Search Results (1,065)

The widespread dissemination of fake news across different domains exerts a negative impact on social order. Current fake news detection models face two major challenges. First, the issue of domain shift constrains the generalization capability of models in cross-domain scenarios. Second, general neural networks struggle to extract features between distant words in text, resulting in poor quality of original features and adversely affecting the final detection outcomes. In response to the aforementioned issues, this paper proposes a multi-domain fake news detection framework based on multi-view hybrid attention enhancement. Firstly, superior original feature extraction is achieved through Recurrent Convolutional Neural Networks (RCNN) and Bidirectional Long Short-Term Memory (BiLSTM). Secondly, a hybrid attention mechanism is established between features and domains across multiple views—including news semantics, sentiment, and style—thereby forming domain-specific memory. This enables the model to achieve more precise classification of news within specific, subdivided domains. Finally, experiments conducted on the public dataset Weibo21 demonstrate that the proposed method attains F1 scores of 93.26% and 85.31% on Chinese and English datasets. Full article

Along with some known and less known results, we discuss new insights relating combinatorics of words and the ordering of rationals from a dynamical systems point of view, somehow continuing along the path started in previous works of the first author. We obtain in particular a set of results that structure and enrich the correspondence between the Stern–Brocot (SB) ordering of rational numbers and the corresponding ordering of Farey–Christoffel (FC) words; a class of words that, since their appearance in the literature at the end of the 18th century, have revealed numerous relationships with other fields of mathematics. Among the results obtained here is the construction of substitution rules that act on the FC words in a parallel way to the maps on the positive reals that generate the permuted SB tree both vertically and horizontally. We further show that these rules naturally induce a map of the space of (infinite) Sturmian sequences into itself. Finally, a complete correspondence is obtained between the vertical and horizontal motions on the SB tree and the geodesic motions along scattering geodesics and the horocyclic motion along Ford circles in the upper half-plane, respectively. Full article

Corporate reputation teams rely on media monitoring and qualitative research, both limited in speed and coverage when digital narratives form rapidly. This paper proposes SAPIENT (Sentinel-Augmented Population Intelligence for Emerging Narrative Tracking), a multi-agent system that links a sentinel layer over public text streams with a simulation layer that runs moderated, repeatable in silico focus-group sessions. The sentinel layer ingests social media, news, and forum text to produce a compact signal state (topics, sentiment, anomaly scores, risk labels), which conditions the simulation layer through an orchestrator. Persona agents and a moderator follow an Agentic Focus Group (AFG) protocol with repeated runs, variance reporting, and human review gates. We describe four sustainability communication scenarios: greenwashing backlash prediction, greenhushing risk assessment, campaign pre-testing, and crisis communication simulation. Nine experiments span 280 AFG runs across 20 conditions, three LLM backends (Claude Sonnet 4, GPT-4o, and Gemini 2.5 Flash), and a preregistered pilot human validation study with 54 participants. Signal conditioning improved simulation specificity ($p=0.012$). Cross-lingual sessions revealed a sentiment asymmetry between English and Turkish ($p=0.001$) with preserved persona rank ordering ($r=0.81$, $p=0.015$). Cross-model comparison showed consistent persona differentiation across all three backends (Pearson $r>0.92$, $p<0.002$ for all pairs). Sentiment was robust to prompt paraphrasing ($p=0.061$, n.s.), though credibility was sensitive to prompt wording ($p<0.001$). All significant results from Experiments 1–8 survived Benjamini–Hochberg correction. A preregistered pilot with 54 human participants on Prolific replicated the predicted credibility ranking across framing variants ($p=0.004$) but not the sentiment ranking, identifying a specific calibration target for future work. Full article

During the early years of interaction between humans and computer systems, user authentication and identification was carried out with the support of knowledge-based factors (something the user knows: passwords, PINs, etc.) and tokens (something the user possesses: credentials, RFID cards, etc.) or a combination of both. In other words, the user presents a token and a password to the system in order to gain access. These solutions pose major challenges: Knowledge-based systems, which rely on secrets like passwords, are vulnerable to those secrets being guessed, shared, or forgotten. On the other hand, tokens are also vulnerable; some, despite implementing encryption, attract cyber attackers who can forge them, and users can share or lose them. In the search for more robust methods, the use of biometrics has been considered. Full article

Background/Objectives: We investigated whether ingestion of caffeine (~1 h before) was beneficial to subsequent morning (07:30 h), mood, strength and cognitive measures. Methods: Fourteen recreationally active males were recruited and completed six sessions: (i) one repetition maximum (1RM) for bench press and back squat; (ii) two familiarization sessions of strength measures; (iv) three experimental conditions administered in a double-blinded, randomized counterbalanced design order, either caffeine (Caffeine [CAFF], 300 mg or 2.8–4.3 mg/kg body weight), placebo (Placebo [PLAC]) ingested at 06:30 h, or no-pill control (No Pill [NoPill]). For each experimental session, on arrival at the laboratory, rectal and skin temperature were measured as well as a battery of cognitive performance through a battery of tests (trail-making test, Rey’s auditory verbal learning test, and Stroop word–colour interference test). Thereafter, maximum voluntary contraction on an isometric chair (MVC) without and with stimulation was conducted, and three repetitions were performed at 40, 60 and 80% of 1RM for bench press and back squat. Average power (AP), average velocity (AV), peak velocity (PV), mean propulsive velocity (MPV), average acceleration (RDV), displacement (D) and time-to-peak velocity (tPV) were recorded using MuscleLab linear encoders. Rating of perceived exertion and effort was asked after each set (RPE). The data was analysed using a general linear model with repeated measures. Results: MVC peak-force values with and without stimulation showed a significant increase in the CAFF condition compared to values for NoPill and with stimulation PLAC conditions (stim: Δ9.0 and 8.7%; no stim: 8.3%; p < 0.05; η²_p = 0.33 and 0.42). Greater muscle % activation was achieved for the CAFF than the other conditions (~6%, p ≤ 0.042; η²_p = 0.33). In the non-stimulated MVC, RPE was perceived as easier (4.8%, p = 0.04). AV and MPV values were higher in both bench press (Δ3.3 and 4.6%) and back squat (Δ7.7 and 9.2%) in CAFF than the PLAC condition (p = 0.031; η²_p = 0.24 and 0.23 and 0.24 and 0.32). CAFF improved auditory total recall compared to NoPill (9.5%, p = 0.040; η²_p = 0.22). Conclusions: Early morning ingestion of caffeine improved MVC to levels observed by others in the evening, as well as some aspects of bench press, back squat and recall performance. Caffeine ingestion had no effect on core temperature, mood, tiredness, alertness or other measures of cognitive performance. Full article

Infected URLs are always regarded as a serious threat to cybersecurity, serving as pathways to phishing, maliciousness, and other offenses. Although transformer-based models have demonstrated good performance in malicious URL detection, their high computational cost and latency make them impractical for deployment in real-time or resource-constrained systems. Allocated on the basis of knowledge distillation (KD), lightweight models tend to be efficient but are commonly not sufficiently discriminative to distinguish between malicious and benign URLs with non-cataclysmic lexical overlaps, particularly when dealing with an imbalanced dataset. In order to address these issues, we propose Contra-KD, a lightweight transformer model that incorporates contrastive learning (CL) and KD. This proposed framework imposes structured embedding matching, allowing the student model to learn more meaningful and generalized depictions. Contra-KD uses a compact 6-layer student transformer architecture based on ELECTRA to scale parameters up and can achieve more than 90% computational fidelity with a high accuracy. In this scheme, CL improves the feature of discrimination by semantically clustering similar URLs and separating different URLs. This tendency serves to limit confusion, especially when a common lexical trait is held between two words and/or in the presence of adversarial obfuscation. Through a large-scale publicly available Kaggle dataset of 651,191 URLs in imbalanced scenarios, the proposed Contra-KD can achieve 99.05% accuracy, 99.96% ROC-AUC, and 98.18% MCC which are superior to their counterparts including lightweight models and transformer-based ones. To summarize, Contra-KD proposes an efficient transformer architecture that is both small and effective in computation while delivering stable detection performance. Full article

Introduction: It is estimated that up to 75% of pregnant women complain of dyspnea at some point during pregnancy. Asthma is the most common chronic pulmonary disease complicating pregnancy. Well controlled asthma does not affect pregnancy negatively. However, asthma exacerbations are linked with several adverse perinatal outcomes. As diligent treatment of asthma significantly reduces the number of asthma exacerbations, it is important to properly detect asthmatic patients among pregnant women in order to provide them with better care. The most efficient way to diagnose asthma is to perform spirometry with a reversibility test. There are no studies that have examined the safety of performing spirometry and, more specifically, a reversibility test, during pregnancy. Objectives: In this systematic review we aimed to review current available data regarding the safety of performing spirometry and a reversibility test during pregnancy. Patients and methods: For this systematic review, we searched PubMed, Scopus and Cochrane databases. We used the following search terms: (pregnancy); (spirometry); (lung function test); (pulmonary function test); (reversibility test); (post-bronchodilator challenge); (safety). Results: We collected reports of spirometry performed on pregnant women and analyzed them for complications that occurred during the procedure. Out of 13,594 records identified for the aforementioned search words, we included 78 documents that met the inclusion criteria. In total, the studies consisted of over 33,405 spirometry attempts performed by 10,617 pregnant women. Additionally, the reversibility test was conducted in nine studies. In all of the selected articles, there were no reports of adverse events occurring while performing spirometry. Conclusions: In this systematic review we aimed to summarize the current available data about the safety of performing spirometry during pregnancy. Several studies have investigated pulmonary function tests during pregnancy. No studies reported any adverse events that occurred while performing the procedure. In order to better characterize the safety profile of spirometry, including during pregnancy, further prospective studies systematically reporting on adverse symptoms during spirometry are required. Full article

Atmospheric Rivers (ARs) are filamentary moisture pathways responsible for a large fraction of extreme precipitation and often occur as interacting filament bundles within the same synoptic regime. Existing diagnostics typically analyze ARs in isolation, despite the frequent coexistence and interaction of multiple filaments. We introduce a topological framework for AR analysis based on framed braids and framed braidoids, which encodes both the geometric interaction of AR centroids and the internal evolution of moisture transport. In this approach, AR filaments are represented as strands whose time-ordered crossings form braid words, while moisture-based framing captures internal intensification or weakening along each filament. Applying this framework to reanalysis-derived Atmospheric River track data, we construct braid and framed braid representations over sliding time windows and analyze a strongly interacting multi-filament AR episode in the North Pacific. The results show that braid-based indicators capture structural reorganizations and moisture intensification episodes that are not apparent from centroid geometry or IVT magnitude alone, offering a complementary structural perspective on atmospheric moisture transport. Full article

The creation and application of classification category labels are essential tasks for transforming complex information into structured knowledge. Categories are used for summary and reporting purposes and have historically been identified by domain experts based on their past experiences and norms. Our interest lies in the general case where expert-generated category lists require improvement, and unsupervised learning, on its own, struggles to effectively identify categories for multi-class classification of human-generated texts. We hypothesise that including an annotated knowledge graph (KG) in an embedding process will positively impact unsupervised clustering performance. Our goal is to identify clusters that can be labelled and used for classification. We look at unsupervised clustering of Maintenance Work Order (MWO) texts. MWOs capture vital observations about equipment failures in process and heavy industries. The selected KG contains a mapping of equipment types to their inherent function based on the IEC 81346-2 international standard for classification of objects in industrial systems. Performance is assessed by statistical analysis, subject matter experts, and Normalized Mutual Information score. We demonstrate that Word2Vec Bi-LSTM and Sentence-BERT NN embedding methods can leverage equipment inherent function information in the KG to improve failure mode cluster identification for the MWO. Organisations seeking to use AI to automate assignment of a failure mode code to each MWO currently need test sets classified by humans. The results of this work suggest that a semantic layer containing a knowledge graph mapping equipment types to inherent function, and inherent function to failure modes could assist in quality control for automated failure mode classification. Full article

Terms are specialized words and expressions used in particular disciplines, cultures, or fields. They usually carry precise meanings and aim to describe referents accurately and clearly. Due to differences in culture, history, and other factors across countries, the development of indigenous Chinese linguistic terms plays a vital role in bridging cultural gaps and promoting the dissemination of Chinese culture. These terms not only explain specific words in Chinese and describe unique linguistic phenomena, but also embody the core concepts and academic traditions of Chinese linguistics, thereby contributing to the global spread and development of Chinese civilization. In order to achieve cross-linguistic dissemination of indigenous terms, we construct a linguistically informed bilingual corpus encompassing a broad spectrum of linguistic subfields, together with novel methods for the automatic extraction and cross-linguistic alignment of terminologies. The resulting corpus contains over 22,000 aligned sentence pairs across nine linguistic domains, providing a robust foundation for bilingual term mining. Building upon this resource, we further propose a multi-channel graph neural network (MCGNN) that jointly models semantic, syntactic, sequential, and co-occurrence relations, thereby enabling multi-perspective reasoning and achieving more accurate bilingual term extraction and alignment. Experimental results demonstrate that our approach substantially improves the accuracy and consistency of bilingual term extraction, alleviates the resource scarcity in the linguistic domain, and provides a solid foundation for future research and applications in cross-linguistic knowledge sharing and academic communication. Full article

Liquid is a fundamental requirement for life as we understand it, but whether that liquid has to be water is not known. We propose the hypothesis that ionic liquids (ILs) and deep eutectic solvents (DES) constitute a class of non-aqueous planetary liquids capable of persisting on a wide range of bodies where stable liquid water cannot exist. This hypothesis is motivated by key physical properties of ILs and DES. Many exhibit vapor pressures orders of magnitude lower than that of water and remain liquid across exceptionally wide temperature ranges, from cryogenic to well above terrestrial temperatures. These properties permit stable liquids to exist where liquid water would rapidly evaporate or freeze and outside of bulk phases as persistent microscale reservoirs—such as thin films and pore-filling droplets. In other words, ILs and DES can persist in environments without requiring oceans, thick atmospheres, or narrowly regulated climate conditions. We further hypothesize that ILs and DES could act as solvents for non-Earth-like life, based on their polar nature and the demonstrated stability and functionality of proteins and other biomolecules in ionic liquids. More speculatively, our hypothesis extends to the idea that ILs and DES could enable prebiotic chemistry by providing long-lived, protective liquid environments for complex organic molecules on bodies such as comets and asteroids, where liquid water is absent. Additionally, based on the occurrence of DES-like mixtures as protective intracellular liquids in desiccation-tolerant plants, we propose that ILs and DES might be solvents that life elsewhere purposefully evolves. We review protein and other biomolecule studies in ILs and DES and outline planetary environments in which ILs and DES might occur by discussing available anions and cations. We present strategies to advance the IL/DES solvent hypothesis using laboratory studies, computational chemistry, planetary missions, analysis of existing spectroscopic datasets, and modeling of liquid microniches and chemical survival on small bodies. Full article

Amidst rapid urbanization and modernization, numerous traditional villages in China face severe challenges, including landscape homogenization and the erosion of their distinctive characteristics. Addressing this issue requires a method capable of systematically identifying, analyzing, and reconstructing both the landscape and its underlying cultural features. This study proposes a digital analytical approach that integrates multimodal artificial intelligence with landscape language theory to address the homogenization of cultural landscapes in traditional Chinese villages. Taking Xinye Village in Zhejiang Province as a case study, the research systematically decodes its landscape spatial narratives and underlying cultural genes. This framework systematically deconstructs village landscapes across four levels: “vocabulary, context, grammar, and semantics”. The village image database is first automatically recognized and statistically analyzed by computer vision technology, which extracts 31 core landscape vocabulary items from three main categories and nine subcategories. Second, Retrieval-augmented Generation technology is employed to synthesize from the constructed domain-specific corpus, a natural context structured around Yuhua Mountain and Daofeng Mountain, as well as a cultural context based on ancestral hall order, connected through folk activities, and idealized by farming and reading passed down through generations. Building on this framework, a multimodal model was used to examine the spatial composition and combinatorial laws of landscape features. Six essential dimensions—spatial layout, visual order, element combination, functional relationships, circulation layout, and scale correlations—revealed the spatial grammar of shuikou landscape. Lastly, the semantic values conveyed by the landscape vocabulary were thoroughly analyzed across three dimensions—form, function, and culture—by integrating a knowledge base. This work creates a landscape language atlas of Xinye Village by combining these studies and using a linguistic model of “character-word-sentence-paragraph”. By methodically deciphering the clan’s cultural code of “farming and reading passed down through generations”, this clearly reconstructs the spatial narrative logic from micro-elements to macro-patterns. This research not only advances the study of landscape language in traditional villages from qualitative description toward a systematic, digital, and interpretable paradigm but also provides an operational theoretical and methodological foundation for the in-depth interpretation, conservation, and transmission of traditional village cultural landscapes. Full article

When extracting entities, relations, and their associated words from scientific literature, it is imperative to consider the supporting role of feature words on the extraction results. These feature words can provide local semantic information and be combined with the global feature representation of the sentence, improving the accuracy of information extraction. However, existing methods, when fusing local semantic feature words with global features, due to ineffective distinction between the influence of feature words and non-feature words, result in limited enhancement on model performance. To solve this problem, we propose a feature words adversarial scheme (FWAS) with dual pointer method. This method implements a dynamic filtering mechanism for feature words through feature pointers, in order to semantically enhance the encoding of the original text. Simultaneously, an inverse feature pointer is designed to establish a negative weight decay mechanism, weakening interference of non-key vocabulary. During joint training, annotation information for entity relations is introduced to supervise the dual feature selection mechanism. Experimental results on three public scientific information extraction datasets demonstrate that our method consistently outperforms strong baselines, achieving up to 4.9% improvement in F1-score. This method offers a new perspective for information extraction tasks in scientific and technical literature and provides scalable optimization directions for subsequent research. Full article

Sinitic, often referred to simply as ‘Chinese’, is a well-differentiated major branch of the Sino-Tibetan family, further divided into ten commonly recognized groups (Mandarin, Jin, Wu, Gan, Xiang, Hui, Hakka, Yue, Min, and Pinghua), identified mainly on the basis of phonological criteria. Sinitic as a whole stands out for being typologically quite distant from the rest of Sino-Tibetan (i.e., the so-called ‘Tibeto-Burman’ languages). Sinitic languages overwhelmingly possess verb-medial basic constituent order and isolating/analytic morphology, while Tibeto-Burman languages are dominantly verb-final and exhibit more complex and varied morphological profiles. Moreover, the Sinitic languages themselves show a considerable degree of internal variation, involving aspects such as word order, morphology, and grammaticalization patterns, among others. The development of Sinitic has often been driven by contact, both within the family and with unrelated (non-Sinitic) languages. For instance, Northern Sinitic shows ‘Altaic’ features due to contact with Mongolic, Turkic, and Tungusic languages, while Southern Sinitic is closer to the Mainland Southeast Asian areal type due to contact with Tai-Kadai, Hmong-Mien, and Mon-Khmer. We also find Sinitic varieties in the Northwest possessing basic verb-final order and postposed markers of case and evidentiality, again due to contact (with Mongolic and Tibetic), as well as other areas of convergence, which contribute to the complexity of the typology of Sinitic. Full article

This article adopts a comparative mythology framework in order to situate creation myths within a broad cultural context. It examines how different societies conceptualize the emergence of the universe through the interconnected notions of thought and word. The study demonstrates that, despite cultural diversity, these narratives articulate shared principles concerning the mental and linguistic foundations of existence while preserving tradition-specific expressions. The analysis is based on qualitative content analysis of primary mythological texts drawn from Hindu, Maori, Maya, Maiana, Dogon, Polynesian, Ancient Egyptian, and Turkish traditions, encompassing sources ranging from the Rig Veda and the Popol Vuh to the theology of Ptah and Dogon doctrines of word-based creation. These materials were examined through hermeneutic reading practices and comparatively evaluated using concept-oriented analytical categories. The findings indicate that cosmogonic myths operate beyond mere narrative description by structuring coherent models of creation in which cognitive intention and verbal articulation play constitutive roles. Full article