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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).

Integrating financial technologies with green initiatives is critical to the sustainable development agenda. This is particularly true for newly developed smart cities like Tongzhou, the sub-city center of Beijing. To assess the adoption of green fintech in Tongzhou, this paper extends the EnergyAugmented Technology Acceptance Model (EA-TAM) to incorporate two green factors – environmental awareness and green knowledge. This paper applies structural equation modeling techniques to analyze data from 403 respondents who live, work, or study in Tongzhou and finds allhypothesized constructs significant. Since green knowledge is significant to the adoption of green fintech, this paper further divides the sample into a high-education group (162 respondents with university-or-above degrees) and a low-education group (251 respondents with post-secondary-orlower degrees) to evaluate the impact of education. All the hypothesized factors are significant to the high-education group,but environmental awareness and perceived usefulness are insignificant to the low- education group. Hence, the results provide evidence that people in the newly developed smart city adopt green fintech due to their environmental sensitivity. The adoption of green fintech is more environmentally sensitive for people with high education levels.

Battery electric vehicles (BEVs) are living up to their claims as consumers choose them more frequently. The increasing demand for sustainable vehicles translates into the global need for specific components, materials, and infrastructures and drives the regulatory frameworks in each country. While BEVs offer environmental benefits and global business opportunities, the technology has not yet gained mainstream acceptance. Thus, this work aims to investigate the characteristics of BEV users and their role in the diffusion of products to larger segments, as this may vary from country to country. For this purpose, a survey based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) (Venkatesh et al., 2012) framework and structural equation modeling (SmartPLS) was adopted. The results indicated that, except for the constructs of effort expectancy (EE) and social influence (SI), the predictors in the model performed well in this context. Current users are satisfied with their vehicles and are supportive of BEVs in the future. The analysis also revealed that in addition to the availability of financial resources, early adopters are attracted by new technologies in a way that leads them to make decisions outside of the traditional influence of the other members of society. It is suggested to leverage the perceived benefits of status, differentiation, or uniqueness motives, to appeal to those seeking to appear trendy and tech-savvy in society. Companies and policymakers should acknowledge the peculiarities of early customers in their communication strategies to reach a wider audience around the globe and encourage the adoption of BEV technology.

Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.

Critical thinking (CT), as a form of higher-order thinking, is intended to help individuals form reasonable reflection and judgment to deal with increasingly severe employment situations. As the primary workforce in the labor market, undergraduates must possess a strong critical thinking disposition (CTD) to make better use of CT. Despite extensive research on components of CTD from the perspective of educational practices, there is limited emphasis on investigating the components and their relationships of CTD in the labor market and the impact of gender differences. Therefore, this study presented an analysis of 1535 Chinese undergraduates (Mage = 20.89; SD = 1.43) using the Employer-Employee-Supported Critical Thinking Disposition Inventory (2ES-CTDI), aiming to explore the CTD that undergraduates should possess before entering the labor market. The relationships among the components were examined using SmartPLS4.0 in conjunction with Partial Least Squares Structural Equation Modeling (PLS-SEM). Additionally, a multigroup analysis (PLS-MGA) with a measurement invariance (MI) test was conducted to validate the moderating effects of gender. The findings indicate that (a) self-efficacy has a significant negative effect on habitual truth-digging, and boys are more affected than girls, instant judgment plays a competitive partial mediating role in this relationship; (b) self-efficacy has a significant positive effect on instant judgment, and boys are more likely to make instant judgments than girls; (c) instant judgment significantly positively affects habitual truth-digging. These findings highlight the dynamic equilibrium among the internal components of CTD in the labor market and call for increased attention from educators to the importance of gender differences in the cultivation process.

The Revenue Management (RM) problem in airlines for a fixed capacity, single resource and two classes has been solved before by using a standard formalism. In this paper we propose a model for RM by using the semi-classical approach of the Quantum Harmonic Oscillator. We then extend the model to include external factors affecting the people’s decisions, particularly those where collective decisions emerge.

In the first half of the 20th century, Macao’s three traditional handicraft industries were: making matches, firecrackers, and incense, and these three became important pillars of Macao's economy. These industries were supported by four other industries, namely: shipbuilding, wine, printing and the clothing that are integral to understanding the history of Macao. The purpose of this research has been to document the importance of Macao's seven traditional industries, in particular the firecracker factories with special reference to the Yick Loong factory. This dissertation makes a significance contribution to the subject of the historical development of Macao’s traditional handicraft industries in reviewing each of the traditional industries and in particular detailing the Yick Loong factories operations and the understanding of the local Macao people to the past importance of this operation. These industries need to be discussed so that society can have a deeper understanding of the importance of them, as up to now they have been little explored. So, by visiting the location of the Yick Loong Firecracker Factory, gathering knowledge, and conducting in-depth research and in books and through a survey about the Macao firecracker business, the author has been able to show that there appears to be limited understanding of the significance of the firecracker industry in Macao. Though, people most recall the Yick Loong factory’s name when prompted about naming a firecracker factory in Macao and associate firecrackers with the Lunar New Year celebrations. However, they seem to have limited understanding of the significance of the firecrackers to Macao, except for childhood memories of them being released.

Inclusive preschool education in Macao is in its infancy and resource teachers (RTs) play a pivotal role in supporting kindergarten teachers. This study aimed to investigate Macao kindergarten teachers’ demand for and satisfaction of RTs’ support services in inclusive education. Using a mixed method design, the researcher administered a self-developed questionnaire (including a need scale and a satisfaction scale) to 70 kindergarten teachers and interviewed four kindergarten teachers. Based on the quantitative and qualitative data analyses, results showed that: 1) Kindergarten teachers had a high demand for RTs’ support services no matter whether they had previously received such services, and kindergarten teachers who had received RTs’ support services (N = 43) were moderately satisfied with their support services. 2) There was a moderate correlation between the demand and satisfaction scales and respective dimensions. The need level was significantly higher than the satisfaction level on all six dimensions. Teachers reported highest scores on both their need and satisfaction on the dimension of Cooperation, Interaction, and Communication. 3) Kindergarten teachers’ need level increased significantly when the number of inclusive students taught during the school year was three or more, compared to a number below three. Teachers’ satisfaction level did not differ significantly across any demographic variables. 4) Kindergarten teachers were satisfied with RTs’ support services in terms of improved classroom management by handling inclusive students’ problem behaviors, pull-out instruction, test-taking assistance, reduction of IEP workload, and the provision of professional support to parents. 5) Kindergarten teachers were dissatisfied with RTs’ support services in terms of cooperative interaction and communication, co-teaching, assessment of inclusive students’ performance, and supervision of RTs' work. Based on the kindergarten teachers’ expectations for RTs’ support services, the implications of this study were discussed to provide meaningful and evidence-based directions for the research and practice of inclusive preschool education in Macao. 澳門學前融合教育處於發展階段，資源教師在支持幼稚園教師學前融合教育中起著舉足輕重的作用。本研究旨在探究澳門幼稚園教師對資源教師支援服務的需求及滿意度情況。通過問卷調查法和半結構訪談法相結合的混合研究方法，研究者採用自編問卷調查了澳門70名幼稚園教師並訪談了4名幼稚園教師，其中問卷的需求和滿意度量表均分別由六個維度組成，包括：「專業知能」、「擬定和執行個別化教育計劃」、「教學資源與課程」、「成績評量」、「合作互動溝通」、「家長溝通」。量化及質性數據分析結果顯示：1）無論之前是否接受過資源教師的支援服務，幼稚園教師對資源教師的支援服務都有著較高的需求，而接受過資源教師支援服務的幼稚園教師（N = 43）對其支援服務的滿意度則達到一般滿意的水準；2）需求與滿意度量表及各維度之間呈現中度正相關，同時需求程度在各維度上均顯著高於滿意度；需求與滿意度得分最高的都是「合作互動溝通」維度；3）在不同的背景變量下，需求程度只有在「本學年為多少位融合生提供教學」該變量上有顯著差異，當融合生人數達到3名或以上時，教師對資源教師的需求有所增加，而滿意度在不同的背景變量下則沒有顯著差異；4）幼稚園教師對資源教師在改善融合生行為問題造成的班級影響、抽離教學、測考協助、減少IEP工作量、以專業角度向家長提供支援服務方面感到滿意；5）幼稚園教師對資源教師在合作互動溝通、合作教學模式、成績評量、對資源教師工作監管等方面感到不太滿意。最後，結合幼稚園教師對資源教師所提出的期望，筆者提出本研究的啟示，包括完善資源教師的政策規定、加強資源教師的專業化程度、拓展幼稚園教師與資源教師合作教學的模式，以及促進雙方之間的溝通與合作互動關係，為本澳學前融合教育的研究與實踐提供有意義的、循證的方向。