Introducción: La calidad del sueño y el estrés académico son variables relevantes en la salud mental universitaria, debido a su impacto en el bienestar y el rendimiento académico. Aunque diversos estudios reportan asociación entre ambas, los resultados no son concluyentes en todos los contextos ni carreras profesionales. Objetivo: Determinar la correlación entre la calidad del sueño y el estrés académico en estudiantes de Derecho de una universidad privada de Trujillo, Perú, 2025. Metodología: Estudio de tipo básico, con enfoque cuantitativo, diseño no experimental, transversal y alcance correlacional. La muestra estuvo conformada por 51 estudiantes seleccionados mediante muestreo no probabilístico por conveniencia. La calidad del sueño se evaluó con el Pittsburgh Sleep Quality Index (PSQI) y el estrés académico con el Inventario SISCO SV-21. Se realizó análisis descriptivo mediante frecuencias y porcentajes. La normalidad se evaluó con Kolmogorov-Smirnov (p < .05), empleándose el coeficiente Rho de Spearman para el análisis correlacional (α = 0.05). Resultados: Predominaron niveles medios de calidad del sueño (50.0 %) y estrés académico (46.4 %). La correlación global fue positiva baja y no significativa (ρ = 0.223; p = 0.116). Se identificaron asociaciones significativas entre calidad del sueño y síntomas de estrés (ρ = 0.296; p = 0.035), y entre disfunción diurna y estrés académico total (ρ = 0.500; p < 0.001). Conclusiones: No se evidenció correlación global significativa; sin embargo, se observaron asociaciones en dimensiones específicas, especialmente en el plano sintomático y funcional.
Literature (General), French literature - Italian literature - Spanish literature - Portuguese literature
Contemporary automated scientific discovery has focused on agents for generating scientific experiments, while systems that perform higher-level scientific activities such as theory building remain underexplored. In this work, we formulate the problem of synthesizing theories consisting of qualitative and quantitative laws from large corpora of scientific literature. We study theory generation at scale, using 13.7k source papers to synthesize 2.9k theories, examining how generation using literature-grounding versus parametric knowledge, and accuracy-focused versus novelty-focused generation objectives change theory properties. Our experiments show that, compared to using parametric LLM memory for generation, our literature-supported method creates theories that are significantly better at both matching existing evidence and at predicting future results from 4.6k subsequently-written papers
Hita Kambhamettu, Bhavana Dalvi Mishra, Andrew Head
et al.
Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This requires iteratively reviewing literature and refining an idea based on what a researcher reads; yet when an idea changes, the literature that matters often changes with it. Most tools offer limited support for this interplay: literature tools help researchers understand a fixed body of work, while ideation tools evaluate ideas against a static, pre-curated set of papers. We introduce literature-initiated pivots, a mechanism where engagement with literature prompts revision to a developing idea, and where that revision changes which literature is relevant. We operationalize this in LitPivot, where researchers concurrently draft and vet an idea. LitPivot dynamically retrieves clusters of papers relevant to a selected part of the idea and proposes literature-informed critiques for how to revise it. A lab study ($n{=}17$) shows researchers produced higher-rated ideas with stronger self-reported understanding of the literature space; an open-ended study ($n{=}5$) reveals how researchers use LitPivot to iteratively evolve their own ideas.
Waqar Husain, A. Haddad, Muhammad Ahmad Husain
et al.
The Big Five Inventory (BFI) is a popular measure that evaluates personality on the Big-Five model. Apart from its utilization across cultures, the literature did not reveal any meta-analysis for the reliability of the different versions of the BFI and its translations. The current study carried out a reliability generalization meta-analysis (REGEMA) to establish the reliability of the BFI across cultures and languages. We searched 30 databases for the relevant studies from 1991 to mid-November 2024. The studies that we intended to include in our meta-analysis required to have utilized the BFI (44 items) and the BFI-2 (60 items) and have reported Cronbach's alpha or McDonald's omega reliability estimates. Our coded variables included BFI version, sample size, population type, age, gender, clinical state, and reliability. A total of 57 studies (datapoints) published in 34 research articles (involving 43,715 participants; 60.24% women; Mean age = 30.08) from various cultures and languages were finally included. These studies used BFI and BFI-2 in Arabic, Chinese, Croatian, Czech, Danish, Dutch, English, French, German, Indonesian, Italian, Japanese, Malay, Norwegian, Polish, Portuguese, Russian, Serbian, Spanish, Swahili, and Turkish. Data analysis was conducted using the metafor and meta packages in R. The average correlation was computed using a random-effects model and reliability coefficients indicated effect size. I2 and Cochran's Q tests were used to examine heterogeneity, with prediction intervals suggesting genuine influences around the pooled estimate. Using funnel plots, regression-based tests (e.g., Egger's regression, rank correlation), and trim-and-fill imputation, publication bias was adjusted to estimate unbiased effects. We calculated the individual and combined reliability of the BFI and BFI-2 across languages and cultures. The results revealed the reliability of all five factors used in the BFI/BFI-2. The BFI estimates provide the following results: openness is estimated at 0.77 (95% CI: 0.75; 0.80); conscientiousness is estimated at 0.80 (95% CI: 0.78; 0.82); extraversion is also estimated at 0.80 (95% CI: 0.79; 0.82); agreeableness is estimated at 0.73 (95% CI: 0.71; 0.76); and neuroticism is estimated at 0.80 (95% CI: 0.79; 0.82). The BFI-2 estimates are as follows: openness is estimated at 0.83 (95% CI: 0.82; 0.84); conscientiousness is estimated at 0.86 (95% CI: 0.85; 0.87); extraversion is estimated at 0.85 (95% CI: 0.84; 0.86); agreeableness is also estimated at 0.80 (95% CI: 0.79; 81); and neuroticism is estimated at 0.89 (95% CI: 0.88; 0.89). The current meta-analysis represents the first reliability analysis of the BFI and the first comparison between its two different versions, the BFI (44 items) and the BFI-2 (60 items). The generalized reliability of both the BFI and BFI-2 were established. The findings confirm that the BFI and BFI-2 have good reliability across all five factors.
Sarah Gomulinski, Vianney Gandillot, F. Valet
et al.
ABSTRACT Aims Understanding and integrating patients' preferences into clinical practice can enhance personalized care, improve patient's adherence to treatment, and lead to better therapeutic outcomes. The aim of this scoping review was to map the existing literature investigating patients' preferences in periodontal and implant therapy while identifying key areas for future research and development. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses for Scoping Reviews (PRISMA‐ScR) guidelines, an electronic search was conducted in four databases (PubMed, Google Scholar, Cochrane Library, and ScienceDirect) in July 2024 to identify studies evaluating patients' preferences for periodontal and implant therapy. Results The literature search yielded 384 studies, of which eight articles met the inclusion criteria. These studies were conducted between 2003 and 2019 in Brazil, China, Austria, Italy, Germany, Canada, USA, Chile, France, Spain, and Portugal. A total of 1642 patients were included. Preferences were assessed using various quantitative and mixed methodologies. Results indicate a strong preference for treatments aimed at preserving teeth, favoring conservative approaches. When teeth cannot be restored, most patients prefer an implant‐supported fixed partial denture to avoid damaging adjacent teeth with a conventional tooth‐supported fixed partial denture. In this context, treatment predictability is ranked as the most important factor. While no sociodemographic factors appeared to be associated with preferences in periodontal treatments, several predictors were identified for dental implant therapy. Younger patients, women, individuals with higher education levels, and those with high perceived dental health showed a higher willingness to pay for dental implants. Conclusion The literature on patients' preferences in periodontal and implant therapies is scarce. Several trends are identified but further longitudinal studies are needed to explore patients' preferences over time and the role of sociodemographic and cultural segmentation criteria.
Sérgio Santos-Silva, H. M. Gonçalves, W. H. van der Poel
et al.
Rat hepatitis E virus (rat HEV) is an emerging zoonotic virus detected in rodents worldwide, with increasing evidence of presence in environmental sources such as surface water, wastewater and bivalves. This systematic review compiles and analyzes all the published research on rat HEV contamination in these matrices, as well as its implications for human health. A comprehensive literature search was conducted using databases such as PubMed, Scopus, Web of Science, and Mendeley, including studies published up until 27 May 2025. Studies were included if they evaluated rat HEV in water- or food-related matrices using molecular detection. The risk of bias was not assessed. The certainty of evidence was not formally evaluated. Limitations include reliance on PCR methods without infectivity confirmation. Following PRISMA inclusion and exclusion criteria, eight eligible studies were analyzed. The results show high detection rates of rat HEV RNA in influent wastewater samples from several high-income European countries, namely Sweden, France, Italy, Spain and Portugal. Lower detection rates were found in effluent wastewater and surface waters in Sweden. In bivalve mollusks sampled in Brazil, rat HEV RNA was detected in 2.2% of samples. These findings show the widespread environmental presence of rat HEV, particularly in urban wastewater systems. While human infections by rat HEV have been documented, the true extent of rat HEV zoonotic potential remains unclear. Given the risks associated with this environmental rat HEV contamination, enhanced surveillance, standardized detection methods, and targeted monitoring programs in food production and water management systems are essential to mitigate potential public health threats. Establishing such programs will be crucial for understanding the impact of rat HEV on human health.
The wild boar (Sus scrofa) has experienced significant population growth as well as geographic expansion across Europe over the past 15 years, leading to increased concerns regarding its role in the transmission of zoonotic pathogens. Among these, Babesia spp. and Anaplasma spp. are of particular importance due to their impact on both wildlife and domestic animals. This study systematically reviews the prevalence and distribution of Babesia and Anaplasma spp. in wild boars and associated tick vectors across multiple European countries, synthesizing data from literature published between 2010 and 2024. A comprehensive search of Scopus, Google Scholar, and PubMed databases was conducted using predefined keywords related to babesiosis, anaplasmosis, wild boars, Europe, and tick-borne diseases. A total of 281 studies were initially retrieved, of which 19 met the inclusion criteria following relevance assessment. Data extraction focused on pathogen identification, diagnostic methods, sample type, host species, and prevalence rates. Molecular detection methods, primarily PCR and sequencing, were the most used diagnostic tools. Results indicate substantial regional variations in the prevalence of Babesia and Anaplasma spp. A. phagocytophilum was detected in wild boar populations across multiple countries, with the highest prevalence rates observed in Slovakia (28.2%) and Poland (20.34%). Conversely, lower prevalence rates were recorded in France (2%) and Portugal (3.1%). Babesia spp. showed higher prevalence rates in Italy (6.2%), while its detection in other regions such as Romania and Spain was minimal or absent. Notably, spleen and multi-organ samples (spleen/liver/kidney) exhibited higher positivity rates compared to blood samples, suggesting an organotropic localization of these pathogens. The findings underscore the role of wild boars as reservoirs for tick-borne pathogens and highlight their potential to contribute to the epidemiological cycle of these infections. The increasing distribution of wild boars, coupled with climate-driven shifts in tick populations, may further facilitate pathogen transmission. Future studies should focus on integrating molecular, serological, and ecological approaches to improve surveillance and risk assessment. Standardized methodologies across different regions will be essential in enhancing comparative epidemiological insights and informing targeted disease management strategies.
A. D’Addabbo, Raffaella Matarrese, F. Lovergine
et al.
Xylella fastidiosa (Xf) is a pathogenic bacterium which causes severe damage to plants and has been detected in various countries, including Italy, France, Portugal, Spain, Lebanon, Iran, and Israel. In Europe, the first outbreak was observed in olive plants in Apulia, Italy, in 2013. The ease of its transmission, coupled with its ability to remain latent within plants for extended periods, has facilitated its rapid expansion, causing severe damage to the regional olive industry. The early detection of Xf infections is therefore crucial for the containment of its spread and, thus, to minimize crop yield losses. Recent studies described in the literature have assessed the potential of remote sensing for monitoring Xf through applicable machine learning models. In particular, high-resolution hyperspectral and thermal remote sensing imageries acquired by airborne platforms have demonstrated an ability to detect the early symptoms of Xf infection in olive trees. However, further analyses are needed to address technical challenges and validate their effectiveness in vast areas. In this paper, we propose to answer some of these crucial questions, which are also relevant to the future task of setting up an operational system to detect Xf on a large scale. First, we assess whether the size of a data set, composed of a limited number of labelled examples, is sufficient to train accurate classifiers. Then, we evaluate whether a classifier that is trained on data from a specific area can detect infected trees in other places, which are potentially different in terms of cultivars and overall agricultural management. The obtained results demonstrate that with as few as 200 labelled data points (even unbalanced between the two classes of interest of “infected” and “not infected”), it is possible to train classifiers to support the detection of Xf, also across a wide area, obtaining overall classification accuracies greater than 74%.
O presente artigo propõe uma reflexão sobre as intersecções entre os elementos do insólito (Roas, 2014; Todorov, 1981; Trevisan, 2023), a Crítica Literária Feminista (Duarte, 1990; Gazolla, 1990; Trevisan; Zaratin; Faqueri, 2023) e os Estudos Comparados de Literatura (Coutinho, 2013). A análise parte do mapeamento de uma linhagem de autoria feminina na literatura contemporânea, especialmente a partir da segunda metade do século XX, cujas escritoras constroem protagonistas inseridas em universos ficcionais marcados pela misoginia e pela violência, tensionadas e ressignificadas pela presença do insólito. Busca-se, assim, evidenciar o diálogo entre essas produções e seus contextos histórico-sociais, observando de que modo os procedimentos estéticos da narrativa configuram uma vertente singular do insólito, reconhecida por alguns estudiosos como “insólito feminista”.
Literature (General), French literature - Italian literature - Spanish literature - Portuguese literature
Eleonora Cappuccio, Andrea Esposito, Francesco Greco
et al.
Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.
Malgré la récurrence du thème de l’art dans l’œuvre de Patrick Grainville, la critique s’est attachée à l’étude de son style, caractérisé par le baroquisme et le sensualisme. L’Atelier du peintre (1988), l’un de ses premiers textes consacrés à la peinture, ne fait pas exception, alors même que l’écriture représente un exemple parfait du bon usage des transpositions artistiques et qu’il s’inscrit dans une possible continuité du « roman de l’artiste » propre au XXIe siècle. Notre étude propose l’analyse détaillée de l’utilisation des référents picturaux (L’Atelier du peintre de Gustave Courbet, Les Arnolfini de Jan Van Eyck ainsi que le genre pictural de la Vanité) pour mettre en relief la richesse et la singularité du roman, au-delà des aspects habituellement remarqués. Une méthodologie comparant les relations et intersections du texte littéraire avec la peinture et l’histoire de l’art nous permettra de faire valoir la fonction structurante de ces références dans l’agencement et la résolution du noyau proprement narratif.
Philology. Linguistics, French literature - Italian literature - Spanish literature - Portuguese literature
The intersections of mental health and computing education is under-examined. In this systematic literature review, we evaluate the state-of-the-art of research in mental health and well-being interventions, assessments, and concerns like anxiety and depression in computer science and computing education. The studies evaluated occurred across the computing education pipeline from introductory to PhD courses and found some commonalities contributing to high reporting of anxiety and depression in those studied. In addition, interventions that were designed to address mental health topics often revolved around self-guidance. Based on our review of the literature, we recommend increasing sample sizes and focusing on the design and development of tools and interventions specifically designed for computing professionals and students.
AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-driven approaches. While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they can complement each other. To address this, we develop the first method that combines literature-based insights with data to perform LLM-powered hypothesis generation. We apply our method on five different datasets and demonstrate that integrating literature and data outperforms other baselines (8.97\% over few-shot, 15.75\% over literature-based alone, and 3.37\% over data-driven alone). Additionally, we conduct the first human evaluation to assess the utility of LLM-generated hypotheses in assisting human decision-making on two challenging tasks: deception detection and AI generated content detection. Our results show that human accuracy improves significantly by 7.44\% and 14.19\% on these tasks, respectively. These findings suggest that integrating literature-based and data-driven approaches provides a comprehensive and nuanced framework for hypothesis generation and could open new avenues for scientific inquiry.
The rapid growth of research in Pattern Analysis and Machine Intelligence (PAMI) has rendered literature reviews essential for consolidating and interpreting knowledge across its many subfields. In this work, we present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions: (i) identifying structural and statistical regularities in existing surveys; (ii) developing quantitative strategies that help researchers navigate and prioritize within the expanding review corpus; and (iii) critically assessing emerging AI-generated review systems. To support this study, we construct RiPAMI, a large-scale database containing more than 3,000 review articles, and combine narrative synthesis with statistical analysis to capture structural and content-level features. Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices. Building on these insights, we propose practical, article-level strategies for indicator-guided navigation that move beyond simple citation counts. Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization, yet also highlights enduring weaknesses in reference retrieval, coverage of recent work, and the incorporation of visual elements. Together, these findings provide both a critical appraisal of existing review practices and a forward-looking perspective on how AI-generated reviews can evolve into trustworthy, customizable, and transformative complements to traditional human-authored surveys.
In this paper, we propose a method to automatically classify AI-related documents from large-scale literature databases, leading to the creation of an AI-related literature dataset, named DeepDiveAI. The dataset construction approach integrates expert knowledge with the capabilities of advanced models, structured across two global stages. In the first stage, expert-curated classification datasets are used to train an LSTM model, which classifies coarse AI related records from large-scale datasets. In the second stage, we use Qwen2.5 Plus to annotate a random 10% of the coarse AI-related records, which are then used to train a BERT binary classifier. This step further refines the coarse AI related record set to obtain the final DeepDiveAI dataset. Evaluation results demonstrate that the entire workflow can efficiently and accurately identify AI-related literature from large-scale datasets.
Davide Garassino, Vivana Masia, Nicola Brocca
et al.
This paper aims to provide a comparison between texts produced by French and Italian politicians on polarizing issues, such as immigration and the European Union, and their chatbot counterparts created with ChatGPT 3.5. In this study, we focus on implicit communication, in particular on presuppositions and their functions in discourse, which have been considered in the literature as a potential linguistic feature of manipulation. This study also aims to contribute to the emerging literature on the pragmatic competences of Large Language Models.
In the dynamic landscape of contemporary business, the wave in data and technological advancements has directed companies toward embracing data-driven decision-making processes. Despite the vast potential that data holds for strategic insights and operational efficiencies, substantial challenges arise in the form of data issues. Recognizing these obstacles, the imperative for effective data governance (DG) becomes increasingly apparent. This research endeavors to bridge the gap in DG research within the Operations and Supply Chain Management (OSCM) domain through a comprehensive literature review. Initially, we redefine DG through a synthesis of existing definitions, complemented by insights gained from DG practices. Subsequently, we delineate the constituent elements of DG. Building upon this foundation, we develop an analytical framework to scrutinize the collected literature from the perspectives of both OSCM and DG. Beyond a retrospective analysis, this study provides insights for future research directions. Moreover, this study also makes a valuable contribution to the industry, as the insights gained from the literature are directly applicable to real-world scenarios.