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Ecological corridors are geographic features designated to allow the movement of wildlife populations between habitats that have been fragmented by human landscapes. Corridors can be a pivotal aspect in wildlife conservation because they preserve a suitable habitat for isolated populations to live and intermingle. Here, two students simulate the effect of introducing a safety corridor for cheetahs, based on real tracking data on cheetahs in Namibia.

The mountain chain of the Western Ghats on the Indian peninsula, a UNESCO World Heritage site, is home to about 200 frog species, 89 of which are endemic. Distinctive to each frog species, their vocalizations can be used for species recognition. Manually surveying frogs at night during the rain in elephant and big cat forests is difficult, so being able to autonomously record ambient soundscapes and identify species is essential. An effective machine learning (ML) species classifier requires substantial training data from this area. The goal of this study was to assess data augmentation techniques on a dataset of frog vocalizations from this region, which has a minimal number of audio recordings per species. Consequently, enhancing an ML model’s performance with limited data is necessary. We analyzed the effects of four data augmentation techniques (Time Shifting, Noise Injection, Spectral Augmentation, and Test-Time Augmentation) individually and their combined effect on the frog vocalization data and the public environmental sounds dataset (ESC-50). The effect of combined data augmentation techniques improved the model's relative accuracy as the size of the dataset decreased. The combination of all four techniques improved the ML model’s classification accuracy on the frog calls dataset by 94%. This study established a data augmentation approach to maximize the classification accuracy with sparse data of frog call recordings, thereby creating a possibility to build a real-world automated field frog species identifier system. Such a system can significantly help in the conservation of frog species in this vital biodiversity hotspot.

Here, beginning from an interest in fractals, infinitely complex shapes. The authors investigated the fractal object that results from crumpling a sheet of paper. They determined its fractal dimension using continuous Chi-squared analysis, thereby testing and validating their model against the more conventional least squares analysis.

Here the authors investigate whether there are mathematical inaccuracies of perspective in artists' paintings that are undetectable with our naked eyes. Using the cross-ratio method, they find that there are three significant errors in various famous paintings which increase as the structures in the paintings recede from the viewer.

Each day we are flooded with new items that promise us a better experience at a better price. This forces buyers to continuously chose between sticking to what they know, or trying something new. In turn, companies need to be aware of the factors affecting consumer choices, that too within the different fractions of society. In this study the authors investigate the effect of survey-based price setting on profits made based on African American teen purchases, and how African-American teen loyalty to a particular brand affects their willingness to pay a higher price than the market average for their preferred brand items.

The authors compare air quality in the presence and absence of a living wall in a high school hallway in Brooklyn, NY.

The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.

Digital compasses are essential in technology that we use in our everyday lives: phones, vehicles, and more. Li and Liu address the accuracy of these devices by presenting a new algorithm for accurately calibrating low-cost magnetometers.