Cascade Region Research Articles

Scaling Effects of the Weissenberg Number in Electrokinetic Oldroyd-B Fluid Flow Within a Microchannel.

This study attempts to extend previous research on electrokinetic turbulence (EKT) in Oldroyd-B fluid by investigating the relationship between the Weissenberg number ( ) and the second-order velocity structure function ( ) under applied electric fields. Inspired by Sasmal's demonstration in Sasmal (2022) of how heterogeneous zeta potentials induce turbulence above a critical , we develop a mathematical framework linking to turbulent phenomena. Our analysis incorporates recent findings on AC (Zhao & Wang, 2017) and DC (Zhao & Wang 2019) EKT, which have defined scaling laws for velocity and scalar structure functions in the forced cascade region. Our finding shows that and , for a length scale , and , where is a velocity fluctuations quantity and denotes the time relaxation parameter. This work establishes a positive correlation between and turbulent flow phenomena through a rigorous analysis of velocity structure functions, thereby offering a mathematical foundation for building the design and optimization of EKT-based microfluidicdevices.

Explainable AI to identify radiographic features of pulmonary edema

Abstract Background Pulmonary edema is a leading cause for requiring hospitalization in patients with congestive heart failure. Assessing the severity of this condition with radiological imaging becomes paramount in determining the optimal course of patient care. Purpose This study aimed to develop a deep learning methodology for the identification of radiographic features associated with pulmonary edema. Materials and Methods This retrospective study used a dataset from the Medical Information Mart for Intensive Care database comprising 1000 chest radiograph images from 741 patients with suspected pulmonary edema. The images were annotated by an experienced radiologist, who labeled radiographic manifestations of cephalization, Kerley lines, pleural effusion, bat wings, and infiltrate features of edema. The proposed methodology involves 2 consecutive stages: lung segmentation and edema feature localization. The segmentation stage is implemented using an ensemble of 3 networks. In the subsequent localization stage, we evaluated 8 object detection networks, assessing their performance with average precision (AP) and mean AP. Results Effusion, infiltrate, and bat wing features were best detected by the Side-Aware Boundary Localization (SABL) network with corresponding APs of 0.599, 0.395, and 0.926, respectively. Furthermore, SABL achieved the highest overall mean AP of 0.568. The Cascade Region Proposal Network network attained the highest AP of 0.417 for Kerley lines and the Probabilistic Anchor Assignment network achieved the highest AP of 0.533 for cephalization. Conclusion The proposed methodology, with the application of SABL, Cascade Region Proposal Network, and Probabilistic Anchor Assignment detection networks, is accurate and efficient in localizing and identifying pulmonary edema features and is therefore a promising diagnostic candidate for interpretable severity assessment of pulmonary edema.

Monolithic integration of deep ultraviolet and visible light-emitting diodes for radiative sterilization application

The demand for effective sterilization methods, particularly in the wake of the Covid-19 pandemic, has sparked interest in the use of deep ultraviolet (DUV) radiation for disinfection. The high risk of skin/eye exposure to the high-energy DUV radiation requires the integration of DUV and visible (VIS) LED chips to sterilize and indicate its operation simultaneously in the portable sterilization devices. However, conventional double-chip integration suffers from high power consumption and fabrication complexity. This study sets out to explore the monolithic integration of DUV and VIS LEDs for the radiative sterilization application. This is accomplished by cascading AlGaN/AlGaN/AlGaN multiple quantum wells (QWs) and GaN/InGaN/GaN QWs through the compositional grading AlGaN cascade region. The inevitable overflown electrons from DUV QWs are deliberately introduced into the VIS QWs, allowing for the electron–hole recombination and the simultaneous emission of VIS light. Both experiment and simulation results confirm the feasibility of the proposed dual-wavelength LED integration. The proposed DUV&VIS LED shows an external quantum efficiency and wall-plug efficiency of 2.03% and 1.54% at 40 mA, respectively. This study establishes a quantitative framework for the monolithic integration of DUV and VIS LEDs for radiative sterilization, which has the potential to replace the current technique of using discrete DUV and VIS double-chip configurations.

(Invited) Transistor-Injected Quantum-Cascade Devices: Novel Sources for Mid-Wave Infrared Emission

Quantum Cascade Lasers (QCLs) [1] are important sources for coherent mid-wave infrared (MWIR) light that find widespread use in applications such as molecular spectroscopy, free-space optical communications, and defense countermeasures. These applications demand tunability of emission wavelength and high output powers. The conventional QCL is a two-terminal n-i-n device in which an externally applied bias establishes the necessary electric field in the cascade region for proper alignment of the quantum states, defines the emission wavelength, and also determines the current through the cascade structure. Because QCLs are 2-terminal devices, changing bias directly changes both wavefunction overlap and energy separation. The spectral location and magnitude of the gain peak thus has a strong dependence on applied voltage, impacting the capability to modulate. To overcome these limitations, the transistor-injected quantum-cascade laser (TI-QCL) has been proposed [2]. The TI-QCL is a three-terminal device in which the cascade region is placed within a heterojunction bipolar transistor. In forward-active mode the current injection is controlled by a forward-biased emitter-base junction, and the reverse bias on the base-collector junction is used to control the electric field for alignment of the quantum states. This novel structure enables the decoupling of emission wavelength and output power. This extra control positions the TI-QCL to impact a wide range of applications such as spectroscopy with its wide tunability, and free-space communication with its capability to separately modulate power and frequency.Previously, transistor characteristics and near-infrared (NIR) band-to-band base emission was demonstrated in a GaAs design [3, 4] before moving to an InP-based design [5]. We will review the progress made to push the TI-QCL concept towards full functionality, focusing on InP-based epitaxial designs. A self-consistent Schrodinger-Poisson solver and non-equilibrium Green’s function method is used to aid in the active-region design of the structure, which is optimized for a peak emission wavelength of 8.27 μm as shown in Figure 1 [6]. A wide wavelength tunability from approximately 7.8 μm to 8.8 μm is projected based on computational analysis. The InP-based structure is grown using molecular beam epitaxy by IntelliEpi; a cross-section of the material is depicted in Figure 2. It contains an active-region composed of 30 repeated periods of In0.41Ga0.59As/In0.36Al0.64As strain-balanced superlattice. The devices are fabricated using standard III-V processing. An image of a fabricated TI-QCL bar consisting of a sloped, 20 μm wide emitter mesa and evaporated SiO2 as the passivation layer is depicted in Figure 3. The fabricated devices are diced and cleaved into 2 to 4 mm x 500 μm die and mounted onto In-plated copper mounts followed by wire bonding. The devices are electrically characterized at room temperature, 77 K, and 18 K. Under cryogenic temperatures, the common-base characteristics exhibit negative differential resistance (NDR) in collector current at all emitter injection currents as shown in Figure 4, which signifies tunneling transport through the quantum cascade superlattice region. The base current also exhibits corresponding NDR behavior and switching in the band-to-band NIR light (1.57 m) output from the In0.53Ga0.47As base is experimentally observed when biased in a region where transport through the cascade region is rejected due to quantum state misalignment.

MD Simulations of Collision Cascades in α-Ti. The Residual Number of Radiation Defects, Cascade Relaxation Time, and Displacement Cascade Region Morphology

MD Simulations of Collision Cascades in α-Ti. The Residual Number of Radiation Defects, Cascade Relaxation Time, and Displacement Cascade Region Morphology

MD Simulations of Collision Cascades in α-Ti. The Residual Number of Radiation Defects, Cascade Relaxation Time and Displacement Cascade Region Morphology

Molecular dynamics (MD) simulations were applied to study radiation damage formation in collision cascades created by the recoil of primary knock-on atoms (PKA) with PKA energies Epka = 5, 10, 15, 20 and 25 keV in α-Ti at temperatures T = 100, 300, 600 and 900 K. A series of 24 collision cascades with the same set of (Epka, T) parameters has been simulated to generate representative sampling. Sapling size was justified a posteriori. The number of Frenkel pairs Nfp and cascade relaxation time were obtained as functions of (Epka, T). It was shown that the average ⟨Nfp(Epka,T)⟩ fits within 0.3NRT, provided the threshold displacement energy is chosen in 28-40 eV range depending on the irradiation temperature. Under high PKA energy/low irradiation temperature conditions, displacement cascade region splits into subcascades expended along high-energy recoil trajectories. Cascade relaxation time does not depend on Epka in this case. Contrary, under low PKA energy/ high temperature simulation conditions, most of collision cascades possess equiaxial shape, and their relaxation time grows with PKA energy increase.

A Domain-Adaptive Tree-Crown Detection and Counting Method Based on Cascade Region Proposal Networks

Rapid and accurate tree-crown detection is significant to forestry management and precision forestry. In the past few decades, the development and maturity of remote sensing technology has created more convenience for tree-crown detection and planting management. However, the variability of the data source leads to significant differences between feature distributions, bringing great challenges for traditional deep-learning-based methods on cross-regional detection. Moreover, compared with other tasks, tree-crown detection has the problems of a poor abundance of objects, an overwhelming number of easy samples and the existence of a quantity of impervious background similar to the tree crown, which make it difficult for the classifier to learn discriminative features. To solve these problems, we apply domain adaptation (DA) to tree-crown detection and propose a DA cascade tree-crown detection framework with multiple region proposal networks, dubbed CAS-DA, realizing cross-regional tree-crown detection and counting from multiple-source remote sensing images. The essence of the multiple region proposal networks in CAS-DA is obtaining the multilevel features and enhancing deeper label classifiers gradually by filtering simple samples of source domain at an early stage. Then, the cascade structure is integrated with a DA object detector and the end-to-end training is realized through the proposed cascade loss function. Moreover, a filtering strategy based on the planting rules of tree crowns is designed and applied to filter wrongly detected trees by CAS-DA. We verify the effectiveness of our method in two different domain shift scenarios, including adaptation between satellite and drone images and cross-satellite adaptation. The results show that, compared to the existing DA methods, our method achieves the best average F1-score in all adaptions. It is also found that the performance between satellite and drone images is significantly worse than that between different satellite images, with average F1-scores of 68.95% and 88.83%, respectively. Nevertheless, there is an improvement of 11.88%~40.00% in the former, which is greater than 0.50%~5.02% in the latter. The above results prove that in tree-crown detection, it is more effective for the DA detector to improve the detection performance on the source domain than to diminish the domain shift alone, especially when a large domain shift exists.

Interactions between displacement cascades and grain boundaries in NiFe single-phase concentrated solid solution alloys

Single-phase concentrated solid solution alloys (SP-CSAs) are promising structural materials with excellent irradiation resistance. It is very important for their application in nuclear reactor structures to understand the interactions between their grain boundaries (GBs) and irradiation-induced defects. With molecular dynamics (MD) simulations, the displacement cascades in NiFe SP-CSAs with Σ5(210) symmetrical tilt GB were studied. Compared with pure Ni, NiFe has higher sink strength of GB for vacancies but has little effect on the interstitial absorption of GB. The formation energies of interstitials and vacancies near the GB are more dispersed, so that the point defects are more difficult to aggregate, which is beneficial for the recombination of interstitials and vacancies near the GB. Both the formation energies of point defects and the sink strengths of GB for point defects could be changed with the SP-CSA compositions. When the Fe atom concentration is higher than 30 at.%, the point defects are easier to annihilate. Both the effects of the PKA energy and the PKA-GB distance on the displacement cascades damage can be explained with the size of overlapping region between the cascade region and GB. In addition, the formation of SP-CSAs helps to annihilate the vacancies in NiFe with Σ37(750) symmetrical tilt GB and Σ5 twist GB.

Generalized Knowledge Distillation for Unimodal Glioma Segmentation from Multimodal Models

Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods.

CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection

CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection

The Role of Subduction Zone Processes in the Cultural History of the Cascade Region

The Cascadia subduction zone continuously shapes the landscape of the Pacific Northwest of North America and the cultures of its inhabitants. The impacts of subduction processes on Pacific Northwest societies and cultures are varied, but Native Americans and European settler cultures alike have described geological processes through oral histories and have relied on resources provided by the subduction zone. Indigenous peoples focus many aspects of their religious practices and art around the geohazards of the Cascadia region, and our melded modern cultures continue to take part in storytelling related to subduction zone hazards through movies and other forms of narration.

Role of temperature in the formation of induced magnetic anisotropy during ion-beam synthesis of iron silicides

Thin films with the uniaxial magnetic anisotropy were synthesized by Fe+ implantation into single-crystal silicon in the external magnetic field. The induction of the magnetic anisotropy is due to the directional atomic pair ordering (Néel-Taniguchi model). The applicability of this model is confirmed by experiments on the rotation of the direction of the easy magnetization axis by repeated low-dose implantation in an external magnetic field. The induced anisotropy is formed in the collisions cascade region characterized by the so-called «dynamic temperature». The value of the dipole interaction coefficient, calculated taking into account the dynamic temperature, agrees with the results obtained in other works. The value of anisotropy constant is inversely proportional to temperature. Possible mechanism leading to such dependency is discussed.

Biomass stocks in California’s fire-prone forests: mismatch in ecology and policy

Restoration of fire-prone forests can promote resiliency to disturbances, yet such activities may reduce biomass stocks to levels that conflict with climate mitigation goals. Using a set of large-scale historical inventories across the Sierra Nevada/southern Cascade region, we identified underlying climatic and biophysical drivers of historical forest characteristics and projected how restoration of these characteristics manifest under future climate. Historical forest conditions varied with climate and site moisture availability but were generally characterized by low tree density (∼53 trees ha−1), low live basal area (∼22 m2 ha−1), low biomass (∼34 Mg ha−1), and high pine dominance. Our predictions reflected broad convergence in forest structure, frequent fire is the most likely explanation for this convergence. Under projected climate (2040–2069), hotter sites become more prevalent, nearly ubiquitously favoring low tree densities, low biomass, and high pine dominance. Based on these projections, this region may be unable to support aboveground biomass >40 Mg ha−1 by 2069, a value approximately 25% of current average biomass stocks. Ultimately, restoring resilient forests will require adjusting carbon policy to match limited future aboveground carbon stocks in this region.

Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images

The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in the Pearl River Delta and Jianghan plain in China. It plays a key role in preserving biodiversity, enhancing the nutrient cycle, and increasing crop production. However, DPS is rarely mapped at a large scale with satellite data, due to the limitations in the training dataset and traditional classification methods. This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable shapes and orientations. The mCascade R-CNN yielded the most accurate detection of DPS, with an average precision (AP) value that was 2.71% higher than Cascade R-CNN and 11.84% higher than You Look Only Once-v4 (YOLOv4). The area of oilseed rape growing on the dikes accounted for 3.42% of the total oilseed rape planting area. This study demonstrates the potential of the deep leaning methods combined with high-resolution satellite images in detecting integrated agriculture systems.

Individual cascade annealing in BCC tungsten: effects of size and spatial distributions of defects.

To investigate effects of size and spatial distributions of defects from primary damage to annealing of an individual cascade, molecular dynamics (MD) and object kinetic Monte Carlo (OKMC) are applied for simulating cascade generation and annealing. MD cascade simulations of tungsten are carried out with two typical embedded atom method potentials for cascade energies in the range from 0.1 to 100 keV at 300 K. The simulation results show that even though the number of survival defects varies slightly, these two potentials produce very different interstitial cluster (IC) size distribution and defect spatial distribution with cascade energies larger than 30 keV. Furthermore, OKMC is used to model individual cascade annealing. It demonstrates that larger-sized ICs and closely distributed SIAs in the cascade region will induce a much higher recombination fraction for individual cascade annealing. Therefore, special attention should be paid to the size and spatial distributions of defects for primary damage in the multi-scale simulation framework.

Investigation of the interaction between hydrogen and irradiation defects in titanium by using positron annihilation spectroscopy

The formation of mono-vacancy, vacancy clusters and hydrogen-vacancy complexes with 30 keV H ion-irradiated pure titanium at different doses and temperatures was measured using by Positron annihilation spectroscopy (PAS). Results show a large number of HmVn clusters and vacancy-like defects in the samples irradiated at for room temperature, and that the formation of HmVn (m > n) at the sample irradiated at a high dose inhibits the increase of the S parameter. At increased irradiation temperature, the shrinkage of vacancy clusters and the effective open volume of defects decrease the S parameters. The high-temperature irradiation results in decreased vacancy-type defect concentration, and some hydrogen atoms diffuse from the cascade region to the track region, forming a large number of hydrogen-vacancy complexes in the track region. The coincidence Doppler broadening spectroscopy, an element analysis method, used to detect hydrogen in the ion-irradiated pure titanium sample, and results show hydrogen-related peaks in the high-momentum region, which may be due to the information of positron annihilation in the covalent bond formed by the H and the Ti elements. The increased radiation dose and temperature contribute to the formation of the hydrogen vacancy-complex, and the positron annihilation in high-momentum regions easily obtain hydrogen-related information.

On Independence of the Thermal-Spike Temperature in Pure Metals of the Implanted Ion Energy and Atomic Mass

The optical emission spectra of pure metals Fe, Zr, Ta and W are measured during their irradiation with Ar+, Kr+ and Xe+ ions (5, 10, 15 and 20 keV). It is found out that the energy and ion type in this energy range do not appreciably affect the thermal emission spectral profile of these targets and hence do not influence the thermal spike temperature formed in the zones of passing dense cascades of atomic displacements. It is shown that a constant temperature regime of the thermal peaks (T = const) is fulfilled in the case where the average radius R of the cascade region is proportional to the square root of the incident ion energy$$ \left(R\sim \sqrt{E}\right) $$, and the major part of the thermal energy is released at the cascade periphery of a fixed length. As a result, the energy per atom of the medium, released in this region, does not vary with the ion energy and cascade radius. It is also shown that the thermal spike temperature for all ion types essentially depends on the target material.

Remote Sensing Airport Detection Based on End-to-End Deep Transferable Convolutional Neural Networks

Rapid intelligent detection of airports from remote sensing images is required to accomplish autonomous intelligent landing of unmanned aerial vehicles (UAVs) and other tasks. To address the insufficiency of traditional models in detecting airports under complicated backgrounds from remote sensing images, we propose an end-to-end remote sensing airport hierarchical expression and detection model based on deep transferable convolutional neural networks. Based on transfer learning, we solve the fundamental problem of overfitting due to the inadequate number of labeled remote sensing images by transferring the network model from natural image source domain to remote sensing image target domain. In addition, we introduce a cascade region proposal network with soft-decision nonmaximal suppression to improve the network structure and the performance of our method under complex backgrounds. Moreover, we use skip-layer feature fusion and hard example mining methods to improve the object expression ability and the training efficiency. Finally, the experimental results demonstrate that the method established in this letter can quickly and effectively detect different types of airports over complex backgrounds and obtain better detection performance than the other detection methods.

Cascade region proposal and global context for deep object detection

Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. For region proposal we propose a novel cascade structure which can effectively improve RPN proposal quality without incurring heavy extra computational cost. For object recognition we re-implement global context modeling with a few modifications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016 validation set). Besides, we apply the idea of pre-training extensively and show its importance in both steps. Together with common training and testing tricks, we improve Faster R-CNN baseline by a large margin. In particular, we obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test set and 36.8% on the COCO test-std set.

The Effect of Irradiation with Si+ Ions on Resistive Switching in Memristive Structures Based on Yttria-Stabilized Zirconia

We have studied the resistive switching in memristive structures based on 40-nm-thick yttria-stabilized zirconia (YSZ) films exposed to 6-keV Si+ ion irradiation to a total dose of 5.4 × 1015 cm–2. It is established that the ion irradiation leads to increased stability of the parameters of resistive switching in YSZ based memristive structures. This effect is related to the fact that the diameter of conducting filaments in irradiated structures is limited by lateral dimensions of the region of atomic displacement cascades (i.e., the region occupied by point defects produced by bombarding ions). The oxidation of ion-modified filaments during resistive switching proceeds more effectively and leads to increasing resistance in the high-resistance state of memristive structures.