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This article reviews the role that the Macao Special Administrative Region (SAR) plays in China’s cultural and public diplomacy through training programmes organised by the Macao Forum and tailored for the elite of the world’s Portuguese-speaking countries (PSCs). It begins with a review of China’s approach to key instruments of its soft-power offensive and strategy towards the developing world, followed by an overview of Beijing’s linkages with each PSC. Formulated as an expression of China’s cultural diplomacy towards the PSCs, the seminars of the Macao Forum serve as a kind of cooperation in which the provider party—rather than delivering tangible goods and services such as food, money, loans or infrastructure—actually offers grey matter in the form of ideas for initiatives in public policies and reforms, in order to foster further economic development and administrative rationalisation. Adding to an intense debate and substantial literature that discuss quantitatively and qualitatively China’s role in and aid provision to Africa, the authors argue that China, through the Macao Forum’s training programmes, courts the developing PSCs by building the capacity of their human capital, targeting in particular those in the public and private sectors who are in a position to implement their ideas.

By using both, the weak-value formulation as well as the standard probabilistic approach, we analyze the Hardy's experiment introducing a complex and dimensionless parameter ($\epsilon$) which eliminates the assumption of complete annihilation when both, the electron and the positron departing from a common origin, cross the intersection point $P$. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when $\epsilon=1$; however, even in this case we can interpret this result as a natural consequence of the fact that the particles can cross the point $P$, but at different times due to a natural consequence of the energy-time uncertainty principle.

Electronic government is increasingly dominant in the study of public administration. In analysing people's behavioural factors towards the adoption of e-services, most previous studies targeted the adult population, while those on government employees are minimal. Government employees have an essential function in the process of government operation; they can be regarded as the principal medium of communication between the service provider (government) and the end-users (citizens). This study was designed to understand the government employees' behavioural factors on their intentions towards adopting e-government services. A set of semi-structured interview questions was developed based on the prior literature on the Theory of Planned Behaviour (TPB) and e-government studies. Ten in-depth interviews were conducted in Macao SAR (Special Administrative Region). In addition to analysing the three primary constructs of TPB, the factor of Trust and some enablers and hindrances were identified. Significant findings were yielded while investigating how the government employees perceived the e-services and how they regarded the general public's perception of this issue. This contextualisation would help policymakers look at this issue from different perspectives and design feasible interventions according to group alignment strategies.

In any physical system, when we move from short to large scales, new spacetime symmetries emerge which help us to simplify the dynamics of the system. In this letter we demonstrate that certain variations on the symmetries of general relativity at large scales generate the effects equivalent to dark matter ones. In particular, we reproduce the Tully-Fisher law, consistent with the predictions proposed by MOND. Additionally, we demonstrate that the dark matter effects derived in this way are consistent with the predictions suggested by MOND, without modifying gravity.

This dissertation consists of three essays, covering the topics of foreign trade, offshoring and international rivalry. In particular, Chapter 1 analyzes the strategic capacity allocation of an international oligopoly. Because a line of products shares specific inputs that are fixed in the short run, a multiproduct oligopolist faces a capacity constraint in the production. Not being able to produce the desirable quantities to meet demand, an oligopolist strategically allocates its capacity among different products against its rival. If the market were monopolistic, a firm would mainly concern the effective profitability of a product when allocating its capacity and when responding to a capacity expansion. Identical duopolists that compete in a Cournot fashion should have identical capacity allocation. However, in a sequential game, while the Stackelberg leader allocates all its scarce capacity towards the more profitable product, the follower should still allocate some capacity towards the unprofitable product. This matches the observation that Boeing, the incumbent in the large commercial aircrafts (LCA) industry, specializes in smaller planes, while Airbus allocates resources more evenly towards both superjumbo planes and smaller planes. Chapter 2 provides an explanation to the observation that international oligopolists, which are similar in many ways (subject to the same state of technology, have equal market shares, etc.), may engage in significantly different degrees of offshoring. Different from previous studies, which considered fragmentation to be affected by global exogenous factors only, this essay sees fragmentation as an endogenous variable. A firm can invest on R&D of its own fragmentation technology to enable certain degrees of fragmentation, so that offshoring of those fragmented subparts can be achieved. An important implication of endogenous fragmentation is that the government now has a policy alternative to export subsidy. Very often, when export subsidy is prohibited under an FTA, a government has incentive to subsidize fragmentation of a firm, which can stimulate both export and offshoring. Chapter 3 investigates Macao's and Singapore's questionable goal to diversify among two tourism services—gambling and convention. Macao has a cost advantage in gambling while Singapore has a cost advantage in convention. When a city operates as a regional monopoly, the simple multiproduct model shows that it is optimal for a city to diversify in response to an expansion in the markets of the tourism services. If the two cities operate as a Cournot duopoly instead, there will be a higher degree of product differentiation between the cities. Yet, both cities diversify more when there is a market expansion. On the other hand, Osaka is a potential entrant. The three-city model shows that if Osaka's relative cost of producing convention is even lower than Singapore’s, both Macao and Singapore will produce greater proportions of gambling compared to the two-city case. In general, Macao and Singapore respond to Osaka’s rivalry by strategizing their product mixes to avoid head-on competition with Osaka.

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.

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.

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.

Family business are the dominant form of business in the world, and Chinese family business (CFB) is a unique type of family business that relies on collective action to survive. This paper argues that in CFBs, entrepreneurial actions are transgenerational collective endeavors, and successors are groomed as stewards of the family legacy. Work-life relationship in CFBs is about synergy and not balance because the family identity is the business identity, and vice-versa. Using five in-depth case studies, this research introduces an alternative understanding of CFBs and proposes a model of work-life synergy in transgenerational entrepreneurship based on discussion of five theory-based propositions. This model explains that through emphasizing on the business family's shared value and entrepreneurial legacy, elements of trust, shared identity and stewardship of family members are enhanced which leads to collective action and goal of the business family, resulting in transgenerational entrepreneurship. Limitations and future research are presented.

The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.