A nonlinear analytical model for tensile failure prediction of pseudo-ductile composite laminates

In this study, the tensile nonlinear responses of composite laminates with [±θn] s and [±θn∕0] s layups are investigated. An analytical model that integrates the progressive failure, shear nonlinearity, fiber rotation, and fragmentation is established to characterize the nonlinear tensile behavior. A nonlinear factor is used to describe the shear nonlinearity of the resin matrix, which is governed by shear stress, while progressive damage indexes are determined by normal stresses. The degree of fiber rotation and the fragmentation between layers are analytically formulated. Tensile results from experiments conducted in this study and from others in the literature are used to verify the model’s prediction accuracy. The proposed model provides acceptably good predictions of nonlinear behavior for pseudo-ductile carbon fiber reinforced composite laminates. A sensitivity analysis shows that the dominant model parameter changes from axial modulus to shear modulus, and eventually to transverse modulus as the off-axial angle increases from 0◦ to 9

Fusion-Eval: Integrating Evaluators with LLMs

Evaluating Large Language Models (LLMs) is a complex task, especially considering the intricacies of natural language understanding and the expectations for high-level reasoning. Traditional evaluations typically lean on human-based, model-based, or automatic-metrics-based paradigms, each with its own advantages and shortcomings. We introduce "Fusion-Eval", a system that employs LLMs not solely for direct evaluations, but to skillfully integrate insights from diverse evaluators. This gives Fusion-Eval flexibility, enabling it to work effectively across diverse tasks and make optimal use of multiple references. In testing on the SummEval dataset, Fusion-Eval achieved a Spearman correlation of 0.96, outperforming other evaluators. The success of Fusion-Eval underscores the potential of LLMs to produce evaluations that closely align human perspectives, setting a new standard in the field of LLM evaluation

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

Optical force-induced nonlinearity and self-guiding of light in human red blood cell suspensions

Osmotic conditions play an important role in the cell properties of human red blood cells (RBCs), which are crucial for the pathological analysis of some blood diseases such as malaria. Over the past decades, numerous efforts have mainly focused on the study of the RBC biomechanical properties that arise from the unique deformability of erythrocytes. Here, we demonstrate nonlinear optical effects from human RBCs suspended in different osmotic solutions. Specifically, we observe self-trapping and scattering-resistant nonlinear propagation of a laser beam through RBC suspensions under all three osmotic conditions, where the strength of the optical nonlinearity increases with osmotic pressure on the cells. This tunable nonlinearity is attributed to optical forces, particularly the forward scattering and gradient forces. Interestingly, in aged blood samples (with lysed cells), a notably different nonlinear behavior is observed due to the presence of free hemoglobin. We use a theoretical model with an optical force-mediated nonlocal nonlinearity to explain the experimental observations. Our work on light self-guiding through scattering bio-soft-matter may introduce new photonic tools for noninvasive biomedical imaging and medical diagnosis.Comment: 20 Pages, 5 figures, accepted for publication in Light, Science & Applicatio

Myosin concentration underlies cell size–dependent scalability of actomyosin ring constriction

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Journal of Cell Biology 195 (2011): 799-813, doi:10.1083/jcb.201101055.In eukaryotes, cytokinesis is accomplished by an actomyosin-based contractile ring. Although in Caenorhabditis elegans embryos larger cells divide at a faster rate than smaller cells, it remains unknown whether a similar mode of scalability operates in other cells. We investigated cytokinesis in the filamentous fungus Neurospora crassa, which exhibits a wide range of hyphal circumferences. We found that N. crassa cells divide using an actomyosin ring and larger rings constricted faster than smaller rings. However, unlike in C. elegans, the total amount of myosin remained constant throughout constriction, and there was a size-dependent increase in the starting concentration of myosin in the ring. We predict that the increased number of ring-associated myosin motors in larger rings leads to the increased constriction rate. Accordingly, reduction or inhibition of ring-associated myosin slows down the rate of constriction. Because the mechanical characteristics of contractile rings are conserved, we predict that these findings will be relevant to actomyosin ring constriction in other cell types.Work in the laboratories of M.K. Balasubramanian and G. Jedd is supported by research funds from Singapore Millennium Foundation and the Temasek Life Sciences Laboratory.2012-05-2

THE MECHANISM OF PEROXISOMAL TAIL-ANCHORED PROTEIN BIOGENESIS

Ph.DDOCTOR OF PHILOSOPH

Recognition of deformable object category and pose

Abstract — We present a novel method for classifying and estimating the categories and poses of deformable objects, such as clothing, from a set of depth images. The framework presented here represents the recognition part of the entire pipeline of dexterous manipulation of deformable objects, which contains grasping, recognition, regrasping, placing flat, and folding. We first create an off-line simulation of the deformable objects and capture depth images from different view points as training data. Then by extracting features and applying sparse coding and dictionary learning, we build up a codebook for a set of different poses of a particular deformable object category. The whole framework contains two layers which yield a robust system that first classifies deformable objects on category level and then estimates the current pose from a group of predefined poses of a single deformable object. The system is tested on a variety of similar deformable objects and achieves a high output accuracy. By knowing the current pose of the garment, we can continue with further tasks such as regrasping and folding. I