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This article discusses the new gaming law in Macau with emphasis on the critical aspects concerning the gaming operators, concession regime, and other regulatory obligations.1 Thanks to the gaming liberalization commenced in 2001,2 Macau has experienced tremendous economic growth. The past two decades have seen the rapid development of large-scale integrated resorts, and Macau now ranks among the world's major gaming jurisdictions.3 Policy and regulatory challenges have also emerged along with the growth of the junket-driven VIP business in casinos.4 With the recent amendment of Law No. 16/2001 and the subsequent enactment of Law No. 16/2022, Macau has strengthened the legal underpinnings of its system of gaming regulation to oversee various groups involved in casinos and their industry practices. The present study is among the first to review the scope and impact of the revised gaming law, and associated managerial and operational implications for Macau casinos. Topics covered include policy directions, concession requirements, industry participants, gaming taxes, and fair business practices. This study could provide insights into the “Macau 2.0” project and how casinos are to be operated and managed over the next decade. This article could also provide practical guidance for policy makers charged with formulating gaming policy and regulation in other jurisdictions.

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.

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.

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.

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.

Reproduction of the sea cucumber Apostichopus japonicus is critical for aquaculture production. Gonadal development is the basis of reproduction, and lipids, which are among the main nutrients required for gonadal development, directly affect reproduction. We investigated whether gonadal and intestinal lipid metabolism differed between male and female A. japonicus. Transcriptome analysis of the intestines of sexually mature male and female wild-caught individuals revealed differences in gene expression, with 27 and 39 genes being up-regulated in females and males, respectively. In particular, the expression of the fatty acid synthase gene was higher in males than in females. Metabolome analysis of the gonads identified 141 metabolites that were up-regulated and 175 metabolites that were down-regulated in the testes compared with the ovaries in the positive/negative mode of an LC-MS/MS analysis. A variety of polyunsaturated fatty acids were found at higher concentrations in the testes than in the ovaries. 16 s rDNA sequencing analysis showed that the composition and structure of the intestinal microbiota were similar between males and females. These results suggest that sex differences in intestinal metabolism of A. japonicus are not due to differences in the microbiota, and we speculate that gonadal metabolism may be related to intestinal morphology. This information might be useful in improving the reproductive efficiency of sea cucumbers in captivity.

Reaction of ultramafic rocks with seawater and subsequent serpentinization has been considered one of the most important factors controlling the formation of ultramafic-hosted seafloor massive sulfide (UM-SMS) deposits. However, the mineralization processes responsible for these deposits remain poorly understood, in particular because they are less abundant as compared with their basaltic counterparts. In this work, serpentinites with different alteration grades collected at the Tianzuo hydrothermal field (THF), Southwest Indian Ridge, were studied. Mineralogical and chemical analyses were performed in the secondary opaque minerals resulting from serpentinization to understand the role of this process during the formation of UM-SMS deposits. Our results show that these opaque minerals mainly consist of magnetite, hematite, pentlandite, and minor pyrite, suggestive of high but varying oxygen and sulfur fugacities. The hematite is characterized by an enrichment in Mg, Si, Ni, and Co as compared with magnetite. Pentlandite associated with hematite has elevated and consistent Ni contents as compared with that associated with magnetite. These results indicate that breakdown and decomposition of primary silicate and sulfide minerals during serpentinization has controlled the sources of ore-forming materials. Concentrations of Te are variable and show a positive correlation with Ni in pentlandite associated with magnetite or hematite, suggesting that gabbroic intrusions provided additional material to the hydrothermal system. Oxidation and sulfidation conditions are ideal for the formation of trisulfur ion S3− in THF, which can significantly improve the capability of hydrothermal fluids for leaching ore-forming metals from the wall rocks, promoting the formation of THF. In addition of reduced systems, hydrothermal fluids with high oxygen and sulfur fugacities triggered by extensive seawater infiltration can most likely also develop in ultramafic-hosted systems. These results suggest that the areas with well-developed fractures are promising candidates for further exploration of UM-SMS deposits along mid-oceanic ridges.

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.

<abstract><p>About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ &gt; million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.</p></abstract>

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.

In government studies, electronic government has become a hot topic in recent decades. Many scholars believe that soon, the government might not be able to operate smoothly without the help of ICTs as the Internet has been overwhelming people's daily lives already. In analyzing people's behavioral factors towards adopting e-government services, most studies targeted the adult population, while those in the hard-to-reach groups are minimal. This study was designed especially to understand the behavioral factors of the younger generation aged between 18 and 24 and the senior citizens above 60 on their adoption of e-government services in Macao SAR. Sixteen in-depth interviews were conducted based on the semi-structured interview questions developed from the prior literature on the Theory of Planned Behavior and e-government studies. Six significant findings are yielded, which could serve as an important reference for policymakers designing e-government policy and promoting its implementation strategy. These behavioral factors also contribute empirical data to support the theoretical framework of TPB in the context of Macao SAR e-government services.

Hydrothermal activity on mid-ocean ridges is an important mechanism for the delivery of Zn from the mantle to the surface environment. Zinc isotopic fractionation during hydrothermal activity is mainly controlled by the precipitation of Zn-bearing sulfide minerals, in which isotopically light Zn is preferentially retained in solid phases rather than in solution during mineral precipitation. Thus, seafloor hydrothermal activity is expected to supply isotopically heavy Zn to the ocean. Here, we studied sulfide-rich samples from the Duanqiao-1 hydrothermal field, located on the Southwest Indian Ridge. We report that, at the hand-specimen scale, late-stage conduit sulfide material has lower δ66Zn values (−0.05 ± 0.15 ‰; n = 19) than early-stage material (+0.13 ± 0.15 ‰; n = 10). These lower values correlate with enrichments in Pb, As, Cd, and Ag, and elevated δ34S values. We attribute the low δ66Zn values to the remobilization of earlier sub-seafloor Zn-rich mineralization. Based on endmember mass balance calculations, and an assumption of a fractionation factor (αZnS-Sol.) of about 0.9997 between sphalerite and its parent solution, the remobilized Zn was found consist of about 1/3 to 2/3 of the total Zn in the fluid that formed the conduit samples. Our study suggests that late-stage subsurface hydrothermal remobilization may release isotopically-light Zn to the ocean, and that this process may be common along mid-ocean ridges, thus increasing the size of the previously identified isotopically light Zn sink in the ocean.

The physiological mechanisms underlying variation in aggression in fish remain poorly understood. One possibly confounding variable is the lack of standardization in the type of stimuli used to elicit aggression. The presentation of controlled stimuli in videos, a.k.a. video playback, can provide better control of the fight components. However, this technique has produced conflicting results in animal behaviour studies and needs to be carefully validated. For this, a similar response to the video and an equivalent live stimulus needs to be demonstrated. Further, different physiological responses may be triggered by live and video stimuli and it is important to demonstrate that video images elicit appropriate physiological reactions. Here, the behavioural and endocrine response of male Siamese fighting fish Betta splendens to a matched for size conspecific fighting behind a one-way mirror, presented live or through video playback, was compared. The video playback and live stimulus elicited a strong and similar aggressive response by the focal fish, with a fight structure that started with stereotypical threat displays and progressed to overt attacks. Post-fight plasma levels of the androgen 11-ketotestosterone were elevated as compared to controls, regardless of the type of stimuli. Cortisol also increased in response to the video images, as previously described for live fights in this species. These results show that the interactive component of a fight, and its resolution, are not needed to trigger an endocrine response to aggression in this species. The study also demonstrates for the first time in a fish a robust endocrine response to video stimuli and supports the use of this technique for researching aggressive behaviour in B. splendens.

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

YouTube has become increasingly popular for marketing purposes. As corporate and user-generated content is widely available on this platform, beauty-related professionals need to understand how to create videos that make their products more appealing and stand out from the clutter. In this study, we examine four factors (i.e., perceived usefulness of the information, perceived credibility of the information, attitude toward the purchase, and perceived video characteristics) that affect the purchase intentions of female consumers. After viewing beauty-related videos, a sample of 204 female consumers was analyzed by structural equation modeling. The findings showed that videos with more views, likes, and comments tend to have a greater effect on the respondents' intentions to purchase. Also, the factors of perceived usefulness of the information, perceived credibility of the information, and attitude toward the purchase exhibited a significant effect on the intention to buy beauty-related products. The result showed that perceived video characteristics (such as quality and visuals) did not significantly influence the purchase intention, however, there is evidence that this factor should not be ignored by content creators. Finally, our research provides insights, strategies, and future directions for industry practitioners and marketers.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.

Approximately 50 million people are suffering from epilepsy worldwide. Corals have been used for treating epilepsy in traditional Chinese medicine, but the mechanism of this treatment is unknown. In this study, we analyzed the transcriptome of the branching coral Acropora digitifera and obtained its Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG) and Gene Ontology (GO) annotation. Combined with multiple sequence alignment and phylogenetic analysis, we discovered three polypeptides, we named them AdKuz1, AdKuz2 and AdKuz3, from A. digitifera that showed a close relationship to Kunitz-type peptides. Molecular docking and molecular dynamics simulation indicated that AdKuz1 to 3 could interact with GABAA receptor but AdKuz2–GABAA remained more stable than others. The biological experiments showed that AdKuz1 and AdKuz2 exhibited an anti-inflammatory effect by decreasing the aberrant level of nitric oxide (NO), IL-6, TNF-α and IL-1β induced by LPS in BV-2 cells. In addition, the pentylenetetrazol (PTZ)-induced epileptic effect on zebrafish was remarkably suppressed by AdKuz1 and AdKuz2. AdKuz2 particularly showed superior anti-epileptic effects compared to the other two peptides. Furthermore, AdKuz2 significantly decreased the expression of c-fos and npas4a, which were up-regulated by PTZ treatment. In addition, AdKuz2 reduced the synthesis of glutamate and enhanced the biosynthesis of gamma-aminobutyric acid (GABA). In conclusion, the results indicated that AdKuz2 may affect the synthesis of glutamate and GABA and enhance the activity of the GABAA receptor to inhibit the symptoms of epilepsy. We believe, AdKuz2 could be a promising anti-epileptic agent and its mechanism of action should be further investigated.

Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.