Steady state sedimentation of ultrasoft colloids

The structural and dynamical properties of ultra-soft colloids - star polymers - exposed to a uniform external force field are analyzed applying the multiparticle collision dynamics approach, a hybrid coarse-grain mesoscale simulation approach, which captures thermal fluctuations and long-range hydrodynamic interactions. In the weak field limit, the structure of the star polymer is nearly unchanged, however in an intermediate regime, the radius of gyration decreases, in particular transverse to the sedimentation direction. In the limit of a strong field, the radius of gyration increases with field strength. Correspondingly, the sedimentation coefficient increases with increasing field strength, passes through a maximum and decreases again at high field strengths. The maximum value depends on the functionality of the star polymer. High field strengths lead to symmetry breaking with trailing, strongly stretched polymer arms and a compact star polymer body. In the weak field linear response regime, the sedimentation coefficient follows the scaling relation of a star polymer in terms of functionality and arm length

Digit Recognition Using Single Layer Neural Network with Principal Component Analysis

This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA). The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application. There are many algorithms applied to this computer vision problem and many more algorithms are continuously developed on this to make the handwritten recognition classify digits more accurately with less computation involved. The proposed model in this paper aims to reduce the features to reduce computation requirements and successfully classify the digit into 10 categories (0 to 9). The system designed consists of backward propagation (BP) neural network and is trained and tested on the MNIST dataset of handwritten digit. The proposed system was able to obtain 98.39% accuracy on the MNIST 10,000 test dataset. The Principal Component Analysis (PCA) is used for feature extraction to curtail the computational and training time and at the same time produce high accuracy. It was clearly observed that the training time is reduced by up to 80% depending on the number of principal component selected. We will consider not only the accuracy, but also the training time, recognition time and memory requirements for entire process. Further, we identified the digits which were misclassified by the algorithm. Finally, we generate our own test dataset and predict the labels using this system

Applications of Tannins in Industry

Tannins are water-soluble natural polyphenols mainly present in plant-based materials, including food. Tannins play a very significant role as a raw material for sustainable green industries. Therefore, they are mainly used in diverse types of industries such as leather, feed, fisheries, beverages, etc. They also find application as potential medicinal agents, antioxidants, metal chelators; and cater as inhibitors of harmful pro-oxidative enzymes and of lipid peroxidation process. Recently, several important properties like antiseptics, anticarcinogenic, and anti-inflammatory of tannins have been documented in the human that make them suitable candidates for pharmaceuticals and nutraceutical industries. Because of current concerns related to synthetic compounds used in the human health and food industries, which leave highly adverse effects on the human body and environment, tannins can offer an alternative to these harmful chemicals in recently emerging industries

A General Class of Chain-Type Estimators in the Presence of Non-Response Under Double Sampling Scheme

General class chain ratio type estimators for estimating the population mean of a study variable are examined in the presence of non-response under a double sampling scheme using a factor-type estimator (FTE). Properties of the suggested estimators are studied and compared to those of existing estimators. An empirical study is carried out to demonstrate the performance of the suggested estimators; empirical results support the theoretical study