Search Results (23)

Population ageing and the growing burden of immune-mediated disease have prompted efforts to quantify immunosenescence with clinically usable biomarkers. Immune ageing clocks have been built from immunophenotyping, transcriptomics, proteomics, epigenomics and adaptive receptor repertoires, but heterogeneous task definitions, assay protocols and evaluation criteria limit comparability and translation. We review major immune data modalities and outline an end-to-end workflow from cohort design and assay standardisation to preprocessing, feature engineering, model development, validation and recalibration. We propose a task–modality–model taxonomy separating (i) chronological age clocks, (ii) outcome-anchored risk clocks and (iii) cell lineage/state clocks, while treating bulk blood transcriptomics (whole blood or PBMC) as a molecular-layer modality that can support either age-scale or outcome-anchored tasks depending on supervision. Across studies, common limitations include batch effects, compositional confounding, endpoint mismatch, scarce external validation and limited mechanistic anchoring. We conclude with priorities for the field, including multimodal integration, longitudinal designs with digital phenotypes, tissue- and cell-type-specific models, and pathway-grounded clocks that can be linked to interventions. 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

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

Background: Rhythmic sensory stimulation, including structured musical interventions, has gained renewed interest as a non-pharmacological strategy that may modulate cortical excitability and network stability in focal epilepsy. Although several small studies have reported changes in seizure frequency or epileptiform activity during rhythmic or music exposure, the underlying mechanisms and translational relevance remain insufficiently synthesized. Objective: This narrative review summarizes clinical evidence on music-based and rhythmic sensory interventions in focal epilepsy, outlines plausible neurophysiological mechanisms related to neural entrainment and large-scale network regulation, and discusses emerging opportunities for digital delivery of rhythmic protocols in everyday self-management. Methods: A structured search of recent clinical, neurophysiological, and rehabilitation literature was performed with emphasis on rhythmic auditory, tactile, and multimodal stimulation in epilepsy or related conditions. Additional theoretical and translational sources addressing oscillatory dynamics, entrainment, timing networks, and patient-centered digital tools were reviewed to establish a mechanistic framework. Results: Existing studies—although limited by small cohorts and heterogeneous methodology—suggest that certain rhythmic structures, including specific musical compositions, may transiently modulate cortical synchronization, reduce epileptiform discharges, or alleviate seizure-related symptoms in selected patients. Evidence from neurologic music therapy and rhythmic stimulation in other neurological disorders further supports the concept that externally delivered rhythms can influence timing networks, attentional control, and interhemispheric coordination. Advances in mobile health platforms enable structured rhythmic exercises to be delivered and monitored in real-world settings. Conclusions: Music-based and rhythmic sensory interventions represent a promising but underexplored adjunctive approach for focal epilepsy. Their effectiveness likely depends on individual network characteristics and on the structure of the applied rhythm. Digital integration may enhance personalization and adherence. Rigorous clinical trials and mechanistic studies are required to define optimal parameters, identify responders, and clarify the role of rhythmic stimulation within modern epilepsy care. Full article

Ensuring consistent quality and safety in agri-food processing is a strategic priority for firms seeking compliance with international standards such as ISO 9001 and ISO 22000. Traditional inspection practices in Ecuador’s food industry remain largely destructive, labor-intensive, and subjective, limiting real-time decision-making. This study developed a non-destructive, ISO-aligned framework for process-level quality control by integrating digital (RGB) imaging for surface-level inspection, hyperspectral imaging (HSI) for internal-quality prediction (e.g., moisture, firmness, and freshness), near-infrared spectroscopy (NIRS) for compositional and authenticity analysis, and deep learning (DL) models for automated classification of ripeness, maturity, and defects. Experimental results across four flagship commodities—bananas, cacao, coffee, and shrimp—achieved classification accuracies above 88% and ROC AUC values exceeding 0.90, confirming the robustness of AI-driven, multimodal (RGB–HSI–NIRS) inspection under semi-industrial conveyor conditions. Beyond technological performance, the findings demonstrate that digital inspection reinforces ISO principles of evidence-based decision-making, conformity verification, and traceability, thereby operationalizing the Plan–Do–Check–Act (PDCA) cycle at digital speed. The study contributes theoretically by advancing the conceptualization of Quality 4.0 as a socio-technical transformation that embeds AI-driven sensing and analytics within management standards, and practically by providing a roadmap for Ecuadorian SMEs to strengthen export competitiveness through automated, real-time, and auditable quality assurance. Full article

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

This study aims to explore teachers’ perceptions and students’ strategies when integrating AI-mediated informal digital learning of English tools (AI-IDLE) into career ESL writing instruction. This case study involved six university instructors and over 300 students in an English writing course. Although AI-IDLE has broadened English access beyond classrooms, existing research on writing skills often neglects students’ diverse strategies that correspond to their professional aspirations, as well as teachers’ perceptions. The data included a demographic questionnaire, think-aloud protocols for real-time assessment of cognitive processes during the task, and semi-structured interviews for teachers’ validation. Findings reveal three major student strategies: (1) explicit genre understanding, (2) student-driven selection of digital multimodal tools—such as Grammarly, ChatGPT, Canva with Magic Write, and Invideo—to integrate text with images, sound, and layout for improved rhetorical accessibility, and (3) alignment with students’ post-graduation career needs. Students’ work with these AI tools demonstrated that when they created projects aligned with professional identities and future job needs, they became more aware of how to improve their writing; however, the teachers expressed hopes and doubts about the tools’ effectiveness and authenticity of the students’ work. Suggestions to use AI-IDLE to improve writing were provided. Full article

The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor alignment with daily life. This review surveys the current landscape of at-home healthcare technologies, including wearable vital sign monitors, digital diagnostics and body composition assessment tools. We synthesize insights from the existing literature for this narrative review, highlighting strengths and limitations in sensing accuracy, user experience and integration into daily health routines. Special attention is given to the role of AI in enabling real-time insights, adaptive feedback and predictive monitoring across these devices. To examine persistent adoption challenges from a user-centered perspective, we reflect on the Pi-CON methodology, a conceptual framework previously introduced to stimulate discussion around passive, non-contact, and continuous data acquisition. While Pi-CON is highlighted as a representative methodology, recent external studies in multimodal sensing, RFID-based monitoring, and wearable–ambient integration confirm the broader feasibility of unobtrusive, passive, and continuous health monitoring in real-world environments. We conclude with strategic recommendations to guide the development of more accessible, intelligent and user-aligned smart healthcare solutions. Full article

Digital platforms have become primary channels for cultural heritage transmission, yet how individuals visually represent their personal heritage online remains unexplored. This study investigates the visual patterns in personal heritage representation across digital platforms, examining whether platform affordances or demographics influence these patterns. Through the LAVIS multimodal AI system, we analyzed 588 heritage images from Instagram and “Personas y Patrimonios”, combining automated content, composition, color, and saturation analyses with human validation. Our findings revealed that intimate, portable objects—particularly jewelry (22.79%)—dominate personal heritage representations, with no content differences between platforms or genders. Small but statistically significant platform differences emerged in color patterns (Cohen’s d = −0.215) and compositional attention (Cohen’s d = 0.147), while gender showed no significant differences in any visual dimension. These findings may indicate that personal heritage representation follows universal visual patterns, emphasizing personal bonds that transcend both platform affordances and demographic differences. These results advance understanding of personal digital heritage communication by identifying the universal patterns in its visualization. Beyond establishing a methodological framework for AI-assisted heritage image analysis, this research provides practical insights for heritage educators and digital platform designers while illuminating how biographical objects function in digital environments, ultimately underscoring the pivotal role of imagery in contemporary cultural transmission. Full article

Reading and writing in the 21st century have evolved from traditional text-based formats to multimodal literacy, integrating linguistic, visual, auditory, and spatial modes to enhance communication and comprehension. While multimodal reading has been widely studied, multimodal writing remains underexplored, despite its growing importance in K–12 education across disciplines. Multimodal composing demands advanced self-regulation as students navigate multiple digital tools and platforms. Self-regulated learning strategies, particularly the self-regulated strategy development model, offer a promising approach to support students in planning, monitoring, and revising multimodal compositions. However, a comprehensive framework linking self-regulation and multimodal composition is lacking. This article addresses this gap by synthesizing findings from two studies—one in preservice teacher education and another in a first-grade classroom—along with existing research to propose a self-regulated multimodal composing framework. This framework aims to guide educators in fostering students’ autonomy and competence in multimodal composing. By integrating self-regulation strategies with multimodal composition processes, the SRMC framework provides actionable insights for instructional practices, helping teachers support diverse learners in today’s digitally mediated classrooms. The article discusses implications for pedagogy and future research, advocating for greater emphasis on self-regulated multimodal composing in literacy education. Full article

This commentary addresses a problem of practice related to student disengagement in technology-rich classrooms, where learners are digitally connected but socially and academically disconnected. Although not an empirical study, the commentary draws on instructional examples from secondary- and graduate-level teaching. The authors examine how digital literacy instruction can strengthen engagement, reading comprehension, and ethical participation in online environments. The article highlights strategies such as the workshop model, multimodal composition, digital content curation, and the use of mentor texts to support critical thinking and collaborative learning. These practices aim to develop students’ analytical skills, awareness of audience, and recognition of their own positionality in digital spaces. Across courses, the authors reflected on increased student engagement when digital tools were used not simply for task completion but to support inquiry, discourse, and authentic creation for real audiences. Full article

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article

With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article

Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system. Full article

Background: This project focuses on the relevance of using a health literacy approach to educating children about obesity prevention. The Habits of Health and Habits of Mind© model was used to write Electronic Texts for Health Literacy© to encourage actions that support obesity prevention. Guided by the Integrative Theory of Behavioral Prediction, the design template for a new genre of digital texts called Electronic Texts for Health Literacy© emerges for exploring children’s frame of mind, health literacy skills, and behavioral intentions toward obesity prevention. Methods: Online materials from selected websites were strategically reviewed for improving obesity prevention and child health literacy. The digital resources were juxtaposed with the Electronic Texts for Health Literacy©, with the latter written by and for children. Discussion: Health educators who use a constructivist pedagogy can help students to write health literacy narratives about obesity prevention, then read and talk about their multimodal compositions to further the practice and development of their health literacy skills. Children with obesogenic body frames can also gain from cowriting visual–textual–gestural health narratives with their peers or health professionals. Co-constructed narratives can help children make deeper connections about their identity, frame of mind, and social agency. Summary: Although this untested resource is available as a new genre of digital text, health educators could nudge children toward developing a stronger frame of mind and behavioral intentions toward obesity prevention when they write health literacy narratives that focus on decision making, goal setting, and communication in the context of eating nutritious foods and participating in physical activities. Full article