Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Data-driven models have been constructed for Dual Phase (DP) and Interstitials Free (IF) steels using an evolutionary approach. DP steel data are utilized from an existing database, while for the IF steels, data generated at an integrated steel plant have been used. The objective function for Ultimate Tensile Strength (UTS) and % elongation, created as data-driven models, is simultaneously optimized for an optimum strength-ductility balance and the results indicate the possibilities of developing steels with better mechanical properties than what are known to have been existing so far.     

(Two-Dimensional)<Superscript>2</Superscript> whitening reconstruction for newborn recognition

Recently, various feature extraction techniques and its variations have been proposed for computer vision. However, most of these techniques are sensitive to the images acquired in uncontrolled environment. The illumination, expression and occlusion for face images result in random error entries in the 2days matrix representing the face. Techniques such as Principal Component Analysis (PCA) do not handle these entries explicitly. This paper proposes a (Two-dimensional)² whitening reconstruction (T²WR) pre-processing step to be coupled with the PCA algorithm. This combined method would process illumination & expression variations better than standalone PCA. This technique has been compared with state-of-the-art Two-dimensional whitening reconstruction (TWR) pre-processing method. The final results clearly indicate the reason for better performance of T²WR over TWR. The histograms plotted for both these algorithms show that T²WR makes for a smoother frequency distribution than TWR. The proposed method indicated increased recognition rate and accuracy with increasing number of training images; up to 93.82 % for 2 images, 94.76 % for 4 images and 97.42 % for 6 training images.     

Newborn face recognition using deep convolutional neural network

Multimedia Tools and Applications - Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as...     

RETRACTED ARTICLE: A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

Multimedia Tools and Applications - With the increasing number of the images, how to effectively manage and use these images becomes an urgent problem to be solved. The classification of the images...     

Design optimization of a small-scale hydropower harvesting device

Structural and Multidisciplinary Optimization - This paper presents a semi-analytical model that facilitates the optimum design of small-scale hydropower systems, so that maximum possible energy...     

Potential nitrous oxide (N2O) emission from aquaculture in Nepal

Nitrous oxide has a greenhouse potential 310 times that of carbon dioxide (CO₂) over a 100-year lifespan. N₂O has been produced in the aquaculture system via nitrification and denitrification. This study mainly concerns estimation of the annual N₂O emission from aquaculture in Nepal and potential emission by the years 2020 and 2030. Theoretically, this study estimates emission of N₂O-N from aquaculture in Nepal in 2020 and 2030 to be 1.1 × 10⁸ g N₂O-N (±5.2%) and 2.24 × 10⁸ g of N₂O-N (±2.5%) of 95% confidence limit, respectively, with the emission factor 1.69 g of N₂O-N/kg of fish. In terms of carbon dioxide equivalent (CO₂eq), these values are 5.36 × 10¹⁰ g and 1.1 × 10¹¹ g, respectively, in 2020 and 2030, which accounts for 0.57% and 0.70% of Nepal’s CO₂ emission in 2020 and in 2030, respectively. This study also briefly deals with the parameters that stimulate N₂O emission and some potential technologies likely to reduce the N₂O emission in aquaculture. Greenhouse gas emission from aquaculture should be included by all countries in their estimation of global warming and climate change matters.