An isogeometric analysis for elliptic homogenization problems

A novel and efficient approach which is based on the framework of isogeometric analysis for elliptic homogenization problems is proposed. These problems possess highly oscillating coefficients leading to extremely high computational expenses while using traditional finite element methods. The isogeometric analysis heterogeneous multiscale method (IGA-HMM) investigated in this paper is regarded as an alternative approach to the standard Finite Element Heterogeneous Multiscale Method (FE-HMM) which is currently an effective framework to solve these problems. The method utilizes non-uniform rational B-splines (NURBS) in both macro and micro levels instead of standard Lagrange basis. Beside the ability to describe exactly the geometry, it tremendously facilitates high-order macroscopic/microscopic discretizations thanks to the flexibility of refinement and degree elevation with an arbitrary continuity level provided by NURBS basis functions. A priori error estimates of the discretization error coming from macro and micro meshes and optimal micro refinement strategies for macro/micro NURBS basis functions of arbitrary orders are derived. Numerical results show the excellent performance of the proposed method

Isogeometric analysis for functionally graded microplates based on modified couple stress theory

Analysis of static bending, free vibration and buckling behaviours of functionally graded microplates is investigated in this study. The main idea is to use the isogeometric analysis in associated with novel four-variable refined plate theory and quasi-3D theory. More importantly, the modified couple stress theory with only one material length scale parameter is employed to effectively capture the size-dependent effects within the microplates. Meanwhile, the quasi-3D theory which is constructed from a novel seventh-order shear deformation refined plate theory with four unknowns is able to consider both shear deformations and thickness stretching effect without requiring shear correction factors. The NURBS-based isogeometric analysis is integrated to exactly describe the geometry and approximately calculate the unknown fields with higher-order derivative and continuity requirements. The convergence and verification show the validity and efficiency of this proposed computational approach in comparison with those existing in the literature. It is further applied to study the static bending, free vibration and buckling responses of rectangular and circular functionally graded microplates with various types of boundary conditions. A number of investigations are also conducted to illustrate the effects of the material length scale, material index, and length-to-thickness ratios on the responses of the microplates.Comment: 57 pages, 14 figures, 18 table

Deep combination of radar with optical data for gesture recognition: role of attention in fusion architectures

Multimodal time series classification is an important aspect of human gesture recognition, in which limitations of individual sensors can be overcome by combining data from multiple modalities. In a deep learning pipeline, the attention mechanism further allows for a selective, contextual concentration on relevant features. However, while the standard attention mechanism is an effective tool when working with Natural Language Processing (NLP), it is not ideal when working with temporally- or spatially-sparse multi-modal data. In this paper, we present a novel attention mechanism, Multi-Modal Attention Preconditioning (MMAP). We first demonstrate that MMAP outperforms regular attention for the task of classification of modalities involving temporal and spatial sparsity and secondly investigate the impact of attention in the fusion of radar and optical data for gesture recognition via three specific modalities: dense spatiotemporal optical data, spatially sparse/temporally dense kinematic data, and sparse spatiotemporal radar data. We explore the effect of attention on early, intermediate, and late fusion architectures and compare eight different pipelines in terms of accuracy and their ability to preserve detection accuracy when modalities are missing. Results highlight fundamental differences between late and intermediate attention mechanisms in respect to the fusion of radar and optical data

Molecular mechanism of MBX2319 inhibition of Escherichia coli AcrB multidrug efflux pump and comparison with other inhibitors

Efflux pumps of the resistance nodulation division (RND) superfamily, such as AcrB, make a major contribution to multidrug resistance in Gram-negative bacteria. The development of inhibitors of the RND pumps would improve the efficacy of current and next-generation antibiotics. To date, however, only one inhibitor has been cocrystallized with AcrB. Thus, in silico struc- ture-based analysis is essential for elucidating the interaction between other inhibitors and the efflux pumps. In this work, we used computer docking and molecular dynamics simulations to study the interaction between AcrB and the compound MBX2319, a novel pyranopyridine efflux pump inhibitor with potent activity against RND efflux pumps of Enterobacteriaceae species, as well as other known inhibitors (D13-9001, 1-[1-naphthylmethyl]-piperazine, and phenylalanylarginine-ß-naphthyl-amide) and the binding of doxorubicin to the efflux-defective F610A variant of AcrB. We also analyzed the binding of a sub- strate, minocycline, for comparison. Our results show that MBX2319 binds very tightly to the lower part of the distal pocket in the B protomer of AcrB, strongly interacting with the phenylalanines lining the hydrophobic trap, where the hydrophobic por- tion of D13-9001 was found to bind by X-ray crystallography. Additionally, MBX2319 binds to AcrB in a manner that is similar to the way in which doxorubicin binds to the F610A variant of AcrB. In contrast, 1-(1-naphthylmethyl)-piperazine and phenylalanylarginine-ß-naphthylamide appear to bind to somewhat different areas of the distal pocket in the B protomer of AcrB than does MBX2319. However, all inhibitors (except D13-9001) appear to distort the structure of the distal pocket, impairing the proper binding of substrates

An Efficient Solution to the Mixed Shop Scheduling Problem Using a Modified Genetic Algorithm

The mixed job shop scheduling problem is one in which some jobs have fixed machine orders and other jobs may be processed in arbitrary orders. In past literature, optimal solutions have been proposed based on adaptations of classical solutions such as by Johnson, Thompson and Giffler among many others, by pseudopolynomial algorithms, by simulation, and by Genetic Algorithms (GA). GA based solutions have been proposed for flexible Job shops. This paper proposes a GA algorithm for the mixed job shop scheduling problem. The paper starts with an analysis of the characteristics of the so-called mixed shop problem. Based on those properties, a modified GA is proposed to minimize the makespan of the mixed shop schedule. In this approach, sample instances used as test data are generated under the constraints of shop scheduling problems. A comparison of our results based on benchmark data indicate that our modified GA provides an efficient solution for the mixed shop scheduling problem