Does L1 transfer influence Chinese speakers' intuition of adjective ordering in English

L1 influence on second language acquisition has been shown by linguists in many areas. This study is to look into the L1 influence on English adjective ordering among Chinese ESL learners. We hypothesized that if there was a certain adjective ordering "rule" existing in both Chinese and English, it would facilitate Chinese ESL learners' performance on producing such order in English. The results of our experiment suggested that there was a statistically significant interaction between proficiency level (native vs. non-native) and adjective categories ( non-absolute + absolute vs. absolute + absolute vs. non-absolute + non-absolute). More specifically, Chinese ESL learners performed the best in the "nom-absolute + absolute" category that exists in both English and Chinese compared to the other two categories that only exist in English. This finding indicates that L1 influence may play a role in second language acquisition of adjective ordering

Finite strain modelling for multiphase flow in dual scale porous media during resin infusion process

Resin infusion is a pressure-gradient-driven composite manufacturing process in which the liquid resin is driven to flow through and fill in the void space of a porous composite preform prior to the heat treatment for resin solidification. It usually is a great challenge to design both the infusion system and the infusion process meeting the manufacturing requirements, especially for large-scale components of aircraft and wind turbine blades. Aiming at addressing the key concerns about flow fronts and air bubble entrapment, the present study proposes a modelling framework of the multiphase flow of resin and air in a dual scale porous medium, i.e. a composite preform. A finite strain formulation is discussed for the fluid-solid interaction during an infusion process. The present study bridges the gap between the microscopic observation and the macroscopic modelling by using the averaging method and first principle method, which sheds new light on the high-fidelity finite element modelling

Moment of inertia and torque performance sensorless measurement for HDD used spindle motors

Master'sMASTER OF ENGINEERIN

Quantifying the Evidential Value of Celebrity Endorsement: A p-Curve Analysis

Celebrity endorsements have long been used as a promotional tool in marketing communication. However, literature has documented inconsistent findings on the effects of celebrity endorsements compared to no endorsement or noncelebrity endorsements, suggesting a close examination about the reliability and robustness of celebrity endorsements is needed. This study conducted a p-curve analysis among two sets of published studies based on different comparison groups (celebrity endorsements vs. no celebrity endorsement; celebrity endorsements vs. noncelebrity endorsements) to investigate if both sets of studies contain an evidential value. The significantly right-skewed p curve suggests that both sets of published studies have some integrity. However, the studies that compared celebrity endorsements with no celebrity endorsements showed low statistical power. Theoretical and methodological implications for celebrity endorsement research were discussed

Fast Regions-of-Interest Detection in Whole Slide Histopathology Images

Detecting and localizing pathological region of interest (ROI) over whole slide pathological image (WSI) is a challenging problem. To reduce computational complexity, we introduced a two-stage superpixel-based ROI detection approach. To efficiently construct superpixels with fine details preserved, we utilized a novel superpixel clustering algorithm which cluster blocks of pixel in a hierarchical fashion. The major reduction of complexity is attributed to the combination of boundary update and coarse-to-fine refinement in superpixel clustering. The former maintains the accuracy of segmentation, meanwhile, avoids most of unnecessary revisit to the ‘non-boundary’ pixels. The latter reduces the complexity by faster localizing those boundary blocks. Detector of RoI was trained using handcrafted features extracted from super-pixels of labeled WSIs. Extensive experiments indicates that the introduced superpixel clustering algorithm showed lifted accuracy on lung cancer WSI detection at much less cost, compared to other classic superpixel clustering approaches. Moreover, the clustered superpixels do not only facilitate a fast detection, also deliver a boundary-preserving segmentation of ROI in whole slide images

Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.Comment: 10 pages. Accepted by the ACM BCB 201

Multivariate regression models in estimating the behavior of FRP tube encased recycled aggregate concrete

This study applied newly developed multivariate statistical models to estimating the mechanical properties of recycled aggregate concrete cylinder encased by fiber reinforced polymer (FRP). Two different types of RFPs were applied, namely flax FRP and polyester FRP. Ten independent variables were predefined including the FRP type and cylinder size. It was found that several mixed models outperformed the traditional linear regression approach, based on the accuracy and residual value distribution. Individual factor analysis indicated that the fiber thickness and layer number had more significant impacts on the strength and strain of FRP-encased concrete’s transitional point, compared to their impacts at the ultimate state