In this article the authors looked at the effect of spending time with a therapy dog before and after stressful events. They found that interacting with a therapy before a stressful event showed more significant reduction in signs of stress compared to interacting with a therapy dog after stressful events have already occurred.
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The effects of algaecides on Spirulina major and non-target organism Daphnia magna
Algal blooms pose a threat to ecosystems, but the methods used to combat these blooms might harm more than just the algae. Halepete, Graham, and Lowe-Schmahl demonstrate negative effects of anti-algae treatments on a cyanobacterium (Spirulina major), and the water fleas (Daphnia magna) that live alongside these cyanobacteria.
Read More...Modeling Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from Helium to Xenon using STO-nG basis sets
The energy of an atom is extremely useful in nuclear physics and reaction mechanism pathway determination but is challenging to compute. This work aimed to synthesize regression models for Pople Gaussian expansions of Slater-type Orbitals (STO-nG) atomic energy vs. atomic number scatter plots to allow for easy approximation of atomic energies without using computational chemistry methods. The data indicated that of the regressions, sinusoidal regressions most aptly modeled the scatter plots.
Read More...Using NLP to ascertain changes in the fast-fashion industry based on UN sustainable development goals
Here, the authors sought to evaluate the efforts of fast fashion clothing companies towards sustainability, specifically in regards to the United Nations Sustainable Development Goals. The authors used natural language processing to investigate the sustainability reports of fast fashion companies focusing on terms established by the UN. They found that the most consistently addressed areas were related to sustainable consumption/production, with a focus on health and well-being emerging during the recent pandemic.
Read More...Error mitigation of quantum teleportation on IBM quantum computers
Quantum computers can perform computational tasks beyond the capability of classical computers, such as simulating quantum systems in materials science and chemistry. Quantum teleportation is the transfer of quantum information across distances, relying on entangled states generated by quantum computing. We sought to mitigate the error of quantum teleportation which was simulated on IBM cloud quantum computers.
Read More...Analyzing aerosol variation during the COVID-19 pandemic lockdown using satellite data
In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.
Read More...Using economic indicators to create an empirical model of inflation
Here, seeking to understand the correlation of 50 of the most important economic indicators with inflation, the authors used a rolling linear regression to identify indicators with the most significant correlation with the Month over Month Consumer Price Index Seasonally Adjusted (CPI). Ultimately the concluded that the average gasoline price, U.S. import price index, and 5-year market expected inflation had the most significant correlation with the CPI.
Read More...Effects of Photoperiod Alterations on Stress Response in Daphnia magna
Here, seeking to better understand the effects of altered day-night cycles, the authors considered the effects of an altered photoperiod on Daphnia magna. By tracking possible stress responses, including mean heart rate, brood size, and male-to-female ratio they found that a shorter photoperiod resulted in altered mean heart rates and brood size. The authors suggest that based on these observations, it is important to consider the effects of photoperiod alterations and the stress responses of other organisms.
Read More...Prediction of molecular energy using Coulomb matrix and Graph Neural Network
With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.
Read More...Novel biaryl imines and amines as potential competitive inhibitors of dihydropteroate synthase
In this study, the authors design a series of new biaryl small molecules to target and block the binding pocket of the enzyme dihydropteroate synthase, which is important for prokaryotic biosynthesis of folic acid and could serve as better antimicrobial compounds.
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