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In this paper, microcrack patterns in a quartzite are quantified using fractal geometry based methods. Since the quartzite does not show a mesoscopic foliation, the fabric was recognized using anisotropy of magnetic susceptibility (AMS) analysis. Microcracks were investigated in thin sections prepared along the three principal planes of the AMS ellipsoid. Point load tests were performed on cores drilled parallel as well as perpendicular to the magnetic foliation. After experimental deformation, thin sections were prepared in two orientations – (a) parallel to the plane of failure (i.e., parallel to the direction of loading), (b) perpendicular to the plane of failure (i.e., perpendicular to the direction of loading), and microcrack patterns in these sections were investigated. The box-counting method of fractal analysis was first applied to microcracks traced from SEM images from each thin section of the experimentally undeformed as well as deformed samples to establish the fractal nature of the microcrack pattern. It was found that in thin sections perpendicular to the direction of loading, the box (fractal) dimension tends to marginally increase. This is inferred as a manifestation of the increase in complexity of the pattern. The software AMOCADO, which is based on the modified Cantor Dust method of fractal analysis, was applied to microcrack pattern from each thin section in order to quantify the pattern anisotropy. It is noted that the anisotropy significantly reduces in sections perpendicular to the loading direction. SEM data are presented to demonstrate that this reduction in anisotropy is on account of generation and/or growth of new cracks in random orientations. It is envisaged that the approach adopted in this investigation maybe useful in rock mechanics and mineral-resource applications in future.

The Indian rock pythons (Python molurus) are classified as a near-threatened snake species by the International Union for the Conservation of Nature and Natural Resources (IUCN); they are native to the Indian subcontinent and have experienced population declines caused primarily by poaching and habitat loss. We hand-captured the 14 rock pythons from villages, agricultural lands, and core forests to examine the species' home ranges. We later released/translocated them in different kilometer ranges at the Tiger Reserves. From December 2018 to December 2020, we obtained 401 radio-telemetry locations, with an average tracking duration of (444 ± 212 days), and a mean of 29 ± SD 16 data points per individual. We quantified home ranges and measured morphometric and ecological factors (sex, body size, and location) associated with intraspecific differences in home range size. We analyzed the home ranges of rock pythons using Auto correlated Kernel Density Estimates (AKDE). AKDEs can account for the auto-correlated nature of animal movement data and mitigate against biases stemming from inconsistent tracking time lags. Home range size varied from 1.4 ha to 8.1 km2 and averaged 4.2 km2. Differences in home range sizes could not be connected to body mass. Initial indications suggest that rock python home ranges are larger than other pythons.