Technological variables predictors of academic stress in nursing students in times of COVID-19

Objective: to analyze which technological variables, derived from the use of electronic devices, predict academic stress and its dimensions in Nursing students. Method: analytical cross-sectional study carried out with a total of 796 students from six universities in Peru. The SISCO scale was used and four logistic regression models were estimated for the analysis, with selection of variables in stages. Results: among the participants, 87.6% had a high level of academic stress;time using the electronic device, screen brightness, age and sex were associated with academic stress and its three dimensions;the position of using the electronic device was associated with the total scale and the stressors and reactions dimensions. Finally, the distance between the face and the electronic device was associated with the total scale and size of reactions. Conclusion: technological variables and sociodemographic characteristics predict academic stress in nursing students. It is suggested to optimize the time of use of computers, regulate the brightness of the screen, avoid sitting in inappropriate positions and pay attention to the distance, in order to reduce academic stress during distance learning.

SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance;the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation of COVID-19 lesion. We collected multinational CT scan data from China, Italy and Russia and conducted extensive comparative and ablation studies. The experimental results demonstrated that our method outperforms state-of-the-art models and can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% vs. U-Net++: 84.25%), sensitivity (our: 93.28% vs. U-Net++: 89.85%) and Hausdorff distance (our: 19.99 mm vs. U-Net++: 26.79 mm), respectively.

Bridging the Digital Divide: Distance Music Learning among Older Adult Musicians

The purpose of this study was to examine the experiences of conductor-teachers and older adult musicians in a New Horizons ensemble engaged in distance online music-making and music learning. This study employed intrinsic and particularistic qualitative case study designs in which older adult musicians and conductor-teachers of a New Horizons orchestra were interviewed and observed for one year during the COVID-19 pandemic. Primary data sources included verbatim interview transcripts of 11 older adult musicians and the principal conductor, e-mail correspondences, video recordings, and the principal conductor's journal entries. Findings distilled from the data included (a) the information communication technology (ICT) and music learning technology (MLT) introduced and the technological pedagogical and content knowledge (TPACK) needed to teach orchestra members and (b) how orchestra members navigated both ICT and MLT to engage in meaningful music-making and music learning in a distance learning environment. Implications for research and practice include challenging implicit assumptions and messages regarding technology use among older adult musicians, continuing post-COVID distance music learning that may lead to promising models for informal music learning, and continued connectivity beyond the locality of the rehearsal hall.

The Impact of Disadvantage on Higher Education Engagement during Different Delivery Modes: A Pre- versus Peri-Pandemic Comparison of Learning Analytics Data

The pandemic forced many education providers to pivot rapidly their models of education to increased online provision, raising concerns that this may accentuate effects of digital poverty on education. Digital footprints created by learning analytics systems contain a wealth of information about student engagement. Combining these data with student demographics can provide significant insights into the behaviours of different groups. Here we present a comparison of students' data from disadvantaged versus non-disadvantaged backgrounds on four different engagement measures. Our results showed some indications of effects of disadvantage on student engagement in a UK university, but with differential effects for asynchronously versus synchronously delivered digital material. Pre-pandemic, students from disadvantaged backgrounds attended more live teaching, watched more pre-recorded lectures, and checked out more library books than students from non-disadvantaged backgrounds. Peri-pandemic, where teaching was almost entirely online, these differences either disappeared (attendance and library book checkouts), or even reversed such that disadvantaged students viewed significantly fewer pre-recorded lectures. These findings have important implications for future research on student engagement and for institutions wishing to provide equitable opportunities to their students, both peri- and post-pandemic.

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

An optimisation approach for determining the efficiency of vital medical devices in intensive care units with COVID-19 patients using Apriori algorithm

Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.

Mining association rules for classification using frequent generator itemsets in arules package

Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.

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.

Evaluation of Online Education in the Era of COVID-19 Pandemic: A Review from Students, Parents, and Teachers' Perspectives

Qualitative content analysis is used in this study to review related online education since the outbreak of COVID-19. The aim of this study was to summarize the impact of online teaching on the education industry during the pandemic, sum up the viewpoints of all kinds of people to draw conclusions, and conclude the practical countermeasures. Based on the result of the analysis, firstly, we think that students and teachers are satisfied with online education, but parents have expressed dissatisfaction with this kind of education. Secondly, this paper lists the advantages and common problems of online teaching during study at home from different aspects. According to deficits, we summarize the solutions from three aspects: network equipment, teaching, and self-adjustment. This research is of great significance. It is not only beneficial to the development of educational platforms and personalized teaching but also helps formulate education policy to reduce the burden of education.

The Effect of Course Format Change on Learning Behaviors and Performance of Students

This study investigates how the course format change caused by covid-19 pandemic affected learning behaviors and performance of college students enrolled in a large introductory history course. Clickstream log files capturing how students were interacting with online learning contents were analyzed to identify the learning behaviors of students before and after the mid-semester course format change. The non-parametric regression model was developed to examine the relationship between learning performance of students and course format change. Although the frequency of accessing learning resources decreased during the first three weeks after the course format change, it had a relatively small effect on the learning performance of students. The quantile regression model indicates the mid-semester course format change is associated with about 3.3% decrease in the learning performance of students. These results suggest that students were quite resilient and their learning during the pandemic was not as bad as we feared.

A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).

Examining Attitudes and Online Distractors in Turkish EMI Context during Emergency Remote Teaching

This study aimed to examine the attitudes of students enrolled in an English preparatory program of a Turkish state university towards the use of emergency remote teaching as a mode of distance education in the 2019-2020 COVID-19 outbreak and to reveal online distractors students experienced throughout this process. A total of 270 EFL students participated in the study;93 of these participants were female, and 177 were male. The study employed an explanatory sequential design, in which firstly quantitative data were collected using a scale ([alpha] = 0.871), and then qualitative data were gathered through open-ended questions followed by semi-structured ones. The quantitative data were analyzed through descriptive and inferential statistics using SPSS software while the qualitative data were analyzed through a thematic analysis conducted by the researcher and two other experts ([kappa] = 0.70). The results showed that the students held partially positive attitudes towards the use of emergency remote teaching as a mode of distance education. There were significant differences between the students' overall attitudes and their gender, digital literacy, technological accessibility, and perceived language success. The relationship between the online distractors students experienced during Emergency Remote Teaching and their attitudes was also discussed. The conclusions were made in the light of the findings, and implications and suggestions for further research were stated.

Orchestrating Image Retrieval and Storage Over a Cloud System

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ([Formula Omitted], [Formula Omitted], and [Formula Omitted]). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.

Report on the Condition of Education 2023. NCES 2023-144

The "Report on the Condition of Education" is a congressionally mandated annual report from the National Center for Education Statistics (NCES). Using the most recent data available (at the time this report was written) from NCES and other sources, the report contains key indicators on the condition of education in the United States at all levels, from prekindergarten through postsecondary, as well as labor force outcomes and international comparisons. There are core indicators that are updated every year and spotlight indicators that provide in-depth analyses on topics of interest to education agencies, policymakers, researchers, and the public. At the broadest level, the Condition of Education Indicator System is organized into five sections: family characteristics;preprimary, elementary, and secondary education;postsecondary education;population characteristics and economic outcomes;and international comparisons. The Report on the "Condition of Education 2023" encompasses key findings from the Condition of Education Indicator System. The full contents of the Indicator System can be accessed online through the website or by downloading PDFs for the individual indicators. [For "The Condition of Education 2023": At a Glance, see ED628291. For the "Report on the Condition of Education 2022. NCES 2022-144," see ED619870.]

RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images

In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.

Joint Transmission Scheme and Coded Content Placement in Cluster-Centric UAV-Aided Cellular Networks

Recently, as a consequence of the COVID-19 pandemic, dependence on telecommunication for remote learning/working and telemedicine has significantly increased. In this context, preserving high Quality of Service (QoS) and maintaining low-latency communication are of paramount importance. In cellular networks, the incorporation of unmanned aerial vehicles (UAVs) can result in enhanced connectivity for outdoor users due to the high probability of establishing Line of Sight (LoS) links. The UAV's limited battery life and its signal attenuation in indoor areas, however, make it inefficient to manage users' requests in indoor environments. Referred to as the cluster-centric and coded UAV-aided femtocaching (CCUF) framework, the network's coverage in both indoor and outdoor environments increases by considering a two-phase clustering framework for Femto access points (FAPs)' formation and UAVs' deployment. Our first objective is to increase the content diversity. In this context, we propose a coded content placement in a cluster-centric cellular network, which is integrated with the coordinated multipoint (CoMP) approach to mitigate the intercell interference in edge areas. Then, we compute, experimentally, the number of coded contents to be stored in each caching node to increase the cache-hit-ratio, signal-to-interference-plus-noise ratio (SINR), and cache diversity and decrease the users' access delay and cache redundancy for different content popularity profiles. Capitalizing on clustering, our second objective is to assign the best caching node to indoor/outdoor users for managing their requests. In this regard, we define the movement speed of ground users as the decision metric of the transmission scheme for serving outdoor users' requests to avoid frequent handovers between FAPs and increase the battery life of UAVs. Simulation results illustrate that the proposed CCUF implementation increases the cache-hit-ratio, SINR, and cache diversity and decrease the users' access delay, cache redundancy, and UAVs' energy consumption.

Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Technological Devices and Digital Competences: A Look into the Digital Divides for University Continuity during the COVID-19 Pandemic

Virtual education has gained great relevance in recent years, due to the pandemic. The access to electronic devices and services represents an urgent necessity and thus the concern for acquiring digital competences, which allow a proper interaction within the teaching–learning process. Recent studies have demonstrated the importance of having digital resources and the adaptability of their use from the university students' homes during the pandemic crisis. This research intends to identify the relevant challenges regarding the accessibility to technological devices and digital competences that university students had to face to obtain suitable learning during the lockdown, due to the pandemic. The sample information consisted of 9326 Peruvian university students. The data was obtained from the National Homes Survey from the Statistics and Information National Institute, and it was distributed in twenty-five regions (in groups of five macro-regions) over a period of three years (2019–2021). The results showed significant differences in the number of students with internet access from home: between 40% and 60% access classes with a desktop or laptop, and digital competences have improved in the last year. This is evidence that digital divides set limits on the opportunities for a quality education.