Notes

Optimal Consensus using Dual Problem Function of Cell IoT Based on Edge Computing.
In contrast with economic outcomes, the only educational differential effect between the two groups is Pell recipients' 41% greater likelihood to consider dropping a course mostly because of concerns that their grade would jeopardize their financial assistance. Other vulnerable students, such as first-generation students and transfer students, were relatively harder hit. To the extent that they seem to rely less on financial aid and more on income from wage and salary jobs, both their educational and employment outcomes were more negatively impacted by the pandemic relative to students whose parents also attended college or those who began college as freshmen.There is a large and growing population of dual language learners (DLLs) represented in early intervention programs in the United States, the majority of which are from Spanish-speaking families. In order to adequately serve these families, educators and speech-language pathologists must work closely with parents and provide them with culturally responsive strategies and activities that align with their language background and interaction styles. The purpose of this convergent parallel mixed methods study was to identify culturally consistent early literacy strategies specifically for parents of two- to three-year-old DLLs. Findings from a descriptive study that included 94 young DLLs and their parents engaged in a book reading task plus findings from an integrative literature review were converged to identify potential parent-implemented strategies that may support early literacy in young DLLs. From this process, a total of 26 strategies were identified in the categories of enhanced interaction, engagement with texts or storybooks, questioning behaviors, and other language enhancement. Fifteen of the strategies had compelling strength based on available work. Use of these strategies in pilot programs and future treatment studies is recommended.Artificial intelligence (AI)-aided general clinical diagnosis is helpful to primary clinicians. Machine learning approaches have problems of generalization, interpretability, etc. Dynamic Uncertain Causality Graph (DUCG) based on uncertain casual knowledge provided by clinical experts does not have these problems. This paper extends DUCG to include the representation and inference algorithm for non-causal classification relationships. As a part of general clinical diagnoses, six knowledge bases corresponding to six chief complaints (arthralgia, dyspnea, cough and expectoration, epistaxis, fever with rash and abdominal pain) were constructed through constructing subgraphs relevant to a chief complaint separately and synthesizing them together as the knowledge base of the chief complaint. A subgraph represents variables and causalities related to a single disease that may cause the chief complaint, regardless of which hospital department the disease belongs to. Verified by two groups of third-party hospitals independently, total diagnostic precisions of the six knowledge bases ranged in 96.5-100%, in which the precision for every disease was no less than 80%.The sudden appearance of COVID-19 has put the world in a serious situation. Due to the rapid spread of the virus and the increase in the number of infected patients and deaths, COVID-19 was declared a pandemic. This pandemic has its destructive effect not only on humans but also on the economy. Despite the development and availability of different vaccines for COVID-19, scientists still warn the citizens of new severe waves of the virus, and as a result, fast diagnosis of COVID-19 is a critical issue. Chest imaging proved to be a powerful tool in the early detection of COVID-19. This study introduces an entire framework for the early detection and early prognosis of COVID-19 severity in the diagnosed patients using laboratory test results. It consists of two phases (1) Early Diagnostic Phase (EDP) and (2) Early Prognostic Phase (EPP). In EDP, COVID-19 patients are diagnosed using CT chest images. In the current study, 5, 159 COVID-19 and 10, 376 normal computed tomography (CT) images of Egyptians were used as a dataset to train 7 different convolutional neural networks using transfer learning. Data augmentation normal techniques and generative adversarial networks (GANs), CycleGAN and CCGAN, were used to increase the images in the dataset to avoid overfitting issues. 28 experiments were applied and multiple performance metrics were captured. Classification with no augmentation yielded 99.61 % accuracy by EfficientNetB7 architecture. By applying CycleGAN and CC-GAN Augmentation, the maximum reported accuracies were 99.57 % and 99.14 % by MobileNetV1 and VGG-16 architectures respectively. In EPP, the prognosis of the severity of COVID-19 in patients is early determined using laboratory test results. In this study, 25 different classification techniques were applied and from the different results, the highest accuracies were 98.70 % and 97.40 % reported by the Ensemble Bagged Trees and Tree (Fine, Medium, and Coarse) techniques respectively.In the traditional electroplating industry of Acrylonitrile Butadiene Styrene (ABS), quality control inspection of the product surface is usually performed with the naked eye. However, these defects on the surface of electroplated products are minor and easily ignored under reflective conditions. If the number of defectiveness and samples is too large, manual inspection will be challenging and time-consuming. We innovatively applied additive manufacturing (AM) to design and assemble an automatic optical inspection (AOI) system with the latest progress of artificial intelligence. The system can identify defects on the reflective surface of the plated product. Based on the deep learning framework from You Only Look Once (YOLO), we successfully started the neural network model on graphics processing unit (GPU) using the family of YOLO algorithms from v2 to v5. Finally, our efforts showed an accuracy rate over an average of 70 percentage for detecting real-time video data in production lines. We also compare the classification performance among various YOLO algorithms. Our visual inspection efforts significantly reduce the labor cost of visual inspection in the electroplating industry and show its vision in smart manufacturing.When romantic partners' personal goals conflict, this can negatively affect personal goal outcomes, such as progress. In a concurrent mixed methods study, we investigated whether goal conflict and negation of goal conflict were associated with goal outcomes (progress, confidence, motivation) and what strategies partners used during the COVID-19 pandemic to negotiate goal conflict. Survey participants (n = 200) completed a daily diary for a week and weekly longitudinal reports for a month and interview participants (n = 48) attended a semi-structured interview. Results showed that higher goal conflict was associated with lower goal outcomes, and successful negotiation of goal conflict was associated with better goal outcomes. Qualitative analyses identified three goal conflict negotiation strategies (compromise, integration, concession). Conversations focused on both practical and emotional needs and included respectful communication and space from conflict (timeout or avoidance). The mixed methods results suggest that goal conflict was low during the pandemic and participants were often able to negotiate goal conflict resulting in better goal outcomes.The present study aims to examine the relationship between urban vitality, healthy environment and density through the city of Istanbul, which is going through the Covid-19 outbreak. In this context, an online survey was conducted to measure the assessments of the residents living in districts with different density categories regarding the neighborhoods and the city they live in. The evaluations made by the citizens in the dimensions of vitality, mobility, safety, healthiness, cleanliness, orderliness were reduced to two main factors as "urban vitality" and "healthy environment" using Principal Components Analysis. Then, the evaluations regarding these six variables and two factors were subjected to cross-inquiries with the personal, residential and district characteristics. Urban residents were also asked to evaluate the city life before and after the Covid-19 outbreak. The main findings of the study reveal that there is a statistically significant difference between the density levels of the districts in terms of the perception of urban vitality and some sub-variables of healthy environment. Also, there is an observed change in the thoughts about urban life in Istanbul due to the outbreak.The world has adopted unprecedented lockdown as the key method to mitigate COVID-19; yet its effect on pandemic outcomes and health disparities remains largely unknown. Adopting a multilevel conceptual framework, this research investigates how city-level lockdown policy and public transit system shape mobility and thus intra-city health disparities, using New York City as a case study. With a spatial method and multiple sources of data, this research demonstrates the uneven impact of the lockdown policy and public transit system in shaping local pandemic outcomes. Census tracts with people spending more time at home have lower infection and death rates, while those with a higher density of transit stations have higher infection and death rates. Residential profile matters and census tracts with a higher concentration of disadvantaged population, such as Blacks, Hispanics, poor and elderly people, and people with no health insurance, have higher infection and death rates. Spatial analyses identify clusters where the lockdown policy was not effective and census tracts that share similar pandemic characteristics. Through the lens of mobility, this research advances knowledge of health disparities by focusing on institutional causes for health disparities and the role of the government through intervention policy and public transit system.The latest evidence suggests neurological symptoms of long covid, such as brain fog, are caused by an immune reaction - and should be reversible, reports Jessica Hamzelou.The state has kept a tight lid on covid-19, but experts say there is little use holding out longer.Scholars have linked cost and life stress to lower voter turnout with clear implications for voting during the COVID-19 pandemic. We ask whether COVID-19 reduces turnout intention and how election agencies can mitigate this effect. We use a series of six survey and conjoint experiments implemented in samples totalling over 28,000 Canadian respondents collected between July and November of 2020 to show that 1) priming people to think about COVID-19 reduces turnout intention, especially among those who feel most threatened by the disease; 2) safety measures for in-person voting, such as mandatory masks and physical distancing, can improve safety perceptions and willingness to vote in-person, and 3) providing people information about safety precautions for in-person voting mitigates the negative effect of priming COVID-19. These studies illustrate the importance of both the implementation and communication of measures by election agencies designed to make people safe - and feel safe - while voting in-person.
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