Nonlinear dynamic response and vibration of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) shear deformable plates with temperature-dependent material properties and surrounded on elastic foundations

Based on Reddy’s third-order shear deformation plate theory, the nonlinear dynamic response and vibration of imperfect functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates on elastic foundations subjected to dynamic loads and temperature are presented. The plates are reinforced by single-walled carbon nanotubes which vary according to the linear functions of the plate thickness. The plate’s effective material properties are assumed to depend on temperature and estimated through the rule of mixture. By applying the Airy stress function, Galerkin method and fourth-order Runge–Kutta method, nonlinear dynamic response and natural frequency for imperfect FG-CNTRC plates are determined. In numerical results, the influences of geometrical parameters, elastic foundations, initial imperfection, dynamic loads, temperature increment, and nanotube volume fraction on the nonlinear vibration of FG-CNTRC plates are investigated. The obtained results are validated by comparing with those of other authors.     

Development of g-C3N4/BiVO4 Binary Component Heterojunction as an Advanced Visible Light-Responded Photocatalyst for Polluted Antibiotics Degradation

Topics in Catalysis - g-C3N4/BiVO4, binary component heterojunction materials, were successfully synthesized for novel photocatalytic tetracycline (TC) decomposition. In the prepared binary...     

It’s the journey,not the destination: the effect of school travel mode on student achievement

ABSTRACT

According to data from the National Household Travel Survey, 49.3 percent of American children in Kindergarten through sixth grade either walked or biked to school in 1969. By 2017, only 11 percent of elementary children still walked or biked to school. In this study, we examine the effect of school transport mode on a child’s academic achievement using data from a nationally representative dataset of American children. We rely on instrumental variables regression to isolate the effect of mode on achievement. Our results suggest children who are dropped off from private vehicles, and to a lesser extent, walk to school, have higher test scores than children who ride the bus.     

Correction to: Nitrogen balance in paddy fields under flowing-irrigation condition

The article Nitrogen balance in paddy fields under flowing-irrigation condition, written by Makoto Saiki, Thi Phuong Mai Nguyen, Junko Shindo, Kei Nishida, was originally published Online First...     

Flexural–torsional behavior of thin-walled composite space frames

A general analytical model based on the first-order shear deformable beam theory applicable to thin-walled composite space frames with arbitrary lay-ups under external loads is presented. This model accounts for all the structural coupling coming from the material anisotropy. The seven governing equations are derived from the principle of the stationary value of total potential energy. A displacement-based one-dimensional 14 degree-of-freedom space beam model which includes the effects of shear deformation, warping is developed to solve the problem. Numerical results are obtained to investigate the effects of fiber orientation on flexural–torsional responses of thin-walled composite space frame under vertical load.     

Experimental and numerical investigations on the thermal response of multilayer glass fibre/unsaturated polyester/organoclay composite

Organoclay glass fibre reinforced polymer (GFRP) nanocomposites are fabricated using the vacuum assisted resin transfer moulding. The unsaturated polyester resin is prepared with and without organoclay involving mechanical mixing, sonication, dilution solvent and heat treatment. Three levels of organophilic clay content are added, and its influences on the fire performance of composite samples are investigated. A novel numerical procedure combining pyrolysis analysis of the organoclay‐composites and the fire dynamic simulation of the combustion process are developed to validate the thermal responses obtained from the cone calorimetry experiments. Kinetic parameters obtained from the TGA tests and pyrolysis analyses are used as inputs for the models measuring the fire growth index and total heat release. To account for multilayer composite structure and organoclay distribution, three numerical models are proposed including composite (CPS), component (CPN) and CPN‐layer models. While CPS model assumes the homogeneity of the composite, later models consider multilayer effects with uniform (CPN model) or concentrated (CPN‐layer model) distribution of organoclay. Numerical results are compared with experimental ones in terms of total heat release, fire growth index. Finally, the fire resistance and total smoke release of the polyester/glass composites with the addition of organoclay will be evaluated taking into account influences of the fabrication processes.     

An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves preprocessing stage which removes the presence of noise in the input image. Then, the watershed algorithm is applied to segment the preprocessed images. Followed by, feature extraction takes place by leveraging EOPSO-CNN model. Finally, the extracted feature vectors are provided to a Decision Tree (DT) classifier to classify the DR images. The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way. The simulation outcome offered the maximum classification with accuracy, sensitivity, and specificity values being 98.47%, 96.43%, and 99.02% respectively.     

Additive manufacturing of polymer-derived ceramic matrix composites

This study presents a fabrication method and identifies processing bounds for additively manufacturing (AM) ceramic matrix composites (CMCs), comprising a silicon oxycarbide (SiOC) ceramic matrix. A digital light projection printer was used to photopolymerize a siloxane-based preceramic resin containing inert ceramic reinforcement. A subsequent pyrolysis converted the preceramic polymer to SiOC. Particle reinforcements of 0 to 40% by volume in the green state were uniformly dispersed in the printed samples to study their effects on pyrolysis mass loss and shrinkage, and CMC notch sensitivity and strength. Both particle and whisker reinforcements toughened the glassy SiOC matrix (1 MPa m^1/2), reaching values >3 MPa m^1/2. Bending strengths of >300 MPa (>150 MPa (g cm⁻³)⁻¹) and a Weibull modulus of 10 were measured on AM samples without surface finish. We identified two pore formation mechanisms that placed processing bounds on sample size and reinforcement volume fraction. Methods for increasing these bounds are discussed. With properties commensurate to traditionally processed technical ceramics, the presented process allows for free-form fabrication of high-performance AM CMC components.