BLDE University

This study investigates the relationship between geological and geotechnical properties of granite formations in the Mudgal region of Karnataka, India. The project includes geological surveys and laboratory testing to better understand the impact of mineralogical composition, texture, and structure on the engineering qualities of these rocks. Natural joints enable massive granite block extraction without wire saw cutting, influencing rock mass gradation. The geological assessments reveal that the study area hosts granite deposits of varying sizes, forms and structural attributes. Joints and natural discontinuities in these formations significantly impact block extraction, affecting their economic viability. Importantly, these granites exhibit limited weathering, rendering them suitable for construction purposes. Geotechnical tests expose correlations between mineral composition, grain size and rock strength. The present study covers petrographical study, Bulk unit weight variations, water absorption, specific gravity and compressive strength of granites. Higher quartz content and smaller grain sizes are found to enhance uniaxial compressive strength. In addition, using regression analysis, a novel empirical relationship is established, providing essential equations for predicting rock qualities based on mineralogical structures.

Precision livestock management has shown promise in maintaining the well-being and productivity of cow herds. Lumpy skin disease (LSD) is a global problem for cow populations that affects food security and results in large financial losses. It is imperative to detect LSD in herds early and take prompt action to stop its spread. In this research work, a thorough method for the automatic identification of LSD in cattle that uses deep learning techniques is presented. Here, the performance of five listed convolutional neural network (CNN) models has already been trained: DenseNet, ResNet, InceptionV3, VGG, and Xception, which are investigated in detecting LSD in cow images. These models are refined using a large-scale dataset collected from a veterinary clinic and Kaggle, which includes pictures of calves that show signs of LSD infection. Work exploits the pre-trained representations obtained by these models on general image recognition tasks and adapts them to the LSD detection task, specifically using transfer learning. A comparison study is carried out to evaluate these models’ accuracy and computational effectiveness. This work also looks into how resilient the models are to changes in lighting, environmental factors, and cow breeds.

The consequences of varying pressure drop on a Herschel-Bulkley fluid as it flows through two-dimensional non-uniform porous tube have been explored. The governing equations of the model were solved analytically under long wavelength and low Reynolds number to determine the behavior of the fluid within the given conditions. Exact solutions are derived for the velocity field, stream functions, and pressure gradient. The observations disclose that an increase in the porous thickening of the wall enhances the pressure rise. The rise in Darcy’s numbers reduces the pressure rise. This suggests an inverse relationship between Darcy’s number and pressure difference. Also, the impact of various parameters involved in the solutions is presented graphically. The article concludes with a discussion on the trapping phenomena for Herschel-Bulkley fluid.

An infant presented with the abrupt onset of esotropia of the left eye, followed by an esotropia of the right eye over the subsequent 48 hours. Physical examination revealed bilateral papilledema, and apart from esotropia of both eyes ([figure 1][1]), the neurological assessment was unremarkable.