Search

Rapid and accurate system evolution predictions are crucial in scientific and engineering research. However, the complexity of processing systems, involving multiple physical field couplings and slow convergence of iterative numerical algorithms, leads to low computational efficiency. Hence, this paper introduces a systematic deep-learning-based surrogate modeling methodology for multi-physics-coupled process systems with limited data and long-range time evolution, accurately predicting physics dynamics and considerably improving computational efficiency and generalization. The methodology comprises three main components: (1) generating datasets using a sequential sampling strategy, (2) modeling multi-physics spatio-temporal dynamics by designing a heterogeneous Convolutional Autoencoder and Recurrent Neural Network, and (3) training high-precision models with limited data and long-range time evolution via a dual-phase training strategy. A holistic evaluation using a 2D low-temperature plasma processing example demonstrates the methodology’s superior computational efficiency, accuracy, and generalization capabilities. It predicts plasma dynamics approximately 105times faster than traditional numerical solvers, with a consistent 2% relative error across different generalization tasks. Furthermore, the potential for transferability across various geometries is explored, and the model’s transfer capability is demonstrated with two distinct geometric domain examples.

Complicated deformation problems are frequently encountered in medical image registration tasks. Although various advanced registration models have been proposed, accurate and efficient deformable registration remains challenging, especially for handling the large volumetric deformations. To this end, we propose a novel recursive deformable pyramid (RDP) network for unsupervised non-rigid registration. Our network is a pure convolutional pyramid, which fully utilizes the advantages of the pyramid structure itself, but does not rely on any high-weight attentions or transformers. In particular, our network leverages a step-by-step recursion strategy with the integration of high-level semantics to predict the deformation field from coarse to fine, while ensuring the rationality of the deformation field. Meanwhile, due to the recursive pyramid strategy, our network can effectively attain deformable registration without separate affine pre-alignment. We compare the RDP network with several existing registration methods on three public brain magnetic resonance imaging (MRI) datasets, including LPBA, Mindboggle and IXI. Experimental results demonstrate our network consistently outcompetes state of the art with respect to the metrics of Dice score, average symmetric surface distance, Hausdorff distance, and Jacobian. Even for the data without the affine pre-alignment, our network maintains satisfactory performance on compensating for the large deformation. The code is publicly available at https://github.com/ZAX130/RDP.

This article presents a novel 30.5-dBm output power 1 transmitter and 2 receivers (1T2R) heterogeneously integrated (HI) phased-array frequency-modulated continuous-wave (FMCW) radar transceiver using local oscillator (LO) phase shifting (PS). Two $X$ -band GaAs-based 6-bit PSs are designed and placed at the LO path. Two $W$ -band GaN-based power amplifiers (PAs) are proposed, amplifying the FMCW signals with 30.5 dBm at output. The antenna in package (AiP) is designed and fabricated in a high-resistivity silicon wafer by combining through silicon ring-trench and artificial magnetic conductor (AMC), where the $1\times4$ antenna array has a high radiation efficiency (70%), a broadband (12%), and a high gain (11.8 dBi) in the $W$ -band. Two $W$ -band SiGe-based transceiver systems on a chip, GaN-based PAs, AMC-based AiPs, and two $X$ -band GaAs PSs are HI with a transceiver size of $31\times17$ mm2 by using our in-house silicon-based MEMS photosensitive composite film given the advantages of high-density integration and fabrication accuracy. The HI radar application is measured with an effective isotropic radiated power of 41 dBm with a measured 2 cm range resolution. The beam scanning performance is also measured and presented using three corner reflectors located at different azimuths. Simulated and measured beamforming results show good agreement. The HI radar sensor has a size of $70\times70$ mm2 with a weight of only 200 g.

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expertise to make large scale annotations. Self/semi-supervised learning methods have been shown to improve the performance by exploiting unlabeled data. However, they are still lack of mining local semantic discrimination and exploitation of volume/sequence structures. In this work, we propose a semi-supervised representation learning method with two novel modules to enhance the features in the encoder and decoder, respectively. For the encoder, based on the continuity between slices/frames and the common spatial layout of organs across subjects, we propose an asymmetric network with an attention-guided predictor to enable prediction between feature maps of different slices of unlabeled data. For the decoder, based on the semantic consistency between labeled data and unlabeled data, we introduce a novel semantic contrastive learning to regularize the feature maps in the decoder. The two parts are trained jointly with both labeled and unlabeled volumes/sequences in a semi-supervised manner. When evaluated on three benchmark datasets of medical volumes and sequences, our model outperforms existing methods with a large margin of 7.3% DSC on ACDC, 6.5% on Prostate, and 3.2% on CAMUS when only a few labeled data is available. Further, results on the M&M dataset show that the proposed method yields improvement without using any domain adaption techniques for data from unknown domain. Intensive evaluations reveal the effectiveness of representation mining, and superiority on performance of our method. The code is available at https://github.com/CcchenzJ/BootstrapRepresentation.

Ultrasound (US) is important for diagnosing infant developmental dysplasia of the hip (DDH). However, the accuracy of the diagnosis depends heavily on expertise. We aimed to develop a novel automatic system (DDHnet) for accurate, fast, and robust diagnosis of DDH. An automatic system, DDHnet, was proposed to diagnose DDH by analyzing static ultrasound images. A five‐fold cross‐validation experiment was conducted using a dataset containing 881 patients to verify the performance of DDHnet. In addition, a blind test was conducted on 209 patients (158 normal and 51 abnormal cases). The feasibility and performance of DDHnet were investigated by embedding it into ultrasound machines at low computational cost. DDHnet obtained reliable measurements and accurate diagnosis predictions. It reported an intra‐class correlation coefficient (ICC) on αangle of 0.96 (95% CI: 0.93–0.97), βangle of 0.97 (95% CI: 0.95–0.98), FHC of 0.98 (95% CI: 0.96–0.99) and PFD of 0.94 (95% CI: 0.90–0.96) in abnormal cases. DDHnet achieved a sensitivity of 90.56%, specificity of 100%, accuracy of 98.64%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 98.44% for the diagnosis of DDH. For the measurement task on the US device, DDHnet took only 1.1 seconds to operate and complete, whereas the experienced senior expert required an average 41.4 seconds. The proposed DDHnet demonstrate state‐of‐the‐art performance for all four indicators of DDH diagnosis. Fast and highly accurate DDH diagnosis is achievable through DDHnet, and is accessible under constrained computational resources.

Hyperspectral imaging (HSI) is a widely used technology, yet hard to implement in real-time anomaly detection due to its extensive data flow volume. An autoencoder structured hybrid optical–electrical neural network method is proposed in this work that realizes feature exaction and anomaly detection during the hyperspectral data acquisition process to address such issues. In the proposed method, a digital micromirror device functions as the core optical processor to extract low-level features from the hyperspectral data flow. Weight binarization and a conditional subnetwork are utilized to suit optical computation. Pretraining by artificial data is implemented to ease the training data burden. Case studies on defect detection and foreign object detection have demonstrated that the proposed method can significantly reduce the sampling time by orders of magnitude without loss of detection accuracy.

Quantification of left ventricular (LV) ejection fraction (EF) from echocardiography depends upon the identification of endocardium boundaries as well as the calculation of end-diastolic (ED) and end-systolic (ES) LV volumes. It's critical to segment the LV cavity for precise calculation of EF from echocardiography. Most of the existing echocardiography segmentation approaches either only segment ES and ED frames without leveraging the motion information, or the motion information is only utilized as an auxiliary task. To address the above drawbacks, in this work, we propose a novel echocardiography segmentation method which can effectively utilize the underlying motion information by accurately predicting optical flow (OF) fields. First, we devised a feature extractor shared by the segmentation and the optical flow sub-tasks for efficient information exchange. Then, we proposed a new orientation congruency constraint for the OF estimation sub-task by promoting the congruency of optical flow orientation between successive frames. Finally, we design a motion-enhanced segmentation module for the final segmentation. Experimental results show that the proposed method achieved state-of-the-art performance for EF estimation, with a Pearson correlation coefficient of 0.893 and a Mean Absolute Error of 5.20% when validated with echo sequences of 450 patients.

This paper presents the model predictive control (MPC) application on the solar power system with microturbine and thermochemical energy storage (TCES). To investigate the potential of a solar-powered turbine, a solar receiver and a TCES are introduced to the Brayton cycle as the replacement of the combustor. MPC is applied to offer the constrained multi-variable real-time optimization control. To increase the practicability in the solar industry, a custom-made multi-modeling approach is proposed based on the close relationship between direct normal irradiance and system states. Feedback correction mechanisms are designed to improve the prediction and target tracking accuracies. During the regulatory control under both extreme and realistic conditions, the multi-MPC (MMPC) shows stronger adaptability and reliability than proportional–integration–differentiation (PID) and higher tracking accuracies than the single-linear-model-based MPC. Although the control performance could be further improved by employing nonlinear MPC (NMPC), the much longer optimization time of NMPC was unsuitable for real-time control. MMPC is further adapted to track the grid demand, which is technically unachievable by PID in the current system. While the output power precisely follows the demand, the performance parameters can still stay close to their design values, retaining a high system efficiency. Overall, the proposed MMPC enables power demand tracking operation of solar air turbine systems, and can ensure high stability and system efficiency.

A spiro ent-clerodane homodimer with a rare 6/6/6/6/6-fused pentacyclic scaffold, spiroarborin (1), together with four new monomeric analogues (2–5), were isolated from Callicarpa arborea. Their structures were elucidated by comprehensive spectroscopic data analysis, quantum-chemical calculations, and X-ray diffraction. A plausible biosynthetic pathway of 1was proposed, and a biomimetic synthesis of its derivative was accomplished. Compound 1showed a potent inhibitory effect by directly binding to the YEATS domain of the 11-19 leukemia (ENL) protein with an IC50value of 7.3 μM. This gave a KDvalue of 5.0 μM, as recorded by a surface plasmon resonance binding assay.

In this article, we propose the development of a recurrent neural network (RNN)-based model predictive controller (MPC) for a plasma etch process on a three-dimensional substrate using inductive coupled plasma (ICP) analysis. Specifically, the plasma etch process is simulated by a multiscale model: (1) A macroscopic fluid model is applied to simulate the gas flows and chemical reactions of plasma. (2) A kinetic Monte Carlo (kMC) model is applied to simulate the etching process on the substrate. Subsequently, proper orthogonal decomposition (POD) is used to derive the empirical eigenfunctions of the plasma model. Then the empirical eigenfunctions are utilized as basis functions within a Galerkin’s model reduction framework to compute a low-order system capturing dominant dynamics of the plasma model. Additionally, RNN is introduced to approximate dynamics of both the reduced-order plasma system and the microscopic etch process. The training data for the RNN models are generated from discrete sampling of open-loop simulations. A probability distribution function is also involved to present the stochastic characteristic of the kMC model. The trained RNN models are then implemented as the prediction model in the development of MPC to achieve desired control objectives. Closed-loop simulation results are presented to compare the performance of the model predictive controller and a proportional-integral (PI) controller, which show that the proposed MPC framework is effective and exhibits better performance than does a PI controller.

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancertissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSegachieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSegcan be accessed at https://github.com/Serian1992/ImgBio.

Recently, cytoophidium, a nonmembrane-bound intracellular polymeric structure, has been shown to exist in various organisms, including tumor tissues, but its function and mechanism have not yet been examined. Examination of cytoophidia-assembled gene inosine monophosphate dehydrogenase (IMPDH) and cytidine triphosphate synthetase (CTPS) mRNA levels showed that only IMPDH1 levels were significantly higher in the clear cell renal cell carcinoma (ccRCC). IMPDH1 was positively correlated with the metastasis-related gene Y-box binding protein 1 (YB-1) and served as an independent prognostic factor in ccRCC. Kaplan-Meier analysis indicated that patients with tumors that expressed high IMPDH1 levels had a shorter overall survival (OS) and disease-free survival (DFS). Furthermore, detection of cytoophidia by immunofluorescence staining in ccRCC tissues showed that IMPDH1-assembled cytoophidia are positively associated with tumor metastasis. Mechanistically, IMPDH1 and YB-1 formed an autoregulatory positive feedback loop: IMPDH1 maintained YB-1 protein stabilization; YB-1 induced IMPDH1 expression by binding to the IMPDH1 promoter motif. Functionally, IMPDH1-assembled cytoophidia physically interacted with YB-1 and translocated YB-1 into the cell nucleus, thus correlating with ccRCC metastasis. Our findings provide the first solid theoretical rationale for targeting the IMPDH1/YB-1 axis to improve metastatic renal cancer treatment.

Two-dimensional (2D) nanomaterials hold great potential for cancer theranostic applications, yet their clinical translation faces great challenges of high toxicity and limited therapeutic/diagnostic modality. Here, we have created a kind of symbiotic 2D carbon–2D clay nanohybrids, which are composed of a novel 2D carbon nanomaterial (carbon nanochips, or CNC), prepared by carbonizing a conjugated polymer polydiiodobutadiyne, and a 2D layered aluminosilicate clay mineral montmorillonite (MMT). Intriguingly, with the formation of the nanohybrids, MMT can help the dispersion of CNC, while CNC can significantly reduce the hemolysis and toxicity of MMT. The symbiotic combination of CNC and MMT also leads to a synergistic anti-cancer theranostic effect. CNC has a strong absorption and high photothermal conversion efficiency in the second near-infrared region (NIR-II, 1000–1700 nm), while MMT contains Fe3+that can facilitate the generation of reactive oxygen species from highly expressed H2O2in tumor microenvironment. The nanohybrids not only enable a synergy of photothermal therapy and chemodynamic therapy to suppress the extremely rapid growth of RM1 tumors in mice but also allow for dual photoacoustic and magnetic imaging to guide the drug delivery and NIR-II irradiation execution, hence establishing a highly efficient and biosafe “all-in-one” theranostic platform for precision nanomedicine.

The present work extends known finite dimensional Luenberger observer and Kalman filter designs to the realm of linear transport-reaction systems frequently present in chemical engineering practice. A unified modelling framework for distributed parameter systems (DPS) which does not account for any type of spatial approximation or order reduction is developed. The Cayley–Tustin transformation of continuous linear distributed parameter system yields structure and properties preserving discrete distributed parameter models, amenable to observer and filter design developments. Designs presented here explore well known state reconstruction methodologies starting from least square estimation, continuous and discrete Luenberger observers and one-step predictor Kalman filter realization. Simple implementation and realization account for the appealing nature of the discrete system observers and filter designs for linear transport-reaction systems. The simulation scenarios cover the majority of representative examples found in the common engineering process control practice.

Histopathology images are important for cancer diagnosis and prognosis. We extracted quantitative morphological features from breast cancers images and systematically analyzed their relationships with proteins and mRNAs. We observed concordant correlation patterns between image-protein and image-RNA and identified four cancer-related biological processes associated with morphological features related to different tumor components. Further, we observed that proteomic data reveal unique protein-related biological processes associated with morphology. Finally, prognostic morphological features were identified, and their roles are consistent with the underlying biological processes.

Ultrasound (US) has become one of the most commonly performed imaging modalities in clinical practice. It is a rapidly evolving technology with certain advantages and with unique challenges that include low imaging quality and high variability. From the perspective of image analysis, it is essential to develop advanced automatic US image analysis methods to assist in US diagnosis and/or to make such assessment more objective and accurate. Deep learning has recently emerged as the leading machine learning tool in various research fields, and especially in general imaging analysis and computer vision. Deep learning also shows huge potential for various automatic US image analysis tasks. This review first briefly introduces several popular deep learning architectures, and then summarizes and thoroughly discusses their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation. Finally, the open challenges and potential trends of the future application of deep learning in medical US image analysis are discussed.

The integral cross sections (ICSs) of the electron scattering of nitrogen molecule are of great importance to the understanding of many different aspects of atmospheric physics. In the present work, the generalized oscillator strengths (GOSs) of the valence shell excitations of b′1Πu, b′1Σu+, c31Πu, c4′1Σu+, and o31Πuof nitrogen have been determined by fast electron scattering at an incident electron energy of 1500 eV and an energy resolution of 70 meV. By cross‐checking strictly the present results with the previous electron scattering and inelastic X‐ray scattering (IXS) ones, the accuracy and reliability of the GOSs for the corresponding dipole‐allowed transitions of nitrogen are tested stringently. Based on the accurate GOSs of these dipole‐allowed transitions, their ICSs are obtained systematically from the threshold to 5000 eV for the first time with the aid of the BE‐Scaling (B is the binding energy and E is the excitation energy) method, and the corresponding ICSs at the moderate and high energies are the only available data to the best of our knowledge. The ICSs of nitrogen are of great importance to the understanding of many different aspects of atmospheric physicsThe BE‐scaled ICSs of nitrogen are obtained from the reliable GOSs cross‐checked by the electron scattering and IXS measurementsThe ICSs of nitrogen molecule are provided systematically from the threshold to 5000 eV for the first time

A desert-grown Halimodendron halodendron ethylene-responsive element binding factor gene (HhERF2), which encodes a 245 amino acids protein containing a conserved AP2/EREBP domain, was isolated through the rapid amplification cDNA end (RACE) method. Sequence and phylogenetic analysis indicated that HhERF2 was classified into the B-2 group of ERF subfamily. Semiquantitative RT-PCR showed that HhERF2 was greatly induced by treatments with high-salt, drought and low temperature in H. halodendron seedlings. The expression vector containing HhERF2 and Populus euphratica dehydration- responsive element binding protein (PeDREB2a) genes driven by rd29A promoter was constructed and transferred into cotton (Gossypium hirsutum L.) by non-tissue culture Agrobacterium-mediated genetic transformation system. The transformation and expression of HhERF2 and PeDREB2a were identified by PCR and RT-PCR. Analyses of physiological function indicated that transgenic cottons had improved seeds germination, tolerance to drought and highsalt stresses. Agronomic evaluation in the field exhibited that transgenic lines presented a dwarf phenotype and improved further in the yield and characters. These results demonstrated that overexpressed both HhERF2 and PeDREB2a transcription factor genes in cotton induced elevated tolerance to drought and high-salt stresses.

This work proposes a multiscale model-based operation framework for large-area plasma-enhanced chemical vapor deposition (PECVD) of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping. The results of a multiscale process model indicate that in order to manufacture a thin film with a diameter of 20 cm and a uniform thickness with surface microstructure that optimizes light trapping: a) a sinusoidally-grated wafer surface should be used in which the grating period and depth should correspond to values that lead to film surface roughness on the order of visible light wavelength range, and b) the substrate temperature should be regulated, along several concentric zones across the substrate, to compensate for a radially non-uniform deposition rate of the film on the wafer owing to gas-phase transport phenomena. Due to the dependence of film growth rate on substrate temperature, the wafer surface is separated into four concentric zones, each with an independent heating element. Simulations demonstrate that the use of appropriate sinusoidal wafer grating and the regulation of substrate temperature provide a viable and effective way for the large-area PECVD of thin film silicon solar cells with uniform thickness and film surface microstructure that optimizes light trapping.

Using Fe (NO3)3⋅9H2O, Y(NO3)3⋅6H2O and citric acid as the main raw material, the YFeO3 nanoparticles were synthesized by ultrasonic assisted process and calcination. The YFeO3 nanoparticles were characterized by thermogravimetry and differential thermal analysis (TG-DTA), powder X-ray diffraction (XRD), scanning electron microscopy (SEM) and UV-visible diffuse reflectance spectroscopy (DRS). The results show that the perovskite structureYFeO3 (YFeO3-800) can be obtained through the calcination of ultrasonic processed YFeO3 precursors at 800 °C, and the resulting product has a particle size of 70 nm and an optical band gap of 2.0 eV. Consequently, the YFeO3-800 nanoparticles show high photocatalytic activity for decomposition of methyl orange under visible-light irradiation.

Using Er (NO3)3·5H2O and Fe (NO3)3·9H2O as major materials, the ErFeO3 nanoparticles were prepared by successive microwave processing and high temperature calcination. The samples were characterized by thermogravimetric and differential thermal analysis (TG-DTA), powder X-ray diffraction (XRD), scanning electron microscopy (SEM) and UV-visible diffuse reflectance spectroscopy (DRS). It was found that the single phase ErFeO3 can be obtained through the calcination of microwave processed ErFeO3 precursors at 800°C, and the resulting product was approximation flake of 20-80 nm. The visible-light photocatalytic activity of ErFeO3 nanoparticles was investigated in experimental simulation wastewater containing 10 mg/L methyl orange (MO). The results show that ErFeO3 nanoparticles appear high photocatalytic activity for the decomposition of MO under visible-light irradiation.

In this paper, EU smart city evaluation system is introduced, and every index and its characteristic is explained particularly. Smart city construction of foundation is created by innovation. They emphasized green, low carbon economy and lifestyle and encouraging public participation, advocate social forces to participating in urban development. From the ranking result, it is known that the EU smart city construction has the following characteristics: The level of EU smart city development is not balanced. Smart move, smart people and the smart life is better overall development. The development of the city itself is not balanced.

Purpose: A multi-leaf collimator leaf sequencing comparison program in the sense that it translates beam intensity maps into the least number of MLC field segments was presented. Methods: The IMRT leaf sequencing calculation program based on Galvin, Bortfeld algorithms was constructed. The output of the leaf sequencing program were the number of segment and the total number of monitor unit. Results: Assuming 15 x 15 bixel fields with an average of 10 intensity levels, Bortfeld algorithm could yield better result on example 1 while Galvin algorithm yielded better results on example 2. Conclusions: This represented a useful tool for optimizing the leaf sequencing of static multi-leaf collimator that can yield shorter treatment time and higher utilization of photons by comparing with the total number of monitor units and number of segments of existed typical algorithms. It was an effective strategy and could be applied in commercial Treatment Planning Systems.

Under the terrible environment in the aero-engine, the blade, made of Ti-5Al-2Sn-2Zr-4Mo-4Cr titanium alloy, is prone to result in fatigue failure. So improving the titanium alloy performance is becoming very significant. In this paper, the microstructure and microhardness of Ti-5Al-2Sn-2Zr-4Mo-4Cr titanium alloy with and without laser shock peening (LSP) were examined and compared by XRD, TEM and microhardness test. The XRD tests pointed out that LSP generated the microstrains and grains refinement in the material surface. The TEM results indicated that great high density dislocations were generated and evolved into the dislocation wall, small-angle boundary and large-angle boundary. The nanocrystallites were formed and became more and more uniform after 4GW/cm2. The grains nanocrystallization in the surface layer helps to improve the material performance. The microhardness test result showed that LSP could increase the hardness by 20 percent or so. And the affected depth is about 700μm.

Abstract This paper aims at constructing a general mathematical frame for the equilibrium theory of open or closed biomembranes. Based on the generalized potential functional, the equilibrium differential equation for open biomembrane (with free edge) or closed one (without boundary) is derived. The boundary conditions for open biomembranes are obtained. Besides, the geometrically constraint equation for the existence, formation and disintegration of open or closed biomembranes is revealed. The physical and biological meanings of the equilibrium differential equation and the geometrically constraint equation are discussed. Numerical simulation results for axisymmetric open biomembranes show the effectiveness and convenience of the present theory.

In this work, a complex deposition process including two types of molecules whose growth behaviors are very different and influenced by long range interactions is investigated. The study of this process is motivated by recent experimental results on the growth of high-κ dielectric thin films using plasma-enhanced chemical vapor deposition (PECVD). A multi-component kinetic Monte-Carlo (kMC) model is developed for the deposition. The dependence of the surface microstructure of the thin film, such as island size and surface roughness, on substrate temperature are studied. The surface morphology is found to be strongly influenced by these two factors and growth regimes governed by short and long range interactions are observed. Furthermore, a kMC model-predictive control scheme which uses the substrate temperature to control the final surface roughness of the thin film is proposed. The closed-loop simulation results demonstrate that robust deposition with controlled thin film surface roughness can be achieved under the proposed kMC model-predictive controller.

In this work, we present a methodology for real-time carbon content feedback control of a plasma-enhanced metal organic chemical vapor deposition process using optical emission spectroscopy. Initially, an estimation model of carbon content of ZrO2 thin films based on real-time optical emission spectroscopy data is presented. Then, a feedback control scheme, which employs the proposed estimation model and a proportional-integral controller, is developed to achieve carbon content control. Using this approach, a real-time control system is developed and implemented on an experimental electron cyclotron resonance high density plasma-enhanced chemical vapor deposition system at UCLA to demonstrate the effectiveness of real-time feedback control of carbon content. Experimental results of the deposition process under both open-loop and closed-loop operations are shown and compared. The advantages of operating the process under real-time feedback control in terms of higher productivity, reduced process variation and lower carbon content are demonstrated

Not Available

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. We propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model was trained using well-annotated breast cancertissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSegachieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generated pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimated the epithelial and stromal ratios and performed correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that were highly correlated with tissue ratios suggest that the same tissue was associated with similar biological processes in different breast cancer subtypes, whereas each subtype also had its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSegcan be accessed at https://github.com/Serian1992/ImgBio.

The article presents a comparative analysis of the current situation of how girls majoring in Physical Education (P.E.) and those not majoring in P.E. handle psychological pressure. It was found that sports activities help strengthen the ability of college students to release pressure. Results indicate that the psychological pressure experienced by P.E. majors is lower than that of ordinary women students.