Search Results (117)

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like the anterior temporal lobe and temporoparietal junction, and GC to a fronto-striatal procedural network encompassing Broca’s area and the basal ganglia. Moreover, a central theme is the integration of Chomsky’s theories of Universal Grammar (UG), which posits an innate human linguistic endowment, with their neurobiological correlates. This integration analysis bridges foundational models that first mapped syntax (Friederici’s work) to distinct neural pathways with contemporary network-based theories that view grammar as an emergent property of dynamic, inter-regional neural oscillations. Furthermore, we examine the genetic factors influencing ID and GC, including genes implicated in neurodevelopmental and neurodegenerative disorders. A comparative anatomical perspective across human and non-human primates illuminates the evolutionary trajectory of the language-ready brain. Also, we emphasize that, clinically, ID and GC serve as sensitive neurocognitive markers whose power lies in their often-dissociable profiles. For instance, the primary decline of ID in Alzheimer’s disease contrasts with the severe grammatical impairment in nonfluent aphasia, aiding in differential diagnosis. Importantly, as non-invasive and scalable metrics, ID and GC also provide a critical complement to gold-standard but costly biomarkers like CSF and PET. Finally, the review considers the emerging role of AI and Natural Language Processing (NLP) in automating these linguistic analyses, concluding with a necessary discussion of the critical challenges in validation, ethics, and implementation that must be addressed for these technologies to be responsibly integrated into clinical practice. Full article

With accelerating urbanization, the preservation and adaptive renewal of historic urban environments have emerged as critical challenges in the field of urban science. Among various morphological attributes, spatial openness plays a fundamental role in shaping visual perception and influencing human well-being, but remains insufficiently examined within the context of historic streetscapes. This study investigates the spatial configuration of Tangchang Ancient Town in Chengdu, China, to elucidate the relationship between spatial openness and perceptual responses. A mixed-methods approach was employed, integrating semantic differential (SD) surveys with a suite of spatial analysis techniques, including GIS-based viewshed analysis, depth-to-height ratios, building density, and street curvature metrics. The empirical findings reveal that increased spatial openness is positively associated with visual comfort, while reduced openness contributes to a heightened sense of enclosure and psychological stress. Mediating factors, such as sky visibility and natural lighting conditions, were identified as significant, with elevation angle and curvature further enriching the explanatory framework. Drawing on these insights, this study proposes a set of context-sensitive spatial design strategies tailored to varying degrees of openness. These include enhancing vertical openness through building form regulation, improving lighting and sky access, integrating vegetation more effectively, and activating corner spaces to support spatial legibility and visual interest. This research contributes to the growing discourse on evidence-based urban design by linking quantifiable spatial parameters with perceptual and affective outcomes. The proposed framework offers practical guidance for the sustainable conservation and transformation of historic urban areas undergoing contemporary urbanization pressures. Full article

PHP is the most widely used server-side programming language, but it remains highly susceptible to diverse classes of vulnerabilities. Static Application Security Testing (SAST) tools are commonly adopted for vulnerability detection; however, their evaluation lacks systematic criteria capable of quantifying information loss and uncertainty in analysis. Existing approaches, often based on small real-world case sets or heuristic sampling, fail to control experimental entropy within test cases. This uncontrolled variability makes it difficult to measure the information gain provided by different tools and to accurately differentiate their performance under varying levels of structural and semantic complexity. In this paper, we have developed a systematic evaluation framework for PHP SAST tools, designed to provide accurate and comprehensive assessments of their vulnerability detection capabilities. The framework explicitly isolates key factors influencing data flow analysis, enabling evaluation over four progressive dimensions with controlled information diversity. Using a benchmark instance, we validate the framework’s feasibility and show how it reduces evaluation entropy, enabling the more reliable measurement of detection capabilities. Our results highlight the framework’s ability to reveal the limitations in current SAST tools, offering actionable insights for their future improvement. Full article

Immersive technologies are increasingly acknowledged as powerful tools in sustainability education, capable of fostering deeper engagement and emotional resonance. This study investigates the potential of 360° VR storytelling to enhance learning through embodied knowledge, attitudinal change, and emotional awareness. Conducted within the EMOTIONAL project, the research explores a first-person narrative told from the perspective of a ceramic object rooted in Italian cultural heritage, designed to facilitate meaningful, affective learning. The present study addresses the following research questions: RQ1 Can 360° VR story-living narrations effectively promote embodied learning and semantic and attitudinal shifts in the context of sustainability education? RQ2 What added insights can be gained from integrating subjective assessments with physiological measures? To this end, a hybrid assessment framework was developed and validated, combining subjective self-report tools (including attitudinal scales, semantic differential analysis, and engagement metrics) with objective physiological measures, specifically Electrodermal Activity (EDA). Sixty participants, including students and entrepreneurs, experienced the immersive narrative, and a subset underwent physiological tracking to evaluate the effectiveness of the experience. The findings show that immersive storytelling can enhance emotional and cognitive engagement, producing shifts in semantic interpretation, self-perceived knowledge, and attitudes toward material culture. A convergence of high emotional engagement, embodied learning, and technology acceptance was observed, although individual differences emerged based on prior experience and disciplinary background. EDA data offered complementary insights, identifying specific moments of heightened arousal during the narrative. The study demonstrates that emotionally driven immersive narratives (supported by integrated assessment methods) can make abstract sustainability values more tangible and personally resonant, thereby fostering more reflective and relational approaches to sustainable consumption and production. Full article

Time series clustering plays a vital role in various analytical and pattern recognition tasks by partitioning structurally similar sequences into semantically coherent groups, thereby facilitating downstream analysis. However, building high-quality clustering models remains challenging due to three key issues: (i) capturing dynamic shape variations across sequences, (ii) ensuring discriminative cluster structures, and (iii) enabling end-to-end optimization. To address these challenges, we propose KDiscShapeNet, a structure-aware clustering framework that systematically extends the classical k-Shape model. First, to enhance temporal structure modeling, we adopt Kolmogorov–Arnold Networks (KAN) as the encoder, which leverages high-order functional representations to effectively capture elastic distortions and multi-scale shape features of time series. Second, to improve intra-cluster compactness and inter-cluster separability, we incorporate a dual-loss constraint by combining Center Loss and Supervised Contrastive Loss, thus enhancing the discriminative structure of the embedding space. Third, to overcome the non-differentiability of traditional K-Shape clustering, we introduce Differentiable k-Shape, embedding the normalized cross-correlation (NCC) metric into a differentiable framework that enables joint training of the encoder and the clustering module. We evaluate KDiscShapeNet on nine benchmark datasets from the UCR Archive and the ETT suite, spanning healthcare, industrial monitoring, energy forecasting, and astronomy. On the Trace dataset, it achieves an ARI of 0.916, NMI of 0.927, and Silhouette score of 0.931; on the large-scale ETTh1 dataset, it improves ARI by 5.8% and NMI by 17.4% over the best baseline. Statistical tests confirm the significance of these improvements (p < 0.01). Overall, the results highlight the robustness and practical utility of KDiscShapeNet, offering a novel and interpretable framework for time series clustering. Full article

As an essential medium for the cultural narrative of architectural space, studying the cognitive transformation mechanisms of traditional ethnic decorative patterns is critical for their effective preservation and innovative application. This research focuses on typical decorative motifs found in She ethnic architectural heritage, systematically classifying them into five categories—animal, plant, human figure, totem, and geometric—based on symbolic themes, formal structure, and cultural function. Correspondingly, 20 sets of standardized black-and-white line drawing stimuli were developed for experimental use. Methodologically, this study utilized the EyeLink 1000 eye-tracking system to acquire real-time gaze metrics, including fixation duration and saccadic amplitude, as well as pupil dilation responses from participants engaged in a controlled pattern observation task. Immediately after observation, participants completed a semantic differential assessment using a five-point Likert scale. Data analysis employed descriptive statistics, analysis of variance (ANOVA), Kruskal–Wallis tests, and Bonferroni-adjusted post hoc comparisons (α = 0.05). Attention allocation was further examined through heatmaps and gaze trajectory visualizations to provide comprehensive insight into visual engagement. Two principal findings were identified: first, male participants showed a predominant focus on holistic structural composition and cultural symbol representation, whereas female participants exhibited a processing bias towards fine details; second, concrete symbols imbued with historical significance elicited more pronounced emotional responses, while abstract geometric patterns necessitated formal reconstruction to enhance cognitive accessibility. These findings offer empirical support for gender-inclusive architectural design strategies and inform practical approaches for safeguarding cultural heritage within contemporary architectural environments. Consequently, modern reinterpretation of traditional decorative patterns should balance cultural narrative fidelity with functional adaptation, achieving inclusive expression through contextual reconstruction and interactive design strategies. Future research directions include expanding participant demographics to encompass cross-cultural cohorts and incorporating multimodal neuroimaging techniques to elucidate the underlying cognitive and affective mechanisms, thereby advancing the sustainable transmission and innovation of ethnic cultural heritage. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

Power cabinet OCR enables real-time grid monitoring but faces challenges absent in generic text recognition: 7.5:1 scale variation between labels and readings, tabular layouts with semantic dependencies, and electrical constraints (220 V ± 10%). We propose SACC (Structure-Aware and Condition-Constrained), an end-to-end framework integrating structural perception with domain constraints. SACC comprises (1) MAF-Detector with adaptive dilated convolutions ($r\in \{1,3,5\}$) for multi-scale text; (2) SA-ViT, combining Vision Transformer with GCN for tabular structure modeling; and (3) DCDecoder, enforcing real-time electrical constraints during decoding. Extensive experiments demonstrate SACC’s effectiveness: achieving 86.5%, 88.3%, and 83.4% character accuracy on PCSTD, YUVA EB, and ICDAR 2015 datasets, respectively, with consistent improvements over leading methods. Ablation studies confirm synergistic improvements: MAF-Detector increases recall by 12.3SACC provides a field-deployable solution achieving 30.3 ms inference on RTX 3090. The co-design of structural analysis with differentiable constraints establishes a framework for domain-specific OCR in industrial and medical applications. Full article

This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves overall population health. The research proposes a conceptual model with four main elements: classifying countries according to their Gini index along with their performance on SDG 3 and SDG 10; analyzing how income inequality and progress on SDG 10 influence health outcomes (SDG 3); categorizing countries based on the strength of links between inequality measures and well-being indicators; and interpreting these results in the context of Ukraine’s European integration aspirations. Methodologically, cluster analysis, correlation and regression models, and semantic differentiation are applied. The findings show that a reduction in income inequality positively affects health and well-being. Nonetheless, Ukraine continues to face considerable structural and institutional hurdles. From a governance standpoint, the study highlights the need for cohesive policies that integrate economic, health, and social dimensions. Effective public management should coordinate national reforms to match EU healthcare and social policy standards. Strengthening institutions, ensuring fair access to healthcare services, and adopting inclusive policy instruments remain crucial to advancing both SDG 3 and SDG 10 targets, as well as supporting Ukraine’s broader integration with the European Union. Full article

As a core task in remote sensing image processing, change detection plays a vital role in dynamic surface monitoring for environmental management, urban planning, and agricultural supervision. However, existing methods often suffer from missed detection of small targets and pseudo-change interference, stemming from insufficient modeling of multi-scale feature coupling and spatio-temporal differences due to factors such as background complexity and appearance variations. To this end, we propose a Differential-Perception-Enhanced Multi-Scale Attention Network for Remote Sensing Image Change Detection (MDNet), an optimized framework integrating multi-scale feature extraction, cross-scale aggregation, difference enhancement, and context modeling. Through the parallel collaborative mechanism of the designed Multi-Scale Feature Extraction Module (EMF) and Cross-Scale Adjacent Semantic Information Aggregation Module (CASAM), multi-scale semantic learning is strengthened, enabling fine-grained modeling of change targets of different sizes and improving small-target-detection capability. Meanwhile, the Differential-Perception-Enhanced Module (DPEM) and Transformer structure are introduced for global–local coupled modeling of spatio-temporal differences. They enhance spectral–structural differences to form discriminative features, use self-attention to capture long-range dependencies, and construct multi-level features from local differences to global associations, significantly suppressing pseudo-change interference. Experimental results show that, on three public datasets (LEVIR-CD, WHU-CD, and CLCD), the proposed model exhibits superior detection performance and robustness in terms of quantitative metrics and qualitative analysis compared with existing advanced methods. Full article

What if machines could seamlessly translate between the visual richness of images and the semantic depth of language with mathematical precision? This paper presents a theoretical and empirical analysis of five novel cross-modal Wasserstein adversarial translation networks that challenge conventional approaches to cross-modal understanding. Unlike traditional generative models that rely on stochastic noise, our frameworks learn deterministic translation mappings that preserve semantic fidelity across modalities through rigorous mathematical foundations. We systematically examine: (1) cross-modality consistent dual-critical networks; (2) Wasserstein cycle consistency; (3) multi-scale Wasserstein distance; (4) regularization through modality invariance; and (5) Wasserstein information bottleneck. Each approach employs adversarial training with Wasserstein distances to establish theoretically grounded translation functions between heterogeneous data representations. Through mathematical analysis—including information-theoretic frameworks, differential geometry, and convergence guarantees—we establish the theoretical foundations underlying cross-modal translation. Our empirical evaluation across MS-COCO, Flickr30K, and Conceptual Captions datasets, including comparisons with transformer-based baselines, reveals that our proposed multi-scale Wasserstein cycle consistent (MS-WCC) framework achieves remarkable performance gains—12.1% average improvement in FID scores and 8.0% enhancement in cross-modal translation accuracy—compared to state-of-the-art methods, while maintaining superior computational efficiency. These results demonstrate that principled mathematical approaches to cross-modal translation can significantly advance machine understanding of multimodal data, opening new possibilities for applications requiring seamless communication between visual and textual domains. Full article

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

The research was initiated by the Plečnik House gallery (Ljubljana, Slovenia) and focuses on the 3D architectural reconstruction of the unrealised monument of the Czech military leader Jan Žižka, designed by the Slovenian architect Jože Plečnik. In addition, the experience with the 3D reconstructed monument in the exhibition “Plečnik and the Sacred” was analysed. Using the available references and interpretative approaches, a digital and 3D-printed reconstruction was created that retains Plečnik’s architectural style. The experimental phase included a detailed interpretation of the studied references, 3D modelling, 3D printing, exhibition and experience analysis. The dimensions of the finished 3D-printed model are 52.80 × 55.21 × 44.60 cm. It was produced using stereolithography (SLA) for figurative elements and fused deposition modelling (FDM) for architectural components. The reconstruction was evaluated using participant testing, including semantic differential analysis, comparative studies, and knowledge-based questionnaires. The results showed that architectural elements were reconstructed with an average similarity score of 1.97 out of 5. Statues followed with a score of 1.81, and props, though detailed, met audience expectations, scoring 1.61. Clothing received the lowest score of 1.40. This research emphasises the importance of a hypothetical digital 3D reconstruction of never constructed monument for broader understanding of Plečnik’s legacy. Full article