Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

ANTIBACTERIAL ACTIVITY OF VULGAROL A EXTRACTED FROM THE LEAVES OF SYZYGIUM CUMINI

Objectives: Extraction, purification and identification of antimicrobial compounds from leaves of Syzygium cumini.  Methods: The antibacterial activity of crude extract of Syzygium cumini leaves were investigated against pathogenic Gram positive and Gram negative bacteria. Petroleum ether extract was used for the separation of compound using thin layer chromatography and separated compound identified by using Gas Chromatography-Mass Spectroscopy.Results:Extracts prepared in various organic solvents such as chloroform, methanol, petroleum ether, acetone and ethanol showing antibacterial activity against Salmonella typhimurium, Pseudomonas aeruginosa and Staphylococcus aureus, but significant activity found with petroleum ether extract. The purified compound from petroleum ether extract showing antibacterial activity was identified as diterpenoids i.e. Vulgarol A by Gas Chromatography-Mass Spectroscopy. Vulgarol A shows more potential antibacterial activity against Gram negative organisms such as P. aeruginosa.Conclusion:This study concludes that the extracted compound Vulgarol A from Syzygium cumini leaves could be used as an active pharmaceutical ingredient to control infectious diseases caused by P. aeruginosa. Keywords: Syzygium cumini, diterpenoids, P. aeruginosa, GC-M

Ultrasound assisted adsorption of basic dye onto organically modified bentonite (nanoclay)

162-167Sonochemical adsorption of basic methylene blue dye (CI 52015) into organophilic bentonite (nanoclay) is explored. Tetrabutyl ammonium chloride (TBAC) modified nanoclay has showed amorphous exfoliated nature, while N-cetyl-N,N,N trimethyl ammonium bromide (CTAB) modified nanoclay has revealed intercalating crystalline nature, as supported by X-ray analysis. Presence of NH₂, CH₂ groups onto nanoclay platelet is confirmed from FTIR spectrum analysis. In presence of ultrasound, due to decrease in diffusion resistance, both modified nanoclays exhibit higher adsorption due to an easy insertion of dye into clay platelets. Calculated Langmuir adsorption isotherm, Q₀ and Langmuir constant ‘b’ for TBAC modified nanoclay, respectively were 0.34 mg/mg 0.246. Intercalating agent has a significant contribution during overall process

Homology Modeling and Docking Studies of TMPRSS2 with Experimentally Known Inhibitors Camostat Mesylate, Nafamostat and Bromhexine Hydrochloride to Control SARS-Coronavirus-2

The rapid outbreak of SARS-Coronavirus 2 (SARS-CoV-2) caused a serious global public health threat. The spike ‘S’ protein of SARS-CoV-2 and ACE2 of the host cell are being targeted to design and discover new drugs to control Covid-19 disease. Similarly, a transmembrane serine protease, TMPRSS2 of the host cell has been found to play a significant role in proteolytic cleavage of viral spike protein priming to the receptor ACE2 present in human cell. However, three dimensional structure and inhibition mechanism of TMPRSS2 is yet to be explored experimentally. Hence, in the present study we have generated a homology model of TMPRSS2 and studied its binding properties with experimentally studied inhibitors viz. Camostat mesylate, Nafamostat and Bromhexine hydrochloride (BHH) using molecular docking technique. Docking analysis revealed that the Camostat mesylate and its structural analogue Nafamostat interacts strongly with residues His296, Ser441 and Asp435 present in catalytic triad of TMPRSS2. However, BHH interacts with Gln438 and other residues present in the active site pocket of TMPRSS2 through hydrophobic contacts effectively. Thus, these results revealed the inhibition mechanism of TMPRSS2 by known inhibitors Camostat mesylate, Nafamostat and Bromhexine hydrochloride in detail at the molecular level. However, Camostat mesylate shows strong binding as compared to other two inhibitors. This structural information could also be useful to design and discover new inhibitors of TMPRSS2, which may be helpful to prevent the entry to SARS-Coronavirus 2 in human cell

Structural insights and inhibition mechanism of TMPRSS2 by experimentally known inhibitors Camostat mesylate, Nafamostat and Bromhexine hydrochloride to control SARS-coronavirus-2: A molecular modeling approach

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has been responsible for the cause of global pandemic Covid-19 and to date, there is no effective treatment available. The spike ‘S’ protein of SARS-CoV-2 and ACE2 of the host cell are being targeted to design new drugs to control Covid-19. Similarly, a transmembrane serine protease, TMPRSS2 of the host cell plays a significant role in the proteolytic cleavage of viral ‘S’ protein helpful for the priming of ACE2 receptors and viral entry into human cells. However, three-dimensional structural information and the inhibition mechanism of TMPRSS2 is yet to be explored experimentally. Hence, we have used a molecular dynamics (MD) simulated homology model of TMPRSS2 to study the inhibition mechanism of experimentally known inhibitors Camostat mesylate, Nafamostat and Bromhexine hydrochloride (BHH) using molecular modeling techniques. Prior to docking, all three inhibitors were geometry optimized by semi-empirical quantum chemical RM1 method. Molecular docking analysis revealed that Camostat mesylate and its structural analogue Nafamostat interact strongly with residues His296 and Ser441 present in the catalytic triad of TMPRSS2, whereas BHH binds with Ala386 along with other residues. Comparative molecular dynamics simulations revealed the stable behavior of all the docked complexes. MM-PBSA calculations also revealed the stronger binding of Camostat mesylate to TMPRSS2 active site residues as compared to Nafamostat and BHH. Thus, this structural information could be useful to understand the mechanistic approach of TMPRSS2 inhibition, which may be helpful to design new lead compounds to prevent the entry of SARS-Coronavirus 2 in human cells

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-