The innovative concept of multiple remote tower operation (MRTO) is where a single air traffic controller (ATCO) provides air traffic services to two or more different airports from a geographically separated virtual Tower. Effective visual scanning by the air traffic controller is the main safety concern for human-computer interaction, as the aim of MRTO is a single controller performing air traffic management tasks originally carried out by up to four ATCOs, comprehensively supported by innovative technology. Thirty-two scenarios were recorded and analyzed using an eye tracking device to investigate the above safety concern and the effectiveness of multiple remote tower operations. The results demonstrated that ATCOs' visual scan patterns showed significant task related variation while performing different tasks and interacting with various interfaces on the controller's working position (CWP).ATCOs were supported by new display systems equipped with pan tilt zoom (PTZ) cameras allowing enhanced visual checking of airport surfaces and aircraft positions.Therefore, one ATCO could monitor and provide services for two airports simultaneously. The factors influencing visual attention include how the information is presented, the complexity of that information, and the characteristics of the operating environment. ATCO's attention distribution among display systems is the key human-computer interaction issue in single ATCO performing multiple monitoring tasks.

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The research aim is to develop a better design of auditory alerts that can improve air traffic controllers' situation awareness. Method: Participants are seventy-seven qualified Air Traffic Controllers. The experiment was conducted in the Air Traffic Control operational rooms of the Irish Aviation Authority at Shannon and Dublin. Participants were advised that the trials were in relation to the COOPANS Air Traffic Control. ANOVA with two between-subject factors (alerting designs and experience levels) were conducted to analyze the ATCO's response time for three critical events. Bonferroni test was performed for post-hoc analysis on mean differences of response time. Results: There is a significant difference in ATCO's response time between acoustic alert and semantic alert across STCA, APW and MSAW. No significant main effect of controllers' experience on ATCO's response time for STCA and APW. Also, there is no significant interaction between alerting design and experience level on ATCO's response time across STCA, APW and MSAW. Conclusion: The results demonstrated that the acoustic alert deployed within the COOPANS ATM system provides level-1 Situational Awareness to ATCO's compared with an semantic alert which provides not only level-1 of situational awareness for perceived alerts, but also level-2 and level-3 of situational awareness to assist ATCO understanding of critical events and therefore develop more suitable solutions. Consequently, humancentered design of a semantic alert can significantly speed up ATCO's response to STCA, and APW. Furthermore, the sematic alert could alleviate expertise differences by promoting quicker response times for both novice and experienced air traffic controllers.

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The issue of equality in the between-and within-level structures in Multilevel Confirmatory Factor Analysis (MCFA) models has been influential for obtaining unbiased parameter estimates and statistical inferences. A commonly seen condition is the inequality of factor loadings under equal level-varying structures. With mathematical investigation and Monte Carlo simulation, this study compared the robustness of five statistical models including two model-based (a true and a mis-specified models), one design-based, and two maximum models (two models where the full rank of variance-covariance matrix is estimated in between level and within level, respectively) in analyzing complex survey measurement data with level-varying factor loadings. The empirical data of 120 3rd graders' (from 40 classrooms) perceived Harter competence scale were modeled using MCFA and the parameter estimates were used as true parameters to perform the Monte Carlo simulation study. Results showed maximum models was robust to unequal factor loadings while the design-based and the miss-specified model-based approaches produced conflated results and spurious statistical inferences. We recommend the use of maximum models if researchers have limited information about the pattern of factor loadings and measurement structures. Measurement models are key components of Structural Equation Modeling (SEM); therefore, the findings can be generalized to multilevel SEM and CFA models. Mplus codes are provided for maximum models and other analytical models.