Crack and flaw identification in elastodynamics using Kalman filter techniques

Filter-driven optimization based on the extended Kalman filter concept is used here for the numerical solution of crack and flaw identification problems in elastodynamics. The mechanical modeling of the studied two-dimensional problem, which includes the effect of unilateral contact along the sides of the crack, is done with the help of the boundary element method. The effect of various dynamical test loads and the applicability of this method for crack and defect identification in disks are investigated. The work has been supported by the German Research Foundation (DFG). Partial support has been provided by a Greek-German Research Cooperation Grant (IKYDA2001). This support is greatfully acknowledged.     

Defect identification in 3-D elastostatics using a genetic algorithm

An inverse problem in engineering mechanics is considered where the position and the geometry of three-dimensional, ellipsoidal defects are identified by using measurements of the mechanical response under static loading on the external surface of the structure. The problem is solved by appropriate combination of genetic optimization (GO) and boundary element method (BEM) and following previously published two-dimensional problems. The three-dimensional case presents some additional difficulties. Furthermore, the function of several genetic operators and the effect of the parameters of genetic optimization on the efficiency of the solution has been numerically examined.     

Direct Segmentation of Algebraic Models for Reverse Engineering

In Reverse Engineering a physical object is digitally reconstructed from a set of boundary points. In the segmentation phase these points are grouped into subsets to facilitate consecutive steps as surface fitting. In this paper we present a segmentation method with subsequent classification of simple algebraic surfaces. Our method is direct in the sense that it operates directly on the point set in contrast to other approaches that are based on a triangulation of the data set. The segmentation process involves a fast algorithm for k-nearest neighbors search and an estimation of first and second order surface properties. The first order segmentation, that is based on normal vectors, provides an initial subdivision of the surface and detects sharp edges as well as flat or highly curved areas. One of the main features of our method is to proceed by alternating the steps of segmentation and normal vector estimation. The second order segmentation subdivides the surface according to principal curvatures and provides a sufficient foundation for the classification of simple algebraic surfaces. If the boundary of the original object contains such surfaces the segmentation is optimized based on the result of a surface fitting procedure.     

Wave propagation in delaminated beam

The wave propagation model investigated herein is based on the known fact that material discontinuities affect the propagation of elastic waves in solids. The change in certain material characteristics, such as a local change in stiffness or inertia caused by a crack or the presence of material damage, will affect the propagation of transmitted elastic waves and will modify the received signal. Wave frequencies associated with the highest detection sensitivity depend, among other things, on the type of structure, the type of material, and the type of damage. This paper presents a method of wave propagation, which can be further used to detect small delaminations in beam-like structures. The considered beam is modelled by spectral finite elements.     

Constraint Lingo: towards high‐level constraint programming

Logic programming requires that the programmer convert a problem into a set of constraints based on predicates. Choosing the predicates and introducing appropriate constraints can be intricate and error prone. If the problem domain is structured enough, we can let the programmer express the problem in terms of more abstract, higher‐level constraints. A compiler can then convert the higher‐level program into a logic‐programming formalism. The compiler writer can experiment with alternative low‐level representations of the higher‐level constraints in order to achieve a high‐quality translation. The programmer can then take advantage of both a reduction in complexity and an improvement in runtime speed for all problems within the domain. We apply this analysis to the domain of tabular constraint‐satisfaction problems. Examples of such problems include logic puzzles solvable on a hatch grid and combinatorial problems such as graph coloring and independent sets. The proper abstractions for these problems are rows, columns, entries, and their interactions. We present a higher‐level language, Constraint Lingo, dedicated to problems in this domain. We also describe how we translate programs from Constraint Lingo into lower‐level logic formalisms such as the logic of propositional schemata. These translations require that we choose among competing lower‐level representations in order to produce efficient results. The overall effectiveness of our approach depends on the appropriateness of Constraint Lingo, our ability to translate Constraint Lingo programs into high‐quality representations in logic formalisms, and the efficiency with which logic engines can compute answer sets. We comment on our computational experience with these tools in solving both graph problems and logic puzzles. Copyright © 2004 John Wiley & Sons, Ltd.     

Generalized model of vibro-isolation systems with seated human body for single-axis transmission of vibration

A generalized model of vibration isolation systems with a human body in a seated position is developed. The system dynamics is modeled for a single-axis transmission of vibration, that is, longitudinal x, lateral y and vertical z, to limit whole-body vibration exposure. The developed model can be successfully employed to describe the propagation of vibration waves from the source to a vibrating object. The proposed mathematical model can be widely used to protect a human body against vibration.     

The State‐of‐the‐Art in Predictive Visual Analytics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.     

SARS-CoV-2 Infection Dysregulates Cilia and Basal Cell Homeostasis in the Respiratory Epithelium of Hamsters

Similar to many other respiratory viruses, SARS-CoV-2 targets the ciliated cells of the respiratory epithelium and compromises mucociliary clearance, thereby facilitating spread to the lungs and paving the way for secondary infections. A detailed understanding of mechanism involved in ciliary loss and subsequent regeneration is crucial to assess the possible long-term consequences of COVID-19. The aim of this study was to characterize the sequence of histological and ultrastructural changes observed in the ciliated epithelium during and after SARS-CoV-2 infection in the golden Syrian hamster model. We show that acute infection induces a severe, transient loss of cilia, which is, at least in part, caused by cilia internalization. Internalized cilia colocalize with membrane invaginations, facilitating virus entry into the cell. Infection also results in a progressive decline in cells expressing the regulator of ciliogenesis FOXJ1, which persists beyond virus clearance and the termination of inflammatory changes. Ciliary loss triggers the mobilization of p73⁺ and CK14⁺ basal cells, which ceases after regeneration of the cilia. Although ciliation is restored after two weeks despite the lack of FOXJ1, an increased frequency of cilia with ultrastructural alterations indicative of secondary ciliary dyskinesia is observed. In summary, the work provides new insights into SARS-CoV-2 pathogenesis and expands our understanding of virally induced damage to defense mechanisms in the conducting airways.     

Wireless link prediction and triggering using modified Ornstein–Uhlenbeck jump diffusion process

Through time domain observation, typical wireless signal strength values seems to exhibit some forms of mean-reverting and discontinuous “jumps” behaviour. Motivated by this fact, we propose a wireless link prediction and triggering (LPT) technique using a modified mean-reverting Ornstein–Uhlenbeck (OU) jump diffusion process. The proposed technique which we refer as OU-LPT is an integral component of wireless mesh network monitoring system developed by ICT FP7 CARrier grade wireless MEsh Network project. In particular, we demonstrate how this technique can be applied in the context of wireless mesh networks to support link switching or handover in the event of predicted link degradation or failure. The proposed technique has also been implemented and evaluated in a real-time experimental testbed. The results show that OU-LPT technique can significantly enhance the reliability of wireless links by reducing the rate of false triggers compared to a conventional linear prediction technique and therefore offers a new direction on how wireless link prediction, triggering and switching process can be conducted in the future.