Uncovering the Nonlinear Dynamics of Origami Folding

Origami, the ancient art of paper folding, has found lots of different applications in various branches of science, including engineering. However, most of the studies on engineering applications of origami have been limited to static or quasistatic applications. Origami folding, on the other hand, could be a dynamic process. The intricate nonlinear elastic properties of origami structures can lead to interesting dynamic characteristics and applications. Nevertheless, studying the dynamics of folding is still a nascent field. In this dissertation, we try to expand our knowledge of fundamentals of origami folding dynamics. We look at the problem of origami folding dynamics from two different perspectives: 1) How can we utilize folding-induced mechanical properties for dynamic applications? and 2) How can we fold origami structures using dynamic excitations? In order to answer these questions, we conduct three different projects. Regarding the first perspective, we study a unique asymmetric quasi-zero stiffness (QZS) property from the pressurized fluidic origami cellular structure, and examine the feasibility and efficiency of using this nonlinear property for low-frequency vibration isolation. In another project, we analyze the feasibility of utilizing origami folding techniques to create an optimized jumping mechanism. And finally, regarding the second perspective, we examine a rapid and reversible origami folding method by exploiting a combination of resonance excitation, asymmetric multi-stability, and active control. In addition to these studies, Witnessing the rich and nonlinear dynamic characteristics of origami structures, in this dissertation we introduce the idea of using origami structures as physical reservoir computing systems and investigate their potentials in sensing and signal processing tasks without relying on external digital components and signal processing units

Architected Origami Materials: How Folding Creates Sophisticated Mechanical Properties

Origami, the ancient Japanese art of paper folding, is not only an inspiring technique to create sophisticated shapes, but also a surprisingly powerful method to induce nonlinear mechanical properties. Over the last decade, advances in crease design, mechanics modeling, and scalable fabrication have fostered the rapid emergence of architected origami materials. These materials typically consist of folded origami sheets or modules with intricate 3D geometries, and feature many unique and desirable material properties like auxetics, tunable nonlinear stiffness, multistability, and impact absorption. Rich designs in origami offer great freedom to design the performance of such origami materials, and folding offers a unique opportunity to efficiently fabricate these materials at vastly different sizes. Here, recent studies on the different aspects of origami materialsâ geometric design, mechanics analysis, achieved properties, and fabrication techniquesâ are highlighted and the challenges ahead discussed. The synergies between these different aspects will continue to mature and flourish this promising field.Origami, the ancient art of paper folding, has become a framework of designing and constructing architected materials. These materials consist of folded sheets or modules with intricate geometries, and feature many unique and desirable mechanical properties. Recent progress in architected origami materials is highlighted, especially the foldingâ induced mechanics, and the challenges ahead are discussed.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147779/1/adma201805282_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147779/2/adma201805282.pd

The Neuroprotective Effects of Flaxseed Oil Supplementation on Functional Motor Recovery in a Model of Ischemic Brain Stroke: Upregulation of BDNF and GDNF

Cerebral ischemic stroke is a common leading cause of disability. Flaxseed is a richest plant-based source of antioxidants. In this study, the effects of flaxseed oil (FSO) pretreatment on functional motor recovery and gene expression and protein content of neurotrophic factors in motor cortex area in rat model of brain ischemia/reperfusion (I/R) were assessed. Transient middle cerebral artery occlusion (tMCAo) in rats was used as model brain I/R. Rats (6 in each group) were randomly divided into four groups of Control (Co+normal saline [NS]), Sham (Sh+NS), tMCAo+NS and tMCAo+FSO. After three weeks of pretreatment with vehicle or FSO (0.2 ml~800 mg/kg body weight), the rats were operated in sham and ischemic groups. Ischemia was induced for 1 h and then reperfused. After 24 h of reperfusion, neurological examination was performed, and animals were sacrificed, and their brains were used for molecular and histopathological studies. FSO significantly improved the functional motor recovery compared with tMCAo+NS group (P<0.05). A significant reduction in brain-derived neurotrophic factor (BDNF) and glial cell-derived neurotrophic factor (GDNF) mRNAs and protein levels were observed in the tMCAo+NS group compared with Co+NS and Sh+NS group (P<0.05). A significant increase of BDNF and GDNF mRNAs and proteins was recorded in the tMCAo+FSO group compared with Co+NS, Sh+NS and tMCAO+NS groups (P<0.05). The results of the current study demonstrated that pretreatment with FSO had neuroprotective effects on motor cortex area following cerebral ischemic stroke by increasing the neurotrophic factors (BDNF, GDNF)

A convolutional neural network segments yeast microscopy images with high accuracy

The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of approximate to 2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually. Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation