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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.

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.

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.

Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.

This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.

Since early times, the effects of a booming sector in other sectors of a small economy have been of interest to scholars. There is a general perception that the booming Gaming sector has contributed to the overall growth in Macau through the trickle-down effect, passing on the benefits of growth to other sectors. After the liberalization of the gaming industry in 2002, this booming sector experienced several years of exponential growth, becoming the driving industry for Macao’s economy. Several scholars and researchers have dedicated their studies to the effects of the casino gaming industry as a booming sector in such a small economy. However, there is a gap in what concerns measuring the influence of the Gaming sector as a driving industry for several other sectors or following industries of Macau’s economy. The purpose of this research study is to investigate in what measure the Gaming sector in Macao leveraged the other economic sectors and how related or correlated are the different industries of Macao’s Economy. A protocol-driven understanding of the state of the art on the interrelations between economic sectors and different techniques used to study those inter-relations was conducted through a systematic literature review. Given the limited available data on the Gross Value Added (GVA), or Gross Domestic Product (GDP) on the supply side, as a central measure of economic activity in the different sectors, several possible interpolation models using auxiliary high-frequency data (indicators) were compared, to achieve the optimal model for interpolation of each variable. Several forecasts for the future performance of Macau's four major economic sectors were presented based on different regression techniques. Autoregressive Integrated Moving Average (ARIMA) models were developed to assess the dependence of the future performance of a sector’s GVA on its past performance. Optimal Vector Autoregressive (VAR) models were created to identify the explanatory power of some sectors of Macau’s economy in others. Based on available auxiliary data in high-frequency (quarterly) it was possible to interpolate the quarterly GVA per economic sector, available only in low-frequency (annually), for the major sectors of Macao’s economy. Some sectors have a considerable explanatory power on the performance of other sectors, however, the proposed regression models did not identify a clear relation between the performance of the Gaming sector and the performance of other major sectors from Macao’s economy

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.

It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.

The adoption of project management techniques is a crucial decision for corporate governance in construction companies since the management of areas such as risk, cost, and communications is essential for the success or failure of an endeavor. Nevertheless, different frameworks based on traditional or agile methodologies are available with several approaches, which may create several ways to manage projects. The primary purpose of this work is to investigate the adequate project management methodology for the construction industry from a general perspective and consider a case study from Macau. The methodology considered semi-structured interviews and a survey comparing international and local project managers from the construction industry. The interviews indicate that most construction project managers still follow empirical methods with no specific methodology but consider the adoption of traditional waterfall approaches. In contrast, according to the survey, most project managers and construction managers agree that the project's efficacy needs to increase, namely in planning, waste minimization, communication increase, and focus on the Client's feedback. In addition, there seems to be a clear indication that agile methodology could be implemented in several types of projects, including hospitality development projects. A hybrid development approach based on the Waterfall and Agile methodologies as a tool for the project management area may provide a more suitable methodology for project managers to follow.

Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. From a global perspective, multicultural influences and societal conditions are crucial to consider. Neuroscience applications in international marketing and consumer behavior is an emergent and multidisciplinary field aiming to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. This research aimed to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different branding advertisements. The nonadvertisement exposition was also monitored while gathering survey responses at the end of each phase. A feature extraction module with a data analytics module was implemented to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.

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.

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.