Use of information technology and problems faced by students in the teaching-learning process in higher education during the 2019 Covid pandemic

The 2019 Covid pandemic has changed the interaction of human life in the education sector. The teaching-learning process in higher education uses online communication techniques more, both synchronous and synchronous. Various information technology-based applications are used for students learning to obtain learning resources and to conduct teaching-learning interactions. This is useful, but there are also many problems faced by students. This study aims to explore online communication techniques and the problems that arise in using these techniques. The research object included 80 students of the master's program. The research design used mixing methods, supported by google form as a data collection technique. Descriptive statistics were used for qualitative data analysis. The results showed that widely used online techniques by students were Google Meet, WhatsApp, Google Search, Google Scholar, Zoom Meeting, Sipejar, YouTube, and Scopus.Com. Meanwhile. Problems were related to the use of Zoom Meeting, Sipejar, Google Meet, WhatsApp, Scopus.Com, YouTube, Google Scholar, and Google Search. The source of these problems included network problems, mastery of applications, ease of use, costs, the amount of data, the troubles in the using process, and so on. Based on these problems, effective strategies for using online communication techniques are suggested. © 2023 Author(s).

Online Psycho-Educational Intervention in Mathematical Learning Difficulties

The present study shows the results of six case studies referring to an intervention applied to mathematical learning difficulties. Participants were 8 to 12 years old. The intervention considered mathematics as a language and it is theoretically based on Bronfenbrenner's bioecological model, Vygotsky's sociocultural theory and Peircean semiotics. The objective was to work on the development of academic skills associating mathematics with interactional social skills. The analysis was based on qualitative data collected during the intervention process and quantitative data from scales and instruments with pre- and post-intervention measures. However, due to the COVID-19 pandemic context, some methodological issues were affected, mainly because the evaluations took place before and in the midst of the pandemic. Social impacts of the pandemic have unevenly affected participants, especially adolescents and children. The pandemic had a worse effect on adolescents than on children, especially regarding procedures that involve memory, and those with attentional problems also had worse results.

Optimal selection of cruise ship waste disposal ports

As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

The impact of COVID-19 restrictions on social relationships of forensic psychiatric outpatients with preexisting social network-related problems: A mixed methods study

This study aimed to explore the impact of the COVID-19 restrictions on social relationships of forensic psychiatric outpatients with preexisting social network-related problems. Data from 70 participants of an ongoing randomized controlled trial, investigating the effectiveness of a social network intervention among forensic psychiatric outpatients, were examined. Demographic characteristics, quality of social relationships, loneliness, and social support were assessed at baseline. During the COVID-19 pandemic, an additional questionnaire that contained quantitative and qualitative questions regarding the impact of COVID-19 restrictions on social relationships was administered. Participants showed high levels of loneliness and dissatisfaction with social relationships before COVID-19. The majority of forensic outpatients perceived no changes on social relationships due to the COVID-19 restrictions. Qualitative results revealed some participants already lived socially isolated. Negative changes on social relationships were related to deterioration of social contacts, interruption of daytime activities, changed mental health care, and well-being. Emotional loneliness predicted deteriorated general and romantic relationships. These findings suggest that social relationships of forensic patients with preexisting social network-related problems remain of concern throughout the COVID-19 pandemic. [ FROM AUTHOR] Copyright of Journal of Forensic Psychology Research & Practice is the property of Taylor & Francis Ltd 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.)

The effect of the COVID-19 pandemic on perimenopausal symptoms

Aim: Menopause is the period of transition from the era of female reproduction to the period of loss of reproductive ability associated with the regression of ovarian functions. Perimenopause period is;It covers premenopausal (2 years before menopause) and menopausal (first 2 years after menopause). The aim of this study was to investigate the effect of the COVID-19 pandemic on menopause symptoms of women who contracted COVID-19 infection during the perimenopausal period when they were more sensitive psychosocially to the pandemic restrictions. Material and method: The study included 103 women aged 45-55 years, who presented at the Gynaecology and Obstetrics Clinic of Turhal State Hospital because of menopause symptoms between June 2021 and August 2021. The women were separated into 2 groups as 32 women who had contracted COVID-19 infection during the previous 6 months and recovered, and 71 women who had not had COVID-19. The groups were compared in respect of age, gravida, parity, body weight, menopause status, and not taking regular exercise using the Menopause Symptom Evaluation Scale. Results: Menopause status (p=0.002), not taking regular exercise (p<0.001), sleep problems (p=0.002), hot flashes (p<0.001), anxiety (p<0.001), and joint-muscle complaints (p=0.002) were determined at statistically significantly higher rates in the COVID-19 group compared to the non-COVID-19 group. Conclusion: The status of not taking regular exercise, thought to be associated with the COVID-19 pandemic restrictions, was observed to increase menopause symptoms. Hot flashes, anxiety and sleep problems in particular were found to be significant complaints in menopausal patients who had been infected with COVID-19. It must be taken into consideration that these could be associated with previous COVID-19 infection. © 2022, Pamukkale University. All rights reserved.

A Multivent System for Non-invasive Ventilation: Solving the Problem of Ventilator Shortage during the COVID-19 Pandemic

The COVID-19 pandemic has caused a severe global problem of ventilator shortage. Placing multiple patients on a single ventilator (ventilator sharing) or dual patient ventilation has been proposed and conducted to increase the cure efficiency for ventilated patients. However, the ventilator-sharing method needs to use the same ventilator settings for all the patients, which cannot meet the ventilation needs of different patients. Therefore, a novel multivent system for non-invasive ventilation has been proposed in this study. The close loop system consists of the proportional valve and the flow-pressure sensor can regulate the airway pressure and flow for each patient. Multiple ventilation circuits can be combined in parallel to meet patients’ventilation demands simultaneously. Meanwhile, the mathematical model of the multivent system is established and validated through experiments. The experiments for different inspired positive airway pressure (IPAP), expired positive airway pressure (EPAP), inspiratory expiratory ratio (I:E), and breath per minute (BPM) have been conducted and analyzed to test the performance of the multivent system. The results show that the multivent system can realize the biphasic positive airway pressure (BIPAP) ventilation mode in non-invasive ventilation without interfering among the three ventilation circuits, no matter the change of IPAP, EPAP, I:E, and BPM. However, pressure fluctuation exists during the ventilation process because of the exhaust valve effect, especially in EPAP control. The control accuracy and stability need to be improved. Nevertheless, the novel designed multivent system can theoretically solve the problem of ventilator shortage during the COVID-19 pandemic and may bring innovation to the current mechanical ventilation system. Author

Study of musculoskeletal problems among long-term computer users in Ahmedabad City

Background: COVID-19 made many changes in life of persons and even after post COVID era these changes are integral to our life. Some of the changes were online classes, work from home, and online gaming. Computer work leads to static position of neck, shoulders, and upper limbs for extended hours. This leads to higher risk of developing visual, musculoskeletal and psychological problems. Aims and Objectives: The present study was carried out to determine prevalence of musculoskeletal health disorders, assess work distribution, and their probable interaction with musculoskeletal health problems in computer users of Ahmedabad city. Material(s) and Method(s): A cross-sectional study was carried out over a period of 1-year time among 800 participants to study the musculoskeletal problems among computer users. Result(s): Out of 800 participants, 76.75% of participants had any computer related musculoskeletal problem. If participants work more than 4 h in a single spell prevalence of musculoskeletal problems was 82.95%. Regular exercise has significant role in preventing computer-related musculoskeletal problems. Conclusion(s): Computer-related musculoskeletal problems have relation with number of hours spent in single spell, total daily working hours, and years of computer-related work.Copyright © 2023, Mr Bhawani Singh. All rights reserved.

Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

How Has the Pandemic Affected Social Values? From the Perspective of Preservice Teachers, Is It Erosion or Gain?

This research aims to examine from the perspective of pre-service teachers how values, which have a great function in ensuring social order and welfare, maintaining healthy interpersonal relations, adapting the behaviour of the individual with the expectations of social life, and preventing possible social problems, are affected by the pandemic process.The research was carried out using phenomenology method, which is a qualitative research method. The study group of the research consists of twenty-five pre-service teachers. While determining the study group, easily accessible sampling method was used. In the research, a questionnaire containing four open questions developed by the researchers was used as a data collection tool. Content analysis was used in the analysis of the data. As a result of the research, it was determined that the pandemic positively affected some values such as altruism, benevolence, solidarity, gratitude, resignation, cleanliness, giving importance to being healthy, and negatively affected some values such as hospitality, freedom, equality, kindness, perseverance, and aesthetics. On the other hand, it is seen that some values such as patience, solidarity, savings and being scientific take their place among the values that both erode and gain from due to the differences in the perspective of pre-service teachers towards life. In addition, it was concluded that the pre-service teachers have a concern that the eroded values will force humanity to face problems such as various health problems, an asocial life, emotional deprivation, depression, digital addiction, selfishness, unemployment, anxiety, and impoliteness in the future.

Towards Timeline Generation with Abstract Meaning Representation

Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach. © 2023 ACM.

Contact Tracing over Uncertain Indoor Positioning Data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE

Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study

Background. Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms among Chinese nurses via machine learning (ML). Aim. To predict nurses' depressive symptoms and identify the relevant factors by machine learning (ML) algorithms. Methods. A self-administered smartphone questionnaire was delivered to nurses to evaluate their depressive symptoms;1,431 questionnaires and 28 internal and external features were collected. In the training set, the use of maximum relevance minimum redundancy ranked the features' importance. Five ML algorithms were used to establish models to identify nurses' depressive symptoms using different feature subsets, and the area under the curve (AUC) determined the optimal feature subset. Demographic characteristics were added to the optimal feature subset to establish the combined models. Each model's performance was evaluated using the test set. Results. The prevalence rate of depressive symptoms among Chinese nurses was 31.86%. The optimal feature subset comprised of sleep disturbance, chronic fatigue, physical fatigue, exhaustion, and perceived organisation support. The five models based on the optimal feature subset had good prediction performance on the test set (AUC: 0.871–0.895 and accuracy: 0.798–0.815). After adding the significant demographic characteristics, the performance of the five combined models slightly improved;the AUC and accuracy increased to 0.904 and 0.826 on the test set, respectively. The logistic regression analysis results showed the best and most stable performance while the univariate analysis results showed that external and internal personal features (AUC: 0.739–0.841) were more effective than demographic characteristics (AUC: 0.572–0.588) for predicting nurses' depressive symptoms. Conclusions. ML could effectively predict nurses' depressive symptoms. Interventions to manage physical fatigue, sleep disorders, burnout, and organisational support may prevent depressive symptoms.

Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples' interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users' search logs to reflect humans' underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples' cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks. © 2023 ACM.

Problems of Mathematics Teachers in Teaching Mathematical Content Online in Nepal

The study aimed to explore the problems of teachers in teaching mathematical contents through the online mode during COVID-19 in Nepal. A cross-sectional survey study was carried out among 415 mathematics teachers from basic school to the university level. A self-constructed questionnaire was administered online, and the data were analyzed using the t-test, ANOVA, and the hierarchical multiple regression. The result shows that Algebra, Statistics, Vectors, Geometry, and Analysis are problematic areas for teachers teaching mathematics online. The institution types, ICT training status, and years of using the laptop by teachers at the secondary level were found to be the key factors determining the problem of mathematical content teaching during online instruction although the level of problems varied with respect to the teachers' age and experience at the university level. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

Incremental Donor Approach for Immune Plasma Algorithm on Solving Path Planning Problem of UCAV

Immune Plasma algorithm (IP algorithm or IPA) that models the implementation details of a medical method popularized with the COVID-19 pandemic again known as the immune or convalescent plasma has been introduced recently and used successfully for solving different engineering optimization problems. In this study, incremental donor (ID) approach was first developed for controlling how many donor individuals will be chosen before the treatment of receivers representing the poor solutions of the population and then a promising IPA variant called ID-IPA was developed as a new path planner. For analyzing the contribution of the ID approach on the solving capabilities of the IPA, a set of experimental studies was carried out and results of the ID-IPA were compared with different well-known meta-heuristic algorithms. Comparative studies showed that controlling the incrementation of donor individuals as described in the ID approach increases the qualities of the final solutions and improves the stability of the IP algorithm. © 2022 IEEE.

Using Multiple Models to Learn about COVID-19

Furthermore, multiple scientific disciplines, such as immunology, genetics, epidemiology, and microbiology, contribute to our understanding of the pandemic. [...]COVID-19 is a complex socioscientific issue (SSI), meaning that science concepts related to the virus have real-world implications for problems in society (Zeidler 2014). SSI-based teaching and learning creates opportunities for students to grapple with real-world problems relevant to their own lives and that require consideration and evaluation of multiple, sometimes competing, factors associated with the issue. The modeling activities were embedded in a broader unit designed for high school biology classes;descriptions of the full unit and the individual modeling activities can be accessed online at https:// epiclearning.web.unc.edu/covid (Sadler et al. 2021). For this aspect of the work, we chose a Netlogo computer simulation (www.jacobkelter.com/infection-model) that allowed students to identify patterns and make sense of underlying cause-and-effect relationships associated with social distancing-two of the NGSS crosscutting concepts.

iAssist: An online wellness platform to elevate the physical and mental health of the elderly

The physical and mental health of older adults is a critical issue that is often overlooked. With the recent increase in the number of people infected with the new variants of coronavirus, we are facing several problems, including a dearth of high-quality medical care. iAssist aims to be a platform that primarily focuses on the social benefit of promptly delivering medical aid to the elderly in our nation. It enables a variety of functions, such as doctor appointments, medicine orders, and lab appointments under one roof, with the goal of assisting caregivers, such as family members and healthcare professionals. Additionally, it offers a chatbot component that uses a social media messaging service, to inform users of new developments and assist in swiftly answering user questions. The technology stack used in iAssist makes the platform efficient and user-friendly for everyone involved. © 2022 IEEE.

Connection, desperation and disillusionment: Exploring student wellbeing at a university in South Africa during the COVID-19 pandemic

University students' mental health and wellbeing has been a global public health issue of increasing concern in recent years, with a growing body of empirical evidence suggesting university students are a 'very high-risk population' for mental disorders and psychological distress. Pre-existing mental health challenges among university students have consequently been compounded by the global COVID-19 pandemic. A sample of 20 students registered in the education faculty at a large urban university in South Africa participated in a Photovoice study. The research required them to capture three photos or images of their experiences of wellbeing during the pandemic. The findings showed that students experienced mental health concerns and disillusionment with higher education. Their wellbeing was associated with a sense of connection with themselves, their peers and the campus space, and the cultivation of resilience.