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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.

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.

There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges.

The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.

Information and communication technologies (ICTs) are highly associated with the study of e-government, and many scholars believe that within the coming decades, government operation and policy decision-making cannot persist without the use of ICTs (Van Dijk, 2018). This thesis aims to generate a conceptual framework of the behavioral factors that could contribute to the acceleration of the implementation of e-government services in Macao SAR. Rather than regarding e-government services as a goal to be realized in traditional practice through evaluating the outcome, a process-oriented study was conducted. The e-government services are regarded as advanced tools in the 21st century to transform Macao into a smart city. The design of the process-oriented approach and the comparative study of four groups of Macao citizens' behavioral intentions are solidly supported by the research gaps identified in the literature review of e-government studies in an international perspective and the actual context of local Macao studies. Under the framework of the Theory of Planned Behavior (Ajzen, 1985, 1991), the behavioral factors of the general public and civil servants are investigated through a qualitative approach, and the findings are triangulated from various aspects. Firstly, a systematic literature review of TPB was conducted thoroughly to better understand the current study of e-government around the world. Secondly, a content and thematic analysis of the official documents and articles from local press media and research institutes related to the topic of e-government services was carried out to demonstrate a more comprehensive picture of the current problems of implementing iv and adopting e-services in Macao SAR. Observations in some government premises that provided e-services and 40 in-depth interviews were conducted to generate detailed and first-hand data. Key issues were extracted from the interviewees’ narratives and daily actual usages. Different conceptual models for different age groups and civil servant group were formed. Special attention was paid to analyzing the "hard-to-reach" groups' behavioral intentions. Research limitations identified from the previous literature were overcome partially in this study too. After comparing the similarities and differences, a new conceptual model of significant behavioral factors that affect the behavioral intentions in adopting e-government services was built. Results and findings from the analysis could be used to develop effective interventions by the government policymakers in responding to the behavioral change of the general public in the aspect of e-government services acceptance and adoption

At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.

In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.

"As an atheistic religious tradition, Buddhism conventionally stands in opposition to Christianity, and any bridge between them is considered to be riddled with contradictory beliefs on God the creator, salvific power and the afterlife. But what if a Buddhist could also be a Classical Theist? Showing how the various contradictions are not as fundamental as commonly thought, Tyler Dalton McNabb and Erik Baldwin challenge existing assumptions and argue that Classical Theism is, in fact, compatible with Buddhism. They draw parallels between the metaphysical doctrines of both traditions, synthesize their ethical and soteriological commitments and demonstrate that the Theist can interpret the Buddhist's religious experiences, specifically those of emptiness, as veridical, without denying any core doctrine of Classical Theism. By establishing that a synthesis of the two traditions is plausible, this book provides a bold, fresh perspective on the philosophy of religion and reinvigorates philosophical debates between Buddhism and Christianity"--

Launonen and Mullins argue that if Classical Theism is true, human cognition is likely not theism-tracking, at least, given what we know from cognitive science of religion. In this essay, we develop a model for how classical theists can make sense of the findings from cognitive science, without abandoning their Classical Theist commitments. We also provide an argument for how our model aligns well with the Christian doctrine of general revelation.

"Providing an overview of key issues in theory and practice, Replication Research in Education is designed to identify and discuss the benefits and challenges facing replication studies in education. Both clear and practical, this ground-breaking volume covers how to introduce, develop, conduct, report and discuss these studies, and the issues they raise for policy and practice. Bridging theory and practice, this book considers what replication research should look like, how it should be conducted and how to judge when it has been successful. It enables researchers to plan and conduct studies successfully, from their earliest stages through to completion. This key text: brings together in a single volume, existing issues, claims and counter-claims, discourses and practices of replication introduces, covers, and extends this field of research, indicating its possibilities and limits expands and adds to existing discussions and practices will enable researchers to design, conduct, evaluate and critique studies. The comprehensive and exhaustive coverage of issues and practices within Replication Research in Education make it a "must read" for all novice and experienced educational researchers who are considering, conducting, and reviewing replication studies in education"--

Toda a obra e pensamento do Padre Manuel Antunes se revestem de características de grande abrangência e de capacidade de abertura à inovação, na perspetiva de que o pensamento crítico, sendo perscrutador do desconhecido, enquanto questiona o conhecimento adquirido ou em pesquisa, não se pode fechar em si mesmo ou separar partes do conhecimento de um todo que constitui o universo, e o homem como parte deste, já que se objetiva a compreensão última do Todo. Encontramos, portanto, traços de transdisciplinaridade na obra e pensamento do Padre Manuel Antunes indicando um pioneirismo relativamente ao movimento da transdisciplinaridade que arranca com o primeiro congresso da área e a respetiva carta daí resultante. Neste artigo, os autores propõem uma análise crítica da obra do Padre Manuel Antunes à luz dos princípios fundacionais encontrados na Carta da Transdisciplinaridade de 1994.