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Emotional intelligence (EI) has been an emerging topic for psychological, educational, and management researchers and consultants in recent years. However, existing literature has concentrated on demonstrating the effects of EI on either the mental health or on job outcomes such as job attitudes and performance. There is relatively little discussion concerning how EI, as a set of interrelated abilities about handling emotions, is developed. Understanding how EI is developed may be the significant first step for organizations to develop effective EI training programs. As an exploratory effort, we borrowed the basic argument from theories in human development to argue that life experiences affect EI development. Based on samples of university students from Singapore and Hong Kong, whether one of the parents was a full-time parent was a significant predictor of the students' EI. This finding was cross-validated with a sample of graduate students in Taiwan. Furthermore, age as a proxy for life experiences for this graduate student sample was found to be a significant predictor of EI. Implications for EI research and training are discussed.

Hospitals can predetermine the admission rate and facilitate resource allocation based on valid emergency requests and bed capacity estimation. The excess unoccupied beds can be determined with the help of forecasting the number of discharged patients. Extracting predictive features and mining the temporal patterns from historical observations are crucial for accurate and reliable forecasts. Machine learning algorithms have demonstrated the ability to learn temporal knowledge and make predictions for unseen inputs. This paper utilizes several machine learning algorithms to forecast the inpatient discharges of Singapore hospitals and compare them with statistical methods. A novel ensemble deep learning algorithm based on random vector functional links is established to predict inpatient discharges. The ensemble deep learning framework is optimized in a greedy layer-wise fashion. Several forecasting metrics and statistical tests are utilized to demonstrate the proposed method’s superiority. The proposed algorithm statistically outperforms the benchmark with a ranking of 1.875. Finally, practical implications and future directions are discussed. [ABSTRACT FROM AUTHOR]