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The Young's Modulus of a structural material is a measure of its elasticity and is defined as the ratio of the tensile stress to tensile strain. This study aims to investigate the Young's Modulus of pasta with different diameters.

In this study the authors look at elastic modulus and stiffness of steel rules with vary lengths of cracks. They found that cracks decreased the overall elastic modulus and bending stiffness of the ruler. This work has applications to structural engineering and the design of items such as airplanes and bridges.

A common form of Acne is caused by a species of bacterium called Cutibacterium acnes. By using a predictive algorithm and structural analysis, the authors identified 5 small molecules with high affinity to growth factors in Catibacterium acnes. This has potential implications for supplemental skincare products.

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

Bubbles! In this study, the authors investigate the effects that different materials, temperature, and distance have on the formation of water bubbles on the surface of copper and steel. They calculated mathematical relations based on the outcomes to better understand whether interstitial hydrogen present in the d-block metals form hydrogen bonds with the water bubbles to account for the structural and mechanical stability.

Caenorhabditis elegans xpa-1 and him-1 are orthologs of human XPA and human SMC1A, respectively. Mutations in the XPA are correlated with Xeroderma pigmentosum, a condition that induces hypersensitivity to ultraviolet (UV) radiation. Alternatively, SMC1A mutations may lead to Cornelia de Lange Syndrome, a multi-organ disorder that makes patients more sensitive to UVinduced DNA damage. Both C. elegans genes have been found to be involved in protection against UV radiation, but their combined effects have not been tested when they are both knocked down. The authors hypothesized that because these genes are involved in separate pathways, the simultaneous knockdown of both of these genes using RNA interference (RNAi) in C. elegans will cause them to become more sensitive to UV radiation than either of them knocked down individually. UV protection was measured via the percent survival of C. elegans post 365 nm and 5.4x10-19 joules of UV radiation. The double xpa-1/him-1 RNAi knockdown showed a significantly reduced percent survival after 15 and 30 minutes of UV radiation relative to wild-type and xpa-1 and him-1 single knockdowns. These measurements were consistent with their hypothesis and demonstrated that xpa-1 and him-1 genes play distinct roles in resistance against UV stress in C. elegans. This result raises the possibility that the xpa-1/him-1 double knockdown could be useful as an animal model for studying the human disease Xeroderma pigmentosum and Cornelia de Lange Syndrome.

This paper looks at the antibacterial and toxic effects of silver nanoparticles (AgNPs) on Escherichia coli bacteria and Drosophila melanogaster fruit flies. They modified the AgNPs size, concentration, and surface coating to determine the effects on each of the organisms. For both organisms, increased AgNP concentration demonstrated increased toxicity but particle size and surface coating had opposing effects.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

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.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.