The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.
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Modeling the moving sofas in circular hallways using geometric methods
Investigation of the largest rigid shape that can be moved through a circular hallway of unit width with an arbitrary turn angle
Read More...Predictive modeling of cardiovascular disease using exercise-based electrocardiography
The authors looked factors that could lead to earlier diagnosis of cardiovascular disease thereby improving patient outcomes. They found that advances in imaging and electrocardiography contribute to earlier detection of cardiovascular disease.
Read More...Simple solving heuristics improve the accuracy of sudoku difficulty classifiers
The Mount Laurel doctrine: A case study in housing affordability and the labor market in New Jersey
The authors explored the effects of the Mount Laurel Doctrine on housing affordability, unemployment rate, and civilian labor force in Burlington County, New Jersey compared to nearby counties.
Read More...Development of novel biodegradable bioplastics for packaging film using mango peels
Here the authors explored the development of biodegradable bioplastic films derived from mango peels as a sustainable solution to plastic pollution and greenhouse gas emissions from fruit waste. They optimized the film's mechanical properties and water resistance through adjusting processing conditions and incorporating plasticizers and a hydrophobic coating, ultimately demonstrating its potential as a bacteriostatic and biodegradable alternative to conventional plastic food wrap.
Read More...Disputing the green valley theory of galaxy evolution
Quantifying coliform bacteria in ground beef to evaluate food safety guidelines
The authors looked at the presence of coliform bacteria present in ground beef after cooking it various CDC standards. They found that no coliform bacteria was present when CDC guidelines for cooking ground beef were properly followed.
Read More...Vineyard vigilance: Harnessing deep learning for grapevine disease detection
Globally, the cultivation of 77.8 million tons of grapes each year underscores their significance in both diets and agriculture. However, grapevines face mounting threats from diseases such as black rot, Esca, and leaf blight. Traditional detection methods often lag, leading to reduced yields and poor fruit quality. To address this, authors used machine learning, specifically deep learning with Convolutional Neural Networks (CNNs), to enhance disease detection.
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