Buckling and postbuckling of axially-loaded CNT-reinforced composite cylindrical shell surrounded by an elastic medium in thermal environment

Buckling and postbuckling behaviors of nanocomposite cylindrical shells reinforced by single walled carbon nanotubes (SWCNTs), surrounded by an elastic medium, exposed to a thermal environment and subjected to uniform axial compression are investigated in this paper. Material properties of carbon nanotubes (CNTs) and isotropic matrix are assumed to be temperature dependent, and effective properties of nanocomposite are estimated by extended rule of mixture. The CNTs are embedded into matrix via uniform distribution (UD) or functionally graded (FG) distribution along the thickness direction. Governing equations are based on Donnell’s classical shell theory taking into account von Karman-Donnell nonlinear terms and interaction between the shell and surrounding elastic medium. Three-term form of deflection and stress function are assumed to satisfy simply supported boundary conditions and Galerkin method is applied to obtain load-deflection relation from which buckling and postbuckling behaviors are analyzed. Numerical examples are carried out to analyze the effects of CNT volume fraction and distribution types, geometrical ratios, environment temperature and surrounding elastic foundation on the buckling loads and postbuckling strength of CNTRC cylindrical shells

Nonlinear buckling of CNT-reinforced composite toroidal shell segment surrounded by an elastic medium and subjected to uniform external pressure

Buckling and postbuckling behaviors of Toroidal Shell Segment (TSS) reinforced by single-walled carbon nanotubes (SWCNT), surrounded by an elastic medium and subjected to uniform external pressure are investigated in this paper. Carbon nanotubes (CNTs) are reinforced into matrix phase by uniform distribution (UD) or functionally graded (FG) distribution along the thickness direction. Effective properties of carbon nanotube reinforced composite (CNTRC) are estimated by an extended rule of mixture through a micromechanical model. Governing equations for TSSs are based on the classical thin shell theory taking into account geometrical nonlinearity and surrounding elastic medium. Three-term solution of deflection and stress function are assumed to satisfy simply supported boundary condition, and Galerkin method is applied to obtain nonlinear load-deflection relation from which buckling loads and postbuckling equilibrium paths are determined. The effects of CNT volume fraction, distribution types, geometrical ratios and elastic foundation on the buckling and postbuckling behaviors of CNTRC TSSs are analyzed and discussed

Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instance

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning.

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES)

Review: Possible removal of heavy metal and selective rare-earth ions by polymeric and nano-composite materials.

Heavy metal contamination in aqueous environment from natural processes and human’s activities including pesticides usage, mining, factories discharging and leaking irritation shows an extreme threat to environment and human health. The control of rising amount of heavy metal in environment not only requires critical observations at their original sources but also demands efficient removal methods. However, each conventional method is only suitable in a certain range of use. For example, inexpensive materials and convenient techniques are necessary for large scale heavy meal treatment, while high performance system to remove almost trace amount of heavy metals are critically required for drinking water treatment. On the other hands, heavy metal treatments must be applicable in some crucial environment conditions. Among solutions for environmental heavy metal control, polymeric materials have showed many advantages to enhance the performance of conventional heavy metal adsorbents. Possessing functional chemical moieties on their side chains and backbones, polymers can act as main metal ion adsorption component and/or effective supports for the adsorption property. In addition, the utilization of polymeric components can result in a low corrosion, selectivity, controllability, and recyclability of absorbents when operated in real environment conditions. This article summarizes the preparation techniques and desirable properties of polymer for removal of toxic heavy metal ion as well as the possibility of polymers in collecting some rare noble metal ions. Keywords. Heavy metal ions, nano-composite, polymer

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Spondylolysis-induced Multilevel Lumbar Spondylolisthesis; Challenges in Lumbar Spine Surgery

Lumbar spondylolysis and multilevel lumbar spondylolysis account for 4.4-5.8% and 0.3% of the general population, and multilevel lumbar spondylolysis resulting in spondylolisthesis is even rarer. Herein, we report two cases of three-level lumbar spondylolisthesis because of spondylolysis: A 49-year-old woman was admitted to the hospital for dull lower back pain over the past 8 months, with exacerbating symptoms when standing and walking. Spasticity at lumbar region and radiculopathy at S1 nerve root was found on examination and a 63-year-old man was admitted to the hospital because of numbness and perianal sensory disturbances with difficulty urinating 2 weeks ago, the symptoms gradually increased to the time of examination. Both patients were diagnosed with multilevel lumbar spondylolisthesis because of spondylolysis and were indicated for posterior lumbar interbody fusion (PLIF). After surgery, both patients recovered well without any significant complications. The improved treatment results suggest the application of PLIF technique to treat spondylolysis-induced multilevel lumbar spondylolisthesis