Browse Articles

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.

Semiconductors are the center of the fourth industrial revolution as they are key components for all electronics. Exposed wafers made of silicon (Si), which can easily oxidize, convert to silicon dioxide (SiO₂). The surface of SiO₂ wafers consists of many Si-OH bonds, allowing them to easily bond with water, resulting in a “wet” or hydrophilic condition. We sought to determine a way to modify the surface of SiO₂ wafers to become hydrophobic to ensure safe wet cleaning.

With disruption of DNA repair pathways pertinent to the timeline of cancer, thorough evaluation of mutations relevant to DNA repair proteins is crucial within cancer research. One such mutation includes S815L PMS2 - a mutation that results in significant decrease of DNA repair function by PMS2 protein. While mutation of PMS2 is associated with significantly increased colorectal and endometrial cancer risk, much work is left to do to establish the functional effects of the S815L PMS2 mutation in ovarian cancer progression. In this article, researchers contribute to this essential area of research by uncovering the tumor-progressive effects of the S815L PMS2 mutation in the context of ovarian cancer cell lines.

Lithium-ion batteries, a breakthrough in chemistry that enabled the electronic revolution we live today have become an essential part of our day-to-day life. A phone battery running out after a heavy day of use with limited opportunities for recharging is a well-known and resented experience by almost everyone. How then can we make batteries more efficient? This paper proposes the use of a different type of separator, that improves the charging and discharging capacities of lithium ions compared to the classical separator. This and similar attempts to improve Lithium-ion battery function could facilitate the development of higher-performance batteries that work longer and withstand harsher use.

We systematically evaluated the effects of raw material composition, heat treatment, and mechanical properties on 13-8PH stainless steel alloy. The results of the neural network models were in agreement with experimental results and aided in the evaluation of the effects of aging temperature on double shear strength. The data suggests that this model can be used to determine the appropriate 13-8PH alloy aging temperature needed to achieve the desired mechanical properties, eliminating the need for many costly trials and errors through re-heat treatments.

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

Quorum sensing (QS) is the process in which bacteria recognize and respond to the surrounding cell density, and it can be inhibited by certain antimicrobial substances. This study showed that illumination intensity data is insufficient for evaluating QS activity without proper statistical modeling. It concluded that modeling illumination intensity through time provides a more accurate evaluation of QS activity than conventional cross-sectional analysis.

One common age-related health problem is the loss of bone mineral density (BMD), which can lead to a variety of negative health outcomes, including increased risk of spinal fracture. In this study, the authors investigate risk factors that may be predictive of an individual's risk of spinal fracture. Their findings provide valuable information that clinicians can use in patient evaluations.

Predicting antibody structures and antibody-antigen complexes using AlphaFold

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.