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People are quick to accept the assumption that a light will appear dimmer the farther away they are, citing the inverse square relationship that illuminance obeys as rationale. However, repeated observations of light sources maintaining their brightness over large distances prompted us to explore how the brightness, or perceived illuminance of a light varies with the viewing distance from the object. We hypothesized that since both the illuminance of the light source and image size decrease at the same rate, then the concentration, or intensity of the image remains unchanged, and subsequently the perceived illuminance.

Here recognizing the growing amount of plastic waste in the oceans, the authors sought to develop and test laser imaging for the identification of waste in water. They found that while possible, limitations such as increasing depth and water turbidity result in increasing blurriness in laser images. While their image processing methods were somewhat insufficient they identified recent methods to use deep learning-based techniques as a potential avenue to viability for this method.

Greenspaces (urban and wetland areas that contain vegetation) are beneficial to reducing pollution, while airplanes are a highly-polluting method of transportation. The authors examine the intersection of these two environmental factors by processing satellite images to reveal what percentage of flight paths go over greenspaces at major US airports.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

Here, seeking to better understand the effects of gadolinium-based contrast agents, dyes typically used for MRI scans, the authors evaluated the activity of catalase found in beef liver both with and without gadodiamide when exposed to hydrogen peroxide. They found that gadioamide did not significantly inhibit catalase's activity, attributing this lack of effects to the chelating agent found in gadodiamide.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

Recognizing a potential link between mental focus and physical endurance, here, the authors considered the effects of mental visualization on strength endurance. By comparing the number of repetitions completed in sets where the lifter was aware of the weight to be lifted against sets where the lifter was kept unaware, they found that the lifter was able to maintain strength endurance when unable to accurately visualize the weight they lifted in this exploratory study.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

The authors looked at whether retinal features, specifically lens clarity, optic nerve cupping, and hyperreflective foci, can serve as biomarkers for neuro-ophthalmological diseases, which share pathological mechanisms with neurodegeneration and may indicate broader neurological risk. Using multimodal clinical data and machine learning, they investigated the potential of retinal imaging as a complementary diagnostic tool for more accurate and accessible detection of neurological disorders.