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Objective: Over the past decade, arbitration has grown in popularity as a method of resolving commercial disputes worldwide. However, this practice is relatively new in Macao SAR. Recently, official plans were announced to make Macao as a seat of arbitration for commercial disputes between China and Portuguese-speaking countries (Hereinafter PSCs). This article is dedicated to explores the possibility of Macao undertaking and implementing such a role. Accordingly, this article addresses the following issues: What are the strengths and weaknesses of Macao as a seat and eventually as venue for hosting international commercial arbitration between Chinese and PSCs entrepreneurs?Methodology: A mixed-method approach of legal doctrinal and empirical research was used in this article. We first included a thorough study of the concept of arbitration followed by analysis of various legal journals and legislations, including Macao, China, and PSCs’ arbitration laws. An empirical research was then used to collect data by surveying and interviewing with both lawyers and arbitration practitioners from Macao, China and PSCs.Results: This article argues that the strength of Macao resides in the similarities between its legal system and that of the China and PSCs and the languages advantage (Chinese and Portuguese both official languages). In spite of this, arbitration is still relatively underutilized in the region, and there is a limited number of arbitrators and legal professionals with bilingual proficiency.Contributions: This article contributes to the identification of the opportunities and challenges that Macao faces in its potential future development as a seat/venue of arbitration between China and the PSCs.

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.

Macau has long been considered to be an example of remarkable economic growth. With the opening of the gaming sector in 2002, the casino and hospitality sector flourished, creating employment opportunities but also imposing several challenges on managers. Since Macau endeavors to be positioned as the center for international business with Portuguese-speaking countries and a platform for trading with China’s Greater Bay Area (GBA), it becomes essential for international enterprises to understand the local dynamics. In light of the limited research available, this study aims to identify management challenges from the perspectives of senior executives in different industries based in Macau. Our findings point out that managers must contend with several issues, such as the lack of a skilled local talent pool, high turnover rates, employees' work attitudes, and a tightly controlled immigration policy. It is also imperative for international managers to nurture relationships and pay attention to the local culture. Our results suggest that Macau has to develop a highly skilled local workforce to attract international companies, while local organizations also have to create an attractive working environment to compete in the marketplace.

Substitute foods are increasingly popular to reduce our environmental footprint and promote food security. As the world population is expected to grow and food resources become scarce, insects as food have recently gained attention as a viable alternative. In the present study, a model grounded on the Theory of Planned Behavior (TPB) is proposed and analyzed through structural equation modeling software (SmartPLS) to assess consumers intentions toward insects as food. Except for subjective norm, both attitude and perceived behavioral control were key determinants of intention and, in turn, of actual use behaviour. Despite insects being consumed in nearly 1/4 of the sample (for instance in Chinese medicine), the study found that respondents were on average relatively unwilling to use them as a dietary habit. Also, it appeared that men were more likely to consume insects as food than women. The insights of our study have important implications for practitioners and policymakers seeking to promote sustainable nutritional practices among consumers. This study is particularly relevant for Macau, as the city positions itself as a "UNESCO Creative City of Gastronomy" with the aim to develop internationally a unique and sustainable food image.

China’s economy has entered a critical period of structural adjustment. The developing green industries and the transforming traditional industries have increasing demand for finance, making ""green finance"" increasingly essential. While China's green finance is in the development stage, some newly developed zones serve as pilots for the launch of green financial products. An example is Tongzhou District of Beijing, which aims to expand Beijing’s space, promote the coordinated development of Beijing-Tianjin-Hebei, and explore the optimal development mode of the densely populated economic areas. This thesis aims to study consumer acceptance of green financial technology (fintech) in the case of Tongzhou District. This thesis extended the commonly applied theoretical model for the problem of study, the Energy Augmented Technology Acceptance Model (EA-TAM), to analyze the impacts of perceived usefulness, perceived ease of use, attitude toward use, intention, usage intention, environmental awareness, and green knowledge on the acceptance of green fintech in Tongzhou District. The survey collected 403 valid responses from people that had been active in Tongzhou District. The quantitative analysis is based on structural equation modeling techniques, including reliability analysis, validity analysis, standard method deviation test, and hypothesis testing. The analytical results show that all the hypothesized factors are significant. In addition, the sample is divided into different gender groups and education groups, so that the impacts of the socio-demographic characteristics can be explored. Males’ environmental awareness and green knowledge are insignificant in determining their acceptance of green fintech. The low-educated group’s acceptance of green fintech does not significantly depend on environmental awareness and perceived usefulness

Macau, Macau Business, MAG, MB, MB Featured, Opinion | Despite the welcome optimism expressed at the government’s plans to resurrect Macau’s economy, its economic recovery will continue to suffer from having had the rug pulled from under its feet by the zero-Covid policy, however well intentioned.

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.

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.

The various volumes coordinated by Pierre Nora to pursue a history of the places of memory in France have become a multidisciplinary theoretical reference for those who, like us, seek to reconstruct the memories with which the land of the Potiguara aborigines of Brazil is organized today. In the introduction to the voluminous work that he directed for eight years, Nora explained his epistemic understanding of the notion of “places of memory”, stressing that a “lieu de mémoire” is any significant entity that, material or immaterial in nature, through a human will or the wear and tear of time, has become a symbolic element of a community's memorial heritage. The French historian also added that, since memory is the fundamental structure of this generally lengthy process, it was convenient to understand it as a phenomenon of emotions and magic that only accommodates the facts that feed it. Strictly speaking, memory is always vague, and reminiscent, stirring both general impressions and fine symbolic details. Furthermore, memory is always vulnerable to transference, repressed and imagined memories, censorship, and all kinds of projections. (Nora, 1984). In this article, we try to understand that the places of memory are also almost always what comes to us, stays, and selects the past. The reserve where they live appears as a symbolic locus to which the Potiguara aborigines cling with all their strength to preserve what remains of their past.

The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.

Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.

Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.

This study presents the intrinsic value of Moody’s Corporation, a leading credit rating agency in the U.S. The results of the valuation were compared to the market value of Moody’s Corporation of the same date. The aim of the research is to provide a perspective to the investors on whether the actual value of the Company was overvalued or undervalued in the market, and how much the volatility of the stock price by the change of some factors. Both qualitative and quantitative analyses were applied in the research. The historical data, economic outlook, and the Company’s strategies were collected to be the metrics to determine the intrinsic value and provide an analysis of the prospects of Moody’s Corporation. Three valuation models were applied in the research to estimate the intrinsic value of the Company’s common stock. The cost of debt, cost of equity, the weighted average cost of capital, and the market risk premium were introduced and calculated in the research as they were the critical components in the valuation process. Since the valuation was based on assumptions and historical data to determine future growth, which indicates that the results could be changed due to uncertain factors. This study demonstrates that there was some discrepancy between the stock’s market price and the intrinsic value per share of Moody’s Corporation as of December 31, 2021

Starbucks Corporation (hereinafter “Starbucks” or “the Company”) is a worldwide coffee retailer, which operates over 33,000 stores located in over 83 countries nowadays. The purpose of the study is to estimate Starbucks’ intrinsic value as of December 31, 2021 and identify whether the Company was overvalued or undervalued. Several analyses give investors and shareholders an insight into how the Company may develop or identify the ability to generate positive returns from investing in Starbucks. This study is mainly separated into two aspects. The first part specifically discusses the Company overview, industry analysis, and economic outlook, which includes SWOT analysis, PESTEL analysis, Porter's five forces analysis, and value chain analysis to identify external and internal factors that may influence the Company. The second part focuses on financial analyzes, including both historical and forecasted financial statement. Three valuation models and a sensitive analysis are applied to understand the Company’s financial conditions and performance. Starbucks’ intrinsic value is derived from the three discounted cash flow models, indicating the market overvalued the Company’s stock price as of December 31, 2021. Finally, investors and shareholders can understand more about Starbucks’ capital structure, financial highlights, and intrinsic value, because this set of information is critical for existing investors and potential investors to make investment decisions

Oracle Corporation (hereinafter referred to as Oracle, ORCL, or ‘the Company’) is an American multinational company that provides solutions of products and services that serve the enterprises’ information technology (IT) environment. This thesis is to conduct a business analysis of the Company from a financial perspective, determining the Company’s intrinsic value as of May 31, 2021, and comparing it with the respective market value. Thus, this thesis will study, evaluate, and present an overview of the Company, an analysis of the Company’s market, industry, strategy, financial performance, including external and internal factors, a ten-year pro forma financial statement forecast, and the techniques of using the three discounted cash flow models to estimate the intrinsic value of Oracle. The obtained results from the three valuation models, including the Enterprise Discounted Cash Flow (EDCF) model, the Adjusted Present Value (APV) model, and the Discounted Economic Profit (DEP) model, show that the Company’s intrinsic values were estimated at $74.57, $75.21, and $74.82, respectively. When the results were used to compare with the market price of Oracle’s shares as of May 31, 2021, at $78.74, it reflects that the Company was overvalued

Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.