Since 2005, the call for the military to conduct ?softer? missions, such as humanitarian assistance and partner training, has increased. These ?medical stability operations? are in fact to be given comparable priority to combat operations. Military leadership understands their the value of Medical Stability Operations across the range of military operations from shaping through postdisaster or ...

Attention Bias Modification Treatment (ABMT), an emerging treatment for anxiety disorders, is thought to modify underlying, stable patterns of attention. Therefore, ABMT research should take into account the impact of attention bias stability on attention training response, especially in pediatric populations. ABMT research typically relies on the dot-probe task, where individuals detect a prob...

[Purpose] This study was aimed at investigating the preliminary therapeutic efficacy and usefulness of canoe game-based virtual reality training for stroke patients. [Subjects and Methods] Ten stroke patients were randomly assigned to an experimental group (EG; n=5) or a control group (CG; n=5). Patients in both groups participated in a conventional rehabilitation program, but those in the EG a...

In this paper, a novel neuro-fuzzy based method combined with a feature selection technique is proposed for online dynamic voltage stability status prediction of power system. This technique uses synchronized phasors measured by phasor measurement units (PMUs) in a wide-area measurement system. In order to minimize the number of neuro-fuzzy inputs, training time and complication of neuro-fuzzy ...

CONTEXT Instability resistance training (IRT; unstable surfaces and devices to strengthen the core or trunk muscles) is popular in fitness training facilities. OBJECTIVE To examine contradictory IRT recommendations for health enthusiasts and rehabilitation. DATA SOURCES A literature search was performed using MEDLINE, SPORT Discus, ScienceDirect, Web of Science, and Google Scholar databases...

The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding has demonstrated impressive performance on a variety of supervised tasks, but its generalization properties have not been studied. We...