This paper describes the functionalization of the surfaces of gold nanoshells, which consist of silica nanoparticles coated with a continuous thin layer of gold. Previous studies have shown that gold nanoshells exhibit optical properties similar to those of metal colloids (e.g., strong optical absorptions and large third-order nonlinear optical polarizabilities). In contrast to metal colloids, however, the plasmon resonance of the nanoshells can be tuned to specific wavelengths across the visible and infrared range of the electromagnetic spectrum by adjusting the relative size of the dielectric core and the thickness of the gold overlayer. In efforts to develop new strategies for protecting and manipulating these nanoparticles, this paper describes the functionalization of the surfaces of gold nanoshells with self-assembled monolayers derived from the adsorption of a series of alkanethiols. The nanoshells are characterized by transmission electron microscopy, UV−vis spectroscopy, FTIR spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy and by examining their relative solubility in a variety of organic solvents.

Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO -MLP and 97% for APGWO -MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

Plant disease, especially crop plants, is a major threat to global food security since many diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in agricultural productivity. Farmers have to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Most of them built their models based on limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on plant leaves with small disease blobs, which can only be detected with higher resolution images, by an artificial neural network (ANN) approach. After a pre-processing step using a contrast enhancement method, all the infested blobs are segmented for the whole dataset. A list of several measurement-based features that represents the blobs are chosen and then selected based on their influences on the model's performance using a wrapperbased feature selection algorithm, which is built based on a hybrid metaheuristic. The chosen features are used as inputs for an ANN. We compare the results obtained using our methods with another approach using popular CNN models (AlexNet, VGG16, ResNet-50) enhanced with transfer learning. The ANN's results are better than those of CNNs using a simpler network structure (89.41% vs 78.64%, 79.92%, and 84.88%, respectively). This shows that our approach can be implemented on low-end devices such as smartphones, which will be of great assistance to farmers on the field. INDEX TERMS Neural network, image classification, plant disease, feature selection, precision agriculture.

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