The effect of other neuroendocrine hormones in the regulation of adipose tissue thermogenesis.
Read More...Exploring a new mechanism controlling thermogenesis of adipose tissue
The effect of other neuroendocrine hormones in the regulation of adipose tissue thermogenesis.
Read More...Assessing the accuracy and efficiency of simplified gridded ion thruster simulations
The authors used a particle-in-cell simulation to determine the effects on extensive and intensive metrics. They found that preliminary simulations could be run quickly with much lower particle counts before more technically demanding and comprehensive simulations are performed.
Read More...Are Asian foods healthier than Western foods: Evidence collected from St. Louis area grocery stores
The authors compare nutritional content of foods found in Western versus Asian grocery stores to determine whether one cultural diet is healthier than the other.
Read More...A multi-dimensional analysis of NFL red zone efficiency
Here the authors investigated the relationship between offensive play-calling styles and scoring success within the NFL's red zone by analyzing play-by-play data and expected points metrics. Their findings suggest that a conservative approach to play design and execution is more strongly associated with maximizing efficiency and point-value gains than aggressive strategies.
Read More...Deep learning for pulsar detection: Investigating hyperparameter effects on TensorFlow classification accuracy
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.
Read More...Training neural networks on text data to model human emotional understanding
The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.
Read More...Identifying factors, such as low sleep quality, that predict suicidal thoughts using machine learning
Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.
Read More...Simulating natural selection via autonomous agents: Environmental factors create unstable equilibria
Natural selection shapes the evolution of all organisms, and one question of interest is whether natural selection will reach a "stopping point": a stable, ideal, value for any particular trait. Madhan and Kanagavel tackle this question by building a computer simulation of trait evolution in organisms.
Read More...Analysis of biofertilization impacts on Pisum sativum
This study explored the various effects of three different produce-based biofertilizers on pea plant growth, using red apple, pear, strawberry, and control treatments. It was hypothesized that the application of fruit biomatter would increase the growth of pea plants, with the application of strawberry biomatter having the most significant effect due to strawberries containing a higher nutrient content compared to pears and apples. Analysis confirmed the hypothesis. The application of strawberry biomatter could prove to be an effective way to increase plant growth in commercial agriculture.
Read More...Analysis of professional and amateur tennis serves using computer pose detection
The authors looked at the dynamics of tennis serves from professional and amateur athletes.
Read More...