British or American? Iranian EFL learners’ Perceptions toward English Accents: Exploring possible relationships

Native English accents (British and American) are known as highly favored and accepted varieties compared to other existing accents in English as foreign language (EFL) context. Notwithstanding the research accomplished on EFL learners’ attitudes toward either of the accents (British or American), studies are still scant regarding the investigation of their perspectives in detail toward one of the accents specifically within the context of Iran. The aim of this study is to examine the Iranian EFL learners’ attitudes toward the two major known English accents (British and American. Additionally, the study highlights the major factors contributing to the learners’ preferences toward either of the accents (British or American). To that aim, a developed and validated questionnaire was distributed among 108 EFL learners selected from two of the major EFL contexts (universities and private institutes). The results indicated that the majority of the learners preferred American over British English accent. Besides, factor analysis revealed that American English exposure, lack of guidance, and lack of reinforcement toward British accent were among the mentioned factors accepted by learners for their preference of American. Finally, the study concludes with interpretations regarding the learners’ decision making issues in either of the two major accents, and recommendations are provided for revisiting the EFL learners’ attitudes and insights toward native English accents

Tracking Interpersonality in Research Article Abstract: A Diachronic Study of Dynamic Nature of Genre

This study reports on interpersonality in a diachronic-contrastive investigation in Research Article (RA) abstracts. The study analyzed a corpus of 180 RA abstracts from two journalsof Psychological Bulletin and Personality and Individual Differences over the last three decades. This paper uses Hylands (2005b) Stance Model of Interaction and Hyland Tses (2005) Classification of Sentences Containing Evaluative that in order to explore interpersonality. The results of this study revealed that authors of these journals adopted different stance and positioning over time in their writing. In addition, the findings of this paper did not corroborate previous research findings that RA abstracts exhibit high number of boosters. In relation to writing pedagogy, the results of this study can help the scholars to frame their papers in order to publish them in English-medium journals

Count Data Modeling and Classification Using Statistical Hierarchical Approaches and Multi-topic Models

In this thesis, we propose and develop various statistical models to enhance and improve the efficiency of statistical modeling of count data in various applications. The major emphasis of the work is focused on developing hierarchical models. Various schemes of hierarchical structures are thus developed and analyzed in this work ranging from purely static hierarchies to dynamic models. The second part of the work concerns itself with the development of multitopic statistical models. It has been shown that these models provide more realistic modeling characteristics in comparison to mono topic models. We proceed with developing several multitopic models and we analyze their performance against benchmark models. We show that our proposed models in the majority of instances improve the modeling efficiency in comparison to some benchmark models, without drastically increasing the computational demands. In the last part of the work, we extend our proposed multitopic models to include online learning capability and again we show the relative superiority of our models in comparison to the benchmark models. Various real world applications such as object recognition, scene classification, text classification and action recognition, are used for analyzing the strengths and weaknesses of our proposed models

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Limbitis Secondary to Autologous Serum Eye Drops in a Patient with Atopic Keratoconjunctivitis

Purpose. Report a case of limbitis secondary to autologous serum eye drops in a patient with atopic keratoconjunctivitis. Design. Interventional case report. Methods. A 32-year-old African American female with atopic keratoconjunctivitis (AKC) presented with chronic dry eye and diffuse punctate epithelial erosions refractory to conservative treatment. She was initially managed with cyclosporine ophthalmic 0.05% in addition to preservative-free artificial tears and olopatadine hydrochloride 0.2% for 6 months. She was later placed on autologous serum eye drops (ASEDs) and 4 weeks into treatment developed unilateral limbitis. The limbitis resolved shortly after stopping ASEDs in that eye; however, the drops were continued in the contralateral eye, which subsequently developed limbitis within 2 weeks. ASEDs were discontinued in both eyes, and the patient has remained quiet ever since. Results. Patient with a history of AKC and no prior history of limbitis developed limbitis shortly after starting ASEDs, which resolved promptly after discontinuation of therapy with no subsequent recurrence of inflammation. Conclusion. ASEDs are widely used in the treatment of complicated or treatment refractory dry eye. The potential side effects should be kept in mind when prescribing ASEDs for any patient, especially in those with underlying immunological diseases and circulating inflammatory factors

A variational Bayes model for count data learning and classification

Several machine learning and knowledge discovery approaches have been proposed for count data modeling and classification. In particular, latent Dirichlet allocation (LDA) (Blei et al., 2003a) has received a lot of attention and has been shown to be extremely useful in several applications. Although the LDA is generally accepted to be one of the most powerful generative models, it is based on the Dirichlet assumption which has some drawbacks as we shall see in this paper. Thus, our goal is to enhance the LDA by considering the generalized Dirichlet distribution as a prior. The resulting generative model is named latent generalized Dirichlet allocation (LGDA) to maintain consistency with the original model. The LGDA is learned using variational Bayes which provides computationally tractable posterior distributions over the model׳s hidden variables and its parameters. To evaluate the practicality and merits of our approach, we consider two challenging applications namely text classification and visual scene categorization

Single-Layer versus Double-Layer Laparoscopic Intracorporeally Sutured Gastrointestinal Anastomoses in the Canine Model

This study shows that the 1-layer gastrointestinal suture technique is feasible, safe and has fewer complications compared with a 2-layer suture technique

Distribution and pollution level of nickel and vanadium in sediments from south part of the Caspian Sea, Iran

765-771Distribution and pollution level of nickel and vanadium in sediment from south part of the Caspian Sea, north of Iran, were studied. Sediment samples obtained by Van Veen Grab from four stations, including, Turkaman, Amirabad, Fereydunkenar and Noushahr along the south part of the Caspian Sea, during fall of 2015 and april, summer and winter of 2016. The concentrations of metal were ranged from 21.63 µg/g to 55.45 µg/g for nickel and from 58.23 µg/g to 146.27 µg/g for vanadium in sediments samples collected from all stations. There was significant difference in metals concentration between different stations along the Caspian Sea (P < 0.05), and the highest mean concentration of metals was absorbed in Fereydunkenar estuary, followed by Amirabad, Turkaman and Noushahr, respectively. The results showed that there were significant differences between metals pollution during four seasons (P < 0.05), and the highest concentration of metals were absorbed in dry season (summer) and the lowest concentration in wet season (winter). There was a positive correlation between nickel and vanadium concentration in sediment samples, and the Pearson correlation was (r = 0.67) between nickel and vanadium in sediment samples. The positive correlation between heavy metals can be related to same source of both metals in the environment. Based on our results, anthropogenic activities such as oil industry and agriculture activities are the main sources of pollution in the coasts along south part of Caspian Sea