![Analysis of Milorganite’s ability to sustain growth of <i>Ocimum basilicum</i> in simulated Martian soil](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBcTRQIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--f219425c7ec584ca3a6e45d33476fc90e0c9f0f9/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/feature.png)
The authors test whether basil can grow in a simulated Martian soil improved with a waste-based fertilizer called Milorganite.
Read More...Analysis of Milorganite’s ability to sustain growth of Ocimum basilicum in simulated Martian soil
The authors test whether basil can grow in a simulated Martian soil improved with a waste-based fertilizer called Milorganite.
Read More...Optimal pH for indirect electrochemical oxidation of isopropyl alcohol with Ru-Ti anode and NaCl electrolyte
In this study, the authors determine optimal pH levels for maximizing isopropanol degradation in water. This has important applications for cleaning up polluted wastewater in the environment.
Read More...Collaboration beats heterogeneity: Improving federated learning-based waste classification
Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.
Read More...Sex differences in linear polyubiquitination in the entorhinal cortex during fear memory formation
The authors explore sex-specific differences in the formation of fear memories across several rat brain regions.
Read More...Phytoplankton Plastid Proteomics: Cracking Open Diatoms to Understand Plastid Biochemistry Under Iron Limitation
In many areas of the world’s oceans, diatoms such as Thalassiosira pseudonana are limited in growth by the availability of iron (Fe), which is an essential nutrient for diatoms. The authors of this study examined if Fe-limitation makes a significant difference in the proteins expressed within the chloroplast, the power source for diatoms, utilizing a new plastid isolation technique specific to diatoms and completing 14 mass spectrometry experiments.
Read More...Heat conduction: Mathematical modeling and experimental data
In this experiment, the authors modify the heat equation to account for imperfect insulation during heat transfer and compare it to experimental data to determine which is more accurate.
Read More...The Effects of Micro-Algae Characteristics on the Bioremediation Rate of Deepwater Horizon Crude Oil
Environmental disasters such as the Deepwater Horizon oil spill can be devastating to ecosystems for long periods of time. Safer, cheaper, and more effective methods of oil clean-up are needed to clean up oil spills in the future. Here, the authors investigate the ability of natural ocean algae to process crude oil into less toxic chemicals. They identify Coccochloris elabens as a particularly promising algae for future bioremediation efforts.
Read More...Studying habitability of the exoplanents Kepler-504 b, Kepler-315 b, and Kepler-315 c
The authors explore how similar exoplanets are to Earth and whether they could be inhabited by humans and other living organisms.
Read More...Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance
In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.
Read More...Automated classification of nebulae using deep learning & machine learning for enhanced discovery
There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.
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