Hasil untuk "American literature"

Zhi Zhang, Yan Liu, Zhejing Hu et al.

Compounding errors pose a significant challenge in automatic literature review generation, as inaccuracies can cascade across multi-stage retrieval and generation workflows. Existing self-correction strategies often lack mechanisms to effectively track and consolidate verified information throughout the process, making it difficult to prevent error accumulation and propagation. In this paper, we propose Structure-Guided Memory Consolidation (SGMC), a novel framework that incrementally consolidates and verifies information using structured representations at each stage of the literature review pipeline. SGMC consists of three key modules: Tree-Guided Memory for hierarchical literature retrieval and outline generation, Hub-Guided Memory for evidence extraction and iterative content refinement, and Self-Loop Memory for proactive error correction via historical feedback. Extensive experiments on public benchmarks and a newly constructed large-scale dataset demonstrate that SGMC achieves state-of-the-art performance in citation accuracy and content quality, significantly mitigating compounding errors in long-form literature review generation.

Xiao Hu, Yuansheng Lian, Ke Zhang et al.

This study proposes an interpretable prediction framework with literature-informed fine-tuned (LIFT) LLMs for truck driving risk prediction. The framework integrates an LLM-driven Inference Core that predicts and explains truck driving risk, a Literature Processing Pipeline that filters and summarizes domain-specific literature into a literature knowledge base, and a Result Evaluator that evaluates the prediction performance as well as the interpretability of the LIFT LLM. After fine-tuning on a real-world truck driving risk dataset, the LIFT LLM achieved accurate risk prediction, outperforming benchmark models by 26.7% in recall and 10.1% in F1-score. Furthermore, guided by the literature knowledge base automatically constructed from 299 domain papers, the LIFT LLM produced variable importance ranking consistent with that derived from the benchmark model, while demonstrating robustness in interpretation results to various data sampling conditions. The LIFT LLM also identified potential risky scenarios by detecting key combination of variables in truck driving risk, which were verified by PERMANOVA tests. Finally, we demonstrated the contribution of the literature knowledge base and the fine-tuning process in the interpretability of the LIFT LLM, and discussed the potential of the LIFT LLM in data-driven knowledge discovery.

Ziang Wang, Amir Aryani

This technical report presents a natural language processing (NLP)-based approach for systematically classifying scientific literature on childhood speech disorders. We retrieved and filtered 4,804 relevant articles published after 2015 from the PubMed database using domain-specific keywords. After cleaning and pre-processing the abstracts, we applied two topic modeling techniques - Latent Dirichlet Allocation (LDA) and BERTopic - to identify latent thematic structures in the corpus. Our models uncovered 14 clinically meaningful clusters, such as infantile hyperactivity and abnormal epileptic behavior. To improve relevance and precision, we incorporated a custom stop word list tailored to speech pathology. Evaluation results showed that the LDA model achieved a coherence score of 0.42 and a perplexity of -7.5, indicating strong topic coherence and predictive performance. The BERTopic model exhibited a low proportion of outlier topics (less than 20%), demonstrating its capacity to classify heterogeneous literature effectively. These results provide a foundation for automating literature reviews in speech-language pathology.

Chenyang Shao, Yong Li, Fengli Xu

The rapid development of AI agent has spurred the development of advanced research tools, such as Deep Research. Achieving this require a nuanced understanding of the relations within scientific literature, surpasses the scope of keyword-based or embedding-based retrieval. Existing retrieval agents mainly focus on the content-level similarities and are unable to decode critical relational dynamics, such as identifying corroborating or conflicting studies or tracing technological lineages, all of which are essential for a comprehensive literature review. Consequently, this fundamental limitation often results in a fragmented knowledge structure, misleading sentiment interpretation, and inadequate modeling of collective scientific progress. To investigate relation-aware retrieval more deeply, we propose SciNetBench, the first Scientific Network Relation-aware Benchmark for literature retrieval agents. Constructed from a corpus of over 18 million AI papers, our benchmark systematically evaluates three levels of relations: ego-centric retrieval of papers with novel knowledge structures, pair-wise identification of scholarly relationships, and path-wise reconstruction of scientific evolutionary trajectories. Through extensive evaluation of three categories of retrieval agents, we find that their accuracy on relation-aware retrieval tasks often falls below 20%, revealing a core shortcoming of current retrieval paradigms. Notably, further experiments on the literature review tasks demonstrate that providing agents with relational ground truth leads to a substantial 23.4% performance improvement in the review quality, validating the critical importance of relation-aware retrieval. We publicly release our benchmark at https://anonymous.4open.science/r/SciNetBench/ to support future research on advanced retrieval systems.

Raymond Fok, Joseph Chee Chang, Marissa Radensky et al.

Comprehensive literature review requires synthesizing vast amounts of research -- a labor intensive and cognitively demanding process. Most prior work focuses either on helping researchers deeply understand a few papers (e.g., for triaging or reading), or retrieving from and visualizing a vast corpus. Deep analysis and synthesis of large paper collections (e.g., to produce a survey paper) is largely conducted manually with little support. We present DimInd, an interactive system that scaffolds literature review across large paper collections through LLM-generated structured representations. DimInd scaffolds literature understanding with multiple levels of compression, from papers, to faceted literature comparison tables with information extracted from individual papers, to taxonomies of concepts, to narrative syntheses. Users are guided through these successive information transformations while maintaining provenance to source text. In an evaluation with 23 researchers, DimInd supported participants in extracting information and conceptually organizing papers with less effort compared to a ChatGPT-assisted baseline workflow.

Juan Llivisaca-Villazhañay, Pablo Flores-Siguenza, Rodrigo Guamán et al.

This study identifies key drivers for ERP implementation in small- and medium-sized enterprises (SMEs) in Austro–Ecuador and examines their impact on operational efficiency, strategic adaptability, and digital transformation. Motivated by the limited empirical evidence on ERP adoption in Latin American SMEs, this research aims to provide Austro–Ecuadorian insights that contribute to innovation management practices in emerging economies. To identify the critical success factors (CSFs) influencing ERP implementation, a four-phase methodology was employed, encompassing a CSF literature review, data collection and case analysis from 55 SMEs, multiple correspondence analysis (MCA), and descriptive ERP analysis. Statistical analysis of the surveyed SMEs, primarily from manufacturing sectors, revealed that while a significant portion (37%) lacked ERP experience, 22.9% were in the process of implementing or actively using systems such as Oracle’s J.D. Edwards Enterprise One and SAP. The MCA highlighted ERP system configuration, vendor relationships, and user training as significant factors for successful ERP implementation, reported by 54.5% of the companies. Quadrant analysis further emphasized the influence of IT structure and legacy systems on implementation characteristics, with cluster analysis identifying three distinct groups of companies based on their ERP strategies. The findings underscore the importance of top management support, business process re-engineering, and external consultants for successful ERP adoption in SMEs, providing practical insights for optimizing innovation management in the digital era. Future research should investigate the long-term impacts of ERP systems on organizational performance and innovation sustainability.

Susen Halank

The Apple TV+ series Dickinson culminates decades of feminist and queer scholarship on the poet Emily Dickinson, positioning her as a queer icon while challenging conventional portrayals of her life and legacy. Through the lens of feminist and queer studies, this paper examines the series’ portrayal of Dickinson’s journey to poetic self-identification, her resistance to patriarchal constraints, and her impact on contemporary culture. I argue that the series constructs a narrative that merges notions of the biopic with a coming-of-age story as well as historical facts with millennial sensibilities, resulting in a very effective re-writing of Dickinson’s life based on feminist scholarship. I claim that Dickinson not only redefines the poet’s public image but also overcomes restrictions of the biopic genre and thus becomes part of a feminist (and queer) counter-public sphere that resonates with contemporary audiences.

Huawei Ji, Cheng Deng, Bo Xue et al.

With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before further processing. However, parsing diverse structured texts in academic literature remains challenging due to the lack of datasets that cover various text structures. In this paper, we introduce AceParse, the first comprehensive dataset designed to support the parsing of a wide range of structured texts, including formulas, tables, lists, algorithms, and sentences with embedded mathematical expressions. Based on AceParse, we fine-tuned a multimodal model, named AceParser, which accurately parses various structured texts within academic literature. This model outperforms the previous state-of-the-art by 4.1% in terms of F1 score and by 5% in Jaccard Similarity, demonstrating the potential of multimodal models in academic literature parsing. Our dataset is available at https://github.com/JHW5981/AceParse.

Harleen Kaur Bagga, Jasmine Bernard, Sahil Shaheen et al.

Sarcasm is hard to interpret as human beings. Being able to interpret sarcasm is often termed as a sign of intelligence, given the complex nature of sarcasm. Hence, this is a field of Natural Language Processing which is still complex for computers to decipher. This Literature Survey delves into different aspects of sarcasm detection, to create an understanding of the underlying problems faced during detection, approaches used to solve this problem, and different forms of available datasets for sarcasm detection.

Hengxing Cai, Xiaochen Cai, Junhan Chang et al.

Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including biology, chemistry, material, and medicine. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement. We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis. SciAssess and its resources are available at \url{https://github.com/sci-assess/SciAssess}.

Feiyang Wang, Qiaozhi Bao, Zixuan Wang et al.

This article improves the Transformer model based on swarm intelligence optimization algorithm, aiming to predict the emotions of employment related text content on American social media. Through text preprocessing, feature extraction, and vectorization, the text data was successfully converted into numerical data and imported into the model for training. The experimental results show that during the training process, the accuracy of the model gradually increased from 49.27% to 82.83%, while the loss value decreased from 0.67 to 0.35, indicating a significant improvement in the performance of the model on the training set. According to the confusion matrix analysis of the training set, the accuracy of the training set is 86.15%. The confusion matrix of the test set also showed good performance, with an accuracy of 82.91%. The accuracy difference between the training set and the test set is only 3.24%, indicating that the model has strong generalization ability. In addition, the evaluation of polygon results shows that the model performs well in classification accuracy, sensitivity, specificity, and area under the curve (AUC), with a Kappa coefficient of 0.66 and an F-measure of 0.80, further verifying the effectiveness of the model in social media sentiment analysis. The improved model proposed in this article not only improves the accuracy of sentiment recognition in employment related texts on social media, but also has important practical significance. This social media based data analysis method can not only capture social dynamics in a timely manner, but also promote decision-makers to pay attention to public concerns and provide data support for improving employment conditions.

Bela Bajnok

A brief overview and history of the American Mathematics Competitions.

Anna D. Guseva

Globalization as a factor of the formation of new behavioral patterns in the communicative practice of the host culture is closely related to the phenomenon of character type borrowings, which include the lexeme superman. The purpose of the investigation is to study the peculiarities of linguistic understanding of the character type borrowing superman in Russian-language media discourse. The study was conducted on the basis of data from the National Corpus of the Russian Language and included an analysis of the thematic contexts of the use of the lexeme nominating the character type borrowing superman in texts belonging to various genres of media discourse. The character type “superman” has been established to be embedded in the system of basic concepts of Russian culture and form the following value oppositions: in thematic contexts related to the field “Art, Culture and Leisure” the behavior of Superman, the hero of American films, based on the cult of physical strength and permissiveness, is interpreted as contrary to the moral ideals laid down in classical Russian literature; in thematic contexts related to the field of “Army and Armed Conflicts”, the opposition “superman” – “Russian warrior” is constituted according to the parameter of ability/inability to accomplish a feat; in thematic contexts related to the field of “Politics and Public Life” it is shown that in American society “superman” is understood as a symbol of the fight against enemies and for social justice. In the Russian-speaking space it acquires negative connotations, since it describes a politician as the one not able to instantly solve social problems of society; in the thematic area of “Health and Medicine” the image of Superman reflects his commitment to a healthy lifestyle and is characterized by a high level of self-control, which is frowned upon in Russian-language media discourse. We conclude that the linguistic understanding of the borrowed character types in the media space introduces new conceptual features, due to the value system of the host culture.

Poompak Kusawat, Surat Teerakapibal

Purpose: Global adoption of the internet and mobile usage results in a huge variation in the cultural backgrounds of consumers who generate and consume electronic word-of-mouth (eWOM). Unsurprisingly, a research trend on cross-cultural eWOM has emerged. However, there has not been an attempt to synthesize this research topic. This paper aims to bridge this gap. Methodology: This research paper conducts a systematic literature review of the current research findings on cross-cultural eWOM. Journal articles published from 2006 to 2021 are included. This study then presents the key issues in the extant literature and suggests potential future research. Findings: The findings show that there has been an upward trend in the number of publications on cross-cultural eWOM since the early 2010s, with a relatively steeper increase toward 2020. The findings also synthesize cross-cultural eWOM research into four elements and suggest potential future research avenues. Value: To the best of the authors' knowledge, there is currently no exhaustive/integrated review of cross-cultural eWOM research. This research fills the need to summarize the current state of cross-cultural eWOM literature and identifies research questions to be addressed in the future.

Steelyana Evi, Kinanti Dhea

Public-private partnerships (PPPs) have become an increasingly important tool for delivering infrastructure and public services worldwide. In Toll Road development, PPP scheme is often being used to deliver public infrastructure. PPPs have received a lot of attention from academics as a new approach to public procurement. To enhance our knowledge of PPPs, this study aims to analyze the trend of articles discussing collaborative governance in toll road developments with PPP schemes in Asian, African, American, Australian and European countries during the 2000–2020 period and analyze the most discussed topic in PPP research specifically in collaborative governance. The relationship between PPP research and Public Sector Accounting Theory is also analyzed. Using a qualitative method with a Systematic Literature Review approach, this study analyzed published articles from reputable international journals. Result of this study shows a positive trend in the quantity of research articles that discuss collaborative governance in toll road development by PPP scheme. Moreover, it found also that frequently discussed topics in PPP research are Risk Management, Value for Money, Critical Success Factors, PPP Challenges, PPP Evolution and Financing. This study found that Public Sector Accounting Theory (Governance Theory, Institutional Theory and Stakeholders Theory) have influenced most the PPP scheme in toll road development.

Ardeshir Shojaeinasab, Masoud Jalayer, Homayoun Najjaran

A comprehensive literature review has always been an essential first step of every meaningful research. In recent years, however, the availability of a vast amount of information in both open-access and subscription-based literature in every field has made it difficult, if not impossible, to be certain about the comprehensiveness of one's survey. This subsequently can lead to reviewers' questioning of the novelties of the research directions proposed, regardless of the quality of the actual work presented. In this situation, statistics derived from the published literature data can provide valuable quantitative and visual information about research trends, knowledge gaps, and research networks and hubs in different fields. Our tool provides an automatic and rapid way of generating insight for systematic reviews in any research area.

Carol Neidle, Augustine Opoku, Dimitris Metaxas

The American Sign Language Linguistic Research Project (ASLLRP) provides Internet access to high-quality ASL video data, generally including front and side views and a close-up of the face. The manual and non-manual components of the signing have been linguistically annotated using SignStream(R). The recently expanded video corpora can be browsed and searched through the Data Access Interface (DAI 2) we have designed; it is possible to carry out complex searches. The data from our corpora can also be downloaded; annotations are available in an XML export format. We have also developed the ASLLRP Sign Bank, which contains almost 6,000 sign entries for lexical signs, with distinct English-based glosses, with a total of 41,830 examples of lexical signs (in addition to about 300 gestures, over 1,000 fingerspelled signs, and 475 classifier examples). The Sign Bank is likewise accessible and searchable on the Internet; it can also be accessed from within SignStream(R) (software to facilitate linguistic annotation and analysis of visual language data) to make annotations more accurate and efficient. Here we describe the available resources. These data have been used for many types of research in linguistics and in computer-based sign language recognition from video; examples of such research are provided in the latter part of this article.

Chisala D. Meki, Esper J. Ncube, Kuku Voyi

Abstract Background Waterborne diarrhea diseases are among the leading causes of morbidity and mortality globally. These diseases can be mitigated by implementing various interventions. We reviewed the literature to identify available interventions to mitigate the risk of waterborne diarrheal diseases. Methods We conducted a systematic database review of CINAHL (Cumulative Index to Nursing and Allied Health Literature), PubMed, Web of Science Core Collection, Cochrane library, Scopus, African Index Medicus (AIM), and LILACS (Latin American and Caribbean Health Sciences Literature). Our search was limited to articles published between 2009 and 2020. We conducted the review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement checklist. The identified studies were qualitatively synthesized. Results Our initial search returned 28 773 articles of which 56 studies met the inclusion criteria. The included studies reported interventions, including vaccines for rotavirus disease (monovalent, pentavalent, and Lanzhou lamb vaccine); enhanced water filtration for preventing cryptosporidiosis, Vi polysaccharide for typhoid; cholera 2-dose vaccines, water supply, water treatment and safe storage, household disinfection, and hygiene promotion for controlling cholera outbreaks. Conclusion We retrieved few studies on interventions against waterborne diarrheal diseases in low-income countries. Interventions must be specific to each type of waterborne diarrheal disease to be effective. Stakeholders must ensure collaboration in providing and implementing multiple interventions for the best outcomes. Systematic review registration PROSPERO CRD42020190411 .

Judith Cornelius, Charlene Whitaker-Brown, Jaleesa Smoot et al.

BackgroundAfrican Americans have a higher incidence rate of heart failure (HF) and an earlier age of HF onset compared to those of other racial and ethnic groups. Scientific literature suggests that by 2030, African Americans will have a 30% increased prevalence rate of HF coupled with depression. In addition to depression, anxiety is a predictor of worsening functional capacity, decreased quality of life, and increased hospital readmission rates. There is no consensus on the best way to treat patients with HF, depression, and anxiety. One promising type of treatment—cognitive behavioral therapy (CBT)—has been shown to significantly improve patients’ quality of life and treatment compliance, but CBT has not been used with SMS text messaging reminders to enhance the effect of reducing symptoms of depression and anxiety in racial and ethnic minority patients with HF. ObjectiveThe objectives of our study are to (1) adapt and modify the Creating Opportunities for Personal Empowerment (COPE) curriculum for delivery to patients with HF by using an SMS text messaging component to improve depression and anxiety symptoms, (2) administer the adapted intervention to 10 patients to examine the feasibility and acceptability of the approach and modify it as needed, and (3) examine trends in depression and anxiety symptoms postintervention. We hypothesize that patients will show an improvement in depression scores and anxiety symptoms postintervention. MethodsThe study will comprise a mixed methods approach. We will use the eight steps of the ADAPT-ITT (assessment, decision, administration, production, topical expert, integration, training, and testing) model to adapt the intervention. The first step in this feasibility study will involve assembling individuals from the target population (n=10) to discuss questions on a specific topic. In phase 2, we will examine the feasibility and acceptability of the enhanced SMS text messaging intervention (TXT COPE-HF [Texting With COPE for Patients With HF]) and its preliminary effects with 10 participants. The Beck Depression Inventory will be used to assess depression, the State-Trait Anxiety Inventory will be used to assess anxiety, and the Healthy Beliefs and Lifestyle Behavior surveys will be used to assess participants’ lifestyle beliefs and behavior changes. Changes will be compared from baseline to end point by using paired 2-tailed t tests. An exit focus group (n=10) will be held to examine facilitators and barriers to the SMS text messaging protocol. ResultsThe pilot feasibility study was funded by the Academy for Clinical Research and Scholarship. Institutional review board approval was obtained in April 2021. Data collection and analysis are expected to conclude by November 2021 and April 2022, respectively. ConclusionsThe study results will add to the literature on the effectiveness of an SMS text messaging CBT-enhanced intervention in reducing depression and anxiety among African American patients with HF. International Registered Report Identifier (IRRID)PRR1-10.2196/32550

Tracy X. Chen, Marion Schmitz, Joseph M. Mazzarella et al.

We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multi-dimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union (IAU) for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in the Literature is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors.