Search Results (6,151)

Angiogenesis is a spatially regulated hallmark of colorectal cancer (CRC) progression, yet current analytical frameworks fail to resolve how nutraceutical bioactive compounds interact with angiogenic signalling within the heterogeneous tumour microenvironment. This review advances a central hypothesis: that the spatial localisation of palm oil mill effluent (POME)-derived bioactive compounds within CRC tumour tissues is predictive of their functional anti-angiogenic activity. POME—the largest waste stream of palm oil processing—contains a chemically diverse array of bioactives, including tocotrienols, phenolics, carotenoids, and fatty acids, with reported antioxidant, anti-inflammatory, and anti-angiogenic properties. However, the existing evidence is predominantly derived from bulk in vitro analyses, limiting mechanistic conclusions about compound behaviour within spatially organised tumour architectures. To address this gap, we propose an integrated framework positioning mass spectrometry imaging (MSI)—across matrix-assisted laser desorption/ionisation (MALDI), desorption electrospray ionisation (DESI), and secondary ion mass spectrometry (SIMS) platforms—as the analytical bridge between compound localisation and angiogenic function. By enabling the label-free, spatially resolved co-localisation of POME-derived compounds with key angiogenic mediators, including VEGF, HIF-1α, and NF-κB, within intact CRC tissues, MSI provides a mechanistic platform that transcends the limitations of conventional molecular analyses. A four-component translational roadmap is outlined, encompassing POME bioactive profiling, spatial compound mapping, angiogenic co-localisation analysis, and functional validation. Critically, the existing evidence on oil palm-derived bioactives is appraised with respect to study quality, mechanistic depth, and translational limitations, identifying the most analytically tractable candidate compounds for spatial investigation. Collectively, this framework positions POME valorisation within a precision nutraceutical oncology paradigm, offering a spatially informed strategy for anti-angiogenic intervention in CRC while simultaneously addressing the environmental burden of palm oil processing waste. Full article

Sparse light field imaging often limits the quality of 3D scene reconstruction due to insufficient viewpoint coverage, resulting in incomplete or inaccurate reconstructions. This work introduces a hybrid CNN–LSTM-based framework to address this issue by generating novel camera poses and the corresponding synthesized novel views, effectively densifying the light field representation. The CNN extracts spatial features from the sparse input views, while the LSTM predicts temporal and positional dependencies, enabling smooth interpolation of novel poses and views. The proposed method integrates these synthesized views with the original sparse dataset to produce a comprehensive set of images. Our approach was evaluated on several datasets, including challenging datasets. The inference capability of our method was tested extensively, and it showed good generalization across diverse datasets. The effectiveness of the framework was evaluated not only with local light field fusion (LLFF) but also with NeRF and 3D Gaussian Splatting, which are considered state-of-the-art reconstruction methods. Overall, the enriched dataset generated by our method led to consistent improvements in 3D reconstruction quality, including higher depth estimation accuracy, reduced artifacts, and enhanced structural consistency. Most importantly, LSTM-based approaches have so far attracted limited attention in the context of generating novel views. While LSTMs have been widely applied in sequential data domains such as natural language processing, their use for image generation conditioned on camera poses remains largely unexplored, which underscores the novelty and significance of the proposed work. This approach provides a scalable and generalizable solution to the sparsity problem in light fields, advancing the capabilities of computational imaging, photorealistic rendering, and immersive 3D scene reconstruction. The results firmly establish the proposed method as a robust and versatile tool for improving reconstruction quality in sparse-view settings. Full article

Objectives: This study aimed to compare the retention and fit precision of removable partial denture circumferential clasps fabricated from cast cobalt–chromium, 3D-printed cobalt–chromium, and polyether ether ketone. Methods: A maxillary right first premolar abutment was prepared. Eighty circumferential clasps were allocated into three material groups: cast Co–Cr (n = 20), 3D-printed Co–Cr (n = 20), and PEEK (n = 40). The terminal third of metal retentive clasps was designed to engage 0.25 mm and 0.50 mm undercuts. PEEK clasps were fabricated with two designs: partial (two-thirds) and full-arm undercut engagement. Each group was examined for retentive forces after 1440 cycles (simulating 1 year). Initial and final retentive forces were recorded. Clasp deformation was assessed by measuring inter-arm distance before and after cycling using digital photography and ImageJ software. Results: All clasp groups demonstrated a statistically significant reduction in retention after 1440 cycles (p < 0.05). At both undercut depths, cast and 3D-printed Co–Cr clasps exhibited significantly higher retentive forces than PEEK (p < 0.001). Within the PEEK group, full-arm engagement showed significantly higher retention than partial engagement at the 0.25 mm undercut (p < 0.001), whereas no significant difference was observed between designs at the 0.50 mm undercut (p = 0.406). Fit precision revealed a significant increase in inter-arm distance after cycling (p < 0.05). PEEK clasps exhibited significantly smaller dimensional changes than Co–Cr clasps (p < 0.02). Conclusions: Clasp material, undercut depth, and design significantly influenced retention and fit precision. Co–Cr clasps maintained higher retentive forces, whereas PEEK clasps demonstrated reduced deformation after cycling. Full article

Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows. Full article

Strong light interference severely degrades imaging system performance. This paper presents a novel digital micromirror device (DMD)-based imaging system for robust, strong light suppression and long-distance detection. Our design strategically places the DMD at the primary image plane, utilizing a large F-number objective for extended depth of field. The relay imaging system employs a tilted image plane in a near-symmetric configuration to effectively balance DMD-induced aberrations, which avoids the off-axis layout and overall tilt of the relay system itself and greatly simplifies system alignment. Stray light analysis verifies the rationality of the structural design, and MTF tests confirm that the assembly performance of the prototype meets the design requirements. The system can achieve clear imaging of buildings at 1 km, which demonstrates its long-distance imaging capability. With an entrance pupil power density of 4.68 × 10⁻⁴ W/cm², strong light interference suppression has been successfully achieved via the DMD regional flipping method. This system offers an efficient solution for long-range imaging in strong light environments. Full article

Background: Epidural procedures benefit from a pre-procedural informed estimation of epidural depth, as anticipating the approximate distance can support safer needle placement and reduce technical difficulties during analgesia or anesthesia procedures. The influence of ethnicity has been established across different populations worldwide; however, there is a lack of Saudi-specific MRI data on epidural depth among the adult population. Aim of this Study: To measure the skin to epidural space distance (SED) at the lumbar interspaces L3–L4 and L4–L5 in the Saudi adult population using magnetic resonance imaging (MRI) and to examine its correlations with age, sex, height, weight, and body mass index (BMI). Methods: In this retrospective cross-sectional study, sagittal T1-weighted lumbar MRI images of the spine of 169 adult Saudi patients were studied. The age group ranged from 20 to 70 years, with an equal distribution of males and females. The skin to epidural space distance (SED) was measured at the L3–L4 and L4–L5 interspaces, and its correlations with age, sex, height, weight, and BMI were analyzed. Results: The average measurement of skin to epidural space distance (SED) was 59.08 mm in L3–L4, and 63.21 in L4–L5. BMI and weight showed strong positive correlations with SED across both levels. Female sex was associated with longer SED values at L4–L5. There was no significant correlation between SED and age or height of the patients. Conclusions: MRI-based assessment of SED revealed strong correlations with weight and BMI, but no correlation with height, age, and sex. These findings support the individualized estimation of epidural depth and needle length selection to enhance procedural safety and reduce complications. Full article

With the continuous development of drilling and reservoir stimulation technologies, the drilling depth of enhanced geothermal system projects is getting deeper and deeper, and the surrounding rock stress of dry hot rock reservoirs is also increasing. Therefore, it has become an inevitable demand for geothermal exploitation to study the evolution law of fracture seepage characteristics of granite under high temperature and ultra-high pressure. To reveal the evolutionary patterns of seepage characteristics in deep-seated hot dry rock fractures, an independently developed ultra-high pressure rock triaxial mechanical testing system was employed to investigate the seepage characteristics of fractured granite under varying temperatures (25–150 °C) and triaxial stresses (50–100 MPa). The study explores the influence of temperature on the seepage characteristics of granite fractures under ultra-high triaxial stress conditions. The results indicate that: (1) In the temperature range of 25–125 °C, as the rock temperature increases, the permeability of the Specimens showed a continuously decreasing trend due to the effect of thermal expansion. (2) In the temperature range of 125–150 °C, as the rock temperature increases, the permeability continues to decrease under low triaxial stress (50 MPa). However, under high triaxial stress (75 MPa) and extremely high triaxial stress (100 MPa), the permeability shows a slight increase instead. This phenomenon is attributed to free surface dissolution. (3) Quantitative analysis of the mesoscopic morphological data of the rock fracture surfaces after testing, combined with SEM images from scanning electron microscopy, confirms that within the high-temperature range of 125–150 °C, the differing levels of triaxial stress determine the variation in the dominant mechanism governing the evolution of the Specimen fracture surfaces, which in turn leads to the divergence in the trend of their permeability changes. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

Traditional Chinese paper cutting represents an important intangible cultural heritage. Can artificial intelligence (AI) reactivate the heritage in a new style? The aim of this study was to use AI to reactivate temple arts and paintings by converting them into the style of traditional Chinese paper cuttings. Thirty sets of old images taken 18 years ago and 10 images of ancient paintings from the National Palace Museum were restyled in Nano Banana (Pro)^®. Related design elements included integrated isolated parts, visual depth, details, and solid and void alternation. Three-dimensional stone and wood sculptures were reconstructed using Rodin^® or Meshy^® and converted into AR models in Sketchfab^®. From the generated 2D images and their 3D representations, a reactivated style of Chinese paper cutting was developed that can be interacted with in the AR smartphone platform or RP in the physical world. Approximately 370 images were regenerated, and 167 versions of models were reconstructed. AI should be considered part of culture. Rethinking traditional folk art highlights demand for the cross-reference and cross-reactivation of heterogeneous art forms. This AI model interprets novel 3D structural and visual details and creates a unique 2D and 3D identity for each subject. Full article

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

Vision-based pose estimation has been widely applied in unmanned aerial vehicle (UAV) navigation. However, existing visual pose estimation algorithms are highly sensitive to camera imaging distortion, which degrades estimation accuracy, and often suffer from noticeable jitter between frames in dynamic scenarios. To address these issues, this paper proposes an improved visual pose estimation algorithm built upon the Perspective Similar Triangle (PST) geometric model. Using a planar fiducial marker as the observation target, the single-frame pose estimation problem is reformulated as a hierarchical geometric inference framework, including image point distortion correction, depth recovery based on planar similar triangle constraint, and rigid transformation estimation between the camera and world coordinate systems. This formulation improves pose estimation accuracy under distorted imaging conditions. To accommodate distortion variations in practical scenarios, a radial distortion coefficient update method is further designed to adaptively adjust the radial distortion parameters under single-frame observations, ensuring that the distortion model remains consistent with the actual imaging distortion and providing reliable model inputs for distortion correction in pose estimation. In addition, to enhance pose stability in dynamic scenarios, a multi-frame optical center consistency constraint (MOCCC) method is introduced to optimize the pose estimation for more stability. By constraining pose estimation across adjacent frames using the mean optical center over multiple frames as the optimization objective, the proposed method effectively suppresses pose jitter caused by single-frame observation noise. Finally, a three-degree-of-freedom (3-DOF) attitude motion platform is established, and both static and dynamic experimental scenarios are designed to validate the accuracy and stability of the proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves high accuracy and high stability pose estimation under imaging distortion and small perturbations, exhibiting good robustness and suitability for practical UAV visual navigation applications. Full article

The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations in skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and effective treatment. However, achieving precise and efficient segmentation remains a major challenge, as existing methods often struggle to capture complex lesion characteristics. To address this challenge, we propose a novel deep learning framework that integrates the PVT v2 backbone with two key modules: the Spatial-Aware Feature Enhancement (SAFE) module and the Multiscale Dual Cross-attention Fusion (MDCF) module. The SAFE module enhances multi-scale encoder features through a dual-branch architecture, which adaptively extracts offset information to integrate fine-grained shallow details with deep semantic information, thereby bridging the feature gap across network depths. The MDCF module establishes bidirectional cross-attention between decoder and encoder features, followed by multi-scale deformable convolutions that capture lesion boundaries and small fragments across heterogeneous receptive fields, thereby enriching semantic details while suppressing background interference. The proposed model was evaluated on two public benchmark datasets (ISIC 2016 and ISIC 2018), achieving Intersection over Union (IoU) scores of 87.33% and 83.67%, respectively. These results demonstrate superior performance compared to current state-of-the-art methods and indicate that our framework significantly enhances skin lesion image analysis, offering a promising tool for improving early detection of skin cancer. Full article

This study experimentally investigates the dynamics of air bubble plumes in water under varying thermal and hydrodynamic conditions using a two-dimensional Particle Image Velocimetry (PIV) system. The experimental setup consists of a transparent acrylic tank equipped with a bubble generator, a controlled heating system, and a synchronized PIV arrangement to capture both bubble motion and the induced liquid flow field. Experiments were conducted over a range of water temperatures (21–60 °C), air flow rates, and water depths (200–600 mm) to systematically quantify their coupled influence on bubble plume behavior. The results demonstrate that bubble rising velocity (defined here as the mean vertical, buoyancy-driven component of bubble motion measured in the fully developed plume region) increases with water temperature, gas flow rate, and water depth. For a fixed gas flow rate and water depth, increasing the water temperature from 40 °C to 60 °C resulted in an approximately twofold increase in bubble rising velocity, primarily due to reduced liquid viscosity and enhanced buoyancy forces. Bubble velocity also increased with gas flow rate and water depth, reflecting stronger momentum input and extended acceleration distances within taller water columns. PIV-resolved velocity fields further reveal that the surrounding fluid velocity increases proportionally with bubble rising velocity and temperature, confirming a strong coupling between bubble motion and plume-induced circulation. The surrounding liquid velocity reached approximately 30–60% of the corresponding bubble rising velocity, depending on operating conditions. These findings provide quantitative experimental insight into the coupled effects of thermal conditions, gas injection rate, and liquid depth on bubble–liquid interactions. The results contribute valuable validation data for multiphase flow modeling and offer practical relevance for thermal–hydraulic, chemical, and environmental engineering applications involving bubble-driven transport processes. Full article

Background/Objectives: This study aimed to develop a nomogram-based prediction tool to estimate visual field (VF) progression in patients with bilateral myopic normal-tension glaucoma (mNTG) by integrating key structural and vascular parameters. Methods: This retrospective cohort study included 150 eyes from 75 treatment-naïve patients with mNTG. All subjects were followed for at least five years with at least six reliable VF examinations. Key structural features, including the lamina cribrosa steepness index (LCSI) via enhanced-depth imaging optical coherence tomography (OCT) and choroidal microvascular dropout (cMvD) via OCT angiography (OCTA), were evaluated. VF progression was determined by event-based glaucoma progression analysis (GPA). To construct the predictive nomogram, clustered logistic regression with forward selection and 1000 bootstrap iterations was used to identify independent predictors. Results: Of the 150 eyes, 58 (38.7%) exhibited VF progression. Multivariable analysis identified steeper LCSI and the presence of parapapillary cMvD at baseline as significant independent predictors of progression. The resulting nomogram demonstrated excellent predictive accuracy, with an AUC of 0.922 and a C-index of approximately 0.92, indicating strong discriminative ability. Conclusions: This nomogram, incorporating structural (LCSI) and vascular (cMvD) markers, may offer a useful individualized tool for predicting VF progression in mNTG. This tool could assist in the early identification of high-risk patients and supports personalized treatment planning to optimize long-term visual outcomes. Full article

Agile parkour in unstructured environments poses significant challenges for quadruped robots, requiring both dynamic motion generation and terrain adaptability. Recent advances such as Adversarial Motion Priors (AMP) have shown promise in learning dynamic behaviors through motion imitation, but the resulting policies are typically specialized and struggle to generalize across varying terrains. However, existing AMP-based approaches largely lack explicit environmental awareness, leading to limited adaptability and revealing a clear gap in achieving general agile locomotion. To address this limitation, we propose a novel terrain-conditional AMP framework that extends adversarial motion priors by conditioning the discriminator on explicit terrain features, enabling the learning of terrain-aware motion representations adaptable to diverse environments. To improve practical applicability, we further leverage a vision-based policy distillation scheme, where a teacher policy with privileged terrain height information supervises a student policy operating only on forward-looking depth images. This enables agile, perception-driven locomotion in real time. To the best of our knowledge, this is the first work to integrate environmental information into adversarial motion priors and jointly learn a vision-based policy through policy distillation for agile quadruped locomotion. Experiments on terrains such as platforms, gaps, stairs, slopes, and debris show that the proposed method achieves more efficient training convergence and higher success rates compared to pure AMP-based and RL-based methods. These results highlight the effectiveness of the proposed framework and represent a step toward perception-driven agile locomotion for quadruped robots in complex environments. Full article