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Interculturality is considered a constant given in the development of most major religious movements during the process of propagation coming into contact with diverse tongues, mores, and sentiments. And one of the chief, if not decisive, instruments contributing to this ever dynamic spread and reception of beliefs and cultures is translation. Christianity purports to be an incarnational religion, where the Word made flesh expresses the di-vine in human terms. Its doctrines are enshrined in a faith tradition that is developed largely through interpretation and translation. This short paper will cut into this sacral literary tradition by paralleling two influential mod-ern Christian thinkers, John Henry Newman from the Anglophone school, and Joseph Ma Xiangbo from the Orient, to see how attempts at translating the ideas and works of people from distinct cultural milieux is both reflec-tive of the necessary developmental nature of Christian teachings in the historical continuum of time and space, and indicative of the intellectual challenges that never cease to accompany the literary effervescence stem-ming from comparative religious studies.
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Following the World Health Organization proclaims a pandemic due to a disease that originated in China and advances rapidly across the globe, studies to predict the behavior of epidemics have become increasingly popular, mainly related to COVID-19. The critical point of these studies is to discuss the disease's behavior and the progression of the virus's natural course. However, the prediction of the actual number of infected people has proved to be a difficult task, due to a wide range of factors, such as mass testing, social isolation, underreporting of cases, among others. Therefore, the objective of this work is to understand the behavior of COVID-19 in the state of Ceará to forecast the total number of infected people and to aid in government decisions to control the outbreak of the virus and minimize social impacts and economics caused by the pandemic. So, to understand the behavior of COVID-19, this work discusses some forecast techniques using machine learning, logistic regression, filters, and epidemiologic models. Also, this work brings a new approach to the problem, bringing together data from Ceará with those from China, generating a hybrid dataset, and providing promising results. Finally, this work still compares the different approaches and techniques presented, opening opportunities for future discussions on the topic. The study obtains predictions with R2 score of 0.99 to short-term predictions and 0.93 to long-term predictions.
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Current global shifts in education towards inclusive early childhood education are deeply engineered by the crisis of educational exclusion. In responding to exclusion, teachers have mainly utilized dominant western theories to plan and implement inclusive teaching. In this chapter, we draw on a non-western philosophy, a Nichiren Buddhist (Soka) philosophy, to provide a ‘kaleidoscopic’ lens through which to create inclusive educational learning spaces that engender full participation of all children. The Soka education philosophy is a humanist concept which can guide teachers when preparing to create inclusive education. The aims of this chapter are threefold: The first is an exploration of the Nichiren Buddhist (Soka) philosophy. The second aim is to highlight how this philosophy can enable teachers to unleash the unlimited potential of children in inclusive learning settings. Thirdly, we argue that grounding early childhood teacher education in this philosophy can help improve the effectiveness of inclusive educational experience for all children.
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In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach.
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A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.
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The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.