Factors affecting the public higher education institution (PHEI) acceptance of online meetings applications during COVID-19 pandemic: an empirical study

PurposeIn order to ensure effectiveness of staff's performance using online meetings applications during coronavirus disease (COVID-19), having the behavioural intention is mandatory for staff to measure, test, and manage the staff's data. Understanding of Public Higher Education Institution (PHEI) staffs' intention and behaviour toward online meetings platforms is needed to develop and implement effective and efficient strategies. The objectives of this paper to identify the factors that affect staff to use online meetings applications, to develop a model that examining the factors that affect PHEI staff to online meetings applications and to validate the proposed model. This study used a cross-sectional quantitative correlational study with using UTAUT2 model by validating the model and mediating variables to enhance the model's explanatory power and to make the model more applicable to PHEI staff's behavioural intention.Design/methodology/approachThe data were collected in Malaysia from March to May 2021. The survey took place using Google form and was send to PHEI staff for answer. This research particularly chooses PHEI as the location to carry out the research due to two main factors. Statistical analysis and hypotheses were tested using structural equation modelling based on the optimisation technique of partial least squares. SmartPLS software, Version 3.0 (Hair et al., 2010) was used to conduct the analysis. A conceptualised estimation model was "drawn in” the partial least squares structural equation modeling (PLS-SEM) to analyse the consequences of the variables' relationships. In essence, the PLS-SEM simulation was carried out in a model by assessing and computing various parameters that included elements like validity, durability, and item loading. Henseler et al. (2009) suggested a two-step method that includes PLS model parameter computing. This is accomplished by first solving the estimation model in the structural model independently before calculating the direction coefficients. The results of data analysis using SmartPLS findings and interpretation of the data are addressed. The questionnaire was extensively examined to ensure that the data obtained were presented in a clear and intelligible manner, with the use of figures, and graphs.FindingsThis current study found that the usability of the material, the reliability of operating, the impact of the PHEI staff's views on its usage, and finally the familiarity with the online meetings platforms influenced PHEI staff's behavioural intention for adoption and long-term use of online meeting platforms using UTAUT2. The staff's behavioural intention for using online meeting platforms was significantly influenced by the effort expectancy, facilitating conditions and habit of online meeting platforms. There was a clear association between "Habit” and "Behavioural Intention” for the usage of information technology in learning in several studies (El-Masri and Tarhini, 2017;Uur and Turan, 2018;Mosunmola et al., 2018;Venkatesh et al., 2003). As a consequence of the utility of online meeting platforms in daily staff meetings and learning activities, this technology has been adopted.Originality/valueThis study used UTAUT2 and structural equations modelling in this study to assess respondents' perspectives on the use of online meetings platforms in PHEI, since users' perspective is a significant factor in the adoption and acceptance of online meeting applications. Staff's behavioural intention to use online meeting platforms was effectively enhanced by "Effort Expectancy,” "Facilitating Conditions” and "Habit” in this study. The study shows that identifying PHEI staff's perspectives will effectively increase the staff's aversion to utilising online meeting platforms for online meetings purposes.

Can Pharma Quality Go High Tech?

"There were questions of how to handle tech transfer, factory and site acceptance tests, and regulatory inspections when the world was no longer travelling," he says. Since July, Stracquantanio says, usage of the platform has expanded by a factor of 17, and new site deployments by a factor of ten. The company is now using the remote approach with all major global regulatory agencies. Since Catalent hosts or performs more than 500 audits each year, new technology has allowed Catalent to keep pace, while observing social distancing requirements, Montano adds. Live facility tours are filmed with a 360-degree camera, and 3-D tours with a 3-D mapping camera, says Sandy Immerman, vice president of Information Technology (IT), Pharma Services. [...]this year, Immerman says, Thermo Fisher has hosted multiple regulatory inspections and more than 125 client audits, focusing efforts on current good manufacturing practice (CGMP) activities in work that has involved FDA, the European Medicines Agency, which issued formal guidance on the use of this technology last May (3), the Russian Ministry of Health (MoH), and the Swedish Medical Products Agency.

Molecular Diagnostics on Smartphone

During the COVID-19 pandemic, point-of-care genetic testing (POCT) devices were used for on-time and on-site detection of the virus, which helped to prevent and control the spread of the pandemic. Smartphones, which are widely used electronic devices with many functions, have the potential to be used as a molecular diagnostic platform for universal healthcare monitoring. Several integrated diagnostics platforms for the real-time and end-point detection of COVID-19 were developed using the functions of smartphones, such as the operating system, power, sound, camera, data storage, and display. These platforms use the 5V output power of smartphones, which can be amplified to power a micro-capillary electrophoresis system or a thin-film heater, and the CMOS camera of smartphones can capture the color change during a colorimetric loop-mediated isothermal amplification test and detect fluorescence signals. Smartphones can also be used with self-written web-based apps to enable automatic and remote pathogen analysis on POCT platforms. Our lab developed a handheld micro-capillary electrophoresis device for end-point detection of SARS-CoV-2, as well as an integrated smartphone-based genetic analyzer for the qualitative and quantitative colorimetric detection of foodborne pathogens with the help of a custom mobile app. © 2023 SPIE.

Vaccination Identification System using Raspberry Pi

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes Covid-19, an infectious illness. A methodology was created to track the vaccination history of people with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that causes Covid-19, an infectious illness. The system operates on a Raspberry Pi processor that is designed to authenticate the vaccination records of individuals. The Vaccination Identification System consists of various components connected to the Raspberry Pi Zero 2W microprocessor, Pi camera, an LCD display, LED indicators, a buzzer, a DC servo motor, and a PCB converter. The proposed system grants access to vaccinated individuals and denies access to those who are not vaccinated.

Dr.'s Eye: The Design and Evaluation of a Video Conferencing System to Support Doctor Appointments in Home Settings

The spread of COVID-19 has encouraged the practice of using video conferencing for family doctor appointments. Existing applications and off-the-shelf devices face challenges in dealing with capturing the correct view of patients' bodies and supporting ease of use. We created Dr.'s Eye, a video conferencing prototype to support varying types of body exams in home settings. With our prototype, we conducted a study with participants using mock appointments to understand the simultaneous use of the camera and display and to get insights into the issues that might arise in real doctor appointments. Results show the benefits of providing more flexibility with a decoupled camera and display, and privacy protection by limiting the camera view. Yet, challenges remain in maneuvering two devices, presenting feedback for the camera view, coordinating camera work between the participant and the examiner, and reluctance towards showing private body regions. This inspires future research on how to design a video system for doctor appointments. © 2023 ACM.

Development of an acquisition system to monitoring asynchronous tele-ultrasound for lung patients

Lung ultrasound imaging allows the detection and evaluation of the lung damage generated by COVID-19. However, several infrastructure and logistical limitations prevent them from being carried out in isolated and remote areas. In this work, a system for the acquisition of medical images through asynchronous tele-ultrasounds was developed. The system is based on a graphical user interface, which records the three video cameras, the ultrasound image and the accelerometer simultaneously. The interface was developed according to the Volume Sweep Imaging acquisition protocol. The translational and rotational movement of the transducer are tracked and monitored by the accelerometer and the position of the transducer is obtained from the images acquired by the three video cameras. The results show a correct functioning of the system overall, being viable to be implemented for data acquisition and calculation of error, although in order to validate the error calculation there is still more research to be done. © 2023 SPIE.

Mask Detection and Classification in Thermal Face Images

Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.

Design and Implementation of a Non-contact Thermometer that Records Body Temperature Individually using Forehead Wrinkle Image

With the novel coronavirus disease (COVID-19) pandemic, there has been a significant increase in the opportunities to measure body temperature when using public facilities and to check one's daily health condition. Standing thermometer and handheld devices have been introduced due to their non-contact features. On the other hand, the standing thermometer requires a large space for installation and the person need to move to the front of the device. In this study, we propose a non-contact thermometer that identifies individuals by acquiring images of wrinkles from a camera module and records body temperature individually. The wrinkle image is cropped and then gamma correction removal and grayscaling are applied. The images are fed into a convolutional neural network for the identification. The experiment using images obtained with a smartphone camera from 12 subjects confirmed that individuals could be identified with an average F-value of 0.94. The experiment using images obtained with the proposed device from five subjects confirmed that the identification accuracy exceeded F-value of 0.93 for all subjects. © 2022 ACM.

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.

Automated Face Mask Detection using Artificial Intelligence and Video Surveillance Management

Surveillance camera has become an essential, ubiquitous technology in people's daily lives, whether applicable for home surveillance or extended to public workplace detection. The importance of the camera is irreplaceable in terms of the agent for an enclosed system to function correctly. The goal of ubiquitous computing is to keep different devices or technology communicating seamlessly, allowing them to expand to other areas instead of limiting it to one device. However, many research papers have been released on how the camera can aid in the current situation where COVID-19 is still raging worldwide, especially in crowded places. This paper aims to suggest a method by which surveillance cameras on the university campus can automatically detect student face mask status and notify them. Alongside that, this concept of applying a video management system within the university campus will assist in the automation of invigilating the student's daily mask status from the number of embedded surveillance cameras around the campus. © 2023 IEEE.

The couch as a classroom: exploring the school environment of low-income Latine adolescents during the COVID-19 pandemic

PurposeThe purpose of this paper is to uncover what the at-home educational environments of low-income Latine adolescents looked like during the COVID-19 pandemic and how these environments influenced students' participation in their online classes. Additionally, the findings highlight students' perspectives on their varied engagement in virtual instruction.Design/methodology/approachData for this study were collected via an online survey that included both open and close-ended questions. Students were able to share about their behaviors and comfort in their online classes, as well as provide photos of the areas from which they joined their online classes. Quantitative and qualitative data analysis methods were used.FindingsMany students in the sample expressed discomfort with themselves and their homes being seen on camera and shared having to juggle multiple responsibilities during online classes. Photos uploaded revealed that at-home educational environments often lacked the resources afforded by in-school instruction, with students joining classes from areas that may not be conducive to learning.Originality/valueResearch has highlighted the ways in which the COVID-19 pandemic may have exacerbated existing issues of educational equity, but the systemic reasons for these inequities remain understudied. The results from this study highlight the ways in which disparate at-home learning environments may help to explain unequal engagement in online classes.

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

Monitoring Social Distancing using CNN for minimizing COVID-19

SARS-CoV-2 is the name of the highly infectious Coronavirus that brought the disease to the world. Preventing Covid by keeping the partition among people since it is not possible to get relatable information of an individual about its contamination. The WHO considers 6-ft to be a safe separation for individuals who take all other necessary precautions (masks, sanitizing, etc.)[1]. The undertaking targets utilizing Artificial Insight to implement this social separation openly put by continually checking the separation between individuals shown in a video feed, also alarming the dependable individual to initiate the required moves. This video feed can be used without any problem gathered by the prior framework across the general population places like CCTV Cameras. This would enable us to continually observe the separation between any two people in a public place. The adaptability is exceptionally arranged highly, so that cameras are introduced at practically on every open spots[2]. © 2022 IEEE.

Control and Monitoring of Thermographic Chambers by Means of PLC and HMI

Covid-19 disease affects the individual's body in different ways. Most of the infected people present various symptoms of complexity. This article develops the design of a system of control and monitoring of people through the use of thermographic cameras, which includes an intelligent control system for the detection of people with symptoms of Covid-19, which at the same time allows estimating a reading of parameters obtained from the thermographic camera, the possible suspected cases of people entering the Continental University. The development of the proposed system will allow obtaining real-time data of each user entering the Continental University, these parameters obtained will be stored in a SQL database that is linked to an HMI screen where the temperature of each person is displayed, if in case they exceed the established temperature ranges, instant access to the facility is restricted. The results of the research showed that the system design contributes to the prevention and mass propagation of Covid-19. © 2022 IEEE.

Research on Intelligent Disinfection-Vehicle System Design and Its Global Path Planning

We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.

Willingness of Spanish University Students to Connect their Camera during the COVID-19 Pandemic

As a result of the pandemic generated by covid-19, educational institutions began to teach their classes remotely. During these, the majority of students opted to keep the webcam turned off, causing demotivation and uncertainty among teachers. The different studies that inves­tigate this behavior present atomized and unconnected reasons. This is why this research intended to offer a holistic view of the reasons that led students to activate or not activate the camera during the pandemic. Data obtained through a questionnaire administered to 305 students from different Spanish universities revealed a tendency to follow the decision of the majority regarding whether or not to connect the camera during classes, as well as a reluctance to show themselves to classmates in relaxed environments. On the other hand, although no significant differences were found between men and women regarding the frequency of use, male participants attributed less importance to both the projected personal im­age and the connection resources available. Results also show that the importance attributed both to the generation of so­cial presence in the classroom and to the academic qualification is a predictor of its frequency of use. These results suggest the use of strategies that encourage higher education students to connect the webcam during the teaching through videoconferencing systems.Alternate : Em decorrência da pandemia gerada pela covid-19, as instituições de ensino passaram a ministrar suas aulas remotamente, por meio de sistemas de videoconferência, durante os quais a maioria dos alunos optou por manter a webcam desligada, causando desmotivação e incerteza entre os professores. Os diferentes estudos que investigam esse comportamento apresentam motivos atomizados e desconexos, por isso esta pesquisa oferece uma visão holística dos motivos que levaram os alunos a ativar ou não a câmera durante a pandemia do covid-19. Com base nos dados obtidos através de um questionário aplicado a 305 estudantes de diferentes universidades espanholas, esta pesquisa explicativa identifica dimensões de natureza social, pessoal, econômica e acadêmica que, combinadas, parecem revelar tais razões. Os resultados mostram uma tendência em seguir a decisão da maioria de conectar ou não a câmera durante as aulas, bem como uma relutância em se mostrar aos colegas em ambientes descontraídos. Por outro lado, embora não tenham sido encontradas diferenças significativas entre homens e mulheres quanto à frequência de uso, os participantes do sexo masculino atribuíram menos importância tanto à imagem pessoal projetada quanto aos recursos de conexão disponíveis ao manter o telefone ligado. Além disso, mostram que a importância atribuída tanto à geração de presença social em sala de aula quanto à qualificação acadêmica são preditores de sua frequência de uso. Esses resultados sugerem o uso de estratégias não impostas que estimulem estudantes do ensino superior a conectarem a webcam durante o ensino por meio de sistemas de videoconferência.Alternate : Como resultado de la pandemia generada por la covid-19, las instituciones edu­cativas pasaron a impartir sus clases de manera remota durante las que el alumnado optó, de forma mayoritaria, por mantener apagada la cámara web, provocando desmotivación e incertidumbre entre el profesorado. Los distintos estudios que investigan este comportamiento presentan motivos atomizados e inconexos, por lo que la presente investigación se propuso ofrecer una visión holística de los motivos que llevaron al alumnado a activar o no la cámara durante dicha pandemia. Los datos obtenidos a través de un cuestionario suministrado a 305 alumnos de distintas universida­des españolas revelan una tendencia a seguir la decisión de la mayoría con respecto a conectar o no la cámara durante las clases, así como una reticencia a mostrarse ante compañeros en entornos relajados. Por otra parte, si bien no se encontraron diferencias significativas entre hombres y mujeres ace ca de la frecuencia de uso, los participantes de género masculino atribuyeron me­nor importancia tanto a la imagen personal proyectada como a los recursos de conexión disponibles. Asimismo, se encontró que la importancia atribuida tanto a la generación de presencia social en el aula como a la calificación académica es un predictor de su frecuencia de uso. Estos resultados sugieren el uso de estrategias que incentiven al alumnado de educación superior a conectar la cámara web durante la impartición de docencia por medio de sistemas de videoconferencia.

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.

Drawing COVID-19. The Viral Image

The Prime Ministerial Decree of 9 March 2020 has caused the spread of social campaigns (#corona-virus, #iorestoacasa), whose ‘pandemic' effect has influenced the collective imagination more than the COVID-19 virus which, spread all over the world, has changed everyone's life globally. In a hyper-technological era, this microscopic virus has shown that even evolved communities are fragile, questioning many false certainties. The society of the global image has reacted and, as a symbolic language, has responded with countless forms of visual and multimedia communication (magazines covers, cartoons, comics, video clips) to spread new messages on social networks or television channels: to stay home, to renounce social life, to use quarantine to rediscover forgotten activities. This contribution examines the topic from a social and graphic point of view, analyzing contexts and languages such as: the proliferation of photographic images of deserted cities, which have portrayed the same places with different eyes and effects;the manipulation of the iconic and/or rhetorical force of artistic masterpieces or cinematographic titles, reinterpreted according to inspirations due to coronavirus psychosis, or of well-known advertising brands to obtain ironic puns with an equally ‘viral' effect;the redesign of the logos of famous multinationals in the name of the rule of social distancing;the creation of video clips to analyze, through the now usual video call screen, problems and typical behavior of the quarantine. The goal is to confirm how quickly the communication of the visual image on social channels was a favorable condition for learning the rules and behaviors to observe during the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Advanced Dance Choreography System Using Bidirectional LSTMs

Recently, the craze of K-POP contents is promoting the development of Korea's cultural and artistic industries. In particular, with the development of various K-POP contents, including dance, as well as the popularity of K-POP online due to the non-face-to-face social phenomenon of the Coronavirus Disease 2019 (COVID-19) era, interest in Korean dance and song has increased. Research on dance Artificial Intelligent (AI), such as artificial intelligence in a virtual environment, deepfake AI that transforms dancers into other people, and creative choreography AI that creates new dances by combining dance and music, is being actively conducted. Recently, the dance creative craze that creates new choreography is in the spotlight. Creative choreography AI technology requires the motions of various dancers to prepare a dance cover. This process causes problems, such as expensive input source datasets and the cost of switching to the target source to be used in the model. There is a problem in that different motions between various dance genres must be considered when converting. To solve this problem, it is necessary to promote creative choreography systems in a new direction while saving costs by enabling creative choreography without the use of expensive motion capture devices and minimizing the manpower of dancers according to consideration of various genres. This paper proposes a system in a virtual environment for automatically generating continuous K-POP creative choreography by deriving postures and gestures based on bidirectional long-short term memory (Bi-LSTM). K-POP dance videos and dance videos are collected in advance as input. Considering a dance video for defining a posture, users who want a choreography, a 3D dance character in the source movie, a new choreography is performed with Bi-LSTM and applied. For learning, considering creativity and popularity at the same time, the next motion is evaluated and selected with probability. If the proposed method is used, the effort for dataset collection can be reduced, and it is possible to provide an intensive AI research environment that generates creative choreography from various existing online dance videos.