Direct ink writing of vancomycin-loaded polycaprolactone/ polyethylene oxide/ hydroxyapatite 3D scaffolds

Novel inks were formulated by dissolving polycaprolactone (PCL), a hydrophobic polymer, in organic solvent systems; polyethylene oxide (PEO) was incorporated to extend the range of hydrophilicity of the system. Hydroxyapatite (HAp) with a weight ratio of 55–85% was added to the polymer-based solution to mimic the material composition of natural bone tissue. The direct ink writing (DIW) technique was applied to extrude the formulated inks to fabricate the predesigned tissue scaffold structures; the influence of HAp concentration was investigated. The results indicate that in comparison to other inks containing HAp (55%, 75%, and 85%w/w), the ink containing 65% w/w HAp had faster ink recovery behavior; the fabricated scaffold had a rougher surface as well as better mechanical properties and wettability. It is noted that the 65% w/w HAp concentration is similar to the inorganic composition of natural bone tissue. The elastic modulus values of PCL/PEO/HAp scaffolds were in the range of 4–12 MPa; the values were dependent on the HAp concentration. Furthermore, vancomycin as a model drug was successfully encapsulated in the PCL/PEO/HAp composite scaffold for drug release applications. This paper presents novel drug-loaded PCL/PEO/HAp inks for 3D scaffold fabrication using the DIW printing technique for potential bone scaffold applications.     

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.     

Direct writing of textured ceramics using anisotropic nozzles

Direct writing is a unique means to align anisotropic particles for the fabrication of textured ceramics by templated grain growth (TGG). We show that alignment of tabular barium titanate (BT) template particles (20–40 μm width and 0.5–2 μm thickness) in a PIN-PMN-PT matrix powder (d₅₀ = 280 nm) is significantly improved during direct writing using anisotropic nozzles at high printing rates. The particle orientation distribution in as-printed filaments, and the texture orientation distribution in sintered ceramic filaments are shown to directly correlate with COMSOL Multiphysics-predicted torque distributions for direct writing with aspect ratio 2, 3 and 5 oval nozzles. Electromechanical strain properties of the textured piezoelectric ceramics significantly improved relative to random ceramics when printed with anisotropic nozzles. Simulations of aspect ratio 20 nozzles and nozzles with interior baffles demonstrate significantly increased torque and near elimination of constant shear stress cores (i.e. plug flow).     

Flexible electronic skin with high performance pressure sensing based on PVDF/rGO/BaTiO3 composite thin film

The wearable intelligent electronic product similar to electronic skin has a great application prospect. However, flexible electronic with high performance pressure sensing functions are still facing great challenges. In this paper, the highly sensitive flexible electronic skin (FES) based on the PVDF/rGO/BaTiO₃ composite thin film was fabricated using the near-field electrohydrodynamic direct-writing (NFEDW) method. The PVDF/rGO/BaTiO₃ composite solution was directly written on flexible substrate by the NFEDW method to fabricate FES with micro/nano fiber structure, which has the function of sensing pressure with high sensitivity and fast response. The surface morphology and microstructure were characterized by SEM, AFM, and optical microscope in detail. The fabricated FES has high sensitivity (59 kPa⁻¹) and faster response time (130 ms). FES has been successfully applied to the detection of human motion and subtle physiological signals. The experimental results show that FES has good stability and reliability. FES can recognize human motion, and it has a broad application prospect in the field of wearable devices.     

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.     

傣族织锦的艺术特征与传承

文章阐述了傣族织锦的艺术特征及传承发展现状,对其纹样、内涵及应用进行了分析,提出了傣锦传承和发展的措施与方法,为傣锦的保护和传承提供参考。     

显色图像分析技术在水稻叶耳花青甙显色目测分级中的应用

目的基于颜色特征的叶耳花青甙显色分级研究。方法以水稻倒二叶叶耳花青甙显色测试为切入点,在Emgu Cv3.0图像分析软件基础上,对完成图像分割的目标区域提取红绿蓝(red green blue, RGB)、色调饱和度亮度(hue saturation value, HSV)颜色特征,利用SPSS软件对颜色特征和测试分级数据进行相关性、回归等统计分析,建立颜色特征多元回归模型。结果叶耳花青甙显色强度与R(红色)、G(绿色)、B(蓝色)、H(色调)、V(亮度)极显著负相关;所有颜色特征值中,G值与叶耳花青甙显色强度相关性最显著,是一元和多元回归主要自变量; G值建立的一元回归模型中, R~2为0.980;多元回归模型R~2值为0.994。结论回归模型的拟合效果好,用这两个模型均可完成叶耳花青甙显色强度分级。