Guidance of collective cell migration by substrate geometry.

Collective behavior refers to the emergence of complex migration patterns over scales larger than those of the individual elements constituting a system. It plays a pivotal role in biological systems in regulating various processes such as gastrulation, morphogenesis and tissue organization. Here, by combining experimental approaches and numerical modeling, we explore the role of cell density ('crowding'), strength of intercellular adhesion ('cohesion') and boundary conditions imposed by extracellular matrix (ECM) proteins ('constraints') in regulating the emergence of collective behavior within epithelial cell sheets. Our results show that the geometrical confinement of cells into well-defined circles induces a persistent, coordinated and synchronized rotation of cells that depends on cell density. The speed of such rotating large-scale movements slows down as the density increases. Furthermore, such collective rotation behavior depends on the size of the micropatterned circles: we observe a rotating motion of the overall cell population in the same direction for sizes of up to 200 μm. The rotating cells move as a solid body, with a uniform angular velocity. Interestingly, this upper limit leads to length scales that are similar to the natural correlation length observed for unconfined epithelial cell sheets. This behavior is strongly altered in cells that present a downregulation of adherens junctions and in cancerous cell types. We anticipate that our system provides a simple and easy approach to investigate collective cell behavior in a well-controlled and systematic manner.

Differential geometry and design coupling in MDO

The notion of coupling within a design, particularly within the context of Multidisciplinary Design Optimization (MDO), is much used but ill-defined. There are many different ways of measuring design coupling, but these measures vary in both their conceptions of what design coupling is and how such coupling may be calculated. Within the differential geometry framework which we have previously developed for MDO systems, we put forth our own design coupling metric for consideration. Our metric is not commensurate with similar types of coupling metrics, but we show that it both provides a helpful geo- metric interpretation of coupling (and uncoupledness in particular) and exhibits greater generality and potential for analysis than those similar metrics. Furthermore, we discuss how the metric might be profitably extended to time-varying problems and show how the metric's measure of coupling can be applied to multi-objective optimization problems (in unconstrained optimization and in MDO). © 2013 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

On the application of differential geometry to MDO

Multidisciplinary Design Optimization (MDO) is a methodology for optimizing large coupled systems. Over the years, a number of different MDO decomposition strategies, known as architectures, have been developed, and various pieces of analytical work have been done on MDO and its architectures. However, MDO lacks an overarching paradigm which would unify the field and promote cumulative research. In this paper, we propose a differential geometry framework as such a paradigm: Differential geometry comes with its own set of analysis tools and a long history of use in theoretical physics. We begin by outlining some of the mathematics behind differential geometry and then translate MDO into that framework. This initial work gives new tools and techniques for studying MDO and its architectures while producing a naturally arising measure of design coupling. The framework also suggests several new areas for exploration into and analysis of MDO systems. At this point, analogies with particle dynamics and systems of differential equations look particularly promising for both the wealth of extant background theory that they have and the potential predictive and evaluative power that they hold. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

The influence of room geometry on the overturning of smoke owing to a floor fire

The present paper explores the influence of room geometry on the overturning of smoke owing to a centrally located floor fire, and examines the implications on smoke filling times. The focus is on presenting practical design guidelines based on the theoretical predictions of the model of Kaye and Hunt. An engineering platform is developed for the prediction of smoke filling times, and a rational basis is provided by way of which smoke behaviour can be specified for simple room designs. The time taken for smoke to fill a room to a given height is critically affected by the room aspect ratio and the characteristic size of the buoyancy source. At large times, taller (small aspect ratio) rooms are shown to fill with smoke at a faster rate than wide (large aspect ratio) rooms owing to large-scale overturning and engulfing of ambient air during the initial transients. Larger area sources of buoyancy also decrease significantly the smoke filling times, with important implications for fire and smoke safety design. Simplified design curves incorporating the main findings have been developed for use as a tool by practising fire-safety engineers.

Influence of tire geometry on the horn effect

A reciprocal-configuration Boundary Element Method calculation of acoustic radiation characteristics has been implemented for a generic tire geometry. The influence of the geometric parameters on the radiation characteristics has been studied. The degree of amplification of noise sources on the tire belt is strongly affected by the overall tire width. In contrast, the tire radius predominantly influences the pattern of the varying amplification around the belt, rather than its absolute level. Radiusing the tire's 'shoulder' region is potentially beneficial in terms of lowering amplification levels, for a tire of fixed overall width. However, it is less effective than maintaining sharp shoulders and reducing the overall width. Thus, for an acoustically optimal belted tire, the overall width should be as small as possible, even if this leads to a larger diameter. The width should not be increased in order to accommodate a radiused crown region. Copyright © (2012) by the Institute of Noise Control Engineering (INCE).

Vibration decay along deepwater moorings with complex geometry and configuration, as a tool for truncation

This paper is concerned with the difficulties in model testing deepwater structures at reasonable scales. An overview of recent research efforts to tackle this challenge is given first, introducing the concept of line truncation. Passive truncation has traditionally been the preferred method by industry; however, these techniques tend to suffer in capturing accurately line dynamic response and so reproducing peak tensions. In an attempt to improve credibility of model test data the proposed truncation procedure sets up the truncated model, based on line dynamic response rather than quasi-static system stiffness. Vibration decay of transverse elastic waves due to fluid drag forces is assessed and it is found that below a certain length criterion, the transverse vibrational characteristics for each line are inertia driven, hence with respect to these motions the truncated model can assume a linear damper whose coefficient depends on the local line properties and vibration frequency. Initially a simplified taut string model is assumed for which the line is submerged in still water, one end fixed at the bottom the other assumed to follow the vessel response, which can be harmonic or random. A dimensional analysis, supported by exact benchmark numerical solutions, has shown that it is possible to produce a general guideline for the truncation length criterion, which is suitable for any kind of line with any top motion. The focus of this paper is to extend this work to a more complex line configuration of a conventional deepwater mooring line and so enhance the generality of the truncation guideline. The paper will close with an example case study of a spread mooring system, applying this method to create an equivalent numerical model at a reduced depth that replicates exactly the static and dynamic characteristics of the full depth system. Copyright © 2012 by ASME.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.

Blind image restoration based on the theory of high-dimensional space geometry

In practical situations, the causes of image blurring are often undiscovered or difficult to get known. However, traditional methods usually assume the knowledge of the blur has been known prior to the restoring process, which are not practicable for blind image restoration. A new method proposed in this paper aims exactly at blind image restoration. The restoration process is transformed into a problem of point distribution analysis in high-dimensional space. Experiments have proved that the restoration could be achieved using this method without re-knowledge of the image blur. In addition, the algorithm guarantees to be convergent and has simple computation.

Geometry parameter optimization of large cross-sectional S-shaped bent rib waveguides for silicon based optical switches

地址: Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing 100083, Peoples R China

High dimensional imagery geometry and it's applications

Because of information digitalization and the correspondence of digits and the coordinates, Information Science and high-dimensional space have consanguineous relations. With the transforming from the information issues to the point analysis in high-dimensional space, we proposed a novel computational theory, named High dimensional imagery geometry (HDIG). Some computational algorithms of HDIG have been realized using software, and how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is demonstrated in this paper. As the applications, two kinds of experiments of HDIG, which are blurred image restoration and pattern recognition ones, are given, and the results are satisfying.

Information geometry theory of high-dimension space and application for speaker independent continuous digit speech recognition

In the light of descriptive geometry and notions in set theory, this paper re-defines the basic elements in space such as curve and surface and so on, presents some fundamental notions with respect to the point cover based on the High-dimension space (HDS) point covering theory, finally takes points from mapping part of speech signals to HDS, so as to analyze distribution information of these speech points in HDS, and various geometric covering objects for speech points and their relationship. Besides, this paper also proposes a new algorithm for speaker independent continuous digit speech recognition based on the HDS point dynamic searching theory without end-points detection and segmentation. First from the different digit syllables in real continuous digit speech, we establish the covering area in feature space for continuous speech. During recognition, we make use of the point covering dynamic searching theory in HDS to do recognition, and then get the satisfying recognized results. At last, compared to HMM (Hidden Markov models)-based method, from the development trend of the comparing results, as sample amount increasing, the difference of recognition rate between two methods will decrease slowly, while sample amount approaching to be very large, two recognition rates all close to 100% little by little. As seen from the results, the recognition rate of HDS point covering method is higher than that of in HMM (Hidden Markov models) based method, because, the point covering describes the morphological distribution for speech in HDS, whereas HMM-based method is only a probability distribution, whose accuracy is certainly inferior to point covering.

An algorithm for removing facial makeup disturbances based on high dimensional imaginal geometry

In this paper, a novel algorithm for removing facial makeup disturbances as a face detection preprocess based on high dimensional imaginal geometry is proposed. After simulation and practical application experiments, the algorithm is theoretically analyzed. Its apparent effect of removing facial makeup and the advantages of face detection with this pre-process over face detection without it are discussed. Furthermore, in our experiments with color images, the proposed algorithm even gives some surprises.