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Chronic bad breath, or halitosis, is a problem faced by nearly 50% of the general poluation, but existing treatments such as liquid mouthwash or sugar-free gum are imperfect and temporary solutions. In this study, the authors investigate potential alternative treatments using natural ingredients such as Manuka Honey and Licorice root extract. They found that Manuka honey is almost as effective as commercial mouthwashes in reducing the growth of P gingivalis (one of the main bacteria that causes bad breath), while Licorice root extract was largely ineffective. The authors' results suggest that Manuka honey is a promising candidate in the search for new and improved halitosis treatments.

Honey possesses distinctive antimicrobial properties; however, it is unknown which compounds in honey are responsible for these properties. In this research study, Abdel-Azim et al. examine the role of osmotic stress in honey and the antibacterial efficacy of both Manuka and raw pasture honey.

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.

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.

Manufacturers that produce products using fused filament fabrication (FFF) 3D printing technologies have control of numerous build parameters. This includes the number of solid layers on the exterior of the product, the percentage of material filling the interior volume, and the many different types of infill patterns used to fill their interior.This study investigates the hypothesis that as the density of the part increases, the mechanical properties will improve at the expense of build time and the amount of material required.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

Here, recognizing that alcohol manufacturers may target ethnic minorities and youths with specific forms of advertisements based on previous studies, the authors considered how alcohol storefronts differ depending on the community they are located in. Specifically, they looked at differences between Metro-Dtroit suburban communities of high- and low-incomes. They found that alcohol stores in the low-income areas had more and larger alcohol and malt liquor advertisements per store along with being within 1,000 feet of a school.

A bottleneck in deleting algal blooms is that current data section is manual and is reactionary to an existing algal bloom. These authors made a custom-designed Seek and Destroy Algal Mitigation System (SDAMS) that detects harmful algal blooms at earlier time points with astonishing accuracy, and can instantaneously suppress the pre-bloom algal population.

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.