Contrastive Analysis of Bilingual and Monolingual EFL Learners' Syntactic Errors in Translation

This study aimed at the analysis of syntactic errors in translation done by both bilingual and monolingual EFL learners. Research on the subject of the study implied that there might be differences between monolinguals and bilingual learners of foreign languages. The gaps of studies on differences between monolinguals and bilingual’s translations from Persian into English language presumed as research question of the study. Through a quantitative and experimental analysis, the researcher collected data from two universities of IAU of South Tehran (monolingual) and Jihad University of Kermanshah (bilingual) using students majoring in translation studies. The OPT test was applied to specify eligible students for the study and then a Persian literary text was offered to students to measure their syntactic errors as introduced by Keshavarz’s (1996) model of error analysis. From 100 participants 36 monolinguals and 24 bilinguals were eligible for the study whose translations were scored by the two raters. The results of the study indicated that there is a significant difference between the scores of monolingual and bilingual translators. In addition, the rate of literal errors and approximation was more than other errors that were reported as the result of language learning strategies and communicative strategies. However, new studies are suggested to investigate the types of errors made by monolinguals and bilinguals and graduate students with advanced level of language learning

The CYP17 MSP AI (T-34C) and CYP19A1 (Trp39Arg) variants in polycystic ovary syndrome: A case-control study

Background: Polycystic ovary syndrome (PCOS) is a common and chronic disorder of endocrine glands where genetic factors play a major role in the susceptibility to the disease. The cytochrome (CYP) 17 enzyme is essential for androgens biosynthesis. Also, the CYP19 enzyme converts the androgens to the aromatic estrogens.Objective: We aimed to investigate the association of CYP 17 MSP AI (T-34C) and CYP 19A1 (Trp39Arg) variants with the pathogenesis of PCOS in a population from Western Iran with Kurdish ethnic background.Materials and Methods: The present case-control study consisted of 50 patients with PCOS and 109 controls. The CYP17 T-34C and CYP19A1 (Trp39Arg) polymorphisms were identified by polymerase chain reaction-restriction fragment length polymorphism. The serum lipid and lipoprotein profile were detected by the Bionic Diagnostic Kits. Estradiol, dehydroepiandrosterone (DHEA), and sex hormone-binding globulin (SHBG) levels were measured using the chemiluminescent method.Results: The serum levels of estradiol and SHBG in PCOS patients were lower than controls (p < 0.001 and p =0.06, respectively). However, the level of DHEA was higher (p= 0.01) in patients compared to controls. The higher frequency of CYP17 TC genotype in patients (30%) compared to controls (15.6%) was associated with 2.31-fold susceptibility to PCOS (p = 0.038). The frequency of CYP19 TC genotype was 6.4% in controls and10% in patients (p = 0.42).Conclusion: The present study suggests that CYP17 TC genotype could be associated with the risk of PCOS. Also, the study indicated the sex steroid hormones level alteration and the lower level of SHBG in PCOS patients compared to healthy individuals

SSHA: Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model

Current human-based surveillance systems are prone to inadequate availability and reliability. Artificial intelligence-based solutions are compelling, considering their reliability and precision in the face of an increasing adaption of surveillance systems. Exceedingly efficient and precise machine learning models are required to effectively utilize the extensive volume of high-definition surveillance imagery. This study focuses on improving the accuracy of the methods and models used in automated surveillance systems to recognize and localize human violence in video footage. The proposed model uses an I3D backbone pretrained on the Kinetics dataset and has achieved state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets, respectively. The semi-supervised hard attention mechanism has enabled the proposed method to fully capture the available information in a high-resolution video by processing the necessary video regions in great detail.Comment: 11 pages, 4 figures, 4 equations, 3 tables, 1 algorith

Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades

Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Method: We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. Results: Testing research can be divided into 16 high-level topics and 18 subtopics. Creation guidance, automated test generation, evolution and maintenance, and test oracles have particularly strong connections to other topics, highlighting their multidisciplinary nature. Emerging keywords relate to web and mobile apps, machine learning, energy consumption, automated program repair and test generation, while emerging connections have formed between web apps, test oracles, and machine learning with many topics. Random and requirements-based testing show potential decline. Conclusions: Our observations, advice, and map data offer a deeper understanding of the field and inspiration regarding challenges and connections to explore.Comment: To appear, Journal of Systems and Softwar

Identifying the Invisible Impact of Scholarly Publications: A Multi-Disciplinary Analysis Using Altmetrics

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The field of ‘altmetrics’ is concerned with alternative metrics for the impact of research publications using social web data. Empirical studies are needed, however, to assess the validity of altmetrics from different perspectives. This thesis partly fills this gap by exploring the suitability and reliability of two altmetrics resources: Mendeley, a social reference manager website, and Faculty of F1000 (F1000), a post- publishing peer review platform. This thesis explores the correlations between the new metrics and citations at the level of articles for several disciplines and investigates the contexts in which the new metrics can be useful for research evaluation across different fields. Low and medium correlations were found between Mendeley readership counts and citations for Social Sciences, Humanities, Medicine, Physics, Chemistry and Engineering articles from the Web of Science (WoS), suggesting that Mendeley data may reflect different aspects of research impact. A comparison between information flows based on Mendeley bookmarking data and cross-disciplinary citation analysis for social sciences and humanities disciplines revealed substantial similarities and some differences. This suggests that Mendeley readership data could be used to help identify knowledge transfer between scientific disciplines, especially for people that read but do not author articles, as well as providing evidence of impact at an earlier stage than is possible with citation counts. The majority of Mendeley readers for Clinical Medicine, Engineering and Technology, Social Science, Physics and Chemistry papers were PhD students and postdocs. The highest correlations between citations and Mendeley readership counts were for types of Mendeley users that often authored academic papers, suggesting that academics bookmark papers in Mendeley for reasons related to scientific publishing. In order to identify the extent to which Mendeley bookmarking counts reflect readership and to establish the motivations for bookmarking scientific papers in Mendeley, a large-scale survey found that 83% of Mendeley users read more than half of the papers in their personal libraries. The main reasons for bookmarking papers were citing in future publications, using in professional activities, citing in a thesis, and using in teaching and assignments. Thus, Mendeley bookmarking counts can potentially indicate the readership impact of research papers that have educational value for non-author users inside academia or the impact of research papers on practice for readers outside academia. This thesis also examines the relationship between article types (i.e., “New Finding”, “Confirmation”, “Clinical Trial”, “Technical Advance”, “Changes to Clinical Practice”, “Review”, “Refutation”, “Novel Drug Target”), citation counts and F1000 article factors (FFa). In seven out of nine cases, there were no significant differences between article types in terms of rankings based on citation counts and the F1000 Article Factor (FFa) scores. Nevertheless, citation counts and FFa scores were significantly different for articles tagged: “New finding” or “Changes to Clinical Practice”. This means that F1000 could be used in research evaluation exercises when the importance of practical findings needs to be recognised. Furthermore, since the majority of the studied articles were reviewed in their year of publication, F1000 could also be useful for quick evaluations