Browse Articles

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 were interested in whether they could observe the effects of climate change by analyzing historical temperature data of two U.S. cities. They predicted that they should observe a warming trend in both cities. Their results showed that despite yearly variations, warming trends can be observed both in Rochester, NY and Seattle, WA which fit the predictions of climate change forecasts.

This study investigates the migration intentions of Syrian high school and university students amid ongoing conflict and economic instability. Drawing on survey data, the research examines how academic stage influences migration motivations, preferred destinations, and return intentions. The findings reveal a widespread desire to emigrate, driven by educational, economic, and security concerns, highlighting significant implications for Syria’s future workforce and post-conflict recovery.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

The authors re-examine the galactic kinematics of relic galaxy NGC 1277, recently identified as dark matter deficient, by reproducing its rotation curve with data from the George and Cynthia Mitchell Spectrograph.

Here the authors aimed to find the best preservative combinations to stop bacterial growth in food, using data modeling and biochemical experiments. They discovered that single preservatives are often not enough, with varying effectiveness against different bacteria, and suggest future research into combining preservatives for better results.

This study explores how to predict and minimize distortion in 3D printed parts, particularly when using affordable PLA filament. The researchers developed a model using a gradient boosting regressor trained on 3D printing data, aiming to predict the necessary CAD dimensions to counteract print distortion.

Adolescence is a critical period for self-identity formation, heavily influenced by feedback from social networks. This research examined the interplay between social support from parents and peers and self-concept development in adolescents using data from the National Longitudinal Study of Adolescent to Adult Health. While individual support from parents and peers did not directly impact self-concept, their combined interaction significantly influenced it, highlighting the importance of various social supports in fostering healthy self-concept development and overall adolescent well-being.

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.

Authors address the gender disparity in STEM fields, examining changes in gender diversity across male-dominated undergraduate programs over 19 years at 24 top universities. Analyzing data from NCES IPEDS, it identifies STEM as persistently male-dominated but notes increasing gender diversity in many disciplines, particularly in recent years. Results indicate that higher-ranked universities in disciplines like computer science and mechanical engineering show a weak correlation with improved gender diversity, suggesting effective initiatives can mitigate the gender gap in STEM, despite ongoing challenges.