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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.

Arabadopsis, “the fruit fly of plants”, is an easy to grow plant system for genetic manipulation. Here, researchers tested the effects of varied light conditions on plants with specific mutations in the light sensing pathways.

This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.

The authors studied the impact of drying blood as a storage solution for space travel.

The authors looked at how storage temperature of potatoes can impact the sweetness of potatoes (due to the break down of the starch into simple sugars).

The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

Impact of carbon tax and dividend on financial security

The authors describe the design and testing of new guide RNAs targeting the DMPK gene, which is responsible for myotonic dystrophy.

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.