An Experimental Investigation of Performance and Emission in Ethanol Fuelled Direct Injection Internal Combustion Engines with Zirconia Coating

This article presents the experimental investigation of use of neat ethanol (95% Ethanol +5% water) as a fuel in a four stroke single cylinder engine as regards to performance and emission characteristics. Two different ignition modes viz. (i) High Compression (16.5:1) Spark Ignition with high-pressure manifold injection of ethanol and (ii) Ultra High Compression (44.4:1) Compress Ignition with Direct Injection of ethanol have been experimentally analyzed with and without zirconia surface coating. As a benchmark, the results have been compared with data from the same engine run with diesel as fuel. In the first mode, the brake thermal efficiency with ethanol as fuel was found almost equal to that of diesel. However, the emissions were found to be significantly lower. In the second mode, the brake thermal efficiency was found to fall in between the diesel and ethanol manifold injection modes of operation. More significantly, the cost of running the engine was found to be lower than the operating cost incurred by using diesel. This assumes importance in the wake that ethanol can be obtained from non-fossil resources

Isolation of phytoconstituents from the flowers of <em>Couroupita guianensis</em>

709-713Couroupita guianensis is used extensively as an ingredient in many Ayurveda preparations which cure gastritis, scabies, bleeding piles, dysentery, and scorpion poison. The flower has been subjected to sequential extraction using petroleum ether, chloroform, ethyl acetate and methanol solvents. A new compound I Cycloart-24-en-3-ol-4'-exomethylene heptadeconate along with stigmasterol II, p-coumaric acid III, o-coumaric acid IV, caffeic acid V and quercetin VI have been isolated by column chromatography and characterised using IR, 1H and 13C&nbsp;NMR and MS spectral data. Compound I, III, IV and V are reported for the first time from C. guianensis

Drying of carrot slices in a triple pass solar dryer

An indirect triple pass forced convection solar dryer was developed and its performance was evaluated for drying of carrot slices. The drying experiments were carried out under the meteorological conditions of Coimbatore city in India during the year 2016. The experimental set-up consists of a blower, triple pass packed bed air collector (using sand) with wire mesh absorber plate, and a drying chamber. The air mass flow rate was optimized to 0.062 kg/s. The initial moisture content of the carrot slices was reduced from 87.5% (on wet basis) to the final moisture content of 10% (wet basis) in 6 h duration. The thin layer drying characteristics were analyzed using twelve mathematical models available in open literature. The results showed that the pick-up efficiency of the dryer was varied in the range between 14 and 43% with an average air collector thermal efficiency of 44% during the experimentation. The drying characteristics of carrot slices was predicted with good degree of accuracy using Wang and Singh drying model

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. Therefore, efficient detection depends on the selection of an optimal IoT traffic feature set and the learning algorithm that classifies the IoT traffic. There is a flaw in the existing anomaly detection systems because the feature selection algorithms do not identify the most appropriate set of features. In this article, a layered paddy crop optimization (LPCO) algorithm is suggested to choose the optimal set of features. Furthermore, the use of smart devices generates tremendous traffic, which can be labelled as either normal or attack using a capsule network (CN) approach. Five network traffic benchmark datasets are utilized to evaluate the proposed approach, including NSL KDD, UNSW NB, CICIDS, CSE-CIC-IDS, and UNSW Bot-IoT. Based on the experiments, the presented approach yields assuring results in comparison with the existing base classifiers and feature selection approaches. Comparatively, the proposed strategy performs better than the current state-of-the-art approaches

LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge

Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. Therefore, efficient detection depends on the selection of an optimal IoT traffic feature set and the learning algorithm that classifies the IoT traffic. There is a flaw in the existing anomaly detection systems because the feature selection algorithms do not identify the most appropriate set of features. In this article, a layered paddy crop optimization (LPCO) algorithm is suggested to choose the optimal set of features. Furthermore, the use of smart devices generates tremendous traffic, which can be labelled as either normal or attack using a capsule network (CN) approach. Five network traffic benchmark datasets are utilized to evaluate the proposed approach, including NSL KDD, UNSW NB, CICIDS, CSE-CIC-IDS, and UNSW Bot-IoT. Based on the experiments, the presented approach yields assuring results in comparison with the existing base classifiers and feature selection approaches. Comparatively, the proposed strategy performs better than the current state-of-the-art approaches