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We report a method of color-image retrieval based on fuzzy correlation, in which α-cut relations in fuzzy set theory are applied to defining color match and height match of color peaks for synthesizing fuzzy correlation of two color histograms, and RGB space is partitioned into six sub-regions in the experiment for the regional color comparisons. Experimental results show that the efficiency of the color-image retrieval can be effectively improved by this approach.

We report for the first time the theoretical analysis and experimental results of a white-light reconstructed monochromatic 3-D image synthesizing tomograms by multiple rainbow holography with vertical-area partition (VAP) approach. The theoretical and experimental results show that 3-D monochromatic image can be synthesized by recording the master hologram by VAP approach without any distortions either in gray scale or in geometrical position. A 3-D monochromatic image synthesized from a series of medical tomograms is presented in this paper for the first time

This chapter contains sections titled: Recording Plane Division Multiplexing (RDM) in Digital Holography for Resolution Enhancement Development of Digital Holographic Tomography Digital Holographic Interferometry for Phase Distribution Measurement References

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection Network (OSAD-Net) that firstly estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OSAD-Net can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a large-scale purpose-driven affordance dataset v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103 object categories. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods and may also facilitate downstream vision tasks, such as scene understanding, action recognition, and robot manipulation. Specifically, we conducted comprehensive experiments on PADv2 dataset by including 11 advanced models from several related research fields. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is available at https://github.com/lhc1224/OSAD_Net.

Affordance grounding aims to locate objects’ “action possibilities” regions, an essential step toward embodied intelligence. Due to the diversity of interactive affordance, i.e., the uniqueness of different individual habits leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels. Human has the ability that transforms various exocentric interactions into invariant egocentric affordance to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from the exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. However, there is some “interaction bias” between personas, mainly regarding different regions and views. To this end, we devise a cross-view affordance knowledge transfer framework that extracts affordance-specific features from exocentric interactions and transfers them to the egocentric view to solve the above problems. Furthermore, the perception of affordance regions is enhanced by preserving affordance co-relations. In addition, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from 36 affordance categories. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. The code is available via: github.com/lhc1224/Cross-View-AG.

Vital pulp therapy (VPT) is considered a conservative treatment for preserving pulp viability in caries and trauma-induced pulpitis. However, Mineral trioxide aggregate (MTA) as the most frequently used repair material, exhibits limited efficacy under inflammatory conditions. This study introduces an innovative nanocomposite hydrogel, tailored to simultaneously target anti-inflammation and dentin mineralization, aiming to efficiently preserve vital pulp tissue. The L-(CaP-ZnP)/SA nanocomposite hydrogel was designed by combining L-Arginine modified calcium phosphate/zinc phosphate nanoparticles (L-(CaP-ZnP) NPs) with sodium alginate (SA), and was characterized with TEM, SEM, FTIR, EDX, ICP-AES, and Zeta potential. In vitro, we evaluated the cytotoxicity and anti-inflammatory properties. Human dental pulp stem cells (hDPSCs) were cultured with lipopolysaccharide (LPS) to induce an inflammatory response, and the cell odontogenic differentiation was measured and possible signaling pathways were explored by alkaline phosphatase (ALP)/alizarin red S (ARS) staining, qRT-PCR, immunofluorescence staining, and Western blotting, respectively. In vivo, a pulpitis model was utilized to explore the potential of the L-(CaP-ZnP)/SA nanocomposite hydrogel in controlling pulp inflammation and enhancing dentin mineralization by Hematoxylin and eosin (HE) staining and immunohistochemistry staining. In vitro experiments revealed that the nanocomposite hydrogel was synthesized successfully and presented desirable biocompatibility. Under inflammatory conditions, compared to MTA, the L-(CaP-ZnP)/SA nanocomposite hydrogel demonstrated superior anti-inflammatory and pro-odontogenesis effects. Furthermore, the nanocomposite hydrogel significantly augmented p38 phosphorylation, implicating the involvement of the p38 signaling pathway in pulp repair. Significantly, in a rat pulpitis model, the L-(CaP-ZnP)/SA nanocomposite hydrogel downregulated inflammatory markers while upregulating mineralization-related markers, thereby stimulating the formation of robust reparative dentin. The L-(CaP-ZnP)/SA nanocomposite hydrogel with good biocompatibility efficiently promoted inflammation resolution and enhanced dentin mineralization by activating p38 signal pathway, as a pulp-capping material, offering a promising and advanced solution for treatment of pulpitis.

First-person hand-object interaction anticipation aims to predict the interaction process over a forthcoming period based on current scenes and prompts. This capability is crucial for embodied intelligence and human-robot collaboration. The complete interaction process involves both pre-contact interaction intention (i.e., hand motion trends and interaction hotspots) and post-contact interaction manipulation (i.e., manipulation trajectories and hand pose with contact). Existing research typically anticipates only interaction intention while neglecting manipulation, resulting in incomplete predictions and an increased likelihood of intention errors due to the lack of manipulation constraints. To address this issue, we propose a novel model, PEAR (phrase-based hand-object interaction anticipation), which jointly anticipates interaction intention and manipulation. To handle interaction uncertainty, we employ a twofold approach. Firstly, we perform cross-alignment of verbs, nouns, and images to reduce the diversity of hand movement patterns and object functional attributes, thereby mitigating intention uncertainty. Secondly, we establish bidirectional constraints between intention and manipulation using dynamic integration and residual connections, ensuring consistency among elements and thus overcoming manipulation uncertainty. To rigorously evaluate the performance of the proposed model, we collect a new task-relevant dataset, EGO-HOIP, with comprehensive annotations. Extensive experimental results demonstrate the superiority of our method.

Mouth ulcers, a highly prevalent ailment affecting the oral mucosa, leading to pain and discomfort, significantly impacting the patient’s daily life. The development of innovative approaches for oral ulcer treatment is of great importance. Moreover, a deeper and more comprehensive understanding of mouth ulcers will facilitate the development of innovative therapeutic strategies. The oral environment possesses distinct traits as it serves as the gateway to the digestive and respiratory systems. The permeability of various epithelial layers can influence drug absorption. Moreover, oral mucosal injuries exhibit distinct healing patterns compared to cutaneous lesions, influenced by various inherent and extrinsic factors. Furthermore, the moist and dynamic oral environment, influenced by saliva and daily physiological functions like chewing and speaking, presents additional challenges in local therapy. Also, suitable mucosal adhesion materials are crucial to alleviate pain and promote healing process. To this end, the review comprehensively examines the anatomical and structural aspects of the oral cavity, elucidates the healing mechanisms of oral ulcers, explores the factors contributing to scar-free healing in the oral mucosa, and investigates the application of mucosal adhesive materials as drug delivery systems. This endeavor seeks to offer novel insights and perspectives for the treatment of oral ulcers.

INTRODUCTION: Modern urban lifestyles have fundamentally transformed children's growth environments, indirectly contributing to the proliferation of Nature-Deficit Disorder (NDD). This condition has elevated the prevalence of childhood obesity, attention deficit disorders, and mental health issues to epidemic levels. Existing interventions face significant limitations, particularly in their inability to seamlessly integrate into the daily routines of urban children. OBJECTIVES: Grounded in Socio-Ecological Theory, Self-Determination Theory (SDT), and the Technology Acceptance Model(TAM), this study aims to propose and design a novel IoT-based interactive ecological intervention system. The goal is to create a unified framework that effectively bridges the gap between urban living and nature engagement. METHODS: The system incorporates modular hardware design and a gamified task-driven mechanism, organically integrating outdoor exploration with indoor planting. Through user surveys involving 100 urban children aged 6–12, we constructed and applied partial least squares structural equation modeling (PLS-SEM) to evaluate the intervention's effectiveness. This analysis examined the impact paths of perceived ease of use(PEOU)and gamification experience(GAM)on nature connectedness (NC)and pro-nature behavioral intention(BI), while testing the mediating role of gamification experience. Field observations were also conducted with representative users. CONCLUSION: The structural equation modelling (SEM)results indicate strong explanatory power for the model, with R²values for key variables ranging from 0. 396 to 0. 477. The study confirms that gamification experience plays a critical and significant mediating role between perceived ease of use and nature connectedness (indirect effect β=0. 241, p=0. 003). A more noteworthy finding is that the primary driver of children's future pro-nature behavioral intention is gamification experience itself (β=0. 376, p<0. 001), rather than the nature connectedness it fosters. This research thus concludes that, in digital health interventions for children, intrinsic enjoyment derived from gamification experience serves as a more pivotal long-term motivator for sustained engagement than the educational value of nature connectedness. These insights not only validate a promising prototype intervention system but, more importantly, offer essential theoretical and practical guidance for designing more effective child-focused intervention products in the future.

Masticatory dysfunction induces astrocyte hyperplasia and disrupts brain cholesterol homeostasis, but the specific mechanism by which unilateral mastication drives cognitive decline remains unclear. A rat model of experimental unilateral mastication (EUM) was established via unilateral maxillary anterior and posterior tooth extraction, characterized by asymmetric masseter electromyography (EMG), muscle fiber type distribution, and acetylcholinesterase activity. Behavioral tests assessed learning and memory. Hippocampal changes were analyzed for astrocyte reactivity and synaptic integrity. Targeted cholesterol metabolomics profiled cholesterol species, with correlation analyses exploring links between metabolic perturbations and synaptic dysfunction. EUM rats showed accelerated learning deficits and memory decline. Hippocampal analyses revealed reactive astrocytosis and synaptic degeneration. Metabolomics identified hippocampal cholesterol ester accumulation, with dysregulated biosynthesis and efflux/metabolism pathways. Correlation analyses linked cholesterol dysregulation to synaptic impairment, with reduced 24‐hydroxycholesterol as a key mediator. Unilateral mastication impacts brain health via hippocampal cholesterol dysregulation, suggesting targeting cholesterol metabolism may mitigate associated cognitive decline. These findings highlight a potential connection between masticatory dysfunction and cognitive decline mediated by cholesterol metabolic pathways, providing mechanistic insights into oral‐brain axis interactions with implications for preventing cognitive impairment. Unilateral mastication, a common oral habit, induces cognitive decline in rats by disrupting hippocampal cholesterol metabolism. It triggers astrocyte hyperplasia, enhances cholesterol synthesis, impairs transport/degradation, and reduces 24‐hydroxycholesterol (24‐OHC), ultimately damaging synaptic function. Our study identifies cholesterol metabolic pathways as potential targets for preventing oral‐brain axis‐related cognitive impairment.