High-quality ZnO nanorod based flexible devices for electronic and biological applications

Vertically aligned zinc oxide nanorods (ZnO NRs) were synthesized on kapton flexible sheets using a simple and cost-effective three-step process (electrochemical seeding, annealing under ambient conditions, and chemical solution growth). Scanning electron microscopy studies reveal that ZnO NRs grown on seed-layers, developed by electrochemical deposition at a negative potential of 1.5 V over a duration of 2.5 min and annealed at 200 degrees C for 2 h, consist of uniform morphology and good chemical stoichiometry. Transmission electron microscopy analyses show that the as-grown ZnO NRs have single crystalline hexagonal structure with a preferential growth direction of < 001 >. Highly flexible p-n junction diodes fabricated by using p-type conductive polymer exhibited excellent diode characteristics even under the fold state.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Fuzzy optimization model for water quality management of a river system - Closure

A fuzzy waste-load allocation model, FWLAM, is developed for water quality management of a river system using fuzzy multiple-objective optimization. An important feature of this model is its capability to incorporate the aspirations and conflicting objectives of the pollution control agency and dischargers. The vagueness associated with specifying the water quality criteria and fraction removal levels is modeled in a fuzzy framework. The goals related to the pollution control agency and dischargers are expressed as fuzzy sets. The membership functions of these fuzzy sets are considered to represent the variation of satisfaction levels of the pollution control agency and dischargers in attaining their respective goals. Two formulations—namely, the MAX-MIN and MAX-BIAS formulations—are proposed for FWLAM. The MAX-MIN formulation maximizes the minimum satisfaction level in the system. The MAX-BIAS formulation maximizes a bias measure, giving a solution that favors the dischargers. Maximization of the bias measure attempts to keep the satisfaction levels of the dischargers away from the minimum satisfaction level and that of the pollution control agency close to the minimum satisfaction level. Most of the conventional water quality management models use waste treatment cost curves that are uncertain and nonlinear. Unlike such models, FWLAM avoids the use of cost curves. Further, the model provides the flexibility for the pollution control agency and dischargers to specify their aspirations independently.

CLXXX. Vegetable Proteins. I. The Proteins of Dolichos Lab Lab

The field bean (Dolichos lab lab ; Tamil name, Mochai ; Kanarese, Avarai) is a legume which is widely cultivated in South India often as a mixed crop with cereals. The kernel of the seed enters into the diet of may South Indian households, and in the Mysore State the seed are used as a delicacy when they are green for over four months in the year. The haulm, husk and pods are commonly used a fodder. As the kernel which is widely used as an article of food and considered to be very nutritious, contains about 24% of protein hitherto uninvestigated and as the quality of protein plays an important role in nutrition, the present work was undertaken.