Influence of nanoadditives on ignition characteristics of Kusum (Schleichera oleosa) biodiesel

Biodiesel obtained from inedible sources emerged as a productive approach in Indian energy scenario due to the scarcity of food resources come up with extensive usage of edible crops. Kusum (Schleichera oleosa) oil is abundantly available in India and can be used as feedstock to produce biodiesel. However, issues such as higher viscosity, poor stability, and lower calorific value result in poor ignition characteristics, hence limiting its use in combustion applications. An improvement in performance and emission characteristics can be achieved by doping nanoparticles in Kusum biodiesel (KBD). The present work examines the impact of a metal compound and carbon‐primarily based nanoparticles on the evaporation time and ignition probability of the KBD. During the experimental process, different fuel samples of KBD were prepared by amalgamating nanoparticles; then, a sequence of hot plate (stainless steel) ignition test was conducted on these test fuels. The comparative assessment of neat biodiesel and the biodiesel fuel doped with 30 ppm each of alumina (Al2O3), and multiwalled carbon nanotubes (MWCNTs) nanoparticles were carried out. The Kusum oil was converted to biodiesel using two‐stage transesterification process. In the initial stage, refined oil was gone through the acid catalyst esterification process followed by the transesterification reaction. The prepared methyl ester was confirmed and characterized using GC‐MS technique. The thermophysical and spray properties of the test fuels including density, viscosity, calorific value, cloud/pour point, Sauter mean diameter (SMD), and specific surface area (SSA) were also calculated. The experimental result showed a significant increase in ignition probability and heat conduction properties due to improved surface area/volume ratio. Also, lower evaporation time was noted for metal/carbon‐based nanoparticles doped biodiesel as compared with neat biodiesel.     

Continuous‐discrete filters for bearings‐only underwater target tracking problems

In this work, we develop a continuous‐discrete shifted Rayleigh filter (CD‐SRF) and a continuous‐discrete sparse‐grid Gauss‐Hermite filter (CD‐SGHF) for a real‐life passive underwater bearings‐only target tracking problem. The stochastic difference equation describing the process model is derived from its continuous equivalent using Ito‐Taylor expansion of order 1.5. The performance of the proposed filters is compared in terms of root mean square error (RMSE), track divergence and computational time. For a fair comparison, popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss–Hermite filter (GHF) are implemented. The effect of initial uncertainty, measurement noise covariance and sampling time on filtering accuracy is also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer–Rao lower bound (CRLB). From simulation results, it was observed that CD filters performed with higher accuracy than their discrete equivalents, with CD‐SRF proving to be the most accurate among all the filters.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

A soft-error resilient low power static random access memory cell

Analog Integrated Circuits and Signal Processing - Advent and rapid development of on-chip computation in applications based on internet of things has opened space for integration of human life...     

Antenna Design: Micro Strip Patch for Spectrum Utilization in Cognitive Radio Networks

Wireless Personal Communications - A micro strip patch antenna with multiple parasitic patches for Cognitive Radio Network applications is presented to enhance the bandwidth. Multiple resonances...     

Modified Binary Multiplier Architecture to Achieve Reduced Latency and Hardware Utilization

Wireless Personal Communications - Arithmetic Logic Units (ALUs) are very important components of the processor, which performs various arithmetic and logical operations such as multiplication,...     

Sorption of Tertiary Butyl Mercaptan to Indoor Materials in Contact with Air or Water

This study reports sorption of the malodorant 2-methyl-2-propanethiol, commonly known as tertiary butyl mercaptan (TBM), to selected indoor materials. The phase distribution of TBM in gas-solid and aqueous-solid systems was evaluated using batch reactors. Sorbents used in the study included two carpets, two wallpapers, a soil, and granular activated carbon (GAC). Sorption was studied for gaseous and aqueous TBM concentrations spanning three orders of magnitude and contact times ranging from 1 to 28 days. The phase distribution data were plotted and fitted using linear and Freundlich relationships. Results indicated that all solids sorbed environmentally significant quantities of TBM, with the likelihood of producing concentrations above the odor threshold during subsequent remediation using mechanical ventilation. TBM retention by sorbents was greater from air than from water. The malodorant partitioned readily into wallpapers and slowly into the carpet materials. Sorption was nonlinear in the case of GAC and the nonlinearity appeared to increase with sorption contact time. GAC sorbed TBM strongly from both air and water.     

Comparative stability of aspartame and neotame in yoghurt

The comparative stability of aspartame and neotame was monitored in yoghurt during its processing, fermentation and storage. A solid‐phase extraction method was suggest changing it to developed for the isolation of aspartame and neotame. Pasteurisation (85 °C/30 min) resulted in approximately 47% and 3% loss of aspartame and neotame, respectively. During fermentation, 3% loss of aspartame was observed, but no loss of neotame. There was no significant effects on the stability of either aspartame or neotame during storage (4–7 °C/15 days). The results indicated that neotame was more stable than aspartame under both pasteurisation and fermentation conditions; however, during storage, both sweeteners exhibited excellent stability.