Blockchain can be used to build a peer-to-peer, secure, and smart transaction system. As a horizontal technology that has changed several fields of industry, blockchain has tremendous potential to transform healthcare systems as well. In this article, a systematic review is conducted to critically evaluate 64 articles on blockchain-based healthcare systems, published between 2016 and January 2020 in 21 conferences, 33 journals, and ten online sources. The aim of this article is to answer three main questions. First, what are the applications of blockchain in the healthcare systems, and what are the structures and challenges of applying blockchain to a specific healthcare domain? Second, what are the technical, temporal, and spatial aspects of the currently developed blockchain applications for different healthcare domain? Third, what are the future research directions in designing and implementing blockchain-based healthcare systems? Statistical facts about the technical aspects of these 64 articles show that most of the proposed blockchain-based healthcare systems use private blockchain and Ethereum platforms; furthermore, the majority of the authors are affiliated with research institutions in the USA and China. We also discuss potential future research directions, e.g., integrating the blockchain in artificial intelligence based solutions, cloud-computing-based solutions, and parallel blockchain architecture.

Entrepreneurship is a multilevel concept that connects social, environmental, and economic aspects of sustainability for societal developments, entrepreneurial processes, and market transformations. Social sustainability covers various social aspects and concerns that modern societies should achieve to improve their social infrastructures. Entrepreneurship is one of the main pivots that can strongly affect achieving social sustainability goals. Therefore, considering the current efforts of countries to reach their optimum level of social sustainability and strategic decision-making for long-term goals requires evaluating the current entrepreneurial performance in the first step. In this article, we aim to develop a multicriteria evaluation model using Shannon's entropy and measurement of alternatives and ranking according to the compromise solution to evaluate countries with the most influential entrepreneurial activities which contribute to socially sustainable development goals. As a case study, member countries of the Group of Seven (G7) are selected to assess their entrepreneurial performance using seven key indicators. These indicators are employees by business size, enterprises by business size, starting a business, women inventors, running a business, self-employed with employees, and young self-employed and are chosen for the analysis. The results show that Italy and the United States of America have been the pioneers in utilizing entrepreneurial activities to improve their social sustainability.

Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting.