C－isotope composition and significance of the Sinian on the Tarim plate

102 samples of limestones,mudrocks shales and tillites from the Sinian in Kuluketage have been analyzed ,Four formations in the Sinian belong to glacial deposits in which the differences in δ^13C are distinct (-14.7‰- 4.2‰),Distinctly negative δ^13C excursions for three times support the viwe of three glacial deposits in this area suggested by previous authors,except for the durations of them ,δ^13C values are generally positive in the Beiyixi Formation of lower part of the Lower Sinian ,representing probably the rock records of a continental rift related to break-up of the Rodinian supercontinent,There are three times of distinct excursions of δ^13C curve in all strata,The first one is from positive values at the bottom to about -5‰ at the top of the Beiyixi Formation ,The second one,from 0‰-3‰ in the Arletonggou Formation of upper part of the Lower Sinian to -3.4‰---14.4‰ in the Teruiaiken Formation.The third one,from positive values in the Zhamoketi Formation of lower part of the Upper Sinian to negative values in glacial varves at the top of the Hankeerqiaoke Formation.     

A Proof of the Duality of the DINA Model and the DINO Model

The Deterministic Input Noisy Output “AND” gate (DINA) model and the Deterministic Input Noisy Output “OR” gate (DINO) model are two popular cognitive diagnosis models (CDMs) for educational assessment. They represent different views on how the mastery of cognitive skills and the probability of a correct item response are related. Recently, however, Liu, Xu, and Ying demonstrated that the DINO model and the DINA model share a “dual” relation. This means that one model can be expressed in terms of the other, and which of the two models is fitted to a given data set is essentially irrelevant because the results are identical. In this article, a proof of the duality of the DINA model and the DINO model is presented that is tailored to the form and parameterization of general CDMs that have become the new theoretical standard in cognitively diagnostic modeling.     

Swelling behaviors and pervaporation properties of CA/PAN composite membranes for separation of caprolactam/water mixtures

The composite membranes with cellulose acetate (CA) as the separating layer material and polyacrylonitrile (PAN) as the supporting layer material were prepared for separating caprolactam (CPL) from CPL/water mixtures by pervaporation technique. The swelling experiments were carried out to investigate the effects of swelling time and CPL concentration on the degree of swelling. The results showed that the CA membrane reached swelling equilibrium within 24 h, and the degree of swelling first slightly decreased then increased with the increase of CPL concentration in the feed under the experimental conditions. Respectively comparing flux with permeance, and separation factor with selectivity, we found out that the separation performance of the CA/PAN composite membrane is more strongly dependent on its hydrophilic/hydrophobic nature as well as on the effects of operating parameters, such as feed composition concentration and feed temperature.     

Effect of updating training on fluid intelligence in children

Recent studies have indicated that working memory (WM) training can improve fluid intelligence.However,these earlier studies confused the impact of WM storage and central executive function on the effects of training.The current study used the running memory task to train the updating ability of 9-11 year-old children using a double-blind controlled design.The results revealed that children’s fluid intelligence was significantly improved by memory-updating training.Overall,our findings suggest that the increase in fluid intelligence achieved with WM training is related to improving central executive function.     

Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques

The global growth of the Internet and the rapid expansion of social networks such as Facebook make multilingual sentiment analysis of social media content very necessary. This paper performs the first sentiment analysis on code-mixed Bambara-French Facebook comments. We develop four Long Short-term Memory (LSTM)-based models and two Convolutional Neural Network (CNN)-based models, and use these six models, Naïve Bayes, and Support Vector Machines (SVM) to conduct experiments on a constituted dataset. Social media text written in Bambara is scarce. To mitigate this weakness, this paper uses dictionaries of character and word indexes to produce character and word embedding in place of pre-trained word vectors. We investigate the effect of comment length on the models and perform a comparison among them. The best performing model is a one-layer CNN deep learning model with an accuracy of 83.23 %.     

Cobalt doped titanate nanotubes: Synthesis and properties

Cobalt oxide doped titanate nanotubes are synthesized with a simple hydrothermal treatment of mixed Co_3O_4 and TiO_2 powders. The formed tubular nanostructure, chemical composition,and the elemental distribution are analyzed using TEM, BET, FTIR, XRD, and XPS. The electrocatalytic activity towards oxygen evolution reactions and photodegradation against Rhodamine B are investigated. It has been found out that the oxygen evolutions starts at 0.8 V and reaches 0.98 mA ·cm~(–1) at 1.4 V vs. SCE. For photodegradation of Rhodamine B, the concentration decreased to 24% after 1 h irradiation using the sample with a mass percentage of 5% cobalt. The results demonstrate that the cobalt oxide doped titanate nanotubes are good candidates as electrocatalysts and photocatalytic materials.     

Laccases of prokaryotic origin: enzymes at the interface of protein science and protein technology

The ubiquitous members of the multicopper oxidase family of enzymes oxidize a range of aromatic substrates such as polyphenols, methoxy-substituted phenols, amines and inorganic compounds, concomitantly with the reduction of molecular dioxygen to water. This family of enzymes can be broadly divided into two functional classes: metalloxidases and laccases. Several prokaryotic metalloxidases have been described in the last decade showing a robust activity towards metals, such as Cu(I), Fe(II) or Mn(II) and have been implicated in the metal metabolism of the corresponding microorganisms. Many laccases, with a superior efficiency for oxidation of organic compounds when compared with metals, have also been identified and characterized from prokaryotes, playing roles that more closely conform to those of intermediary metabolism. This review aims to present an update of current knowledge on prokaryotic multicopper oxidases, with a special emphasis on laccases, anticipating their enormous potential for industrial and environmental applications.