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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

COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.

This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.

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.

The spontaneous symmetry breaking phenomena applied to Quantum Finance considers that the martingale state in the stock market corresponds to a ground (vacuum) state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena completely ignores the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cases. However, when we deal with the martingale condition, it comes out that the kinetic terms can also behave as potential terms and then reproduce a shift on the effective location of the vacuum (martingale). In this paper, we analyze the effective symmetry breaking patterns and the connected vacuum degeneracy for these special circumstances. Within the same scenario, we analyze the connection between the flow of information and the multiplicity of martingale states, providing in this way powerful tools for analyzing the dynamic of the stock markets.

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...

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.

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

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

China growing awareness of sustainability has brought out relevant aspects to move towards a green environment. Since its subscription in 2016, China has been committed to implementing the Paris Agreement, and the Greater Bay Area (GBA) development plan prioritizes ecology and pursuing green development. The primary purpose of this research is to perceive the companies' insights concerning the implementation of sustainable buildings’ projects in Macau. For this multi-case study analysis, primary data was gathered from interviews with two groups involved in the construction projects’ lifecycle: Consultants and Contractors, to analyze different perceptions and concerns. The interviews considered two different themes about the main topic: (1) Perception on Companies’ Experience in Sustainable Projects; (2) Key Drivers towards Sustainable Buildings’ Projects’ Implementation. In conclusion, according to the analyzed data, it is essential to notice that companies’ background and the market particularities affect their corporate performance specially connected to the green construction frameworks. The data also indicate that it is necessary to move towards regulations and policies to change corporate and people's mindset.

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.