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Modeling and predicting of mental workload are among the most important issues in studying human performance in complex systems. Ample research has shown that the amplitude of the P300 component of event-related potential (ERP) is an effective real-time index of mental workload, yet no computational model exists that is able to account for the change of P300 amplitude in dual-task conditions compared with that in single-task situations. We describe the successful extension and application of a new computational modeling approach in modeling P300 and mental workload - a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. Based on the neurophysiological mechanisms underlying the generation of P300, the current modeling approach accurately accounts for P300 amplitude both in temporal and intensity dimensions. This approach not only has a basis in its biological plausibility but also has the ability to model and predict workload in real time and can be applied to other applied domains. Further model developments in simulating other dimensions of mental workload and its potential applications in adaptive system design are discussed.