Correction to: Buckling of magneto-electro-hygro-thermal piezoelectric nanoplates system embedded in a visco-Pasternak medium based on nonlocal theory

Microsystem Technologies - The original version of this article unfortunately contained a mistake. Farzad Ebrahimi was not listed among the authors.     

Visceral Obesity and Its Shared Role in Cancer and Cardiovascular Disease: A Scoping Review of the Pathophysiology and Pharmacological Treatments

The association between obesity, cancer and cardiovascular disease (CVD) has been demonstrated in animal and epidemiological studies. However, the specific role of visceral obesity on cancer and CVD remains unclear. Visceral adipose tissue (VAT) is a complex and metabolically active tissue, that can produce different adipokines and hormones, responsible for endocrine-metabolic comorbidities. This review explores the potential mechanisms related to VAT that may also be involved in cancer and CVD. In addition, we discuss the shared pharmacological treatments which may reduce the risk of both diseases. This review highlights that chronic inflammation, molecular aspects, metabolic syndrome, secretion of hormones and adiponectin associated to VAT may have synergistic effects and should be further studied in relation to cancer and CVD. Reductions in abdominal and visceral adiposity improve insulin sensitivity, lipid profile and cytokines, which consequently reduce the risk of CVD and some cancers. Several medications have shown to reduce visceral and/or subcutaneous fat. Further research is needed to investigate the pathophysiological mechanisms by which visceral obesity may cause both cancer and CVD. The role of visceral fat in cancer and CVD is an important area to advance. Public health policies to increase public awareness about VAT’s role and ways to manage or prevent it are needed.     

A control-theoretic approach to distributed discrete-valued decision-making in networks of sensing agents

We address the problem of global sensor fusion for the purpose of distributed decision-making, from a control-theoretic perspective. In particular, we introduce a quasi-linear stochastic distributed protocol, using which a network of sensing agents can reach agreement in order to take a collective action. Using control-theoretic methods, we design the parameters of our protocol - which include weights in the local update rules used by the agents and a finite stopping time - to achieve agreement in a fair and rapid manner. We show analytically that the developed protocol achieves fair agreement with certainty in the noise-free case and achieves fair agreement with high probability even in the presence of communication noise and assuming very little information storage capability for the agents. Our development is illustrated throughout with a canonical example motivated by autonomous vehicle control.     

Adaptive stabilization of a class of nonlinear systems using high-gain feedback

This note presents a high-gain feedback stabilizing control algorithm in which the high-gain parameter is adapted on-line. The algorithm is developed for a class of nonlinear systems which can be viewed as the nonlinear counterpart of uniform rank systems. The system can be unknown except for a number of vital pieces of information. For single-input single-output linear systems such information is usually required in the traditional adaptive control literature.     

A Detection-Theoretic Model of Echo Inhibition.

A detection-theoretic analysis of the auditory localization of dual-impulse stimuli is described, and a model for the processing of spatial cues in the echo pulse is developed. Although for over 50 years "echo suppression" has been the topic of intense theoretical and empirical study within the hearing sciences, only a rudimentary understanding of its mechanisms has emerged. In this article, psychometric functions and results from matching studies are used in developing a model that specifies the perceived position of the echo pulse as a normal deviate, with an expectation that is a logistic function of the echo delay and a variance that depends on interaural time difference. Loss of information in the echo event is quantified as a decline in the efficiency with which the binaural system receives information from the lag impulse. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Deconvolution methods for mitigation of transverse blurring in optical coherence tomography.

Imaging resolution in optical coherence tomography (OCT) is a key determinant for acquiring clinically useful optical biopsies of tissues. In contrast to light or confocal microscopy, the axial and transverse resolutions in OCT are independent and each can be analyzed individually. A method for mitigating transverse blurring and the apparent loss of transverse resolution in OCT by means of Gaussian beam deconvolution is presented. Such a method provides better representation of a specimen by using known physical parameters of a lens. To implement this method, deconvolution algorithms based on a focal-dependent kernel are investigated. First, the direct inverse problem is investigated using two types of regularization, truncated singular value decomposition, and Tikhonov. Second, an iterative expectation maximization algorithm, the Richardson-Lucy algorithm, with a beam-width-dependent iteration scheme is developed. A dynamically iterative Richardson-Lucy algorithm can reduce transverse blurring by providing an improvement in the transverse point-spread-function for sparse scattering samples in regions up to two times larger than the confocal region of the lens. These deblurring improvements inside and outside of the confocal region, which are validated experimentally, are possible without introducing new optical imaging hardware or acquiring multiple images of the same specimen. Implementation of this method in sparse scattering specimens, such as engineered tissues, has the potential to improve cellular detection and categorization.     

An analytical solution for static stability of multi-scale hybrid nanocomposite plates

An analytical answer to the buckling problem of a composite plate consisted of multi-scale hybrid nanocomposites is presented here for the first time. In other words, the constituent material of the structure is made of an epoxy matrix which is reinforced by both macro- and nanosize reinforcements, namely, carbon fiber (CF) and carbon nanotube (CNT). The effective material properties such as Young’s modulus or density are derived utilizing a micromechanical scheme incorporated with the Halpin–Tsai model. To present a more realistic problem, the plate is placed on a two-parameter elastic substrate. Then, on the basis of an energy-based Hamiltonian approach, the equations of motion are derived using the classical theory of plates. Finally, the governing equations are solved analytically to obtain the critical buckling load of the system. Afterward, the normalized form of the results is presented to emphasize the impact of each parameter on the dimensionless buckling load of composite plates. It is worth mentioning that the effects of various boundary conditions are covered, too. To show the efficiency of presented modeling, the results of this article are compared to those of former attempts.

     

Postbuckling analysis of piezoelectric multiscale sandwich composite doubly curved porous shallow shells via Homotopy Perturbation Method

In this study postbuckling behaviors of multiscale composite sandwich doubly curved piezoelectric shell with a flexible core and MR layers by employing Homotopy Perturbation Method in hygrothermal environment has been investigated. By using Reddy third shear deformable theory the face sheets and third-order polynomial theory of the flexible core the strains and stresses are obtained. A mathematical model for the multiscale composite layered shell with a flexible core and magnetorheological layer (MR) that incorporates the nonlinearity of the in-plane and the vertical displacements of the core is assumed. Three-phase composite shells with polymer/Carbon nanotube/fiber and polymer/Graphene platelet/fiber either uniformly or non-uniformly based on different patterns according to Halpin–Tsai model have been considered. The governing equations of multiscale shell have been derived by implementing Hamilton’s principle. Meanwhile, simply supported boundary conditions are employed to the shell. For investigating correctness and accuracy, this paper is validated by other previous researches. Finally, different parameters such as temperature rise, various distribution patterns, magnetic fields and curvature ratio are considered in this article. It is found these parameters have significant effect on the frequency–amplitude curves.

     

An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005.