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A thiol and amine group-based single crystal of L-cysteine ethyl ester hydrochloride (CE) is grown by the solvent evaporative method. By using single crystal XRD analyses the unit cell geometry estimation with crystal nature is assessed. UV visible spectra show the percentage of transmittance range and the edge of lower absorption wavelength is assessed. By using the lower absorption wavelength, various optical constants such as optical energy band gap, absorption coefficient, conductivity response, and refractive index values from various relations are evaluated. Using Wemple Di-Domenico single oscillator technique, oscillator parameters and different dispersion factors are evaluated. The nonlinear factors like third order susceptibility, absorption coefficient, and refractive index are also estimated. The experimental second order efficiency with its phase matching condition of the grown CE crystal is also verified.

The number of research activities to find alternative fuels for Internal Combustion Engines has increased tremendously in recent years, owing to depleting oil reserves and growing climate concerns. In this scenario, biodiesel is one of the few promising alternatives that can potentially replace conventional fuel. When vegetable oil is used for frying food items, it undergoes physiochemical changes. After a while, it is discarded as waste cooking oil (WCO) which can be used in the production of biodiesel. Since WCO is a degraded raw material, it is important to understand its effect on the quality of biodiesel produced in terms of engine performance and engine emission. In current research study, total polar matter (TPM) is considered as a measure of quality for waste cooking oil. Sunflower oil and palm olein were used in this study for comparison since both exhibit different fatty acid compositions. Among the properties considered in this study, the results found that the kinematic viscosity of the biodiesel gets highly affected by total polar matter content of waste cooking oil. Further, the study also identified correlations to predict the kinematic viscosity of biodiesel from total polar matter content of WCOs. The authors found no significant difference in engine performance during engine tests between the biodiesels produced from fresh oils and waste cooking oils. However, biodiesels produced from WCOs emitted slightly higher carbon monoxide than the biodiesel produced from fresh oils. On the contrary, nitric oxide and smoke emissions from biodiesels produced from waste cooking oils and fresh oils were similar. Even though waste cooking oil is a degraded feedstock, the biodiesel produced from it has no adverse effect on engine performance and emissions. Therefore, WCO oil can be considered as a promising feedstock in the sustainable production of biodiesel.

The organic Ethyltriphenylphosphonium bromide dihydrate (ETPBD) single crystal is grown by slow evaporation method at room temperature. The unit cell parameters and crystal system were obtained by the single crystal X-ray diffraction technique. The optical parameters and absorption wavelength assessment are made by UV–Visible spectroscopic technique. The second harmonic generated efficiency is evaluated by using the Kurtz-Perry technique. The polarizability value, charge distribution, HOMO-LUMO energy, natural bond orbital and thermodynamical properties of the ETPBD crystal are estimated from the quantum chemical calculation with the help of DFT. The intermolecular interaction of ETPBD crystal is also analyzed by Hirshfeld surface analysis. The various functional groups were analyzed by spectroscopic studies of FTIR and FTRAMAN by experimentally identified functional groups that were compared with theoretically computed values.

Summary: Anomaly event recognition and identification has a crucial part in several areas, particularly in night vision environments. Conventional techniques of event recognition are hugely based upon data extracted from certain images for classification purposes. This needs users to select suitable features to establish the feature depictions for actual images per definite situations. Manual feature selection is laborious as well as heuristic tasks and the features obtained in this manner generally have worse robustness. Here, a Faster Region‐based Convolutional fused Social Generative Adversarial Network (FRC‐SGAN) is designed for anomaly event recognition in a night vision environment. At the cloud, key frame extraction, pre‐processing, feature extraction, human detection (HD) and anomalous event recognition are carried out. Initially, input video from the database is subjected to perform pre‐processing. The visibility enhancement is utilized for pre‐processing. Thereafter, features like ResNet features, texture features and statistical features are extracted. Then, HD is accomplished by DeepJoint segmentation with chord distance. Finally, anomalous detection is done by FRC‐SGAN that is the incorporation of Fast Regional Convolutional Neural Network (FR‐CNN) and Social Generative Adversarial Network (SGAN). In addition, FRC‐SGAN acquired 90.8% of accuracy, 89.7% of precision, and 89.2% of recall. [ABSTRACT FROM AUTHOR]