Investigating syntactic complexity and language-related error patterns in EFL students’ writing: corpus-based and epistemic network analyses

Students’ writing proficiency is measured through holistic and analytical ratings in writing assessment; however, recent studies suggest that measurement of syntactic complexity in second language writing research has become an effective measure of writing proficiency. Within this paradigm, we investigated how automated measurement of syntactic complexity helped distinguish the writing proficiency of students from two Higher Education institutions. In addition, we also examined language-related errors in students’ writing to further indicate the differences in the error patterns of the two groups. Data was drawn from a corpus of 1,391 sentences, comprising 58 texts produced by first-year undergraduate students from Myanmar and Hungary. Automated tools were used to measure the syntactic complexity of students’ writing. We performed a corpus-based analysis, focusing on syntactic complexity, while language-related error patterns in writing were investigated through an epistemic network approach. Findings suggested that the Myanmar students tended to write longer essays comprising simpler sentences, whereas the Hungarian students preferred shorter texts with more complex sentences. Most complexity measures were also found to distinguish the texts produced by the two groups: length of production units, sentence complexity, and subordination indices. An examination of the language-related error patterns revealed statistically significant differences in the error patterns in student writing: errors were found to be more prevalent in Myanmar students’ essays. Implications for enhancing teaching L2 writing in educational contexts are discussed

Television screen time, but not computer use and reading time, is associated with cardio-metabolic biomarkers in a multiethnic Asian population: A cross-sectional study

10.1186/1479-5868-10-70International Journal of Behavioral Nutrition and Physical Activity10

The Anatomical Numerical Measurement of Posterior Cruciate Ligament: A Vietnamese Cadaveric Study

BACKGROUND: The posterior cruciate ligament (PCL) is crucial to restrain the posterior translation of the tibia. Its anatomical structure is complex. A proper understanding of PCL anatomy may assist surgeon in reconstructing anatomically native PCL. AIM: To describe the anatomical numerical measurement of the PCL in Vietnamese adults. METHODS: Twenty-one fresh cadaveric knees were examined. The macroscopic details of the intra-articular PCL, the attachment of the anterolateral bundle (ALB), posteromedial bundles (PMB) to the femur and tibia were analysed. We used a digital camera to photograph the cadaveric specimens and used the ImageJ software to analyse the collected images. RESULTS: The ALB and PMB length were 35.5 ± 2.78 and 32.6 ± 2.28 mm, respectively. The smallest and the biggest diameter of middle third of the PCL were 5.9 ± 0.71 and 10.0 ± 1.39 mm, respectively. The area of cross section of middle third of the PCL was 53.6 ± 12.37 mm2. The femoral insertion area of ALB and PMB were 88.4 ± 16.89 and 43.5 ± 8.83 mm2, respectively. The distance from the central point of femoral ALB, PMB, and total PCL insertion to the Blumensaat line were 5.5 ± 0.91, 11.5 ± 1.98, and 7.6 ± 1.42 mm, respectively. The shortest distance from medial femoral cartilage rim to the central point of femoral ALB, PMB, and total PCL insertion were 7.0 ± 0.79, 7.3 ± 0.95, and 7.8 ± 1.73 mm, respectively. The tibial insertion area of ALB and PMB were 84.5 ± 12.52 and 47.8 ± 6.20 mm2 respectively. The shortest distance from the posterior cartilage corner of the medial tibial plateau to the central point of ALB, PMB, and total PCL insertion to tibia were 8.5 ± 1.02, 9.4 ± 1.11, and 8.3 ± 1.1 mm, respectively. The central point of tibial PCL insertion was 9.7±1.08 mm below cartilage plane of the medial tibial plateau. CONCLUSION: This study describes the detailed anatomical measurement of the PCL and its bundles in adults

Multi-layer heterogeneous ensemble with classifier and feature selection.

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep learning models. These design principles have inspired other sub-fields of machine learning including ensemble learning. In recent years, there are some deep homogenous ensemble models introduced with a large number of classifiers in each layer. These models, thus, require a costly computational classification. Moreover, the existing deep ensemble models use all classifiers including unnecessary ones which can reduce the predictive accuracy of the ensemble. In this study, we propose a multi-layer ensemble learning framework called MUlti-Layer heterogeneous Ensemble System (MULES) to solve the classification problem. The proposed system works with a small number of heterogeneous classifiers to obtain ensemble diversity, therefore being efficiency in resource usage. We also propose an Evolutionary Algorithm-based selection method to select the subset of suitable classifiers and features at each layer to enhance the predictive performance of MULES. The selection method uses NSGA-II algorithm to optimize two objectives concerning classification accuracy and ensemble diversity. Experiments on 33 datasets confirm that MULES is better than a number of well-known benchmark algorithms

A homogeneous-heterogeneous ensemble of classifiers.

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module

Physical Properties of Normal Grade Biodiesel and Winter Grade Biodiesel

In this study, optical and thermal properties of normal grade and winter grade palm oil biodiesel were investigated. Surface Plasmon Resonance and Photopyroelectric technique were used to evaluate the samples. The dispersion curve and thermal diffusivity were obtained. Consequently, the variation of refractive index, as a function of wavelength in normal grade biodiesel is faster than winter grade palm oil biodiesel, and the thermal diffusivity of winter grade biodiesel is higher than the thermal diffusivity of normal grade biodiesel. This is attributed to the higher palmitic acid C16:0 content in normal grade than in winter grade palm oil biodiesel

THE IMPACT OF NATIONAL CULTURE ON BILATERAL TRADE IN VIETNAM

The purpose of this paper is to examine whether or to what extent national culture influences bilateral trade flows between Vietnam and its trading partners. Using a panel dataset of 52 countries from 2001 till 2011 and six cultural dimensions of Hofstede, the regression analysis performed by gravity model shows that national culture and bilateral trade flows between Vietnam and trading partners are significantly correlated. This study's implications may help macro-policy makers devise better export promotion policies and boost the volume of bilateral trade between Vietnam and other countries around the world

Highly-efficient electrochromic performance of nanostructured TiO2 films made by doctor blade technique

Electrochromic TiO2 anatase thin films on F-doped tin oxide (FTO) substrates were prepared by doctor blade method using a colloidal solution of titanium oxide with particles of 15 nm in size. The films were transparent in the visible range and well colored in a solution of 1 M LiClO4 in propylene carbonate. The transmittances of the colored films were found to be strongly dependent on the Li+ inserted charges. The response time of the electrochromic device coloration was found to be as small as 2 s for a 1 cm2 sample and the coloration efficiency at a wavelength of 550 nm reached a value as high as 33.7 cm2 C−1 for a 600 nm thick nanocrystalline-TiO2 on a FTO-coated glass substrate. Combining the experimental data obtained from in situ transmittance spectra and in situ X-ray diffraction analysis with the data from chronoamperometric measurements, it was clearly demonstrated that Li+ insertion (extraction) into (out of) the TiO2 anatase films resulted in the formation (disappearance) of the Li0.5TiO2 compound. Potential application of nanocrystalline porous TiO2 films in large-area electrochromic windows may be considered

Genome-wide association study of a panel of vietnamese rice landraces reveals new QTLs for tolerance to water deficit during the vegetative phase

Background: Drought tolerance is a major challenge in breeding rice for unfavorable environments. In this study, we used a panel of 180 Vietnamese rice landraces genotyped with 21,623 single-nucleotide polymorphism markers to perform a genome-wide association study (GWAS) for different drought response and recovery traits during the vegetative stage. These landraces originate from different geographical locations and are adapted to different agrosystems characterized by contrasted water regimes. Vietnamese landraces are often underrepresented in international panels used for GWAS, but they can contain original genetic determinants related to drought resistance. Results: The panel of 180 rice varieties was phenotyped under greenhouse conditions for several drought-related traits in an experimental design with 3 replicates. Plants were grown in pots for 4 weeks and drought-stressed by stopping irrigation for an additional 4 weeks. Drought sensitivity scores and leaf relative water content were measured throughout the drought stress. The recovery capacity was measured 2 weeks after plant rewatering. Several QTLs associated with these drought tolerance traits were identified by GWAS using a mixed model with control of structure and kinship. The number of detected QTLs consisted of 14 for leaf relative water content, 9 for slope of relative water content, 12 for drought sensitivity score, 3 for recovery ability and 1 for relative crop growth rate. This set of 39 QTLs actually corresponded to a total of 17 different QTLs because 9 were simultaneously associated with two or more traits, which indicates that these common loci may have pleiotropic effects on drought-related traits. No QTL was found in association with the same traits in both the indica and japonica subpanels. The possible candidate genes underlying the quantitative trait loci are reviewed. Conclusions: Some of the identified QTLs contain promising candidate genes with a function related to drought tolerance by osmotic stress adjustment