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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.

"In 2021/2022 academic year, there are 2,244 SEN students in Macau and its growth rate is 36% in ten years. However, at the present, there are 38 schools providing the related education to them. Admittedly, this is an unbalanced supply and demand. In fact, the teachers who work at inclusive education schools are bearing all responsibility to teach SEN students and their mental health is worth to attention. Moreover, there are 1,224 SEN students in primary (2021/2022 academic year), it accounting for 55% of all. That is, the numbers of their teachers are the most and they are representative. Therefore, exploring primary teachers’ burnout at inclusive education schools becomes the topic of this study, even more important, it is including to compare normal and resource teachers. This topic is rarer currently in Macau. On the one hand, emotional exhaustion, depersonalization and (reduced) personal accomplishment are dimensions of burnout (Maslach et al., 1996). These become the dependent variables of this study. According Ecological Systems Theory (Bronfenbrenner, 1979) and the factors of self-efficacy in inclusive education (Sharma et al., 2012), the following hypotheses are provided to guide this study: (1) normal teachers’ emotional exhaustion, depersonalization and reduced personal accomplishment are higher than those in resource teachers; (2) teachers’ attitudes into inclusive education, (3) teachers’ self-efficacy to use inclusive instruction (SEII), (4) teachers’ self-efficacy in collaboration (SEC) and (5) teachers’ self-efficacy in v managing behavior (SEMB) both are negatively related to emotional exhaustion, depersonalization and reduced personal accomplishment; (6) teachers’ stress of Covid19 is positively related to emotional exhaustion, depersonalization and reduced personal accomplishment. On the other hand, quantitative methodology, and snowball sampling are used in this research. At last, 132 responds are collected, including 100 normal teachers and 32 resource teachers. They are from 48 inclusive education schools in Macau. All data were analyzed by SPSS 25.0. The results of this study are followed: (1) teachers’ emotional exhaustion level is middle, their depersonalization and reduced personal accomplishment levels both are low; (2) resource teachers’ emotional exhaustion and depersonalization are higher than normal teachers; (3) teachers’ attitudes into inclusive education negatively related to depersonalization but positively related to reduced personal accomplishment; (4) in the factors of self-efficacy, only SEII is negatively related to reduced personal accomplishment; (5) teachers’ stress of Covid-19 is positively related to emotional exhaustion, depersonalization and reduced personal accomplishment."

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.

COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.

With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

In the face of the Covid-19 pandemic, the fashion industry was surprised and quickly had to adapt to digital media. However, the relationship between fashion and the multiplicity of screens is not new. Fashion emerged and took its first steps with Cinema, in Modernity. Although there are times when these two systems are further apart from each other, the alliance survived. To analyse contemporaneity, we take as main reference the studies of Gilles Lipovetsky, and his reflections on aesthetic capitalism. The fashion system has many Western fields of life, including art and technology. In this article we discuss how this relationship of fashion adapts and develops with aesthetic capitalism and post-digital art while we analyse representative artefacts from/about fashion. We propose to put the recent digital fashion artefacts in dialogue with post-digital aesthetics theories, discussing the blurred boundaries between the digital and the post-digital, and proposing the instantiation of a post-digital creation cycle applied to fashion artefacts.

In the light of the many kinds of journeys that have been considered pilgrimages, this book uses phenomenology as a method to examine the claim that pilgrimage is a journey to the ‘center’ during which pilgrims seek meaning s for themselves. First, by analyzing a phenomenology of Christian pilgrimage, this work attempts to identify what commonalities, as well as differences, exist between Christian pilgrimage and secular pilgrimage in terms of ‘natural attitude’. Next, by using a phenomenological method, such as transcendental reduction, the distinction between these two types of pilgrimage could be clarified that the happiness sought in Christian pilgrimage is both intentionally spiritual and sustainable, while primarily intellectual or sensory in secular pilgrimage. Lastly, this work seeks to establish whether or not ‘being at leisure’ is the primary element for pilgrims whose aim is to attain an understanding of happiness during a pilgrimage

Fishes show remarkably diverse aggressive behaviour. Aggression is expressed to secure resources; adjusting aggression levels according to context is key to avoid negative consequences for fitness and survival. Nonetheless, despite its importance, the physiological basis of aggression in fishes is still poorly understood. Several reports suggest hormonal modulation of aggression, particularly by androgens, but contradictory studies have been published. Studies exploring the role of chemical communication in aggressive behaviour are also scant, and the pheromones involved remain to be unequivocally characterized. This is surprising as chemical communication is the most ancient form of information exchange and plays a variety of other roles in fishes. Furthermore, the study of chemical communication and aggression is relevant at the evolutionary, ecological and economic levels. A few pioneering studies support the hypothesis that aggressive behaviour, at least in some teleosts, is modulated by “dominance pheromones” that reflect the social status of the sender, but there is little information on the identity of the compounds involved. This review aims to provide a global view of aggressive behaviour in fishes and its underlying physiological mechanisms including the involvement of chemical communication, and discusses the potential use of dominance pheromones to improve fish welfare. Methodological considerations and future research directions are also outlined.