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Virtual reality, a computer-generated 3D environment, allows one to navigate and possibly interact, resulting in real-time simulation of one or more of the user’s five senses (Gutierrez et al., 2008; Vince, 2004). Virtual tours and places have swiftly become popular in education, professional training, arts, exhibitions, and medication and rehabilitation. The empirical studies derived from the PhD thesis research aim to identify the conditions for Macao’s single-user experience to achieve mindfulness in virtual reality through immersion and interactivity.

Research in ubiquitous networked music systems has unveiled the potential of behavioural-driven interaction interfaces as an effective model to cope with network communication delays in remote musical performances. Most of the techniques developed under these premises are based on digital music interfaces implemented on laptop computers or tablet devices, where a certain degree of gestural control comes as an added dimension. The purpose of this paper is to present an implementation of such type of interfaces in the form of a physical tangible musical instrument, contemplating multiple expressive possibilities. This is viable at the current stage of technological development thanks to leveraging 3D printing and laser cutting technologies for effective prototyping and testing of such a device. The paper seeks to demonstrate that this approach opens a wide range of possibilities for creating musical instruments with versatility and expressiveness beyond what is usually accomplished in traditional instruments. This implementation, entitled “Radial String Chimes,” is presented with its advantages, the challenges it faces, and the methods used to create it. Finally, the paper will offer suggestions for further developing such an instrument to unlock its potential.

Digital inclusion in Macao is in the very beginning stage, and disability inclusion practice on social media in producing and promoting accessible social media content and needs is hard to find. This study aims to analyse the factors that influence communication staff's practice of disability inclusion on social media by using the combined employee behaviour and communication process model to provide suggestions to management who wants to promote disability inclusion practice on social media.The Service Centre for the Deaf of the Macau Deaf Association (MDA) was selected as the object for this case study. The online social media used for MDA's communication was analysed, and semi-structured in-depth interviews with members of the Association were conducted. The research findings showed that, except for the reward structure, factors examined from the work environment in terms of organisation, supervision and co-workers; communication staff themselves; outcomes of accessible social media communication; audience and feedbacks show relations with disability inclusion practices on social media. The delivery of inclusive culture, the influential power of disability stakeholders and the positioning of social media platforms are the key influencing factors.The interesting part of this study is that people without disabilities seem to be excluded from the disability inclusion practice carried out on MDA's Facebook. Their social media content is highly accessible to deaf and hard-of-hearing audiences yet seems not to involve the general public. The study object is a good model for producing accessible content, yet the optimisation of promoting social media accessibility needs further exploration.

Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.

The classification of emotions based on facial expressions have been a new topic of research in recent years, especially in marketing and consumer behavior areas. However, there is lack of studies to understand how the research topic is developed in terms of bibliometric data. Therefore, the purpose of this work is to provide a bibliometric analysis of the research on the analysis of facial expressions for marketing and consumer behavior, identifying the state of the art, the latest research direction, and other indicators. We extracted data from Web of Science (WOS) platform, considering its core database, resulting in a total of 117 articles. The software Vosviewer was used to analyze the data and graphically visualize the results. This study indicates some of the most influential authors citations and coupling analysis in this specific field, identifies journals with the most published articles, and provide trends of the research area based on the analysis of keywords and corresponding number of articles per year. The results shows that 11 articles (9.4%) were cited more than 100 times, and the two most prolific authors published 5 articles, and the two most influential authors are Bouaziz Sofien and Pauly mark(270 citations) in this field. Of the 117 articles retrieved by WOS, more than 70% were published in high impact journals. The bibliometric analysis of the existing work in this study provides a valuable and reliable reference for researchers in this field and makes a reasonable prediction of the research direction trends.

This research aims to evaluate a Macau tea brand's social media advertising effectiveness with neuromarketing tools, including physiological monitoring that can measure emotional arousal. This research bridges the gap of social media marketing on Instagram for brands through the neuromarketing method. Data from 40 respondents were collected with iMotions software using neuroscientific tools. This research uses the stimuli of Guanding Teahouse, a newly established Macau tea brand, to evaluate social media advertising effectiveness. The neuroscientific tools – Galvanic Skin Response (GSR) sensors, Eye-tracking, Facial Expression Analysis (FEA) and emotion analysis are used to do the experiment. The data analysis was drawn from one representative respondent to measure the emotions and attention on the Instagram advertisements. Video 1 recorded 9 GSR peaks and Video 2 recorded 12 GSR peaks, both videos attention is ranging between 96-98 indexes. Results show that advertising videos should focus more on the products than the model. Moreover, the participant is more interested in Video 2, but the effectiveness of advertising is showing a lower focus on the brand and the tea. Future studies should consider comparing the video advertising effectiveness of Instagram stories and Instagram reels to prevent disruption of video on the stories ad.

Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.

Resumo Os bibliotecários são profissionais que se devem manter constantemente atualizados, procurando ativamente o desenvolvimento das suas competências profissionais, pelas próprias características das funções que desempenham. Porém, é importante perceber o que pensam estes atores acerca da sua atual formação de base, concretamente se esta tem vindo a acompanhar as tendências de atualização. Assim, partindo de uma análise prévia ao conteúdo de diversos documentos orientadores, emanados de associações internacionais, no sentido de encontrar as competências preconizadas para a área de atuação, é realizado um questionário ao nível nacional e internacional a futuros profissionais, estudantes na área das ciências documentais. O principal objetivo é o de procurar entender, comparativamente, que habilidades e competências são valorizadas para o futuro desempenho profissional. O questionário foi aplicado a grupos de estudantes de ciências da documentação e informação em Portugal, Macau e República Checa, evidenciando-se as perspetivas comuns entre os inquiridos sobre as competências necessárias e aquelas que são recebidas na formação inicial, concluindo-se que existem ainda algumas assimetrias entre as necessidades da profissão e a formação recebida, na perspetiva dos futuros profissionais, independentemente do território a que pertencem.

The UTAUT-2 offers the most comprehensive assessment of individual acceptance and use of technology to date. In particular, the theoretical additions of “hedonic motivation”, “price value”, and “habit”, made the model suitable for studying technology in a consumer context. However, a review of the literature revealed that the construct of habit has been dropped from a large number of studies. There are several reasons for this, including that the technologies examined were relatively new for the respondents to form a routine behavior. Therefore, this study aims to explore whether the construct can be used as a key predictor of future intention to use an innovation rather than an acquired practice among technology users. For this purpose, a conceptual model based on the theoretical additions to the UTAUT-2 is proposed and analyzed with structural equation modeling (SmartPLS). Our results showed significant relationships between the predictors and the behavioral intention to use battery electric vehicles (BEV) technology, and, in particular, depicted the construct of habit as the strongest factor in the decision to adopt the technology. In light of our findings, the construct of habit (HT) should be used in research together with the other UTAUT-2 predictors to assess individuals’ perceptions of possible future habitual behaviors.

Small and medium-sized enterprises (SMEs) can benefit significantly from open innovation by gaining access to a broader range of resources and expertise using absorptive capacitive, and increasing their visibility and reputation. Nevertheless, multiple barriers impact their capacity to absorb new technologies or adapt to develop them. This paper aims to perform an analysis of relevant topics and trends in Open Innovation (OI) and Absorptive Capacity (AC) in SMEs based on a bibliometric review identifying relevant authors and countries, and highlighting significant research themes and trends. The defined string query is submitted to the Web of Science database, and the bibliometric analysis using VOSviewer software. The results indicate that the number of scientific publications has consistently increased during the past decade, indicating a growing interest of the scientific community, reflecting the industry interest and possibly adoption of OI, considering Absorptive. This bibliometric analysis can provide insights on the most relevant regions the research areas are under intensive development.

A growing number of people are becoming aware of the environmental footprint that our actions have on the environment. Research indicates that a key factor leading to the adoption of an electric vehicle is consumers’ high environmental concern. Indeed, the environmental concern (EC) construct is commonly associated with the purchase of sustainable and eco-friendly products in literature. Our study challenges this assumption. We examined how the environmental factor influenced the behavioral intention of Macau residents to adopt battery-electric vehicle (BEV) technology. For this purpose, we conducted a study based on the UTAUT-2 framework and used structural equation modeling (SmartPLS) to analyze the data. As a result, the choice of vehicles did not depend on the consumers’ level of concern. It appeared that consumers strongly perceived the benefits of a cleaner environment, however, when it comes to technology, environmental benefits are nice to have, rather than the primary incentive to purchase BEVs. Researchers should consider the role of environmental concern as a background factor in technology acceptance models, rather than a direct predictor of behavior. It is also recommended that marketers correctly consider this element when developing their product communications strategies, to appeal to the desired segment of customers.

This study is an attempt to understand and describe the compositional principles of Chaoshan traditional houses (CTH) through a computational space syntax. In this approach, the space syntax is used to describe and verify the compositional rules of Chaoshan houses. Chaoshan rural residence is a classical Lingnan style building in Chaoshan area of eastern Guangdong province, associated with Teo-Swa people, a Han Chinese minority. This study takes the example the prototypes existing in the village of Zhupu, Haojiang District, Shantou city as a case study, to analyse the spatial form of the residences. The Zhupu village houses date from the Qing Dynasty - Qianlong period, around 1700 AD. The hypothesis of this study is that CTH buildings are a result of a space compositional rule system that can be described and replicated through a computational design methodology. This study will establish a computational architectural syntax, and is the first stage of an extended research work on the evolution of Chaoshan residential types. The understanding of this evolution may help, as future work, to develop urban strategies for adaptation of the CTH heritage buildings to the contemporary living conditions. As the result of this study is a computational 3D graphics modelling algorithm, the ability of the system to generate the house layouts is not limited to the reconstruction of existing typologies of CTH and its variations. The same algorithm will allow the generation of new housing schemes, with adaptation to design variables extracted from a particular site and region.

The Black-Scholes equation is famous for predicting values for the prices of Options inside the stock market scenario. However, it has the limitation of depending on the estimated value for the volatility. On the other hand, several Machine learning techniques have been employed for predicting the values of the same quantity. In this paper we analyze some fundamental properties of the Black-Scholes equation and we then propose a way to train its free-parameters, the volatility in particular. This with the purpose of using this parameter as the fundamental one to be learned by a Machine Learning system and then improve the predictions in the stock market.

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.

The contemporary city is a complex system where a multitude of factors and dwellers interact, creating a multi-layered spatial structure that supports the urban life. To successfully research and intervene in this context, one needs to develop fitting approaches and methods to be able to analyze it in depth.

The degree of economic integration in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), as reflected in the mobility of trade and capital flows, has been strengthened by free trade agreements, but obstacles including border effects, capital controls, differences of exchange rate systems and inadequate cross-regional coordination remain. Digital renminbi (e-CNY) has been tested in Shenzhen, a core GBA city since April 2020. If e-CNY is adopted in the GBA, the area will effectively become a single currency zone. Whether the GBA constitutes an “optimum currency area” (OCA) depends on its degree of economic integration. This paper computes real interest rate differential (RID), uncovered interest rate differential (UID) and deviation from purchasing power parity (PPD) of each regional pair based on data of interest rates, exchange rates and price indexes from 2016M2 to 2022M7. All UID, PPD and RID series have means within about 1 percent point from 0, indicating high degrees of financial integration, real integration and economic integration. With the exception of Guangdong-Macau RID, all series are stationary, implying mean-reverting behavior. Hence, the parities are expected to hold both in the short run and in the long run, which is a condition for an OCA in the GBA. Furthermore, the regression analysis finds that the test launch of e-CNY in Shenzhen (adjusted for the COVID-19 outbreak) has significant impacts on all RIDs, Guangdong-Macau PPD and Hong Kong-Macau PPD. With merely two and a half years of test launch, the introduction of e-CNY already had impacts on overall economic integration in the GBA.