Search Results (646)

Background/Objectives: Fluoroscopy-guided procedures are widely used across surgical and interventional specialties but expose operators to ionizing radiation with associated stochastic and deterministic effects. The aim is to characterize occupational radiation exposure, evaluate the effectiveness of shielding strategies, assess long-term cancer risks, and identify compliance patterns. Methods: This structured narrative review summarizes evidence on operator dose, shielding effectiveness, compliance with protective practices, and long-term cancer risk. A search of PubMed, Scopus, Embase, and Web of Science (limited to January 2000–March 2024) identified 62 records; 27 full texts were reviewed, and 16 studies met the inclusion criteria. Results: Across studies, unshielded chest exposure averaged 0.08–0.11 mSv per procedure, and eye exposure averaged 0.04–0.05 mSv. Lead aprons reduced exposure by about 90% at 0.25 mm and 99% at 0.5 mm, thyroid collars reduced neck dose by 60–70%, and lead glasses reduced ocular dose 2.5–4.5-fold. Compliance with aprons and thyroid collars was high, whereas lead glasses and lower-body shielding were inconsistently used. Limited epidemiologic data suggested a higher cancer burden in exposed surgeons, and Biologic Effects of Ionizing Radiation (BEIR) VII–based modeling projected increased lifetime risks of solid cancers in chronically exposed operators. Conclusions: Protective equipment substantially reduces operator dose, but exposure variability and inconsistent shielding practices persist and justify standardized monitoring, stronger enforcement of radiation safety protocols, and longitudinal studies. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

A cultural heritage augmented reality system overlays virtual information onto real-world heritage sites, enabling intuitive exploration and interpretation with spatial and temporal contexts. This study presents the design and implementation of a cognitive Mobile Augmented Reality (MAR) system that integrates KCHDM-based ontologies with large language models (LLMs) to facilitate intelligent exploration of urban heritage. While conventional AR guides often rely on static data, our system introduces a Semantic Retrieval-Augmented Generation (RAG) pipeline anchored in a structured knowledge base modeled after the Korean Cultural Heritage Data Model (KCHDM). This architecture enables the LLM to perform dynamic contextual reasoning, transforming heritage data into adaptive question-answering (Q&A) and interactive knowledge-testing quizzes that are precisely grounded in both historical and spatial contexts. The system supports on-site AR exploration and map-based remote exploration to ensure robust usability and precise spatial alignment of virtual content. To deliver a rich, multisensory experience, the system provides multimodal outputs, integrating text, images, models, and audio narration. Furthermore, the integration of a knowledge sharing repository allows users to review and learn from others’ inquires. This ontology-driven LLM-integrated MAR design enhances semantic accuracy and contextual relevance, demonstrating the potential of MAR for socially enriched urban heritage experiences. Full article

While text-to-image and customized generation methods demonstrate strong capabilities in single-image generation, they fall short in supporting immersive applications that require coherent 360° panoramas. Conversely, existing panorama generation models lack customization capabilities. In panoramic scenes, reference objects often appear as minor background elements and may be multiple in number, while reference images across different views exhibit weak correlations. To address these challenges, we propose a diffusion-based framework for customized multi-view image generation. Our approach introduces a decoupled feature injection mechanism within a dual-UNet architecture to handle weakly correlated reference images, effectively integrating spatial information by concurrently feeding both reference images and noise into the denoising branch. A hybrid attention mechanism enables deep fusion of reference features and multi-view representations. Furthermore, a data augmentation strategy facilitates viewpoint-adaptive pose adjustments, and panoramic coordinates are employed to guide multi-view attention. The experimental results demonstrate our model’s effectiveness in generating coherent, high-quality customized multi-view images. Full article

Remote sensing image semantic segmentation (RSISS) aims to assign a correct class label to each pixel in remote sensing images and has wide applications. With the development of artificial intelligence, RSISS based on deep learning has made significant progress. However, existing methods remain more focused on predefined semantic classes and require costly retraining when confronted with new classes. To address this limitation, we propose the hierarchical guidance open-vocabulary semantic segmentation framework for remote sensing images (named HG-RSOVSSeg), enabling flexible segmentation of arbitrary semantic classes without model retraining. Our framework leverages pretrained text-embedding models to provide class common knowledge and aligns multimodal features through a dual-stream architecture. Specifically, we propose a multimodal feature aggregation module for pixel-level alignment and a hierarchical visual feature decoder guided by text feature alignment, which progressively refines visual features using language priors, preserving semantic coherence during high-resolution decoding. Extensive experiments were conducted on six representative public datasets, and the results showed that our method has the highest mean mIoU value, establishing state-of-the-art performance in the field of open-vocabulary semantic segmentation of remote sensing images. Full article

Background/Objectives: Cluttering in childhood and adolescence is characterised by unstable speech timing, excessive coarticulation, irregular rate and reduced intelligibility, yet the developmental mechanisms underpinning these behaviours remain partially understood. This review synthesises empirical and conceptual evidence to examine cluttering through the lenses of speech motor development, neurocognitive mechanisms, task demands and allied-health practice. Four research questions guided the review, focusing on motor characteristics, developmental and neurocognitive mechanisms, task dependence and clinical implications. Methods: Following the PRISMA guidelines, a comprehensive search across seven databases identified studies examining cluttering in children and adolescents. Screening and full-text review were conducted in Covidence by two reviewers, with disagreements resolved by the first author. Twelve studies met the inclusion criteria. Data were extracted into a structured evidence table, and findings were synthesised. Results: Across studies, cluttering emerged as a developmental motor–cognitive integration disorder. Speech motor systems, linguistic formulation and executive control showed difficulty aligning under real-world communicative demands, leading to timing instability, articulatory blurring and reduced intelligibility. Symptoms were strongly influenced by task complexity, with spontaneous and extended discourse eliciting the most pronounced breakdowns. Conclusions: Cluttering reflects a developmental vulnerability in coordinating speech motor, linguistic and executive processes. Understanding cluttering in this way challenges narrow rate-based definitions and supports more nuanced approaches to assessment and intervention. Significant evidence gaps remain, particularly in longitudinal, mechanistic, multilingual and ecologically valid research. This developmental motor–cognitive framework strengthens the conceptual foundations of cluttering and clarifies its relevance to children’s motor development. Full article

Background/Objectives: Mobile health (mHealth) interventions can enhance chronic disease management, but their integration into public healthcare systems remains complex. DiabeText is the first SMS-based intervention in Spain delivering personalized diabetes self-management support using electronic health record data. This study explored perceived barriers and facilitators to the implementation of DiabeText in the Spanish public health context from the perspective of key stakeholders. Methods: A qualitative study was conducted using semi-structured interviews with 14 purposively selected stakeholders involved in digital health, diabetes care, data protection, and healthcare management across several Spanish regions. Interviews were thematically analyzed using Braun and Clarke’s approach and guided by the Implementation Research Logic Model. Results: Participants reported several barriers, including concerns regarding data protection, uncertainty about long-term sustainability, insufficient training and engagement of healthcare professionals and low digital literacy among certain patient groups. Facilitators included favorable institutional momentum for digital innovation, funding availability, perceived clinical utility and scalability of DiabeText, and growing patient familiarity with digital tools. Recommended strategies included integration into existing healthcare systems and workflows, professional training and use of familiar communication platforms. Conclusions: Effective implementation of DiabeText requires addressing regulatory, organizational, and equity-related barriers while leveraging institutional support and readiness for innovation. Early involvement of healthcare professionals, robust data governance, and investment in digital literacy are essential to ensure sustainable and equitable adoption. These findings provide actionable insights to support the integration of mHealth tools into chronic disease care in Spain and similar settings. Full article

Multi-modal entity alignment (MMEA) identifies identical real-world entities across two multi-modal knowledge graphs. Most existing methods heavily rely on costly manually labeled seed alignments; in response, self-supervised MMEA has emerged to reduce this dependency. However, current self-supervised approaches suffer from two key issues: (1) low-quality pseudo-labels (sparse and noisy) weakening self-supervised signals; and (2) inter-modal semantic inconsistencies (structure, text, vision) hindering unified entity representations. To resolve these issues, we propose OPICE, an Ontology-guided Pseudo-label Generation and Inter-modal Consistency Enhancement for self-supervised MMEA. It adopts a robust pseudo-label generation strategy to produce more initial alignments with less noise, and it uses an inter-modal consistency enhancement module to narrow inter-modal semantic gaps for unified representations. Experiments on FB–DB15K and FB–YAGO15K show that OPICE achieves state-of-the-art performance, improving Hit@1 by 6.8% on average over the strongest self-supervised baseline and being competitive with most supervised baselines under standard reported settings. Full article

Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, limiting their adaptability to specific sub-domains. This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. By integrating iterative optimization, consistency-guided filtering, and prompt-based extraction, the proposed method achieves a balance between precision and coverage, enabling the robust extraction of standardized subject–predicate–object triples from raw long texts. This makes it highly effective for knowledge graph construction and downstream reasoning tasks. We applied the parameter-efficient open-source model Qwen3-14B, and experimental results on the SciERC dataset show that, under strict matching (i.e., ensuring the exact matching of all components), our method achieved an F1 score of 0.358, outperforming the baseline model’s F1 score of 0.349. Under fuzzy matching (allowing some parts of the triples to be unmatched), the F1 score reached 0.447, outperforming the baseline model’s F1 score of 0.392, demonstrating the effectiveness of our approach. Ablation studies validate the robustness and generalization potential of our method, highlighting the contribution of each component to the overall performance. Full article

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

Effective Automated Geometric Problem Solving (AGP) requires a deep integration of visual perception and textual comprehension. To address this, we propose a dual-stream fusion model that injects deep semantic understanding from a Pre-trained Language Model (PLM) into the geometric diagram parsing pipeline. Our core innovation is a Semantic-Guided Cross-Attention (SGCA) mechanism, which uses the global semantic intent of the problem text to direct attention toward key visual primitives. This yields context-enriched visual representations that serve as inputs to a Graph Neural Network (GNN), enabling relational reasoning that is not only perception-driven but also context-aware. By explicitly bridging the semantic gap between text and diagrams, our approach delivers more robust and accurate predictions. To the best of our knowledge, this is the first study to introduce a semantic-guided cross-attention mechanism into geometric diagram parsing, establishing a new paradigm that effectively addresses the cross-modal semantic gap and achieves state-of-the-art performance. This is particularly effective for parsing problems involving geometric symmetries, where textual cues often clarify or define symmetrical relationships not obvious from the diagram alone. Full article

Rapid technological innovation has made navigating millions of new patent filings a critical challenge for corporations and research institutions. Existing patent recommendation systems, largely constrained by their static designs, struggle to capture the dynamic pulse of an ever-evolving technological ecosystem. At the same time, their “black-box” decision-making processes severely limit their trustworthiness and practical value in high-stakes, real-world scenarios. To address this impasse, we introduce TEAHG-EPR, a novel, end-to-end framework for explainable patent recommendation. The core of our approach is to reframe the recommendation task as a dynamic learning and reasoning process on a temporal-aware attributed heterogeneous graph. Specifically, we first construct a sequence of patent knowledge graphs that evolve on a yearly basis. A dual-encoder architecture, comprising a Relational Graph Convolutional Network (R-GCN) and a Bidirectional Long Short-Term Memory network (Bi-LSTM), is then employed to simultaneously capture the spatial structural information within each time snapshot and the evolutionary patterns across time. Building on this foundation, we innovatively introduce uncertainty modeling, learning a dual “deterministic core + probabilistic potential” representation for each entity and balancing recommendation precision with exploration through a hybrid similarity metric. Finally, to achieve true explainability, we design a feature-guided controllable text generation module that can attach a well-reasoned, faithful textual explanation to every single recommendation. We conducted comprehensive experiments on two large-scale datasets: a real-world industrial patent dataset (USPTO) and a classic academic dataset (AMiner). The results are compelling: TEAHG-EPR not only significantly outperforms all state-of-the-art baselines in recommendation accuracy but also demonstrates a decisive advantage across multiple “beyond-accuracy” dimensions, including explanation quality, diversity, and novelty. Full article

The rise of artificial intelligence has led to the emergence of geoscience knowledge graphs (GeoKG) as effective tools for organizing and representing complex knowledge. The growing complexity of geoscience data calls for innovative strategies for structuring and interpreting extensive information. Conventional knowledge extraction methods often rely on manual annotation and deep learning techniques, which can be costly and inefficient. Herein, we leverage a large language model (LLM) to address the challenges of knowledge extraction and fusion in creating a knowledge graph focused on gold deposits. First, we developed an ontology explicitly designed for gold deposits, drawing on insights from geological experts. Next, we formulate a prompt to guide the LLM to accurately extract geological entities and their semantic relationships in accordance with the knowledge graph schema. Subsequently, we conducted geological entity alignment and integration to construct the gold deposit knowledge graph, which encompasses over 3738 entities and 3900 semantic relationships. Finally, we identified an optimal configuration balancing F1-score and computational cost through comparative experiments on locally deployed models with varying parameters. Our findings demonstrate that an LLM can effectively capture long-range contextual relationships to identify geological entities and their semantic connections, demonstrating strong performance in handling diverse expressions. Full article

Background/Objectives: Combined chromosome 7 gain and chromosome 10 loss (+7/−10) is the most frequent cytogenetic alteration and a defining diagnostic criterion for isocitrate dehydrogenase wild-type (IDHwt) glioblastoma. Despite the association with poor prognosis, its clinical and therapeutic significance remains unclear. We aim to systematically review its clinical significance, focusing on prevalence, prognostic value, and potential association with therapeutic resistance in adult patients. Methods: PubMed, Embase, CENTRAL, Scopus, EBSCOhost, and Web of Science were searched from inception to April 2025, using controlled vocabulary and free-text terms. Eligible studies included adult glioblastoma with molecular confirmation of combined chromosome 7 gain and chromosome 10 loss and reported survival or treatment response. Quality was assessed qualitatively, and findings were synthesized descriptively. Results: Of 3249 records, 5 observational studies (523 patients) were included. The signature was present in 60% to 70% of glioblastoma cases and frequently co-occurred with epidermal growth factor receptor amplification and telomerase reverse transcriptase promoter mutations. This alteration was consistently associated with shorter survival (mean, 8–70 weeks) compared with tumors lacking the alteration (19–170 weeks). In one study, the signature was more common in radioresistant tumors (9/20 vs. 1/10). Molecular evidence suggests that this alteration arises early in tumorigenesis. Conclusions: The +7/−10 cytogenetic alteration, common in glioblastoma, is frequently associated with aggressive clinical behavior. While exploratory data suggest a possible association with radiotherapy response, current evidence is insufficient to establish a predictive or therapeutic role. Its principal clinical value lies in diagnosis, molecular classification, and risk stratification. Incorporating cytogenetic testing for this alteration into routine glioblastoma workup may improve risk stratification and guide individualized management. Full article

Personalized face image generation is essential for Artificial Intelligence-Generated Content (AIGC) applications such as personalized digital avatars and user-customized media creation. However, existing diffusion-based approaches still suffer from insufficient identity consistency and limited text editability. In this work, we propose CoFaDiff, a diffusion-based face generation framework designed for coordinating identity consistency and text-driven editability. To enhance identity consistency, our method integrates a dual-encoder scheme that jointly leverages CLIP and ArcFace to capture both semantic and discriminative facial features, incorporates a progressive curriculum learning strategy to obtain more robust identity representations, and adopts a hybrid IdentityNet–IPAdapter architecture that explicitly models facial location, pose, and corresponding identity embeddings in a unified manner. To enhance text-driven editability, we introduce three complementary optimization strategies: First, long-prompt fine-tuning is employed to reduce the model’s dependency on identity conditions. Second, a semantic alignment loss is incorporated to regularize the influence of identity embeddings within the semantic space of the pretrained diffusion model. Third, during classifier-free guided sampling, we modulate the strength of the identity condition by stacking different numbers of zero-valued identity tokens, enabling users to flexibly balance identity consistency and text editability according to their needs. Experiments on FFHQ and IMDB-WIKI demonstrate that CoFaDiff achieves superior identity consistency and text editability compared to recent baselines. Full article