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Learning mechanisms have been postulated to be one of the primary reasons why different individuals have similar or different emotional responses to music. While existing studies have largely examined mechanisms related to learning in terms of cultural familiarity or recognition, few studies have conceptualized it in terms of an individual’s level of familiarity with musical style, which could be a better reflection of an individual’s composite musical experiences. Therefore, the current study aimed to bridge this research gap by investigating the electrophysiological correlates of the effects of familiarity with musical style on music-evoked emotions. 49 non-musicians listened to 12 musical excerpts of a familiar musical style (Japanese animation soundtracks) and eight musical excerpts of an unfamiliar musical style (Greek Laïkó music) with their eyes closed as electroencephalography is being recorded. Participants rated their felt emotions after each musical excerpt is played. Behavioral ratings showed that music of the familiar musical style was felt as significantly more pleasant as compared to the unfamiliar musical style while no significant differences in arousal were observed. In terms of brain activity, music of the unfamiliar musical style elicited higher (1) theta power in all brain regions (including frontal midline), (2) alpha power in frontal region, and (3) beta power in fronto-temporo-occipital regions as compared to the familiar musical style. This is interpreted to reflect the need for greater attentional resources when listening to music of an unfamiliar style, where listeners are less familiar with the syntax and structure of the music as compared to music of a familiar style. In addition, classification analysis showed that unfamiliar and familiar musical styles can be distinguished with 67.86% accuracy, Thus, clinicians should consider the musical profile of the client when choosing an appropriate selection of music in the treatment plan, so as to achieve better efficacy.

Planning, scheduling, and the control of resources and activities are key elements to survive and compete. Humans play a critical role in these activities. Actually, most systems could be viewed as an environment surrounded by human beings. Over the years, some novel solutions have been proposed to solve the problems of human systems and environments in modeling, control, and management. Advanced computing systems and artificial neural network approaches continue to be one of the most promising solutions.The purpose of this special issue was to provide details on the development of advanced artificial neural network approaches and their applications to modeling, control, and the management of human systems and environments. The target audiences were researchers in information management, system engineering, environmental protection, as well as practicing managers and engineers. After a strict review, five articles from researchers around the world were finally accepted.Predicting the stock market is an important facet of financial forecasting, attracting great interest from stock buyers and sellers, investors, policy makers, applied researchers, and many others who are involved in the capital market. Neural networks have been used extensively for stock market forecasting. S. Banik, M. Anwer, and M. K. Khan conducted a comparative study to predict the stock index values using soft computing models and a time series model. They used wellknown models such as the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models, while considering the generalized autoregressive conditional heteroscedastic (GARCH) model as a time series model. The experimental results showed that the use of soft computing models is more successful than the time series model. S. K. Boddhu and J. C. Gallagher described a novel frequency grouping based analysis technique, developed to qualitatively decompose the evolved controllers into explainable functional control blocks. They also provided a summary of their previous work related to evolving flight controllers for two categories of controllers and demonstrated the applicability of the newly developed decomposition analysis for both categories. Their proposed methodology has been successfully applied to autonomous and nonautonomous controllers, and it has been demonstrated that the methodology can indeed be used to decompose the evolved controllers into logically explainable control blocks for further control analysis.K. H. Lim, K. P. Seng, and L.-M. Ang developed the Lyapunov theory-based radial basis function neural network (RBFNN) for traffic sign recognition. Their methodology, inserted multidimensional inputs into the RBF nodes, linked to multiple weights. An iterative weight adaptation scheme was then designed based on the Lyapunov-stability theory to obtain a set of optimum weights. After comparing the performances of the proposed classifier to some existing conventional techniques, the simulation results revealed th...