Conformal deformations of a specific class of lorentzian manifolds with non-irreducible holonomy representation

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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Immunological discrimination of β-tubulin isoforms in developing mouse brain. Post-translational modification of non-class-III β-tubulins

Biochemical Journal ◽

10.1042/bj2880919 ◽

1992 ◽

Vol 288 (3) ◽

pp. 919-924 ◽

Cited By ~ 21

Author(s):

I Linhartová ◽

P Dráber ◽

E Dráberová ◽

V Viklický

Keyword(s):

Mouse Brain ◽

Specific Class ◽

Class Iii ◽

Post Translational Modification ◽

Beta Tubulin ◽

Developmentally Regulated ◽

Post Translational Modifications ◽

Tubulin Isotypes ◽

Tubulin Isoforms ◽

Developing Mouse Brain

Individual beta-tubulin isoforms in developing mouse brain were characterized using immunoblotting, after preceding high-resolution isoelectric focusing, with monoclonal antibodies against different structural regions of beta-tubulin. Some of the antibodies reacted with a limited number of tubulin isoforms in all stages of brain development and in HeLa cells. The epitope for the TU-14 antibody was located in the isotype-defining domain and was present on the beta-tubulin isotypes of classes I, II and IV, but absent on the neuron-specific class-III isotype. The data suggest that non-class-III beta-tubulins in mouse brain are substrates for developmentally regulated post-translational modifications and that beta-tubulins of non-neuronal cells are also post-translationally modified.

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Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Applied Sciences ◽

10.3390/app11146368 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6368

Author(s):

Fátima A. Saiz ◽

Garazi Alfaro ◽

Iñigo Barandiaran ◽

Manuel Graña

Keyword(s):

Ad Hoc ◽

Data Augmentation ◽

Semantic Network ◽

Semantic Networks ◽

Stereo Image ◽

Generative Adversarial Networks ◽

Specific Class ◽

Adversarial Networks ◽

Augmentation Techniques ◽

Image Acquisition System

This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

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UHPLC-IM-Q-ToFMS analysis of maradolipids, found exclusively in Caenorhabditis elegans dauer larvae

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03172-3 ◽

2021 ◽

Vol 413 (8) ◽

pp. 2091-2102

Author(s):

Michael Witting ◽

Ulrike Schmidt ◽

Hans-Joachim Knölker

Keyword(s):

Caenorhabditis Elegans ◽

Developmental Stage ◽

Ion Mobility ◽

Environmental Conditions ◽

Multidimensional Data ◽

Specific Class ◽

Lipid Profiling ◽

C Elegans ◽

Tandem Mass Spectrometric ◽

Dauer Larvae

AbstractLipid identification is one of the current bottlenecks in lipidomics and lipid profiling, especially for novel lipid classes, and requires multidimensional data for correct annotation. We used the combination of chromatographic and ion mobility separation together with data-independent acquisition (DIA) of tandem mass spectrometric data for the analysis of lipids in the biomedical model organism Caenorhabditis elegans. C. elegans reacts to harsh environmental conditions by interrupting its normal life cycle and entering an alternative developmental stage called dauer stage. Dauer larvae show distinct changes in metabolism and morphology to survive unfavorable environmental conditions and are able to survive for a long time without feeding. Only at this developmental stage, dauer larvae produce a specific class of glycolipids called maradolipids. We performed an analysis of maradolipids using ultrahigh performance liquid chromatography-ion mobility spectrometry-quadrupole-time of flight-mass spectrometry (UHPLC-IM-Q-ToFMS) using drift tube ion mobility to showcase how the integration of retention times, collisional cross sections, and DIA fragmentation data can be used for lipid identification. The obtained results show that combination of UHPLC and IM separation together with DIA represents a valuable tool for initial lipid identification. Using this analytical tool, a total of 45 marado- and lysomaradolipids have been putatively identified and 10 confirmed by authentic standards directly from C. elegans dauer larvae lipid extracts without the further need for further purification of glycolipids. Furthermore, we putatively identified two isomers of a lysomaradolipid not known so far. Graphical abstract

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Four-dimensional homogeneous Lorentzian manifolds

Monatshefte für Mathematik ◽

10.1007/s00605-013-0588-9 ◽

2013 ◽

Vol 174 (3) ◽

pp. 377-402 ◽

Cited By ~ 7

Author(s):

Giovanni Calvaruso ◽

Amirhesam Zaeim

Keyword(s):

Lorentzian Manifolds

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FINITE ACTION KLEIN–GORDON SOLUTIONS ON LORENTZIAN MANIFOLDS

International Journal of Geometric Methods in Modern Physics ◽

10.1142/s0219887806001739 ◽

2006 ◽

Vol 03 (07) ◽

pp. 1349-1357 ◽

Cited By ~ 17

Author(s):

V. V. KOZLOV ◽

I. V. VOLOVICH

Keyword(s):

Mass Spectrum ◽

Elliptic Equations ◽

Gordon Equation ◽

Infinite Family ◽

De Sitter ◽

Lorentzian Manifolds ◽

Klein Gordon ◽

Finite Action ◽

Discrete Mass ◽

Klein Gordon Equation

The eigenvalue problem for the square integrable solutions is studied usually for elliptic equations. In this paper we consider such a problem for the hyperbolic Klein–Gordon equation on Lorentzian manifolds. The investigation could help to answer the question why elementary particles have a discrete mass spectrum. An infinite family of square integrable solutions for the Klein–Gordon equation on the Friedman type manifolds is constructed. These solutions have a discrete mass spectrum and a finite action. In particular the solutions on de Sitter space are investigated.

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Torsional Characteristics of Carbon Nanotubes: Micropolar Elasticity Models and Molecular Dynamics Simulation

Nanomaterials ◽

10.3390/nano11020453 ◽

2021 ◽

Vol 11 (2) ◽

pp. 453

Author(s):

Razie Izadi ◽

Meral Tuna ◽

Patrizia Trovalusci ◽

Esmaeal Ghavanloo

Keyword(s):

Carbon Nanotubes ◽

Molecular Dynamics ◽

Scale Effects ◽

Couple Stress Theory ◽

Dynamics Simulation ◽

Local Theory ◽

Beam Model ◽

Specific Class ◽

Micropolar Theory ◽

Material Parameters

Efficient application of carbon nanotubes (CNTs) in nano-devices and nano-materials requires comprehensive understanding of their mechanical properties. As observations suggest size dependent behaviour, non-classical theories preserving the memory of body’s internal structure via additional material parameters offer great potential when a continuum modelling is to be preferred. In the present study, micropolar theory of elasticity is adopted due to its peculiar character allowing for incorporation of scale effects through additional kinematic descriptors and work-conjugated stress measures. An optimisation approach is presented to provide unified material parameters for two specific class of single-walled carbon nanotubes (e.g., armchair and zigzag) by minimizing the difference between the apparent shear modulus obtained from molecular dynamics (MD) simulation and micropolar beam model considering both solid and tubular cross-sections. The results clearly reveal that micropolar theory is more suitable compared to internally constraint couple stress theory, due to the essentiality of having skew-symmetric stress and strain measures, as well as to the classical local theory (Cauchy of Grade 1), which cannot accounts for scale effects. To the best of authors’ knowledge, this is the first time that unified material parameters of CNTs are derived through a combined MD-micropolar continuum theory.

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Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽

10.1515/phys-2020-0123 ◽

2020 ◽

Vol 18 (1) ◽

pp. 951-960

Author(s):

Haiqing Zhang ◽

Jun Han

Keyword(s):

Remote Sensing ◽

Recognition Rate ◽

Three Dimensional ◽

Remote Sensing Image ◽

Optical Remote Sensing ◽

Specific Class ◽

Three Dimensional Model ◽

Image Information ◽

Information Classification ◽

Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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