Explore the vast range of titles available.

\"Hugh Craig and Brett Greatley-Hirsch extend the computational analysis introduced in Shakespeare, Computers, and the Mystery of Authorship (edited by Hugh Craig and Arthur F. Kinney; Cambridge, 2009) beyond problems of authorship attribution to address broader issues of literary history. Using new methods to answer long-standing questions and challenge traditional assumptions about the underlying patterns and contrasts in the plays of Shakespeare and his contemporaries, Style, Computers, and Early Modern Drama sheds light on, for example, different linguistic usages between plays written in verse and prose, company styles and different character types. As a shift from a canonical survey to a corpus-based literary history founded on a statistical analysis of language, this book represents a fundamentally new approach to the study of English Renaissance literature and proposes a new model and rationale for future computational scholarship in early modern literary studies\"-- Provided by publisher.

Background Systematic reviews (SRs) are an important source of information about healthcare interventions. A key component of a well-conducted SR is a comprehensive literature search. There is limited evidence on the contribution of non-English reports, unpublished studies, and dissertations and their impact on results of meta-analyses. Methods Our sample included SRs from three Cochrane Review Groups: Acute Respiratory Infections (ARI), Infectious Diseases (ID), Developmental Psychosocial and Learning Problems (DPLP) ( n  = 129). Outcomes included: 1) proportion of reviews that searched for and included each study type; 2) proportion of relevant studies represented by each study type; and 3) impact on results and conclusions of the primary meta-analysis for each study type. Results Most SRs searched for non-English studies; however, these were included in only 12% of reviews and represented less than 5% of included studies. There was a change in results in only four reviews (total sample = 129); in two cases the change did not have an impact on the statistical or clinical significance of results. Most SRs searched for unpublished studies but the majority did not include these (only 6%) and they represented 2% of included studies. In most cases the impact of including unpublished studies was small; a substantial impact was observed in one case that relied solely on unpublished data. Few reviews in ARI (9%) and ID (3%) searched for dissertations compared to 65% in DPLP. Overall, dissertations were included in only nine SRs and represented less than 2% of included studies. In the majority of cases the change in results was negligible or small; in the case where a large change was noted, the estimate was more conservative without dissertations. Conclusions The majority of SRs searched for non-English and unpublished studies; however, these represented a small proportion of included studies and rarely impacted the results and conclusions of the review. Inclusion of these study types may have an impact in situations where there are few relevant studies, or where there are questionable vested interests in the published literature. We found substantial variation in whether SRs searched for dissertations; in most reviews that included dissertations, these had little impact on results.

ChatGPT is a 175-billion-parameter natural language processing model that is already involved in scientific content and publications. Its influence ranges from providing quick access to information on medical topics, assisting in generating medical and scientific articles and papers, performing medical data analyses, and even interpreting complex data sets. The future role of ChatGPT remains uncertain and a matter of debate already shortly after its release. This review aimed to analyze the role of ChatGPT in the medical literature during the first 3 months after its release. We performed a concise review of literature published in PubMed from December 1, 2022, to March 31, 2023. To find all publications related to ChatGPT or considering ChatGPT, the search term was kept simple (“ChatGPT” in AllFields). All publications available as full text in German or English were included. All accessible publications were evaluated according to specifications by the author team (eg, impact factor, publication modus, article type, publication speed, and type of ChatGPT integration or content). The conclusions of the articles were used for later SWOT (strengths, weaknesses, opportunities, and threats) analysis. All data were analyzed on a descriptive basis. Of 178 studies in total, 160 met the inclusion criteria and were evaluated. The average impact factor was 4.423 (range 0-96.216), and the average publication speed was 16 (range 0-83) days. Among the articles, there were 77 editorials (48,1%), 43 essays (26.9%), 21 studies (13.1%), 6 reviews (3.8%), 6 case reports (3.8%), 6 news (3.8%), and 1 meta-analysis (0.6%). Of those, 54.4% (n=87) were published as open access, with 5% (n=8) provided on preprint servers. Over 400 quotes with information on strengths, weaknesses, opportunities, and threats were detected. By far, most (n=142, 34.8%) were related to weaknesses. ChatGPT excels in its ability to express ideas clearly and formulate general contexts comprehensibly. It performs so well that even experts in the field have difficulty identifying abstracts generated by ChatGPT. However, the time-limited scope and the need for corrections by experts were mentioned as weaknesses and threats of ChatGPT. Opportunities include assistance in formulating medical issues for nonnative English speakers, as well as the possibility of timely participation in the development of such artificial intelligence tools since it is in its early stages and can therefore still be influenced. Artificial intelligence tools such as ChatGPT are already part of the medical publishing landscape. Despite their apparent opportunities, policies and guidelines must be implemented to ensure benefits in education, clinical practice, and research and protect against threats such as scientific misconduct, plagiarism, and inaccuracy.

For the purpose of this study, A statistical test of Biblical books was conducted using the recently discovered probability models for text homogeneity and text change point detection. Accordingly, translations of Biblical books of Tigrigna and Amharic (major languages spoken in Eritrea and Ethiopia) and English were studied. A Zipf-Mandelbrot distribution with a parameter range of 0.55 to 0.88 was obtained in these three Bibles. According to the statistical analysis of the texts’ homogeneity, the translation of Bible in each of these three languages was a heterogeneous concatenation of different books or genres. Furthermore, an in-depth examination of the text segmentation of prat of a single genre—the English Bible letters revealed that the Pauline letters are heterogeneous concatenations of two homogeneous segments.

Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.

Objective To employ big data mining to provide a visualization analysis of Case-Based Learning (CBL) application in Chinese and international medical education, with the aim of observing the potential applications of CBL. Methods All included literature was obtained from the Web of Science (WoS) core collection database, Chinese core periodicals database, Chinese Social Sciences Citation Index (CSSCI), Chinese Science Citation Database of China National Knowledge Infrastructure (CNKI), Wangfang Database, and CQVIP Database. CiteSpace software (6.1.6R6) was used to conduct an in-depth investigation from four perspectives: quantitative analysis of literature, network analysis of co-occurring authors, network analysis of co-occurring research institutions, keyword clustering and burst analysis. Results A total of 721 Chinese articles and 537 English articles were included, demonstrating an exponential growth trend. Notably, no author exhibited a prolific publication rate within a short timeframe. Bursting keywords in English literature encompassed topics related to students' learning, teaching curriculum, methods, and location. In contrast, Chinese literature focused on students' learning, teaching methods, courses, application fields as well as national policy and the Ministry of Education of the People’s Republic of China (MOE) guidance. The keyword clusters include research on care, community practice, special projects and groups, teaching methods, and capacity development of participants in English literature. For Chinese literature, the clusters include research national policy guidance, teaching reform, mode and evaluation and various disciplines. Conclusion CBL holds immense potential for implementation across diverse disciplines, community practices, and special projects within medical education. The practical application of CBL is continuously evolving in response to changing times and can be seamlessly integrated into different contexts influenced by environmental factors and policies.

This study investigated the research trends in language learning in a virtual reality environment by conducting a content analysis of findings published in the literature from 2004 to 2013 in four top ranked computer-assisted language learning journals: Language Learning ＆ Technology, CALICO Journal, Computer Assisted Language Learning, and ReCALL. Data from 29 articles were cross-analyzed in terms of research topics, technologies used, language learning settings, sample groups, and methodological approaches. It was found that the three most popular research topics for learners were interactive communication; behaviors, affections, and beliefs; and task-based instruction. However, the analysis results highlight the need for the inclusion of the impact of teacher. The data also revealed that more studies are utilizing triangulation of measurement processes to enable in-depth analysis. A trend of gathering data through informal learning procedures was also observed. This article concludes by highlighting particular fields related to VR in which further research is urgently needed.

The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. We aimed to conduct a scoping review of the literature to address the question, \"What are the current and potential impacts of AI technologies on health equity in oncology?\" Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

This meta-analysis examined if students ' writing performance is improved by reading interventions in studies (k = 54 experiments; 5,018 students) where students were taught how to read and studies (k = 36 investigations; 3,060 students) where students ' interaction with words or text was increased through reading or observing others read. Studies included in this review involved true- or quasi-experiments (with pretests) written in English that tested the impact of a reading intervention on the writing performance of students in preschool to Grade 12. Studies were not included if the control condition was a writing intervention, treatment students received writing instruction as part of the reading intervention (unless control students received equivalent writing instruction), control students received a reading intervention (unless treatment students received more reading instruction than controls), study attrition exceeded 20%, less than 10 students were included in any experimental condition, and students attended a special school for students with disabilities. As predicted, teaching reading strengthened writing, resulting in statistically significant effects for an overall measure of writing (effect size [ES] = 0.57) and specific measures of writing quality (ES = 0.63), words written (ES = 0.37), or spelling (ES = 0.56). The impact of teaching reading on writing was maintained over time (ES = 0.37). Having students read text or observe others interact with text also enhanced writing performance, producing a statistically significant impact on an overall measure of writing (ES = 0.35) and specific measures of writing quality (ES = 0.44) or spelling (ES = 0.28). These findings provide support that reading interventions can enhance students' writing performance.

This state-of-the-art paper foregrounds mixed-methods research (MMR) in language teaching and learning by discussing and critically reviewing issues related to this newly developed research paradigm. The paper has six sections. The first provides a context for the discussion of MMR through an introductory review of quantitative and qualitative paradigms. In the second section we discuss the nature and scope of MMR, its underlying principles, and its techniques and procedures. In the third section we discuss trends in MMR in language teaching and learning, and review 40 published papers in 30 journals related to this field, covering one decade (2002–2011). Issues and challenges facing MMR and its researchers are discussed in the fourth section, while in the fifth we discuss the significance of replicating MMR studies in language teaching and learning. Finally, we conclude by presenting prospects and avenues for further developing mixed-methods research.