In this study, the authors determined whether tautomerization dynamics in protic and aprotic solvents displayed differences in reaction rates and in the proportion of the keto and enol tautomers present.
Read More...Deuterated solvent effects in the kinetics and thermodynamics of keto-enol tautomerization of ETFAA
In this study, the authors determined whether tautomerization dynamics in protic and aprotic solvents displayed differences in reaction rates and in the proportion of the keto and enol tautomers present.
Read More...Changes in Aromanian language use and the Aromanian ethnolinguistic group’s reaction to decline
The Aromanian language and culture is quickly declining towards extinction. In this new research article, Ganea and Lascu provide evidence that, although the use of the Aromanian language is less prevalent among younger individuals, participants overwhelming support the preservation of Aromanian language and culture.
Read More...Hammett linear free-energy relationships in the biocatalytic hydrolysis of para-substituted nitrophenyl benzoate esters
As the world moves towards more eco-friendly methods for chemical synthesis, there's a strong interest in employing enzymes in chemical synthetic processes. Here, the authors explore how the activity of enzymes such as trypsin, lipase and nattokinase is affected by the electronic effects of the substrate they are acting on.
Read More...The impact of timing and magnitude of the El Niño- Southern Oscillation on local precipitation levels and temperatures in the Bay Area
Understanding the relationships between temperature, MEI, SPI, and CO2 concentration is important as they measure the major influencers of California’s regional climate: temperature, ENSO, precipitation, and atmospheric CO2. In this article, the authors analyzed temperature, Multivariate El Niño-Southern Oscillation Index (MEI), and Standard Precipitation Index (SPI) data from the San Francisco Bay Area from 1971 to 2016. They also analyzed CO2 records from Mauna Loa, HI for the same time period, along with the annual temperature anomalies for the Bay Area.
Read More...Population Forecasting by Population Growth Models based on MATLAB Simulation
In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.
Read More...A Novel Model to Predict a Book's Success in the New York Times Best Sellers List
In this article, the authors identify the characteristics that make a book a best-seller. Knowing what, besides content, predicts the success of a book can help publishers maximize the success of their print products.
Read More...Human comprehension of 4-dimensional rotation
The authors looked at the ability subjects to rotation a 4D cube and how the ability to practice cube rotation impact their ability to understand 4D space.
Read More...Correlating inlet gas composition to conversion efficiency in plasma-assisted landfill gas reforming
The escalating crisis of climate change, driven by the accumulation of greenhouse gases from human activities, demands urgent and innovative solutions to curb rising global temperatures. Plasma-based methane (CH4) and carbon dioxide (CO2) reforming offers a promising pathway for carbon capture and the sustainable production of hydrogen fuel and syngas components. To advance this technology, particularly in terms of energy efficiency and selectivity, it is essential to enhance the conversion efficiencies of CO2 and CH4.
Read More...Contribution of Indian Women to the National GDP
The authors assessed the degree of women participation in India's economy as a way to estimate woman's participation in India's economic growth.
Read More...Forecasting air quality index: A statistical machine learning and deep learning approach
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.
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