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Lower alcohols have long been the figureheads of diesel/biodiesel additives in characterizing renewable fuels. Next-generation alcohol like n-octanol occupied the reified position due to their better fuel properties. In this paper, combustion, performance and, emission of different graphene-oxide nanoparticles (nanoGO) added jatropha biodiesel, n-octanol and petrodiesel blends are investigated in a 4-stroke DI diesel engine. This article also aims to optimize the engine inputs accountable for better performance and emission characteristics of a diesel engine running with nanoGO dispersed biodiesel/diesel/higher alcohol blends. Full Factorial Design-based Response Surface Methodology (RSM) is utilized to model the experiments using Design-Expert software to optimize engine responses. Validation of the developed model is carried out using sophisticated error and performance metrics, namely, TheilU2, Kling-Gupta Efficiency (K-G Eff), and Nash-Sutcliffe coefficient of efficiency (N-S Eff) along with the conventional statistical database. The model optimized engine inputs of 3.898% n-Octanol, and 49.772 ppm nanoGO at 99.2% load with a desirability index of 0.997 as the optimum engine parameters. The experimental validation revealed that the model optimized blend at full load witnessed a reduction of 15.6% CO, 21.78% HC.u, and 3.26% NOx emission compared to petrodiesel. However, a slight increase in brake specific energy consumption (2.95%) is also recorded because of the lower heating value of the blend.

Topological photonics provides a new platform to control and manipulate the flow of light in photonic devices that exhibit topological phase transitions. One way to introduce such transition of geometric phases in photonic waveguides is to modulate the structure of the photonic crystals, array waveguides, or the metamaterials in such a way that it eventually changes the bulk topology. Such topological waveguides localize the light at the boundary of the two distinct bulk systems with different topological invariant. Based on this fundamental physics, we demonstrate a robust and controlled propagation of light in a class of specialty optical fiber which exhibits the topological phase transition between a nontrivial one-dimensional (1D) periodic cylinder and its topologically trivial counterpart. The key idea is to explore the possibility of topological phase transition of 1D photonic bands over the momentum space by tuning the unit-cell dimension and its individual thicknesses. We have systematically computed the trivial and the nontrivial topological invariants, the Zak phase, of designed cylindrical 1D lattices with inversion symmetry over the first Brillouin zone. To establish the presence and the robustness of the localized edge mode, we have studied the reflectance of the geometry in the presence of disorder. Also, the simulated field profile and the back-reflectionless propagation of the edge mode through the fiber have been shown. Besides conventional fibers, such topological optical fibers are definitely a waveguide geometry for robust delivery of light and thus would be very significant to open up a new paradigm in guided wave photonics.

This paper investigates the consequence of linseed methyl ester and diethyl ether on the performance and emissions of the existing single cylinder compression ignition engine. Here biodiesel is mix...

We report an Aubrey-Andre-Harper (AAH) model based quasi-periodic lossless evanescently coupled waveguide lattice to study the unconventional physics of light localization. We find an exclusively new way to establish localization phase transition in a 1D-AAH lattice by observing the slope variation of the band and accordingly study the modal characteristics to reveal the fact that a higher value of quasi-periodic modulation strength is imperative for observing a signature of fully localized light states having higher eigenenergy. This analytical concept has numerically been implemented in the proposed quasi-periodic lattice to achieve light localization, where we have shown that the supported states not only depend on localization phase transition parameter, but also on the specific location of excitation which is due to quasi-periodic nature of lattice. Furthermore, we have investigated a unique effect of the presence of disorder on light localization phenomenon, where it has been reported that the presence of off-diagonal disorder, which is otherwise detrimental, favours light localization in the proposed structure due to its quasi-periodic nature. The findings indeed have the potential to open up a fertile platform to manipulate light in passive photonic devices.

Objective: To investigate an anthrax outbreak affecting multi-species of animals including human in five remote tribal villages in West Midnapur district, India, with no previous history of anthrax cases for decades. Methods: A systematic epidemiological investigation, along with bacteriological examination, cultural isolation, biochemical and molecular characterization of the samples (blood, dried meat, pieces of bones) was carried out to con firm diagnosis, identify transmission routes and risk factors to recommend suitable control measures. Results: Samples from nine animals were confirmed for presence of Bacillus anthracis by characteristic morphology, biochemical profile, McFadyean reaction in blood smear, ‘medusa head’ appearance of colonies and PCR based detection of pXO1 plasmid. Epidemiological investigation revealed that the human patients contracted the infection during butchering or while handling contaminated animal products. In total, 14 animals died and 11 people got infected with cutaneous anthrax during the outbreak period. Conclusions: Anthrax continues to be a persisting problem in Indian subcontinent causing considerable morbidity and mortality in animals and human. Collaborative efforts of human and animal health officials through various controlled measures viz., ring vaccination, early treatment of human cases, quarantine of affected animals, safe disposal of carcass and public health campaign effectively controlled the zoonotic anthrax outbreak.

This paper investigates use of artificial neural network (ANN) model in prediction of brake specific energy consumption (BSEC), nitrogen oxides (NOx), unburnt hydrocarbon (UHC), and carbon dioxide (CO2) emissions of a single cylinder diesel engine operates with diesel-palm biodiesel-ethanol blends. The engine is run at different load form 20–100% and 1500 rpm constant speed. The fuel used in this present study are diesel and six different diesel-palm biodiesel-ethanol blends. The Levenberg-Marquardt back propagation training algorithm with logistic-sigmoid activation function results best prediction of performance and emission characteristics with accurate overall correlation coefficient (R) (0.99329–0.99875) and minimum mean square error (MSE) (0.000179082–0.000465809). The mean absolute percentage errors (MAPE) are observed to be in range of 2.32–4.54% with the acceptable margin of mean square relative error (MSRE). Furthermore, experimental and ANN predicted data are compared in fuzzy interface system (FIS) to find optimum engine operating parameters. Compared to other blends, at 20% load, D85BD10E5 blend exhibits the highest MPCI (multi performance characteristics index) values of 0.718 and 0.705 for experimental and ANN predicted data respectively. Robustness and reliability of the proposed techniques clearly explain the application of ANN and fuzzy logic system in the prediction and optimization of engine parameters.