Hasil untuk "English literature"

Marine Holborow

J. Fernández-Alemán, Inmaculada Carrión Señor, P. Lozoya et al.

T. Shamliyan, Kristine M. C. Talley, R. Ramakrishnan et al.

Gillian A. O’Reilly, Lauren T. Cook, D. Spruijt-Metz et al.

Farnaz Salehian, Jahanpour Alipour, Somayyeh Zakerabasali

<h4>Introduction</h4>Advanced telerehabilitation technology helps physiotherapists monitor the patient's treatment process after Total Knee Replacement. This study aimed to review the research on telerehabilitation after Total Knee Replacement, synthesize the findings related to their applications and features, and address evaluations made on them.<h4>Methods</h4>A systematic search was conducted in PubMed, Scopus, and Web of Science from 2019 through 2023. The authors selected the articles based on keywords and criteria and reviewed them in terms of title, abstract, and full text. Full-text articles and English language, focusing on telerehabilitation for TKR patients, preferably including mobile health applications and consistent with the research question, were considered for more review. Then, the MMAT (mixed methods appraisal) tool was used to assess each article. Finally, the selected articles were evaluated. The systematic review was registered through PROSPERO with registration ID: PROSPERO CRD42024533040.<h4>Result</h4>After reviewing databases, 183 articles were retrieved, then 84 duplicate articles were removed. Of the remaining 99 articles, 40 were deleted after reviewing the title and abstract because they were grey literature or irrelevant to TKR surgery, and 41 were deleted after reviewing the full text. Finally, 18 articles were included in this study and analyzed. The United States was the most common country that developed a telerehabilitation system. The most common use of this technology has been in education, treatment, and subsequent monitoring. Sensor and wearable activity trackers are the most common equipment used in studies. The most common study designs were randomized controlled trials (RCT) and observational studies, each of which accounted for 9 (50%) and 4 (22.22%), respectively.<h4>Conclusion</h4>Telerehabilitation can be as effective as traditional rehabilitation in improving the condition of patients after TKA. However, it is suggested that improvements should always be made to achieve better results of telerehabilitation. Most of the TKA apps in the reviewed studies showed significant effectiveness. Information and communication technology are used to provide high-quality, low-cost, continuous treatments. Soon, telerehabilitation will play a more prominent role and will be more popular.

Sabrina Sabrina, Hayatun Nur, Ika Ika et al.

This study explores the optimization of English pronunciation skill through the use of HelloTalk, an AI-powered learning app. The research employed a quasi-experimental design, involving 17 English Department students of Universitas Serambi Mekkah as the research sample. Data were collected through pretest and posttest, assessing the aspect of pronunciation accuracy. After the pretest, a four-week intervention was given to the students through the use of HelloTalk to see if their English pronunciation competence could improve. The data was analyzed statistically using SPSS 16.0, and the statistical analysis showed a significant improvement in their pronunciation skill before and after the intervention, indicated by the mean scores of the pre-test and post-test (49.71 and 79.12, respectively). The findings also showed a strong correlation between the use of HelloTalk and students’ pronunciation skills, particularly after the use of HelloTalk features, such as Partner Matching, Text Chat, Voice Messages, Voice and Video Calls, Moments Feed, and Community Interaction that enable students to engage in direct verbal communication and exchange messages with native speakers of English. Pronunciation Help with voice recognition and Correction Feature also AI-driven features that also contribute to the considerable improvement. This research is expected to contribute to the existing body of literature by providing insight that HelloTalk can be an effective learning tool to increase articulation accuracy in English. Future research could explore the effect of HelloTalk on other pronunciation aspects, such as intonation, stress, rhythm, and pitch.

Daniela Surico, Alessandro Vigone, Carlotta Monateri et al.

<i>Background and Objectives</i>: The isthmocele is a pouch-shaped defect in the anterior uterine wall, site of a previous cesarean section, due to a scar defect or dehiscence. The prevalence could be underestimated, but the rate of cesarean section is still high in the world. The preferable technique to correct this anomaly is not clearly indicated in the literature. Our objective is to evaluate the literature on the surgical treatment of isthmocele in pre-Cesarean women treated with minimally invasive technique. Our hypothesis is that robotic treatment is more effective than other procedures in women desirous of having children. <i>Materials and Methods</i>: The words “isthmocele”, “laparoscopy”, “robot” and “cesarean scar pregnancy” were searched on the main online scientific search sources (PubMed, Google Scholar, Scopus, WES, and Embase, etc.). We included articles in English and French, chosen for the relevance to the topic. We have decided to include also surgical corrections of isthmocele linked to pregnancies at the site of the defect, with particular attention to video training explanation. <i>Results</i>: We analyzed the literature about the minimally invasive surgery for the repair of an isthmocele, evaluating 20 articles. Comparing several surgical techniques, robotic-assisted laparoscopy could be an effective method to correct the defect, without high risk of intraoperative complications. <i>Conclusions</i>: As indicated in the literature, robotic tailored excision and repair of isthmocele (and of concomitant cesarean scar pregnancy) could be advantageous and safe, and it is necessary to promote video-training about this technique.

Hana Farida, Fadhila Faiza Amalia

This research attempted to understand how dehumanization along with objectification occurred in prostitution. Dehumanization occurred when people treated others less than humans by denied their human uniqueness and nature and added negative attributes to refer to them as animals or objects. This research discussed how the victims of prostitution suffered dehumanization and how this dehumanization affected the victims, positioning them as objects, as portrayed in Somaly Mam and Ruth Marshall’s The Road of Lost Innocence. This research is qualitative-descriptive research. The main data is Somaly Mam and Ruth Marshall’s The Road of Lost Innocence, while supporting data is obtained from books, articles, and journals. In analyzing the data, the researcher applied the dehumanization theory of Haslam and the consequences of dehumanization as described by Bastian and Haslam. The results of this research showed the victims of prostitution suffered dehumanization based on Haslam’s theory: first, animalistic dehumanization, where the brothel owner viewed victims as animals, trained the victims as animals, etc. Second, mechanistic dehumanization occurred when a victim was sold into the brothel by her family, and the owner of the brothel treated the victims like goods that could be traded, etc. This researcher also discovered that this dehumanization affected the victim's cognitive and emotional responses.

Beth Bjorkman, Sean English, Johnathan Koch et al.

Power domination is a graph theoretic model which captures how phasor measurement units (PMUs) can be used to monitor a power grid. Fragile power domination takes into account the fact that PMUs may break or otherwise fail. In this model, each sensor fails independently with probability $q\in [0,1]$ and the surviving sensors monitor the grid according to classical power domination. We study the expected number of observed nodes under the fragile power domination model. We give a characterization for when two networks and initial sensor placements will behave the same according to this expectation. We also show how to control the behavior of this expectation by adding structure to a network.

Lucas H. English, Sam Roberts, Stephen D. Bartlett et al.

Utility-scale quantum computers require quantum error correcting codes with large numbers of physical qubits to achieve sufficiently low logical error rates. The performance of quantum error correction (QEC) is generally predicted through large-scale numerical simulations, used to estimate thresholds, finite-size scaling, and exponential suppression of logical errors below threshold. The connection of QEC to models from statistical mechanics provides an alternative tool for analysing QEC performance. However, predicting the behaviour of these models also requires large-scale numerical simulations, as analytic solutions are not generally known. Here we exploit an exact mapping, from a toric code under bit-flip noise that is post-selected on being syndrome free to the exactly-solvable two-dimensional Ising model on a torus, to derive an analytic solution for the logical failure rate across its full domain of physical error rates. In particular, this mapping provides closed-form expressions for the logical failure rate in four distinct regimes: the path-counting, below-threshold (ordered), near-threshold (critical), and above-threshold (disordered) regimes. Our framework places a number of familiar and long-standing numerical observations on firm theoretical ground. It also motivates explicit ansätze for the conventional QEC setting of non-post-selected codes whose statistical mechanics mappings involve random-bond disorder. Specifically, we introduce an effective surface tension model for the below-threshold regime, and a new scaling ansatz for the near-threshold regime, derived from an analysis of the ground state energy cost distribution. By bridging statistical mechanics theory and quantum error correction practice, our results offer a new toolkit for designing, benchmarking, and understanding topological codes beyond current computational limits.

Abbas Zarei, Elham Shishegarha

Reducing anxiety in foreign language learning has long been a concern for many teachers. This study focused on exploring the effects of three dynamic assessment models on L2 speaking and listening anxiety. The participants were 120 pre-intermediate Iranian learners of English at a language institute in Qazvin, Iran. The learners were randomly assigned to four groups (three experimental groups and one control group). Before the treatment, the students’ homogeneity was checked using Oxford Placement Test (OPT). Then, all the groups were given listening and speaking anxiety questionnaires as pretests. During 10 sessions, the first group received listening and speaking instruction using Buddof’s Learning Potential Measurement Approach (LPM); the second group was treated with Guthke’s Lerntest Approach; the third group was treated with Testing-the-Limits Approach.  Lastly, the control group was taught conventionally in a teacher-fronted way.  The same questionnaires were given to the participants in the twelfth session as posttests. Data were analyzed using two one-way analysis of covariance procedures. Significant differences were found among the groups’ listening and speaking anxiety mean scores on the posttests after controlling for the initial differences. Those experimental groups that received testing-the-limits and Lerntest approaches had a lower level of listening and speaking anxiety on the posttest. It was concluded that employing dynamic assessment models can decrease speaking and listening anxiety among EFL learners and enhance their productivity. The findings can have important implications for students, teachers and materials designers.

Manon Reusens, Philipp Borchert, Jochen De Weerdt et al.

Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investigates whether the quality of LLM responses varies depending on the demographic profile of users. Considering English as the global lingua franca, along with the diversity of its dialects among speakers of different native languages, we explore whether non-native English speakers receive lower-quality or even factually incorrect responses from LLMs more frequently. Our results show that performance discrepancies occur when LLMs are prompted by native versus non-native English speakers and persist when comparing native speakers from Western countries with others. Additionally, we find a strong anchoring effect when the model recognizes or is made aware of the user's nativeness, which further degrades the response quality when interacting with non-native speakers. Our analysis is based on a newly collected dataset with over 12,000 unique annotations from 124 annotators, including information on their native language and English proficiency.

Lynnette Hui Xian Ng, Luo Qi Chan

Translation of code-mixed texts to formal English allow a wider audience to understand these code-mixed languages, and facilitate downstream analysis applications such as sentiment analysis. In this work, we look at translating Singlish, which is colloquial Singaporean English, to formal standard English. Singlish is formed through the code-mixing of multiple Asian languages and dialects. We analysed the presence of other Asian languages and variants which can facilitate translation. Our dataset is short message texts, written as informal communication between Singlish speakers. We use a multi-step prompting scheme on five Large Language Models (LLMs) for language detection and translation. Our analysis show that LLMs do not perform well in this task, and we describe the challenges involved in translation of code-mixed languages. We also release our dataset in this link https://github.com/luoqichan/singlish.

Eric Yanchenko

Oral exams are a powerful tool to assess student's learning. This is particularly important in introductory statistics classes where students struggle to grasp various topics like the interpretation of probability, $p$-values and more. The challenge of acquiring conceptual understanding is only heightened when students are learning in a second language. In this paper, I share my experience administering oral exams to an introductory statistics class of non-native English speakers at a Japanese university. I explain the context of the university and course, before detailing the exam. Of particular interest is the relationship between exam performance and English proficiency. The results showed little relationship between the two, meaning the exam seemed to truly test student's statistical knowledge rather than their English ability. I close with encouragements and recommendations for practitioners hoping to implement similar oral exams, focusing on the unique difficulties faced by students not learning in their mother tongue.

Lisa Wang, Adam Meyers, John E. Ortega et al.

Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the omitted particle X ('DE'). In news article titles from the Penn Chinese Discourse Treebank, we developed a targeted dataset to fine-tune Hugging Face Chinese to English translation models, specifically improving how this critical function word is handled. This focused approach not only complements the broader strategies suggested by previous studies but also offers a practical enhancement by specifically addressing a common error type in Chinese-English translation.

M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani et al.

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.

Maoling Fu, Maoling Fu, Wenshuai Song et al.

BackgroundThe improvement in survival of preterm infants is accompanied by an increase in neonatal intensive care unit (NICU) admissions. Prolonged length of stay in the NICU (LOS-NICU) increases the incidence of neonatal complications and even mortality and places a significant economic burden on families and strain on healthcare systems. This review aims to identify risk factors influencing LOS-NICU of newborns and to provide a basis for interventions to shorten LOS-NICU and avoid prolonged LOS-NICU.MethodsA systematic literature search was conducted in PubMed, Web of Science, Embase, and Cochrane library for studies that were published in English from January 1994 to October 2022. The PRISMA guidelines were followed in all phases of this systematic review. The Quality in Prognostic Studies (QUIPS) tool was used to assess methodological quality.ResultsTwenty-three studies were included, 5 of which were of high quality and 18 of moderate quality, with no low-quality literature. The studies reported 58 possible risk factors in six broad categories (inherent factors; antenatal treatment and maternal factors; diseases and adverse conditions of the newborn; treatment of the newborn; clinical scores and laboratory indicators; organizational factors).ConclusionsWe identified several of the most critical risk factors affecting LOS-NICU, including birth weight, gestational age, sepsis, necrotizing enterocolitis, bronchopulmonary dysplasia, and retinopathy of prematurity. As only a few high-quality studies are available at present, well-designed and more extensive prospective studies investigating the risk factors affecting LOS-NICU are still needed in the future.

Weixin Liang, Mert Yuksekgonul, Yining Mao et al.

The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse. The published version of this study can be accessed at: www.cell.com/patterns/fulltext/S2666-3899(23)00130-7

Kristina Schaaff, Tim Schlippe, Lorenz Mindner

In this paper we analyze features to classify human- and AI-generated text for English, French, German and Spanish and compare them across languages. We investigate two scenarios: (1) The detection of text generated by AI from scratch, and (2) the detection of text rephrased by AI. For training and testing the classifiers in this multilingual setting, we created a new text corpus covering 10 topics for each language. For the detection of AI-generated text, the combination of all proposed features performs best, indicating that our features are portable to other related languages: The F1-scores are close with 99% for Spanish, 98% for English, 97% for German and 95% for French. For the detection of AI-rephrased text, the systems with all features outperform systems with other features in many cases, but using only document features performs best for German (72%) and Spanish (86%) and only text vector features leads to best results for English (78%).

Mustapha Eddahibi, Mohammed Mensouri

This paper is about the development of a machine translation system tailored specifically for LATEX mathematical documents. The system focuses on translating English LATEX mathematical documents into Arabic LATEX, catering to the growing demand for multilingual accessibility in scientific and mathematical literature. With the vast proliferation of LATEX mathematical documents the need for an efficient and accurate translation system has become increasingly essential. This paper addresses the necessity for a robust translation tool that enables seamless communication and comprehension of complex mathematical content across language barriers. The proposed system leverages a Transformer model as the core of the translation system, ensuring enhanced accuracy and fluency in the translated Arabic LATEX documents. Furthermore, the integration of RyDArab, an Arabic mathematical TEX extension, along with a rule-based translator for Arabic mathematical expressions, contributes to the precise rendering of complex mathematical symbols and equations in the translated output. The paper discusses the architecture, methodology, of the developed system, highlighting its efficacy in bridging the language gap in the domain of mathematical documentation