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Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents

An et al. | Aug 23, 2024

Adults’ attitudes toward non-alcoholic beer purchases and consumption by children and adolescents
Image credit: The authors

Consumption of non-alcoholic beverages, like non-alcoholic beer, is growing in popularity in the United States. These beverages raise important societal questions, such as whether minors should be allowed to purchase or consume non-alcoholic beer. An and An investigate this issue by surveying adults to see if they support minors purchasing and consuming non-alcoholic beer.

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3D Printed Polymer Scaffolds for Bone Tissue Regeneration

Jayatissa et al. | Apr 26, 2019

3D Printed Polymer Scaffolds for Bone Tissue Regeneration

Scientists are always on the quest to improve the body's healing abilities and broken bones are no exception. In this article, the authors investigate properties of 3D-printed biocompatible polymers used to improve bone healing. With such efforts, we can hope to, one day, improve bone scaffolding materials in ways that make the natural healing processes more efficient, reducing the time needed for recovery from bone fractures.

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Testing Various Synthetic and Natural Fiber Materials for Soundproofing

Karuppiah et al. | Jun 15, 2017

Testing Various Synthetic and Natural Fiber Materials for Soundproofing

Noise pollution negatively impacts the health and behavioral routines of humans and other animals, but the production of synthetic sound-absorbing materials contributes to harmful gas emissions into the atmosphere. The authors of this paper investigated the effectiveness of environmentally-friendly, cheap natural-fiber materials, such as jute, as replacements for synthetic materials, such as gypsum and foam, in soundproofing.

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Machine learning predictions of additively manufactured alloy crack susceptibilities

Gowda et al. | Nov 12, 2024

Machine learning predictions of additively manufactured alloy crack susceptibilities

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.

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Suppress that algae: Mitigating the effects of harmful algal blooms through preemptive detection & suppression

Natarajan et al. | Jul 17, 2023

Suppress that algae: Mitigating the effects of harmful algal blooms through preemptive detection & suppression
Image credit: Sharanya Natarajan

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.

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Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Sikdar et al. | Jan 10, 2023

Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

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Utilizing the Magnus effect to produce more downforce than a standard wing

Angiras et al. | Aug 15, 2022

Utilizing the Magnus effect to produce more downforce than a standard wing

Here, seeking a better solution to produce downforce that keeps a vehicle grounded at high speeds than wings which tend to result in degraded car performance due to increased air resistance, the authors considered using the Magnus effect as a replacement. The authors found that a spinning cylinder generated significantly more downforce through the Magnus effect than a standard wing at all wind speeds as simulated through the use of a leaf blower. They suggest that a cylinder could be a potential replacement for a wing when downforce is a priority.

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A study on the stretching behavior of rubber bands

Davuluri et al. | Jan 18, 2022

A study on the stretching behavior of rubber bands

Here, the authors considered the stretching behavior of rubber bands by exposing the rubber bands to increasing loads and measuring their stretch response. They found that a linear stretch response was observed for intermediate loading steps, but this behavior was lost at lower or higher loads, deviating from Hooke's Law. The authors suggest that studies such as these can be used to evaluate other visco-elastic structures.

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