Diverse bio-sensing and therapeutic applications of plasmon enhanced nanostructures

Substantial advancements have been observed over the years in the research and development of Localized Surface Plasmon Resonance (LSPR). A variety of current and future applications involving anisotropic plasmonic nanoparticles include biosensors, photothermal therapies, photocatalysis, and various other fields. Amongst various other applications, plasmonic enhancements are deployed in Surface Enhanced Raman Spectroscopy (SERS) mediated bio-sensing, absorption spectroscopy based analyte quantification, and fluorescence spectroscopy-based biomolecular detection up to femtomolar level and even on the level of single molecules. LSPR based healthcare diagnostics and therapeutics have grown much faster than expected, with an increased number of published original research articles and reviews. Despite the extensive literature available, a comprehensive review with a focused emphasis on recent advances in the field of plasmonic particle anisotropy, plasmonic nanostructure, plasmonic coupling mediated enhanced LSPR intensity and their diverse applications in biosensing is needed. This article focuses on LSPR properties of anisotropic nanostructures like spherical gold nanoparticles (AuNP), gold nanorod (AuNR), gold nanostar (AuNs), gold nanorattles (AuNRT), gold nanoholes (AuNH), dimeric nanostructures and their role in plasmonic enhancements for targeted biosensing and therapeutic research. The contemporary state of the art biosensing development around SERS has also been discussed. A detailed literature analysis of recent development in micro-surgery, photothermal tumor killing, biosensor development for detection up to single molecule level, high-efficiency drug delivery are covered in this article. Furthermore, recent and advanced technologies including Spatially Offset Raman Spectroscopy (SORS), Surface Enhanced Resonance Raman Spectroscopy (SERRS), and Surface Enhanced Spatially Offset Raman Spectroscopy (SESORS) are presented citing their importance in biosensing. We complement this review article with relevant theoretical frameworks to understand finer nuances within the literature that is discussed.     

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the “p” value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.     

基于NB分类器重访概率预测的Web缓存替换策略

Web缓存是用来解决网络访问延迟和网络拥塞问题，缓存替换策略直接影响缓存的命中率。为此，提出一种朴素贝叶斯（NB）分类器重访概率预测的Web缓存替换策略；根据用户之前访问日志，通过分区操作提取多项特征来表示每次访问的对象，并构建特征数据集；训练NB分类器，用来确定缓存中对象被再次访问的概率，为对象分配权重；结合LRU策略来合理删除一些对象。仿真结果表明，提出的策略在保证较高命中率的同时有效降低了执行时间。     

Equilibria,kinetics and mechanism for the degradation of the cytotoxic compound L-NG-nitroarginine

L-N^G-nitroarginine (LNNA), an analog of L-arginine, is a competitive inhibitor of nitric oxide synthase which causes the selective reduction of blood flow to tumor cells. Despite the potential of LNNA to function as an adjuvant in cancer therapies, its poor solubility and stability have hindered the development of an injectable formulation of LNNA that is suitable for human administration. This work, for the first time, details a systematic study on the determination of equilibrium K_a constants and the rate law of LNNA degradation. The four K_a values of LNNA were determined to be 1.03, 1.10?×?10^?2, 2.51?×?10^?10, and 1.33?×?10^?13 M. From the kinetic and equilibrium studies, we have shown that the deprotonated form of LNNA is the main form of LNNA that undergoes degradation in aqueous media at room temperature. The rate law of LNNA degradation was found to be first order with respect to OH^? concentration and first order with respect to LNNA^? concentration. The rate constant at 25?°C and 1?atm was determined to be 0.04453 M^?1min^?1. A base catalyzed mechanism of LNNA degradation was proposed based on the kinetic study. The mechanism was found to be very useful in explaining the discrepancies and changes of the rate law at different pH values. It is thus recommended that LNNA should be formulated as a concentrated solution in acidic conditions for maximum chemical stability during storage and be diluted with a basic solution to near physiological pH just before administration.     

槽式太阳能集热管内相变微胶囊悬浮液的热力性能分析

针对传统使用水基和油基的太阳能集热器换热效果低和管壁热应力大的问题，以相变微胶囊悬浮液为工作流体，对抛物型槽式太阳能集热器进行了三维建模。采用蒙特卡罗射线追踪法结合有限容积法和有限元法的方法求解了太阳能集热管的光?热?力耦合问题，采用欧拉?欧拉多相流模型研究了相变微胶囊悬浮液在集热管内的流动换热特性。结果表明，相变微胶囊悬浮液强化了集热管内的对流换热，不仅降低了集热管的沿程壁温，且减少了集热管的周向温差，均化了集热管温度分布。集热管周向等效热应力呈花瓣型分布，对应的5个高温度梯度的位置附近(圆周角θ=5°, 90°, 175°, 225°和315°)出现等效应力局部峰值。吸热管内壁面θ=90°处轴向热应力为压应力，作用于整个管程，而径向热应力和切向热应力为拉应力，主要作用在进出口端。相变微胶囊悬浮液浓度越高，强化换热效果越好，集热管热应力越小，但产生的压降也随之增大。     

基于机器视觉的玻璃缺陷检测

随着科学技术的进步,高端显示屏产品对平板玻璃的质量要求越来越高,玻璃的表面缺陷检测技术也因此备受关注。传统的人眼检测方法工作量大且准确率低,已经无法满足生产实际要求。研究了一种基于机器视觉的玻璃质量检测系统,采用先进的CCD成像技术和背光式照明获取图像,用MATLAB图像处理工具箱对采集到的图像进行灰度值化、滤波降噪和阈值分割处理,实现对缺陷区域的特征提取和识别。最后用BP神经网络对玻璃表面的三种缺陷进行分类,该神经网络识别的平均误差率为9.84%,表明此检测方法具有一定的应用价值。     

基于动态行为学习的空中目标识别方法

针对空中移动目标识别问题,提出了一种基于动态行为学习的识别方法。首先,从数据源中提取目标要素,经过标注形成目标数据特征集;其次,基于应用场景进行特征组合,形成特征模型要素;然后,基于深度学习算法进行模型训练,得到评估最优的模型参数;最后,利用训练后的模型进行目标识别处理。通过数值仿真验证,该方法能够有效地识别无业务规则的目标身份。