Search Results (474)

Against the backdrop of intensifying global population aging, ensuring the sustainable provision of age-friendly spaces has become an important domain of urban policy intervention. A close examination of the supply–demand matching of age-friendly spaces is therefore essential for policymakers seeking to achieve social and environmental sustainability in an aging society. However, existing approaches to assessing this alignment primarily rely on quantitative analyses of geographical spatial distribution, lacking methods to evaluate the structural alignment of spatial functional types. To address this gap, this study proposes and validates a type-based quantitative approach to examining the alignment between policy supply and everyday demands for age-friendly spaces. By integrating policy text analysis, questionnaire surveys, activity logs, and behavior snapshots, the study identifies the types of age-friendly spaces mentioned by policies and those demanded in daily life, and quantitatively evaluates their alignment using a matching model. The results show that the older adults’ spatial demands shift progressively from life-oriented spaces to survival-oriented spaces as age increases and health declines. More importantly, a significant structural imbalance is evident: survival-oriented spaces are oversupplied, while life-oriented spaces remain in short supply. This study provides a diagnostic method for assessing the provision of age-friendly spaces and provides practical implications for local governments in formulating more balanced, responsive, and sustainable supply strategies. Full article

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

Background/Objectives. Abdominal computed tomography (CT) radiology reports contain critical information for abdominal aortic aneurysm (AAA) management, including aneurysm presence, size, rupture status, and prior repair. However, this information is often embedded within lengthy, heterogeneous reports, making manual extraction inefficient. We evaluated the performance of multiple large language models (LLMs) for automated extraction of AAA-related findings from radiology reports. Methods. We retrospectively analyzed 500 abdominal CT reports mentioning AAA from an urban academic health system (2020–2024). Ground truth labels were established by manual review. Four open-source LLMs (Qwen2.5-7B-Instruct, Llama3-Med42-8B, GPT-OSS-20B, and MedGemma-27B-text-it) were evaluated for extraction of aneurysm presence, size, morphology, rupture status, impending rupture, and prior aortic repair. Model outputs were compared with ground truth using exact-match accuracy, and inter-model agreement was assessed using Fleiss’ kappa. Reasoning traces were examined to characterize correct and incorrect model behavior. Results. Accuracy for identifying AAA presence ranged from 0.90 to 0.95 (κ = 0.851), and prior aortic repair from 0.90 to 0.97 (κ = 0.793). Accuracy for aneurysm size ranged from 0.67 to 0.88 (κ = 0.340), with low κ’s due to class imbalance or dimension misselection. Rupture and impending rupture were identified with accuracies exceeding 0.90 across models, though agreement was lower (κ = 0.485 and 0.589), reflecting low event prevalence. Larger models (GPT-OSS-20B, MedGemma-27B) generally outperformed smaller models. Reasoning analysis revealed strengths in measurement prioritization but recurrent errors, including dimension misselection, over-inference of prior repair, and conservative classification of rupture-related findings. Conclusions. LLMs can accurately extract clinically relevant AAA information from radiology reports with interpretable reasoning, with larger and medically trained models outperforming smaller or general-purpose models. Performance varies by task and model, underscoring the need for careful validation and human-in-the-loop deployment in clinical settings. Full article

Diagnosis of cancer is one of the hardest problems faced in modern medicine and involves integrating different data sources such as medical images, genomic profiles and clinical records. Traditional machine learning methods have difficulty handling the high-dimensional and complex correlation properties of multimodal medical data. In view of this, we propose a new Quantum-Enhanced Multimodal Fusion Network (QEMFN) framework to break through traditional image–text matching based on quantum computing principles for CT imaging with genomic sequencing data and EHR information. Our approach utilizes variational quantum circuits for feature encoding, quantum kernel methods for crossmodal attention, and hybrid quantum–classical architectures for final classification. We realize the framework using Google Cirq quantum computing library and validate it on publicly available datasets including TCIA (The Cancer Imaging Archive), TCGA (The Cancer Genome Atlas), and MIMIC-III clinical database. The matched multimodal cohort comprises 847 lung cancer patients, 623 colorectal cancer patients, and 401 liver cancer patients with complete imaging, genomic, and clinical records, assembled via de-identified patient ID linkage across the three archives. The experiment takes steps toward the realization of quantum-enhanced diagnostic systems and offers a path for subsequent experimental confirmation. We theoretically analyze the potential quantum advantage, present detailed implementation details using Cirq, and describe a roadmap to clinical translation for quantum-enhanced diagnostic tools. Full article

Southern Europe is a climate change hotspot, with rising temperatures threatening the region’s tourism industry. While existing research has focused on supply-side adaptations and macro-level demand shifts, this study examines the demand side—tourist experience and adaptive behaviors at the micro-experiential level, with implications for broader discussions of resilience and adaptive capacity. The study analyzed 6466 TripAdvisor reviews from 11 open-air heritage sites in Italy, Greece, Spain, and Malta, linking reviews to site-relative thermal exposure tertiles and applying dictionary-based text matching with mixed-effects models. Under hotter conditions, tourists were significantly more likely to mention heat, thermal discomfort, and coping resources such as shade and drinking water, with co-occurrence patterns indicating that discomfort and relief-oriented infrastructure are narrated together. Yet overall satisfaction ratings remained uniformly high and statistically unaffected by temperature, revealing a paradox wherein experiential strain intensifies while evaluative scores stay stable. These findings suggest patterns consistent with behavioral and cognitive adaptive responses, whereby positive evaluations are maintained despite heightened thermal stress, and indicate that narrative-based indicators may capture experiential shifts that conventional satisfaction metrics miss. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

The suicide rate in the U.S. has increased in the last two decades despite continued efforts to mitigate risks. This study explored potential variability in suicide rates by occupation in Hawaiʻi by analyzing 2430 death certificates from 2013 to 2023. Of these, 1988 suicide deaths occurred among individuals aged 20 to 64 years old, who constituted the study sample. Suicide death was identified using ICD-10 underlying cause of death codes X60–X84 and Y87. Occupations were coded using the 1990 Census Bureau classification scheme, and nearly all records included open-text occupation information, supplemented by business/industry descriptions. The numerator was calculated as the 11-year suicide total by occupation; the denominator was based on the annual PERWT population estimate (U.S. Census/IPUMS data) over the same 11-year period. The mean victim age was 41, and 78% were males, with notable variation by occupation. Occupations with the highest suicide rates included carpenters, construction, farmers and fishers, musicians and artists, as well as landscapers and groundskeepers, comparable to national and a few other state-specific reports. The findings are limited, constrained by potential confounding factors and the multi-factorial nature of risk of suicide, imprecise numerator and denominator match, as well as the influence of retirees in the computation of rates. Full article

Reliable evaluation of emotional expression in large language model (LLM) outputs remains methodologically under-specified, particularly for long-form generation where label-only correctness provides limited evidence of affective reliability. A claim-conditioned framework is introduced for cross-model comparison under matched elicitation conditions, with TEAS (Text Emotion Adherence Score) as its core continuous metric. Defined in a shared prototype space induced by a frozen reference encoder, TEAS combines affective separability with entropy-aware uncertainty, enabling reliability assessment beyond discrete agreement within a fixed evaluator. Evaluation is conducted on a controlled synthetic corpus under a ground-truth-free, claim-conditioned protocol across four widely used LLM families (Gemini, GPT, Grok, and Mistral). In addition to overall comparative ordering, auxiliary diagnostic measures are reported to localize failure modes and support interpretation of model behavior, together with Holm-corrected pairwise comparisons, sequence-level drift analysis, and local hyperparameter sensitivity analysis. Empirical results show stable endpoint separation, aggregation-sensitive differences among close models, measurable sequence-level degradation, and stable relative orderings under tested local parameter variations. Overall, the study provides an interpretable and statistically grounded protocol for assessing emotion-expression reliability in LLM-generated text within a fixed reference space rather than as a human gold measure of emotional truth. Full article

Segment Anything Model (SAM)-based few-shot segmentation models traditionally rely solely on annotated reference images as prompts, which inherently limits their accuracy due to an over-reliance on visual cues and a lack of semantic context. This reliance leads to incorrect segmentation, where visually similar objects from different categories are incorrectly identified as the target object. We propose Vision and Language Reference Prompt into SAM (VLP-SAM), a novel few-shot segmentation model that integrates both visual information of reference images and semantic information of text labels into SAM. VLP-SAM introduces a vision-language model (VLM) with pixel–text matching into the prompt encoder for SAM, effectively leveraging textual semantic consistency while preserving SAM’s extensive segmentation knowledge. By incorporating task-specific structures such as an attention mask, our model achieves superior few-shot segmentation performance with only 1.4 M learnable parameters. Evaluations on PASCAL-5ⁱ and COCO-20ⁱ datasets demonstrate that VLP-SAM significantly outperforms previous methods by 6.8% and 9.3% in mIoU, respectively. Furthermore, VLP-SAM exhibits strong generalization across unseen objects and cross-domain scenarios, highlighting the robustness provided by textual semantic guidance. This study offers an effective and scalable framework for few-shot segmentation with multimodal prompts. Full article

Construction safety texts are commonly used only for descriptive statistical analysis, and systematic approaches for uncovering latent semantic risk correlations remain limited. In particular, risk identification and prioritization under unlabeled conditions remain challenging. To address this issue, this study proposes a semantic risk association and ranking framework based on Sentence-BERT (SBERT). First, a domain-specific keyword library is constructed, and representative risk terms are extracted through tokenization, stop-word removal, and TF-IDF weighting. A fine-tuned SBERT model is then employed to generate sentence embeddings. FAISS-based similarity search is applied to match safety inspection records with historical accident reports, enabling automatic identification and ranking of the most relevant accident types. In addition, a seven-day inspection window is introduced to capture the temporal accumulation effect of hazards and support risk assessment without explicit labels. Experiments conducted on 1368 accident reports and 484 inspection records show that the proposed framework achieves an accuracy of 0.75, a recall of 1.00, and an F1-score of 0.8571. Cross-project validation yields an F1-score of 0.5607, and the performance remains stable under 10% noise interference. The results demonstrate that the proposed semantic risk association and ranking framework is effective and robust for practical construction safety management. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article

This paper presents the first prototype of a speech-to-sign language translation system for Kazakh Sign Language (KRSL). The proposed pipeline integrates the NVIDIA FastConformer model for automatic speech recognition (ASR) in the Kazakh language and addresses the challenges of sign language translation in a low-resource setting. Unlike American or British Sign Languages, KRSL lacks publicly available datasets and established translation systems. The pipeline follows a multi-stage process: speech input is converted into text via ASR, segmented into phrases, matched with corresponding gestures, and visualized as sign language. System performance is evaluated using word error rate (WER) for ASR and accuracy metrics for speech-to-sign translation. This study also introduces the first KRSL dataset, consisting of 1200 manually recreated signs, including 95% static images and 5% dynamic gesture videos. To improve robustness under resource-constrained conditions, a Weighted Hybrid Similarity Score (WHSS)-based gesture matching method is proposed. Experimental results show that the FastConformer model achieves an average WER of 10.55%, with 7.8% for isolated words and 13.3% for full sentences. At the phrase level, the system achieves 92.1% accuracy for unigrams, 84.6% for bigrams, and 78.3% for trigrams. The complete pipeline reaches 85% accuracy for individual words and 70% for sentences, with an average latency of 310 ms. These results demonstrate the feasibility and effectiveness of the proposed system for supporting people with hearing and speech impairments in Kazakhstan. Full article

Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent $40-50\%$ gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article

This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams—where players act as words and collective play conveys meaning—the proposed methodology models tactical configurations as compositional semantic structures. Each player is represented as a multidimensional vector integrating technical, physical, and psychological attributes; team profiles are aggregated through contextual weighting into a higher-level semantic representation. Within this shared vector space, tactical templates such as high press, counterattack, or possession build-up are encoded analogously to linguistic concepts. Their alignment with team profiles is evaluated using vector-distance metrics, enabling the computation of tactical “fit” and opponent-exploitation potential. A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations, accompanied by fine-grained diagnostic insights at the attribute level. Evaluation through synthetic scenarios and a pilot study with real match data establishes internal consistency and feasibility of the approach; operational validity in live coaching contexts remains an open question for future prospective validation. Beyond football, the framework offers a potentially generalizable approach for collective decision-making in team-based domains—ranging from basketball and hockey to cooperative robotics and human–AI coordination systems. The paper concludes by outlining future directions toward real-world data integration, predictive simulation, and the validation work required before operational deployment. Full article

Background and Objective: Accurate classification of intestinal polyps is crucial for preventing colorectal cancer but is hindered by visual similarity among subtypes and endoscopic variability. While deep learning aids in diagnosis, single-modal models face efficiency–accuracy trade-offs and ignore pathological semantics. We propose a multimodal framework that integrates endoscopic images with structured pathological descriptions to bridge this gap. Methods: We propose LPA-Tuning CLIP, which incorporates three key innovations: replacing CLIP’s instance-level contrastive loss with cross-modal projection matching (CMPM) with ID loss to explicitly optimize intraclass compactness and interclass separation through label-aware image-text similarity matrices; introducing structured clinical semantic templates that encode WHO diagnostic criteria into hierarchical text prompts for consistent pathology annotations; and developing medical-aware augmentation that preserves lesion features while reducing domain shifts. Results: The experimental results demonstrate that our proposed method achieves an accuracy of 85.8% and an F1 score of 0.862 on the internal test set, establishing a new state-of-the-art performance for intestinal polyp classification. Conclusions: This study proposes a multimodal polyp classification paradigm that achieves 85.8% accuracy on three-subtype classification via endoscopic image-pathology text joint representation learning, outperforming unimodal baselines by 8.7% and a multimodal baseline by 4.3%. Full article