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The authors develop a method for detecting fake AI-generated images from real images.

Chess engines, or computer programs built to play chess, outperform even the best human players. Kaushikan and Park investigate the inner workings of these chess engines by studying popular chess engines' evaluations of which side of a chess match is most likely to win, and how this is affected by the number of pieces and controlled squares on each side.

Counterintuitive in nature, the Mpemba effect asserts that hot liquid freezes faster than cold liquid. While noted throughout history by scientific minds like Aristotle, the phenomenon remains in contention with varying hypotheses for the effect proposed alongside the effect’s rise in popularity. Contributing to the research efforts surrounding the Mpemba effect, the authors in this article explore the effect in different liquids ranging in physical properties and intermolecular forces to determine potential parameters attributable to producing the Mpemba effect.

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

Here, recognizing that years of training in saber fencing could expectedly result in optimized movements that result in elite skill levels, the authors used motion tracking and statistical analysis to assess the difference in velocity and blade tip velocity of novice and elite fencers during a vertical blade thrust. They found statistically significant differences in blade tip velocity and elbow joint angle kinematics.

The authors look at how different food types impact the ability of bacteria to produce electricity.

In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

Control of voluntary and involuntary movements is one of the most important aspects of human neurological function, but the mechanisms of motor control are not completely understood. In this study, the authors use transcranial magnetic stimulation (TMS) to stimulate a portion of the motor cortex while subjects performed either discrete (e.g. throwing) or rhythmic (e.g. walking) movements. By recording electrical activity in the muscles during this process, the authors showed that motor evoked potentials (MEPs) measured in the muscles during TMS stimulation are larger in amplitude for discrete movements than for rhythmic movements. Interestingly, they also found that MEPs during transitions between rhythmic and discrete movements were nearly identical and larger in amplitude than those recorded during either rhythmic or discrete movements. This research provides important insights into the mechanisms of neurological control of movement and will serve as the foundation for future studies to learn more about temporal variability in neural activity during different movement types.