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Capturing Harmful Air Pollutants Using an Electrospun Mesh Embedded with Zinc-based Nanocrystals

Doppalapudi et al. | May 12, 2020

Capturing Harmful Air Pollutants Using an Electrospun Mesh Embedded with Zinc-based Nanocrystals

Zeolithic imidazolate framework-8 (ZIF-8) is a specific metal-organic framework that has favorable qualities for use in an air filter and is known to be capable of adsorbing particulate matter. Therefore, the objective of this experiment was to determine the effectiveness of ZIF-8 in adsorbing polar, gaseous air pollutants, specifically nitrogen dioxide and hydrogen sulfide. In order to determine effectiveness, the percent change in concentration for various gases after the application of ZIF-8 crystals was measured via Fourier-transform infrared spectroscopy (FTIR). The work highlights crystals as a potentially promising alternative or addition to current filter materials to reduce atmospheric pollution.

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Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning

Chong et al. | May 01, 2023

Rhythmic lyrics translation: Customizing a pre-trained language model using stacked fine-tuning
Image credit: Pixabay

Neural machine translation (NMT) is a software that uses neural network techniques to translate text from one language to another. However, one of the most famous NMT models—Google Translate—failed to give an accurate English translation of a famous Korean nursery rhyme, "Airplane" (비행기). The authors fine-tuned a pre-trained model first with a dataset from the lyrics domain, and then with a smaller dataset containing the rhythmical properties, to teach the model to translate rhythmically accurate lyrics. This stacked fine-tuning method resulted in an NMT model that could maintain the rhythmical characteristics of lyrics during translation while single fine-tuned models failed to do so.

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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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Leveraging E-Waste to Enhance Water Condensation by Effective Use of Solid-state Thermoelectric Cooling

Joshi et al. | Dec 02, 2020

Leveraging E-Waste to Enhance Water Condensation by Effective Use of Solid-state Thermoelectric Cooling

Water scarcity affects upwards of a billion people worldwide today. This project leverages the potential of capturing humidity to build a high-efficiency water condensation device that can generate water and be used for personal and commercial purposes. This compact environment-friendly device would have low power requirements, which would potentially allow it to utilize renewable energy sources and collect water at the most needed location.

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Determining the Effects of Voice Pitch on Adolescent Perception, Subconscious Bias, and Marketing Success Using Electroencephalography

Guan et al. | Apr 28, 2021

Determining the Effects of Voice Pitch on Adolescent Perception, Subconscious Bias, and Marketing Success Using Electroencephalography

Voice pitch affects perceived authoritativeness, competency, and leadership capacity. In this study, the authors suggest that examining certain measures of brain activity collected using an affordable EEG could predict advertising effectiveness, which may be invaluable in future neuromarketing research. Understanding voice pitch and other factors that cause implicit bias may allow significant advances in marketing, facilitating business success.

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Changing electronic use behavior in adolescents while studying: An interventional psychology experiment

Kumar et al. | Mar 02, 2024

Changing electronic use behavior in adolescents while studying: An interventional psychology experiment
Image credit: RAMSHA ASAD

Here, the authors investigated the effects of an interventional psychology on the study habits of high school students specifically related to the use of electronic distractions such as social media or texting, listening to music, or watching TV. They reported varying degrees of success between the control and intervention groups, suggesting that the methods of habit-breaking for students merits further study.

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Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Düzgezen et al. | Mar 27, 2020

Analysis of Technology Usage of Teens: Correlating Social Media, Technology Use, Participation in Sports, and Popularity

Social media usage is predicted to impact teen well-being and emotional status. This study sought to assess the impact of teen technology usage on their social lives. Surveys of 8th and 9th graders were used to assess compare technology usage between males and females as well as and how social media usage impacts the perception of social environment at school.

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