Using functional magnetic resonance imaging (fMRI), we measured brain activity of human participants while they performed a sentence congruence judgment task in either the visual or auditory modality separately, or in both modalities simultaneously. Significant performance decrements were observed when attention was divided between the two modalities compared with when one modality was selectiv...

The monitoring of cognitive functions is central to the assessment and consecutive management of multiple sclerosis (MS). Though, especially cognitive processes that are central to everyday behavior like dual-tasking are often neglected. We examined dual-task performance using a psychological-refractory period (PRP) task in N = 21 patients and healthy controls and conducted standard neuropsycho...

Dual tasking (e.g., walking or standing while performing a cognitive task) disrupts performance in one or both tasks, and such dual-task costs increase with aging into senescence. Dual tasking activates a network of brain regions including pFC. We therefore hypothesized that facilitation of prefrontal cortical activity via transcranial direct current stimulation (tDCS) would reduce dual-task co...

Current SSA sensor tasking and scheduling is not centrally coordinated or optimized for either orbit determination quality or efficient use of sensor resources. By applying readily available capabilities for centrally generating deconflicted schedules for all available sensors and determining optimal tasking times, both the quality of determined orbits (and thus situational awareness) and the e...

In this work we present the parallel implementation of a hybrid global optimization algorithm assembled specifically to tackle a class of time consuming interatomic potential fitting problems. The resulting objective function is characterized by large and varying execution times, discontinuity and lack of derivative information. The presented global optimization algorithm corresponds to an irre...

A parallel memetic global optimization algorithm suitable for shared memory multicore systems is proposed and analyzed. The considered algorithm combines two well-known and widely used population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with two efficient and parallelizable local search procedures. The sequential version of the algorithm was fi...