A New Method for Sentiment Analysis Using Contextual Auto-Encoders

Sentiment analysis, a hot research topic, presents new challenges for understanding users’ opinions and judgments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, we introduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at the word level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of the auto-encoders, we propose a new model for distributed word vector representation “PMI-SA” using as input pointwise-mutual-information “PMI” word vectors. The resulted continuous word vectors are combined to represent a sentence. An unsupervised sentence embedding method, called Contextual Recursive Auto-Encoders “CoRAE”, is also developed for learning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply compose the vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consists in combining recursively each word with its context words (neighbors’ words: previous and next) by considering the word order. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositional representation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demonstrate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twitter corpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and 97.57% with the Sanders dataset.     

Assessing the determinants of flow experience in the adoption of learning management systems: the moderating role of perceived institutional support

Information technology has moved the focus of students and instructors from blackboards to online learning platforms. In the emerging e-learning revolution, the success of quality education depends on the right use of information and communication technology, including the creation of participants’ flow experience while using learning systems, which are rightly considered a backbone of contemporary education. There is a gap in the e-learning literature, reflecting a lack of understanding of students’ flow experience while using online interactive systems. This paper presents a study investigating the key factors (interpersonal factors and atmospheric cues) affecting flow experience and highlights its massive role in the use of any learning management system (LMS). Moreover, the study examines the moderating role of perceived institutional support (PIS) on student adoption of LMS. Using a survey, data were collected from university students in Pakistan and the data were analysed through structural equation modelling. We found that interpersonal factors and LMS atmospherics have significant effects on the flow experience. It was also found that PIS moderates the relationship between flow experience and the use of LMS. This study contributes to the literature by helping institutions and policy-makers know key factors of effective learning systems that students value and enjoy.     

Electrosynthesis and structural characterization of a novel aryl ether trimer

The structure of a novel trimer formed by three p-tert-butyl anisole moieties via the controlled potential electrolysis of p-tert-butyl anisole was determined by X-ray diffraction method. By cyclic voltammetry, we report a multistep electron transfer, each followed by a chemical reaction, resulting in an ECECEC mechanism. Preparative scale oxidation of p-tert-butyl anisole in dry acetonitrile leads to its two first oligomers. The symmetrical dimer, 2,2′-dimethoxy-5,5′-di-tert-butylbiphenyl, shows that the favored coupling sites are those in the ortho position of the methoxy group. The title compound, namely the 1-methoxy-bis-2,3-(2′-methoxy-5′-tert-butylphenyl)-4-tert-butylbenzene crystallizes in triclinic space group P-1 with a = 10.571(3) Å, b = 11.739(1) Å, c = 12.733(2) Å, α = 74.64(1)°, β = 88.71(2)°, γ = 76.58(2)°, V = 1480.8(5) Å³, and Z = 2. The structural analysis reveals that two p-tert-butyl anisole moieties are linked in ortho position on a third p-tert-butyl anisole fragment.     

Investigation of buoyancy-driven flow and heat transfer in a trapezoidal cavity filled with water–Cu nanofluid

A numerical study is conducted to investigate the transport mechanism of free convection in a trapezoidal enclosure filled with water–Cu nanofluid. The horizontal walls of the enclosure are insulated while the inclined walls are kept at constant but different temperatures. The numerical approach is based on the finite element technique with Galerkin's weighted residual simulation. Solutions are obtained for a wide range of the aspect ratio (AR) and Prandtl number (Pr) with Rayleigh number (Ra = 10⁵) and solid volume fraction (? = 0.05). The streamlines, isotherm plots and the variation of the average Nusselt number at the left hot wall are presented and discussed. It is found that both AR and Pr affect the fluid flow and heat transfer in the enclosure. A correlation is also developed graphically for the average Nusselt number as a function of the Prandtl number as well as the cavity aspect ratio.     

The coating of cadmium compounds with nickel compounds

Micrometer-sized particles of cadmium carbonate (cores) were prepared by homogeneous precipitation from aqueous solution containing urea in the presence of cadmium sulfate. These particles were then homogeneously coated with a layer of nickel hydroxy carbonate by heating their dispersion in aqueous solution, containing nickel sulfate and urea, at 85 °C for 70 min with constant agitation. The same solution mixture produced spheroids of nickel hydroxy carbonate (coating precursors), when heated under similar conditions in the absence of the cadmium carbonate particles. The existence of the coated layer on the cores and its composition was confirmed by various physical methods. The as-prepared carbonated solids (cores, coating precursors, and coated particles) were converted into their oxide forms by calcination at 700 °C. The cores became porous, whereas the coating precursors and coating layer disintegrated into smaller particles during the calcination process.     

Biallelic Variants in EPHA2 Identified in Three Large Inbred Families with Early-Onset Cataract

Hereditary congenital cataract (HCC) is clinically and genetically heterogeneous. We investigated HCC that segregates in three inbred families (LUCC03, LUCC16, and LUCC24). Ophthalmological examinations revealed cataracts with variability related to the age of onset segregating in a recessive manner in these families. Exome sequencing of probands identified a novel homozygous c.2710delG;p.(Val904Cysfs*36) EPHA2 variant in LUCC03 and a known homozygous c.2353G>A;p.(Ala785Thr) EPHA2 variant in the other two recessive families. EPHA2 encodes a transmembrane tyrosine kinase receptor, which is primarily involved in membrane-transport, cell-cell adhesion, and repulsion signaling processes. Computational structural modeling predicts that substitution of a threonine for an alanine p.(Ala785Thr) results in the formation of three new hydrogen bonds with the neighboring residues, which causes misfolding of EPHA2 in both scenarios. Insights from our study will facilitate counseling regarding the molecular and phenotypic landscape of EPHA2-related HCC.     

SURFACE EXCITATION PARAMETER IN QUANTITATIVE SURFACE ANALYSIS BY ELECTRON SPECTROSCOPY

The investigation of influence of surface effects on the energy spectra of electrons is essential for comprehensive understanding of electron-solid interactions as well as quantitative analysis. The accuracy of the analysis depends on the models for elastic and inelastic interactions. Electrons impinging on a solid or escaping from it suffer losses in the surface layer. The energy loss spectra therefore have contributions from surface excitations. The role of surface excitations is characterized by surface excitation parameter (SEP), which indicates the number of surface plasmons created by an electron crossing the surface. The imaginary part of complex self-energy of an electron is related to the energy.‘ loss cross section. SEP is numerically computed using self-energy formalism and compared with the results as described and calculated by different workers.     

ENERGY-LOSS FUNCTIONS DERIVED FROM REELS SPECTRA FOR ALUMINUM

The effective energy loss functions for Al have been derived from differential inverse inelastic mean free path based on the extended Landau approach. It has been revealed that the effective energy loss function is very close in value to the theoretical surface energy loss function in the lower energy loss region but graduaUy approaches the theoretical bulk energy loss function in the higher energy loss region. Moreover, the intensity corresponding to surface excitation in effective energy loss functions decreases with the increase of primary electron energy. These facts show that the present effective energy loss function describes not only surface excitation but also bulk excitation. At last, REELS spectra simulated by Monte Carlo method based on use of the effective energy loss functions has reproduced the experimental REELS spectra with considerable success.