Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

Imagined Speech Classification Using EEG and Deep Learning.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain-computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.

Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA.

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (-0.22) and between humidity and COVID-19 incidence rate (-0.15). The linear regression model showed the model linear coefficients to be 0.92 and -1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

How Does the Local Electrostatic Field Influence Emitted Wavelengths and Bioluminescent Intensities of Modified Heteroaromatic Luciferins?

The firefly chromophore, oxyluciferin, is in the pocket of the firefly luciferase and is surrounded by the side-chains of some amino acid residues. The charged residues produce the local electrostatic field (LEF) around the oxyluciferin. The emitted wavelengths and intensities of the oxyluciferin and its heterocyclic analogs under the LEF are examined. The common overlapping volumes of the HOMO and LUMO explain why the oscillator strengths vary under the LEF. Three average Ex change rates of the first excited energy are introduced to measure what luciferins are more sensitive to the LEF. The first excited energies and intensities in two enzymatic-like microenvironments are simulated via the LEF. The oscillator strengths and the net electric charges of the O6' and the O4 are applied to explain the experimental bioluminescent intensities.

Theoretical investigation into the structural, thermochemical, and electronic properties of the decathio[10]circulene.

For the first time, a theoretical study has been performed on the prototypical decathio[10]circulene (C(20)S(10)) species, which is an analogue of the novel octathio[8]circulene "Sulflower" molecule (C(16)S(8)). Examinations of the singlet and triplet states of C(20)S(10) were made at the B3LYP/6-311G(d) level. Local minima of C(2) and C(s) symmetry were found for the lowest singlet and triplet states, respectively. The stability of C(20)S(10) was assessed by calculating the ΔH°(f) of C(16)S(8) and C(20)S(10) and the ΔH(o) for their decomposition into C(2)S units. Frontier molecular orbital plots show that structural adjacent steric factors along with the twist and strain orientations of C(20)S(10) do not disturb the aromatic π-delocalizing effects. In fact, C(20)S(10) maintains the same p(z) HOMO character as C(16)S(8). These similarities are further verified by density-of-states characterization. Calculated infrared spectra of C(16)S(8) and C(20)S(10) show broad similarities. Molecular electrostatic potential results reveal that eight of the peripheral sulfur atoms are the most electronegative atoms in the molecule, while the interior ten-membered ring exhibits virtually no electronegativity.