It\u27s Not All Just Child\u27s Play: A Psychological Study on the Potential Benefits of Theater Programming With Children

Research suggests there are developmental benefits of theater education. The present study hypothesized that a theater curriculum, utilizing the techniques of Creative Drama and theater games created by Viola Spolin, taught to students at the Stillwater Montessori School to 15 students in grades Kindergarten through Fourth, would contribute to significant increases in children’s self-esteem and empathy. Questionnaires assessing children’s self-esteem and empathy were administered to children, parents, and teachers prior to and following the theater programming. Results were analyzed using a repeated measures ANOVA and indicated that generally there were no significant changes in children’s self-esteem and empathy. However, for females there were significant decreases in Global Self-Worth and self-esteem. According to teachers, children significantly decreased in their Attention to Others’ Feelings. Although responses to the measures did not yield many significant changes in children, there were several observable changes in the children’s listening and nonverbal communication skills and their ability to give and receive praise. Individual progressions concerning participation, involvement, self-esteem, and focus were also observed. Positive feedback from students and teachers indicated the theater programming had a beneficial effect

Vicarious Trauma Exposure and Its Effects on Mental Health among Adolescents and Adults: A Narrative Research Review

Vicarious Trauma Exposure and Its Effects on Mental Health among Adolescents and Adults: A Narrative Research Review Harris Davis, Dept. of Psychology, Angel Whitfield, Sydney Judge, Bailee Beverly, Jenna Minter, Harrison Davis, and Eryn DeLaney and Chloe Walker, Dept. of Psychology Graduate Students, with Dr. Chelsea Williams, Dept. of Psychology A narrative research review was conducted to examine studies that have tested the relation between vicarious trauma (i.e., witnessing abuse of others; Trautmann, S., Reineboth, M., Trikojat, K., Richter, J., Hagenaars, M. A., Kanske, P., & Schäfer, J. (2018). and mental health in adolescents and young adults. Using psycinfo to find research conducted to date, this review indicated that there are various types of trauma that adolescents might be exposed to (e.g., domestic abuse, emotional abuse, and physical abuse) and various forms of mental health issues (e.g., post traumatic stress disorder, depression, and anxiety) that might result from such trauma. (Erolin, K. S., Wieling, E., & Parra, R. E. A. (2014)., , Stowkowy, J., Goldstein, B. I., MacQueen, G., Wang, J., Kennedy, S. H., Bray, S., Lebel, C., & Addington, J. (2020). Findings from this review also suggest that there is a relation between varying types of traumatic exposure and several types of depressive symptoms (Monfort, E., Afzali M. H. (2015), Merza, K., Papp, G., & Szabó, I. K. (2015) and that there are more studies on adults than on adolescents. These effects will be discussed. Gaps in the literature will be referenced and potential future research directions will be acknowledged. In conclusion, this narrative research review emphasizes the importance of knowing the effects of vicarious trauma on adolescents and adults for preventative purposes.https://scholarscompass.vcu.edu/uresposters/1336/thumbnail.jp

Understanding Feature Importance in Streamflow Prediction Using Machine Learning in Snow-Dominated Basins

Reliable prediction of streamflow is vital to ensuring a community’s access to water. Traditional prediction methods often rely on regression models and both station measured snow water equivalent (SWE) and precipitation accumulation. However, climate change is expected to negatively affect SWE’s ability to accurately predict streamflow. With an increased emphasis on the western United States water availability, the goal of this study was to explore input data, or features, and machine learning models that could potentially aid streamflow prediction in the future. For five case study sites, multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and random forest (RF) machine learning models are used in conjunction with station-based, meteorological, and climate input data to examine the make-up of high-performing models. Every possible combination of the features is used to build individual models for each model type (MLR, MARS, and RF). The performance of each model is then judged using the Nash and Sutcliffe coefficient of efficiency (NSE) and ranked against one another for each basin. Results suggest that having a diverse selection of features arising from physical station-based data, large-scale metrological data, and climate indices will produce better models than relying solely on station-based features alone. Rather than individual features standing out as powerful predictors, the representation of all three feature categories (station-based, meteorological, and climate) appeared most important to high-performing models. Each basin’s top performing models had differing feature sets and model types. Overall, this study presents an approach to feature selection in hydrological machine learning modeling that provides improved understanding and assists in the streamflow prediction, benefiting water managers as well as assisting in the foundation of future studies attempting to improve the results of streamflow prediction

Gp91ds-tat, a Selective NADPH Oxidase Peptide Inhibitor, Increases Blood Nitric Oxide (NO) Bioavailability in Bind Limb Ischemia and Reperfusion (I/R)

I/R injury induces cell death and organ dysfunction in part due to a burst of reactive oxygen species that occurs upon the reintroduction of oxygen into the ischemic area, leading to endothelial dysfunction: decreased blood NO and increased hydrogen peroxide (H2O2 ) levels. We’ve previously shown in isolated rat hearts subjected to I/R injury, gp91ds-tat attenuated cardiac contractile dysfunction and reduced infarct size compared to controls presumably by the inhibition of NADPH oxidase induced superoxide release. Superoxide can quench NO via the formation of peroxynitrite and also be converted to H2O2 in blood. We attempted to confirm this hypothesis using a rat hind limb I/R model that permitted real time measurements of changes in blood NO and H2O2. NO or H2O2 microsensors were inserted into both femoral veins in anesthetized male rats. One limb’s femoral artery/vein is subjected to I(30min)/R(45min) while the other served as a non-ischemic sham. Preliminary results show blood NO release significantly increased by the end of reperfusion in gp91ds-tat treated rats (1.2 mg/kg, MW 2452g/mol, n=5) compared to saline treated rats (n=3;

Comparing the effectiveness of TAT and myristoylation of gp91ds on leukocyte superoxide (SO) release

It is well known that adding myristic acid or transactivating (TAT)carrier peptide to native peptides will facilitate cell membranepermeability required for targeting intracellular substrates. However, it is not known if differences exist in the effectiveness of myristic acid versus TAT conjugated peptides. To test this, we compared the dose-response relationship of myristoylated (Myr-peg linker-C-S-TR-I-R-R-Q-L-NH2oxidase assembly inhibitor (gp91ds), ([H]-R-K-K-R-R-Q-R-R-R- CS-T-R-I-R-R-Q-L-NH2gp91ds with glycine-glycine spacer in between TAT and gp91ds (MW=2566 g/mol) on N-Formyl-L-Methionyl-L-Leucyl-L-Phenylalanine (fMLP) induced leukocyte superoxide (SO) release.Myr-peg-gp91ds NADPH oxidase peptide inhibitor significantly attenuated fMLP-induced leukocyte SO release dose dependently (40±11%; 2 μM; n=13), (53±8%; 5μM; n=13), and (64±8%;10μM; n=11, p\u3c0.01) compared to fMLP (1μM; n= 29; change in absorbance 0.196±0.012 from baseline) or native sequence which inhibited the fMLP response by only 11±4% at the highest dose tested (80μM; n=10). The TAT gp91ds inhibitors (both 80μM; n=13, p\u3c0.05) significantly attenuated fMLP-induced leukocyte SO release by 35±11%, but to a lesser extent than the myr-peg linked inhibitor. These results suggest that myr-peg-gp91ds is more cell permeable and therefore can inhibit fMLP-induced SO release from leukocytes at lower doses compared to TAT gp91ds.; MW=1486 g/mol) and TAT conjugated NADPH ; MW=2452 g/mol) or TAT conjugated

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://zenodo.org/record/6525446, baseline models and code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://aircraftverse.onrender.com/.Comment: The dataset is hosted at https://zenodo.org/record/6525446, baseline models and code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://aircraftverse.onrender.com

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals

A two-dimensional, two-electron model atom in a laser pulse: exact treatment, single active electron-analysis, time-dependent density functional theory, classical calculations, and non-sequential ionization

Owing to its numerical simplicity, a two-dimensional two-electron model atom, with each electron moving in one direction, is an ideal system to study non-perturbatively a fully correlated atom exposed to a laser field. Frequently made assumptions, such as the ``single active electron''- approach and calculational approximations, e.g. time dependent density functional theory or (semi-) classical techniques, can be tested. In this paper we examine the multiphoton short pulse-regime. We observe ``non-sequential'' ionization, i.e.\ double ionization at lower field strengths as expected from a sequential, single active electron-point of view. Since we find non-sequential ionization also in purely classical simulations, we are able to clarify the mechanism behind this effect in terms of single particle trajectories. PACS Number(s): 32.80.RmComment: 10 pages, 16 figures (gzipped postscript), see also http://www.physik.tu-darmstadt.de/tqe

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems