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This article presents the development of a virtual platform with the Design Thinking methodology and gamified elements, to strengthen the teaching-learning process in the teachers of the González Suárez Educational Unit. This research arises from the need of a virtual platform with gamified elements that are free, easy to use, interactive and motivational. In this study user stories with the agile POV format, test cases to verify the functionality and abuse cases for robustness and to prevent the vulnerability of the virtual platform are used, as well as a b-Learning approach for testing in the classroom. Obtaining in the end as significant results a high and satisfactory assessment of the use of the virtual platform. © 2022 IEEE.
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Background: With a prevalence of 1-3 cases per million, acquired haemophilia A (AHA) is a rare autoimmune bleeding disorder caused by the presence of neutralizing antibodies against factor VIII. Even though diagnosis of this bleeding disorder is rarely established among children and adolescents, AHA may lead to severe, life-threatening hemorrhage in this age group, and therefore it requires special caution. Case report: 19 year old primigravida with confirmed SARS-CoV-2 infection was admitted to hospital due to prolonged vaginal bleeding six weeks postpartum. All gynaecological causes of uterine bleeding were excluded, Foley catheter was placed, but the bleeding still persisted. Coagulation tests revealed isolated deranged aPTT values. Further haematology evaluation demonstrated factor VIII deficiency, presence of factor VIII inhibiting factors, and the diagnosis of AHA was proposed. The anti-inhibitor coagulant complex drug was introduced and patient has responded positively to the treatment. Conclusion(s): Due to disturbance of immune system, pregnancy and postpartum period represent predilection time for AHA development. Furthermore, viral infection in pregnancy, such as COVID-19, might be considered as an additional risk factor for AHA development and several reported cases of AHA after COVID-19 infection support this hypothesis. Even though AHA is a rare disease, due to its high mortality rate of more than 20%, it should be considered in all cases of unusual bleeding of unknown cause in all age groups. Publication of this case report is approved by Institutional Review Board.Copyright © 2023
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The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.
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Many studies have shown the efficacy of pair programming for students learning to program. However, most of these studies have taken place in an in-person environment, where the driver and navigator are physically sharing a keyboard and screen and can communicate verbally and non-verbally. With the increase in online learning, especially during the COVID-19 pandemic. It is important to know whether these results generalize to an online environment. In this work, we develop a methodology to replicate existing pair programming research in a remote context. Students can fulfill the same driver and navigator roles and share access to a single IDE. However, communication is limited to video chat, and participants can never physically interact. This will allow us to replicate various studies, evaluating the efficacy, perceptions, impacts, and perceptions of solo vs. pair programming. An initial study of 116 students enrolled in an introduction to programming course validated our experimental setup and showed that pair programming positively impacted the completion and correctness of programming exercises in an online environment. With 67.3% of pair programming submissions passing at least one test case, vs. 55.3% in the solo programming condition, and 63.5% of pair programming submissions passing all test cases vs. 45.0% of solo submissions (p < 0.02). This work validates our experimental design and shows promise that future work will be able to replicate many additional pair-programming studies in an online environment. © 2022 Owner/Author.
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Introduction: Acquired hemophilia A (AHA) is a rare autoimmune disease due to anti-factor VIII antibodies. It may be associated with infections and malignancies. The association with Covid vaccine is extremely rare. Immunosuppressive therapy with steroids, cytotoxic agents, is the traditionalmainstay for antibodies eradication. Rituximab standard doses have been used with success. There are few reports on low-dose Rituximab for AHA.We present a case of AHA post Covid-19 vaccination successfully treated with low dose of Rituximab. Method(s): case report Results: A non hemophilic 69-year-old male with no medical history consulted for multiple ecchymosis that spontaneously occurred with no context of trauma. Two months previously he received a second dose of CoronaVac-Sinovac vaccine. Coagulation tests revealed an isolated and prolonged aPTT (100 sec/30s;ratio=3.33) not corrected with normal plasma. The coagulation factors assay revealed an isolated decrease of factor VIII to 1% with a titer of 121 Bethesda units/ml confirming the diagnosis of AHA. Hepatitis B and C and HIV tests were negative. A full body-computed tomography scan was normal. Treatment with Prednisolone 1 mg/kg/d was started with tranexamic acid. Bypassing therapy was not considered because of the absence of life-threatening bleeding. Seventeen days after corticosteroid initiation, a worsening of the ecchymosis was noted with the non-improvement of the aPTT. A low-dose rituximab (100 mg/week) was added for 4 weeks. After 3 doses of Rituximab a complete clinical response was achieved. Factor VIII inhibitor was completely eradicated. Corticosteroid was discontinued. At 3-month follow-up the patient remains in remission without further treatment Discussion/Conclusion:More than 50 cases of AHA following COVID-19 vaccine have been reported. To our knowledge only 2 cases of AHA were successfully treated with low dose of rituximab. Low-dose Rituximab appears to be effective for Factor VIII inhibitor eradication in AHA with a lower cost.
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The time frame of 2020 to present day 2022 primarily highlights the COVID-19 pandemic. The humanity is being largely affected by SARS-CoV-2(The Severe Acute Respiratory Syndrome CoronaVirus 2) because of its highly infectious characteristic which can be even fatal in severe cases. The World Health Organization (WHO), have reported over 544.3 million verified cases of COVID-19 globally till date, including over 6.3 million deaths. The reason why SARS-CoV-2 is considered to be a dangerous illness is due to this relatively high mortality and contagious rates, in addition to asymptomatic individuals also being carriers of the virus. The only way to identify susceptible populations and to attempt to control the spread would be via RT-PCR COVID testing of all individuals, which is time consuming and expensive. The challenges of this testing mechanism and the prolonging end of the pandemic are the primary motivation to bring up an effective system over a large test cases with a reduced time constraints. This paper proposes a combination of the pretrained convolutional neural network, VGG-16(Visual Geometry Group-16) and GRU(Gated Recurrent Unit) to differentiate the Pneumonia and COVID-19 attack from chest X-rays(CXRs). The proposed model employs VGG-16 to extract features from the CXR inputs, and the GRU classifies it. We experimented this model over 6939 CXR images with 3 classes (COVID-19, Pneumonia, and Normal) and the training produced encouraging macro average precision, recall, and f1-score of 0.9525, 0.9524, and 0.9524 respectively. These results indicate hybrid deep learning systems can greatly aid in the early detection of COVID-19 using CXRs and thereby reduce the widespread of the pandemic. We believe that early diagnosis can be easily and effectively done using this model. © 2022 IEEE.
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Minimizing failing test cases is an important preprocessing step on the path of debugging. If much of a test case that triggered a bug does not contribute to the actual failure, then the time required to fix the bug can increase considerably. However, test case reduction itself can be a time‐consuming task, especially if done manually. Therefore, automated minimization techniques have been proposed, the minimizing Delta Debugging and the Hierarchical Delta Debugging (HDD) algorithms being the most well known. In this paper, we investigated the input format of HDD, searching for structures that the algorithm cannot reduce. Motivated by the findings, we have created an algorithmic framework that enabled the use of transformations other than pruning. Furthermore, with the Transformation‐based Minimization framework, we propose to extend HDD and its coarse and recursive variants with a reduction method that does not prune subtrees but replaces them with compatible subtrees further down the hierarchy, called hoisting. We have evaluated various combinations of pruning and hoisting on multiple test suites and found that hoisting can help to further reduce the size of test cases by 27% on average and by 80% as best case compared with the baseline algorithm. [ FROM AUTHOR]
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The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing. © 2022, Springer Nature Switzerland AG.
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This paper describes a framework for enabling students to remotely interact with laboratory computers and equipment for traditional, in-person laboratory courses. This framework was developed due to the closure of campus facilities and the transition to remote course delivery in response to the COVID-19 pandemic. This transition was particularly difficult for hands-on laboratory courses. The laboratory course used as a test-case for the framework was an IoT Edge Computing design course that had an on-campus laboratory equipped with workstation computers, development boards, isolated network segments, and custom sensor hardware. Duplicating this setup would be cost prohibitive for students so remote access to the lab was essential. A detailed description of the lab station architecture, development hardware connectivity requirements, network security issues, student guides, and custom software to support the framework is presented along with the educational goals that drove those decisions. The paper concludes with a discussion of the lessons learned in this first remote delivery offering, a discussion on how remote delivery impacted student mastery of the course materials, what aspects of the remote laboratory might be generalized to other hardware-intensive courses, and what improvements may be made in future iterations of the course. © American Society for Engineering Education, 2021