Neuromarketing: Evaluating Consumer Emotions and Preferences to Improve Business Marketing Management - Zeng, Ian Mei; Marques, João Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
The invention of neuroscience has benefited medical practitioners and businesses in improving their management and leadership. Neuromarketing, a field that combines neuroscience and marketing, helps businesses understand consumer behaviour and how they respond to advertising stimuli. This study aims to investigate the consumer purchase intention and preferences to improve the marketing management of the brand, based on neuroscientific tools such as emotional arousal using Galvanic Skin Response (GSR) sensors, eye-tracking, and emotion analysis through facial expressions classification. The stimuli for the experiment are two advertisement videos from the Macau tea brand “Guanding Teahouse” followed by a survey. The experiment was conducted on 40 participants. 76.2% of participants that chose the same product in the first survey responded with the same choice of products in the second survey. The GSR peaks in video ad 1 measured a total of 60. On the other hand, video ad 2 counted a total of 55 GSR peaks. The emotions in ad1 and ad2 have similar responses, with an attention percentage of 76%. The results showed that ad1 has a higher engagement time of 11.1% and ad2 has 9.6%, but only 19 of the respondent’s conducted engagement in video ad1, and 31 showed engagement in video ad2. The results demonstrated that although ad 1 has higher engagement rates, the respondents are more attracted to video ad 2. Therefore, ad2 has better marketing power than ad 1. Overall, this study bridges the gap of no previous research on measuring tea brand advertisements with the neuroscientific method. The results provide valuable insights for marketers to develop better advertisements and marketing campaigns and understand consumer preferences by personalising and targeting advertisements based on consumers' emotional responses and behaviour of consumers' purchase intentions. Future research could explore advertisements targeting different demographics.

A review on multimodal machine learning in medical diagnostics - Yan, Keyue; Li, Tengyue; Marques, João Alexandre Lobo; Gao, Juntao; Fong, Simon James; Yan, Keyue; Li, Tengyue; Marques, João Alexandre Lobo; Gao, Juntao; Fong, Simon James (Mathematical Biosciences and Engineering, 2023)
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.

PROJECT MANAGEMENT ADOPTION AND MATURITY LEVEL IN INFORMATION TECHNOLOGY INDUSTRY OF MACAU AND HENGQIN - Wong, Ka Seng (University of Saint Joseph, 2023)
Projects are tactical and operational initiatives, and achieving specific outcomes through projects can help organizations achieve strategic goals. The effective use of project management tools and techniques is essential to achieve successful results, since the goal is to maximize the realization of the project's plan by effectively using the budget, time, and resources provided by the project owner to achieve the project's original purpose. The Project Management Maturity Model (PMMM) is a tool for measuring project management capabilities and is essential to improve project and portfolio performance in different industries. The main objective of this research is to analyze and characterize the maturity level and capacity of the IT industry in Macau and HengQin based on the assessment of the PMMM. The research also aims to assess and compare the maturity level in the IT industry in Macau and HengQin. An online survey was conducted and sent to IT project managers from Macau and HenqQin. A total of 34 responses were collected, divided into 3 different parts: Part I - General Information, Part II - Project Management Areas, and Part III - Perception. The results indicate that, in general, Project Managers state that their companies do not follow Project Management standards and best practices, classifying as Low and Very Low essential PM areas such as Planning and Scheduling (68%), Scope (61%) and Communications (64%). From a comparison perspective, project managers in Macau follow less formal frameworks than Hengqin in managing the triple constraints of the project. The collected data also indicate that Macau's communication management and stakeholder engagement are less mature than Hengqin's. Furthermore, the data indicate that maturity level is not necessarily related to education level, which means not higher education has a higher maturity level. Recommendations are provided for the IT industry in both areas, and specific comments are provided for each group or professionals. In conclusion, this work allows a novel characterization and a better understanding of the Project Management adoption and maturity level of the IT Industry in Macau and Hengqin
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph; information technology; maturity level; project management; project management maturity model; project maturity assessment

The Application of Nudge Theory in Ensuring Change Acceptance in the Hospitality-Gaming Industry – A Case Analysis from Macau SAR, China - Tagulao, Thea Clarice Saavedra; Marques, Joao Alexandre Lobo (Association for Computing Machinery, 2022)
As the rate of change increases exponentially, organizations must adapt quickly to the business landscape's volatility, uncertainty, complexity, and ambiguity (VUCA). As a result, organizations must implement agile strategies and practices to ensure their responsiveness and readiness to any changes brought about by internal or external factors. With a greater number of changes, change agents are tasked with implementing various change management methodologies to ensure that change recipients accept change initiatives. This research will look at one of the methodologies used by change agents, the use of nudges from Thaler and Sunstein's Nudge Theory, which is a subtle intervention to influence an individual's decision-making with the goal of steering them towards a specific desired outcome; and analyze their effectiveness towards the change recipients when implemented. Change agents were interviewed on the application of Nudge Theory to change recipients when managing to change initiatives within their respective organizations. The results indicate that the use of nudges created by the change agents can significantly impact the level of resistance from the change recipients. If used correctly, the Nudge Theory can mitigate change resistance, and the success of a change initiative is higher. But, if change recipients are forced to comply, their resistance will be greater, affecting the organization overall.
Keywords: Change Initiatives; Change Resistance; Nudge Theory; Organizational Behavior; Organizational Change Management

Assessment of Methodologies Employed by Change Agents in Managing Organizational Change: A Case Study in the Hospitality-Gaming Industry - Tagulao, Thea Clarice Saavedra (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Modelling the barriers that are preventing e-commerce to thrive – a case study from Portugal, international journal of business excellence - Silva, Susana Costa e; Machado, Joana Costa; Martins, Carla; Duarte, Paulo; Marques, João Alexandre Lobo (International Journal of Business Excellence, 2021)
The identification of barriers for e-commerce to thrive in specific countries is a topic of great interest. This work proposes two models to study the barriers to B2C e-commerce adoption in Portugal, highlighting obstacles less exploited by previous research: the impact of offline shopping pleasure and the influence of the distance to shopping malls on online shopping intent. An online survey was conducted based on different constructs. A multivariate OLS hierarchical regression was used to analyse the proposed models regarding the intention to buy online and the number of online purchases. The results revealed that customer satisfaction is a strong predictor of intent to buy online and that perceived product risk remains a barrier to e-commerce. Consumers living in high urbanised areas have more propensity to buy online. Helpful information is provided regarding the impact of context, culture, product, and individual barriers, showing that multichannel strategies are best suited for success.
Keywords: E-commerce barriers; Online shopping; Portugal; Retail

Towards an efficient prognostic model for fetal state assessment - Silva Neto, Manuel Gonçalves da; Madeiro, João Paulo do Vale; Marques, João Alexandre Lobo; Gomes, Danielo G. (Measurement, 2021)
Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.
Keywords: Cardiotocography; Classification; Fetal state assessment; Prognostic model; System design

Sustainable Practices in Hotel Chains—A Comparative Analysis of Official Annual Hospitality Sustainable Reports from Listed Companies in Macau SAR, China - Santos, Ana Sofia Kong; Marques, João Alexandre Lobo (Springer International Publishing, 2023)
In the last few years, the tourism industry has experienced rapid expansion and diversification, making it one of the fastest-growing financial industries in the world. Consequently, the hotel industry has significantly affected the environment's long-term viability. Many hotels have begun voluntarily implementing environmentally sustainable practices as they become more aware of their ecological footprint. There has been a great deal of discussion about the effects of hotel operations on the environment and tourism sustainability in Macau. It is because of these negative impacts that hoteliers have adopted green practices in an attempt to minimize them. By developing sustainability reports, hotels can set goals, measure performance, and manage change, resulting in better sustainability. It could also be viewed as a strategy to enhance the company’s sustainability reporting to ensure stakeholders know what the company does. The objective of this study is twofold based on the analysis of the official sustainability reports of four major hotel chains. Firstly, seven categories of sustainable practices effectively adopted by these chain hotels are identified and clusterized. Second, it is presented in which areas some hotels performed more efficiently than others, considering the UN Sustainable Development Goals (SDGs) as a reference. The results allow a comprehensive clusterized analysis of the industry in a highly developed gaming and entertainment area of South China and create a clear comparison between relevant players and their concerns about sustainability practices.
Keywords: Environmentally sustainable practices; Macau hotels; Sustainable development goals (SDGs); Sustainable reports; Tourism industry

Analysis of the Impact of Environmentally Sustainable Practices on Macau Hotels Guest Engagement - Santos, Ana Sofia Kong (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Crisis Management – Dystopic Scenario VS Real-World Pandemic: The Case of Saramago’s Blindness that Anticipated the Initial Managerial Approach to Covid-19 Pandemic Taken by Unprepared Governments Worldwide - Rouxinol, Hernâni Tiago Caveirinhas (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices - Rodrigues, Pedro Miguel; Madeiro, João Paulo; Marques, João Alexandre Lobo (Bioengineering, 2023)
In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]
Keywords: n/a

COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review - Ribeiro, Pedro; Marques, João Alexandre Lobo; Rodrigues, Pedro Miguel (Bioengineering, 2023)
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.
Keywords: COVID-19; artificial intelligence; computerized diagnostic systems; image processing; signal processing

Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity - Ribeiro, Pedro; Marques, João Alexandre Lobo; Pordeus, Daniel; Zacarias, Laíla; Leite, Camila Ferreira; Sobreira-Neto, Manoel Alves; Peixoto, Arnaldo Aires; de Oliveira, Adriel; Madeiro, João Paulo do Vale; Rodrigues, Pedro Miguel (Biomedical Signal Processing and Control, 2024)
Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.
Keywords: Accuracy; COVID-19; ECG signals; Machine learning classifiers; Non-linear analysis; –

Influence of management in change readiness maturity of teams in organizations - Reis, Joana Mateus (University of Saint Joseph, 2019)
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Training Strategies for Covid-19 Severity Classification - Pordeus, Daniel; Ribeiro, Pedro; Zacarias, Laíla; de Oliveira, Adriel; Marques, João Alexandre Lobo; Rodrigues, Pedro Miguel; Leite, Camila; Neto, Manoel Alves; Peixoto, Arnaldo Aires; do Vale Madeiro, João Paulo (Springer Nature Switzerland, 2023)
The COVID-19 pandemic has posed a significant public health challenge on a global scale. It is imperative that we continue to undertake research in order to identify early markers of disease progression, enhance patient care through prompt diagnosis, identification of high-risk patients, early prevention, and efficient allocation of medical resources. In this particular study, we obtained 100 5-min electrocardiograms (ECGs) from 50 COVID-19 volunteers in two different positions, namely upright and supine, who were categorized as either moderately or critically ill. We used classification algorithms to analyze heart rate variability (HRV) metrics derived from the ECGs of the volunteers with the goal of predicting the severity of illness. Our study choose a configuration pro SVC that achieved 76% of accuracy, and 0.84 on F1 Score in predicting the severity of Covid-19 based on HRV metrics.
Keywords: COVID-19; Electrocardiogram (ECG); Heart Rate Variability (HRV); disease severity classification; signal processing

The Future of Cosmetics Marketing Strategy: An Exploratory Study into How Neuromarketing Can Deepen its Understanding - Neto, Andreia Da Conceição (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

The Influence of Online Payment Methods on Consumers’ Online Purchase Behavior: The Case of Young People in Macau - Martins, Maria Cordes Aniceto (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

EFFECTIVENESS ANALYSIS OF WATERFALL AND AGILE PROJECT MANAGEMENT METHODOLOGIES – A CASE STUDY FROM MACAU'S CONSTRUCTION INDUSTRY - Marques, Joao Alexandre Lobo; Morais, João José Bragança dos Reis; Alves, José; Gonçalves, Marcus (Revista Gestão em Análise, 2023)
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.
Keywords: PMBOK; agile; construction industry; project management methodologies

IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring - Marques, João Alexandre Lobo; Han, Tao; Wu, Wanqing; Madeiro, João Paulo do Vale; Neto, Aloísio Vieira Lira; Gravina, Raffaele; Fortino, Giancarlo; de Albuquerque, Victor Hugo C. (IEEE Internet of Things Journal, 2021)
The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.
Keywords: Artificial intelligence (AI); Biomedical monitoring; Cloud computing; Feature extraction; Fetal heart rate; Internet of Things; Medical diagnostic imaging; Monitoring; convolutional neural networks (CNNs); feature extraction; fetal monitoring; health analytics; maternal monitoring

Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Forecasting COVID-19 Time Series Based on an Autoregressive Model - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Prediction for Decision Support During the COVID-19 Pandemic - Marques, Joao Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, José; Fong, Simon James (Predictive Models for Decision Support in the COVID-19 Crisis, 2020)

Predictive Models for Decision Support in the COVID-19 Crisis - Marques, João Alexandre Lobo; Gois, Francisco Nauber Bernardo; Xavier-Neto, Jose; Fong, Simon James (SpringerBriefs in Applied Sciences and Technology, 2021)
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.

Artificial neural network-based approaches for computer-aided disease diagnosis and treatment - Marques, João Alexandre Lôbo; Gois, Francisco Nauber Bernardo; Madeiro, João Paulo do Vale; Li, Tengyue; Fong, Simon James (Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data, 2022)
The adoption of computer-aided diagnosis and treatment systems based on different types of artificial neural networks (ANNs) is already a reality in several hospital and ambulatory premises. This chapter aims to present a discussion focused on the challenges and trends of adopting these computerized systems, highlighting solutions based on different types and approaches of ANN, more specifically, feed-forward, recurrent, and deep convolutional architectures. One section is focused on the application of AI/ANN solutions to support cardiology in different applications, such as the classification of the heart structure and functional behavior based on echocardiography images; the automatic analysis of the heart electric activity based on ECG signals; and the diagnosis support of angiogram images during surgical interventions. Finally, a case study is presented based on the application of a deep learning convolutional network together with a recent technique called transfer learning to detect brain tumors using an MRI images data set. According to the findings, the model has a high degree of specificity (precision of 0.93 and recall of 0.94 for images with no brain tumor) and can be used as a screening tool for images that do not contain a brain tumor. The f1-score for images with brain tumor was 0.93. The results achieved are very promising and the proposed solution may be considered to be used as a computer-aided diagnosis tool based on deep learning convolutional neural networks. Future works will consider other techniques and compare them with the one presented here. With the comprehensive approach and overview of multiple applications, it is valid to conclude that computer-aided diagnosis and treatment systems are important tools to be considered today and will be an essential part of the trend of personalized medicine over the coming years.
Keywords: Artificial intelligence; Computer-aided diagnosis and treatment; Deep learning; Medical imaging; Neural networks

Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview - Marques, João Alexandre Lobo; Fong, Simon James; Li, G.; Arraut, Ivan; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: COVID-19; Research cooperation; Research ethics; Scientific research; Technology development

Leadership and Neurosciences - The analysis of emotional arousal during decision-making processes with decision-makers exposed to acute stress - Marques, Joao Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
Corporate leaders are constantly dealing with stress in parallel with continuous decision-making processes. The impact of acute stress on decision-making activities is a relevant area of study to evaluate the impact of the decisions made, and create tools and mechanisms to cope with the inevitable exposure to stress and better manage its impact. The intersection of leadership and neurosciences techniques is called Neuroleadership. In this work, an experiment is proposed to detect and measure the emotional arousal of two groups of business professionals, divided into two groups. The first one is the intervention/stress group, n=30, exposed to stressful conditions, and the control group, n=14, not exposed to stress. The participants are submitted to a sequence of computerized stimuli, such as watching videos, answering survey questions, and making decisions in a realistic office environment. The Galvanic Skin Response (GSR) biosensor monitors emotional arousal in real-time. The experiment design implemented stressors such as visual effects, defacement, unfairness, and time-constraint for the intervention group, followed by decision-making tasks. The results indicate that emotional arousal was statistically significantly higher for the intervention/stress group, considering Shapiro and Mann-Whitney tests. The work indicates that GSR is a reliable stress detector and may be useful to predict negative impacts on executive professionals during decision-making activities.

Crowdsensing-Based Gamification for Collective Assistance for Post-Era of Coronavirus Epidemic in Community Living - Luo, Renfei; Marques, João Alexandre Lôbo; Ong, Kok-Leong; Fong, Simon (International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2020)
Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...

Stock Market Prediction Using Artificial Intelligence: A Systematic Review of Systematic Reviews - Lin, Coka Chinyang; Marques, Joao Alexandre Lobo (2023)
There are many systematic reviews on predicting stock. However, each of them reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review and conclude the systematic reviews on AI and stock to provide particularly useful predictions for making future strategies for stock markets. Keywords that would fall under the broad headings of AI and stock prediction were looked up in two databases, Scopus and Web of Science. We screened 69 titles and read 43 systematic reviews which include more than 379 studies before retaining 10 of them.
Keywords: Deep Learning; Long Short-Term Memory (LSTM); Machine Learning; Neural Networks (NN); Support Vector Machines (SVM)

Health and Well-Being Education: Extending the SCARF Learning Analytics Model for Identifying the Learner Happiness Indicators - Li, Tengyue; Marques, Joao Alexandre Lobo; Fong, Simon (International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 2020)
The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

IDENTIFICATION OF CRITICAL FACTORS TO DETERMINE THE INTENTION OF GOVERNMENT SERVICE MINI-PROGRAM ADOPTION THROUGH MOBILE PAYMENT PLATFORMS IN MACAU AND THE GREATER BAY AREA - Lai Chi Meng; Marques, João Alexandre Lobo (University of Saint Joseph, 2023)
Government service mini-programs have become an integral component of eGovernment in the Greater Bay Area, and successful eGovernment is necessary for building a smart city. Service quality and citizens' trust play a vital role in urban integration and in-depth cooperation in the Bay Area. The ubiquitous nature of mini-programs based on WeChat and Alipay provides excellent flexibility in accessing government services. Technology advantages, mutual recognition of cross-border data, and online transactions bring value and benefits to citizens. However, the mechanism of mini-program adoption has not been elaborated. Homogenization, conflict of regulations, and policy effectiveness are issues of great concern. This study employed Self-Determination Theory and Motivation Theory, proposed an empirical model based on the extended SOR paradigm, and aimed to identify the critical factors determining the intention of government service mini-program adoption from the user’s perspective. Six hundred and nine valid samples were collected from Macau, Hong Kong, Guangzhou, and Shenzhen through online survey platforms. The findings suggested that service quality, trust in eGovernment, ubiquity, and social influence constituted the determinants of intention to adopt. Service quality and ubiquity were salient determinants, and a great extent of service quality and ubiquity could promote perceived value and intention. Citizens' trust in government service mini-programs was reasonable, where benevolence, integrity, and competence were crucial indicators of trust. Social influence amplified and transmitted risk perception while perceived risk significantly reduced intention. Perceived value positively associated with the four determinants and enhanced user intention; it acted as a mediator with high explanatory power in the model. Government support received positive ratings from citizens; it negatively regulated the relationship between intention and the determinants respectively, implying that excessive intervention from the government could lead to inhibition. Finally, we proposed relevant implications and suggestions for the GBA government agents and policymakers
Keywords: Thesis and Dissertations PhD in Government Studies (D-GS); University of Saint Joseph

An Analysis of Celebrities and KOLs Influence Consumer Decisions on the Luxury Fashion Market in Macau - Ip, San U. (University of Saint Joseph, 2022)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Neurofinance: Exploratory Analysis Stock Trader's Decision-Making Process by Real-Time Monitoring of Emotional Reactions - Hsu, Hsin-Tzu; Marques, João Alexandre Lobo (European Conference on Management Leadership and Governance, 2023)
Human emotions can be associated with decision-making, and emotions can generate behaviors. Due to the fact that it could be biased and exhaustively complex to examine how human beings make choices, it is necessary to consider relevant groups of study, such as stock traders and non-traders in finance. This work aims to analyze the connection between emotions and the decision-making process of investors and non-investors submitted to the same set of stimuli to understand how emotional arousal might dictate the decision process. Neuroscience monitoring tools such as Real-Time Facial Expression Analysis (AFFDEX), Eye-Tracking, and Galvanic Skin Response (GSR) were adopted to monitor the related experiments of this paper and its accompanying analysis process. Thirty-seven participants attended the study, 24 were classified as stock traders, and 13 were non-traders; the mean age for the groups was 35 and 25, respectively. The designed experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. On the contrary, investors were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. Future works may consider expanding the study to larger sample size, different demographic groups, and other biometrics for further analysis and conclusions.

NEUROFINANCE - ANALYZING AND UNDERSTANDING STOCK TRADER’S DECISION-MAKING PROCESS BASED ON REAL-TIME MONITORING OF EMOTIONAL REACTIONS - Hsu, Hsin-Tzu (University of Saint Joseph, 2023)
Human emotions can be meticulously associated with decision-making, and emotion can generate behaviours. Due to the fact that it could be bias and exhaustively complex to examine how human beings make choices, important groups of study in finance are stock traders and non-traders. The objective of this work is to analyze the connection between emotions and the decision-making process of investors and non-investors to understand how emotional arousal might dictate the process of deciding policy. As facial expressions are fleeting, neuroscience tools such as AFFDEX (Real-Time Facial Expression Analysis), Eye-Tracking, and GSR (galvanic skin response) were adopted to facilitate the experiment and its accompanying analysis process. Thirty-seven participants attended the study, ranging from 18 to 72 years old; the distribution of investors and non-investors was twenty-four and thirteen, respectively. The experiment initially disclosed a thought-provoking result between the two groups under the certainty and risk-seeking prospect theory; there were more risk-takers among non-investors at 75%, while investors were inclined toward certainty at 79.17%. The implication could be that the non-investing individuals were less complex in thought and therefore pursued higher returns besides a high probability of losing the game. In addition, the automatic emotion classification system indicates that when non-investors confronted a stock trending chart beyond their acquaintance or knowledge, they were psychologically exposed to fear, anger, sadness, and surprise. Investors, on the contrary, were detected with disgust, joy, contempt, engagement, sadness, and surprise, where sadness and surprise overlapped in both parties. Under time pressure conditions, 54.05% of investors or non-investors tend to make decisions after the peak(s) of emotional arousal. Variations were found in the deciding points of the slopes: 2.70% were decided right after the peak(s), 37.84% waited until the emotions turned stable, and 13.51% were determined as the emotional indicators started to slide downwards. Several combinations of emotional responses were associated with decisions. For example, negative emotions could induce passive decision-making, in this case, to sell the stock; nevertheless, it was also examined that as the slope slipped downwards to a particular horizontal point, the individuals became more optimistic and selected the "BUY" option. The support of physiological monitoring tools makes it possible to capture the individuals' responses and discover the science of decision-making. Future works may consider expanding the study to more significant demographic populations for further discoveries
Keywords: Decision-making; Emotions; Investors; Physiological Monitoring; Stock Trading; Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer's Biosignals and Emotions - Goncalves, Marcus V.; Marques, Joao Alexandre Lobo; Silva, Bruno Riccelli Santos; Luther, Valorie; Hayes, Sydney (2022)
Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. Multicultural influences and societal conditions are also crucial aspects to consider from a global perspective. Applying neuroscience tools and techniques in international marketing and consumer behavior is an emergent and multidisciplinary field that aims 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. The primary purpose of this research was to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different types of branding advertisements. The non-advertisement exposition was also monitored during the gathering of survey responses at the end of each phase. A feature extraction module was implemented with a data analytics module 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.
Keywords: Branding Reaction; Consumer Biosignal; Galvanic Skin Response; consumer decision-making; eye-tracking; neuromarketing; neurosciences

Intersectoral Linkages in Booming Sector Economies - Gomes, Sérgio Ricardo Santos; Marques, Joao Alexandre Lobo; Reis, Ricardo Ferreira (ICEME 2022: 2022 13th International Conference on E-business, Management and Economics, 2022)

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures - Gois, Francisco Nauber Bernardo; Marques, João Alexandre Lobo; de Oliveira Dantas, Allberson Bruno; Santos, Márcio Costa; Neto, José Valdir Santiago; de Macêdo, José Antônio Fernandes; Du, Wencai; Li, Ye (Computer and Information Science, 2023)
Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system.

Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures - Gois, Francisco Nauber Bernardo; Marques, João Alexandre Lobo; De Oliveira Dantas, Allberson Bruno; Santos, Márcio Costa; Neto, José Valdir Santiago; De Macêdo, José Antônio Fernandes; Du, Wencai; Li, Ye (Computer and Information Science, 2023)

An Exploratory Study into How the Application of Neurosciences can Assist in Understanding the Decision-Making Process Under Stress - Furtado, Filipa Magalhães de Moura e Mota (University of Saint Joseph, 2021)
Keywords: Thesis and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System - Fong, Simon James; Marques, João Alexandre Lobo; Li, Gloria; Dey, Nilanjan; Crespo, Rubén González; Herrera-Viedma, Enrique; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: COVID-19; Composite Monte Carlo simulation; Healthcare decision-making systems; Prediction

The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic - Fong, Simon James; Marques, João Alexandre Lobo; Li, Gloria; Dey, Nilanjan; Crespo, Rubén González; Herrera-Viedma, Enrique; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: ANN predictor; COVID19; Epidemiology; Fuzzy predictor; PID control; SEAIRD

Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models - Fong, Simon James; Marques, João Alexandre Lobo; Li, G.; Dey, N.; Crespo, Rubén G.; Herrera-Viedma, E.; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: Adaptive SEAIRD model; Adaptive SVEAIRD model; Asymptomatic cases; Prediction models

The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak - Fong, Simon James; Marques, João Alexandre Lobo; Li, G.; Dey, N.; Crespo, Rubén G.; Herrera-Viedma, E.; Gois, F. Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: Artificial neural networks; Epidemiology; Machine learning; Prediction models

Categorization of Foreign Aid Donors: A Critical Review of the Criteria in Light of China's Reemergence as a Donor - Diakite, Ansoumane Douty; Marques, João Alexandre Lobo (Journal of Global South Studies, 2023)
Over the past several decades, the dichotomy between traditional and emerging donors has been based upon the notion that emerging donors (such as China) support authoritarian regimes and use foreign aid to pursue their economic interests at the expense of the poor in the recipient countries. Accordingly, Western donors, media, and scholars portray Chinese aid as non-poverty-focused. This study aims to review and analyze whether the dichotomy between traditional and emerging donors is still relevant in the current aid system and to propose a new and rigorous criterion for recategorizing donors. In terms of methodology, this study relies on secondary data, including scholarly works on traditional and emerging donors and foreign aid policy documents. Conclusions based on the research indicate that the divide between traditional donors and (re)emerging donors is becoming more ambiguous. The literature review indicates that the two donors’ aids had a mixed impact and that their approaches were similar. This paper highlights the importance of developing different recategorization criteria depending on the impact of aid.
Keywords: China and development assistance committee donors; aid policies; categorization of donors; sustainable development goal; traditional and emerging donors

Lead: An iOS Application to Help in the Construction of New Habits - de Aguiar, André Wescley Oliveira; Bezerra, Jagni Dasa Horta; Marques, João Alexandre Lobo; de Alexandria, Auzuir Ripardo (International Journal of Information and Electronics Engineering, 2019)

A prospective analysis about eEmployee retention strategies for Macao hotel industry - Chong, Ka Yee (University of Saint Joseph, 2019)
Keywords: Theses and Dissertations Master of Business Administration (MBA); University of Saint Joseph

Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks - Caldas, Weslley Lioba; Do Vale Madeiro, João Paulo; Pedrosa, Roberto Coury; Gomes, João Paulo Pordeus; Du, Wencai; Marques, João Alexandre Lobo (Computer and Information Science, 2023)

Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks - Caldas, Weslley Lioba; do Vale Madeiro, João Paulo; Pedrosa, Roberto Coury; Gomes, João Paulo Pordeus; Du, Wencai; Marques, João Alexandre Lobo (Computer and Information Science, 2023)
Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 ± 0.02$$accuracy in the multi-classification experiment and also $$0.94 ± 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.
Keywords: Chagas disease; Deep learning; ECG quality assessment

A Quantum Field Formulation for a Pandemic Propagation - Arraut, Ivan; Marques, João Alexandre Lobo; Fong, Simon James; Li, Gloria; Gois, Francisco Nauber Bernardo; Neto, José Xavier (Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
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.
Keywords: COVID-19; Mathematical modeling; Nonlinear analysis; Quantum field theory

On the probability flow in the Stock market I: The Black-Scholes case - Arraut, Ivan; Au, Alan; Tse, Alan Ching-biu; Marques, Joao Alexandre Lobo (arXiv.org, 2020)
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.
Keywords: General Finance