Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

Effect of Distant Education on Children's Academic Achievement in Primary Schools During Corona Pandemic in Dubai Emirate- Field Study

This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.

Angry, Sad, or Scared? Within-valence Mapping of Emotion Words to Facial and Body Cues in 2 to 4-Year-Old Children

The acquisition of emotion words is critical to children's socio-emotional development. Previous studies report that children acquire emotion words gradually during ages 3-5 and beyond. The majority of this work, however, has used demanding tasks for young children (e.g., asking children to label emotion-related facial configurations) and has predominantly relied on facial configurations. Here we designed a child-friendly, word-comprehension task incorporating both facial configurations and body language. In two preregistered online experiments, we asked two to four-year-olds (N = 96) to connect emotion words-happy, sad, angry, and scared-to either facial configurations (Experiment 1) or combined facial and body cues (Experiment 2). We found relatively early competence in understanding emotion words, especially those of the same-valence. All age groups, including 2-year-olds, successfully linked emotion words to corresponding facial configurations (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (though not substantially better) when given additional body cues. Parental reports of children's exposure to and use of masks during the COVID-19 pandemic did not correlate with children's performance in either experiment. Even before children can produce emotion words in an adult-like manner, they possess at least a partial understanding of those words and can map them to emotion cues within valence domains. © 2023 University of California Press. All rights reserved.

The Benefits and Limitations of Teaching English for Specific Purposes Online and Face-to-Face: Sport and Health Sciences Students' Perspectives

The restrictions imposed to limit the spread of COVID-19 resulted in the switch from in-person to online teaching, including the teaching of foreign languages in 2020-2022. This study uses the feedback of students who have studied English for Specific Purposes (ESP) in virtual and classroom settings. The aim is to investigate the benefits and limitations of each approach for language acquisition in ESP for sport and health sciences. A questionnaire to ascertain students' experiences was distributed to 60 students in sport and health sciences who studied ESP at the Latvian Academy of Sport Education. It was found that online and face-to-face studies of ESP in sport and health sciences tended to facilitate the acquisition of different language skills. In the questionnaire, students reported consistent progress in the study of vocabulary and reading the texts related to their area of study during both online and face-to-face studies, but less improvement was reported in the areas of grammar and speaking skills. Overall, synchronous online live classes would facilitate the steady and continuous development of all language skills if supplemented by continuous support from the teaching personnel, regular offline meetings or classes, and access to specially designed online resources.

Newton's law of cooling with a low-cost thermoscope: physics in the time of covid-19

The necessity to teach experimental physics in the pandemic period motivated the development of practices in which students may take measurements with instruments constructed by themselves. In this article, we present an experimental practice to approach Newton's law of cooling with a thermoscope (the earliest device for detecting changes in temperature, forerunner of the thermometer) constructed with household materials. Although the use of a non-calibrated thermoscope, the instrument presented several advantages, visual appeal, ease of handling, ease of data acquisition and good reproducibility. The students can take data, plot graphs, and verify if the Newton's law of cooling holds on the tested circumstances.

Impact of the COVID-19 Pandemic on Food Insecurity in Puerto Rico

This survey (n = 1,356) reports food insecurity (FI) in Puerto Rico (PR) at 38% before, and 40% since, the COVID-19 pandemic. Odds for FI increased for households with annual income <=$15k vs. >=$60k (OR: 3.52;95% CI: 1.20, 10.36);experiencing an income reduction (OR: 2.22 (1.55, 3.18));participating in the United States Department of Agriculture Nutrition Assistance Program (OR: 1.75 (1.14, 2.70));higher food acquisition anxiety (OR: 1.45 (1.29, 1.64));increased home availability of fruit-flavored drinks (OR: 1.52 (1.04, 2.22)), whereas increased fruit availability was protective (OR: 0.63 (0.43, 0.93)). Results heighten the need to examine FI strategies in PR.Copyright © 2021 Taylor & Francis Group, LLC.

Scada Web-Based Instrumentation and Control Laboratory with Real-Time Remote Monitoring and Control System for Columban College

This paper provides a remote access control experiment for students who can't go to the campus because of the COVID-19 pandemic. This paper utilizes the SCADA (supervisory control and data acquisition) using LabView with the Internet of things technology to control the laboratory remotely in real-time. Remote access experiments of a Linear actuator, PID algorithm, Dynamics and Control of Second-order system response, and survey questionnaires were applied and used as an example to show how effective the research study is. The safety of the SCADA system was also considered by using the Virtual Private Network as the primary connection between the student and the server. The remote access laboratory will give a solution to the current problem of the academe for not providing a real-time laboratory equipment experiment. © 2022 IEEE.

Blended Learning to Support Minority Language Acquisition in Primary School Pupils: Lessons From the -Taking Gaelic Home Study'

Gaelic is a minoritised indigenous language of Scotland, with its traditional heartland in the rural north-west of the country. The education system, and in particular Gaelic Medium Education (GME), has been recognised as an important strand of the language maintenance and support initiatives. The provision of GME has grown significantly since its inception in the early 1980s, it remains on the -periphery' of the education system, with around 0.9% of all primary school pupils enrolled in GME settings. The ongoing language shift from Gaelic to English, a process that has been particularly pronounced in the traditional heartlands of the language, and the resulted decline in the use of Gaelic as the language of the home, the family, and the community, raises the question of how GME can contribute to a sustainable future for Gaelic. This article will discuss the findings of a small-scale mixed method practitioner enquiry study, which incorporated parental questionnaires, classroom observations, class-based language assessments and focus groups, to explore the use of blended learning approaches to enhance the development of language skills. The results of this study, conducted initially to evaluate the impact of the Covid-19 school closures on the linguistic proficiency of children in Primary 1 to Primary 3 enrolled in GME, show that pupils who were actively engaged in online learning activities showed a greater confidence and proficiency in their use of Gaelic compared to their peers who had not used these materials to support their learning, as well as increased involvement of caregivers in these Gaelic homework tasks. These findings allow for a re-imaging of approaches to homework in minority language immersion contexts to support the acquisition and use of the minority language beyond the classroom. © Society for the Provision of Education in Rural Education. All Rights Reserved.

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.

The Impact of the Online Project-Based Learning on Students' Communication, Engagement, Motivation, and Academic Achievement

Purpose. Project-based learning (PBL) is a student-centered environment that emphasizes a dynamic classroom approach. It is founded on the idea that students gain a deeper understanding by actively tackling difficulties and issues from the real world. Students gain knowledge in a subject by devoting a significant amount of time to researching and solving a challenging question or interesting issue. Methods & Procedure. PBL was used in online classes during the pandemic of COVID-19. The study concentrated on projects that were created by students themselves under the guidance of teachers with the aim of determining their impact on their communication, engagement, motivation, and academic accomplishment in L2 acquisition context. The sample of the study that consisted of 100 EFL college students deployed this methodology in their weekly lectures. Results. The results show that PBL can have some positive effect on the entire spectrum of parameters under consideration as well as have a substantial impact on communication, engagement, motivation, and academic performance in online education. The findings clearly show that PBL can be a useful methodology for L2 acquisition and can provide students with various challenging activities and topics that increase their motivation and engagement, and thus, academic achievement. © The Author(s) 2023.

Lessons Learned from Online Teaching and Their Implications for Students' Future Careers: Combined PLS-SEM and IPA Approach

As far as students are concerned, there is a well-founded relationship between academic performance and career management from which a special professional path can result, based on the multitude of knowledge, skills, and experiences acquired during the years of study. To this end, the presence and help of teachers participating in the learning process, the teaching activities they are involved in, and their own participation are determinant factors. This research aims to highlight the impact that the above factors have on the professional future of students. For this purpose, 395 respondents, including students in the bachelor's and master's cycles, were involved in the research process, to whom a questionnaire was given in electronic format during two stages: one where the didactic activity was carried out in online format and the other carried out face-to-face. Hypotheses testing was performed using partial least squares structural equation modeling. The present study focuses on two main directions regarding the results obtained. Thus, with respect to the acquisition of knowledge and the development of student skills, it emerged that the effect of the content in the didactic activities on student skills and the development of competencies is strengthened by the skills and degree of involvement of the teaching staff from the university environment. Related to the management of students' careers, the analysis showed that the effect of the content in didactic activities is complemented by the accumulation of knowledge and the formation of student skills. The rigorous economic training resulting from didactic activities constitutes a main pillar in the students' future, even more so depending on how much they perceive that the topics covered in the university courses are of interest to them. The results of this study can serve as theoretical support for future research that addresses the topic of student career management and the implications of university activities on knowledge and skills. In addition, the results can support decisions for the management of higher education institutions regarding the development and implementation of university programs and educational strategies with the aim of increasing the involvement of teachers and students in the teaching–learning process.

An Iot-Based Covid Patient Health Control System

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. A mobile phone-based remote healthcare monitoring framework has been constructed with the help of sensors, an information acquisition unit, a microcontroller (such as Arduino), and a product modification. This framework has the potential to provide continuous web-based data regarding a patient's physiological states (i.e., JAVA). Before transmitting it to the specialist's portable device along with the application, the framework examines the patient's temperature, heart rate, and EEG data. It then displays and saves this information. An Internet of Things-based patient monitoring framework may monitor a patient's health condition in an efficient manner and save the patient's life at the appropriate moment. © 2023 IEEE.

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.

Johan Sverdrup: the Final Lap of a True North Sea Giant

The second phase of Johan Sverdrup came on stream in December 2022. This paper focuses on the execution of Johan Sverdrup phase 2 and describes the assessments and investments for improved oil recovery (IOR) from one of the largest oil fields in Norway. The Johan Sverdrup field development has been called Equinor's ‘digital flagship', and this paper includes the proof of concept for the digital initiatives after more than three years of operation. Despite the Covid-19 pandemic Johan Sverdrup phase 2 has been able to deliver on schedule, under budget, and with an excellent safety record. The paper includes experiences from the concept development and engineering phase to the global contracting strategy, through the construction on multiple building sites in Norway and globally, and until the end of the completion phase offshore Norway. Johan Sverdrup is the third largest oil field on the Norwegian Continental Shelf (NCS), and with recoverable reserves estimated at 2.7 billion barrels of oil equivalents, has the resources to be a North Sea Giant. Start-up of the Johan Sverdrup phase 2 extends and accelerates oil and gas production from the NCS for another five decades. This paper aims to highlight what it took to make Johan Sverdrup a true North Sea Giant, fit for the 21st century: a safe and successful execution of a mega-project, with next-generation facilities adapted to a more digital way of working, with an ambition to profitably recover more than 70% of the resources, while limiting carbon emissions from production to a minimum. In many ways the Johan Sverdrup development has set a new standard for project execution in Equinor. The impact of different variables made during the execution of the project, such as the Covid-19 pandemic, market effects, procurement strategies, value improvement initiatives, execution performance and reservoir characteristics is addressed, as well as describing assessments and investments for improved oil recovery (IOR). Data acquisition, Permanent Reservoir Monitoring (PRM), fibre-optic monitoring of wells, innovative technologies, and digitalization, as well as new ways of working are included. Equinor ´s digital strategy was established in 2017, and Johan Sverdrup was highlighted as a digital flagship at that time and a frontrunner in applying digital solutions to improve safety and efficiency from the development to the operational phase. What has been implemented so far together with experiences will be shared. © 2023, Offshore Technology Conference.

The Slow Food Movement and the Terra-Madre project: food sovereignty and translocal assemblages

Since 2004 the SF Movement has developed a global community of food producers and activists called Terra-Madre. Every two years representatives of the community meet in Turin for five days of workshops and conferences. Due to Covid-19 crisis SF has radically changed the nature of the meeting, presenting a six-month calendar of digital and physical events around the world. The paper examines the exchange occurring between communities as a form of translocal activism and considers to what extent SF can be defined as a movement for food sovereignty. It draws on digital data collection on the online activities and research with participants involving semi-structured interviews and observation notes. The paper reflects on the experience of digital fieldwork and how the new format of Terra-Madre provides insights into SF as a translocal movement characterized by conflicts, common perspectives and emergent capabilities. [ FROM AUTHOR] Copyright of Globalizations is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Design and Implementation of a Real-Time Crowd Monitoring System Based on Public Wi-Fi Infrastructure: A Case Study on the Sri Chiang Mai Smart City

The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. © 2023 Author(s).

AI-Based mHealth App for Covid-19 or Cardiac Diseases Diagnosis and Prognosis

Covid-19 has rapidly spread and affected millions of people worldwide. For that reason, the public healthcare system was overwhelmed and underprepared to deal with this pandemic. Covid-19 also interfered with the delivery of standard medical care, causing patients with chronic diseases to receive subpar care. As chronic heart failure becomes more common, new management strategies need to be developed. Mobile health technology can be utilized to monitor patients with chronic conditions, such as chronic heart failure, and detect early signs of Covid-19, for diagnosis and prognosis. Recent breakthroughs in Artificial Intelligence and Machine Learning, have increased the capacity of data analytics, which may now be utilized to remotely conduct a variety of tasks that previously required the physical presence of a medical professional. In this work, we analyze the literature in this domain and propose an AI-based mHealth application, designed to collect clinical data and provide diagnosis and prognosis of diseases such as Covid-19 or chronic cardiac diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Student satisfaction and perceived learning in an online second language learning environment: A replication of Gray and DiLoreto (2016)

The current study is an approximate replication of Gray and DiLoreto's (2016) study, which proposed a model predicting that course structure, learner interaction and instructor presence would influence students' perceived learning and satisfaction in online learning, with student engagement acting as a mediator between two of the predictors and the outcome variables. Using mixed methods, the current study investigated whether Gray and DiLoreto's model would be able to explain the relationships among the same variables in a computer-assisted language learning environment. A mediation analysis was conducted using survey responses from a sample of 215 college-level students, and qualitative analysis was conducted on the survey responses from a subsample of 50 students. Similar to Gray and DiLoreto's study, positive correlational relationships emerged between the variables. However, the model proposed by Gray and DiLoreto did not fit our data well, leading us to suggest alternative path-analytic models with both student engagement and learner interaction as mediators. These models showed that the role of course organization and instructor presence were pivotal in explaining the variation in students' perceived learning and satisfaction both directly and indirectly via student engagement and learner interaction. Moreover, qualitative analysis of students' responses to open-ended questions suggested that from students' perspectives, course structure was the most salient factor affecting their experiences within online language learning contexts, followed by learner interaction, and then by instructor presence.