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This study focuses on effectively utilizing the biodiesel extracted from Azolla (third-generation biofuel), which is regarded as a renewable energy source, for fueling diesel engines. Biodiesel is unique due to its increased viscosity and different fatty acid composition, which proved difficult to attain better engine performance with a mechanical type injection system. This study expands on the previous investigation in modifying the fuel system when using Azolla biodiesel by using a common rail fuel injection system with wider injection flexibility. Considering the lack of more engine optimization studies for Azolla biodiesel, a parametric study is conducted by changing the fuel injection pressure in the range between 300 bar and 900 bar for diesel engine fueled by B20 (20% Azolla +80% diesel) blend. The experimental engine study revealed that the physical properties of the fuel adversely affect the in-cylinder combustion, which leads to poor engine performance and higher emissions at lower injection pressure (300 bar) for B20 when compared to diesel. As the injection pressure increases, the fuel atomization and other spray characteristics are enhanced and thereby improve the combustion. The Brake Thermal Efficiency (BTE) for B20 at 900 bar injection pressure is 3% higher than the diesel fuel at 300 bar injection pressure under full load conditions. The HC, CO, and smoke emission in the engine exhaust for B20 at 900 bar injection pressure was reduced by 13.3%, 28.5%, and 12.3%, respectively, when compared to diesel. Overall, this study recommends the operation of Azolla biodiesel blend in diesel at 900 bar fuel injection pressure to attain improved engine characteristics. WOS:000862937200005 35977574 Q1

Biofuel is the only novel solution to the increase in the greenhouse effect and bursting energy demand. The catalytic cracking of non-edible vegetable oils, namely castor and mustard was studied to yield gasoline range (C5-C9) hydrocarbons. Hβ (Microporous; pore size2 nm) and AlMCM-41 (Mesoporous; pore size 2 nm-50 nm) materials with different Si/Al ratios were used as catalysts for cracking purposes. Characterization of these catalysts was done by X-ray diffraction, Surface area analyzer, nitrogen sorption studies, TPD and inductively coupled plasma techniques. Used mustard oil was cracked over AlMCM-41 catalysts in a fixed bed catalytic cracking unit at optimized reaction condition (400 °C, 4.6 h

Vehicular Adhoc networks (VANET), a subset of mobile adhoc networks (MANET), is revolutionizing the intelligent transport system. With this advancement, the environment around a car can be more effective with transmitting information for emergency messages and also periodical messages like distance, speed, etc. In today’s world, due to the increasing vehicles on road, there is a need for traffic congestion control due to instances like road construction and management, accidents and weather conditions. This can be achieved by intimating the driver on board about the traffic ahead. In a dense traffic road, information comes from all the vehicles on the segment, hence this information needs to be processed and shared between all the vehicles in the segment. Therefore, there is need for a protocol that all the adhoc vehicles must adhere and detect if the traffic is greater than the threshold, consequently to avoid growing into severe congestion. Many protocols have been proposed for VANET such as AODV, DSDV and AOMDV. The aim of the research is to analyze different existing protocols over different conditions so as to find the optimal protocol for VANET by validating the metrics of network effectiveness such as throughput, delay, etc., and implement traffic congestion detection and rerouting using GPSR protocol in a secured way using RSA algorithm. The VANET system can be simulated over the software such as SUMO and NS2.

The low carbon biofuel (pine oil) derived from the pine tree is endowed with enhanced fuel properties with good octane rating and can be used for SI engine applications. In order to operate pine oil blends with improved fuel efficiency, engine design modification is required. This work investigates the change reflected on the engine characteristics of the port fuelled SI engine due to a thin thermal barrier coating layer formed on the piston crown using the Micro-arc oxidation technique. Furthermore, an endoscopic visualization technique was adopted to compare the combustion flames of uncoated and coated piston engines fuelled with pine oil blends. The coated piston engine fuelled by pine oil blends displayed improved peak in-cylinder pressure and heat release rate. Among the pine oil blends, P20 (20% pine oil + 80% gasoline) blend showed superior engine characteristics at all engine load conditions. The brake thermal efficiency of the coated piston engine fuelled with the P20 blend was improved by 1.8% compared to the uncoated piston fuelled with gasoline. With the P20 blend, the CO, HC emissions were decreased by 8% and 14% respectively and NO emission was increased by 19% for coated piston when compared to gasoline with an uncoated piston engine.

This research work endeavours to study the effect of 2-butoxyethanol blended gasoline on the engine in-cylinder spatial flame propagation, performance, and emission characteristics of a twin-cylinder spark ignition engine at a constant speed of 2400 rpm. The four blends were prepared by adding 1%, 1.5%, 2%, and 2.5% 2-butoxyethanol by volume proportion to the sole gasoline. The visual characteristics of the spatial combustion flame propagation were studied by flame visualization technique using engine endoscope. The captured flame propagation images were analyzed by the combustion diagnostic tool and digital image processing method to compute the flame speed. The results show that the percentage increase in flame speed for 2BE10, 2BE15, 2BE20, and 2BE25 blends is 0.046%, 0.2%, 0.8%, and 1.4% respectively as compared with gasoline. The peak in-cylinder pressure and heat release rate of 2BE25 (97.5% gasoline + 2.5% butoxyethanol) fuel blend is higher by 1.9 bar and 5.06 kJ/m3deg when compared to the sole gasoline whose values are 31.35 bar and 74.322 kJ/m3deg. All the butoxyethanol blends showed a slight improvement in brake thermal efficiency. At full load condition, the CO and HC emissions of 2BE25 blend are decreased by about 15.6% and 9% respectively, whereas NOx emissions of 2BE25 are increased by 10% when compared to that of sole gasoline.

Malware, brief for Malicious Software, is increasing continuously in amounts and sophistication as our digital world continues to develop. Lately, tools for forming malware have been increasing rapidly on the internet, making it more accessible for people without expertise to create malware. Towards the end, the number of malware is growing fast. To deal with the problem, it is necessary to classify malware instantly and accurately. These malware programs play a role such as encrypting or destroying sensible data, stealing, changing or capturing main computing functions, controlling users and perform computer activity without their consent. In this paper, six different algorithms for classification like Linear Regression, Random Forest, Adaboost, Gaussian, Gradient Boosting, Decision Tree has been used for classifying a file as malicious or benign. Based on the results, the Random forest attained 99.43% accuracy and infers that it is suitable for malware prediction.

In the transport sector problems, road safety is a prime concern in emerging nations. Applications about driving assistance are being actively studied to address road safety matters, including humanistic performance defined as one of the principal causes for problems on road safety, which confirms why driving style is currently experiencing extensive research attention. Future driving style prediction will form the basis for eco-driving and energy management strategies. From this aspect, analyzing drivers’ behavior is necessary to improve road safety. The comprehensive survey provides a summary, outline, and structure a large collection of work from various sources and proposes a comparison of the devices used, parameters studied, and classification algorithms used for analyzing driving style. The researches done on the driving style analysis uses a wide variety of devices and several parameters. This analysis shows that a diverse set of parameters that can be used to analyze the style of driving and seeks to understand the various machine learning classification methods and metrics for the classification of driving style.

Driver drowsiness is one of the recent reasons behind accidents that can cause serious death, injury, and economic loss. Driving for long hours, sleepiness, medication, sleep disorders, and health conditions can cause drowsiness in a driver. It is of social concern since many lives including the passengers, drivers and wayfarers are at high risk due to drowsy driving. Detection of drowsiness and alarming the drivers can prevent a large number of accidents and thus the precious life can be saved. Input parameters like heart rate, pulse rate, vehicle steering movement, lane change, head movement, yawning, eye-closure can help to detect the drowsiness in advance. In the past, much research has been carried out to design an efficient driver drowsiness detection system using various measures to determine the drowsiness of the driver. In this paper firstly, we have reviewed the measures attempted by many researchers which are grouped under physiological, vehicle-based, and behavioral-based measures. Secondly, a detailed review of the deep learning approaches used is carried out along with the accuracy level achieved by each author. This detailed review will give a better insight for the young researchers to carry out prospective research in the specific field.

In today's world, the quality of the crops is of utmost importance. Crops need to be effectively cared for, and steps are needed to ensure their healthy growth. Smart Irrigation is a major topic that has been implemented in certain regions, but the accumulation of various sensors is the key to the effective safety of crops. In the chapter, various sensors are being deployed and used in synchronization. The primary ones included in the system are the water level and moisture sensor, which works in correspondence with the water motor; the proximity (PIR) sensor, which works in accordance with the buzzer and the webcam; and finally, the light-dependent resistor (LDR), which works in relation with the artificial light. The analog data received from the sensors are transmitted to the raspberry-pi and then sent over the network using a Wi-Fi module to Ubidots, where the data will be analyzed, and necessary actions will be taken. The components to be used in the system will guarantee overall prolific, scalable, and ardent implementation.

Article number: 8; International audience; Utilizing oil extracted from waste engine oil and waste plastics, by pyrolysis, as a fuel for internal combustion engines has been demonstrated to be one of the best available waste management methods. Separate blends of fuel from waste engine oil and waste plastic oil was prepared by mixing with diesel and experimental investigation is conducted to study engine performance, combustion and exhaust emissions. It is observed that carbon monoxide (CO) emission increases by 50% for 50% waste plastic oil (50WPO:50D) and by 58% for 50% waste engine oil (50WEO:50D) at full load as compared to diesel. Unburnt hydrocarbon (HC) emission increases by 16% for 50WPO:50D and by 32% for 50WEO:50D as compared to diesel at maximum load. Smoke is found to decrease at all loading conditions for 50WPO:50D operation, but it is comparatively higher for 50WEO:50D operation. 50WPO:50D operation shows higher brake thermal efficiency for all loads as compared to 50WEO:50D and diesel fuel operation. Exhaust gas temperature is higher at all loads for 50WPO:50D and 50WEO:50D as compared to diesel fuel operation.

The modern world is moving towards the term ‘Automation’ which makes life simpler and easier. The rapid rise in the number of vehicles has increased the necessity of consideration about the parking system in shopping malls, airports, universities etc. Embedded system design for a real time parking is the latest and the emerging trend in the materialized world. A cost effective, reliable and accurate parking system is of great desire. It is believed that a cost effective, reliable and accurate parking system can save the time and minimize human efforts to a great extent. In this paper we present a embedded system design of a real time parking guidance system using PIC 16F877A micro-controllers which employs the incorporation of wireless communication to provide the user with remote control of elements like lights, sensors, and appliances falling within their working range of the designated environment. This system is designed to help the users by assisting them with parking their vehicles in the car lot. The system will automatically provide all the details in the parking areas on the basis of processing all the sensors data. It is designed to be running on a low budget platform and in the meantime, allowing their developers to eventually expand their controls over a variety of electronic devices.

International audience; The present study investigates the potential of artificial neural network for predicting the performance and emission characteristics of a compression ignition (CI) engine. A number of experiments are performed using diesel, rubber seed oil (RSO) and its methyl ester (RSOME) as the primary fuel, and ethanol as the secondary fuel in a dual fuel engine. The experimental data obtained is used for training and testing the neural network. The predictions are performed using feed forward-back propagation training algorithm. Engine load and ethanol energy share is used as network input parameters, and brake thermal efficiency (BTE), brake specific energy consumption (BSEC), NOx, HC, CO and smoke are the predicted parameters. The prediction performance of the network is measured by comparing it with experimental data. The measurement of statistical error shows that ANN can predict BTE, BSEC, NOx, HC, CO and smoke for a dual fuel engine with high accuracy.