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It has been proven in numerous research that mindfulness can be helpful to reduce stress and chronic pain (Hall, 2014; Lindström, n.d.; Tong et al., 2015). While interactive mindfulness has been one of the focuses in the recent mobile applications market, usually tackling three essential human senses: audio, visual, and touch, each mobile application has quite some different approaches in terms of interactivity. Some focus on the touch and visual, and some on audio (environmental sounds or instructing meditation). Immersing oneself in virtual reality (VR) creates a constant stream of interactivity. Nonetheless, what are the conditions for an (in)tangible virtual reality to be more effective? Under the COVID-19 pandemic and lockdown since the end of 2019, Macao has been facing a social concern that we cannot travel easily to visit our decedents’ graves abroad, let alone the existing concerns of expensive burial services, lack of space, and alternative burial options. Also, taking into consideration that standard funeral service in Macao is often too brief, and getting briefer, thus lacking the opportunity to properly farewell the decedent, this research is proposing a virtual reality 3D model construction of the Chapel of St. Michael, located in St. Michael the Archangel Cemetery in Macao, to be streamed on a 360 virtual tour platform, Kuula. co. By immersing in this virtual reality, the participant is to have a single user experience for mindfulness with the decedent. To ensure valid and reliable results that address the research aims and objectives, a single-user experiment is going to be set up with multiple electronic devices, namely, the smartphone iPhone X with cardboard VR, the tablet iPad Pro, and the Oculus Quest 2. The methodology to collect the data will be using observation and simulation. The experiment will be started with an introduction to the project and conducted with no instruction, allowing users to explore and examine all features in this immersive experience. Along with a post-experience survey (interview + questionnaire), we seek its conditions and impacts on Macao residents in terms of interactive mindfulness and participants’ expectation of testing, for the first time in Macao, a virtual reality grave mourning experience.

<jats:p>Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.</jats:p>

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

In February 2020, Macau became one of the first regions where the pandemic of coronavirus or Covid-19 affected the totality of social and economic life leading to increased anxieties over movement and distance. Although Macau has had very few actual cases of the virus – 46 in total –and no deaths from it, the Macau government rapidly instituted a lock­ down. The aim of this article is to reflect on how the social experience of being in lockdown can provide insights into understanding the type of experience or condition that we provisionally term ‘anxious immobility.’ Such a condition is characterized by a total disruption of everyday rhythms and specifically anxious waiting for the normalization of activity while being the subject of biosocial narratives of quarantine and socially responsible. The paper is based upon 3 months of ethnographic research conducted by two researchers based in Macau. We also reflect upon some aspects of the politics of mobilities in the light of disruptions and friction points between Hong Kong, Macau, mainland China, and the rest of the world.

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.

The role of hormones as modulators of aggressive behavior in fish remains poorly understood. Androgens and corticosteroids, in particular, have been associated with aggressive behavior in fish but it is still not clear if animals adjust the secretion of these hormones to regulate behavior during ongoing fights, in response to fight outcomes in order to adjust aggressive behavior in subsequent fights, or both. With its stereotyped displays and high aggression levels, the Siamese fighting fish Betta splendens is an excellent model to investigate this question. Here, we compared the behavioral and endocrine response of male B. splendens to fights where there is no winner or loser by presenting them with a size-matched live interacting conspecific behind a transparent partition or with a mirror image. The aggressive response started with threat displays that were overall similar in frequency and duration towards both types of stimuli. Fights transitioned to overt attacks and interacting with a live conspecific elicited a higher frequency of attempted bites and head hits, as compared with the mirror image. There was a pronounced increase in plasma androgens (11-ketotestosterone and testosterone) and corticosteroids (cortisol) levels in response to the aggression challenge, independent of stimulus type. Post-fight intra-group levels of these hormones did not correlate with measures of physical activity or aggressive behavior. A linear discriminant analysis including all behavioral and endocrine data was a poor classifier of fish from the conspecific and mirror trials, showing that overall the behavioral and endocrine response to mirror images and conspecifics was similar. The results show that fight resolution is not necessary to induce an evident increase in peripheral levels of androgens and corticosteroids in B. splendens. However, the function of these hormones during present and future aggressive contests remains to be clarified.

Macau, MNA, Opinion | The Macau government recently approved its first reading of a new bill to attract Macau locals to return to Macau to work. Simultaneously, Macau’s Secretary for Social Affairs and Culture was reported as saying that if Macau could create a better environment and conditions, then ‘local talents who are abroad will surely be interested in returning to Macau’.

A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.

Abstract: The Portuguese language poses several challenges for children in the initial phase of learning how to read, particularly in the case of letters that may correspond to more than one phoneme, two letters that correspond to a single phoneme and in the case of words containing complex syllabic structures. The objective of this study was to perform a psycholinguistic analysis of the reading errors of children, attending the 1st (n=175) and 2nd year (n=137) of schooling, specifically in the case of words containing digraphs or complex syllabic structures and to analyse the differences between children’s reading errors in these two years. An oral reading test was used for data collection. A quantitative and qualitative analysis of the type of reading errors was conducted using words with consonant digraphs (ch, nh, lh, gu, rr, ss), and words with complex syllables <CVC and CCV>. This analysis showed that children presented greater difficulties in some specific digraphs and tended to simplify complex syllables, either by adding or deleting phonemes. The quantity and quality of the reading errors of children attending both grades were discussed in light of reading acquisition theories and children’s phonological development.

Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This paper presents an empirical study that evaluates four existing deep learning models—VGG16, DenseNet, ResNet50, and GoogLeNet—utilizing the Facial Expression Recognition 2013 (FER2013) dataset. The dataset contains seven distinct emotional expressions: angry, disgust, fear, happy, neutral, sad, and surprise. Each model underwent rigorous assessment based on metrics including test accuracy, training duration, and weight file size to test their effectiveness in FER tasks. ResNet50 emerged as the top performer with a test accuracy of 69.46%, leveraging its residual learning architecture to effectively address challenges inherent in training deep neural networks. Conversely, GoogLeNet exhibited the lowest test accuracy among the models, suggesting potential architectural constraints in FER applications. VGG16, while competitive in accuracy, demonstrated lengthier training times and a larger weight file size (512MB), highlighting the inherent balance between model complexity and computational efficiency.

As the world becomes more globalized, it is now more important than ever for brands and advertisers to find effective ways to engage with consumers of different cultural backgrounds. Developing marketing that targets people of different cultural backgrounds, or multicultural marketing, carries specific nuances and complexities that may make traditional methods fall short. With this being said, there is still a lack of studies that explore the correlation between consumer's cultural background and their overall brand perception. Neuromarketing has proven to be an effective tool to understanding consumer behavior, by utilizing neuroscience tools. To employ a more sophisticated and in-depth understanding of consumer perception, the current research study makes use of neuroscience tools and aims to study the influence of cultural background in brand perception, while in a controlled environment. Using physiological neuroscience tools, namely, facial expression analysis (FEA), electrodermal activity (EDA), and eye-tracking (ET), a total of thirty-eight individuals, with ages between 19 and 50 years old, from 12 different countries and regions, participated in this research study. Findings suggest that participants of different cultural backgrounds perceive multicultural commercials as more favorable than monocultural commercials. However, future research should be done with a larger sample size, as well as include a wider variety of commercials. Research would also benefit from adopting a statistical analysis to help determine the significance of the results obtained

In the wave of digital transformation, Chinese banks have taken digital and scenario-based finance as primary strategic goals. The goal is to revolutionize the mobile banking experience and encourage frequent use of mobile banking services. However, assessing customer satisfaction with the various financial and contextual services mobile banking provides is crucial. The main objective of this study is to propose a model based on users' perception of financial usage in mobile banking scenarios and how the development of mobile banking finance and scenarios affects users' choice motivations. The study examined the interview records of 12 mobile banking users through qualitative in-depth interviews and utilized Nvivo qualitative analysis software to analyze the interview content. Through repeated thinking, sorting, and differentiating the data, nine core coding categories were formed. The coding was further refined and deepened to include Financial professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived usefulness, and Willingness to use. Based on these categories, a theoretical model of user willingness in the financial scenario of mobile banking has been proposed by referring to the optimized TAM model. The results may provide support to the banking industry in Macau in understanding customers' needs and fostering the positive development of mobile finance and the scene field in Macau

Industrial organization, theory of the firm and boundaries of the firm are well established fields of study involved in the size, structure and scope of a corporate entity (i.e. firm) to the market. However, a key characteristic of corporate entities is that economic concerns (costs and profit) is the overriding or dominant factor. This paper attempts to apply the above mentioned concepts to organizations such as public institutions where economic concerns are secondary considerations, to seek a more objective analysis on what the structure and scope of such organizations should be