Search Results (381)

Laser beams are excellent projection sources due to their high brightness and color purity; however, their high coherence produces speckle noise, which reduces the clarity of images cast by compact projection systems. Existing suppression methods often require complex designs. Here, we propose a simple miniaturized speckle suppression structure (SSS) that consists of a low-absorption particle surface and a micro-vibrating unit. By generating and superimposing different speckle patterns over time, the structure simultaneously reduces both temporal and spatial coherence. A time-varying functional model was developed using a simulation to optimize its dynamic operation. The results of the experimental validation show that at 50 Hz vibration, the speckle contrast decreases from 30.23% to 6.98%, closely matching the simulated prediction of 7.12% and outperforming static configurations by 24%. The results indicate that the SSS is a straightforward, effective solution for enhancing the image quality of compact laser projection displays. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

Background/Objectives: Primary progressive aphasia (PPA) is a clinical syndrome associated with gradual language impairment caused by neurodegenerative disease. While people with post-stroke aphasia often depend on visual and prosodic cues to facilitate language, we hypothesized that people with PPA may have difficulty using such cues due to degeneration in the right hemisphere (albeit less than in the left hemisphere) in PPA. Methods: Eighty-eight outpatients diagnosed with PPA received the Hopkins Auditory Comprehension with Context Assessment (HACCA), a recently developed instrument that systematically titrates both acoustic (prosody) and visual (speaker image) cues in a four-item forced-choice sentence picture matching paradigm assessing comprehension. Patients were grouped based on the effects of cues on accuracy and were examined both by the PPA variant and individually. Results: There was a significant difference between performance classifications across the three cueing conditions as a function of PPA variant (p = 0.014). When individuals with distinct complementary profiles of performance across conditions were examined separately, a small number with logopenic PPA uniquely benefitted from the inclusion of video, while certain patients performed more poorly given any additional cues. HACCA performance across cueing conditions had a strong positive association with other concurrent measures of communication and cognition. Conclusions: Individual patterns of response to prosodic and visual cues provide important insights valuable in refining therapeutic approaches that target the retention of function and support a more robust understanding of the individual variability among patients with this uncommon neurodegenerative syndrome. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

This study proposes a data-driven framework to predict the rock mass-specific fracture energy distributions using microstructural descriptors extracted from SEM-EDS automated characterization images. Ore textures were encoded through unsupervised k-means clustering to identify six representative mineralogical patterns. The resulting cluster proportions were then used as input features for supervised machine learning models, which seek to estimate the parameters of the log-normal distribution (median and standard deviation) adjusted to the experimental fracture energy data. Both models (XGBoost and decision tree regressor) were validated through Leave-One-Out cross-validation and showed high accuracy ($R^2$ of 0.80 and 0.91, respectively) and predict over 85% of the energy distributions matched the experimental ones according to Kolmogorov–Smirnov and Cramér–von Mises tests. The proposed method outperforms traditional empirical approaches by incorporating mineralogical variability and predicting the complete distribution of fracture behavior, representing a step toward more efficient, texture-aware comminution practices. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Large-area metasurfaces were fabricated via a tunable pyramidal interference lithography (PIL) technique, which uses custom-built 2-faced, 3-faced, and 4-faced pyramidal prisms to create metasurfaces with customizable nano- and micro-scale surface feature periodicities. The 2-faced prism produced linear surface relief diffraction gratings, while the 3-faced prism produced metasurfaces with triangular lattices and the 4-faced prism produced metasurfaces with square lattices, all on azobenzene thin films. A double inline prism set-up enabled control over the metasurface feature periodicity, allowing systematic increase in the pattern size. Additional tunability was achieved by placing a prism inline with a lens, allowing precise control over the metasurface feature periodicity. A theoretical model was derived and successfully matched to the experimental results. The resulting metasurfaces were coated with gold and exhibited distinct surface plasmon resonance (SPR) and surface plasmon resonance imaging (SPRi) responses, confirming their functionality. Overall, this work establishes PIL as a cost-effective and highly adaptable metasurface fabrication method for producing customizable periodic metasurfaces for photonic, plasmonic, and sensing applications. Full article

Urethral stricture is a disease of fibrotic narrowing that compromises the urethral mucosa and spongiosum. Oral mucosal graft urethroplasty delivers excellent outcomes in complex cases, yet its procedural demands restrict availability beyond specialized centers. Endoscopic transplantation of oral mucosa has been proposed; while feasibility is shown, clinical efficacy remains suboptimal. We asked whether extracellular vesicles from stem cells of human exfoliated deciduous teeth (SHED-EVs) promote oral mucosa fibroblast (OMF) growth under urethra-mimetic paracrine conditions and whether culture growth phase tunes EV function. SHED-EVs were collected during logarithmic (SHED-EV-L) or stationary (SHED-EV-S) phases under xeno-free conditions, isolated by a standardized workflow, and characterized by nanoparticle tracking analysis. miRNA cargo was profiled with a human miRNA microarray platform and normalized for comparative analyses. OMF proliferation was quantified in a horizontal indirect co-culture with urethral epithelial cells using incubator-based time-lapse imaging. SHED-EV-L produced a sustained pro-proliferative effect across 24–96 h, whereas SHED-EV-S showed a weaker early effect with a late catch-up; both exceeded vehicle at 96 h. Fibrosis-related miRNA heat maps showed culture growth phase-dependent patterns: SHED-EV-L displayed relatively higher signals for miR-31-3p, miR-146b-3p, several let-7 members, and selected miR-181 isoforms, whereas SHED-EV-S showed a marked relative increase of miR-486-3p; miR-21, miR-99/100, and miR-205 were broadly comparable between phases. These findings indicate that culture growth phase is a practical design lever that orients SHED-EV cargo and function, supporting phase-matched formulations for adjunctive transurethral applications and motivating in vivo validation and manufacturing-oriented quality controls. Full article

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this paper introduces a Density Triangle Descriptor (DTD). The proposed method first extracts keypoints from density images generated from LiDAR point clouds, and then constructs a triangle-based global descriptor that is invariant to rotation and translation, enabling robust structural representation. Furthermore, to enhance local discriminative ability, the neighborhood around each keypoint is modeled as a Gaussian distribution, and a local descriptor is derived from the entropy of its probability distribution. During loop closure detection, candidate matches are first retrieved via hash indexing of triangle edge lengths, followed by entropy-based local verification, and are finally refined by singular value decomposition for accurate pose estimation. Extensive experiments on multiple public datasets demonstrate that compared to STD, the proposed DTD improves the average F1 max score and EP by 18.30% and 20.08%, respectively, while achieving a 50.57% improvement in computational efficiency. Moreover, DTD generalizes well to solid-state LiDAR with non-repetitive scanning patterns, validating its robustness and applicability in complex environments. Full article

Increasing consumption of plant-based alternatives is promoted to reduce the environmental impact of food systems, yet adoption remains limited. The aim of this study was to identify distinct consumer segments and examine differences in their perceptions, consumption habits, and trial intentions concerning plant-based dairy alternatives (PBDAs). Conceptually, it advances PBDAs segmentation by jointly incorporating pro-dairy justifications, avoidance of animal-origin considerations, and self-reported PBDAs familiarity, capturing psychological defence mechanisms alongside knowledge-related influences on adoption. Data were collected in a nationwide cross-sectional CAWI survey of 1220 Polish adults responsible for household food purchasing, stratified and quota-matched by gender, age, region, and settlement size. Factor analysis of the segmenting variables was conducted using principal component analysis with varimax rotation, followed by two-step cluster analysis. Alternative cluster solutions were compared using the Bayesian Information Criterion based on the log-likelihood (BIC-LL). The selected five-cluster solution showed acceptable to good clustering quality, as indicated by silhouette-based measures of cohesion and separation. Given the cross-sectional CAWI design and reliance on self-reported measures, the findings do not allow causal inference and should be interpreted as context-specific to the Polish, dairy-centric food culture. Cluster analysis identified five segments that differed in PBDA-related beliefs, product image evaluations, consumption patterns, and trial intentions. PBDA-oriented segments, comprising a dairy-critical segment and a dual-consumption segment, exhibited higher perceived familiarity and stronger ethical and environmental concerns and showed greater PBDA use and willingness to try new products. The dual-consumption segment reported the highest use and trial readiness. In contrast, resistant segments showed stronger dairy attachment, lower perceived familiarity, and more sceptical evaluations of PBDAs’ healthfulness, naturalness, and sensory appeal, and rarely consumed plant-based alternatives. The findings highlight substantial heterogeneity in how Polish dairy consumers perceive PBDAs, emphasising the importance of segment-specific approaches for communication and product development. Tailored strategies can help address the diverse motivations and barriers of consumers, supporting a dietary shift toward more plant-based options. Full article

This paper presents new fast algorithms for the type VII discrete cosine transform (DCT-VII) applied to input data sequences of lengths ranging from 3 to 8. Fast algorithms for small-sized trigonometric transforms enable the processing of small data blocks in image and video coding with low computational complexity. To process the information in image and video coding standards, the fast DCT-VII algorithms can be used, taking into account the relationships between the DCT-VII and the type II discrete cosine transform (DCT-II). Additionally, such algorithms can be used in other digital signal processing tasks as components for constructing algorithms for large-sized transforms, leading to reduced system complexity. Existing fast odd DCT algorithms have been designed using relationships among discrete cosine transforms (DCTs), discrete sine transforms (DSTs), and the discrete Fourier transform (DFT); among different types of DCTs and DSTs; and between the coefficients of the transform matrix. However, these algorithms require a relatively large number of multiplications and additions. The process of obtaining such algorithms is difficult to understand and implement. To overcome these shortcomings, this paper applies a structural approach to develop new fast DCT-VII algorithms. The process begins by expressing the DCT-VII as a matrix-vector multiplication, then reshaping the block structure of the DCT-VII matrix to align with matrix patterns known from the basic papers in which the structural approach was introduced. If the matrix block structure does not match any known pattern, rows and columns are reordered, and sign changes are applied as needed. If this is insufficient, the matrix is decomposed into the sum of two or more matrices, each analyzed separately and transformed similarly if required. As a result, factorizations of DCT-VII matrices for different input sequence lengths are obtained. Based on these factorizations, fast DCT-VII algorithms with reduced arithmetic complexity are constructed and presented with pseudocode. To illustrate the computational flow of the resulting algorithms and their modular design, which is suitable for VLSI implementation, data-flow graphs are provided. The new DCT-VII algorithms reduce the number of multiplications by approximately 66% compared to direct matrix-vector multiplication, although the number of additions decreases by only about 6%. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

Ensuring the integrity of medical images is essential to securing clinical workflows, telemedicine platforms, and healthcare IoT environments. Existing watermarking and reversible data-hiding approaches often modify pixel intensities, reducing diagnostic fidelity, introducing embedding constraints, or causing instability under compression and format conversion. This work proposes a distortion-free, non-embedding authentication framework that leverages the inherent stability of the most significant bit (MSB) patterns in the Non-Region of Interest (NROI) to construct a secure and tamper-sensitive reference for the diagnostic Region of Interest (ROI). The ROI is partitioned into fixed blocks, each producing a 256-bit SHA-256 signature. Instead of embedding this signature, each hash bit is mapped to an NROI pixel whose MSB matches the corresponding bit value, and only the encrypted coordinates of these pixels are stored externally in a secure database. During verification, hashes are recomputed and compared bit-by-bit with the MSB sequence extracted from the referenced NROI coordinates, enabling precise block-level tamper localization without modifying the image. Extensive experiments conducted on MRI (OASIS), X-ray (ChestX-ray14), and CT (CT-ORG) datasets demonstrate the following: (i) perfect zero-distortion fidelity; (ii) stable and deterministic MSB-class mapping with abundant coordinate diversity; (iii) 100% detection of intentional ROI tampering with no false positives across the six clinically relevant manipulation types; and (iv) robustness to common benign Non-ROI operations. The results show that the proposed scheme offers a practical, secure, and computationally lightweight solution for medical image integrity verification in PACS systems, cloud-based archives, and healthcare IoT applications, while avoiding the limitations of embedding-based methods. Full article

Measuring the three-dimensional (3D) concentration of the ubiquitous intermediate OH* across combustion systems, spanning carbon-based fuels to zero-carbon alternatives such as H₂ and NH₃, provides vital insights into flame topology, reaction pathways, and emission formation mechanisms. Optical imaging methods have attracted vital interests due to non-intrusiveness in the combustion process. However, achieving accurate 3D concentration of OH* via imaging in non-axisymmetric flames remains challenging. This work presents a near-ultraviolet (NUV) volumetric emission tomography-based OH* measuring method that integrates a three-layer OH* imaging model, a calibration procedure utilizing narrow-band NUV radiometry, and a threshold-constrained Local Filtered Back-Projection Simultaneous Algebraic Reconstruction Technique (LFBP-SART) algorithm. When applied to a non-axisymmetric Bunsen flame, the method reveals multiple small flame structures matching the fairing pattern in the reconstructed 3D OH* field, with a maximum OH* molar concentration of approximately 0.04 mol/m³ and an overall relative uncertainty of about 8.7%. Given its straightforward requirements, this technique is considered adaptable to other free radicals. Full article