Your search

This article reports a case study of older adults learning English in China. It indicates how, founded on consequentialist ethics, risk analysis, and safeguarding, it was decided to use covert research, drawing on the confluence of risk analysis, risk evaluation, risk management, safeguarding, research ethics, and important contextual and cultural features. Ethical principles of nonmaleficence, beneficence, safeguarding, and protection were addressed, and account was taken of the strength, likelihood, and consequences of risks, safeguards, and benefits, informed by Chinese cultural contexts, values, behaviors, and features of teaching and learning based on andragogy and geragogy. Implications are drawn for teaching and learning with older adults, advocating significant account to be taken of contextual factors.

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.

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.

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

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.

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>

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 21st century, complex problem-solving (CPS) serves as a key indicator of educational achievement. However, the elements of successful CPS have not yet been fully explored. This study investigates the role of strategic exploration and different problem-solving and test-taking behaviors in CPS success, using logfile data to visualize and quantify students’ problemsolving behavior on 10 CPS problems with different characteristics and levels of difficulty. Additionally, in the present study, we go beyond the limits of most studies that focus on students’ problem-solving behavior pattern analyses in European cultures and education systems to examine Arabic students’ CPS behavior. The results show that computer-based assessments of CPS are feasible and valid in Jordanian higher education. The findings also confirm the structural validity of CPS, indicating that the processes of knowledge acquisition (KAC) and knowledge application (KAP) can be distinguished and separated in the problem-solving process. Large differences were identified in students’ test-taking behavior in terms of the efficacy of their exploration strategy. We identified four latent classes based on the students’ exploration strategy behavior. The study thus leads to a better understanding of how students solve problems and behave during the problem-solving process in uncertain situations.

It has been claimed in \cite1, that the idea proposed in \cite2 has certain mistakes based on arguments of energy conditions and others. Additionally, some of the key arguments of the paper are criticized. Here we demonstrate that the results obtained in \cite2 are correct and that there is no violation of any energy condition. The statements claimed in \cite1 are based on three things: 1). Misinterpretation of the metric solution. 2). Language issues related to the physical quantities obtained in \cite1, where the authors make wrong interpretations about certain results over the geometry proposed in \cite2. 3). Non-rigorous evaluations of the vacuum condition defined via the result over the Ricci tensor R_\mu\nu=0.

Abstract The Jesuit missionary Matteo Ricci's teaching on the goodness of human nature in The True Meaning of the Lord of Heaven represents the fruit of the first encounter between Catholicism and Confucianism. This article will consider the Thomistic and neo‐Confucian sources in Ricci's enunciation of the Catholic doctrine on the goodness of human nature in this Chinese catechism. It will illustrate that Ricci developed his teaching, which is fundamentally Thomistic, with the help of terminology borrowed from the Chinese philosophical tradition. His distinction between the good of nature and the good of virtue leads to prioritising the cultivation of human nature. Ricci's teaching reflects the early modern Jesuits’ appreciation of human freedom. It also displays a Catholic reaction to the sixteenth‐century neo‐Confucian intellectual trend that ignored the importance of moral cultivation.

This study investigates career trajectory and work locations of doctoral students trained in Macao and analyses how their career paths are shaped by perceived macro-level factors. Respondents from four applied disciplinary areas were selected for semi-structured in-depth interviews. Research results show that doctoral students who graduated from Macao higher education institutions enjoy good career prospects in Mainland China. Their competitiveness in the research-related job market benefits from having a multi-level support system and a training mode that promotes government–university–industry collaboration. Policies and demand from industrial sectors are involved in students' learning experience through channels such as financial support, project collaboration and networks. Doctoral students in Macao are strategic planners and actors in leveraging their human capital. As Macao becomes an emerging destination for cultivating high-level research labour, findings from this study capture a model of human capital formation in China's cross-system context.

The extent of citizens' trust in government determines the success or failure of e-government initiatives. Nevertheless, the idiosyncrasies of the concept and the broad spectrum of its approach still present relevant challenges. This work presents a systematic literature review on e-government trust while elaborating and summarizing a conceptual analysis of trust, introducing evaluation methods for government trust, and compiling relevant research on e-government trust and intentional behavior. A total of 26 key factors that constitute trust have been identified and classified into six categories: Government trust, Trust in Internet and technology (TiIT), Trust in e-government (TiEG), Personal Beliefs, Trustworthiness, and Trust of intermediary (ToI). The value added of this work consists of developing a conceptual framework of TiEG to provide a significant reference for future in-depth studies and research on e-government trust.

An increasing number of countries have launched their central bank digital currencies (CBDC) in recent years, but the economic impacts of CBDC adoption are underexplored. To empirically assess how CBDC adoption influences regional economic integration, this paper investigates the Greater Bay Area, where China carried out one of its first digital renminbi pilot programs. The Greater Bay Area provides a good example because the growing acceptance of digital renminbi in the area can potentially mitigate transaction costs and risks due to the exchange rate volatility of the Chinese renminbi, Hong Kong dollar, and Macao pataca. CBDC adoption can lead to greater real and financial integrations by facilitating cross-border trade in goods and services. This paper evaluates deviations from uncovered interest rate parity, purchasing power parity, and real interest rate parity across Guangdong, Hong Kong, and Macao based on monthly interest rate and price data from January 2016 to December 2022. The time series have mean values near zero, which validate the parity conditions and indicate high degrees of financial, real, and economic integrations. The Markov regime-switching regression model identifies three regimes: (1) pre-Covid, (2) post-Covid, and (3) post-CBDC. The Covid-19 outbreak brought lower integration and stability, but the launch of the CBDC restored some of the pre-Covid integration and stability. Regimes 1 and 2 are persistent, and transitions from Regime 3 back to Regime 1 are probable. Hence, this study finds evidence that CBDC adoption improves regional economic integration in the short and long run.