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The influence of purpose-of-use on information overload in online social networking

Agarkar et al. | Nov 01, 2022

The influence of purpose-of-use on information overload in online social networking

Here, seeking to understand the effects of social media in relation to social media fatigue and/or overload in recent years, the authors used various linear models to assess the results of a survey of 27 respondents. Their results showed that increased duration of use of social media did not necessarily lead to fatigue, suggesting that quality may be more important than quantity. They also considered the purpose of an individual's social media usage as well as their engagement behavior during the COVID-19 pandemic.

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Defying chemical tagging: inhomogeneities in the wide binary system HIP 34407/HIP 34426

Còdol et al. | Oct 05, 2023

Defying chemical tagging: inhomogeneities in the wide binary system HIP 34407/HIP 34426
Image credit: Pixabay

This assessed the hypothesis that stars in wide binary systems are chemically homogeneous because of their shared origin. Abundances of the HIP 34407/HIP 34426 binary were obtained by analyzing high-resolution spectra of the system. Discrepancies found in the system’s elemental abundances might be an indicator of the presence of rocky planets around this star. Thus, the differences found in chemical composition might demonstrate limitations in the assumptions of chemical tagging.

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A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Ahmed et al. | Jun 09, 2023

A novel encoding technique to improve non-weather-based models for solar photovoltaic forecasting

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

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