scholarly journals Robust Numerical Upscaling of Elliptic Multiscale Problems at High Contrast

2016 ◽  
Vol 16 (4) ◽  
pp. 579-603 ◽  
Author(s):  
Daniel Peterseim ◽  
Robert Scheichl
Keyword(s):  
Diffusion Coefficient ◽  
Orthogonal Decomposition ◽  
High Contrast ◽  
Approximation Properties ◽  
New Approach ◽  
Multiscale Problems ◽  
Optimal Convergence ◽  
Discretization Schemes ◽  
Interpolation Operators ◽  
New Framework

AbstractWe present a new approach to the numerical upscaling for elliptic problems with rough diffusion coefficient at high contrast. It is based on the localizable orthogonal decomposition of ${H^{1}}$ into the image and the kernel of some novel stable quasi-interpolation operators with local $L^{2}$-approximation properties, independent of the contrast. We identify a set of sufficient assumptions on these quasi-interpolation operators that guarantee in principle optimal convergence without pre-asymptotic effects for high-contrast coefficients. We then give an example of a suitable operator and establish the assumptions for a particular class of high-contrast coefficients. So far this is not possible without any pre-asymptotic effects, but the optimal convergence is independent of the contrast and the asymptotic range is largely improved over other discretization schemes. The new framework is sufficiently flexible to allow also for other choices of quasi-interpolation operators and the potential for fully robust numerical upscaling at high contrast.

scholarly journals Semi-Lagrangian Subgrid Reconstruction for Advection-Dominant Multiscale Problems with Rough Data

2021 ◽  
Vol 87 (2) ◽  
Author(s):  
Konrad Simon ◽  
Jörn Behrens
Keyword(s):  
Nonlinear Problems ◽  
Higher Dimensions ◽  
Two Dimensions ◽  
Multiscale Methods ◽  
Galerkin Methods ◽  
Standard Methods ◽  
Multiscale Problems ◽  
Eulerian Method ◽  
Lower Order Terms ◽  
New Framework

AbstractWe introduce a new framework of numerical multiscale methods for advection-dominated problems motivated by climate sciences. Current numerical multiscale methods (MsFEM) work well on stationary elliptic problems but have difficulties when the model involves dominant lower order terms. Our idea to overcome the associated difficulties is a semi-Lagrangian based reconstruction of subgrid variability into a multiscale basis by solving many local inverse problems. Globally the method looks like a Eulerian method with multiscale stabilized basis. We show example runs in one and two dimensions and a comparison to standard methods to support our ideas and discuss possible extensions to other types of Galerkin methods, higher dimensions and nonlinear problems.

scholarly journals Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1994
Author(s):  
Yasmin M. Kassim ◽  
Feng Yang ◽  
Hang Yu ◽  
Richard J. Maude ◽  
Stefan Jaeger
Keyword(s):  
Plasmodium Falciparum ◽  
Parasite Species ◽  
Malaria Diagnosis ◽  
Smear Microscopy ◽  
New Approach ◽  
Development Stages ◽  
Patient Level ◽  
Microscopy Images ◽  
Thick Smear ◽  
New Framework

We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy images for a malaria diagnosis on both image and patient-level. Our framework detects whether a patient is infected, and in case of a malarial infection, reports whether the patient is infected by Plasmodium falciparum or Plasmodium vivax. PlasmodiumVF-Net first detects candidates for Plasmodium parasites using a Mask Regional-Convolutional Neural Network (Mask R-CNN), filters out false positives using a ResNet50 classifier, and then follows a new approach to recognize parasite species based on a score obtained from the number of detected patches and their aggregated probabilities for all of the patient images. Reporting a patient-level decision is highly challenging, and therefore reported less often in the literature, due to the small size of detected parasites, the similarity to staining artifacts, the similarity of species in different development stages, and illumination or color variations on patient-level. We use a manually annotated dataset consisting of 350 patients, with about 6000 images, which we make publicly available together with this manuscript. Our framework achieves an overall accuracy above 90% on image and patient-level.

Integrating New Visions of Education Models and CSR 2.0 Towards University Social Responsibility (USR)

2019 ◽  
pp. 1633-1655
Author(s):  
Catalina Soriana Sitnikov ◽  
Claudiu Bocean ◽  
Sorin Tudor
Keyword(s):  
Social Responsibility ◽  
Business Models ◽  
Ethical Issues ◽  
Socially Responsible ◽  
New Approach ◽  
Doing Business ◽  
Teaching Learning ◽  
Corporate Social ◽  
Education Business ◽  
New Framework

Currently, the adoption of a specific approach to business activities that highlights the strategic importance of corporate social responsibility hereafter CSR is the most important element influencing the existence and continuity of an organization. Thus, there is not a surprise that universities shall identify, in terms of own activities, the possibility to lead their orientation beyond teaching-learning process, towards the operations and institutional activities. At the same time, recent decades have experienced the failure of CSR as a way of doing business, govern or provide solutions and evaluate ethical issues and, thus, of the need to apply and implement a new approach - CSR 2.0. The transition from the current CSR, or 1.0, to CSR 2.0 requires the adoption of five new principles—creativity, scalability, responsiveness, glocality, and circularity—and embedding them within organizations management and culture. The paper will unfold towards two steps: the first, dedicated to the correlation between education (Blessinger's models and frameworks elements) with business (based on higher education business models), and the second, represented by integrating the new built model with the concepts and principles of CSR 2.0 developed by Visser. The new framework can be used to manage the context and processes of a socially responsible university as part of a world influenced by CSR 2.0 principles.

scholarly journals Multicrack Localization in Rotors Based on Proper Orthogonal Decomposition Using Fractal Dimension and Gapped Smoothing Method

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Zhiwen Lu ◽  
Dawei Dong ◽  
Shancheng Cao ◽  
Huajiang Ouyang ◽  
Chunrong Hua
Keyword(s):  
Fractal Dimension ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Smoothing Method ◽  
The Finite Element Method ◽  
New Approach ◽  
Rotor Systems ◽  
Crack Configuration ◽  
Proper Orthogonal ◽  
Robust To Noise

Multicrack localization in operating rotor systems is still a challenge today. Focusing on this challenge, a new approach based on proper orthogonal decomposition (POD) is proposed for multicrack localization in rotors. A two-disc rotor-bearing system with breathing cracks is established by the finite element method and simulated sensors are distributed along the rotor to obtain the steady-state transverse responses required by POD. Based on the discontinuities introduced in the proper orthogonal modes (POMs) at the locations of cracks, the characteristic POM (CPOM), which is sensitive to crack locations and robust to noise, is selected for cracks localization. Instead of using the CPOM directly, due to its difficulty to localize incipient cracks, damage indexes using fractal dimension (FD) and gapped smoothing method (GSM) are adopted, in order to extract the locations more efficiently. The method proposed in this work is validated to be effective for multicrack localization in rotors by numerical experiments on rotors in different crack configuration cases considering the effects of noise. In addition, the feasibility of using fewer sensors is also investigated.

A New Approach for the Diffusion Coefficient Evaluation of Sulfur in CaO-SiO2-Al2O3 Slag

2011 ◽  
Vol 312-315 ◽  
pp. 626-634
Author(s):  
Luckman Muhmood ◽  
Nurini N. Viswanathan ◽  
Seshadri Seetharaman
Keyword(s):  
Diffusion Coefficient ◽  
Sulfide Capacity ◽  
Chemical Diffusion ◽  
Partition Ratio ◽  
Critical Parameters ◽  
New Approach ◽  
Matlab Program ◽  
Order Of Magnitude ◽  
Concentration Changes ◽  
Silver Metal

The Diffusion coefficient of sulfur in a ternary slag with composition of 51.5% CaO- 9.6% SiO2- 38.9% Al2O3 was measured at 1723 K by chemical diffusion from the variation of concentration of sulfur in silver metal. A MATLAB program was developed to find the concentration variation of sulfur in silver metal using various critical parameters like the diffusion coefficient of sulfur in slag available in literature, sulfur partition ratio, sulfide capacity of the slag and the its density. The PS2 and PO2 pressures were calculated from the Gibbs energy of the equilibrium reaction between CaO in the slag and solid CaS and confirming the same by using ThermoCalc. The density of the slag at 1723 K was obtained from earlier experiments. Initially the order of magnitude for the diffusion coefficient was taken from the works of Saito and Kawai but later was modified so that the concentration changes of Sulfur obtained from the program agreed with the experimental results. The diffusion coefficient of sulfur in 51.5% CaO- 9.6% SiO2- 38.9% Al2O3 slag at 1723 K was estimated as 4.14x10-6 cm2/sec.

scholarly journals A new methodology for analyzing vehicle network topologies for critical hacking

2020 ◽  
Author(s):  
Maen Ghadi ◽  
Ádám Sali ◽  
Zsolt Szalay ◽  
Árpád Török
Keyword(s):  
Network Security ◽  
Statistical Techniques ◽  
New Approach ◽  
Large Sample ◽  
Passenger Safety ◽  
Network Topologies ◽  
Vehicle Networks ◽  
Positive Effect ◽  
Vehicle Network ◽  
New Framework

Abstract This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.

Disentangling the Process of Work–Family Conflict

2016 ◽  
Vol 224 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Sascha Haun ◽  
Christian Dormann
Keyword(s):  
Family Conflict ◽  
Organizational Stress ◽  
Work Family Conflict ◽  
New Approach ◽  
Future Studies ◽  
Framework Model ◽  
The Family ◽  
Work Family ◽  
Separate Measurement ◽  
New Framework

Abstract. The purpose of this conceptual article is to deliver a new framework model for research on work–family conflict (WFC), which overcomes existing limitations. By adopting an organizational stress perspective on WFC we show that WFC should be conceptualized as a process. By disentangling its components we point out several problems of WFC research and how our new approach can help to avoid them. Research on WFC often does not comply with the current standards of organizational stress research. Common WFC measures bear the potential of content that overlaps with determinants and outcomes, which might spuriously inflate correlations. To avoid measurement overlap, we propose to operationalize incompatible demands by interaction effects between the work and the family domain. We further acknowledge that incompatible demands increase the need to make role decisions, which affect direct indicators of WFC like role performance. The separate measurement of the components of the WFC process delivers a more objective insight than measures of WFC that do not capture all those components separately. Many problems of WFC research have been addressed before, but this is the first attempt to solve several of them by providing an overall conceptual model. The propositions we derive from this model can easily be tested in future studies. We also point out how our model could be expanded to include other important concepts of the work–family interface.

A Sequential-Quadratic-Programming Algorithm Using Orthogonal Decomposition With Gerschgorin Stabilization

1999 ◽  
Author(s):  
Chin-Pun Teng ◽  
Jorge Angeles
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Hessian Matrix ◽  
Initial Guess ◽  
Orthogonal Decomposition ◽  
Programming Algorithm ◽  
New Approach ◽  
Novel Approach ◽  
Programming Solution ◽  
Sequential Quadratic Programming Algorithm

Abstract This paper introduces a new approach to sequential quadratic programming. Upon application of the Gerschgorin Theorem for the stabilization of the Hessian matrix and of the orthogonal-decomposition algorithm in the quadratic programming solution, this novel approach offers a faster computation and dispenses with a feasible initial guess.

scholarly journals Model-Reduced Variational Data Assimilation

2006 ◽  
Vol 134 (10) ◽  
pp. 2888-2899 ◽  
Author(s):  
P. T. M. Vermeulen ◽  
A. W. Heemink
Keyword(s):  
Diffusion Coefficient ◽  
Data Assimilation ◽  
Original Model ◽  
Variational Data Assimilation ◽  
Reduced Model ◽  
New Approach ◽  
Model Simulations ◽  
Computational Costs ◽  
Recent Estimate ◽  
Reduced Space

Abstract This paper describes a new approach to variational data assimilation that with a comparable computational efficiency does not require implementation of the adjoint of the tangent linear approximation of the original model. In classical variational data assimilation, the adjoint implementation is used to efficiently compute the gradient of the criterion to be minimized. Our approach is based on model reduction. Using an ensemble of forward model simulations, the leading EOFs are determined to define a subspace. The reduced model is created by projecting the original model onto this subspace. Once this reduced model is available, its adjoint can be implemented very easily and can be used to approximate the gradient of the criterion. The minimization process can now be solved completely in reduced space with negligible computational costs. If necessary, the procedure can be repeated a few times by generating new ensembles closer to the most recent estimate of the parameters. The reduced-model-based method has been tested on several nonlinear synthetic cases for which a diffusion coefficient was estimated.

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