Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems

Neural Computing and Applications - Upper extremity (UE) neuromuscular dysfunction critically affects post-stroke patients from performing activities of daily life. In this regard, various...     

Connection Strings Property on ADO Connection Object

［1］Microsoft, Internet information services 5. 0, Resource Guide,Microsoft Press, 2000 ［2］Spencer K. Database connectivity with ASP and ADO, Premier Pr, Wrox Press Inc, 1999 ［3］Infinite solution software, Infinite Solution Press, 2001 ［4］Peterson J. Wh     

Free surface electrospun fibers: The combined effect of processing parameters

In this study, a free surface electrospinning experimental setup was developed based on rotating spiral copper wire electrode and used as the spinneret. The scheme was investigated by varying processing parameters including polymer solution concentration, distance between the electrode and the collector, applied voltage between the electrode, and the collector and wire electrode diameter. An average of fiber diameter ranged between 202 and 543 nm and a relative standard deviation ranged between 11.0 and 26.9% were obtained. The combined effects of processing parameters on the resulting fiber morphology were investigated. The analysis shows that in a multiple variable process like electrospinning, the interaction between the different processing parameters played an important role, rather than one parameter separately in obtaining desired nanofibers. Knowing the relative combined effects of processing parameters on fiber morphology should be useful for process control and prediction of electrospun fiber quality as it has been demonstrated in this study. POLYM. ENG. SCI., 54:189–197, 2014. © 2013 Society of Plastics Engineers     

Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio

Identifying factors that affect crash injury severity and understanding how these factors affect injury severity is critical in planning and implementing highway safety improvement programs. Factors such as driver-related, traffic-related, environment-related and geometric design-related were considered when developing statistical models to predict the effects of these factors on the severity of injuries sustained from motor vehicle crashes at merging and diverging locations. Police-reported crash data at selected freeway merging and diverging areas in the state of Ohio were used for the development of the models. A generalized ordinal logit model also known as partial proportional odds model was applied to identify significant factors increasing the likelihood of one of the five KABCO scale of injury severity: no injuries, possible/invisible injuries, non-incapacitating injuries, incapacitating injuries, or fatal injuries. The results of this study show that semi-truck related crashes, higher number of lanes on freeways, higher number of lanes on ramps, speeding related crashes, and alcohol related crashes tend to increase the likelihood of sustaining severe injuries at freeway merging locations. In addition, females and older persons are more likely to sustain severe injuries especially at freeway merge locations. Alcohol related crashes, speeding related crashes, angle-type collisions, and lane-ramp configuration type D significantly increase the likelihood of severe injury crashes at diverging areas. Poor lighting condition tends to increase non-incapacitating injuries at diverging areas only. Moreover, adverse weather condition increases the likelihood of no-injury and fatal injuries at merging areas only and adverse road conditions tend to increase a range of injury severity levels from possible/invisible injuries to incapacitating injuries at merging areas only.     

Effects of processing parameters on electrospun fiber morphology

This study describes the application of a hybrid neuro-fuzzy inference system to control electrospinning process and how to use this approach for developing an electrospun fiber quality prediction system. An adaptive neuro-fuzzy inference system model has been applied to the use of electrospinning process parameters to study the relationship between electrospinning processing parameters and electrospun fiber morphology. Fiber morphology has been predicted and the impact of each processing parameter has been investigated. It was found that four electrospinning process parameters including: polymer solution concentration, spinning distance, applied voltage, and volume flow rate are the most influential factors to the electrospun fiber morphology. It was observed that the relationship existing between electrospinning processing parameters and nanofiber morphology is nonlinear.