Personal health records can be considered as a noticeable advancement in consumer health informatics. PHRs have been adopted in many nations, whereas other countries are planning to adopt this technology. Understanding the user acceptance of PHRs by organizations and individuals is a critical issue. Many researches discussed the acceptance and adoption of PHRs; few studies were experimental. The main objective of this study is reviewing the related studies that have been made in order to investigate the user acceptance of PHRs through reviewing the theories that have been used, the main factors involved, and any missing aspects to be applied in this field. The review's major components are: topic, location of the study, sample size, statistical tools, models or theories, goals, and findings of the study.

Personal Health Records (PHRs) can be considered as one of the most important health technologies. PHRs enroll the patients directly to their health decision making through giving them the authority to control and share their health information. Testing the user acceptance toward new technology is a vital process. Over the previous decades many models have been used and the latest one was UTAUT2. UTAUT2 have been widely used in e-business and gaming user acceptance researches, whereas, it has been rarely used in health field. This study proposes a combination of UTAUT2 and PMT in order to investigate the user acceptance of PHRs. Relevant factors may be added at the literature review stage. The final model will be used as a framework to investigate the user acceptance of PHRs in Malaysia.

Purpose of the study: To evaluate the efficacy of a CAI called MathTutor in helping autistic students to learn addition in mathematics. Methodology: A pre-post-test experimental model was employed in the study. The study participants included three autistic male students who possessed all the prerequisite skills. Main findings and novelty: The study participants were found to have been benefitted which indicates that MathTutor improves their learning abilities as an effective instructional strategy. The use of CAI enabled the participants to memorise their lessons and actively take part during the entire lesson. Applications of this study: The study is beneficial for people involved in caring for autistic students with the help of computer assisted application. This will ease the burden of the carer takers of autistic children in dealing with their conditions while at the same enabling them to navigate the world.

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets.