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The authors examined the relationships between S&P 500 Technology and Financials equity sectors and their corresponding investment-grade fixed-income markets from 1995 to 2025.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

The authors looked at how different factors related to practicing and playing chess would impact a player's rating.

The authors employ computational protein design to identify a mini-protein with the potential to enhance binding of the tight junction protein, claudin-5, at the blood-blood barrier with therapeutic potential for neurodegenerative diseases.

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

The authors test whether coating stainless steel orthopedic screws in silver will promote calcium precipitation to improve orthopedic implant integration into bone.

The authors looked at different factors that influence specific gravity in beverages, including sweetener used, caffeine, carbonation, and sodium.

Using machine learning to predict the risk of algae bloom

The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

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.