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Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO₂ to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO₂. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO₂ emissions.

In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.

In this study, the authors developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns.

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

The authors looked at how pharmacokinetics changed depending on the use of an in vitro or an in vivo model.

The authors looked at the impact of different harvest and feeding treatments on Heterandria formosa over three generations as a model for changes in marine ecosystems.

The authors looked at the impacts of short-chain fatty acids on muscle fiber formation as well as insulin sensitivity using a model of mouse myoblasts.

The authors looked at whether traditional Chinese medicine remedies that target the lungs and liver would reduce inflammation in a planaria model. They found that the two active compounds they tested were able to decrease induced inflammation by 97-98%.

The authors looked at whether CBD and THC would decrease reproductive rates in a Drosophila melanogaster model. They found that CBD had a greater impact on reducing hatching rates than THC, and that THC resulted in unexpected mortalities.

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.