Assessing Facial Symmetry and Attractiveness using Augmented Reality

Pattern Analysis and Applications - Facial symmetry is a key component in quantifying the perception of beauty. In this paper, we propose a set of facial features computed from facial landmarks...     

Persistent alterations in biological profiles in women with abuse histories: Influence of premenstrual dysphoric disorder.

Objective: To examine dysregulation in biological measures associated with histories of abuse in women and whether women with premenstrual dysphoric disorder (PMDD) differ in their dysregulation. Design: Twenty-five women meeting prospective criteria for PMDD and 42 non-PMDD controls underwent structured interview to determine abuse histories and lifetime Axis I diagnoses, excluding those with current Axis I disorders or using medications. Major Outcome Measures: Plasma cortisol and norepinephrine (NE), heart rate (HR), blood pressure (BP), and vascular resistance index (VRI) were assessed at rest and in response to mental stress. Results: A greater proportion of PMDD women had prior abuse compared with non-PMDD women. Regardless of PMDD status, all abused women had lower plasma NE and higher HRs and tended to have lower plasma cortisol at rest and during stress. Abused women also reported more severe daily emotional and physical symptoms. Greater VRI and BP at rest and during stress were seen only in PMDD women with abuse. Conclusion: There is persistent dysregulation in stress-responsive systems in all abused women that cannot be accounted for by current psychiatric illness or medications, and PMDD women may be differentially more vulnerable to the impact of abuse on measures reflecting α-adrenergic receptor function. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Discrete-particle investigation of friction effect in filling and unsteady/steady discharge in three-dimensional wedge-shaped hopper

The friction effect in filling and unsteady/steady discharge of three-dimensional wedge-shaped hopper was considered numerically by the discrete element method. The presented model is based on contact mechanics of a single spherical particle involving Hooke's law of spring interaction, static and dynamic friction as well as viscous damping forces. The hopper filling process en masse was performed while the modeling of uncontrolled and controlled discharge was simulated. The evolution of the kinetic energy of filling, the distribution of stresses within granular material and stresses acting on the walls, as well as the change in fields of material porosity were investigated at the stages of filling and discharge of the hopper. The variation of flow velocities, the discharged mass fractions as well as discharge fraction rates were analyzed. The friction effect was analyzed by varying inter-particle friction coefficient over the range of 0-0.6. A comparison of the numerical results with available continuum-based predictions was also presented.     

An average stress strain approach to creep analysis of RC uncracked elements

The paper presents a method which enables us to perform creep analysis of RC elements with uncracked cross-sections relying on the evaluation of an average stress strain state in a sense to fulfill Volterra’s integral term in time interval elapsed after loading and the prediction of an actual state at the time considered. The analytically derived expressions for time-dependent stresses, strains and curvatures are very simple, being gained by a newly proposed relation for the ageing coefficient, constitutive law for creep and the classical formulae of strength of materials. The method proposed is mathematically tractable, as well as accounting for the variation of elastic modulus. A validity of the method proposed was proven by direct calculation of the time-dependent parameters in the analysis of various RC elements. The approach gives exact values of the time-dependent stress strain state as those determined using the age-adjusted effective modulus method involving a sophisticated relaxation procedure. The method does not require the introduction of the fictitious restraining actions dramatically simplifying computation and could be used as a simple alternative approach for the exact analysis of RC uncracked elements. Numerical examples and methodology for applicability to RC structures/elements were also developed.     

Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion-based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre-trained CNN model named EfficientNetB0 is fine-tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.     

A novel framework for rapid diagnosis of COVID-19 on computed tomography scans

Pattern Analysis and Applications - Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the...     

Accuracy evaluation of two-dimensional straightness measurement method based on optical meter

The article analyses the accuracy evaluation of non-contact optical mechatronic two dimensional devices for straightness measuring. Measurement principles of the device are based on the evaluation of the position from the phase difference of two signals where one of them is generated by a non-movable photodiode, acting as a reference source, and the other one is attached to the moveable part of the technological machine. The device can be used for measurement, diagnosis and accuracy testing of mechatronic devices, robotics and technological machines.     

Dielectric investigations and theoretical calculations of size effect in lead titanate nanocrystals

The dielectric properties of nanograin ferroelectric lead titanate crystals are presented. The PbTiO3 samples were prepared by pressing nanopowders into plates and were studied experimentally by dielectric permittivity measurements in a wide frequency and temperature range. The TC dependence obtained showed a critical change of behavior with increasing mean nanoparticle size in the 9-nm region. The theoretical calculations based on Monte Carlo simulation were performed to describe the behavior of this material. It was shown that the distribution of nanoparticle sizes in the sample taken into account with the Monte Carlo method describes the dielectric properties of PbTiO3 nanocrystals quite well.     

Application of Bayesian Methods for Age‐Dependent Reliability Analysis

In this article, the authors present a general methodology for age‐dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age‐dependent change of beliefs about reliability parameters (mainly failure rate), when change of belief occurs not only because new failure data or other information becomes available with time but also because it continuously changes due to the flow of time and the evolution of beliefs. The main objective of this article is to present a clear way of how practitioners can apply Bayesian methods to deal with risk and reliability analysis considering ageing phenomena. The methodology describes step‐by‐step failure rate analysis of ageing components: from the Bayesian model building to its verification and generalization with Bayesian model averaging, which as the authors suggest in this article, could serve as an alternative for various goodness‐of‐fit assessment tools and as a universal tool to cope with various sources of uncertainty. The proposed methodology is able to deal with sparse and rare failure events, as is the case in electrical components, piping systems and various other systems with high reliability. In a case study of electrical instrumentation and control components, the proposed methodology was applied to analyse age‐dependent failure rates together with the treatment of uncertainty due to age‐dependent model selection. Copyright © 2013 John Wiley & Sons, Ltd.