VALIDATION OF IMPROVED HARDWARE AND SOFTWARE FOR EXPIRED GAS ANALYSIS INDIRECT CALORIMETRY

The purpose of this investigation was to validate a new system of breath-by-breath expired gas analysis to both an artificial working model of lung ventilation and gas exchange as well as to the Douglas bag technique. In addition, comparisons will be made between expired fractions, ventilation, and computations of VO2, VCO2, and RER between the new system and a commercial mixing chamber system (ParvoMedics) for repeated measurements at rest, steady state and non-steady state cycle ergometry exercise. Post acquisition processing involved custom developed software (LabVIEW), where time to gas equilibration within the mixing bag was determined, as well as differences in equilibrated gas fractions. All testing procedures were repeated 5 times for parametric statistical analyses. Gas concentration (%) results for the compliant 2 L mixing bag was the only method to yield data not significantly different between alveolar and measured. Alveolar % oxygen was significantly lower than mixing bag, mixing chamber, and ParvoMedics. The most responsive method was the mixing bag, with significantly lower % gas data for oxygen for breaths 2 to 5 compared to the mixing chamber and ParvoMedics. The ParvoMedics and mixing bag yielded similar results after breath 6, but data were significantly higher than for alveolar air. The slope data for breaths 0 to breaths 2 was significantly (p \u3c 0.05) lower for the ParvoMedics system compared to the mixing bag and mixing chamber. The mean temporal distribution of 1 L ventilation maneuvers from the mixing bag turbine was 0.999 ± 0.142 L, with a range of 0.96 to 1.03 L. The mean ventilation (STPD) from the ParvoMedics (pneumotach) was significantly lower (p = 0.0027) than the mixing bag turbine. For VE (p = 0.097), VO2 (p = 0.786), and VCO2 (p = 0.178) were not significantly different in the main effect for method and the Intensity x Method interaction (VE: p = 0.721, VO2: p = 0.059, VCO2: p = 0.406). As expected, there was a significant difference for the intensity main effect (p \u3c 0.0001). For FEO2 (p \u3c 0.0001) and FECO2 (p \u3c 0.0001) there were significant findings for the main effects of intensity. However, the Intensity x Method interaction showed no significant differences in FEO2 and FECO2. RER was significantly different in the main effect for method (p = 0.024), intensity (p = 0.0006), and Intensity x Method interaction (p = 0.005). The expired oxygen and carbon dioxide had significant main effects and interactions (p \u3c 0.001). All mean differences between alveolar and mouth end tidal gas % values across 6 breaths were significant (p \u3c 0.01). The mean individual computed dead space volumes were 2.5 ± 0.13 L. The results suggested that the new 2 L mixing bag is capable of accurately reproducing specific gas fractions from reference calibration gas. The new 2 L mixing bag allowed expired air to wash out through the bag. This system, in combination with including anatomical dead space (ADS) as a factor in the determinations, gives more accurate measurements and calculations than a traditional mixing chamber. Additionally, the new mixing bag method has unique aspects that are advantageous to the operation and validity of the system. Although the new system is not used in commercial systems of expired gas analysis indirect calorimetry (EGAIC), this system provides enhanced accuracy and validity

Expanding the Role of Victim-Offender Mediation in the Criminal Justice System: Mediating Cases of Involuntary Manslaughter

Involuntary manslaughter is distinguishable from other types of murder by the perpetrator’s lack of intent to kill. This lack of intent suggests that restorative justice programs, specifically victim-offender mediation, may be a better alternative compared to the traditional adversarial criminal justice system because offenders can express their remorse and victims can receive closure through a facilitated dialogue. Limiting the scope of remedies in criminal proceedings to incarceration has led to serious financial and societal ramifications, as well as harmful psychological and emotional repercussions by failing to address the underlying lasting impacts of crime on victims, offenders, loved ones, and the community at large. Therefore, it is imperative the criminal justice system improves how cases of involuntary manslaughter are processed by implementing victim-offender mediation as a more tailored means to achieving justice

Analysis and consideration of performance test by historical empathy stage: In the case of “History newspaper making" in the subject “East Asian history" at Korean A high school

The purpose of this study is to find the requirements for improving historical empathy through the making of historical newspapers conducted by A High School in Korea. As a result, it was possible to derive the necessity of expanding performance tests, ensuring cooperation among students enabling scaffolding, and changing the role of history teachers. As a result of the analysis and discussion of this research, the three requirements for improving the stage of historical empathy are only limited ones derived from the case study of A high school. In the future, by considering history classes in high schools in Japan as well as Korea, it will be a task to further verify the requirements of history classes in order to improve historical empathy

Trainability, Expressivity and Interpretability in Gated Neural ODEs

Understanding how the dynamics in biological and artificial neural networks implement the computations required for a task is a salient open question in machine learning and neuroscience. In particular, computations requiring complex memory storage and retrieval pose a significant challenge for these networks to implement or learn. Recently, a family of models described by neural ordinary differential equations (nODEs) has emerged as powerful dynamical neural network models capable of capturing complex dynamics. Here, we extend nODEs by endowing them with adaptive timescales using gating interactions. We refer to these as gated neural ODEs (gnODEs). Using a task that requires memory of continuous quantities, we demonstrate the inductive bias of the gnODEs to learn (approximate) continuous attractors. We further show how reduced-dimensional gnODEs retain their modeling power while greatly improving interpretability, even allowing explicit visualization of the structure of learned attractors. We introduce a novel measure of expressivity which probes the capacity of a neural network to generate complex trajectories. Using this measure, we explore how the phase-space dimension of the nODEs and the complexity of the function modeling the flow field contribute to expressivity. We see that a more complex function for modeling the flow field allows a lower-dimensional nODE to capture a given target dynamics. Finally, we demonstrate the benefit of gating in nODEs on several real-world tasks