Analyses and Comparisons of Three Lexical Features in Native and Nonnative Academic English Writing

Built upon the Contrastive Interlanguage Analysis (CIA) framework, this corpus-based research analyzes three lexical features (lexical diversity, lexical sophistication, and cohesion) in native and nonnative English writers\u27 academic writing and examines the potential differences in lexical performance 1) between native and nonnative English writers and 2) across all writers from various language backgrounds. The differences in lexical performance in academic writing between native and nonnative English writers and the unique characteristics of writers from different language backgrounds suggest the necessity of targeted academic writing instruction based upon learner needs. Using text length as the covariate, two Multivariate Analysis of Covariate (MANCOVA) were conducted with language background as the Independent Variable and the three lexical features as the Dependent Variables. The results revealed that nonnative English writers demonstrated significantly lower performance in lexical sophistication than did native English writers. In terms of the comparison between writers from different language backgrounds, the results suggested statistically significant differences in all three aspects of lexical features. Pedagogical implications for vocabulary instruction in academic writing for nonnative English writers include emphasizing the mastery of academic, low-frequency, and discipline-specific vocabulary. In addition, improving nonnative writers\u27 vocabulary size and lexical diversity can offer these learners more options to build cohesion in academic writing at a deeper level. Moreover, the results of this study highlight the wide but often under-considered variability within any language group as individual learner differences come into play, thereby downplaying the idea that writers of any given language tend to perform homogenously. Instructors should acknowledge the unique writing characteristics of different nonnative writers and their varied learner needs. Thus, targeted instruction is essential to provide effective enhancement to nonnative English writers\u27 lexical performance in academic writing

Modelling retention time in a clearwell

Clearwells are large water reservoirs often used at the end of the water treatment process as chlorine contact chambers. Contact time required for microbe destruction is provided by residence time within the clearwell. The residence time distribution can be determined from tracer tests and is the one of the key factors in assessing the hydraulic behaviour and efficiency of these reservoirs. This work provides an evaluation of whether the two-dimensional, depth-averaged, finite element model, River2DMix can adequately simulate the flow pattern and residence time distribution in clearwells. One question in carrying out this modelling is whether or not the structural columns in the reservoir need to be included, as inclusion of the columns increases the computational effort required. In this project, the residence time distribution predicted by River2DMix was compared to results of tracer tests in a scale model of the Calgary Glenmore water treatment plant northeast clearwell. Results from tracer tests in this clearwell were available. The clearwell has a serpentine baffle system and 122 square structural columns distributed throughout the flow. A comparison of the flow patterns in the hydraulic and computational models was also made. The hydraulic model tests were carried out with and without columns in the clearwell. The 1:19 scale hydraulic model was developed on the basis of Froude number similarity and the maintenance of minimum Reynolds numbers in the flow through the serpentine system and the baffle wall at the entrance to the clearwell. Fluorescent tracer slug injection tests were used to measure the residence time distribution in the clearwell. Measurements of tracer concentration were taken at the clearwell outlet using a continuous flow-through fluorometer system. Flow visualization was also carried out using dye to identify and assess the dead zones in the flow. It was found that it was necessary to ensure the flow in the scale model was fully developed before starting the tracer tests, and determining the required flow development time to ensure steady state results from the tracer tests became an additional objective of the work. Tests were carried out at scale model flows of 0.85, 2.06, and 2.87 L/s to reproduce the 115, 280, and 390 ML/day flows seen in the prototype tracer tests. Scale model results of the residence time distribution matched the prototype tracer test data well. However, approximately 10.5 hours was required for flow development at the lowest flow rate tested (0.85 L/s) before steady state conditions were reached and baffle factor results matched prototype values. At the intermediate flow, baffle factor results between the scale model and prototype matched well after only 1 h of flow development time, with improvements only in the Morril dispersion index towards prototype values with increased flow development time (at 5 h). Similar results were seen at the highest flow tested. For fully developed flow, there was little change in the baffle factor and dispersion index results in the scale model with varied flow rate. With the addition of columns to the scale model, there was no significant change in the baffle factor compared to the case compared to without the columns, but up to a 13.9 % increase in dispersion index as compared to the tests in the scale model without columns for fully developed flow. Further, the residence time distribution results from the scale model tests without columns matched the entire residence time distribution found in the prototype tests. However, for the model with columns, the residence time distribution matched the prototype curve well at early times, but departed significantly from it at times later in the tests. It appears the major effect of the addition of columns within a model clearwell is to increase the dispersion index and increase the proportion of the clearwell which operates as a mixed reactor. The results also showed there was good agreement between the physical model tests and River2DMix simulations of the scale model tests for both the flow pattern and residence time distributions. This indicates that a two-dimensional depth-averaged computer model such as River2DMix can provide representative simulation results in the case where the inlet flow is expected to be quickly mixed throughout the depth of flow in the clearwell

Childhood, children and family lives in China

In this chapter we bring into focus those aspects of family lives in China that are concerned with children’s family relationships, and the ways in which such issues are part and parcel of the broader institutionalisation of childhood. We draw on theoretical frameworks in the sociology of childhood and childhood studies (e.g., Prout, 2004; Qvortrup, 2000; Smith and Greene, 2014). Since these theoretical perspectives have developed predominantly in Anglophone literature, some researchers have considered their relevance to, and utility for, China and Chinese childhoods (Goh, 2011; Miao, 2013; Wang YY, 2011, 2014a, 2014b; Zheng, 2012a, 2012b; Ribbens McCarthy et al., 2017). In engaging with existing theories, and applying them to, Chinese children’s family lives, we seek to go beyond any tendency to just ‘add in the missing children’ to existing discussions (Kesby et al., 2006: 186), and give consideration to a variety of cultural and local contexts that characterise China and illuminate why it is necessary to decentre universalist thinking (Jullien, 2008/2014

Creating a frequency-based Turkish-English Loanword Cognates Word List (TELCWL)

This lexical study aims to establish a frequency-based Turkish-English Loanword Cognates Word List (TELCWL) to assist Turkish English learners’ improvement in English language learning and the corresponding pedagogical practice. A final list of 582 Turkish-English loan-based cognate word pairs was derived from the New General Service List (NGSL) and the Frequency Dictionary of Turkish (FDT). For pedagogical purposes, the TELCWL was divided into five sublists with different features of the cognates in spelling and pronunciation. The coverages of the TELCWL were particularly high in discipline and field-specific corpora on average compared to general service written (5%) and spoken corpora (3.5%), accounting for more than 7%. This result suggests that the TELCWL may be more beneficial for enhancing learners’ reading and writing ability; in addition, not only general Turkish English learners but also learners who need to improve their English language proficiency in specific disciplines can benefit from the TELCWL. Further pedagogical implications are made for English instructors regarding the employment of the TELCWL in English classrooms in Turkey

Understanding WeChat User’s Intention to Use Various Functions: from Social Cognitive Perspective

Based upon social cognitive theory, this study explores the effect of personal and environment factors on Wechat user’s continuous intention to use various functions. Online survey is used to collect data from the WeChat users. The results confirms that some personal factors (relationship benefit and performance benefit) have a positive effect on intention to use, while image does not have significant effect. Besides, three social environmental factors, the popularity of WeChat, subjective norm and company guarantee, all have significant impacts. Furthermore, we find that environmental factors’ effects are stronger than personal factors. Finally, we propose our theoretical and practical implications according to the findings of this study

Metformin promotes apoptosis of A549 cells via regulation of p-AMPK protein expression, bax/bcl-2 ratio and ROS levels

Purpose: To investigate the influence of metformin on apoptosis of pulmonary carcinoma cells (A549), and the associated mode of action.Methods: Pulmonary carcinoma cells in logarithmic growth phase were treated with graded concentrations of metformin, and the anti-proliferative and apoptotic effects of the drug were measured using MTT assay and flow cytometry, respectively. The levels of reactive oxygen species (ROS) in A549 cell suspension were determined with 2, 7- dihydrodichlorofluorescein diacetate (DCFH-DA) assay. The expression levels of phosphorylated AMP-activated protein kinase (p-AMPK), mammalian target of rapamycin (mTOR), and bax/bcl-2 ratio were measured using Western blotting and real-time fluorescence quantitative polymerase chain reaction (qRT-PCR).Results: Metformin significantly promoted A549 cell apoptosis, but suppressed its proliferative potential in a dose- and time-based fashion. The levels of ROS, superoxide anion and MDA in A549 cells were significantly and dose-dependently increased by metformin (p < 0.05). Moreover, metformin markedly upregulated the mRNA and protein expressions of p-AMPK as well as bax/bcl-2 ratio, but had no impact on the expression level of mTOR (p < 0.05).Conclusion: Metformin promotes apoptosis in A549 cells via regulation of p-AMPK protein expression, bax/bcl-2 ratio, and ROS levels, and hence may play a role in lung cancer therapy

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

The Development and Application of Organic Rankine Cycle for Vehicle Waste Heat Recovery

The development of engine waste heat recovery (WHR) technologies attracts ever increasing interests due to the rising strict policy requirements and environmental concerns. Organic Rankine Cycle (ORC) can convert low medium grade heat into electrical or mechanical power and has been widely recognized as the most promising heat-driven technologies. A typical internal combustion engine (ICE) converts around 30% of the overall fuel energy into effective mechanical power and the rest of fuel energy is dumped through the engine exhaust system and cooling system. Integrating a well-designed ORC system to ICE can effectively improve the overall energy efficiency and reduce emissions with around 2–5 years payback period through fuel saving. This book chapter is meant to provide an overview of the technical development and application of ORC technology to recover wasted thermal energy from the ICE with a particular focus on vehicle applications