The fourier virtual fields method for the identification of material property distributions

The requirement of a fast and accurate modulus identification technique has arisen in many fields of research, such as solid mechanics, structural health monitoring, medical diagnosis, etc. An inverse technique based on an appropriate interpretation of the principle of virtual work, namely the Virtual Fields Method (VFM), has been proposed in the literature, which is able to return elastic modulus values after a single matrix inversion. An extension of the virtual fields method to the spatial frequency domain in order to determine modulus distributions of materials based on a sine/cosine parameterisation of the unknown modulus is developed in this thesis, and will be called the Fourier-series-based Virtual Fields Method (F-VFM). The technique accepts in-plane (two-dimensional) or volumetric (three-dimensional) deformation measurement data as its input. An efficient numerical algorithm of the F-VFM based on the fast Fourier transform is presented, which can return thousands of unknown Fourier coefficients within a minute thus reducing the computation time by several orders of magnitude compared to a direct implementation of the F-VFM for typical dataset sizes. The F-VFM technique is also adapted to cope with a common situation in experimental mechanics where the knowledge of the boundary conditions is limited. The three versions of the F-VFM in this situation are respectively the experimental traction , windowed traction and Fourier-series traction approaches. The technique is then validated with numerical data from different stiffness patterns. The performance is compared to that of an iterative updating technique based on a genetic algorithm for one of these patterns, and computational effort is demonstrated to be at least five orders of magnitude less for the new F-VFM than for this updating method. The sensitivity of the performance of the F-VFM to noise is also investigated. Finally, the technique is applied to experimental data in both 2-D and 3-D cases with promising results

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

First Principles Prediction Unveils High-Tc_c Superconductivity in YSc2_2H24_{24} Cage Structures

The quest for room-temperature superconductivity has been a long-standing aspiration in the field of materials science, driving extensive research efforts. In this work, we present a novel hydride, YSc$_2$H$_{24}$, which is stable at high pressure using a crystal structure prediction approach with a fixed composition based on known structures. The discovered material is crystalline in a hexagonal unit cell with space group P6/mmm and has a fastinating structure consisting of two distinct cages: Sc@H$_{24}$ and Y@H$_{30}$. By conducting an extensive numerical investigation of lattice dynamics, electron-phonon coupling, and solving the isotropic Eliashberg equation, we have revealed a significant value of $\lambda$ = 2.96 as the underlying factor responsible for the remarkably high critical temperature (T$_c$) of 306-332 K in YSc$_2$H$_{24}$. As pressure increases, the T$_c$ remains above the ambient temperature. Our work has the potential to enhance the existing understanding of high-temperature superconductors, with implications for practical applications. The unique network of these cage-like structures holds great promise for advancing our understanding of high-temperature superconductors, potentially leading to innovative applications

Thermal treatment of polyvinyl alcohol for coupling MoS2 and TiO2 nanotube arrays toward enhancing photoelectrochemical water splitting performance

Solar-driven photoelectrochemical (PEC) water splitting, using semiconductor photo-electrodes, is considered a promising renewable energy source and solution for environmental sustainability. Herein, we report polyvinyl alcohol (PVA) as a binder material for combining MoS2 and TiO2 nanotube arrays (TNAs) to improve PEC water splitting ability. By a thermal treatment process, the formation of the π conjunction in the PVA structure enhanced the PEC performance of MoS2 /TNAs, exhibiting linear sweeps in an anodic direction with the current density over 65 µA/cm2 at 0 V vs. Ag/AgCl. Besides, the photoresponse ability of MoS2 /TNAs is approximately 6-fold more significant than that of individual TNAs. Moreover, a Tafel slope of 140.6 mV/decade has been obtained for the oxygen evolution reaction (OER) of MoS2 /TNAs materials. © 2021 by the authLicensee MDPI, Basel, Switzerla

STRUCTURE, MICROSTRUCTURE, AND PIEZOELECTRIC PROPERTIES OF KNLNS-BNKZ LEAD-FREE CERAMICS UNDER THE EFFECT OF DIFFERENT SINTERING TEMPERATURES

Samples of 0.96(K0.48Na0.48Li0.04)(Nb0.95Sb0.05)O3-0.04Bi0.5(Na0.82K0.18)0.5ZrO3 piezoelectric ceramic were fabricated with conventional ceramic techniques and sintered at different temperatures. The effect of sintering temperature (TS) on the structure, microstructure, and piezoelectric properties of the ceramics was studied in detail. The experimental results showed that with an increase of the TS temperature, the structure of the ceramics transformed from an orthorhombic-tetragonal mixed phase (O-T) at TS £ 1100 °C into a rhombohedral-tetragonal (R-T) mixed phase with a dense microstructure of uniform grain size at TS = 1110 °C. When TS was further increased (TS ³ 1120 °C), the ceramics showed only a rhombohedral phase (R). The ceramics showed the best electrical properties for TS = 1110 °C at which the rhombohedral and tetragonal (R-T) phases coexist. Specifically, the ceramic density reached its highest value (4.22 g/cm3), the electromechanical coupling coefficients kp and kt were 0.46 and 0.50, respectively, and the piezoelectric coefficient d33 was 245 pC/N.Samples of 0.96(K0.48Na0.48Li0.04)(Nb0.95Sb0.05)O3-0.04Bi0.5(Na0.82K0.18)0.5ZrO3 piezoelectric ceramic were fabricated with conventional ceramic techniques and sintered at different temperatures. The effect of sintering temperature (TS) on the structure, microstructure, and piezoelectric properties of the ceramics was studied in detail. The experimental results showed that with an increase of the TS temperature, the structure of the ceramics transformed from an orthorhombic-tetragonal mixed phase (O-T) at TS £ 1100 °C into a rhombohedral-tetragonal (R-T) mixed phase with a dense microstructure of uniform grain size at TS = 1110 °C. When TS was further increased (TS ³ 1120 °C), the ceramics showed only a rhombohedral phase (R). The ceramics showed the best electrical properties for TS = 1110 °C at which the rhombohedral and tetragonal (R-T) phases coexist. Specifically, the ceramic density reached its highest value (4.22 g/cm3), the electromechanical coupling coefficients kp and kt were 0.46 and 0.50, respectively, and the piezoelectric coefficient d33 was 245 pC/N

A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant

Phlogacanthus cornutus: chemical profiles and antioxidant effects

Phlogacanthus cornutus is a rare species and the chemical profiles and the bioactivities of this plant are unknown. In present study, the chemical components of the acetone extract as well as the antioxidant activity of acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus were firstly reported. A total of 33 constituents were identify in the acetone extract of this plant using Gas Chromatography/Mass Spectrometry assay, in which trans-cinnamic acid (21.26%), neophytadiene (6.36%), linolenic acid (5.86%), dihydroagathic acid (5.71%), n-hexadecanoic acid (5.53%), phytol (4.14%) and cis-cinnamic acid (3.23%) were the major compounds. The acetone extract and its fractions such as n-hexane, chloroform and ethyl acetate of P. cornutus showed DPPH radical scavenging activity with IC50 value of 234.31, 185.95, 758.65 and 458.52 µg/mL respectively

Investigation of Some Characterizations of Black TiO2_2 Nanotubes Via Spectroscopic Methods

The black TiO$_2$ with substantial Ti$^3+$ and oxygen vacancies exhibit an excellent photoelectrochemical water-splitting performance due to the improved charge transport the extended visible light response. In this study, black TiO$_2$ nanotube arrays synthesized by the anodization method, and then, they have been investigated some characterizations by spectroscopic methods such as UV-visible reflectance (UV-vis DRS), Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and photoluminescence spectrum. The results showed that some highlighted properties of the black TiO2 nanotube arrays and they could apply for water-splitting effect

Silicon quantum-dots-based optical probe for fluorometric detection of Cr6+ ions

In this report, silicon quantum dots (SiQDs) with the NH2 functional group were synthesized with the hydrothermal method. The as-prepared SiQDs exhibit a strong fluorescence emission peak               at 441 nm when excited at 355 nm and are effectively quenched upon adding Cr6+ ions. Hence, SiQDs were used as an optical probe to detect Cr6+ ions in solutions. The crystal structure of SiQDs was characterized by using X-ray diffraction (XRD). The Fourier-transform infrared spectroscopy (FT-IR) was used to determine the linker groups on the SiQDs surface. The fluorescence spectroscopic technique with an excitation wavelength of 355 nm was used to quantify the Cr6+ ion concentration in the solutions in the range of 0.1–1000 µM. Competition from common coexisting ions, such as K+, Na+, Al3+, Zn2+, and Pb2+, was ignorable. The test with actual samples showed good linearity for the Cr6+ concentration range of 0.1–50 µM