Search Results (1,060)

Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks. Full article

Seagrass meadows are important blue carbon habitats, but their patchy distribution in intertidal zones makes accurate UAV mapping difficult under shallow water cover and complex sediment backgrounds. This study developed a fine-grained semantic segmentation framework with explainability analysis to improve intertidal seagrass extraction from high-resolution UAV visible and multispectral imagery. Exposed seagrass (ESG) and shallow-submerged seagrass (SSG) were mapped separately to represent two observable intertidal states. Visible bands, multispectral bands, and vegetation indices were used as model inputs. U-Net and DeepLabV3+ served as baseline models, while UPerNet-ConvNeXtV2-Tiny was tested under the same settings. Kernel SHAP and permutation importance were used to assess feature contributions. UPerNet-ConvNeXtV2-Tiny achieved the best performance, with an overall accuracy (ACC), mean Intersection over Union (mIoU), and F1 score of 97.45%, 94.63%, and 97.23%, respectively. It outperformed the baseline models in suppressing background interference, preserving patch morphology, and reducing omission errors in weak response and boundary areas, while demonstrating better cross-scenario applicability in independent test areas. Explainability analysis showed that model discrimination was mainly associated with red and green-related features, especially RGB-R, MS-R, MS-G, RGB-G, and NGRDI. ESG and SSG showed different feature dependence patterns, indicating that high-resolution UAV imagery can support accurate seagrass mapping and reveal spectral differences between intertidal seagrass states. These findings provide a practical framework for UAV-based intertidal seagrass mapping and monitoring and offer guidance for feature selection and model explainability analysis. Full article

This study reconsiders the meaning of the term “miqweh” in early rabbinic literature and challenges its common rendering as “immersion pool” or “ritual bath.” Surveying biblical and Second Temple texts, it demonstrates that “miqweh” originally indicated a general “gathering of water” and was not associated with purificatory bathing. Only in early rabbinic sources did the term acquire a specialized, legal-technical sense, referring to pooled water within the narrow context of ritual purification through immersion in water. This semantic shift likely derives from rabbinic interpretation of Leviticus 11:36 and reflects broader patterns of legal abstraction in rabbinic discourse. Crucially, the study shows that “miqweh” never referred to the physical installation or structure containing the water; instead, terms such as “mǝʿārâ” (“cave”) and “bêt haṭṭǝbîlâ” (“place of immersion”) were used for such spaces. These two terms, the study tentatively suggests, likely emerged before the more abstract “miqweh” was coined. Full article

Background: Chronic pain and early cognitive vulnerability frequently co-occur in older women and may share inflammatory mechanisms. Objective: We examined whether dietary inflammatory load, assessed using the dietary inflammatory index (DII), and Mediterranean-derived dietary patterns (Mediterranean diet (MED), Dietary Approaches to Stop Hypertension (DASH), Mediterranean–DASH Intervention for Neurodegenerative Delay (MIND) and anti-inflammatory Mediterranean Diet (AnMED)) are associated with pain and early cognitive outcomes. Methods: We conducted a cross-sectional study among women aged ≥50 years recruited from community pharmacies and healthcare centers in the Comunidad Valenciana (Spain). Dietary intake was assessed using the PREDIMED Food Frequency Questionnaire to derive DII and dietary pattern scores. Outcomes included pain intensity, subjective memory complaints (SMC) and semantic verbal fluency (SVF). Analyses were adjusted for sociodemographic, clinical and lifestyle covariates, with false discovery rate correction. Results: Complete case samples comprised 470 women for SMC and SVF, with 328 also included for pain. Higher DII was consistently associated with greater pain intensity, increased odds of SMC, and lower SVF scores. No dietary pattern was associated with pain after correction. AnMED was associated with lower odds of SMC and higher SVF, while DASH was also positively associated with SVF. Bridge analysis showed that lower DII was associated with both MIND and AnMED, with a stronger association for AnMED. Conclusions: Dietary inflammatory load showed the most consistent associations with pain and early cognitive vulnerability, whereas Mediterranean-derived patterns differed in their inflammatory and cognitive relevance. Full article

The study aimed to examine the role of artificial intelligence in preservice science teachers’ analogical reasoning by comparing the features of analogy designs produced with and without artificial intelligence. The research was conducted with 133 preservice science teachers at a public university in Türkiye. Participants were divided into two conditions: those who designed analogies using artificial intelligence (n = 62) and those who designed analogies without artificial intelligence (n = 71). Analogy design products were analyzed using descriptive analysis, and categorical data derived from these analyses were examined through Pearson’s chi-square tests. In addition, qualitative data obtained from structured interviews with the AI-supported condition were analyzed using content analysis. The results revealed significant differences between the groups in several dimensions of analogy design, presentation format, semantic distance, analogical association, wealth level, and the identification of limitations. Analogies designed with artificial intelligence were more frequently pictorial–verbal, involved both close and remote semantic distance, integrated structural–functional associations, and exhibited extended analogy characteristics. Interview results indicated that preservice science teachers primarily used AI for idea generation, visualization, and creative exploration rather than for generating factual knowledge. These results contribute to the literature by highlighting the potential role of AI in supporting representational transformation processes within science teacher education. Full article

This study investigates the real-time processing of quantifier scope ambiguity using self-paced reading, comparing surface and inverse interpretations of a–every sentences. Reaction time data revealed greater processing difficulty for inverse scope, supporting an account grounded in semantic computation rather than purely heuristic-based parsing. Offline interpretation results further indicate that abstract structural operations are engaged during scope computation. Surprisal estimates derived from a pre-trained autoregressive language model GPT-2 small successfully predicted both the locus and direction of scope effects observed in the human data, with partial convergence in effect magnitude. When surprisal was not considered, language experience, but not working memory, more robustly accounted for variability in complex scope interpretations. Crucially, incorporating individual differences into surprisal-based analyses showed that both language experience and working memory capacity modulate surprisal effects in scope processing: higher proficiency and greater working memory are associated with increased sensitivity to surprisal, whereas lower levels show reduced or even reversed effects, suggesting weaker engagement in expectation-based processing. Together, these findings highlight the interplay among structural complexity, cognitive resources, language experience, and expectation-based mechanisms in shaping real-time semantic processing. Full article

The aim in single-image deraining tasks is to remove rain streaks from degraded images while preserving scene structures and fine details. However, existing deep learning-based methods often face a trade-off between restoration quality and computational efficiency, and many models struggle to capture hierarchical information effectively under complex rain conditions. To address these limitations, we propose a lightweight cross-gated hierarchical transformer for image deraining. The proposed network adopts a five-stage encoder–decoder architecture with Multi-head Feature-wise Attention (MFA) to efficiently model channel-wise dependencies while reducing the computational burden associated with conventional self-attention. In addition, an Enhanced Gated Depthwise Feed-Forward Network (EGDFN) is introduced to obtain refined feature representations with improved efficiency, and a Cross-Level Feature Refinement (CLFR) module is designed to enhance information exchange between corresponding encoder and decoder stages, thereby strengthening hierarchical feature integration and preserving structural details. The network is trained using a single SSIM-based loss, which enhances the structural fidelity of the restored results. Extensive experiments on four synthetic datasets, two real-world datasets, and a downstream semantic segmentation benchmark demonstrate that the proposed method consistently achieves strong restoration performance, producing cleaner outputs with sharper details and improved effectiveness for subsequent vision tasks. Full article

Autonomous driving necessitates a robust 3D perception system that includes accurate object detection, tracking, and segmentation. While recent low-cost camera-based methods have demonstrated promising results, these systems are prone to performance degradation under poor lighting conditions or adverse weather, resulting in considerable localization errors. In this paper, we present a novel approach called Frequency-aware Depth Association Radar-Camera (FDARC) Fusion. This method aims to generate semantically rich and spatially accurate Bird’s-Eye-View (BEV) feature maps by integrating data from both camera and radar sensors. Initially, the image features are enhanced using frequency-aware techniques. Subsequently, these features are transformed into BEV representation with the assistance of depth information estimated from both sensor modalities and radar measurements. This process, known as Depth Association (DA), facilitates more precise BEV representations. Following this, a Temporal and Deformable Cross-Fusion (TDCF) layer is utilized to encode multi-modal feature maps into a unified space-time dimension representation. Extensive experiments conducted on the nuScenes dataset show that FDARC achieves state-of-the-art performance in 3D detection tasks, markedly outperforming baseline models on the nuScenes val set using a ResNet-50 backbone, which attains 53.5% nuScenes Detection Score (NDS) and 44.7% mean Average Precision (mAP). Full article

As the global population continues to age, mental health issues such as depression, anxiety, stress, loneliness, and social isolation among older adults are receiving increasing attention. The built environment is closely associated with older adults’ daily mobility, environmental perception, social participation, and mental health and well-being, but the evidence remains heterogeneous across spatial contexts, environmental indicators, and study designs. Previous umbrella reviews have summarized broad links between the built environment and healthy aging, but less attention has been paid to recent original empirical studies published after the COVID-19 pandemic, the distinction between objective environmental exposure and subjective environmental perception, and the role of social participation as a pathway linking environmental conditions to mental health and well-being. This study employs a systematic literature review approach, searching and screening peer-reviewed empirical studies published between 2015 and January 2026 that focus on the associations between the built environment and older adults’ mental health and well-being. PubMed, Scopus, and Web of Science databases were used for searching, supplemented by manual searching. After title and abstract screening and full-text evaluation, a total of 60 studies were included. Subsequently, a comprehensive analysis was conducted on aspects such as research design, spatial scale, environmental indicators, types of mental health outcomes, and potential pathways of action. In this review, core mental health and well-being outcomes included negative outcomes, such as depression, anxiety, stress, psychological distress, loneliness, and social isolation, and positive outcomes, such as life satisfaction, subjective well-being, psychological well-being, and mental well-being. Social participation was examined as a behavioral and psychosocial pathway rather than as a core outcome. Emerging methods, including street-view image analysis, FCN-based semantic segmentation, and XGBoost-SHAP, were examined because they can refine environmental exposure measurement and support variable-importance interpretation, rather than because they provide causal evidence. The main synthesis suggests that several built environment factors are associated with older adults’ mental health and well-being, although the strength and consistency of evidence vary across outcome types, spatial contexts, and study designs. (1) Exposure to green and blue spaces, quality of public open spaces, walkability and accessibility, accessibility of neighborhood facilities and services, housing and living conditions, and positive environmental perception are mostly associated with lower levels of depression, anxiety, stress, and loneliness, as well as higher levels of life satisfaction, subjective well-being, and psychological well-being. (2) Conversely, adverse environmental exposures such as proximity to roads, pollution, non-vegetated spaces, and high-intensity urbanization are more likely to exacerbate negative psychological outcomes. Existing evidence also suggests that social participation is one of the important behavioral pathways through which the built environment is linked to the mental health of older adults, but it is not the only mechanism. (3) In addition, the direction and intensity of environmental associations remain heterogeneous under different spatial scales, indicator types, and research methods. Overall, this review contributes by organizing recent empirical evidence into a built environment–social participation–mental health and well-being framework, while emphasizing that most findings should be interpreted primarily as evidence of association rather than as stable or uniform causal effects. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article

The canopy closure stage is a critical phase of rice (Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R² of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value (K), initial growth coefficient (a), growth rate coefficient (b), maximum instantaneous growth rate (MGR), and days to maximum growth rate (Tm). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production. Full article

Course recommendation systems based on deep learning have demonstrated powerful feature extraction capabilities in dealing with information overload in massive open online courses (MOOCs), and have become an irreplaceable mainstream method. However, the learner–course interactions are usually scarce in reality, which limits the representation power of course recommendation. In addition, the contribution of learner and course attribute information to course recommendation has not been sufficiently explored by most existing methods. To tackle these challenges, a personalized course recommendation model based on attribute-interaction joint encoding and hypergraph reconstruction (AIHR-PCRM) is proposed in this paper. Specifically, a course hypergraph reconstruction (CHR) method is designed to construct higher-order associations for each course to explore more reliable global collaboration signals. Unlike existing hypergraph constructions that directly take learners as hyperedges, CHR explicitly couples three steps, including invalid learner elimination, high-order reachability induction, and similarity-based hyperedge filtering, to substantially raise the signal-to-noise ratio of the resulting hypergraph. Based on this, a hypergraph global collaborative learning module (HGM) can alleviate the issue of data sparsity. Then, a joint encoding module (JEM) is utilized to enhance learner behavior sequence representations by simultaneously fusing hypergraph-level global signals with attribute-level local semantics. Finally, a bidirectional self-attention module (BSM) is introduced to blend the contextual information of the learner behavior sequence, and to further provide a recommendation. Experimental results on three real-world datasets revealed that the proposed model has already achieved the best recall and ndcg scores compared to those of several existing models. Full article

The integration of domain-specific conversational assistants into administrative and payroll information systems has the potential to improve operational efficiency and decision support in regulated enterprise environments. However, empirical evidence regarding their behavior, perceived usefulness, and operational performance in real-world administrative workflows remains limited. This paper introduces the design and empirical evaluation of a document-grounded conversational assistant for payroll management, implemented using a Retrieval-Augmented Generation (RAG) architecture enhanced with an evidence-grounded response validation layer designed to constrain responses to retrieved payroll documentation, enforce source traceability, and reduce unsupported content generation in administrative workflows. The primary evaluation dataset consisted of 200 controlled interactions generated by 20 experienced payroll administrators who completed predefined payroll-related tasks. In addition, an auxiliary dataset of approximately 800 supplementary interactions, produced during iterative expert testing and operational system validation activities, contributed to the broader operational interaction corpus used for exploratory robustness analysis and deployment-oriented system evaluation. The collected interaction logs were analyzed as an anonymized operational interaction corpus combining controlled participant sessions and supplementary expert-assisted testing activities. Consequently, the reported statistical findings should be interpreted primarily as an anonymized operational interaction dataset rather than strictly controlled inferential evidence. Quantitative assessment included response latency, user-perceived accuracy and satisfaction, and automatic evaluation metrics (BLEU, ROUGE-L, and semantic similarity), complemented by qualitative feedback collected through structured user sessions. The results indicate moderate levels of user-perceived accuracy and satisfaction. Semantic similarity demonstrates strong correlations with human evaluations of response quality, showing stronger associations than traditional n-gram metrics, while response latency shows only weak association with satisfaction within acceptable operational thresholds. The findings provide preliminary evidence that evidence-grounded validation mechanisms may support response traceability and reduction in unsupported content generation in administrative environments. This research contributes a domain-specific RAG-based conversational assistant incorporating evidence-grounded response validation mechanisms, together with an exploratory operational evaluation framework for assessing conversational AI behavior in compliance-sensitive enterprise environments. Full article

We develop a comparative synthesis of quantum-information geometry beyond complex numbers, with emphasis on what different algebraic frameworks contribute to information-processing structure rather than on their formal novelty alone. The organizing idea is a layer-by-layer test of the standard complex Hilbert-space formalism: each non-complex or deformed framework modifies the scalar field, phase group, projective state space, Born-probability semantics, composition rule, measurement geometry, symmetry algebra or representation category. The central thesis is that such frameworks are physically meaningful when they identify which assumptions make complex quantum mechanics operationally stable: positive probabilities, associative multipartite composition, reversible dynamics, experimentally testable phases, locality constraints, informationally complete measurements, error bases and clear operational semantics. Real quantum theory probes the necessity of complex phases and local tomography; quaternionic quantum mechanics probes non-Abelian phase while retaining associativity and admitting complex embeddings; octonionic proposals probe the boundary where exceptional geometry survives but generic circuit composition is obstructed by non-associativity; Jordan algebras test ordered probabilistic state spaces; Clifford algebras and Bott periodicity provide the spinorial and topological grammar connecting gates, Hopf maps and periodic dimensions; and quantum-group or q-deformed constructions probe coproducts, braiding and representation categories rather than scalar amplitudes. We distinguish three roles that are often conflated: genuine hypercomplex kinematics, Hopf-fibration coordinates for ordinary complex multipartite entanglement, and deformed algebraic or categorical structures. The resulting map separates established equivalence and experimental-constraint results from useful representation tools and speculative programs, while identifying concrete open problems for non-complex quantum information. Full article

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article