An extended strain energy density failure criterion by differentiating volumetric and distortional deformation

AbstractWe extend Sih’s strain energy density criterion (Sih, 1974) for crack kinks and material failure by weighting differently the volumetric and distortional parts in the extended strain energy density factor. The work is inspired by the factor that failure by microscopic shearing governed by distortion and microscopic separation controlled by hydrostatic tension represent distinct deformation processes, and should be treated differently as we count their influences to material failure. With the weight parameter introduced to the extended strain energy density factor criterion, we explain satisfactorily several critical experiments which reported crack kink in samples subjected to mixed-mode loading. The extended strain energy density idea is also used to derive a generalized pressure-dependent yielding criterion, which supplies a theoretical basis for those novel strength criteria for materials like bulk metallic glasses. Corresponding methods to determine the two material parameters, the critical strain energy density factor and the weight parameter quantifying the relative contribution by distortion over volumetric deformation, are discussed

Future Orientation, Chronological Age and Product Attributes Preference

This dissertation examines what motivates individuals to prefer certain types of product attributes over others. It is proposed that consumer preference regarding product attributes is fundamentally connected to an individual’s future orientation, i.e., how a person perceives, thinks about, and copes with time left in life. Specifically, it is posited that future orientations play key roles in shaping a person’s criteria in product evaluation. Thus, this dissertation seeks to integrate the study of future orientation with research on socio-emotional selectivity influences on consumption. Building on past research, this study proposes a conceptual model including four constructs: future orientations, chronological age, product evaluation, and preferences. An experimental study was used to investigate the research objectives and calibrate and validate the model. The experiment examines the moderating effect of future orientations and chronological age on consumer preference for hedonic vs. utilitarian attributes. The subjects were randomly assigned to one of two future orientations (expansive and limited) and one of two attributes contexts (hedonic and utilitarian). The sample for this study was drawn from consumers in Metropolitan Atlanta, Georgia. The research results will lead to an improved understanding of how preference varies from individual to individual and changes over time. In particular the research will provide insights about the impact of an individual’s future orientation on product attitude. The findings will advance current theory in both the new product evaluation and preference literature and have implications for the practice of marketing at levels of marketing strategy, product development, integrated marketing communications and loyalty programs

Lytic cycle: A defining process in oncolytic virotherapy

The viral lytic cycle is an important process in oncolytic virotherapy. Most mathematical models for oncolytic virotherapy do not incorporate this process. In this article, we propose a mathematical model with the viral lytic cycle based on the basic mathematical model for oncolytic virotherapy. The viral lytic cycle is characterized by two parameters, the time period of the viral lytic cycle and the viral burst size. The time period of the viral lytic cycle is modeled as a delay parameter. The model is a nonlinear system of delay differential equations. The model reveals a striking feature that the critical value of the period of the viral lytic cycle is determined by the viral burst size. There are two threshold values for the burst size. Below the first threshold, the system has an unstable trivial equilibrium and a globally stable virus free equilibrium for any nonnegative delay, while the system has a third positive equilibrium when the burst size is greater than the first threshold. When the burst size is above the second threshold, there is a functional relation between the bifurcation value of the delay parameter for the period of the viral lytic cycle and the burst size. If the burst size is greater than the second threshold, the positive equilibrium is stable when the period of the viral lytic cycle is smaller than the bifurcation value, while the system has orbitally stable periodic solutions when the period of the lytic cycle is longer than the bifurcation value. However, this bifurcation value becomes smaller when the burst size becomes bigger. The viral lytic cycle may explain the oscillation phenomena observed in many studies. An important clinic implication is that the burst size should be carefully modified according to its effect on the lytic cycle when a type of a virus is modified for virotherapy, so that the period of the viral lytic cycle is in a suitable range which can break away the stability of the positive equilibria or periodic solutions. (C) 2012 Elsevier Inc. All rights reserved

Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation