Browse Articles

A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Dey et al. | Oct 31, 2022

A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

Read More...

Estimating Paleoenvironments Utilizing Foraminiferal Fossils from the Toyohama Formation, Aichi Prefecture, Central Japan

Kimitsuki et al. | Dec 11, 2017

Estimating Paleoenvironments Utilizing Foraminiferal Fossils from the Toyohama Formation, Aichi Prefecture, Central Japan

Foraminifera are a diverse phylum of marine protists that produce elaborate shells. Because of their abundance and morphological diversity, foraminiferal fossil assemblages are used for biostratigraphy, to accurately date sedimentary rocks and to characterize past ocean environments. In this paper, authors collected fossils within the Morozaki Group in central Honshu, Japan, to assess past marine environments and species diversity.

Read More...

Investigating the effects of PVC microplastics on the growth of Chlorella vulgaris

Beaubien et al. | Oct 13, 2026

Investigating the effects of PVC microplastics on the growth of <i>Chlorella vulgaris</i>

This study examined the effect of varying PVC microplastic concentrations on the growth of Chlorella vulgaris in freshwater, utilizing six PVC levels and identical culture conditions. Cell counts revealed that higher PVC concentrations consistently reduced algal growth, and a Spearman correlation test detected a significant negative monotonic association between PVC concentration and viable cell density. These results support the hypothesis that PVC microplastics impair algal growth, underscoring their ecological risk in freshwater environments.

Read More...

Comparative analysis of player ability, game size, and ideal starting positions in Nim games

Sinha et al. | Aug 02, 2026

Comparative analysis of player ability, game size, and ideal starting positions in Nim games
Image credit: Immo Wegmann

Here the authors investigated the impact of player ability versus starting positions in the game of Nim under imperfect play, hypothesizing that player skill becomes the primary determinant of outcomes as pile sizes grow. Through computational simulations and a mathematical model, they demonstrated that favorable starting positions lose their advantage over time and provide key insights to help improve reinforcement learning algorithms in abstract, complex decision-making environments.

Read More...

Effects of Ocean Acidification on the Photosynthetic Ability of Chaetoceros gracilis in the Monterey Bay

Harvell et al. | Jan 16, 2020

Effects of Ocean Acidification on the Photosynthetic Ability of <i>Chaetoceros gracilis</i> in the Monterey Bay

In this article, Harvell and Nicholson hypothesized that increased ocean acidity would decrease the photosynthetic ability of Chaetoceros gracilis, a diatom prolific in Monterey Bay, because of the usually corrosive effects of carbonic acid on both seashells and cells’ internal structures. They altered pH of algae environments and measured the photosynthetic ability of diatoms over four days by spectrophotometer. Overall, their findings indicate that C. gracilis may become more abundant in Monterey Bay as the pH of the ocean continues to drop, potentially contributing to harmful algal blooms.

Read More...

Reinforcement learning in 2-D space with varying gravitational fields

Rousseau et al. | Jun 07, 2025

Reinforcement learning in 2-D space with varying gravitational fields
Image credit: NASA

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

Read More...