Search Results (654)

This study evaluates digital object identifier (DOI) hallucination in large language model (LLM)-generated scholarly citations, with a focus on systematic geographic disparities. To conduct this study, we systematically evaluated four LLMs (GPT-4o-mini, Claude-3-haiku, Gemini-2.0-flash-lite, and DeepSeek V3) using standardized information behavior prompts across ten countries with diverse income levels. The models generated 3451 citations, which we validated using the CrossRef API. The results showed that DOI hallucination follows systematic patterns influenced by model choice, geographic context, and publication recency. Hallucination rates exceeded 80% in lower-income countries and increased sharply for publications from the 2020s across all regions. Fabricated citations—citations that appear structurally complete but contain invalid DOIs—were especially prevalent in countries such as India and Bangladesh. Model-specific factors showed the strongest association with hallucination, followed by income level and publication period. These findings raise concerns about the epistemic reliability of LLM-generated scholarly references and underscore the need for region-aware training, real-time DOI validation, and robust verification protocols in academic contexts. Full article

This study evaluates the relationship between language shift and linguistic change in multigenerational immigrant communities, focusing on North American Norwegian (NAmNo) and German heritage varieties. The research synthesizes current findings on how language shift impacts linguistic structures in moribund heritage varieties. Methods include a qualitative review of diachronic studies, comparing data from different periods to assess changes in tense morphology, language mixing, compositional definiteness, possessive placement, verb placement, argument placement, and phoneme variation. Results indicate that the last generation of heritage speakers demonstrates increased linguistic innovation and variation compared to earlier generations. Key findings show that language shift leads to different input quality and quantity, affecting grammatical stability. The study concludes that sociocultural changes, such as verticalization and domain-specific language use, significantly influence heritage language maintenance and loss. These insights contribute to understanding the dynamics of language shift and its role in heritage language change, offering valuable comparative perspectives across different immigrant communities. Full article

The current agent-based evolutionary models for animal communication rely on simplified signal representations that differ significantly from natural vocalizations. We propose a novel agent-based evolutionary model based on text-to-audio (TTA) models to generate realistic animal vocalizations, advancing from VAE-based real-valued genotypes to TTA-based textual genotypes that generate bird songs using a fine-tuned Stable Audio Open 1.0 model. In our sexual selection framework, males vocalize songs encoded by their genotypes while females probabilistically select mates based on the similarity between males’ songs and their preference patterns, with mutations and crossovers applied to textual genotypes using a large language model (Gemma-3). As a proof of concept, we compared TTA-based and VAE-based sexual selection models for the Blue-and-white Flycatcher (Cyanoptila cyanomelana)’s songs and preferences. While the VAE-based model produces population clustering but constrains the evolution to a narrow region near the latent space’s origin where reconstructed songs remain clear, the TTA-based model enhances the genotypic and phenotypic diversity, drives song diversification, and fosters the creation of novel bird songs. Generated songs were validated by a virtual expert using the BirdNET classifier, confirming their acoustic realism through classification into related taxa. These findings highlight the potential of combining large language models and TTA models in agent-based evolutionary models for animal communication. Full article

This contribution provides new data on Italian first language acquisition and phonological development in preschool children. In total, 104 3- to 6;4-year-old typically developing Italian children were tested with two novel nonword tasks tackling the Italian consonantal system: one for repetition (NWR) and one for discrimination (NWD). NWR data were analyzed in terms of repetition accuracy, featural characteristics, and phonological processes, while NWD was analyzed according to signal detection theory (i.e., A-prime and d-prime) and in terms of discrimination accuracy. The results show the significant role of age on children’s repetition and discrimination abilities: as the children grow older, all the scores improve and the number of errors declines. No complete overlap is found between what children can produce and what they can discriminate, which is in line with what has already been documented in other languages. The findings contribute to the state of the art on the Italian language and provide new perspectives on some methodological issues specific to this language. Full article

While traditional ensemble methods have dominated tabular intrusion detection systems (IDSs), recent advances in foundation models present new opportunities for enhanced cybersecurity applications. This paper presents a comprehensive multi-modal evaluation of foundation models—specifically TabPFN (Tabular Prior-Data Fitted Network), TabICL (Tabular In-Context Learning), and large language models—against traditional machine learning approaches across three cybersecurity datasets: CIC-IDS2017, N-BaIoT, and CIC-UNSW. Our rigorous experimental framework addresses critical methodological challenges through model-appropriate evaluation protocols and comprehensive assessment across multiple data variants. Results demonstrate that foundation models achieve superior and more consistent performance compared with traditional approaches, with TabPFN and TabICL establishing new state-of-the-art results across all datasets. Most significantly, these models uniquely achieve non-zero recall across all classes, including rare threats like Heartbleed and Infiltration, while traditional ensemble methods—despite achieving >99% overall accuracy—completely fail on several minority classes. TabICL demonstrates particularly strong performance on CIC-IDS2017 (99.59% accuracy), while TabPFN maintains consistent performance across all datasets, suggesting robust generalization capabilities. Both foundation models achieve these results using only fractions of the available training data and requiring no hyperparameter tuning, representing a paradigm shift toward training-light, hyperparameter-free adaptive IDS architectures, where TabPFN requires no task-specific fitting and TabICL leverages efficient in-context adaptation without retraining. Cross-dataset validation reveals that foundation models maintain performance advantages across diverse threat landscapes, while traditional methods exhibit significant dataset-specific variations. These findings challenge the cybersecurity community’s reliance on tree-based ensembles and demonstrate that foundation models offer superior capabilities for next-generation intrusion detection systems in IoT environments. Full article

The main purposes of the present paper are to present the mathematical and algorithmic aspects of the ANM/FEM numerical model and to show how it is applied to analyze elastic and thermo-elastic nonlinear solid mechanical problems. ANM is a robust continuation method based on a perturbation technique for solving nonlinear problems dependent on a loading parameter. Historically, this technique has been successfully applied to problems in various fields of solid and fluid mechanics. This paper shows how ANM is used to solve nonlinear elastic and nonlinear thermo-elastic problems involving elastic behavior and geometrical nonlinearities. The implementation of ANM for FEM in the FreeFEM++ language is then presented. The FEM software development platform, called FreeFEM++, is structured to work with variational formulations and, therefore, is well adapted to implement ANM for instability problems in solid mechanics. In order to illustrate the great efficiency of FreeFEM++, scripts will be presented for computing the different steps of ANM continuation for solid elastic structures, considering simple geometries subjected to conservative loading. For the purpose of validation, the problem of a cantilever subjected to an applied force is presented. Next, the new numerical model is applied to study wrinkles appearing in a planar film/substrate system that is subjected to compressive surface forces at the lateral faces of the film. Finally, the model is applied to a spherical film/substrate system subjected to thermo-elastic shrinkage. In both cases, the ANM/FEM prediction method, together with a Newton–Riks correction (if needed), identifies the equilibrium paths efficiently, especially after the post-buckling regime. Full article

Meaning conflation deficiency (MCD) presents a continual obstacle in natural language processing (NLP), especially for low-resourced and morphologically complex languages, where polysemy and contextual ambiguity diminish model precision in word sense disambiguation (WSD) tasks. This paper examines the optimisation of contextual embedding models, namely XLNet, ELMo, BART, and their improved variations, to tackle MCD in linguistic settings. Utilising Sesotho sa Leboa as a case study, researchers devised an enhanced XLNet architecture with specific hyperparameter optimisation, dynamic padding, early termination, and class-balanced training. Comparative assessments reveal that the optimised XLNet attains an accuracy of 91% and exhibits balanced precision–recall metrics of 92% and 91%, respectively, surpassing both its baseline counterpart and competing models. Optimised ELMo attained the greatest overall metrics (accuracy: 92%, F1-score: 96%), whilst optimised BART demonstrated significant accuracy improvements (96%) despite a reduced recall. The results demonstrate that fine-tuning contextual embeddings using MCD-specific methodologies significantly improves semantic disambiguation for under-represented languages. This study offers a scalable and flexible optimisation approach suitable for additional low-resource language contexts. Full article

Synthetic aperture radar (SAR), with its all-weather and all-day active imaging capability, has become indispensable for geoscientific analysis and socio-economic applications. Despite advances in deep learning–based object detection, the rapid and accurate detection of small objects in SAR imagery remains a major challenge due to their extremely limited pixel representation, blurred boundaries in dense distributions, and the imbalance of positive–negative samples during training. Recently, vision–language models such as Contrastive Language-Image Pre-Training (CLIP) have attracted widespread research interest for their powerful cross-modal semantic modeling capabilities. Nevertheless, their potential to guide precise localization and detection of small objects in SAR imagery has not yet been fully exploited. To overcome these limitations, we propose the CLIP-Driven Adaptive Tiny Object Detection Diffusion Network (CDATOD-Diff). This framework introduces a CLIP image–text encoding-guided dynamic sampling strategy that leverages cross-modal semantic priors to alleviate the scarcity of effective positive samples. Furthermore, a generative diffusion-based module reformulates the sampling process through iterative denoising, enhancing contextual awareness. To address regression instability, we design a Balanced Corner–IoU (BC-IoU) loss, which decouples corner localization from scale variation and reduces sensitivity to minor positional errors, thereby stabilizing bounding box predictions. Extensive experiments conducted on multiple SAR and optical remote sensing datasets demonstrate that CDATOD-Diff achieves state-of-the-art performance, delivering significant improvements in detection robustness and localization accuracy under challenging small-object scenarios with complex backgrounds and dense distributions. Full article

Background/Objectives: This systematic review aimed to gather the most recent evidence regarding the link between genetic polymorphisms and physical performance in team sports, with a focus on the practical utility of this information for athlete selection, training personalization, and injury prevention. Methods: Sixteen studies published between 2018 and 2025 were analyzed and selected from six international databases, in accordance with the PRISMA guideline. Only English-language studies were included, which evaluated active athletes in team sports and investigated associations between genetic variations, such as Actinin Alpha 3 (ACTN3 R577X), Angiotensin I Converting Enzyme (ACE I/D), Peroxisome Proliferator-Activated Receptor Alpha (PPARA), Interleukin 6 (IL6), and Nitric Oxide Synthase 3 (NOS3), and physical performance parameters. The methodological quality of the studies was assessed using the Q-Genie tool, with all studies scoring over 45 across all 11 items, indicating high quality. Results: The ACTN3 and ACE genes stood out due to their consistent association with traits such as strength, speed, endurance, and recovery capacity. Other genes, such as PPARA, Fatty Acid Amide Hydrolase (FAAH), Angiotensinogen (AGT), and NOS3, complemented this genetic profile by being involved in the regulation of energy metabolism and injury predisposition. An increasing number of studies have begun to adopt cumulative genotype scores, suggesting a shift from a monogenic approach to complex predictive models. Conclusions: The integration of genetic profiling into the evaluation and management of athletes in team sports is becoming increasingly relevant. Although current evidence supports the applicability of these markers, robust future research conducted under standardized conditions is necessary to validate their use in sports practice and to ensure sound ethical standards. Full article

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement. Full article

The dialects discussed in this article were considered Belarusian in the early 20th century, and later, as a result of the transfer of the administrative (state) border, they became part of the Russian territory and were considered Russian. The changes occurring in these dialects as a result of the influence of the standard Russian language are interesting from various perspectives. Firstly, the linguistic self-identification of dialect speakers changes and the perception of their dialect as less prestigious compared to the standard language is formed. Secondly, linguistic features that dialectologists previously defined as characteristic of the Belarusian language are being replaced by standard Russian ones. By analyzing the linguistic data obtained from the dialect speakers of different generations, we can trace the emergence of variation and then its loss. Observing which linguistic features are subject to change first, and which remain more stable, allows us to examine linguistic changes through the lens of the “hierarchy of borrowings” theory. Additionally, given the linguistic inequality between the dialect and the standard language, we can observe the gradual transformation of the dialect under the influence of the prestigious standard idiom. Therefore, the loss of Belarusian–Russian variation can be viewed as a process of dedialectization, bringing the dialect closer to the standard language. Full article

The extensive deployment of diverse sensors in hospitals has resulted in the collection of various medical time-series data. However, these real-world medical time-series data suffer from limited volume, poor data quality, and privacy concerns, resulting in performance degradation in downstream tasks, such as medical research and clinical decision-making. Existing studies provide generated medical data as a supplement or alternative to real-world data. However, medical time-series data are inherently complex, including temporal data such as laboratory measurements and static event data such as demographics and clinical outcomes, with each patient’s temporal data being influenced by their static event data. This intrinsic complexity makes the generation of high-quality medical time-series data particularly challenging. Traditional methods typically employ Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), but these methods struggle to generate high-quality static event data of medical time-series data and often lack interpretability. Currently, large language models (LLMs) introduce new opportunities for medical data generation, but they face difficulties in generating temporal data and have challenges in specific domain generation tasks. In this study, we are the first to propose an LLM-based framework for modularly generating medical time-series data, QAMT, which generates quality-assured data and ensures the interpretability of the generation process. QAMT constructs a reliable health knowledge graph to provide medical expertise to the LLMs and designs dual modules to simultaneously generate static event data and temporal data, constituting high-quality medical time-series data. Moreover, QAMT introduces a quality assurance module to evaluate the generated data. Unlike existing methods, QAMT preserves the interpretability of the data generation process. Experimental results show that QAMT can generate higher-quality time-series medical data compared with existing methods. Full article

The Trait Emotional Intelligence Questionnaire (TEIQue) is a tool that has been examined in a number of cultural and language variations in an effort to validate it across a range of demographics. The aim of this study is to test the robustness of the TEIQue-Child Short Form’s reliability and validity using a Greek-speaking sample. As a result, seven factors emerge from the analysis, explaining 52.4% of the variance in total. The first factor is named “emotional regulation”, the second factor is named “sociability”, and the third factor is named “positive mood”. The fifth factor is named “low impulsivity”, while the fourth, sixth, and seventh factors are named “lack of persistence”, “emotion perception”, and “adaptability”, respectively. The reliability indices of the factors “emotional regulation”, “sociability”, “positive mood”, “low impulsivity”, and “emotion perception” are all above 70, indicating acceptable reliability. The reliability indices of the factors “lack of persistence” and “adaptability” are almost at acceptable levels (α = 0.69). In conclusion, it seems that the Trait Emotional Intelligence Questionnaire (TEIQue) has undergone extensive validation across diverse linguistic and cultural populations, consistently demonstrating strong psychometric properties, and the TEIQue-CSF is a valid and reliable tool. Full article

Background: Bovine bone-derived xenografts are widely used in regenerative dental procedures due to their osteoconductive properties and volumetric stability. However, their long-term behavior and biological integration remain a subject of debate. This systematic review aims to critically assess the histological and clinical outcomes associated with bovine xenografts over extended follow-up periods. Methods: An electronic search was performed in PubMed, Scopus, and Web of Science, including studies published in the English language from 2005 to 2025 for a total of 217 records, which were initially identified from PubMed, Scopus, and Wos. Results: After duplicate removal, following title/abstract screening and full-text evaluation, 11 studies met the inclusion criteria. These studies reported on the use of bovine-derived xenografts in clinical contexts, assessing parameters such as graft integration, histological remodeling, complication incidence (e.g., chronic inflammation or foreign body reactions), and implant success rates over time. Conclusions: The current evidence suggests that bovine-derived xenografts, particularly Bio-Oss^®, are associated with favorable long-term outcomes in bone regenerative procedures, demonstrating satisfactory graft integration and implant survival rates. However, variations in study design, follow-up duration, and outcome measures warrant further high-quality, long-term randomized clinical trials to confirm these findings and guide clinical decision-making. Full article

Phytoplankton plays a pivotal role in marine ecosystems and global biogeochemical cycles. Accurate identification and monitoring of phytoplankton are essential for understanding environmental dynamics and climate variations. Despite the significant progress made in automatic phytoplankton identification, current datasets predominantly consist of idealized laboratory images, leading to models that demonstrate persistent limitations in the fine-grained differentiation of phytoplankton species. To achieve high accuracy and transferability for morphologically similar species and diverse ecosystems, we introduce a hybrid dataset by integrating laboratory-based observations with in situ marine environmental data. We evaluate the performance of our dataset on contemporary deep learning models, revealing that CNN-based architectures offer superior stability (85.27% mAcc., 93.76% oAcc.). Multimodal learning facilitates refined phytoplankton recognition through the integration of visual and textual representations, thereby enhancing the model’s semantic comprehension capabilities. We present a fine-tuned visual language model leveraging enhanced textual prompts augmented with expert-annotated morphological descriptions, significantly enhancing visual-semantic alignment and allowing for more accurate and interpretable recognition of closely related species (84.11% mAcc., 94.48% oAcc.). Our research establishes a benchmark dataset that facilitates real-time ecological monitoring and aquatic biodiversity research. Furthermore, it also contributes to the field by enhancing model robustness and transferability to diverse environmental contexts and taxonomically similar species. Full article