Search Results (72)

Total internal reflection (TIR) loss is a critical bottleneck limiting light extraction in AlGaN-based deep-ultraviolet (DUV) light-emitting diodes (LEDs), primarily due to the large refractive-index contrast at the light-emitting interface. Here, pyramid-shaped dielectric metasurfaces are designed and fabricated at the sapphire/air interface of flip-chip AlGaN-based DUV LEDs using a scalable nanoimprinting process. The metasurface functions as a light outcoupling layer that modifies the interfacial momentum-matching condition and redistributes photon propagation directions. Experimental results and theoretical simulations show that metasurfaces with different feature sizes enhance light extraction through distinct mechanisms. The subwavelength pyramid nanoarray perturbs the local optical field and provides additional in-plane momentum components, facilitating the coupling of high-angle photons into radiative channels, whereas the larger pyramid void structure mainly promotes photon extraction through geometrical redirection, tilted output interfaces, and dry-etching-induced rough surface scattering. As a result, an average light output power (LOP) enhancement of over 8% is achieved for AlGaN-based DUV LEDs emitting at approximately 275 nm. This work demonstrates a low-cost, scalable, and effective strategy for enhancing the LEE of DUV LEDs, with promising potential for high-efficiency ultraviolet optoelectronic application. Full article

Recently, deep learning has enabled unmanned aerial vehicles (UAVs) to detect human bodies in aerial imagery, which is of particular importance in post-disaster situations such as floods and storms. Yet progress in this domain remains constrained by a familiar obstacle: the shortage of annotated training data. Neural networks, while powerful, are highly sensitive to data volume and diversity. Existing augmentation strategies help reduce this gap but typically introduce only incremental novelty, especially with respect to viewpoint variation, thereby limiting dataset richness. In this work, we propose a complementary strategy that leverages three-dimensional human models reconstructed via photogrammetric techniques. By situating these models within a controlled rendering environment, we generate synthetic imagery across a broad range of elevations and camera angles—perspectives that are rarely captured in conventional UAV datasets. These additions are designed to increase both the variability and the resilience of the training corpus. To evaluate the contribution of this approach, a custom CNN deep convolutional neural classifier was trained and benchmarked on a UAV human vs. non-human patch dataset of 4000 baseline images (128 × 128 px; 2800 train, 600 validation, 600 test), expanded with 3000 photogrammetry-derived synthetic patches (balanced by class) to 7000 total images for the 3DG setting. The primary metric was classification accuracy on the held-out test set, consistent with patch-level evaluation practice; detection-style metrics such as AP/IoU were not applicable to this binary classification protocol. Averaged over five independent training runs, the proposed augmentation improved classification accuracy by 3.02 percentage points over the baseline (88.06 ± 0.97% → 91.08 ± 1.03%), with consistent gains in precision, recall, and F1-score. When combined with standard augmentations (rotation, translation, scaling, flipping), accuracy reached 95.21 ± 0.61%, a gain of 7.15 percentage points over the baseline. These results suggest that photogrammetry-based augmentation offers a practical and effective enhancement for UAV-based human detection pipelines where timely, reliable identification is critical. Full article

The deep classifiers used for chest X-ray diagnosis can be sensitive to the visual frame around the evidence, producing correct labels for the wrong reasons by relying on the background, borders, text marks, or acquisition artifacts. This framing sensitivity reduces their trustworthiness, and they can fail under a distribution shift. Inspired by biological vision, especially figure–ground segregation and selective attention, we propose a bio-inspired adversarial attention alignment training process that encourages evidence-centered decisions without changing the classifier structure. A classifier is first trained with image-level labels. Class activation mapping (CAM) is then used to produce a differentiable heatmap that indicates where the model attends. We treat this heatmap as a generated localization map and train a discriminator to distinguish the generated heatmaps from ground truth masks. The classifier is updated using a joint objective that preserves the classification performance while pushing its CAM heatmap toward a mask-like structure, reducing the reliance on background cues. We also introduce evaluation measures for the test phase, including augmentation inconsistency (prediction flip rate under angle-based augmentations) and framing sensitivity (CAM energy outside the mask). The experiments show improved lung-focused attention and robustness, while requiring masks only during training and no additional inputs at inference. Full article

Longitudinal relaxation time (T1) can be used to assess pancreatic pathology on magnetic resonance imaging (MRI). Although pancreatic T1 values may be influenced by intra-organ fat content, regional variation within the pancreas and the impact of potential confounders have not been comprehensively examined. This study aimed to investigate the nuanced associations between intrapancreatic fat deposition (IPFD) and both regional and total pancreatic T1 relaxation times. Pancreatic T1 relaxation times were quantified with B1-corrected dual flip-angle 3D-VIBE imaging at 3.0 Tesla, whereas IPFD was measured with a high-speed, T2-corrected multi-echo sequence. Linear regression models were constructed to evaluate the association between IPFD and T1 values, with adjustment for relevant covariates. A total of 124 individuals were included in the analysis. IPFD explained 4.6% of the variance in total pancreatic T1 values, with notable regional differences: 1.0% in the head, 3.0% in the body, and 7.7% in the tail of the pancreas. In the fully adjusted model, IPFD was significantly associated with total pancreatic T1 values (p = 0.001), with consistent significant associations observed across all pancreatic regions: head (p = 0.03), body (p = 0.004), and tail (p = 0.002). These findings demonstrate that IPFD is a significant determinant of pancreatic T1 relaxation times. Accordingly, IPFD should be considered a potential confounder in pancreatic T1 assessments and accounted for when interpreting T1 relaxation times on pancreatic MRI in both research and clinical contexts. Full article

In remote sensing image oriented object detection tasks, weakly supervised learning methods based on horizontal bounding boxes have attracted much attention due to their lower annotation costs compared to fully supervised methods. However, remote sensing images, characterized by complex backgrounds, exhibit a wide range of target scales and diverse geometric characteristics across target categories. Existing methods exhibit inadequate exploitation of background and angular information under weak supervision, resulting in compromised perception of dense and high-aspect-ratio targets. Neglecting the imbalance in angle estimation samples further leads to excessively low detection accuracy for few-shot categories. To address the aforementioned issues, this paper proposes a Geometry-Aware Enhancement Network (WSOOD-GAEN) for weakly supervised oriented object detection tasks. First, in the backbone network stage, a channel-space deformable attention module (DAE-ResNet) was constructed. Through deformable sampling and screening of key regions, feature extraction has both morphological adaptability to complex shapes and semantic discriminability of key features in complex backgrounds. Secondly, in the feature pyramid stage, an Angle-Guided Feature Pyramid Network (AG-FPN) is proposed. This module dynamically applies rotation transformation to the sampling offsets of deformable convolutions, thereby enhancing the feature representation of objects with different orientations and scales. Furthermore, an adaptive geometric perception loss (AGL) was designed. Based on the geometric characteristics of different categories, it automatically learns differentiated rotation and flip consistency weights, thereby improving the prediction accuracy of small sample categories. Experiments on the DOTA-v1.0, HRSC, and RSAR datasets validate our approach. Specifically, under the AP75 evaluation metric, the proposed method outperforms existing weakly supervised methods by 1.51%, 9.86%, and 3.28%, respectively. Full article

To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient ($ADC$) of water in human tissue. $ADC$ assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower $ADC$ values. Accurately measuring the $ADC$ requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause $ADC$ underestimation. This study aimed to demonstrate that $ADC$ overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to $ADC$ overestimations. We performed demonstration experiments on fat–water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The $ADC$ values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower $ADC$ values under in-phase conditions and higher $ADC$ values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in $ADC$ overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated $ADC$. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings. Full article

Background: Interstitial myocardial fibrosis is a crucial pathological feature of many cardiovascular disorders. Myocardial fibrosis resulting in extracellular volume (ECV) expansion can be quantified via cardiac MRI (CMR) with T₁ mapping before and after minimally invasive gadolinium (Gd) contrast agent administration. However, longitudinal repetitive ECV measurements are challenging in rodents due to the prolonged scan time with cardiac and respiratory gating that is required for conventional T₁ mapping and the invasive nature of the rodent intravenous lines. Methods: To address these challenges, the objective of this study is to establish a fast, free-breathing, and gating-free ECV procedure using a minimally invasive subcutaneous catheter for in-scanner Gd administration that can allow longitudinal repetitive ECV evaluations in rodent models. This is achieved by the (1) IntraGate sequence for free-breathing, gating-free cardiac imaging; (2) minimally invasive subcutaneous in-scanner Gd administration; and (3) fast T₁ mapping with a varied flip angle (VFA) in conjunction with (4) triple jugular vein blood T₁ normalization. Additionally, full cine CMR (multi-slice short-axis, long-axis 2-chamber, and long-axis 4-chamber) was acquired during the waiting period to assess comprehensive cardiac function and strain. Results: We successfully established a minimally invasive fast ECV quantification protocol to enable longitudinal repetitive ECV quantifications in rodents. Minimally invasive subcutaneous Gd bolus administration induced a reasonable dynamic contrast enhancement (DCE) time course, reaching a steady state in ~20 min for stable T₁ quantification. The free-breathing gating-free VFA T₁ quantification scheme allows for rapid cardiac (~2.5 min) and jugular vein (49 s) T₁ quantification with no motion artifacts. The triple jugular vein T₁ acquisitions (1 pre-contrast and 2 post-contrast) immediately flanking the heart T₁ acquisitions enable accurate myocardial ECV quantification. Our data demonstrated that left-ventricular myocardial ECV quantification was highly reproducible with repeated scans, and the ECV values (0.25) are comparable to reported ranges among humans and rodents. This protocol was successfully applied to the ischemia–reperfusion injury model to detect myocardial fibrosis, which was validated by histopathology. Conclusions: We established a simple, fast, minimally invasive, and robust CMR protocol in rodents that can enable longitudinal repetitive ECV quantification for cardiovascular disease progression. It can be used to monitor disease regression with interventions. Full article

The Voith Schneider Propeller (VSP) operates with blades undergoing an approximately sinusoidal periodic motion along a circular path. Hydrodynamically, the continuous significant variation in the angle of attack between the blades and incoming flow, together with additional inertial effects caused by accelerated rotation, complicates the computation and measurement of hydrodynamic performance. To investigate the unsteady hydrodynamic behavior resulting from this coupled motion, a numerical model incorporating adaptive mesh refinement was developed to simulate VSP performance. Based on insights into the interaction between blade motion and hydrodynamics, an experimental platform was designed using servo motors to achieve precise synchronized blade control, enabling mutual validation between numerical simulations and transient hydrodynamic measurements. Results demonstrate that the coupled blade motion induces nonlinear variations in hydrodynamic forces. Rotational power loss limits VSP efficiency, and a negative thrust regime occurs at high advance coefficients. Rapid blade flipping leads to flow separation, identified as the primary cause of nonlinear lateral forces. The consistency between numerical and experimental results provides reliable data supporting theoretical studies. These findings offer valuable insights for optimizing motion control strategies in cycloidal propeller applications. Full article

To directly address the important issue of MRI geometric distortions in stereotactic radiosurgery (SRS) planning, we performed a phantom study of sequence acquisition optimization. This study analyzed, in particular, the effects of clinically relevant gadolinium (Gd) concentration as filling solution for the phantom, as well as phase encoding reversal direction and flip angle on distortion. We created a rigid geometric grid phantom with 840 fiducial markers for distortion quantification on a 3T MRI scanner. To choose the optimal filling solution, an anthropomorphic RANDO phantom was employed, and 1 mmol/L gadolinium was chosen due to clinical relevance. An automated Python-based software (version 3.7.1) was developed for efficient detection and matching of phantom inserts between MRI and CT scans. A series of MRI acquisition parameter optimizations were systematically evaluated. The standard SRS protocol exhibited the highest average distortion of 1.301 mm. Notably, reversing the phase-encoding direction to anterior–posterior (AP) reduced the mean distortion to 0.725 mm, a 44.27% decrease, while the maximum distortion was reduced by 15.65%. The AP phase sequence maintained acquisition time, SAR, SNR, and CNR within acceptable limits. Additional distortion reduction was achieved by increasing the flip angle from 12° to 18°. In this work, we succeeded in significantly reducing the mean distortion observed in phantom images. As the gadolinium concentration used in the phantom is clinically similar to the gadolinium concentration observed in patients undergoing MRI scans with contrast agents, the achieved distortion reduction is prospectively reproducible in patients. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

In the present study, a polarization rotation switch (PRS)-based all-optical ternary inverter circuit and ternary clocked SR flip-flop (TCSR) are proposed and discussed. The present scheme is designed by the polarization rotation of light in a waveguide coupled with a micro-ring resonator (MRR). The proposed scheme uses linear polarization-encoded light. Here, the ternary (radix = 3) logical states are expressed by the different polarized light. PRS-MRR explores the polarization-encoded methodology, which depends on polarization conversion from one state to another. All-optical ultrafast switching technology is employed to design the ternary NAND gate. We develop the ternary clocked SR flip-flop by employing the NAND gate; it produces a greater number of possible outputs as compared to the binary logic clocked SR flip-flop circuit. The performance of the proposed design is measured by the Jones parameter and Stokes parameter. The results of the polarization rotation-based ternary inverter and clocked SR flip-flop are realized using a pump–probe structure in the MRR. The numerical simulation results are confirmed by the well-known Jones vector (azimuth angle and ellipticity angle) and Stokes parameter (S₁, S₂, S₃) using Ansys Lumerical Interconnect simulation software. Full article

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

The compatibility and safety of contrast media injectors (CMIs) at ultra-high magnetic field strengths remains a critical challenge. This study aimed to investigate a custom-designed CMI powered by a ceramic motor in a newly developed 5T MRI environment, comparing it with a commercial CMI commonly used in a clinic. Three key performance aspects of the CMI were assessed in the 5T environment: translational attraction force, injection flow rates, and total injected volume. Potential imaging artifacts were checked. The custom-designed CMI demonstrated robust performance in the 5T environment, maintaining injection accuracy across all test locations and ensuring translational attraction forces remained within safe thresholds, even in the most challenging positions. Importantly, the custom-designed CMI exhibited no significant radiofrequency (RF) interference, and no imaging artifacts were observed across routine clinical sequences. In contrast, the commercial 3T CMI showed RF interference in several sensitive tests, such as the gradient echo (GRE) sequence with a 0° flip angle and frequency-based detection methods, underscoring the need for field-specific CMI designs tailored to ultra-high field environments. Further tests were performed in monkey livers and a human brain in vivo. The custom-designed CMI proved to be safe, accurate, and fully compatible with the 5T environment. Full article

Background: Dynamic susceptibility contrast perfusion MRI (DSC-MRI) is a promising non-invasive examination to predict histological and molecular characteristics of brain gliomas. However, the diagnostic accuracy of relative cerebral blood volume (rCBV) is heterogeneously reported in the literature. This systematic review and meta-analysis aims to assess the diagnostic accuracy of mean rCBV derived from DSC-MRI in differentiating Isocitrate Dehydrogenase (IDH)-mutant from IDH-wildtype gliomas. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, and EMBASE up to January 2025, following PRISMA guidelines. Eligible studies reported mean CBV values in treatment-naïve gliomas with histologically confirmed IDH status. Pooled estimates of standardized mean differences (SMDs), diagnostic odds ratios (DOR), and area under the receiver-operating characteristic curve (AUC) were computed using a random-effects model. Heterogeneity was assessed via I² statistic. Meta-regression analyses were also performed. Results: An analysis of 18 studies (n = 1733) showed that mean rCBV is significantly lower in IDH-mutant gliomas (SMD = −0.86; p < 0.0001). The pooled AUC was 0.80 (95% CI, 0.75–0.90), with moderate sensitivity and specificity. Meta-regression revealed no significant influence of DSC-MRI acquisition parameters, although a flip angle showed a trend toward significance (p = 0.055). Conclusions: Mean rCBV is a reliable imaging biomarker for IDH mutation status in gliomas, demonstrating good diagnostic performance. However, heterogeneity in acquisition parameters and post-processing methods limits generalizability of results. Future research should focus on standardizing DSC-MRI protocols. Full article