Search Results (87)

Landscape quality significantly impacts residents’ well-being through visual perception, particularly among the elderly who exhibit heightened sensitivity to environmental stimuli. Therefore, this study investigates how landscape configurations influence emotional and physiological responses in older adults under controlled visual conditions. This study selected representative outdoor activity sites in northern Chinese cities and designed five landscape scenarios by adjusting the green coverage ratio (GCR) and landscape composition. Participants (mean age 64.8) reported feelings of pleasure, relaxation, and fatigue while viewing screen-based landscape images, with simultaneous recording of attention-to-interest area (AOIA), pupil diameter range (PD), and electroencephalogram (EEG) data. Research findings reveal a non-linear relationship between the GCR and emotional and physiological responses among elderly populations: when the GCR increased from 18.4% to 38.1%, participants reported significantly heightened feelings of pleasure and relaxation, alongside marked reductions in fatigue-related physiological indicators. However, when the GCR further rose to 48.5%, both reported subjective measures and physiological indicators deteriorated among elderly participants. Under equivalent green coverage conditions, water features within natural settings enhance visual focus on natural elements more effectively than purely green landscapes. Women demonstrated greater sensitivity to changes in the GCR. Correlation analysis further indicated that visual attention among the elderly positively correlated with positive emotions and negatively correlated with fatigue-related physiological responses. This research provides valuable guidance for green space design. Full article

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings. Full article

Addressing the issue of fruit recognition and localization failures in harvesting robots due to severe occlusion by branches and leaves in complex orchard environments, this paper proposes an occlusion avoidance method that combines a lightweight YOLOv8n model, developed by Ultralytics in the United States, with active perception. Firstly, to meet the stringent real-time requirements of the active perception system, a lightweight YOLOv8n model was developed. This model reduces computational redundancy by incorporating the C2f-FasterBlock module and enhances key feature representation by integrating the SE attention mechanism, significantly improving inference speed while maintaining high detection accuracy. Secondly, an end-to-end active perception model based on ResNet50 and multi-modal fusion was designed. This model can intelligently predict the optimal movement direction for the robotic arm based on the current observation image, actively avoiding occlusions to obtain a more complete field of view. The model was trained using a matrix dataset constructed through the robot’s dynamic exploration in real-world scenarios, achieving a direct mapping from visual perception to motion planning. Experimental results demonstrate that the proposed lightweight YOLOv8n model achieves a mAP of 0.885 in apple detection tasks, a frame rate of 83 FPS, a parameter count reduced to 1,983,068, and a model weight file size reduced to 4.3 MB, significantly outperforming the baseline model. In active perception experiments, the proposed method effectively guided the robotic arm to quickly find observation positions with minimal occlusion, substantially improving the success rate of target recognition and the overall operational efficiency of the system. The current research outcomes provide preliminary technical validation and a feasible exploratory pathway for developing agricultural harvesting robot systems suitable for real-world complex environments. It should be noted that the validation of this study was primarily conducted in controlled environments. Subsequent work still requires large-scale testing in diverse real-world orchard scenarios, as well as further system optimization and performance evaluation in more realistic application settings, which include natural lighting variations, complex weather conditions, and actual occlusion patterns. Full article

In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time deployment. The Transformer architecture has become a fundamental component of high-performance models. However, in complex visual environments, shifted window attention mechanisms exhibit inherent limitations: although computationally efficient, local window constraints impede cross-region semantic integration, while deep feature processing obstructs robust representation learning. To address these challenges, we propose DAR-Swin (Dual-Attention Revamped Swin Transformer), enhancing the framework through two complementary attention mechanisms. First, Scalable Self-Attention universally substitutes the standard Window-based Multi-head Self-Attention via sub-quadratic complexity operators. These operators decouple spatial positions from feature associations, enabling position-adaptive receptive fields for comprehensive contextual modeling. Second, Latent Proxy Attention integrated before the classification head adopts a learnable spatial proxy to integrate global semantic information into a fixed-size representation, while preserving relational semantics and achieving linear computational complexity through efficient proxy interactions. Extensive experiments demonstrate significant improvements over Swin Transformer Base, achieving 87.3% top-1 accuracy on CIFAR-100 (+1.5% absolute improvement) and 57.0% mAP on COCO2017 (+1.3% absolute improvement). These characteristics are particularly important for the active and passive safety features of intelligent vehicles. Full article

Public perception of traditional villages’ streetscape is a crucial link for unlocking their benefits in promoting physical and mental health and realizing environmental value transformation. Current studies on the influence mechanisms of rural streetscape characteristics on perception largely rely on subjective ratings and mostly depend on linear models. To address this, this study takes a traditional village in eastern China, which is rich in natural and cultural conditions, as an example and constructs an evaluation framework comprising 29 streetscape feature indicators. Based on multimodal data including electroencephalography (EEG), image segmentation, color, and spatial depth computation, XGBoost-SHAP was employed to reveal the nonlinear influence mechanisms of streetscape features on neurophysiological indicators (alpha-band power spectral density, α PSD) in the traditional rural context, which differs from the blue–green spaces and residential, campus, and urban environments in previous studies. The results indicate that (1) the dominant factors affecting α PSD in traditional villages are tree, color consistency, architectural aesthetics, spatial enclosure index, P_EBG, and road, in descending order. (2) Threshold effects and interaction effects that differ from previous studies on campuses, window views, and other contexts were identified. The positive effect of tree view index on α activity peaks at the threshold of 0.09, beyond which diminishing returns occur. Color complexity, including high color difference from the primary village scheme (i.e., low color consistency, color diversity, and visual entropy), inhibits α activity. The effect of spatial enclosure index (SEI) on α activity exhibits an inverted U-shape, peaking at 0.35. Tree–VE_nats, road–SEI, and building–SEI show antagonistic effects. Road–sky and SEI–P_FG display conditional interaction effects. (3) Based on k-means clustering analysis, the “key factor identification—threshold effect management—multi-factor synergy optimization” design can directionally regulate α PSD, promoting relaxed and calm streetscape schemes. This approach can be applied to urban and rural environment assessment and design, providing theoretical and technical support for scientific decision-making. Full article

Understanding how the human brain processes and interprets multimedia content represents a frontier challenge in neuroscience and artificial intelligence. This study introduces a novel approach to decode semantic information from electroencephalogram (EEG) signals recorded during visual stimulus perception. We present DCT-ViT, a spatial–temporal transformer architecture that pioneers automated semantic recognition from brain activity patterns, advancing beyond conventional brain state classification to interpret higher level cognitive understanding. Our methodology addresses three fundamental innovations: First, we develop a topology-preserving 2D electrode mapping that, combined with temporal indexing, generates 3D spatial–temporal representations capturing both anatomical relationships and dynamic neural correlations. Second, we integrate discrete cosine transform (DCT) embeddings with standard patch and positional embeddings in the transformer architecture, enabling frequency-domain analysis that quantifies activation variability across spectral bands and enhances attention mechanisms. Third, we introduce the Semantics-EEG dataset comprising ten semantic categories extracted from visual stimuli, providing a benchmark for brain-perceived semantic recognition research. The proposed DCT-ViT model achieves 72.28% recognition accuracy on Semantics-EEG, substantially outperforming LSTM-based and attention-augmented recurrent baselines. Ablation studies demonstrate that DCT embeddings contribute meaningfully to model performance, validating their effectiveness in capturing frequency-specific neural signatures. Interpretability analyses reveal neurobiologically plausible attention patterns, with visual semantics activating occipital–parietal regions and abstract concepts engaging frontal–temporal networks, consistent with established cognitive neuroscience models. To address systematic misclassification between perceptually similar categories, we develop a hierarchical classification framework with boundary refinement mechanisms. This approach substantially reduces confusion between overlapping semantic categories, elevating overall accuracy to 76.15%. Robustness evaluations demonstrate superior noise resilience, effective cross-subject generalization, and few-shot transfer capabilities to novel categories. This work establishes the technical foundation for brain–computer interfaces capable of decoding semantic understanding, with implications for assistive technologies, cognitive assessment, and human–AI interaction. Both the Semantics-EEG dataset and DCT-ViT implementation are publicly released to facilitate reproducibility and advance research in neural semantic decoding. Full article

Background/Objectives: Tooth colour perception is critical to aesthetic outcomes in restorative dentistry and patient satisfaction. Psychological and gender-related factors may modulate individual colour perception. This study evaluates the influence of gender and emotional state on tooth colour self-perception in healthy adults. Methods: A prospective observational study was conducted on 100 adults (50 women, 50 men; mean age 32.2 years) without anterior restorations or systemic disease. Tooth shade was assessed by (i) operator visual matching using the VITA Classical A1–D4 guide, (ii) patient self-selection with the same guide, and (iii) spectrophotometric measurement (Spectroshade Micro). Emotional state was measured using the abbreviated Profile of Mood States (POMS-SF); the OHIP-14 was administered to characterise oral health–related quality of life. Statistical analyses included the Chi-squared test, Kendall’s τ, and t-test, with p < 0.05 considered significant. Results: A significant association between gender and the magnitude of patient–operator discrepancy was found (p = 0.013): women showed higher rates of complete agreement or two-step differences, whereas men more frequently exhibited one-step differences. Positive mood parameters (feeling active, energetic, satisfied) correlated with greater patient–operator agreement (τ = 0.17–0.23, p < 0.05). Significant association was neither observed between patient self-selection and spectrophotometric measurement (p = 0.225), nor between facial undertone, facial colour contrast, or depressive mood levels. Conclusions: Gender and emotional state influence subjective tooth colour perception. Positive mood is associated with improved agreement between perceived and clinically assessed colour. These findings support a personalised, gender- and mood-informed approach to shade selection and patient management in aesthetic dentistry. Full article

Background: Sports vision training improves perceptual–motor skills crucial for performance and injury prevention. Despite proven benefits, little is known about its perception and use among coaches in Portugal. Methods: A cross-sectional online survey was completed by active coaches from various sports, gathering sociodemographic data, awareness of visual training, perceived importance of ten visual skills, and implementation in training plans. Statistical analyses included descriptive tests to summarize sample characteristics, t-tests and two-way ANOVA to compare perceived importance of visual skills across sex and sport modalities, Spearman correlations to assess associations with age, and Firth-corrected logistic regression to identify predictors of incorporating visual training into practice plans. Results: Among 155 participants (88.5% men; mean age 36.9 ± 11.8 years), 73.2% reported incorporating visual training, with no association with self-reported knowledge (p = 0.413). Regarding perceived importance, reaction time was rated highest (1.20 ± 0.44), followed by hand–eye/body coordination (1.61 ± 0.71) and anticipation (1.34 ± 0.55). Age negatively correlated with importance given to visual memory, peripheral vision, concentration, depth perception, coordination, and moving-object recognition (p < 0.05). Multivariable analysis showed age (OR = 1.05; p = 0.0206) and volleyball (OR = 2.45; p = 0.031) positively associated with implementation, while higher perceived importance for visual concentration was negatively associated (OR = 0.54; p = 0.0176). Conclusions: Visual training implementation is high but not always linked to formal knowledge. Adoption is influenced by sport and demographics, and the counterintuitive role of visual concentration underscores the need for tailored educational programs to enhance performance and reduce injury risk. Full article

The pursuit of a comprehensive understanding of human consciousness, which includes the subjective experience of perception, is a long-standing endeavor. A multitude of disciplines have sought to elucidate and define consciousness, with a particular emphasis on its etiology. What is the cause of consciousness? One particularly eye-opening idea is that humans attempt to identify the source of consciousness by leveraging their own consciousness, as if something is attempting to elucidate itself. Strikingly, the results of brain-imaging experiments indicate that the brain processes a considerable amount of information outside conscious awareness of the organism in question. Perhaps, the vast majority of decision making, thinking, and planning processes originate from non-conscious brain processes. Nevertheless, consciousness is a fascinating phenomenon, and its intrinsic nature is both intriguing and challenging to ascertain. In the end, it is not necessarily given that consciousness, in particular the phenomenon of perception as the subjective experience it is, is a tangible function or process in the first place. This is why it must be acknowledged that this theoretical paper is not in a position to offer a definitive solution. However, it does present an interesting new concept that may at least assist future research and potential investigations in achieving a greater degree of elucidation. The concept is founded upon a physical (mathematical) phenomenon known as stochastic resonance. Without delving into the specifics, it is relatively straightforward to grasp one of its implications, which is employed here to introduce a novel direction regarding the potential for non-conscious information within the human brain to become conscious through the introduction of noise. It is noteworthy that this phenomenon can be visualized through a relatively simple approach that is provided in the frame of this paper. It is demonstrated that a completely white image is transformed into an image depicting clearly recognizable content by the introduction of noise. Similarly, information in the human brain that is processed below the threshold of consciousness could become conscious within a neural network by the introduction of noise. Thereby, the noise (neurophysiological energy) could originate from one or more of the well-known activating neural networks, with their nuclei being located in the brainstem and their axons connecting to various cortical regions. Even though stochastic resonance has already been introduced to neuroscience, the innovative nature of this paper is a formal introduction of this concept within the framework of consciousness, including higher-order perception phenomena. As such, it may assist in exploring novel avenues in the search for the origins of consciousness and perception in particular. Full article

Macau’s Ching Chau Hill, as a composite entity of modern industrial heritage and natural cultural landscape, faces the dual challenges of conservation and regeneration. This study takes Ching Chau Hill as a case study, integrating structural equation modeling (SEM) with Geographic Information System (GIS) technology and combining the theory of the creative class, to construct an evaluation model of “industrial heritage-creative perception-cultural innovation.” Through questionnaire surveys, data from the creative class were collected, and SEM was employed for path analysis and hypothesis testing, while GIS was used for spatial analysis and visualization. This study systematically explores the creative perception pathways of industrial heritage value from the perspective of the creative class and its driving mechanisms for cultural inheritance and innovation. This study found that the retention rate of industrial structures (73%) and the “sacred-industrial” axis formed by the integrity of the spatial sequence (β = 0.58) together constitute the core of the material attachment path, and there is a significant threshold for the site identity effect: when the material authenticity score exceeds the 3.5 critical point, the identity value jumps by 37.8%, which provides a quantitative basis for the precise protection of “ruin aesthetics”. In the process of transforming cultural inheritance into innovative practice, the participation in creative activities showed a mediating effect of 72.1%, and the driving efficiency of co-creation activities was ten times higher than that of ceremonial guided tours, confirming the core position of “learning by doing” in heritage revitalization. The results show the following: (1) the creative class’s perception of the aesthetic uniqueness and historical memory of Ching Chau Hill’s industrial heritage significantly and positively influences their recognition of its creative value; (2) spatial accessibility and environmental atmosphere are key geographical factors affecting creative perception; (3) recognition of creative value effectively drives the innovative transformation of cultural heritage by stimulating participation willingness and innovative ideas. This study provides a strategy basis with both theoretical depth and practical guidance value for the revitalization and utilization of industrial heritage in post-industrial urban renewal. Full article

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces. 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

The sustainable development of traditional villages faces a core challenge stemming from the disconnect between public perception and spatial planning. To address this issue, this study, taking Baishe Village—a national-level traditional village—as a case study, constructs and applies a “Digital Humanities + Spatial Analysis” research paradigm that integrates text mining, sentiment analysis, visual coding, and spatial analysis based on multimodal social media data (Sina Weibo and Xiaohongshu) from 2013 to 2023. It aims to conduct an in-depth analysis of tourists’ rural image perception structure, emotional tendencies, and their spatial differentiation characteristics, and subsequently propose spatial optimization pathways that promote the revitalization of its cultural landscape and sustainable land use. The main findings reveal the following: (1) In terms of cognitive structure, the rural image presents a ‘settlement-dominated’ four-dimensional structure, with settlement elements such as pit kilns (accounting for more than 70%) as the absolute core. (2) In terms of emotional tendencies, a cognitive tension is formed between the high recognition of architectural heritage value (positive sentiment: 57.44%) and significant dissatisfaction with service facilities. (3) In terms of spatial patterns, a “dual-core-driven” pattern of perceived hotspots emerges, with 83% of tourist activities concentrated in the central–southern main road area, revealing a “revitalization gap” in village spatial utilization. The contribution of this study lies in translating abstract public perceptions into quantifiable spatial insights, thereby constructing and validating a “Digital Humanities + Spatial Analysis” paradigm that fuses multimodal data and links abstract perception with concrete space. This provides a crucial theoretical basis and practical guidance for the living conservation of cultural landscapes, the enhancement of land use efficiency, and refined spatial governance. Full article

This study explores the integration of 3D printing technology by adult learners in Greek Second Chance Schools (SCS), institutions designed to address Early School Leaving and promote Lifelong Learning. Grounded in constructivist and experiential learning theories, the research examines adult learners’ attitudes toward 3D printing technology through a hands-on STEM activity in the context of teaching scientific literacy. The instructional activity was centered on a physics experiment illustrating Archimedes’ principle using a multimodal approach, combining 3D computer modeling for visualization and design with tangible manipulation of a printed object, thereby offering both digital and Hands-on learning experiences. Quantitative data was collected using a structured questionnaire to assess participants’ perception toward the 3D printing technology. Findings indicate a positive trend in adult learners’ responses, finding 3D printing accessible, interesting, and easy to use. While expressing hesitation about independently applying the technology in the future, overall responses suggest strong interest and openness to using emerging technologies within educational settings, even among marginalized adult populations. This work highlights the value of integrating emerging technologies into alternative education frameworks and offers a replicable model for inclusive STEM education and lays the groundwork for further research in adult learning environments using innovative, learner-centered approaches. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article