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  • Open Access
    Authors: 
    Caasi, Nelda B.; Pentang, Jupeth T.;
    Publisher: Zenodo

    {"references": ["Arcebuche, J. M. (2022). STUDENTS' AWARENESS AND USAGE OF OPEN EDUCATIONAL RESOURCES (OER) AS LEARNING TOOL IN THEIR COURSE STUDIES AT THE UNIVERSITY OF THE PHILIPPINES OPEN UNIVERSITY (UPOU). Universal Journal of Educational Research, 1(3). Retrieved from http://ejournals.ph/form/cite.php?id=18200", "Agayon, A., Agayon, A. K., & Pentang, J. (2022). Teachers in the new normal: Challenges and coping mechanisms in secondary schools. International Journal of Humanities and Education Development, 4(1), 67-75. https://doi.org/10.22161/jhed.4.1.8", "Bacomo, A., Daculap, L. P., Ocampo, M., Paguia, C., Pentang, J., Bautista, R. M. (2022). Modular learning efficiency: Learner's attitude and performance towards self-learning modules. IOER International Multidisciplinary Research Journal, 4(2), 60-72. https://doi.org/10.54476/s149512", "Bonilla, M., Camo, J., Lanzaderas, R. A., Lanzaderas, R., & Bonilla, A. (2022). Parental involvement on child's education at home during COVID-19 pandemic. International Journal of Humanities and Education Development, 4(3), 6-13. https://doi.org/10.22161/jhed.4.3.2", "Buar, C. L. (2022). A Phenomenological Study on the Lived Experiences of Physics Students in Laboratory Classes. Universal Journal of Educational Research, 1(2), 10-18. DOI: https://doi.org/10.5281/zenodo.6939564", "Carbonilla, M., Kadusale, G. B., Lucero, R., & Pungyan, M. (2022). Parents' coping mechanism in conquering challenges towards distribution and retrieval of modules. International Journal of Multidisciplinary: Applied Business and Education Research, 3(7), 1249-1256. https://doi.org/10.11594/ijmaber.03.07.04", "Chohan, B. I., & Khan, R. M. (2010). Impact of parental support on the academic performance and self concept of the student. Journal of Research and reflections in Education, 4(1), 14-26. https://ue.edu.pk/jrre/articles/41002.pdf", "De Apodaca, F. R., Gentling, D. G., Steinhaus, J. K., & Rosenberg, E. A. (2015). Parental involvement as a mediator of academic performance among special education middle school students. School Community Journal, 25(2), 35-54. http://www.adi.org/journal/2015fw/ApodacaEtAlFall2015.pdf", "Ghazvini, S. (2011). Relationships between academic self-concept and academic performance in high school students. Procedia-Social and Behavioral Sciences, 15, 1034-1039. https://doi.org/10.1016/j.sbspro.2011.03.235", "Gonzalez-Pienda, J., Nunez, J., Gonzalez-Pumariega, S., Alvarez, L., Roces, C., & Garcia, M. (2002). A structural equation model of parental involvement, motivational and aptitudinal characteristics, and academic achievement. The Journal of Experimental Education, 70(3), 257-287. https://www.tandfonline.com/doi/abs/10.1080/00220970209599509", "Guinto, V. M. R. (2022). The Emotional Response of Filipino Teachers-in-Training to Memes. Universal Journal of Educational Research, 1(2), 19-25. https://doi.org/10.5281/zenodo.6934683", "Hamora, L., Rabaya, M., Pentang, J., Piza\u00f1a, A., & Gamozo, M. (2022). Students' evaluation of faculty-prepared instructional modules: Inferences for instructional materials review and revision. Journal of Education, Management and Development Studies, 2(2), 20-29. https://doi.org/10.52631/jemds.v2i2.109", "Kang, X., & Wu, Y. (2022). Academic enjoyment, behavioral engagement, self-concept, organizational strategy and achievement in EFL setting: A multiple mediation analysis. PLoS ONE 17(4), e0267405. https://doi.org/10.1371/journal.pone.0267405", "Laryea, J. E., Saani, A. J., & Dawson-Brew, E. (2014). Influence of students self-concept on their academic performance in the Elmina township. European Journal of Research and Reflection in Educational Sciences, 2(4), 1-10. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1079.8872&rep=rep1&type=pdf", "Magulod, G. C., Jr., Capulso, L., Delos Reyes, R. J., Luna, A. R., Orte, C. J., Maglente, S., Pentang, J. T., Olitres, B. J., Vidal, C., & Samosa, R. (2021). How to write and publish your thesis. Beyond Books Publication. https://philpapers.org/rec/MAGHTW", "Martinez, A. (2015). Parent involvement and its affects on student academic achievement [Master's thesis, California State University]. https://scholarworks.calstate.edu/downloads/3f4626170", "Neuenschwander, M., Vida, M., Garrett, J., & Eccles, J. (2007). Parents' expectations and students' achievement in two western nations. International Journal of Behavioral Development, 31(6), 594-602. https://doi.org/10.1177%2F0165025407080589", "Pentang, J. (2021). Quantitative data analysis. Holy Angel University Graduate School of Education: Research and Academic Writing. http://dx.doi.org/10.13140/RG.2.2.23906.45764/1", "Sanchez, F. J. P., & Roda, M. D. S. (2003). Relationships between self-concept and academic achievement in primary students. Electronic Journal of Research in Educational Psychology, 1(1), 95-120. https://www.redalyc.org/pdf/2931/293152876003_2.pdf", "Sumayang, K. R., Celendron, K., Declaro, N. P., & Flandez, D. L. (2022). Mainstreaming Learners with Special Needs in a Regular Classroom: A Scoping Review. Universal Journal of Educational Research, 1(3), 106-114. https://www.ujer.org/vol1no3/article132", "Valoroso, M. B., Idulog, M. V. A., & Baslan, C. J. N. (2022). Pandemic era: The role of parents at home in the occurrence of modular distance learning. International Journal of Arts, Sciences and Education, 3(July Special Issue), 99-115. https://ijase.org/index.php/ijase/article/view/167", "Zakaria, W., Turmudi, T., & Pentang, J. (2022). Information and communication technology in elementary schools: A comparison between hybrid and face-to-face learning systems. Profesi Pendidikan Dasar, 9(1), 46-54. http://dx.doi.org/10.23917/ppd.v9i1.17534", "Zhan, Z., & Mei, H. (2013). Academic self-concept and social presence in face-to-face and online learning: Perceptions and effects on students' learning achievement and satisfaction across environments. Computers & Education, 69, 131-138. https://doi.org/10.1016/j.compedu.2013.07.002"]} Parental factors impact students’ self-concept and academic performance during the pandemic. Thus, this study determined the students’ self-concept and academic performance and the parental factors related to it. The research design was descriptive-correlational, and 500 nonrandom college students in West Philippines participated in the study. Researcher-made instruments were used, which were subjected to reliability and validity evaluation. Data were collected online from June 2021 to July 2022 and were analyzed using descriptive (frequency counts and percentage) and inferential statistics (Spearman correlation). Results revealed a positive self-concept and satisfactory academic performance among the students. Besides, parental factors such as educational attainment and school/classroom involvement are significantly related to self-concept and academic performance. Further, self-concept is significantly associated with academic performance. This shows that some parental factors are vital in developing the student’s self-beliefs and supporting their studies and academic endeavors amid the COVID-19 pandemic. Future studies may consider more factors related to academic achievement and self-concept. Other researchers may find the mediation or moderation effect of self-concept between parental factors and students’ scholastic achievement.

  • Open Access
    Authors: 
    Sunardi Sunardi; Arief Ramadhan; Edi Abdurachman; Agung Trisetyarso; Muhammad Zarlis;
    Publisher: Zenodo

    Three years after the COVID-19 pandemic emerged, we have adapted to the new normal, especially in the education field. Learning with video conferences has become our daily activity, and learning tools have gotten more prominent attention to gain student engagement, especially in emergency remote teaching (ERT). Since the trends of metaverse campaigns by meta, augmented reality (AR) has increased recognition in education contexts. However, very little research about the acceptance of augmented reality in video conferences, especially among university students. This paper aims to measure acceptance of AR in video conferences to motivate and inspire students to gain benefits and get impactful technology in the learning process. The research gathered data from a survey of 170 university students (from 5 majors in the study program and 17 different demographic areas) using unified theory of acceptance of technology 2 (UTAUT2). The result reveals that variables significantly impact acceptance: performance expectancy, hedonic motivation, and habit. The least significant but still positive effects are effort expectancy, social influence, and facilitating conditions. The study will provide helpful information on AR technology in video conferences and help top-level management in the university that provides online/distance learning in the early diffusion stage for metaverse in education.

  • Open Access English
    Authors: 
    Juana del Valle-Mendoza; Yordi Tarazona-Castro; Alfredo Merino-Luna; Hugo Carrillo-Ng; Sungmin Kym; Miguel Angel Aguilar-Luis; Luis J. del Valle; Ronald Aquino-Ortega; Johanna Martins-Luna; Isaac Peña-Tuesta; +1 more
    Publisher: BioMed Central
    Country: Spain

    Abstract Background At the end of 2019, a novel coronavirus denominated SARS-CoV-2 rapidly spread through the world causing the pandemic coronavirus disease known as COVID-19. The difference in the inflammatory response against SARS-CoV-2 infection among people living at different altitudes is a variable not yet studied. Methods A descriptive cross-sectional study was performed in two Peruvian cities at different altitudes for comparison: Lima and Huaraz. Five important proinflammatory cytokines were measured including: IL-6, IL-2, IL-10, IFN-γ and TNF-α using ELISA assays. Results A total of 35 COVID-19 patients and 10 healthy subjects were recruited from each study site. The mean levels of IL-6 (p < 0.03) and TNF-α (p < 0.01) were significantly different among the study groups. In the case of IL-6, patients from Lima had a mean level of 16.2 pg/ml (healthy) and 48.3 pg/ml (COVID-19), meanwhile, patients from Huaraz had levels of 67.3 pg/ml (healthy) and 97.9 pg/ml (COVID-19). Regarding TNF-α, patients from Lima had a mean level of 25.9 pg/ml (healthy) and 61.6 pg/ml (COVID-19), meanwhile, patients from Huaraz had levels of 89.0 pg/ml (healthy) and 120.6 pg/ml (COVID-19). The levels of IL-2, IL-10 and IFN-γ were not significantly different in the study groups. Conclusion Patients with COVID-19 residing at high-altitude tend to have higher levels of inflammatory cytokines compared to patients living at sea level, particularly IL-6 and TNF-α. A better understanding of the inflammatory response in different populations can contribute to the implementation of therapeutic and preventive approaches. Further studies evaluating more patients, a greater variety of cytokines and their clinical impact are required.

  • Open Access
    Authors: 
    Abraham Ninian Ejin; Hoe Tung Yew; Mazlina Mamat; Farrah Wong; Ali Chekima; Seng Kheau Chung;
    Publisher: Zenodo

    <span lang="EN-US">The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring.</span>

  • Open Access English
    Authors: 
    Koasidis, Konstantinos; Nikas, Alexandros; van de Ven, Dirk-Jan; Xexakis, Georgios; Forouli, Aikaterini; Mittal, Shivika; Gambhir, Ajay; Koutsellis, Themistoklis; Doukas, Haris;
    Publisher: Zenodo

    To tackle the negative socioeconomic implications of the COVID-19 pandemic, the European Union (EU) introduced the Recovery and Resilience Facility, a financial instrument to help Member States recover, on the basis that minimum 37% of the recovery funds flow towards the green transition. This study contributes to the emerging modelling literature on assessing COVID-19 vis-à-vis decarbonisation efforts, with a particular focus on employment, by optimally allocating the green part of the EU recovery stimulus in selected low-carbon technologies and quantifying the trade-offs between resulting emissions reductions and employment gains in the energy sector. We couple an integrated assessment model with a multi-objective linear-programming model and an uncertainty analysis framework aiming to identify robust portfolio mixes. We find that it is possible to allocate recovery packages to align mitigation goals with both short- and long-term energy-sector employment, although over-emphasising the longer-term sustainability of new energy-sector jobs may be costlier and more vulnerable to uncertainties compared to prioritising environmental and near-term employment gains. Robust portfolios with balanced performance across objectives consistently feature small shares of offshore wind and nuclear investments, while the largest chunks are dominated by onshore wind and biofuels, two technologies with opposite impacts on near- and long-term employment gains.

  • Open Access
    Authors: 
    Kien Trang; An Hoang Nguyen; Long TonThat; Bao Quoc Vuong;
    Publisher: Institute of Advanced Engineering and Science

    <span>Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.</span>

  • Open Access
    Authors: 
    Smriti Mishra; Ranjan Kumar; Sanjay Kumar Tiwari; Priya Ranjan;
    Publisher: Zenodo

    Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.

  • Open Access
    Authors: 
    Mohamad Fani Sulaima; Sharizad Saharani; Arfah Ahmad; Elia Erwani Hassan; Zul Hasrizal Bohari;
    Publisher: Institute of Advanced Engineering and Science

    The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The selforganizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.

  • Open Access
    Authors: 
    Helmi Imaduddin; Brian Aditya Hermansyah;
    Publisher: Institute of Advanced Engineering and Science

    Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.

  • Open Access English
    Authors: 
    Ikrame Aknouch; Adithya Sridhar; Eline Freeze; Francesca Paola Giugliano; Britt J van Keulen; Michelle Romijn; Carlemi Calitz; Inés García-Rodríguez; Lance Mulder; Manon E Wildenberg; +6 more
    Country: Netherlands
    Project: EC | OrganoVIR (812673)

    Human milk is important for antimicrobial defense in infants and has well demonstrated antiviral activity. We evaluated the protective ability of human milk against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in a human fetal intestinal cell culture model. We found that, in this model, human milk blocks SARS-CoV-2 replication, irrespective of the presence of SARS-CoV-2 spike-specific antibodies. Complete inhibition of both enveloped Middle East Respiratory Syndrome Coronavirus and human respiratory syncytial virus infections was also observed while no inhibition of non-enveloped enterovirus A71 infection was seen. Transcriptome analysis after 24h of the intestinal monolayers treated with human milk showed large transcriptomic changes from human milk treatment and subsequent analysis suggested that ATP1A1 downregulation by milk might be of importance. Inhibition of ATP1A1 blocked SARS-CoV-2 infection in our intestinal model, while no effect on EV-A71 infection was seen. Our data indicate that human milk has potent antiviral activity against particular (enveloped) viruses by potentially blocking the ATP1A1-mediated endocytic process. This work was funded under the OrganoVIR project (grant 812673) in the European Union's Horizon 2020 programme, the PPP allowance made available by Health~Holland, Top Sector Life Sciences and Health, to Amsterdam UMC, location Academic Medical Center to stimulate public-private partnerships, and funding from Stichting Steun Emma Kinderziekenhuis. The funders had no role in the design of the study, data analysis, writing of the manuscript, or in the decision to publish the results.