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近年來，博物館的休閒、教育、娛樂功能等日益重要，其對文化教育的貢 獻也越來越受到博物館參觀者的認可。相關的文獻大多數探討於博物館的服務 質量與參觀者參觀博物館體驗的滿意度之間的關係。目前，以參觀者的博物館 體驗如何影響他們重遊博物館的專門論述或深入研究並不多。因此，本研究的 研究目的是以博物館的體驗為中心，探討哪些因素會影響參觀者參觀博物館的 體驗，以及這些因素如何影響參觀者對博物館的重遊意願。本研究以篩選的方 式隨機抽查了 10 位參觀過博物館體驗的參觀者進行半結構式訪談(Semi- structured interview)，深入地了解受訪者的博物館體驗以及影響他們重遊博物 館的因素等問題，並以文獻及理論為基礎探討參觀者在參觀博物館過程會影響 他們的因素。採訪會在博物館附近進行，並採用深度定性訪談法(In-depth qualitative interview)，研究對象為澳門的本地參觀者。

Bridging theory and practice, the up-to-date evidence from these proceedings marks an important contribution to the advancement of children and youth health and well-being professions in the issues of technology, health, stress, inclusion, and resilience. The empirical research reported here examines the perceptions of parents, social workers, counselors, and other helping professionals concerning their awareness of child protection and parent-child relationships. These proceedings serve as a catalyst for action, enabling researchers and practitioners to reference and view the newest research through the lenses of diverse themes that focus on children and youth health and well-being, and to impact the younger population at micro and macro levels. This key text has several important features: 1. It emphasizes the impact of digital technology on well-being among children and young people in this digital age, and how to involve different stakeholders who can help to respond to emergent and existing challenges. 2. It introduces learning disabilities and issues in the field of mental stress and the biopsychology of developmental needs in school settings in addressing the UN Sustainable Development Goals. 3. It advances health knowledge and care practice through practice-oriented research, establishing new benchmarks in health care work, identifying its possibilities and constraints. 4. It enriches knowledge in the field of safeguarding for adults, including parental involvement in identifying and responding to children and youth well-being.

Consumer neuroscience analyzes individuals’ preferences through the assessment of physiological data monitoring, considering brain activity or other bioinformation to assess purchase decisions. Traditional marketing tactics include customer surveys, product evaluations, and comments. For product or brand marketing and mass production, it is important to understand consumer neurological responses when seeing an ad or testing a product. In this work, we use the bi-clustering method to reduce EEG noise and automatic machine learning to classify brain responses. We analyze a neuromarketing EEG dataset that contains EEG data from product evaluations from 25 participants, collected with a 14 channel Emotiv Epoch + device, while examining consumer items. Four components comprised the research methodology. Initially, the Welch Transform was used to filter the EEG raw data. Second, the best converted signal biclusterings are used to train different classification models. Each biclustering is evaluated with a separate classifier, considering F1-Score. After that, the H2O.ai AutoML library is used to select the optimal biclustering and models. Instead of traditional procedures, two thresholds are used. First-threshold values indicate customer satisfaction. Low values of the second threshold reflect consumer dissatisfaction. Values between the first and second criteria are classified as uncertain values. We outperform the state of the art with a 0.95 F1-Score value.

We demonstrate that the flavor oscillation when a neutrino travels through spacetime, is equivalent to permanent changes on the vacuum state condition perceived by the same particle. This can be visualized via the Quantum Yang Baxter equations (QYBE). From this perspective, the neutrino never breaks the symmetry of the ground state because it never selects an specific vacuum condition. Then naturally the Higgs mechanism cannot be the generator of the neutrino masses. The constraints emerging from this model predict a normal mass hierarchy and some specific values for the mass eigenvalues once we fix the mixing angles. Interestingly, the model suggests that the sum of the mix angles is equal to $\pi/2$.

Remote Learning's Impact on Students' Cognitive Development: Evidence from Time Series Assessment [Conference presentation]

The Trinity, i.e., one God in three persons (Father, Son, and Holy Spirit), is the core doctrine of Christianity. It confesses that God truly exists as one, and the three persons truly exist each in themselves. The seemingly paradoxical doctrine was from the beginning of the Church imprinted on the popular faith witnessed in the New Testament, the liturgy, especially the rite of baptism, and the catechetical practice. (Kelly, 1960/1968, pp. 88-90) Yet it took four centuries of controversies and synods to transform the initial faith into a doctrine. The main achievements were made in the 4th century: first, the Ecumenical Council of Nicaea I in 325 acknowledged the common substance of the Trinitarian persons by accepting the Greek term homoouios (homo means same, and ousios means being, substance, or essence); second, in the decades after Nicaea I, the common substance of the three and the distinctiveness of each person are expressed as “mia ousia, treis hypostaseis” (one substance, three persons). 三一论,即一体三位,是基督教的核心教义。它意味着天主为一、真实存在,同时天主拥有三个真实存在的位格,即圣父、圣子、圣神。这一看似自相矛盾的教义从教会建立之初就蕴含在《新约圣经》、礼仪(尤其是圣洗圣事)和信仰实践中。然而,这一原初的信仰要经过四个世纪的理论争辩和教会会议才被转化为教会教义。其中至关重要的是325年的尼西亚大公会议通过接受希腊词“同一体性(homoouios)”一词来表述圣父和圣子之间的关系,认定了天主圣三的共同体性;以及尼西亚大公会议之后,卡帕多西亚教父用希腊语中两个同为表示存在、体质的近义词ousia和hypostasis分别指代天主的同一体性和三位各自的独特存在,由此提出了“mia ousia, treis hypostaseis”(一体三位)这一三一论的经典表述。本文将根据哲学所提供的术语框架,阐述卡帕多西亚教父在三一论神学中对ousia和hypostasis的区分,然后阐释这种区分的神学意义。

After the Covid-19 Pandemic crisis in international economic relations it became evident that climate-smart aspects should be considered when re-establishing a new international trade order. International organizations have proclaimed that this momentum should be used to include climate-smart trade and investment provisions to enable sustainable development. It has been acknowledged that trade has an important role to play in the global response to climate change, providing economies with tools to draw on in their efforts to mitigate climate change and to adapt to its consequences. In this paper we focus the analysis on investigating the digital and sustainable component of trade facilitation measures applied in Western Balkans countries. To evaluate the importance of trade facilitation measures and their digital and sustainable components we apply standard gravity model with the data from UN Global Survey on digital and sustainable trade facilitation. The results show that trade facilitation measures are important for improving and increasing trade among the Western Balkans countries. Especially, measures connected to improving transparency procedures in trade and measures for alleviating trade formalities are most significant for increasing bilateral trade among Western Balkans countries. With a lower level of importance are the measures for improving cross-border paperless trade between these countries.

We demonstrate that black hole evaporation can be modeled as a process where one symmetry of the system is spontaneously broken continuously. We then identify three free parameters of the system. The sign of one of the free parameters governs whether the particles emitted by the black hole are fermions or bosons. The present model explains why the black hole evaporation process is so universal. Interestingly, this universality emerges naturally inside certain modifications of gravity.

<jats:p>Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion.</jats:p>

Intended as an economic and development hub, the Hengqin Cooperation Zone aims to foster collaboration and integration between mainland China, Hong Kong, and Macao, serving as a platform for economic development and innovation among the three regions. The zone's development has increased demand for financial services, often offered through fintech. There is, however, a lack of interoperability between the fintech services currently used in Macao and Hengqin. This may hinder Macao users' adoption of the technology. Thus, our research objective is to identify the factors determining Macao residents' adoption of fintech services in the area and provide insights for service providers, developers, and policymakers. A framework based on the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) was used for this purpose. The responses of 103 Macao residents provided evidence that ease of use significantly and positively impacts the usefulness of the technology. This in turn influences attitudes towards fintech usage. Subjective norms and perceived behavioral control positively impact fintech adoption intentions. The fintech industry and the governments of Macao and Hengqin can work on improving technology's ease of use and usefulness. They can also promote them to Macao users, and provide the resources required for better access to fintech in the zone

The investigation of pharmaceuticals as emerging contaminants in marine biota has been insufficient. In this study, we examined the presence of 51 pharmaceuticals in edible oysters along the coasts of the East and South China Seas. Only nine pharmaceuticals were detected. The mean concentrations of all measured pharmaceuticals in oysters per site ranged from 0.804 to 15.1 ng g–1 of dry weight, with antihistamines being the most common. Brompheniramine and promethazine were identified in biota samples for the first time. Although no significant health risks to humans were identified through consumption of oysters, 100–1000 times higher health risks were observed for wildlife like water birds, seasnails, and starfishes. Specifically, sea snails that primarily feed on oysters were found to be at risk of exposure to ciprofloxacin, brompheniramine, and promethazine. These high risks could be attributed to the monotonous diet habits and relatively limited food sources of these organisms. Furthermore, taking chirality into consideration, chlorpheniramine in the oysters was enriched by the S-enantiomer, with a relative potency 1.1–1.3 times higher when chlorpheniramine was considered as a racemate. Overall, this study highlights the prevalence of antihistamines in seafood and underscores the importance of studying enantioselectivities of pharmaceuticals in health risk assessments.

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

<jats:p>Personalized recommendation plays an important role in many online service fields. In the field of tourism recommendation, tourist attractions contain rich context and content information. These implicit features include not only text, but also images and videos. In order to make better use of these features, researchers usually introduce richer feature information or more efficient feature representation methods, but the unrestricted introduction of a large amount of feature information will undoubtedly reduce the performance of the recommendation system. We propose a novel heterogeneous multimodal representation learning method for tourism recommendation. The proposed model is based on two-tower architecture, in which the item tower handles multimodal latent features: Bidirectional Long Short-Term Memory (Bi-LSTM) is used to extract the text features of items, and an External Attention Transformer (EANet) is used to extract image features of items, and connect these feature vectors with item IDs to enrich the feature representation of items. In order to increase the expressiveness of the model, we introduce a deep fully connected stack layer to fuse multimodal feature vectors and capture the hidden relationship between them. The model is tested on the three different datasets, our model is better than the baseline models in NDCG and precision.</jats:p>

The marine medaka is emerging as a potential behavioral model organism for ocean studies, namely on marine ecotoxicology. However, not much is known on the behavior of the species and behavioral assays lack standardization. This study assesses the marine medaka as a potential model for chemical communication. We investigated how short exposure to visual and chemical cues mediated the stress response to social isolation with the light/dark preference test (LDPT) and the open field test (OFT). After a 5-day isolation period, and 1 h before testing, isolated fish were randomly assigned to one of four groups: (1) placed in visual contact with conspecifics; (2) exposed to a flow of holding water from a group of conspecifics; (3) exposed to both visual and chemical cues from conspecifics; or (4) not exposed to any stimuli (controls). During the LDPT, the distance traveled and transitions between zones were more pronounced in animals exposed to the conspecific's chemical stimuli. The time spent in each area did not differ between the groups, but a clear preference for the bright area in all animals indicates robust phototaxis. During the OFT, animals exposed only to chemical cues initially traveled more than those exposed to visual or both stimuli, and displayed lower thigmotaxis. Taken together, results show that chemical cues play a significant role in exploratory behavior in this species and confirm the LDPT and OFT as suitable tests for investigating chemical communication in this species.

Government service mini-programs (GSMPs) in mobile payment have become integral to the eGovernment in China’s Greater Bay Area (GBA). The ubiquitous nature of WeChat and Alipay provides excellent flexibility for accessing public e-services. Yet, the determinants and mechanisms of adoption have not been identified. A convenience sample was collected from GBA core cities for statistical and SEM analysis. The findings suggest that service quality, trust in eGovernment, ubiquity, and social influence constitute the determinants. A structural model grounded on Self-Determination and Motivation theory is verified, where perceived value and intention contribute a high explanatory power. Benevolence, integrity, and competence are crucial indicators of trust, while social influence amplifies risk perception. Surprisingly, government support negatively moderates the impact of determinants on intention, indicating that over-intervention leads to inhibition. The mechanism illustrates the beneficial impact of GSMPs as the smart government channel and provides insights into addressing service homogeneity and policy applicability. Relevant theoretical and managerial implications are instructive to policymakers and practitioners of smart city innovation and in-depth integration in GBA.

<jats:p>Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals’ nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal’s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model’s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.</jats:p>

Under Macau Arbitration Law (MAL, art 64.1), an award shall be made in writing and shall be signed by the arbitrator or arbitrators. Furthermore, the law provides that in case of arbitral proceedings with more than one arbitrator, the signatures of the majority of all members of the arbitral tribunal shall suffice, provided that the reason for any omitted signature is stated (MAL, art 64.2).