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The increasing demand for energy efficiency and environmental sustainability has driven extensive research into alternative fuels and emission reduction strategies for internal combustion engines. The use of fuel additives, such as hydrogen peroxide and ethanol blends, has emerged as a promising approach to enhance combustion and mitigate harmful emissions. This study investigates the effects of gasoline, ethanol-gasoline, and hydrogen peroxide-ethanol fuel blends on the performance and emissions of a spark-ignition engine across varying engine loads. By exploring the synergistic effects of fuel additives and optimizing engine operating parameters, this research aims to pave the way for reduced emissions, improved fuel efficiency, and a cleaner automotive industry, aligning with global efforts to mitigate the environmental impact of transportation. The results show that both ethanol and hydrogen peroxide significantly improve thermal efficiency, with the combination of both additives providing the highest efficiency gains. Fuel consumption is reduced with the introduction of these additives, particularly with the combined use of E10 and H₂O₂. Emission analysis reveals substantial reductions in carbon monoxide and hydrocarbon emissions, highlighting the environmental benefits of these additives. Contour plots are utilized to visualize and interpret the relationships between engine load, fuel composition, and emissions profiles. [ABSTRACT FROM AUTHOR]

Image enhancement is the process of enhancing specific aspects of an image, such as its borders or contrast. The procedure of restoring a destroyed image is known as image restoration. A multitude of factors, such as low camera resolution, motion blur, noise, and others, can cause images to degrade throughout the acquisition process. Although image restoration techniques can remove haze from a degraded image, they are problematic for use in a real-time system since they necessitate numerous photographs from the same location. The suggested fractional Jaya Bat algorithm (FJBA) provides picture enhancement and blur pixel identification to address this issue. Firstly, the blur pixel identification is done using a deep residual network (DRN) trained with FJBA considering blurry image. FJBA is created by combining the Jaya Bat algorithm (JBA) and fractional notion (FC). Furthermore, a blurred image is deblurred using a fusion convolutional neural network (CNN) approach tuned through Pelican hunter optimization (PHO). PHO stands for Pelican optimization (PO) and hunter prey optimization (HPO). Lastly, the image is enhanced using the neural fuzzy system (NFS) and the image enhancement conditional generative adversarial network (IE-CGAN), which has been fine-tuned using FJBA. The proposed FJBA-NFS-IE-CGAN provided enhanced performance with the highest PSNR of 50.536 dB, SDME of 60.724 dB, and SSIM of 0.963, respectively. [ABSTRACT FROM AUTHOR]

In the present study, performance of the fabricated evacuated U-tube solar collector integrated parabolic reflector (EUSCIPR) and conventional evacuated U-tube solar collector (CEUSC) are analysed experimentally. With reference to humid climatic conditions, a 3D model is developed for comparing the thermal performance of EUSCIPR with a CEUSC. Developed model is validated with field test data and found in good agreement among them. Heat transfer fluid (HTF) temperature difference, energy intake/heat gain and thermal efficiency of the solar collectors are investigated experimentally at various ambient temperatures and solar intensities. From the experimental investigations, it was observed that, in a sunny day, the energy losses incurred across the solar collectors was high during peak hour (1: 20 PM). From the numerical studies, it is found that within the given operating range, the thermal efficiency of the EUSCIPR is 14.1% higher than CEUSC. Further, for a given inlet condition, the contour plots for variation of HTF temperature along the U-tube of EUSCIPR and CEUSC are predicted numerically, and the obtained results are discussed in detail. [ABSTRACT FROM AUTHOR]

Ropalidia rufoplagiata Cameron (Hymenoptera: Vespidae), a polistine species from penisular India, appears to be unique among all known primitively eusocial wasps. A total of 33 out of 46 identified females from an observed colony were found to oviposit on 1-17 occasions. No single predominant egg-layer could be identified during the 45-day period. Of the 17 dissected egg-layers, 12 were mated. All egg-layers showed several oviposition-related behavioural patterns including systematic, but indiscriminate, cannibalism of eggs and larvae, cleaning of empty cells, and guarding of freshly-laid eggs. There was no correlation between the egg-laying activity of the females (whether mated or not), oophagy, and their position in the dominance hierarchy. All nest-maintenance activities were performed exclusively by the egg-layers, while the non-egg-layers were mainly involved in the extranidal task of foraging. No significant morphometric differences between egg-layers and foragers could be discerned. Almost all the older individuals in the colony were egg-layers, while foragers were mainly younger animals. Such a temporal differentiation in reproductive labour suggests the absence of a permanent reproductive caste in this species. [ABSTRACT FROM AUTHOR]