Search Results (365)

This conceptual review article explores the intersection of multimodal writing and multilingualism in a contemporary educational context, with a focus on both secondary and post-secondary classrooms. As digital tools, media platforms, and global communication in interconnected spaces reshape literacy practices, students increasingly communicate and express themselves through a range of modes—visual, audio, textual, and gestural—often in more than one language. This article argues for reimagining and reconceptualizing writing to be a multifaceted literacy practice that integrates multimodal digital tools and that invites multilingual literacy opportunities. Drawing on classroom examples and current research on multimodal writing and translanguaging practices in multilingual spaces, the article explores how educators can support students in developing critical literacy skills through multimodal projects that honor linguistic diversity, cultural identity, and multiple means of expression. The article offers practical strategies for scaffolding multimodal writing in multilingual space, creating inclusive literacy environments where multilingualism and multimodality are seen as a resource, not a barrier. Full article

Background/Objectives: Artificial intelligence (AI) is gaining importance in clinical pharmacology, supporting therapeutic decisions and the prediction of drug interactions, although its applications have significant limitations. The aim of the study was to evaluate the accuracy of the responses of four large language models (LLMs), namely ChatGPT-4o, ChatGPT-3.5, Gemini Advanced 2.0, and DeepSeek, in the field of clinical pharmacology and drug interactions, as well as to analyze the impact of prompting and questions from the National Specialization Examination for Pharmacists (PESF) on the results. Methods: In the analysis, three datasets were used: 20 case reports of successful pharmacotherapy, 20 reports of drug–drug interactions, and 240 test questions from the PESF (spring 2018 and autumn 2019 sessions). The responses generated by the models were compared with source data and the official examination key and were independently evaluated by clinical-pharmacotherapy experts. Additionally, the impact of prompting techniques was analyzed by expanding the content of the queries with detailed clinical and organizational elements to assess their influence on the accuracy of the obtained recommendations. Results: The analysis revealed differences in the accuracy of responses between the examined AI tools (p < 0.001), with ChatGPT-4o achieving the highest effectiveness and Gemini Advanced 2.0 the lowest. Responses generated by Gemini were more often imprecise and less consistent, which was reflected in their significantly lower level of substantive accuracy (p < 0.001). The analysis of more precisely formulated questions demonstrated a significant main effect of the AI tool (p < 0.001), with Gemini Advanced 2.0 performing significantly worse than all other models (p < 0.001). An additional analysis comparing responses to simple and extended questions, which incorporated additional clinical factors and the mode of source presentation, did not reveal significant differences either between AI tools or within individual models (p = 0.34). In the area of drug interactions, it was also shown that ChatGPT-4o achieved a higher level of response accuracy compared with the other tools (p < 0.001). Regarding the PESF exam questions, all models achieved similar results, ranging between 83 and 86% correct answers, and the differences between them were not statistically significant (p = 0.67). Conclusions: AI models demonstrate potential in the analysis of clinical pharmacology; however, their limitations require further refinement and cautious application in practice. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

This paper employs allegory to examine how pupils experience exclusion in mathematics education. Using Dante’s Inferno as a structural frame, I present nine fictional narratives aligned with the nine circles of Hell. These depict recurring learner experiences: displacement, disorientation, mechanical drill, grade-chasing, resistance, doubt, internalised failure, performance without understanding, and withdrawal. The narratives are not verbatim accounts but constructed stories synthesising themes from research, classroom practice, and observed discourse. Through narrative inquiry, each story reframes issues such as language barriers, high-stakes assessment, proceduralism, and stereotype threat—not as individual shortcomings but systemic conditions shaping learner identities. The allegorical mode makes these conditions vivid, positioning mathematics education as a moral landscape where inclusion and exclusion are continually negotiated. The analysis yields three insights: first, forms of exclusion are diverse yet interconnected, often drawing pupils into cycles of silence, resistance, or performance; second, metaphor and fiction can serve as rigorous research tools, allowing affective and structural dimensions of schooling to be understood together; and third, teacher education and policy must confront the hidden costs of privileging narrow forms of knowledge. Reimagining classrooms through Dante’s allegory, this paper calls for pedagogies that disrupt exclusion and open pathways to belonging and mathematical meaning. Full article

Fresnel wave surfaces, or isofrequency light shells, provide a powerful framework for describing electromagnetic wave propagation in anisotropic media, yet their applicability is restricted to reciprocal, lossless materials and far-field radiation. This paper extends the concept by incorporating near-field effects and non-Hermitian responses arising in media with loss, gain, or non-reciprocity. Using the Om-potential approach to macroscopic electromagnetism, we reinterpret near fields as off-shell electromagnetic modes, in analogy with off-shell states in quantum field theory. Formally, both QFT off-shell states and electromagnetic near-field modes lie away from the dispersion shell; physically, however, wavefunctions of fundamental particles admit no external sources (virtual contributions live only inside propagators), whereas macroscopic electromagnetic near-fields are intrinsically source-generated by charges, currents, and boundaries and are therefore directly measurable—for example via near-field probes and momentum-resolved imaging—making “off-shell” language more natural and operational in our setting. We show that photonic density of states (PDOS) distributions near Fresnel surfaces acquire Lorentzian broadening in non-reciprocal media, directly linking this effect to the Beer–Bouguer–Lambert law of exponential attenuation or amplification. Furthermore, we demonstrate how Abraham and Minkowski momenta, locked to light shells in the far field, naturally shift to characterize source structures in the near-field regime. This unified treatment bridges the gap between sources and radiation, on-shell and off-shell modes, and reciprocal and non-reciprocal responses. The framework provides both fundamental insight into structured light and practical tools for the design of emitters and metamaterial platforms relevant to emerging technologies such as 6G communications, photonic density-of-states engineering, and non-Hermitian photonics. Full article

This study explores and models the use of MATLAB technology in multimodal learning interactions to address the challenges of teaching and learning statistics in a multinational postgraduate classroom. The term multimodal refers to the deliberate integration of multiple representational and interaction modes, i.e., visual, textual, symbolic, and interactive computational modelling, within a coherent instructional design. MATLAB is utilised as it is a comprehensive tool for enhancing students’ understanding of statistical skills, practical applications, and data analysis—areas where traditional methods often fall short. International postgraduate students were chosen for this study because their diverse educational backgrounds present unique learning challenges. A qualitative case study design was employed, and data collection methods included classroom observations, interviews, and student work analysis. The collected data were analysed and modelled by conceptualising key elements and themes using thematic analysis, with findings verified through data triangulation and expert review. Emerging themes were structured into models that illustrate multimodal teaching and learning interactions. The novelty of this research lies in its contribution to multimodal teaching and learning strategies for multinational students in statistics education. The findings highlight significant challenges international students face, including language and technical barriers, limited prior content knowledge, time constraints, technical difficulties, and a lack of independent thinking. To address these challenges, MATLAB promotes collaborative learning, increases student engagement and discussion, boosts motivation, and develops essential skills. This study suggests that educators integrate multimodal interactions in their teaching strategies to better support multinational students in statistical learning environments. Full article

Electronic health records (EHRs) are typically stored in relational databases, making them difficult to query for nontechnical users, especially under privacy constraints. We evaluate two practical clinical NLP workflows, natural language to SQL (NL2SQL) for EHR querying and retrieval-augmented generation for clinical question answering (RAG-QA), with a focus on privacy-preserving deployment. We benchmark nine large language models, spanning open-weight options (DeepSeek V3/V3.1, Llama-3.3-70B, Qwen2.5-32B, Mixtral-8 × 22B, BioMistral-7B, and GPT-OSS-20B) and proprietary APIs (GPT-4o and GPT-5). The models were chosen to represent a diverse cross-section spanning sparse MoE, dense general-purpose, domain-adapted, and proprietary LLMs. On MIMICSQL (27,000 generations; nine models × three runs), the best NL2SQL execution accuracy (EX) is 66.1% (GPT-4o), followed by 64.6% (GPT-5). Among open-weight models, DeepSeek V3.1 reaches 59.8% EX, while DeepSeek V3 reaches 58.8%, with Llama-3.3-70B at 54.5% and BioMistral-7B achieving only 11.8%, underscoring a persistent gap relative to general-domain benchmarks. We introduce SQL-EC, a deterministic SQL error-classification framework with adjudication, revealing string mismatches as the dominant failure (86.3%), followed by query-join misinterpretations (49.7%), while incorrect aggregation-function usage accounts for only 6.7%. This highlights lexical/ontology grounding as the key bottleneck for NL2SQL in the biomedical domain. For RAG-QA, evaluated on 100 synthetic patient records across 20 questions (54,000 reference–generation pairs; three runs), BLEU and ROUGE-L fluctuate more strongly across models, whereas BERTScore remains high on most, with DeepSeek V3.1 and GPT-4o among the top performers; pairwise t-tests confirm that significant differences were observed among the LLMs. Cost–performance analysis based on measured token usage shows per-query costs ranging from USD 0.000285 (GPT-OSS-20B) to USD 0.005918 (GPT-4o); DeepSeek V3.1 offers the best open-weight cost–accuracy trade-off, and GPT-5 provides a balanced API alternative. Overall, the privacy-conscious RAG-QA attains strong semantic fidelity, whereas the clinical NL2SQL remains brittle under lexical variation. SQL-EC pinpoints actionable failure modes, motivating ontology-aware normalization and schema-linked prompting for robust clinical querying. Full article

The increasing integration of artificial intelligence agents (AIAs) based on large language models (LLMs) is transforming many spheres of society. These agents act as human assistants, forming Distributed Intelligent Systems (DISs) and engaging in opinion formation, consensus-building, and collective decision-making. However, complex DIS network topologies introduce significant uncertainty into these processes. We propose a quantum-inspired graph signal processing framework to model collective behavior in a DIS interacting with an external environment represented by an influence matrix (IM). System topology is captured using scale-free and Watts–Strogatz graphs. Two contrasting interaction regimes are considered. In the first case, the internal structure fully aligns with the external influence, as expressed by the commutativity between the adjacency matrix and the IM. Here, a renormalization-group-based scaling approach reveals minimal reservoir influence, characterized by full phase synchronization and coherent dynamics. In the second case, the IM includes heterogeneous negative (antagonistic) couplings that do not commute with the network, producing partial or complete spectral disorder. This disrupts phase coherence and may fragment opinions, except for the dominant collective (Perron) mode, which remains robust. Spectral entropy quantifies disorder and external influence. The proposed framework offers insights into designing LLM-participated DISs that can maintain coherence under environmental perturbations. Full article

McMahon Hall, an iconic Brutalist dormitory at the University of Washington, has become the site of an interdisciplinary experiment in cultural memory and machine-assisted storytelling. This article presents a method that combines remote sensing with AI-generated voices to produce a polyvocal narrative of architecture through the perspective of the building itself, its material (concrete), an architect, a journalist, and a bird. Drone photogrammetry and generated 3D models were combined with generative AI (text, image, and voice) to reconstruct the site digitally and imaginatively (AI-driven speculative narratives). Through speculative storytelling, the article and the project explore how cultural memory and perception of built heritage can be augmented by machines, offering plural perspectives that challenge singular historical narratives. The Introduction situates the work at the intersection of digital heritage documentation, AI storytelling, epistemology in machine learning, and spatial computing, emphasizing the perception of heritage through different actors. The Theoretical Framework draws on literature in photogrammetry for heritage preservation, polyvocal narrative, and knowledge frameworks of AI. The Materials and Methods detail the workflow: capturing McMahon Hall via UAV photogrammetry, producing a 3D model, and generating character-driven narratives with large language models and voice synthesis. The resulting multi-voiced narrative and its thematic insights are described. In the Discussion, the implications of this approach for architectural heritage interpretation are considered, including its capacity to amplify diverse voices and the risks of bias or hyperreality in AI-generated narratives. The study argues that this polyvocal, machine-augmented storytelling expands the toolkit of remote sensing and digital heritage by not only documenting the tangible form of the built environment but also speculating on its intangible cultural memory. The Conclusions reflect on how merging spatial computing techniques with AI narratives can support new modes of engagement with architecture, positioning this work as a building block toward richer human-machine co-created heritage experiences. Full article

Teaching practice has long been considered a fundamental and integral part of any teacher education programme, but also very demanding for novice teachers when they are confronted with the reality of the classroom, for the first time in many cases. Teacher educators aim to allow student teachers to experience practice opportunities reflective of their many potential real-world future teaching scenarios, including, for example, teaching online through video conferencing tools or virtual reality. One such mode is teaching in chatrooms, using written language only, which is the focus of this paper. The aims of this research are therefore to investigate the use of corpus based reflective practice (CBRP) using a (written) chatroom corpus with student teachers and evaluate this approach through an exploration of their recounted perceptions. To do this, we conduct a preliminary corpus-based analysis of some of the more salient features of the student teacher chatroom corpus and examine how these align with the student teachers’ reported perceptions. Secondly, we aim to identify and evaluate the nature of the (spoken) discussions in the post-chatroom teaching experience interactions between the teacher educator and student teachers with reference to reflective practice engagement. Full article

Bearing fault diagnosis is crucial for stable operation and safe manufacturing as industry intelligence becomes increasingly advanced. However, under complicated non-linear vibration modes and multiple operating conditions, most of the current diagnostic methods are limited in terms of cross-domain generalization. To address these issues, this study develops a generalized diagnostic framework leveraging Large Language Models (LLMs), integrating multiple enhancements to improve both accuracy and adaptability. Initially, a structured representation approach is designed to transform raw vibration time series into interpretable text sequences by extracting physically meaningful features in both time and frequency domains. This transformation bridges the gap between sequential sensor data and semantic understanding. Furthermore, to explicitly incorporate bearings’ structural parameters and operating condition information, a knowledge-guided prompt tuning strategy based on Low-Rank Adaptation (LoRA-Prompt) is introduced. This mechanism enables the model to adapt more effectively to varying fault scenarios by embedding expert prior knowledge directly into the learning process. Finally, a generalized fault diagnosis method named Knowledge-Guided Semantic Representation and Large Language Model (KG-SR-LLM) is established. Large-scale experiments using 11 public datasets from industrial, aerospace, and energy fields are carried out to extensively evaluate its performance. Based on experiment analysis and a comparison of results, KG-SR-LLM is superior to classical deep learning models by 9.22%, reaching an average diagnostic accuracy of 98.36%. KG-SR-LLM is effective for handling few-shot transfer and cross-condition adaptation tasks. All these results illustrate the theoretical significance and application benefit of KG-SR-LLM for intelligent fault diagnosis of bearings. Full article

This study examines al-Mufaḍḍaliyyāt anthology as a foundational corpus wherein pre-Islamic and early Islamic Arabic poetry emerged not only as a cultural artifact but as a generative locus for theological reflection. Through a close reading of selected poems and nuanced engagement with the figurative language specifically metaphor, personification, and symbolic narrative, the research situates poetry as a mode of epistemic inquiry that articulates religious meaning alongside Qurʾānic revelation. Drawing on ʿAbd al-Qāhir al-Jurjānī’s theory of semantic structure and metaphor, in dialogue with Paul Ricoeur’s conception of metaphor as imaginative cognition, the study proposes that poetic discourse operates as a site of “imaginative theology”, i.e., a space wherein the abstract is rendered sensorially legible and metaphysical concepts are dramatized in affective and embodied terms. The analysis reveals how key Qurʾānic themes including divine will, mortality, ethical restraint are anticipated, echoed, and reconfigured through poetic imagery. Thus, al-Mufaḍḍaliyyāt is not merely a literary corpus vis-à-vis Islamic scripture but also functions as an active interlocutor in the formation of early Islamic moral and theological imagination. This interdisciplinary inquiry contributes to broader discussions on the interpenetration of poetics and theology as well as on the cognitive capacities of literature to shape religious consciousness. Full article

Prenatal exposure to air pollution is a major public health concern due to its potential to impair fetal brain development. This study examined whether maternal inflammatory and oxidative stress biomarkers mediate the association between trimester-specific air pollutant exposure during pregnancy and infant neurodevelopment at one year. We analyzed 87 mother–infant pairs from the OBESO perinatal cohort in Mexico City. Trimester-specific exposure to CO, PM₁₀, PM_2.5, SO₂, and O₃ was estimated using residential geolocation. Biomarkers were measured in the first and third trimesters by protocol, and intra-pregnancy change was calculated as Δ(3T–1T) for cytokines (IL-1β, IL-6, TNFα) and oxidative stress markers (malondialdehyde (MDA), protein carbonyls (PC), and total antioxidant capacity (TAC). Infant neurodevelopment at 12 months was assessed using Bayley-III. Exploratory mediation analyses were conducted, adjusting for gestational age at birth, pre-eclampsia, gestational diabetes, fetal growth restriction, marital status, mode of delivery, and infant sex; bootstrapping was applied to obtain robust estimates. Third-trimester CO exposure was associated with poorer receptive language (coef = 0.754, p = 0.02). PM_2.5 exposure showed direct effects on expressive language in the first (coef = 0.01, p = 0.04) and third trimesters (coef = 0.007, p = 0.015) in models including IL-1β. Third-trimester O₃ and SO₂ exposures were linked to lower expressive scores in models including TNFα (coef = 0.007, p = 0.02), MDA (coef = 0.008, p = 0.04), and PC (coef = 0.007, 95% p = 0.04). Meanwhile PM₁₀ exposure was associated with socio-emotional outcomes in models with IL-6 and TAC (coef = 0.003, p = 0.04). These findings indicate that maternal inflammation and oxidative stress biomarkers did not mediate the associations between prenatal air pollution exposure and infant neurodevelopment, and this study cannot elucidate their specific biological role in neurodevelopment. Full article

With the rapid growth of user demand for large language models (LLMs) in their work, the application market is driving intense competition among large language model providers (LLMPs). Users have different preferences and psychological perceptions towards the charging models of different LLMPs. LLMPs with different intelligence levels must design pricing strategies based on diverse user characteristics. To investigate the impact of user heterogeneity on the strategic pricing of competing LLMPs, this paper establishes a competitive model with two providers, comprising a highly intelligent initial LLM provider and a follower provider. Both providers can independently decide to adopt either a subscription model or a pay-per-use model, resulting in four pricing mode combinations (dual subscription SS, subscription-pay-per-use SD, pay-per-use-subscription DS, dual pay-per-use DD). The study shows that when the pay-per-use model is adopted, the user’s psychological perception of the “tick-tock effect” reduces the provider’s service price and profit, as the perceived psychological cost lowers the user’s valuation of the product, thereby decreasing demand. Furthermore, we analyze the equilibrium strategies for pricing mode selection by the two providers. The results indicate that the subscription model is not always advantageous for providers. Both providers will only choose to adopt the subscription model when both user usage frequency and perceived psychological cost are high. Conversely, when both user usage frequency and perceived psychological cost are low, the two providers will not simultaneously adopt the subscription model. Interestingly, as the product intelligence levels of the two providers converge, their choices of pricing modes are also more inclined to diverge. These insights guide LLMPs to strategically adjust their pricing models based on user behavioral patterns to maximize profitability in the competitive AI market. Full article

There is a pressing need to build a sign-to-text translation system to simplify communication between deaf and non-deaf people. This study investigates the building of a high-performance, lightweight sign language translation system suitable for real-time applications. Two Saudi Sign Language datasets are used for evaluation. We also investigate the effects of the number of signers and number of repetitions in sign language datasets. To this end, eight experiments are conducted in both signer-dependent and signer-independent modes. A comprehensive ablation study is presented to study the impacts of model components, network depth, and the size of the hidden dimension. The best accuracies achieved are 97.7% and 90.7% for the signer-dependent and signer-independent modes, respectively, using the KSU-SSL dataset. Similarly, the model achieves 98.38% and 96.22% for signer-dependent and signer-independent modes using the ArSL dataset. Full article