Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.
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Photometric analysis of Type Ia Supernova 2023jvj
Here the authors conducted a photometric analysis of Supernova (SN) 20234jvj. Through generating a light curve, they determined SN 2023jvj to be a Type Ia supernova located approximately 1.246e8 parasecs away from Earth.
Read More...Cardiovascular Disease Prediction Using Supervised Ensemble Machine Learning and Shapley Values
The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.
Read More...Studying the effects of different anesthetics on quasi-periodic patterns in rat fMRI
The authors looked at the effects of commonly used anesthetics in rodents on brain activity (specifically quasi-periodic patterns). Understanding effects on brain activity is important for researchers to understand when choosing rodent models for disease.
Read More...Anti-inflammatory and pro-apoptotic properties of the polyherbal drug, MAT20, in MCF-7 cells
The authors test potential anti-inflammatory and pro-apoptotic effects of a polyherbal extract formulation on cultured breast cancer cells.
Read More...Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles
While some believe that ride-hailing services offer reduced CO2 emissions compared to individual driving, studies have found that driving without passengers on ride-hailing trips or "deadheading" prevents this. Here, with a mathematical model, the authors investigated if the use of electric vehicles as ride-hailing vehicles could offer reduced CO2 emissions. They found that the improved vehicle efficiency and cleaner generation could in fact lower emissions compared to the use of personal gas vehicles.
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...A chemical and overwintering honey bee apiary field study comparing new and expired amitraz miticide
In this study, the authors test the longevity of a anti-mite compound, amitraz, in commercially-sold strips and the age-dependent efficacy of these strips in preventing honey bee colony collapse by ectoparasitic mite Varroa destructor.
Read More...Structure-activity relationship of berberine and G4 DNA reveals aromaticity’s effect on binding affinity
Berberine is a natural quaternary alkaloid that has anti-microbial and anti-cancer effects. This compound can bind to Guanine Quadruplex (G4) DNA secondary complexes to help inhibit cancer cell proliferation. In this study, the authors investigate whether incorporating large aromatic rings helps to stabilize berberine-G4 interactions.
Read More...Examining the Growth of Methanotrophic Bacteria Immersed in Extremely Low-Frequency Electromagnetic Fields
Scientist are investigating the use of methane-consuming bacteria to aid the growing problem of rising greenhouse gas emissions. While previous studies claim that low-frequency electromagnetic fields can accelerate the growth rate of these bacteria, Chu et al. demonstrate that this fundamental ideology is not on the same wavelength with their data.
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