Snap-through behaviour of a bistable structure based on viscoelastically generated prestress

A novel form of shape-changing bistable structure has been successfully developed through the use of viscoelastically generated prestress. Bistability is achieved through pairs of deflecting viscoelastically prestressed polymeric matrix composite (VPPMC) strips, which are orientated to give opposing cylindrical configurations within a thin, flexible resin-impregnated fibreglass sheet. This arrangement enables the structure to ‘snap through’ between one of two states by external stimulation. Deflection from the VPPMC strips occurs through compressive stresses generated from the non-uniform spatial distribution of nylon 6,6 fibres undergoing viscoelastic recovery. In this study, snap-through behaviour of the bistable structure is investigated both experimentally and through finite element (FE) analysis. By using experimental results to calibrate FE parameter values, the modelling has facilitated investigation into the development of bistability and the influence of modulus ratio (fibreglass sheet: VPPMC strip) on the snap-through characteristics. Experimental results and FE simulation show good agreement with regard to snap-through behaviour of the bistable structure and from this, the bistability mechanisms are discussed

Temporal changes of shear wave velocity and anisotropy in the shallow crust induced by the 10/22/1999 m6.4 Chia-yi, Taiwan earthquake

Temporal changes of seismic velocity and anisotropy in the shallow crust are quantified using local earthquakes recorded at a 200-m-deep borehole station CHY in Taiwan. This station is located directly above the hypocenter of the 10/22/1999, M6.4 Chia-Yi earthquake. Three-component seismograms recorded at this station show clear direct (up-going) and surface-reflected (down-going) P- and S-waves, and S-wave splitting signals. The two-way travel times in the top 200 m is obtained by measuring the time delays between the up-going and down-going waves in the auto-correlation function. The S-wave travel times measured in two horizontal components increase by ~1-2% at the time of Chia-Yi main shock, and followed by a logarithmic recovery, while the temporal changes of S-wave splitting and P-wave are less than 1% and are not statistically significant. We obtain similar results by grouping earthquakes into clusters according to their locations and waveform similarities. This suggests that the observed temporal changes are not very sensitive to the seismic ray path below CHY, but are mostly controlled by the variation of material property in the top 200 m of the crust. We propose that strong ground motions of the Chia-Yi main shock cause transient openings of fluid-filled microcracks and increases the porosity in the near-surface layers, followed by a relatively long healing process. Because we observe no clear changes in the shear wave anisotropy, we infer that the co-seismic damages do not have a preferred orientation. Our results also show a gradual increase of time delays for both the fast and slow S-waves in the previous 7 years before the Chia-Yi main shock. Such changes might be caused by variations of water table, sediment packing or other surficial processes.M.S.Committee Chair: Peng, Zhigang; Committee Member: Assimaki, Dominic; Committee Member: Newman, Andrew V

Pattern-Recognition Processor Using Holographic Photopolymer

proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square-law detector for the purpose of holographic recording of interference fringes. A typical state-of-the-art CCD has a pixel-pitch limited resolution of about 100 lines/mm. In contrast, the holographic photopolymer to be used in the proposed JTOC offers a resolution > 2,000 lines/mm. In addition to being disadvantageous in itself, the low resolution of the CCD causes overlap of a DC term and the desired correlation term in the output image. This overlap severely limits the correlation signal-to-noise ratio. The two-stage nature of the process limits the achievable throughput rate. A further limit is imposed by the low frame rate (typical video rates) of low- and medium-cost commercial CCDs

An evaluation of the scanning electron microscope mirror effect to study viscoelastically prestressed polymeric matrix composites

A viscoelastically prestressed polymeric matrix composite (VPPMC) is produced by applying tensile creep to polymeric fibres, the creep load being removed before the fibres are moulded into a resin matrix. Following matrix curing, the viscoelastically strained fibres impart compressive stresses to the surrounding matrix, counterbalanced by residual tension in the fibres. VPPMCs based on nylon 6,6 fibres in polyester resin have previously demonstrated improvements in mechanical properties of up to 50% compared with control (unstressed) counterparts. Although the associated viscoelastic recovery forces are understood, little is known of the fibre-matrix interactions relating to prestress within VPPMCs. This is addressed by investigating composite samples with the scanning electron microscope mirror effect (SEMME). By comparing results from VPPMC samples with their control counterparts, the findings suggest that there are ∼30% fewer trapped negative charges in the former, implying that the VPPMCs possess higher fibre-matrix interfacial strengths. Tensile test results on similar composite samples support these findings. The effects of resin porosity in SEMME data are also evaluated and our findings suggest that porosity can significantly increase charge trapping

Machine Learning and Genome Annotation: A Match Meant to Be?

By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE

Persistent Homology Based Characterization of the Breast Cancer Immune Microenvironment: A Feasibility Study

Persistent homology is a common tool of topological data analysis, whose main descriptor, the persistence diagram, aims at computing and encoding the geometry and topology of given datasets. In this article, we present a novel application of persistent homology to characterize the spatial arrangement of immune and epithelial (tumor) cells within the breast cancer immune microenvironment. More specifically, quantitative and robust characterizations are built by computing persistence diagrams out of a staining technique (quantitative multiplex immunofluorescence) which allows us to obtain spatial coordinates and stain intensities on individual cells. The resulting persistence diagrams are evaluated as characteristic biomarkers of cancer subtype and prognostic biomarker of overall survival. For a cohort of approximately 700 breast cancer patients with median 8.5-year clinical follow-up, we show that these persistence diagrams outperform and complement the usual descriptors which capture spatial relationships with nearest neighbor analysis. This provides new insights and possibilities on the general problem of building (topology-based) biomarkers that are characteristic and predictive of cancer subtype, overall survival and response to therapy

A Diffusion-based Method for Multi-turn Compositional Image Generation

Multi-turn compositional image generation (M-CIG) is a challenging task that aims to iteratively manipulate a reference image given a modification text. While most of the existing methods for M-CIG are based on generative adversarial networks (GANs), recent advances in image generation have demonstrated the superiority of diffusion models over GANs. In this paper, we propose a diffusion-based method for M-CIG named conditional denoising diffusion with image compositional matching (CDD-ICM). We leverage CLIP as the backbone of image and text encoders, and incorporate a gated fusion mechanism, originally proposed for question answering, to compositionally fuse the reference image and the modification text at each turn of M-CIG. We introduce a conditioning scheme to generate the target image based on the fusion results. To prioritize the semantic quality of the generated target image, we learn an auxiliary image compositional match (ICM) objective, along with the conditional denoising diffusion (CDD) objective in a multi-task learning framework. Additionally, we also perform ICM guidance and classifier-free guidance to improve performance. Experimental results show that CDD-ICM achieves state-of-the-art results on two benchmark datasets for M-CIG, i.e., CoDraw and i-CLEVR

Applying Machine Learning to Crowd-sourced Data from Earthquake Detective

We present the Earthquake Detective dataset - A crowdsourced set of labels on potentially triggered (PT) earthquakes and tremors. These events are those which may have been triggered by large magnitude and often distant earthquakes. We apply Machine Learning to classify these PT seismic events and explore the challenges faced in segregating such low amplitude signals. The data set and code are available online.Comment: Published in AI for Earth Sciences Workshop, NeurIPS 202

Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms