Gas micro-well track imaging detectors for gamma-ray astronomy

We describe our program to develop gas micro-well detectors (MWDs) as three-dimensional charged particle trackers for use in advanced gamma-ray telescope concepts. A micro-well detector consists of an array of individual micro-patterned gas proportional counters opposite a planar drift electrode. The well anodes and cathodes may be connected in X and Y strips, respectively, to provide two-dimensional imaging. When combined with transient digitizer electronics, which record the time signature of the charge collected in the wells of each strip, full three-dimensional reconstruction of charged-particle tracks in large gas volumes is possible. Such detectors hold great promise for advanced Compton telescope (ACT) and advanced pair telescope (APT) concepts due to the very precise measurement of charged particle momenta that is possible (Compton recoil electrons and electron-positron pairs, respectively). We present preliminary lab results, including detector fabrication, prototype electronics, and initial detector testing. We also discuss applications to the ACT and APT mission concepts, based on GEANT3 and GEANT4 simulations

Effect of Brief Biofeedback via a Smartphone App on Stress Recovery: Randomized Experimental Study.

BACKGROUND:Smartphones are often vilified for negatively influencing well-being and contributing to stress. However, these devices may, in fact, be useful in times of stress and, in particular, aid in stress recovery. Mobile apps that deliver evidence-based techniques for stress reduction, such as heart rate variability biofeedback (HRVB) training, hold promise as convenient, accessible, and effective stress-reducing tools. Numerous mobile health apps that may potentially aid in stress recovery are available, but very few have demonstrated that they can influence health-related physiological stress parameters (eg, salivary biomarkers of stress). The ability to recover swiftly from stress and reduce physiological arousal is particularly important for long-term health, and thus, it is imperative that evidence is provided to demonstrate the effectiveness of stress-reducing mobile health apps in this context. OBJECTIVE:The purpose of this research was to investigate the physiological and psychological effects of using a smartphone app for HRVB training following a stressful experience. The efficacy of the gamified Breather component of the Happify mobile health app was examined in an experimental setting. METHODS:In this study, participants (N=140) underwent a laboratory stressor and were randomly assigned to recover in one of three ways: with no phone present, with a phone present, with the HRBV game. Those in the no phone condition had no access to their phone. Those in the phone present condition had their phone but did not use it. Those in the HRVB game condition used the serious game Breather on the Happify app. Stress recovery was assessed via repeated measures of salivary alpha amylase, cortisol, and self-reported acute stress (on a 1-100 scale). RESULTS:Participants in the HRVB game condition had significantly lower levels of salivary alpha amylase during recovery than participants in the other conditions (F2,133=3.78, P=.03). There were no significant differences among the conditions during recovery for salivary cortisol levels or self-reported stress. CONCLUSIONS:These results show that engaging in a brief HRVB training session on a smartphone reduces levels of salivary alpha amylase following a stressful experience, providing preliminary evidence for the effectiveness of Breather in improving physiological stress recovery. Given the known ties between stress recovery and future well-being, this study provides a possible mechanism by which gamified biofeedback apps may lead to better health

A Critique of Automated Approaches to Code Facial Expressions: What Do Researchers Need to Know?

Facial expression recognition software is becoming more commonly used by affective scientists to measure facial expressions. Although the use of this software has exciting implications, there are persistent and concerning issues regarding the validity and reliability of these programs. In this paper, we highlight three of these issues: biases of the programs against certain skin colors and genders; the common inability of these programs to capture facial expressions made in non-idealized conditions (e.g., “in the wild”); and programs being forced to adopt the underlying assumptions of the specific theory of emotion on which each software is based. We then discuss three directions for the future of affective science in the area of automated facial coding. First, researchers need to be cognizant of exactly how and on which data sets the machine learning algorithms underlying these programs are being trained. In addition, there are several ethical considerations, such as privacy and data storage, surrounding the use of facial expression recognition programs. Finally, researchers should consider collecting additional emotion data, such as body language, and combine these data with facial expression data in order to achieve a more comprehensive picture of complex human emotions. Facial expression recognition programs are an excellent method of collecting facial expression data, but affective scientists should ensure that they recognize the limitations and ethical implications of these programs

Bismuth incorporation and the role of ordering in GaAsBi/GaAs structures

The structure and composition of single GaAsBi/GaAs epilayers grown by molecular beam epitaxy were investigated by optical and transmission electron microscopy techniques. Firstly, the GaAsBi layers exhibit two distinct regions and a varying Bi composition profile in the growth direction. In the lower (25 nm) region, the Bi content decays exponentially from an initial maximum value, while the upper region comprises an almost constant Bi content until the end of the layer. Secondly, despite the relatively low Bi content, CuPtB-type ordering was observed both in electron diffraction patterns and in fast Fourier transform reconstructions from high-resolution transmission electron microscopy images. The estimation of the long-range ordering parameter and the development of ordering maps by using geometrical phase algorithms indicate a direct connection between the solubility of Bi and the amount of ordering. The occurrence of both phase separation and atomic ordering has a significant effect on the optical properties of these layers

Simulated Performance of 3-DTI Gamma-Ray Telescope Concepts

We present Monte Carlo simulations of two astronomical gamma-ray telescope concepts based on the ThreeDimensional Track Imager (3- DTI) detector. The 3-DTI consists of a time projection chamber with two-dimensional, crossedstrip micro-well detector readout. The full three- dimensional reconstruction of charged-particle tracks in the gas volume is obtained from transient digitizers, which record the time signature of the charge collected in the wells of each strip. Such detectors hold great promise for advanced Compton telescope (ACT) and advanced pair telescope (APT) concepts due to the very precise measurement of charged particle momenta that is possible (Compton recoil electrons and electron-positron pairs, respectively). We have investigated the performance of baseline ACT and APT designs based on the 3-DTI detector using simulation tools based on GEANT3 and GEANT4, respectively. We present the expected imaging, spectroscopy, polarimetry, and background performance of each design

Medium-Energy Gamma-Ray Astrophysics with the 3-DTI Gamma-Ray Telescope

Gamma-ray observations in the medium energy range (0.50-50.0 MeV) are central to unfolding many outstanding questions in astrophysics. The challenges of medium-energy gamma-ray observations, however, are the low photon statistics and large backgrounds. We review these questions, address the telescope technology requirements, and describe our development of the 3-Dimensional Track Imaging (3-DTI) Compton telescope and its performance for a new mediumenergy gamma-ray mission. The 3-DTI is a large-volume time projection chamber (TPC) with a 2-dimensional gas micro-well detector (MWD) readout

Evaluation of signal processing techniques for the analysis of large civil structures.

Several new methods of determining change in the data signature of a large Cable-Stayed bridge are examined and compared. Two sets of data, one taken in September 1995, and the second in June 2000 are studied. Structural changes are investigated using several techniques; (1) Modal behavior in the .3 to 3 Hz range is investisated using Transmissibility FRFs and the Random Decrement Method, (2) Quasi Periodic behavior in the 3 to 30 Hz frequency range is observed in several tests. Potential causes and characteristics of this behavior are investigated. (3) Some methods of non-linear analysis are applied to the bridge data and changes in behavior are evaluated. Capability and concerns with each method are addressed in conjunction with physical ambient excitation data and its signal properties

Affect Variability and Predictability: Using Recurrence Quantification Analysis to Better Understand How the Dynamics of Affect Relate to Health

Changes in affect over time have been associated with health outcomes. However, previously utilized measurement methods focus on variability of affect (e.g., standard deviation, root mean squared successive difference) and ignore the more complex temporal patterns of affect over time. These patterns may be an important feature in understanding how the dynamics of affect relate to health. Recurrence quantification analysis (RQA) may help alleviate this problem by assessing temporal characteristics unassessed by past methods. RQA metrics, such as determinism and recurrence, can provide a measure of the predictability of affect over time, indexing how often patterns within affective experiences repeat. In Study 1, we first contrasted RQA metrics with commonly used measures of variability to demonstrate that RQA can further differentiate among patterns of affect. In Study 2, we analyzed the associations between these new metrics and health, namely, depressive and somatic symptoms. We found that RQA metrics predicted health above and beyond mean levels and variability of affect over time. The most desirable health outcomes were observed in people who had high mean positive affect, low mean negative affect, low affect variability, and high affect predictability. These studies are the first to demonstrate the utility of RQA for determining how temporal patterns in affective experiences are important for health outcomes

When is Affect Variability Bad for Health? The Association between Affect Variability and Immune Response to the Influenza Vaccination

Objectives—This study addresses methodological and theoretical questions about the association between affect and physical health. Specifically, we examine the role of affect variability and its interaction with mean levels of affect to predict antibody (Ab) levels in response to an influenza vaccination. Methods—Participants (N = 83) received the vaccination and completed daily diary measures of affect four times a day for 13 days. At one and four months post-vaccination, blood was collected from the participants to assess Ab levels. Results—Findings indicate that affect variability and its interaction with mean levels of affect predict an individual’s immune response. Those high in mean positive affect (PA) who had more PA variability were more likely to have a lower Ab response in comparison to those who had high mean PA and less PA variability. Although it did not interact with mean negative affect (NA), NA variability on its own was associated with Ab response, whereby those with less NA variability mounted a more robust immune response. Conclusion—Affect variability is related to immune response to an influenza vaccination and, in some cases, interacts with mean levels of affect. These oscillations in affective experiences are critical to consider in order to unpack the intricacies of how affect influences health. These findings suggest that future researchers should consider the important role of affect variability on physical health-relevant outcomes as well as examine the moderating effect of mean affect levels