The Impact of English Language Study on Intercultural Sensitivity, Ethnocentrism, and Intercultural Communication Apprehension Among Chinese Students

Because of globalization, internationalization and diversification, abetted by the rapid development of science and technology, geographic distance is becoming a less influential factor in communication. As more and more international students go to study in different countries all over the world, it is inevitable for native students to communicate and interact with those students from different cultural backgrounds. Under such circumstances, it is vital to understand the factors that contribute to students’ intercultural sensitivity and the impact of intercultural sensitivity on ethnocentrism and intercultural communication apprehension among Chinese students (in the case of this study) for the sake of developing their proficiency as intercultural communicators in college. Using Chinese university students who study English as their academic major and Chinese university students who are not majoring in English as the samples, the purpose of this study is to investigate the fundamental state of ethnocentrism and intercultural sensitivity among Chinese university students who major in English. The predictability of a measurement of intercultural sensitivity on students’ ethnocentrism and intercultural communication apprehension is also tested in the Chinese university education context. An online survey was conducted using the Intercultural Sensitivity Scale (ISS), the Generalized Ethnocentrism Scale (GENE) and the Personal Report of Intercultural Communication Apprehension (PRICA) to measure the three variables. The results indicated that intercultural sensitivity is negatively related with ethnocentrism and intercultural communication apprehension. Students majoring in English have higher levels of intercultural sensitivity, lower levels of ethnocentrism and lower levels of intercultural communication apprehension compared with students who do not major in English. Further discussion, limitations and suggestions for further researches are provided

An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensions

We present two effective methods for solving high-dimensional partial differential equations (PDE) based on randomized neural networks. Motivated by the universal approximation property of this type of networks, both methods extend the extreme learning machine (ELM) approach from low to high dimensions. With the first method the unknown solution field in $d$ dimensions is represented by a randomized feed-forward neural network, in which the hidden-layer parameters are randomly assigned and fixed while the output-layer parameters are trained. The PDE and the boundary/initial conditions, as well as the continuity conditions (for the local variant of the method), are enforced on a set of random interior/boundary collocation points. The resultant linear or nonlinear algebraic system, through its least squares solution, provides the trained values for the network parameters. With the second method the high-dimensional PDE problem is reformulated through a constrained expression based on an Approximate variant of the Theory of Functional Connections (A-TFC), which avoids the exponential growth in the number of terms of TFC as the dimension increases. The free field function in the A-TFC constrained expression is represented by a randomized neural network and is trained by a procedure analogous to the first method. We present ample numerical simulations for a number of high-dimensional linear/nonlinear stationary/dynamic PDEs to demonstrate their performance. These methods can produce accurate solutions to high-dimensional PDEs, in particular with their errors reaching levels not far from the machine accuracy for relatively lower dimensions. Compared with the physics-informed neural network (PINN) method, the current method is both cost-effective and more accurate for high-dimensional PDEs.Comment: 38 pages, 17 tables, 25 figure

A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks

We present a method for computing the inverse parameters and the solution field to inverse parametric PDEs based on randomized neural networks. This extends the local extreme learning machine technique originally developed for forward PDEs to inverse problems. We develop three algorithms for training the neural network to solve the inverse PDE problem. The first algorithm (NLLSQ) determines the inverse parameters and the trainable network parameters all together by the nonlinear least squares method with perturbations (NLLSQ-perturb). The second algorithm (VarPro-F1) eliminates the inverse parameters from the overall problem by variable projection to attain a reduced problem about the trainable network parameters only. It solves the reduced problem first by the NLLSQ-perturb algorithm for the trainable network parameters, and then computes the inverse parameters by the linear least squares method. The third algorithm (VarPro-F2) eliminates the trainable network parameters from the overall problem by variable projection to attain a reduced problem about the inverse parameters only. It solves the reduced problem for the inverse parameters first, and then computes the trainable network parameters afterwards. VarPro-F1 and VarPro-F2 are reciprocal to each other in a sense. The presented method produces accurate results for inverse PDE problems, as shown by the numerical examples herein. For noise-free data, the errors for the inverse parameters and the solution field decrease exponentially as the number of collocation points or the number of trainable network parameters increases, and can reach a level close to the machine accuracy. For noisy data, the accuracy degrades compared with the case of noise-free data, but the method remains quite accurate. The presented method has been compared with the physics-informed neural network method.Comment: 40 pages, 8 figures, 34 table

An adaptive dimension reduction algorithm for latent variables of variational autoencoder

Constructed by the neural network, variational autoencoder has the overfitting problem caused by setting too many neural units, we develop an adaptive dimension reduction algorithm that can automatically learn the dimension of latent variable vector, moreover, the dimension of every hidden layer. This approach not only apply to the variational autoencoder but also other variants like Conditional VAE(CVAE), and we show the empirical results on six data sets which presents the universality and efficiency of this algorithm. The key advantages of this algorithm is that it can converge the dimension of latent variable vector which approximates the dimension reaches minimum loss of variational autoencoder(VAE), also speeds up the generating and computing speed by reducing the neural units.Comment: 11 pages 12 figure

Principled missing data methods for researchers

The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication

CHEMICAL, ISOTOPE AND MOLECULAR ANALYSIS OF MICROBIAL REDUCTIVE DECHLORINATION OF TETRACHLOROETHYLENE AND TRICHLOROETHYLENE

Tetrachloroethylene (PCE) and trichloroethylene (TCE) are among the most commonly detected groundwater contaminants in the U. S. Microbial reductive dechlorination of this group of contaminants was investigated in different reaction systems, including isolated pure cultures, enrichment cultures and microcosms. Chemical, isotope and molecular analyses were performed to evaluate the feasibility of stable carbon isotope fractionation to quantitatively monitor microbial PCE and TCE reductive dechlorination and the potential factors that may lead to uncertainties of this monitoring technique.Microbial PCE and TCE reductive dechlorination was first analyzed and the product distribution and stable carbon isotope fractionation were determined in two isolated pure cultures (Sulfurospirillum multivoransand Desulfuromonas michiganensis Strain BB1) and one mixed culture, Bio-Dechlor Inoculum (BDITM). S. multivorans and D. michiganensis Strain BB1 produced cis-DCE when PCE or TCE was used as the parent substrate, while the Dehalococcoides-containing BDI was able to completely dechlorinate PCE and TCE to ethylene. Different extents of isotope fractionation were observed among the three cultures. Generally, weaker isotope fractionation occurred during PCE reductive dechlorination (enrichment factors (epsilon bulk) = -1.33 to -7.12 per mil) than that during TCE transformation (epsilon bulk = -4.07 to -15.02 per mil). The different levels of fractionation by individual species/culture might be due to their diversity in the structure of functional enzymes (e.g., reductive dehalogenase), cofactors or rate-limiting steps before enzymatic reactions.In order to evaluate potential impacts of environmental factors (e.g., electron donors and pH) and microbial diversity on isotope fractionation during microbial reductive dechlorination of chlorinated ethylenes, two enrichment cultures (DPF and DPH) stimulated from the same source but in the presence of different electron donors were investigated. These two cultures showed significantly different product distribution and isotope fractionation. Chemical and isotope analyses indicate that electron donors and pH do not directly change the product distribution and only slightly changed extents of isotope fractionation. However, phylogenetic analysis of the 16S rRNA clone libraries of DPF and DPH suggests that electron donors might indirectly influence extents of isotope fractionation by leading to a shift in microbial community composition.At contaminated sites, microbial and abiotic reductive dechlorination may simultaneously occur. To understand the relative contributions of these remediation processes and to evaluate the feasibility of applying isotope fractionation to monitor potentially parallel microbial and abiotic transformation processes, PCE and TCE reductive dechlorination was carried out in a series of well-defined microcosms. Alternative electron accepting processes, e.g., iron-, sulfate-reduction and methanogenesis, were developed to vary contents of biogenic iron and sulfide minerals; electron donors were spiked to stimulate indigenous dechlorinating bacteria. Our results showed that microbial reductive dechlorination was dominant in 21 out of 24 PCE microcosms and 5 out of 8 TCE microcosms. Isotope analysis indicated that weak isotope fractionation occurred in most microcosms, while some of them had very negative epsilon bulk. All of them were within the range of or comparable with the epsilon bulk of microbial reductive dechlorination of PCE and TCE that have been published so far. In addition, compared to the isotope fractionation during PCE and TCE abiotic reductive dechlorination by FeS, the extents of isotope fractionation observed in these microcosms was generally weaker. Higher environmental pH was suggested to be unfavorable for growth of dechlorinating bacteria. Meanwhile, the comparable levels of microbial and abiotic dechlorinated products were only observed in the microcosms with slow microbial reductive dechlorination, suggesting that abiotic dechlorination might be important only when microbial reductive dechlorination is slow. Comparison of geochemical conditions with abiotic product recoveries suggests that high concentrations of Fe(II) and S(-II) solid species produced under sulfate- and iron-reducing conditions are likely important for abiotic reductive dechlorination to occur.In general, the different levels of isotope fractionation during microbial PCE and TCE reductive dechlorination observed in our pure culture, enrichment cultures and microcosm experiments, indicate to us that a number of factors need to be considered in applying isotope fractionation to quantitatively monitor bioremediation of this group of contaminants in the field. This includes whether the appropriate conditions have been selected for development of model enrichment cultures, potential indirect impacts of environmental factors (e.g., pH and electron donors) and impacts of different transformation pathways (e.g., abiotic versus microbial) on the extents of isotope fractionation

Interplay of miRNAs and Canonical Wnt Signaling Pathway in Hepatocellular Carcinoma

Hepatocellular carcinoma is one of the leading causes of cancer death worldwide and the activation of canonical Wnt signaling pathway is universal in hepatocellular carcinoma patients. MicroRNAs are found to participate in the pathogenesis of hepatocellular carcinoma by activating or inhibiting components in the canonical Wnt signaling pathway. Meanwhile, transcriptional activation of microRNAs by canonical Wnt signaling pathway also contributes to the occurrence and progression of hepatocellular carcinoma. Pharmacological inhibition of hepatocellular carcinoma pathogenesis and other cancers by microRNAs are now in clinical trials despite the challenges of identifying efficient microRNAs candidates and safe delivery vehicles. The focus of this review is on the interplay mechanisms between microRNAs and canonical Wnt signaling pathway in hepatocellular carcinoma, and a deep understanding of the crosstalk will promote to develop a better management of this disease

Fe-oxide grain coatings support bacterial Fe-reducing metabolisms in 1.7−2.0 km-deep subsurface quartz arenite sandstone reservoirs of the Illinois Basin (USA)

The Cambrian-age Mt. Simon Sandstone, deeply buried within the Illinois Basin of the midcontinent of North America, contains quartz sand grains ubiquitously encrusted with iron-oxide cements and dissolved ferrous iron in pore-water. Although microbial iron reduction has previously been documented in the deep terrestrial subsurface, the potential for diagenetic mineral cementation to drive microbial activity has not been well studied. In this study, two subsurface formation water samples were collected at 1.72 and 2.02 km, respectively, from the Mt. Simon Sandstone in Decatur, Illinois. Low-diversity microbial communities were detected from both horizons and were dominated by Halanaerobiales of Phylum Firmicutes. Iron-reducing enrichment cultures fed with ferric citrate were successfully established using the formation water. Phylogenetic classification identified the enriched species to be related to Vulcanibacillus from the 1.72 km depth sample, while Orenia dominated the communities at 2.02 km of burial depth. Species-specific quantitative analyses of the enriched organisms in the microbial communities suggest that they are indigenous to the Mt. Simon Sandstone. Optimal iron reduction by the 1.72 km enrichment culture occurred at a temperature of 40oC (range 20 to 60oC) and a salinity of 25 parts per thousand (range 25-75 ppt). This culture also mediated fermentation and nitrate reduction. In contrast, the 2.02 km enrichment culture exclusively utilized hydrogen and pyruvate as the electron donors for iron reduction, tolerated a wider range of salinities (25-200 ppt), and exhibited only minimal nitrate- and sulfate-reduction. In addition, the 2.02 km depth community actively reduces the more crystalline ferric iron minerals goethite and hematite. The results suggest evolutionary adaptation of the autochthonous microbial communities to the Mt. Simon Sandstone and carries potentially important implications for future utilization of this reservoir for CO2 injection