No abstract

This research investigates the effect of fiber, yarn and fabric variables on the bagging behavior of single jersey weft knitted fabrics interpreted in terms of bagging fatigue percentage. In order to estimate the optimum process conditions and to examine the individual effects of each controllable factor on a particular response, Taguchi's experimental design was used. The controllable factors considered in this research are blending ratio, yarn twist and count, fabric structure and fabric density. The findings show that fabric structure has the largest effect on the fabric bagging. Factor yarn twist is second and is followed by fabric density, blend ratio and yarn count. The optimum conditions to achieve the least bagging fatigue ratio were determined.

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior).We find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of learning.Synaptic and myelin plasticity are two crucial mechanisms underlying learning in the brain. Synaptic plasticity, which refers to activity-dependent changes of synaptic coupling, has been

We study the synchronization of small-world networks of identical coupled phase oscillators through the Kuramoto interaction and uniform time delay. For a given intrinsic frequency and coupling constant, we observe synchronization enhancement in a range of time delays and discontinuous transition from the partially synchronized state with defect patterns to a glassy phase, characterized by a distribution of randomly frozen phase-locked oscillators. By further increasing the time delay, this phase undergoes a discontinuous transition to another partially synchronized state. We found the bimodal frequency distributions and hysteresis loops as indicators of the discontinuous nature of these transitions. Moreover, we found the existence of Chimera states at the onset of transitions.