificial intelligence) algorithms in conjunction with imaging techniques can help to identify this disease accurately. This paper presents a new model for automatically detecting COVID-19 from X-ray pictures. The suggested model was created to deliver precise diagnostics for three classes of categorization. © 2022 IEEE.

Smart Assistive 3D Tool to Remedy Children's Learning Difficulties for Distance Education

COVID-19 has accelerated the shift away from the traditional classroom to online learning around the globe. Despite its advantages, students could not easily adapt to the challenges of transition overnight. To remedy academic difficulties that arise with online education, this paper introduces an assistive 3D educational tool for slow learners in elementary school, mainly for Arabic, English, and Math subjects. The proposed tool can be fully integrated with remedial programs to help students who could not adapt to distance learning during the pandemic, slow learners, or even students who cannot attain school. This tool automatically adapts to students' weaknesses and classifies students based on academic performance rather than age. Furthermore, it provides an easy-to-use interface for teachers to customize it using their own content and game scenarios. © 2022 IEEE.

Applications of Microfluidics and Nanotechnologies for Point-of-Care Devices

In this work, applications of microfluidic devices in the field of biomedical engineering will be described, with special focus on point-of-care tools (POC). These are devices offering rapid and easy-to-read diagnostic assays of various diseases or physiological conditions (COVID-19, allergies, cardiovascular diseases, tumours, pregnancy, etc.) that often can be applied by non-medically trained persons. Microfluidic devices play an important role in the development of POC tools. Recent advances in novel fabrication methods and multi-technology approaches allow to overcome common limitations, such as high cost, complex fluidic controls and pumping systems, or requiring specialized labour equipment and well-trained operators. Hence, these devices can be made available for a broad range of patients that might not have sufficient medical facilities. The influence of point-of-care tools on society, medical practice and early diagnosis will be discussed. Copyright © 2022 The Authors.

Inclusiveness and Participation in the Design of Public Spaces: Her City and the Challenge of the Post-Pandemic Scenario

The COVID-19 pandemic made visible the need for greater participation of diverse communities in the planning of cities since it revealed exclusions based on gender, migratory status, and class. As a result, initiatives were launched that applied new technologies, digital platforms, and data-based intelligence to bring alternative solutions to the re-use and re-management of public space. The aim of this article is to analyze Her City, a joint effort of UN-Habitat and Global Utmaning, meant to guide urban actors to implement projects through an open and digitally accessible platform that involves girls and young women in urban planning and design. The authors conducted a review of literature and analyzed a case study that included the application of the Toolbox in Chania, Greece, concluding that although Her City can face challenges in the post pandemic scenario, it could be considered among these new, radical, and forward-thinking technologies, groundbreaking from an urban planning point of view that can address large-scale challenges or opportunities in the design of public spaces.

Communication Channels and their Challenges: An Analysis of Sofware Development Teams during the COVID-19 Pandemic

Software development teams depend on the constant and varied use of technological tools that contribute to the fluidity of development activities. The communication channels provided by these tools contribute to the participatory culture of software development, where requirements include getting involved, learning and co-producing the code. However, the choice of channels to support development is still an open question in the scientific community. Through an investigation of closed remote software teams from a large public educational institution in Brazil during the COVID-19 Pandemic, we identified their preferred communication channels and analyzed them to learn how to improve collaboration. We also drew some comparison with social developers and took a look at the challenges that these channels impose and solutions adopted to mitigate these problems. © 2023 ACM.