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Over the past several decades, the dichotomy between traditional and emerging donors has been based upon the notion that emerging donors (such as China) support authoritarian regimes and use foreign aid to pursue their economic interests at the expense of the poor in the recipient countries. Accordingly, Western donors, media, and scholars portray Chinese aid as non-poverty-focused. This study aims to review and analyze whether the dichotomy between traditional and emerging donors is still relevant in the current aid system and to propose a new and rigorous criterion for recategorizing donors. In terms of methodology, this study relies on secondary data, including scholarly works on traditional and emerging donors and foreign aid policy documents. Conclusions based on the research indicate that the divide between traditional donors and (re)emerging donors is becoming more ambiguous. The literature review indicates that the two donors’ aids had a mixed impact and that their approaches were similar. This paper highlights the importance of developing different recategorization criteria depending on the impact of aid.

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.

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.

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.

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

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.