Search Results (2,699)

Topic modeling in digital news faces the dual challenge of thematic overlap and evolving semantic boundaries, especially in morphologically rich languages like Turkish. To address these obstacles, we propose a topic modeling framework enhanced with knowledge graphs that explicitly incorporates uncertainty in topic assignment. We focus on the diversity of Fuzzy Latent Semantic Analysis (FLSA) and compare the performance with Latent Dirichlet Allocation (LDA), BERTopic, and embedding-based Top2Vec on a corpus drawn from two Turkish news agencies. We evaluate each model using standard metrics for topic coherence, diversity, and interpretability. We propose Shannon entropy of node-degree distributions to measure the network complexity of knowledge graphs as topic similarity. Our results indicate that FLSA achieves perfect topic diversity, 1.000 and improved interpretability, 0.33 over LDA, 0.09 while also enhancing coherence, 0.33 vs. 0.27. Top2Vec demonstrates the strongest coherence, 0.81 and interpretability, 0.78 with high diversity, 0.97, reflecting its capacity to form semantically cohesive clusters. Entropy analysis further shows that FLSA produces the most information-rich topic networks. These findings suggest that fuzzy modeling and embedding-based approaches offer complementary strengths, uncertainty-aware flexibility, and semantic precision, thereby improving topic discovery in complex, unstructured news environments. Full article

Heparin, an essential plasma-derived therapy, acts as a naturally occurring anticoagulant and is essential in various physiological processes. Due to its complex structure, repeating units of sulfated glycosaminoglycan, it attracts attention in the field of commercial pharmaceuticals. In recent decades, significant advancements have been made in the development of economical adsorbents designed especially for the extraction of heparin from the intestinal mucosa of pigs, as evidenced by investments from various pharmaceutical industries. This requirement arises from the demand for efficient, scalable extraction methods for natural sources. In this study, we investigated the application of beta zeolites to increase the recovery of heparin from real porcine mucosa samples, emphasizing materials with greater adsorption surfaces, higher thermal stability, and increased porosity. According to our research, the zeolite CP814E’s macropores and huge surface area allow it to adsorb up to 20.6 mg·g⁻¹ (39%) of heparin from actual mucosa samples. We also investigated the adsorbent’s surface conditions, which are essential for efficient heparin recovery, and adjusted temperature and pH to enhance heparin uptake. These findings demonstrate that zeolite-based adsorbents can enhance the extraction of heparin effectively for use in medicinal applications. Full article

N-methyl-D-aspartate receptors (NMDARs) are ionotropic glutamate channels that play a pivotal role in brain development and the regulation of learning and memory processes. De novo pathogenic variants in four genes encoding NMDA receptor subunits (GRIN1, GRIN2A, GRIN2B, and GRIN2D) have been implicated in a broad spectrum of neurodevelopmental disorders, including developmental delay, intellectual disability, autism spectrum disorders, epilepsy, and movement disorders. Mutations in the GRIN1 and GRIN2B genes, which encode the GluN1 and GluN2B subunits, respectively, are strongly associated with malformations of cortical development, including diffuse dysgyria, bilateral polymicrogyria, hippocampal dysplasia, corpus callosum hypoplasia, and other findings such as ventricular enlargement and basal ganglia abnormalities. Conversely, GRIN2A mutations are associated with heterogeneous and less specific neuroimaging patterns. We reviewed the existing literature on the neuroradiological features associated with GRIN gene mutations, also providing pictorial representations from our patient cohort. The analysis revealed a more consistent association of malformations of cortical development with GRIN1 and GRIN2B variants, likely reflecting the critical role of these genes in neuronal migration and proper development of cortical structures. In comparison, GRIN2A mutations are associated with milder brain abnormalities. An integrated assessment of neuroimaging patterns and GRIN gene variants provides valuable insights for differential diagnosis and supports targeted genetic screening in patients presenting with epileptic encephalopathy, global developmental delay, and autism spectrum disorders. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article

Explaining the relative timing of consonant and vowel articulations (C-V timing) is an important function of speech production models. This article explores how C-V timing might be studied from the perspective of the C/D Model, particularly the prediction that articulations are coordinated with respect to an abstract syllable pulse. Gestural landmarks were extracted from kinematic data from English CVC monosyllabic words in the Wisconsin X-Ray Microbeam Corpus. The syllable pulse was identified using velocity peaks, and temporal lags were calculated among landmarks and the syllable pulse. The results directly follow from the procedure used to identify pulses: onset consonants exhibited stable timing to the pulse, while vowel-to-pulse timing was comparably stable with respect to C-V timing. Timing relationships with jaw displacement and jaw-based syllable pulse metrics were also explored. These results highlight current challenges for the C/D Model, as well as opportunities for elaborating the model to account for C-V timing. Full article

In general, Slovene clitics occur in the second, so-called Wackernagel position of the clause. However, Slovene is exceptional among Wackernagel languages in that the clitic cluster may also occupy the clause-initial position. Imperative sentences have been argued to form an exception to this exception, again allowing the clitic cluster only in the second position. In this paper, we present corpus data that speaks against this second-order exception. We categorize the imperative clauses containing initial clitic clusters found in the corpora into three classes: modally subordinated imperatives, imperatives containing the adversative or the concessive particle, and imperatives occuring as a step in an instruction. We argue that all three classes involve a covert anaphoric element residing in the clause-initial position, yielding an illusion of a clause-initial clitic cluster. In conclusion, initial clitic clusters in Slovene imperatives are not ungrammatical but merely uncommon, and their distribution is ultimately governed by the discourse. We also make a theoretical point, emphasizing that the presented analysis offers support to the view that all discursive information must be represented in syntax. Full article

Analyzing the status of food production in Qinghai Province and exploring the nexus between its ecological conservation and food supply are of critical significance. This study systematically synthesizes the evolution of ecological protection policies in Qinghai Province from 2000 to 2020 and delineates the spatiotemporal evolutionary patterns of grain production in Qinghai Province and their underpinning driving factors. The key findings are as follows. (1) From 2000 to 2020, the corpus of policies governing ecological governance measures in Qinghai Province exhibited a sustained growth trend, with management-oriented policies predominating. (2) The primary grain and meat-producing regions in Qinghai Province are predominantly clustered in the northeastern part, displaying a gradual intensification of concentration. From 2000 to 2020, grain production showed an upward trajectory in the northern region and a downward trend in the southern region, whereas meat production exhibited an ascending trend in both the northern and western regions. (3) Agricultural production conditions represent the principal drivers of grain and meat production in Qinghai Province. Specifically, two driving factors—common cultivated area and total power of agricultural machinery—have exerted significant positive effects on grain and meat production across over 30 counties. Ecological protection conditions have manifested heterogeneous effects across different regions of Qinghai Province; the normalized difference vegetation index (NDVI) has exerted a negative influence on grain and meat production in the eastern region while exerting a positive influence in the western region. Full article

Recent studies have greatly furthered our understanding of the Southern Bantu languages, but questions about the internal relationships of the Southern Bantu language subgroups and the validity of the clade as a whole still remain. This study attempts to reconstruct the consonant inventory of one proposed genetic clade, that of Tsonga-Copi (S50–S60). Using published dictionaries and reference works for each language of the subgrouping, a corpus of cognate vocabulary was assembled. Each term was then matched, where possible, to a reconstruction in the Bantu Lexical Reconstructions 3 (BLR3) database. Sound correspondences were identified and used to reconstruct the consonant inventory of Proto-Tsonga-Copi. In addition to the discovery of several typologically unusual sound changes, the results of this study also lend support to existing and developing hypotheses about both the internal relationships of Southern Bantu clades, as well as the nature of language contact in (pre)historic Southern Africa, particularly the influence of Khoisan and other Bantu languages. Full article

Developing a machine translator from a Korean dialect to a foreign language presents significant challenges due to a lack of a parallel corpus for direct dialect translation. To solve this issue, this paper proposes a pivot-based machine translation model that consists of two sub-translators. The first sub-translator is a sequence-to-sequence model with minGRU as an encoder and GRU as a decoder. It normalizes a dialect sentence into a standard sentence, and it employs alphabet-level tokenization. The other type of sub-translator is a legacy translator, such as off-the-shelf neural machine translators or LLMs, which translates the normalized standard sentence to a foreign sentence. The effectiveness of the alphabet-level tokenization and the minGRU encoder for the normalization model is demonstrated through empirical analysis. Alphabet-level tokenization is proven to be more effective for Korean dialect normalization than other widely used sub-word tokenizations. The minGRU encoder exhibits comparable performance to GRU as an encoder, and it is faster and more effective in managing longer token sequences. The pivot-based translation method is also validated through a broad range of experiments, and its effectiveness in translating Korean dialects to English, Chinese, and Japanese is demonstrated empirically. Full article

Background: Scientific evidence plays a critical role in informing public health decision-making processes. However, the extent, nature, and effectiveness of its use remain uneven across contexts. Despite the increasing volume of literature on the subject, previous syntheses have often suffered from narrow thematic, temporal, or geographic scopes. Objectives: This study undertook a comprehensive systematic literature review spanning 50 years to (i) synthesise current knowledge on the use of scientific evidence in public health decisions, (ii) identify key determinants, barriers, and enablers, (iii) evaluate implementation patterns, and (iv) propose future directions for research and practice. Methods: We adopted the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Moreover, we researched three large databases (Web of Science, Embase, and PubMed), and this study focused on articles published in the English and French languages between January 1974 and December 2024. Studies were analysed thematically and descriptively to identify trends, patterns, and knowledge gaps. Results: This review reveals a growing corpus of scholarship with a predominance of qualitative studies mainly published in public health journals. Evidence use is most frequently analysed at the national policy level. Analyses of the evolution of scientific production over time revealed significant shifts beginning as early as 2005. Critical impediments included limited access to reliable and timely data, a lack of institutional capacity, and insufficient training among policy-makers. In contrast, enablers encompass cross-sector collaboration, data transparency, and alignment between researchers and decision-makers. Conclusions: Addressing persistent gaps necessitates a more nuanced appreciation of interdisciplinary and contextual factors. Our findings call for proactive policies aimed at promoting the use of scientific evidence by improving the accessibility of health data (addressing the absence or lack of data, as well as its reliability, timeliness, and accessibility), and by training decision-makers in the use of scientific evidence for decision making. Furthermore, our findings advocate for better alignment between the agendas of healthcare professionals (e.g., data collection), researchers (e.g., the selection of research topics), and decision-makers (e.g., expectations and needs) in order to develop and implement public health policies that are grounded in and informed by scientific evidence. Full article

This eye tracking study examined the role of lexical frequency in the processing of non-local verbal number agreement by heritage speakers of Spanish. Few prior studies of heritage bilingualism have investigated the role of word frequency in the comprehension or production of morphosyntax, and none have employed a real-time measure of sentence processing, despite the well-known sensitivity of such methods to word frequency and the proposal of some scholars that such online methodologies could be particularly useful in research on heritage speakers. Fifty heritage speakers of Spanish read stimulus sentences containing non-local verbal number agreement that depended on a verb that was either high or low frequency, based on published corpus data. The results suggest that the online integration of verbal agreement was both more immediate and more robust with high frequency verbs than with low frequency verbs. Moreover, an analysis of individual language background variables indicates that faster reading was associated with greater sensitivity to verbal agreement with low frequency verbs. These findings are consistent with theoretical claims that lexical frequency can play an important role in the morphosyntax of heritage speakers, due to reduced exposure to the home language and, particularly, low frequency words. Full article

Aspect-based sentiment analysis (ABSA) plays a pivotal role in understanding the nuances of sentiment expressed in text, particularly in the context of diverse languages and cultures. This paper presents a novel deep convolutional neural network (CNN)-based model tailored for aspect and polarity classification in Hausa movie reviews, as Hausa is an underrepresented language with limited resources and presence in sentiment analysis research. One of the primary implications of this work is the creation of a comprehensive Hausa ABSA dataset, which addresses a significant gap in the availability of resources for sentiment analysis in underrepresented languages. This dataset fosters a more inclusive sentiment analysis landscape and advances research in languages with limited resources. The collected dataset was first preprocessed using Sci-Kit Learn to perform TF-IDF transformation for extracting feature word vector weights. Aspect-level feature ontology words within the analyzed text were derived, and the sentiment of the reviewed texts was manually annotated. The proposed model combines convolutional neural networks (CNNs) with an attention mechanism to aid aspect word prediction. The model utilizes sentences from the corpus and feature words as vector inputs to enhance prediction accuracy. The proposed model leverages the advantages of the convolutional and attention layers to extract contextual information and sentiment polarities from Hausa movie reviews. The performance demonstrates the applicability of such models to underrepresented languages. With 91% accuracy on aspect term extraction and 92% on sentiment polarity classification, the model excels in aspect identification and sentiment analysis, offering insights into specific aspects of interest and their associated sentiments. The proposed model outperformed traditional machine models in both aspect word and polarity prediction. Through the creation of the Hausa ABSA dataset and the development of an effective model, this study makes significant advances in ABSA research. It has wide-ranging implications for the sentiment analysis field in the context of underrepresented languages. Full article

Optimal treatment for non-critically ill multisystem inflammatory syndrome in children (MIS-C) remains unclear. We evaluated short-term outcomes in mild to moderately ill hospitalized MIS-C patients fulfilling CDC 2020 and CDC/CTSE 2023 criteria and treated between April 2020 and March 2022 with either intravenous immunoglobulin (IVIG) monotherapy (Group A, n = 17) or IVIG plus corticosteroids (GC) (Group B, n = 22). Cardiovascular clinical parameters, inflammatory markers, and cardiac imaging were compared on days 1, 3, and 5 relative to day 0. The two groups had no significant differences in demographics or illness severity. Group B showed improvement in heart rate (17.8; 95% CI [9.74, 25.8]), mean blood pressure (5.63 [1.61, 9.64]), and body temperature (1.45 [0.94, 1.95]) by day 1, followed by improvement in albumin (0.43 [0.2, 0.84]), CRP (7.56 [3.0, 12.11]), D-dimer (2344 [488.7, 4200.2]), ferritin (1448 [−609.4, 3505.5]), fibrinogen (110 [44.4, 176]), lymphocyte count (1006 [63.5, 1948]), and NT-proBNP (2901 [−349.3, 6153]) by day 3 and left ventricular ejection fraction by day 4–5 (3.84 [0.55, 8.23]). All results were statistically significant (p < 0.05). Group A required more additional therapies, with no difference in hospital stay. Our study concludes that combined IVIG and GC therapy yielded better short-term outcomes than IVIG monotherapy in this patient population, with improvement in cardiovascular clinical parameters preceding changes in inflammatory markers and cardiac imaging. Full article

This study examines Xuanzang’s methodology for translating the Yogācārabhūmi into Chinese, with particular focus on his translation of passages explaining the central concept of volition (cetanā). Through comparative analysis of Chinese and Tibetan translations—particularly passages for which Sanskrit parallels are not available—this paper investigates textual divergences and interpretative challenges in the two translations. Comprehensive examination of textual evidence across the Yogācārabhūmi corpus confirms that a problematic term in Xuanzang’s Chinese translation—suiyu—authentically reflects the Sanskrit source text, specifically corresponding to the Sanskrit term anupradāna. This allows us greater insight into Xuanzang’s translational strategy and its reception among his disciples. While previous scholarship has traditionally emphasized Xuanzang’s strict fidelity to Sanskrit grammatical structures, this study reveals a more sophisticated approach: he employed suiyu as a translation of anupradāna specifically for technical discussions of consciousness and mental factors, but adopted more idiomatic renderings of anupradāna in general contexts. However, the interpretations of suiyu among his disciples suggest that even this careful methodology sometimes failed to achieve its intended clarity, highlighting the inherent tension between preserving original textual features and ensuring accurate semantic transmission—a fundamental challenge in cross-cultural Buddhist transmission that continues to shape our understanding of Buddhist traditions. Full article