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- Quan-Hoang Vuong (27)
- Anh-Tuan Le (17)
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This work develops a Neural Network (NN) model for the prediction of the tensile modulus of carbon nanotube (CN)/polymer nanocomposites, based on experimental database. A data set composed of 282 configurations is collected from available resources. Considered input variables of the dataset are such as mechanical properties of separated phases, density of polymer matrix, processing method, geometry of CN, modification method at the CN surface, etc. while the problem output is the tensile modulus of nanocomposite. Parametric studies have been performed in finding optimum architecture of the proposed NN model. |
The assembly of primary nanoparticles to form hierarchical ultra-porous architectures is of great interest in various fields because of their extremely large surface area and porosity. In this work, the 3D ultra-porous γ-Fe2O3 nanocubes were synthesized by a simple method, which was derived from perfect Prussian Blue nanocubes by the oxidative decomposition process. The as-synthesized 3D γ-Fe2O3 nanocubes possess a large specific surface area and high porosity, which arise from the self-assembly of ultrafine nanoparticles. The 3D ultra-porous γ-Fe2O3 nanocubes-based sensors showed superior detection of acetone and ethanol with excellent sensitivity and rapid response time. The fantastic gas-sensing platform of 3D γ-Fe2O3 nanocubes could originate from their unique structures and int... |
Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for... |
This study presents a comprehensive method for designing a multifocal contact lens (MCL) with Snell’s law and non-uniform rational B-spline (NURBS) curves. Instead of using thin lens approximation, general mathematical formulas have been developed to achieve the accurate coordinates of points on the anterior lens surface profile of the MCL to meet various given optical power distributions. Then the NURBS curve is adjusted to fit these data points to obtain the smooth front lens surface profile. This method not only improves the accuracy of the optical power profiles of MCLs but also reduces the spherical aberration in near/distance optical zones. The experimental results show that the power profiles of soft MCLs agree with those of the simulation results and original design requirem... |
In this paper, a method for optimizing a cross-section of hollow square steel used in manufacturing a chair is presented. The chair model is de-signed with dimension according to Vietnamese standards. A static analysis model of the chair is established and then solved to find the results such as factor of safety, mass and fatigue life. Response optimization module in Ansys software is applied to optimize the cross-section of chair frame including width and thickness. Different standardized dimension levels of the steel cross-sections are performed to carry out simulated results. Based on required safety factor, mass and fatigue life, some candidate points are given and further evaluated an optimal parameter. |
In this work, an eco-friendly approach for the synthesis of biogenic silver nanoparticles (bio-AgNPs) using botanical extracts in combination with an electrochemical process was carried out. We employed three types of plant extracts, including green tea leaf (GTE), grapefruit peel (GP), and mangosteen peel (MP) extracts to successfully synthesize the bio-AgNPs and optimized the experimental conditions aiming to get the highest synthetic yield. The formation of bio-AgNPs was monitored by UV-Vis spectroscopy via a surface plasmon resonance (SPR) band at about 420–430 nm. Transmission electron microscope (TEM) showed their spherical shape with the size range within 23–55 nm. While X-ray diffraction (XRD) analysis described in detail the crystalline structure of the bio-AgNPs with a fac... |
Acetylserotonin O-methyltransferase (ASMT) is a key enzyme in the synthesis of melatonin. Although melatonin has been shown to exhibit anticancer activity and prevents endocrine resistance in breast cancer, the role of ASMT in breast cancer progression remains unclear. In this retrospective study, we analyzed gene expression profiles in 27 data sets on 7244 patients from 11 countries. We found that ASMT expression was significantly reduced in breast cancer tumors relative to healthy tissue. Among breast cancer patients, those with higher levels of ASMT expression had better relapse-free survival outcomes and longer metastasis-free survival times. Following treatment with tamoxifen, patients with greater ASMT expression experienced longer periods before relapse or distance recurrence... |
This work presents a study on the correlation between the crystallographic texture and the pitting corrosion susceptibility of API 5L steels. The crystallographic textures of API 5L steel samples (codified as S1, S2, S3, and S4) were studied using EBSD measurements. A deterministic model based on symmetric spherical surface harmonics was proposed to describe the strong dependence of the pitting corrosion susceptibility on the crystallographic (grains) orientation of the (body-centered cubic) material. Using the model, pitting corrosion susceptibility index values estimated from EBSD raw data agreed in relative magnitudes (from sample to sample) with the predictions made from X-ray macrotexture measurements, and the results derived from other techniques such as cyclic polarization te... |
A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets including the structural geometrical parameters and the mechanical properties of the materials, was collected from available resources in the literature. The performance of the prediction model has also been validated by comparing with: (i) other ML models such as Artificial neural network, Support vector machine, etc.; and (ii) existing formulations in the literature for predicting load-carrying capacity of square CFST columns (including several codes such as EC4, AISC and ACI). The obtai... |
High performance encapsulation of transparent conductive polymers by spatial atomic layer deposition Poly(3,4-ethylenedioxythiophene) (PEDOT) is a transparent conductive polymer widely used in flexible photonic and optoelectronic devices because of its excellent electrical and optical properties. However, its current range of applications is limited by its poor stability under high humidity and solar radiations. Encapsulation is an attractive solution to this problem and the development of a low-temperature and scalable deposition method is highly desirable. In this study, we report the use of spatial atomic layer deposition (SALD) to deposit ultrathin layers of ZnO, TiO2, and Al2O3. These nanolayers maintain the electrical performance of the conductive polymer and its high optical transmittance. The use of SALD ensures low-cost and flexible processing with pinhole-free high-qualit... |
