A high-throughput chaotic advection microreactor for preparation of uniform and aggregated barium sulfate nanoparticles

A high-throughput (105.5 g/h) passive four-stage asymmetric oscillating feedback microreactor using chaotic mixing mechanism was developed to prepare aggregated Barium sulfate (BaSO₄) particles of high primary nanoparticle size uniformity. Three-dimensional unsteady simulations showed that chaotic mixing could be induced by three unique secondary flows (i.e., vortex, recirculation, and oscillation), and the fluid oscillation mechanism was examined in detail. Simulations and Villermaux–Dushman experiments indicate that almost complete mixing down to molecular level can be achieved and the prepared BaSO₄ nanoparticles were with narrow primary particle size distribution (PSD) having geometric standard deviation, σ_g, less than 1.43 when the total volumetric flow rate Q_total was larger than 10 ml/min. By selecting Q_total and reactant concentrations, average primary particle size can be controlled from 23 to 109 nm as determined by microscopy. An average size of 26 nm with narrow primary PSD (σ_g = 1.22) could be achieved at Q_total of 160 ml/min.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

The use of seed texture features for discriminating different cultivars of stored apples

The aim of this study was to identify the textural features of apple seeds with the highest discriminatory power for distinguishing the seeds of different apple cultivars with the use of discriminative classifiers. The seeds of apple cvs. Gala, Jonagold and Idared were scanned with the use of a flatbed scanner, and the acquired images were processed to calculate textural features from color channels: L, a, b, R, G, B, Y, U, V, H, S, I, X, Y and Z. The selected textures were used to develop discriminative models and distinguish the seeds of the examined apple cultivars. The analyses were performed for color spaces and color channels. The seeds of apple cvs. Gala and Idared were discriminated with 100% accuracy in models based on the textures from Lab and YUV color spaces and color channel L for the Naive Bayes, Multilayer Perceptron and Multi Class classifiers. The discriminatory accuracies of the seeds of all analyzed apple cultivars (Gala, Idared and Jonagold) ranged from 72% to 85%. The discriminatory accuracy of the textures selected from Lab color space for the Naive Bayes classifier reached 85%. The seeds of apple cvs. Gala and Jonagold were discriminated with 78–90% accuracy, and the discriminatory accuracy of the textures from Lab color space and color channel b for the Naive Bayes classifier reached 90%. The seeds of apple cvs. Idared and Jonagold were distinguished with 80–94% accuracy. The models based on textures from Lab color space and color channel b for the Naive Bayes classifier were characterized by 94% discriminatory accuracy. The study demonstrated that textural features are useful for discriminating the seeds of different apple cultivars.     

含不凝性气体蒸汽浸没射流冷凝压力振荡实验研究

实验研究了不凝性气体（空气）含量、水温和蒸汽质量流速对蒸汽浸没射流冷凝压力振荡特性的影响，实验工况横跨冷凝振荡（CO）区和稳定冷凝（SC）区。结果表明：对于纯蒸汽射流，压力振荡主频随水温的升高而降低，振荡强度随水温的升高而升高；在CO区，振荡主频和振荡强度均随蒸汽质量流速的升高而升高；在SC区，振荡主频随蒸汽质量流速的升高而降低，振荡强度基本上不随蒸汽质量流速的变化而发生改变；对于含空气射流，随空气质量分数的增加，振荡主频总体呈下降趋势，振荡强度先迅速下降后小幅上升，在空气质量分数为0.05～0.1区域内振荡主频和振荡强度均存在极小值。     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

Astrocyte Ca2+ Waves and Subsequent Non-Synchronized Ca2+ Oscillations Coincide with Arteriole Diameter Changes in Response to Spreading Depolarization

Spreading depolarization (SD) is a wave of mass depolarization that causes profound perfusion changes in acute cerebrovascular diseases. Although the astrocyte response is secondary to the neuronal depolarization with SD, it remains to be explored how glial activity is altered after the passage of SD. Here, we describe post-SD high frequency astrocyte Ca²⁺ oscillations in the mouse somatosensory cortex. The intracellular Ca²⁺ changes of SR101 labeled astrocytes and the SD-related arteriole diameter variations were simultaneously visualized by multiphoton microscopy in anesthetized mice. Post-SD astrocyte Ca²⁺ oscillations were identified as Ca²⁺ events non-synchronized among astrocytes in the field of view. Ca²⁺ oscillations occurred minutes after the Ca²⁺ wave of SD. Furthermore, fewer astrocytes were involved in Ca²⁺ oscillations at a given time, compared to Ca²⁺ waves, engaging all astrocytes in the field of view simultaneously. Finally, our data confirm that astrocyte Ca²⁺ waves coincide with arteriolar constriction, while post-SD Ca²⁺ oscillations occur with the peak of the SD-related vasodilation. This is the first in vivo study to present the post-SD astrocyte Ca²⁺ oscillations. Our results provide novel insight into the spatio-temporal correlation between glial reactivity and cerebral arteriole diameter changes behind the SD wavefront.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.