Search Results (533)

The integration of Selective Laser Melting (SLM) into high-end manufacturing necessitates robust machine-condition monitoring and subsystem fault classification to navigate the intricate coupling and dynamic transients inherent in these systems. Current diagnostic frameworks often struggle to decouple high-dimensional state variables or track their underlying temporal evolution. To overcome these bottlenecks, this paper develops a spatiotemporal deep learning architecture by coupling Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units. This hybrid approach leverages CNNs to distill multi-dimensional spatial features from subsystem sensor arrays, while LSTMs interpret the sequential dependencies critical for identifying systemic drifts. The proposed framework was validated using an extensive industrial dataset comprising over 310,000 temporal samples across seven critical SLM subsystems, including optical, cooling, and energy units. We systematically investigated three data-handling strategies—feature weighting, balancing, and distribution-based synthesis—to optimize the model’s sensitivity to rare-event anomalies. Benchmarking across six architectural variants reveals that a specific CNN × 3 + LSTM × 1 configuration yields superior diagnostic fidelity, achieving a classification accuracy of 98.81%. Visualization of the feature space confirms high inter-class separability, demonstrating the model’s ability to isolate faults within complex manufacturing cycles. This research offers a scalable paradigm for the intelligent monitoring of SLM equipment and provides a technical benchmark for equipment health management and predictive maintenance in advanced additive manufacturing. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article

(1) Background: This study investigates the development of spatial navigation and long-term memory in the leopard gecko (Eublepharis macularius) to address gaps in understanding reptilian cognitive ontogeny. We aimed to determine if early-life training enhances long-term memory retention and to evaluate the repeatability of individual cognitive performance over time. (2) Methods: Using a modified Morris Water Maze with visual landmarks, we tested 39 individuals across three life stages: juveniles (20 trials), subadults, and adults (10 trials in each later phase). Long-term memory retention was assessed after four and fourteen months. (3) Results: A strong learning effect was observed during the juvenile stage, with geckos significantly improving speed and navigational efficiency. Spatial memory remained stable at the subadult stage (four months post-training), but declined significantly by adulthood (fourteen months post-training), returning to baseline levels. Individual success rates were significantly repeatable during juvenile (R = 0.192) and subadult phases (R = 0.071), although this consistency disappeared in adulthood. (4) Conclusions: These findings indicate that leopard geckos possess substantial spatial learning abilities early in life and exhibit individual cognitive differences. However, spatial memory decays over time without reinforcement. The results highlight the importance of considering developmental stages when evaluating the evolutionary and ecological constraints of reptilian cognition. Full article

The efficient and accurate description of gradient energy anisotropy remains a significant challenge in the phase-field modeling of ferroelectric/antiferroelectric (FE/AFE) composite systems. To address this limitation, we have developed a perturbation model for solving anisotropic gradient energy based on Fourier spectral methods. Through a Fourier-space perturbation scheme, we achieve the ability to treat the full anisotropic gradient energy tensor with spatial variations, overcoming limitations of previous constant-coefficient or isotropic approximations. The application of this model to FE/AFE composites demonstrates exceptional robustness and convergence efficiency. Numerical results indicate that the proposed perturbation scheme can accurately reproduce antiferroelectric phase diagrams and AFE-FE phase transition pathways under varying gradient energy parameters. Furthermore, the algorithm exhibits superior scalability, allowing for a seamless extension to three-dimensional (3D) simulation domains. This capability facilitates the visualization of complex nanodomain structures and reveals the intricate 3D evolution mechanisms of polarization textures. Full article

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries. Full article

Remote sensing image change captioning (RSICC) aims to generate semantic textual descriptions characterizing changes between bi-temporal remote sensing images, with wide applications in disaster assessment and urban planning. However, existing methods face specific drawbacks: CNN-based models have limited ability to capture long-range spatial correlations due to local receptive fields, and Transformer-based models suffer from quadratic complexity while distributing attention uniformly across all spatial positions, resulting in weak perception of salient changes in background-dominated scenes. In this paper, we present PM3Net (Progressive Mask-guided Multigranular Mamba Network), which leverages Mamba state space models with linear complexity for efficient spatiotemporal change modeling. The Progressive Mask-guided Encoder (PME) creates dual-source change masks combining L2 norm spatial differences with cosine distance semantic differences for progressive change feature extraction from detailed structures to high-level semantics. The Mask-guided Feature Enhancement (MFE) module applies mask-weighted refinement and cross-layer fusion to emphasize salient change regions while suppressing background interference, producing multigranular visual representations. Experiments on LEVIR-MCI and WHU-CDC datasets show PM3Net achieves superior results compared to existing methods, with BLEU-4 scores of 66.89 and 73.05, respectively. The results confirm PM3Net’s ability to solve the RSICC task while demonstrating how Mamba models can succeed in this specific field. Full article

Ultra-high-field (UHF) magnetic resonance imaging, defined as imaging at field strengths of 7 Tesla (7T) and above, represents a frontier technology in neuroimaging with emerging applications in prenatal brain research. This narrative review examines the current evidence on the potential benefits of UHF-MRI for investigating embryonic and fetal brain development. Through analysis of 97 studies identified across multiple databases, we find that UHF-MRI offers substantial advantages in spatial resolution, tissue contrast, and anatomical detail compared to conventional clinical field strengths (1.5T and 3T). The primary applications to date have been in ex vivo imaging of post-mortem fetal specimens and preclinical animal models, where UHF-MRI has enabled unprecedented visualization of laminar cortical organization, early sulcation patterns, microstructural development, and subtle anatomical features critical for understanding normal and abnormal neurodevelopment. Key benefits include enhanced delineation of transient developmental zones, improved characterization of cortical folding, superior detection of subtle malformations, and the ability to create high-resolution three-dimensional atlases of fetal brain development. However, significant technical and safety challenges currently limit in utero human applications, including concerns about specific absorption rate, acoustic noise, and fetal motion artifacts. This review identifies critical knowledge gaps and future directions for translating UHF-MRI technology to clinical prenatal diagnostics. Full article

Background: In multicultural healthcare systems such as the Italian one, migrant children may experience dental care as particularly stressful because linguistic and cultural barriers can limit communication, emotional expression, and understanding of the clinical setting. Aim: Understanding the emotional experience of migrant children during dental visits is essential for improving clinical management in pediatric dentistry and orthodontics within multicultural contexts. Because linguistic barriers often limit verbal communication, this study aimed to explore children’s mental representations, emotional states, and perceptions of the dental environment through drawing and to evaluate the clinical implications for communication and therapeutic collaboration. Methods: This qualitative study was conducted in Italy between 2016 and 2025 and analyzed 50 drawings produced by 50 foreign-born migrant children aged 6–13 years, recruited through an educational cooperative in Piacenza. Most participants originated from developing countries and had limited proficiency in Italian, frequently showing a marked “experience gap” in drawing ability that interfered with normative developmental stages described by Lowenfeld. The analysis focused on spatial organization, line quality, color use, posture, interpersonal distance, and representation of the clinical environment, integrating graphic competence assessment with emotional interpretation. Results: Younger children commonly depicted rigid lines, essential settings, and oversized dental unit lamps, whereas older children increasingly represented threatening or disproportionate instruments, aggressive dentists, and omission of the patient figure. Around age 10, drawings became more detailed and colorful, although symbols of closure, such as locked doors, persisted. In adolescents, representations polarized between rich, coherent scenes and extremely essential drawings dominated by fear, rigidity, minimal environments, and symbols of constraint. The findings suggest that drawing may represent a valuable non-verbal clinical and communicative resource for exploring migrant children’s emotional experience of dental care and for identifying signs of anxiety and vulnerability that may not emerge through verbal interaction alone. Conclusions: These findings support the value of a culturally sensitive dental approach integrating drawing, visual aids, multilingual educational materials, and play-based strategies to reduce communication barriers and improve cooperation in migrant children receiving pediatric dental and orthodontic care. Full article

The purported benefits of Virtual Reality for pilot flight simulator training, such as increased immersion and presence, would be of great benefit in training those flight skills that rely on visuospatial awareness. The implementation of this technology for the training of pilots requires careful consideration of its ability to transfer required skills and of any comparative advantages over conventional flight simulators. In order to examine this question, a quasi-transfer-of-training study was conducted using a separate-sample pretest–posttest design. The ability of a low-cost VR simulator to transfer flying skills and mission projection skills, using internally valid measures, during a common flight manoeuvre was evaluated. Results were consistent with improved post-intervention flying performance (g = 0.875) and ‘mission projection’ performance (g = 0.661), with no statistically significant difference between the estimated effect sizes, as well as the combined measure (g = 0.768). The findings indicate that the VR simulator was associated with better performance in the quasi-transfer of basic flying skills, those skills that require understanding of spatial relationships based on visual information, and in the broader training of technique. These findings must, however, be considered in the context of the noted limitations of the technology and the research design. Full article

Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article

Visual mental imagery, the ability to generate and manipulate internal visual experiences without direct sensory input, links perception with memory, planning, and higher cognition. In this targeted narrative review, we synthesize neuroimaging and lesion evidence on the brain basis of visual imagery, with a focus on neuroradiological correlates of the ventral and dorsal visual pathways. Unlike prior cognitive neuroscience reviews that primarily emphasize functional mechanisms, this review is neuroradiology-oriented and integrates lesion patterns and white-matter disconnection to support clinico-radiological interpretation of imagery complaints. Using a dual-stream framework, we contrast ventral occipito-temporal systems that preferentially support object imagery (appearance-based features such as form, faces/objects, and color, with texture remaining under-studied) with dorsal occipito-parietal systems that preferentially support spatial imagery (relations, transformations, and navigation). Across studies, imagery recruitment is strongly task- and stage-dependent: ventral regions are most often engaged during object-focused imagery, whereas parietal regions are prominent during spatial transformation tasks, with evidence for interaction between pathways when demands require both content and spatial operations. Structural and clinico-radiological findings indicate that imagery impairment can arise from focal posterior lesions and posterior neurodegenerative syndromes but also from network disruption affecting long-range connections that support top-down access to posterior representations. Finally, emerging work on aphantasia and hyperphantasia supports a network-level view in which imagery vividness relates to how effectively higher-order systems engage visual representations. We conclude that standardized, stream-sensitive tasks and multimodal approaches combining functional and structural imaging with lesion-based evidence are key to discovering clinically actionable biomarkers of imagery dysfunction. Full article

Semantic change detection (SCD) in remote sensing images aims not only to localize changed regions but also to identify their specific “from–to” semantic transitions. This task remains challenging due to the inherent semantic ambiguity of spectral changes and the presence of pseudo-change noise. While recent vision–language models have shown promise in remote sensing, existing approaches like RemoteCLIP predominantly focus on static scene classification, lacking the ability to explicitly model dynamic temporal transitions. Other adaptations of foundation models (e.g., AdaptVFMs-RSCD) often rely on heavy backbones, incurring prohibitive computational costs. To address these limitations, this paper proposes LGDENet, a lightweight, end-to-end framework that unifies Language-Guided Temporal Contrastive Learning with a noise-robust difference enhancement mechanism. Specifically, we construct a temporal transition prompt learning strategy that aligns visual difference features with textual descriptions of dynamic processes, thereby resolving directional semantic ambiguities. Furthermore, we introduce a Difference Enhancement Module (DEM) that leverages the channel–spatial decoupling property of depthwise separable convolutions to adaptively isolate and suppress irrelevant variations (e.g., registration errors) before feature fusion. Experiments on the SECOND and Landsat-SCD datasets demonstrate that LGDENet achieves state-of-the-art performance, yielding a semantic F1 score ($F_{scd}$) of 87.90% and 88.71%, respectively. Moreover, with a modest parameter count of 33.45 M, it offers a superior trade-off between accuracy and efficiency compared to heavy foundation model-based approaches. Full article

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning. Full article

The sustainable renewal of old residential communities faces increasing challenges in addressing the diverse environmental needs of older residents while respecting spatial constraints. Conventional approaches often treat older adults as a homogeneous group, overlooking how functional and social heterogeneity shape spatial perception. To address this gap, this study examines perceptual priorities in micro-public spaces of old residential communities in Qingdao, China, by classifying 60 community-dwelling older adults into four profiles using the Successful Aging framework. Participants performed free-viewing tasks using eye-tracking to observe 18 areas of interest (AOIs). Results reveal a clear perceptual hierarchy structured by individual profiles. Older adults with lower functional ability (Q3, Q4) allocate significant visual resources to safety-critical elements as a form of compensatory monitoring. Conversely, a systematic perceptual shift from survival-oriented assessment to quality-oriented evaluation was observed as functional and participatory reserves increased. High-participation groups (Q1, Q3) prioritized comfort facilities, while esthetic features attracted sustained attention primarily among the high-function/high-participation group (Q1). These findings provide empirical evidence for differentiated micro-renewal strategies that prioritize perceptual stress reduction and affordance enrichment in old residential communities. Full article