In‐plane shear damage behaviours of 2D needled C/SiC composites

Understanding the in‐plane shear behaviour of composites is essential to establish the design basis for practical applications. This study aims to investigate the shear damage behaviours of 2D needled C/SiC composites by various characterization techniques. The effect of layer arrangement on shear modulus and strength was discussed via shear stress‐strain responses. The shear strain field evolution and uniformity variation were studied by digital image correlation. It shows that the uniformity of shear strain field changes with the shear load, and the shear strain field evolution consist of 5 stages. The electrical resistivity measurement results indicate that structural deformation and damage evolution caused the electrical resistivity change. Furthermore, the damage evolution has a double effect on the electrical resistivity variation. The acoustic emission monitoring shows that the shear damage evolution is a 3‐stage nonlinear process before failure. The shear damages were categorized via acoustic characteristics. Besides, the postfailure behaviours were also discussed in this study.     

Essential oil nanoemulsions: antibacterial activity in contaminated fruit juices

Many human acid tolerant bacterial and fungal pathogens can be transmitted through the consumption of the contaminated fruit juices. We aim to formulate essential oil nanoemulsions (basil, black seed, turmeric, clove & cinnamon), determine their ability to clear contamination by food borne bacterial pathogens from fruit juices. The antibacterial activity of the optimised formulations was tested in the fruit juices against bacterial pathogens causing gastrointestinal tract infections. The minimum bactericidal concentration (MBC) of clove emulsions ranged from 15.6 to 25 μL mL⁻¹. Cinnamon oil emulsion had an MBC ranging between 15 and 31 μL mL⁻¹. At MBC, cinnamon oil emulsions caused a 6log₁₀ decrease in viable counts by 8 h and maintained the sterility of fruit juices for 7 days at ambient temperature. Thus, clove and cinnamon microemulsions can be used as juice additives to control food borne bacterial pathogens and maintain the bacterial sterility of fruit juices.     

The influence of brewing method on bioactive compounds residues in spent coffee grounds of different roasting degree and geographical origin

The content of bioactive compounds in spent coffee grounds (SGC) was studied. SGC were obtained from Coffea arabica beans of different roasting degrees (light and dark) and different geographical origins (Nicaragua, Columbia and Mexico) processed using four brewing methods (mocha, filtered, drip and infusion). The highest caffeine and chlorogenic acid contents were determined in filtered spent coffee extracts. All extracts of light roasted spent coffee grounds showed lower browning index levels in comparison to that from dark roasted spent coffee grounds. Generally, the highest content of total polyphenolic compounds and highest antioxidant capacity were determined in extracts prepared in drip. In conclusion, the results obtained in this study indicate that the spent coffee grounds produced of domestic levels, especially those obtained from filter coffeemaker, could be considered as a good source of natural antioxidants.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

Studying the effect of particle size on the antibacterial activity of some N-nicotinyl phosphoric triamides

Using ultrasonic top-to-down method, nanoparticles of two N-nicotinyl phosphoric triamides: C₅H₄NC(O)NHP(O)R, R?=?4-CH₃-NC₅H₁₀ (1), (CH₃)₃CNH₂ (2) were prepared for the first time and characterized by ³¹P, ¹³C, ¹H NMR, FTIR, scanning electron microscopy, energy dispersive X-ray. The average particle size of 1 and 2 were 60–70 and 40–50?nm, respectively, and the morphology was spherical for 1 and rod for 2. Solid state (powder) antibacterial effect of these compounds and two other similar reported ones, in their macro- and nanosizes, were evaluated with colony counting method on one Gram-positive (Staphylococcus aureus) and one Gram-negative (Escherichia coli) bacteria in Brain–Heart infusion culture medium. Results showed that all the macro- and nanosized compounds, except macrosized 1, were antibacterial and all nanoscaled ones had stronger antibacterial activity than their macroscaled analogues. The most effect of the particle size was observed for 1: by decreasing the particle sizes, the antibacterial activity state of 1 was changed from inactive (for macro) to potent (for nano).     

基于深度学习的彩色以及近红外图像去马赛克

单传感器捕获的彩色-近红外（RGB-NIR）图像存在光谱干扰，从而导致重建出的标准彩色图像（RGB）图像与近红外（NIR）图像存在色彩失真以及细节信息模糊。针对这个问题提出一种基于深度学习的去马赛克方法，通过引入跳远连接与稠密连接解决了梯度消失和梯度弥散问题，使得网络更容易训练，并且提升了网络的拟合能力。首先，用浅层特征提取层提取了马赛克图像的像素相关性以及通道相关性等低级特征；然后，将得到的浅层特征图输入到连续多个的残差稠密块以提取专门针对去马赛克的高级语义特征；其次，为充分利用低级特征与高级特征，将多个残差稠密块提取到的特征进行组合；最后，通过全局跳远连接恢复最终的RGB-NIR图像。在深度学习框架Tensorflow上使用公共的图像与视觉表示组（IVRG）数据集、有植被的户外多光谱图像（OMSIV）数据集和森林（Forest）三个公开数据集进行实验。实验结果表明，所提方法优于基于多级自适应残差插值、基于卷积卷积和神经神经网络以及基于深度残差U型网络的主流的RGB-NIR图像去马赛克方法。     

基于改进粗糙集理论的SAR图像目标增强

图像增强在提高SAR图像舰船目标检测精度方面具有十分重要的意义。由于传统算法不能很好地对SAR图像进行目标增强，提出了基于改进粗糙集理论和引力场强度的目标增强算法。通过借鉴引力场相关理论知识，将粗糙集条件属性集中的梯度属性改进为引力场强度属性，从而实现对原图像的目标增强。与其他算法进行了实验比较，结果表明提出的改进算法相比于其他算法更适用于SAR图像特性，能更好地对舰船目标像素进行针对性增强，具备一定的工程应用价值。     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.