mdpi-ai/citelens huggingface dataset:
Innovative assessment based on multimedia proofs and social network sharing for introductory engineering courses
Field: EDUCATION

Training and education quality are crucial worldwide even more in the area of technical vocation. The assessment validates the performance of the training and education process, however, the design of assessments that validate the acquisition of certain knowledge, abilities or competences is still unsolved and remains open for research. In this paper, the advance in new technologies is used in the assessment process in a step ahead. Since social media is a big part of the daily life for lots of students, an assessment method is proposed that uses social media as the learning motivation. In consequence, the proposed assessment method is completely aligned with the student way of life and this fact motivates the student learning process since generating new high impact social media contents is a very challenging task. The paper shows the results of an assessment innovation project in which the authors develop a methodology for evaluating laboratory practices that measures knowledge of introductory concepts in automatic control engineering courses. The innovation aims to actively involve students in the generation of social media resources through modern technologies that are attractive to them. Under this methodology, students are tasked with creating a series of short videos explaining how to solve a specific problem or elucidate a concept covered in theory lessons. These videos are shared with their peers via a social network. Lecturers evaluate videos considering the quality in the explanation of technical concepts and impact in the social network. Results show that the proposed assessment methodology increases student motivation compared to the traditional assessment process and increases marks.
Fostering effective hybrid human-LLM reasoning and decision making
Field: AI

The impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedicated to overcoming the limitations of current models, the potentials and risks of human-LLM collaboration remain largely underexplored. In this perspective, we argue that enhancing the focus on human-LLM interaction should be a primary target for future LLM research. Specifically, we will briefly examine some of the biases that may hinder effective collaboration between humans and machines, explore potential solutions, and discuss two broader goals—mutual understanding and complementary team performance—that, in our view, future research should address to enhance effective human-LLM reasoning and decision-making.
Democracy is at risk: beliefs of Chilean teachers about the transmission of hate speech in teacher education
Field: EDUCATION

Conversations about hate speech are a complex issue. It is not a new problem; on the contrary, society has been confronted with hate speech against specific communities at different moments. The present study aims to investigate the beliefs of Chilean teachers working in teacher education and their relationship with hate speech that may have occurred in their practice. The methodology was quantitative. The participants were 200 teachers. The data collection instrument was a survey to determine teachers’ beliefs. The results showed that teachers expressed concern about the problem and stated that action must be taken to combat hate speech. At the same time, they argued that their colleagues perpetuate and reproduce hate speech in their practice, which is also a complex situation that needs to be addressed. Finally, there is also a controversy about the limits of freedom of expression.
The sociolinguistic foundations of language modeling
Field: AI

In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling varieties of language, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the concept of a variety of language as developed in sociolinguistics. We then discuss how this perspective could help us better understand five basic challenges in language modeling: social bias, domain adaptation, alignment, language change, and scale. We argue that to maximize the performance and societal value of language models it is important to carefully compile training corpora that accurately represent the specific varieties of language being modeled, drawing on theories, methods, and descriptions from the field of sociolinguistics.
Relationship between physical activity and college students’ life satisfaction: the chain mediating effect of psychological resilience and negative emotions
Field: PSYCHOLOGY

Objective: As the academic pressure, employment competition and mental health problems faced by college students are becoming more and more prominent, paying attention to and improving the quality of life and well-being of college students has become an important issue of widespread concern in all walks of life. This study focuses on the correlation between physical activity and college students’ life satisfaction.Methods: A cross-sectional survey method was applied to 326 college students, using the Physical Activity Rating Scale, the Psychological Resilience Scale, the Depression-Anxiety-Stress Scale, and the Life Satisfaction Scale. For data analysis, demographic analysis of variance, correlation analysis, and chain mediating effect test were conducted sequentially.Results: There were significant differences in psychological resilience, negative emotions, and life satisfaction by gender, and psychological resilience by grade level; there were significant correlations between physical activity and psychological resilience, negative emotions, and life satisfaction among college students (r = 0.541, p < 0.001; r = −0.379, p < 0.001; r = 0.435, p < 0.001); and psychological resilience, negative emotions had significant mediating and chain mediating effects between physical activity and life satisfaction, where the mediating effect of psychological resilience was significantly stronger than the mediating effect of negative emotions and the chain mediating effect of both.Conclusion: There was a correlation between physical activity and life satisfaction among college students, and this relationship was partially mediated by psychological resilience and negative emotions.
Clinical entity-aware domain adaptation in low resource setting for inflammatory bowel disease
Field: AI

The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text. Our research addresses the imperative for robust biomedical entity extraction, focusing specifically on inflammatory bowel disease (IBD). Leveraging novel domain-specific pre-training and entity-aware masking strategies with contrastive learning, we fine-tune and adapt a general language model to be better adapted to IBD-related information extraction scenarios. Our named entity recognition (NER) tool streamlines the retrieval process, supporting annotation, correction, and visualization functionalities. In summary, we developed a comprehensive pipeline for clinical Dutch NER encompassing an efficient domain adaptation strategy with domain-aware masking and model fine-tuning enhancements, and an end-to-end entity extraction tool, significantly advancing medical record curation and clinical workflows.
Radiomics in rectal cancer: current status of use and advances in research
Field: ONCOLOGY

Rectal cancer is a leading cause of morbidity and mortality among patients with malignant tumors in China. In light of the advances made in therapeutic approaches such as neoadjuvant therapy and total mesorectal excision, precise preoperative assessment has become crucial for developing a personalized treatment plan. As an emerging technology, radiomics has gained widespread application in the diagnosis, assessment of treatment response, and analysis of prognosis for rectal cancer by extracting high-throughput quantitative features from medical images. Radiomics thus demonstrates considerable potential for optimizing clinical decision-making. In this paper, we reviewed recent research focusing on advances in the use of radiomics for managing rectal cancer. The review covers TNM staging of tumors, assessment of neoadjuvant therapy outcomes, and survival prediction. We also discuss the challenges and prospects for future developments in translational medicine, particularly the need for data standardization, consistent feature extraction methodologies, and rigorous model validation.
Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review
Field: AI

Background and objective: Very preterm infants are highly susceptible to Neurodevelopmental Impairments (NDIs), including cognitive, motor, and language deficits. This paper presents a systematic review of the application of Machine Learning (ML) techniques to predict NDIs in premature infants.Methods: This review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions.Results: We identified 26 studies that fulfilled the inclusion criteria. In addition, we explore the potential of ML algorithms and discuss commonly used data sources, including clinical and neuroimaging data. Furthermore, the inclusion of omics data as a contemporary approach employed, in other diagnostic contexts is proposed.Conclusions: We identified limitations and emphasized the significance of employing multimodal data models and explored various alternatives to address the limitations identified in the reviewed studies. The insights derived from this review guide researchers and clinicians toward improving early identification and intervention strategies for NDIs in this vulnerable population.
Hispanic-serving HBCUs: towards an anti-colonial meso-relevant theory of organizational identity in sacred spaces of Black education
Field: EDUCATION

Introduction: This study addresses demographic changes at HBCUs and proposes an anti-colonial organizational framework for Historically Black emerging Hispanic Serving Institutions (HB-eHSIs, also referred to as Hispanic-serving HBCUs) to support both Black and Brown students while preserving the historic mission of HBCUs.Methods: We use qualitative methodology and rely on 45–60 minute semi-structured interviews with 15 faculty and administrators from three Historically Black emerging HSIs in Texas to develop the proposed organizational framework.Results: Findings are highlighted through four key tenets, each operationalized based on themes from extant literature and the practices and organizational logics of Black and Brown faculty and staff at HBeHSIs: 1. Tending to white settler colonialism, 2. Tending to fiscal precarity, 3. Tending to sacred spaces, 4. Tending to fallacious notions of essentialism.Discussion: The proposed framework aims to foster solidarity between Black and Brown students and challenge oppressive systems through a radically inclusive approach to serving both communities. Recommendations include reexamining leadership structures, forming coalitions, and creating consortiums to support HBCUs’ evolving needs and diverse student populations. Findings also emphasize the need for dual federal designation for HB-eHSIs to secure funding and legitimacy.
Exploring how course social and cultural environmental features influence student engagement in STEM active learning courses: a control–value theory approach
Field: EDUCATION

Background: Active learning, on average, increases student performance in STEM courses. Yet, there is also large variation in the effectiveness of these implementations. A consistent goal of active learning is moving students towards becoming active constructors of their knowledge. This emphasis means student engagement is of central importance. Thus, variation in student engagement could help explain variation in outcomes from active learning. In this study, we employ Pekrun’s Control–Value Theory to examine the impact of four aspects of course social and cultural environments on student engagement. This theory posits that social and cultural features of the course environment influence students’ appraisals of their ability to control their academic outcomes from the course and the value they see in those outcomes. Control and value in turn influence the emotions students experience in the course and their behaviors. We selected four features of the course environment suggested in the literature to be important in active learning courses: course goal structure, relevance of course content, students’ trust in their instructor, and perceived course competition. Results: We surveyed students in 13 introductory STEM courses. We used structural equation modeling to map how features of the course environment related to control, value, and academic emotions, as well as how control, value, and academic emotions influenced engagement. We found engagement was positively related to control and value as well as the emotion of curiosity. Engagement was negatively related to the emotion of boredom. Importantly, features of the course environment influenced these four variables. All features influenced control: goal structure, relevance, and instructor trust increased it, while competition decreased it. All features except competition were related positively to value. Relevance and instructor trust increased curiosity. Goal structure, relevance, and instructor trust all reduced boredom, while competition increased it. Conclusion: Overall, our study suggests that the way instructors structure the social and cultural environment in active learning courses can impact engagement. Building positive instructor–student relationships, reducing course competition, emphasizing mastery and the relevance of the course to students can all increase engagement in course activities.
Trifluridine/tipiracil regimen in combination with bevacizumab for metastatic colorectal cancer in the third line: an expert opinion
Field: ONCOLOGY

The prolongation of survival along with the preservation of quality of life, possibly avoiding harmful cumulative toxicities, is the primary therapeutic aim for patients with metastatic colorectal cancer (mCRC) in the third-line setting. Several therapeutic options are now available, although some differences across countries in drug approval and the optimal therapeutic sequencing associated with each peculiar patient subgroup represent a clinical challenge for oncologists. Among various options, the SUNLIGHT trial showed how the combination of trifluridine/tipiracil (FTD/TPI) with bevacizumab is effective with an easily manageable toxicity profile compared to FTD/TPI alone. Of note, the efficacy is confirmed independently from KRAS mutational status and also for patients who had breaks in anti-vascular endothelial growth factor (anti-VEGF) therapy. Herein, we describe the current state of the art in the landscape of treatments after the second progression in mCRC. Based on a critical review of the literature aimed to guide clinicians in their daily decision-making, we point out that the combination of FTD/TPI with bevacizumab produces a clinical benefit in unselected mCRC patients. Therefore, the FTD/TPI plus bevacizumab regimen can represent a new standard of care for the treatment of patients with refractory mCRC who have progressed after two lines of therapy.
Just-in-Time News: An AI Chatbot for the Modern Information Age
Field: AI

This study advances AI-powered news delivery by introducing an innovative chatbot capable of providing personalized news summaries and real-time event analysis. This approach addressed a critical gap identified through a comprehensive review of 52 AI chatbot studies. Unlike prior models limited to static information retrieval or predefined interactions, this chatbot harnesses generative AI and real-time data integration to deliver a dynamic and tailored news experience. Its unique architecture combines conversational AI, robotic process automation (RPA), a comprehensive news database (989,432 reports from 2342 sources spanning 27 October 2023 to 30 September 2024), and a large language model (LLM). Within this architecture, LLM generates dynamic queries against the News database for obtain tailored News for the users. Hence, this approach interprets user intent, and delivers LLM-based summaries of the fetched tailored news. Empirical testing with 35 users across 321 diverse news queries validated its robustness in navigating a combinatorial classification space of 53,916,650 potential news categorizations, achieving an F1-score of 0.97, recall of 0.99, and precision of 0.96. Deployed on Microsoft Teams and as a standalone web app, this research lays the foundation for transformative AI applications in news analysis, promising to revolutionize news consumption and empower a more informed citizenry.
Ocular Biometry OCR: a machine learning algorithm leveraging optical character recognition to extract intra ocular lens biometry measurements
Field: AI

Given close relationships between ocular structure and ophthalmic disease, ocular biometry measurements (including axial length, lens thickness, anterior chamber depth, and keratometry values) may be leveraged as features in the prediction of eye diseases. However, ocular biometry measurements are often stored as PDFs rather than as structured data in electronic health records. Thus, time-consuming and laborious manual data entry is required for using biometry data as a disease predictor. Herein, we used two separate models, PaddleOCR and Gemini, to extract eye specific biometric measurements from 2,965 Lenstar, 104 IOL Master 500, and 3,616 IOL Master 700 optical biometry reports. For each patient eye, our text extraction pipeline, referred to as Ocular Biometry OCR, involves 1) cropping the report to the biometric data, 2) extracting the text via the optical character recognition model, 3) post-processing the metrics and values into key value pairs, 4) correcting erroneous angles within the pairs, 5) computing the number of errors or missing values, and 6) selecting the window specific results with fewest errors or missing values. To ensure the models’ predictions could be put into a machine learning-ready format, artifacts were removed from categorical text data through manual modification where necessary. Performance was evaluated by scoring PaddleOCR and Gemini results. In the absence of ground truth, higher scoring indicated greater inter-model reliability, assuming an equal value between models indicated an accurate result. The detection scores, measuring the number of valid values (i.e., not missing or erroneous), were Lenstar: 0.990, IOLM 500: 1.000, and IOLM 700: 0.998. The similarity scores, measuring the number of equal values, were Lenstar: 0.995, IOLM 500: 0.999, and IOLM 700: 0.999. The agreement scores, combining detection and similarity scores, were Lenstar: 0.985, IOLM 500: 0.999, and IOLM 700: 0.998. IOLM 500 was annotated for ground truths; in this case, higher scoring indicated greater model-to-annotator accuracy. PaddleOCR-to-Annotator achieved scores of detection: 1.000, similarity: 0.999, and agreement: 0.999. Gemini-to-Annotator achieved scores of detection: 1.000, similarity: 1.000, and agreement: 1.000. Scores range from 0 to 1. While PaddleOCR and Gemini demonstrated high agreement, PaddleOCR offered slightly better performance upon reviewing quantitative and qualitative results.
Boy’s love fans versus non-fans in the sexual identity and neural response in the digital age’s young females
Field: PSYCHOLOGY

With the omnipresence of online social media, Boys’ Love (BL) culture has found a burgeoning audience among young females. However, we know very little about the audience of this online cultural phenomena, also the potential implications of BL culture to female remain under-explored. Study 1 conducted a survey to investigate the BL audience’s demography data and attitudes to homosexual ect. The results of the questionnaire analysis showed that the sexual orientation and psychological gender of the female BL audiences are more diverse. In addition, we also find the audience spend a lot of time on BL. Study 2 focused on the BL senior fans to explore the neural and behavioral response of female while looking at Boys’ Love(BL) stimuli and Heterosexual love stimuli by fNIRS. Behavioral results showed that there was no main effect of reaction time and accuracy between the BL-fans and non-BL-fans. Neural results confirmed that the Oxy-Hb responses for BL-love stimuli in BL-fans was significantly lower than the non-BL-fans. In addition, the interaction effect showed that the Oxy-Hb responses was significantly higher for BL-love stimuli than for heterosexual love stimuli in non-BL-fans, and no difference was found in BL-fans. This finding, maybe along with the discovery that the more pornography a person was exposed to, the higher the brain dopamine threshold, and the subsequent weakening of the neural response to sexual stimulation. The research leads to the conclusion that long term exposed to Boys’ Love may decrease the reward sensitivity to BL stimuli and weakens the brain’s response of the right ventrolateral prefrontal cortex (rVLPFC) to BL stimuli.
Identification of stress factors in returning migrants in Latvia
Field: PSYCHOLOGY

This study investigates the psychological stress factors faced by return migrants before, during, and after their return to Latvia. Employing a Grounded Theory methodology, we conducted in-depth interviews with 21 return migrants and identified five key themes: pre-return context, identity, perceived social support, psychological wellbeing, and factors that help or hinder re-adjustment. Notably, psychological stress prior to return often exceeds post-return stress, highlighting the critical yet understudied pre-return phase. Key contributors to return migration stress include unmet expectations, feelings of alienation, identity struggles, and inadequate institutional support. By highlighting these stress factors, this research not only enhances the understanding of return migration from a psychological standpoint but also lays the foundational groundwork for the development of a comprehensive theoretical framework that encompasses a broader spectrum of factors influencing return migration stress. The study advocates for a holistic approach to supporting return migrants, emphasizing the integration of psychological resources with practical assistance to foster successful reintegration into their home country.