SciberQuest (United States)

Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p &lt; 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

A novel charge-conserving gather-scatter algorithm for particle-in-cell (PIC) simulations on unstructured grids is presented. The algorithm exactly preserves charge conservation, so additional correction terms are not needed. It is inspired by the exterior calculus of differential forms and on the use of Whitney forms as interpolants for both current and charge deposition onto the grid. The charge conservation is verified without the need for any shape factors and independently of time integration scheme used for the determination of current density.

High resolution, fully kinetic, three dimensional (3D) simulation of collisionless plasma turbulence shows the development of turbulence characterized by sheetlike current density structures spanning a range of scales. The nonlinear evolution is initialized with a long wavelength isotropic spectrum of fluctuations having polarizations transverse to an imposed mean magnetic field. We present evidence that these current sheet structures are sites for heating and dissipation, and that stronger currents signify higher dissipation rates. The analyses focus on quantities such as J·E, electron, and proton temperatures, and conditional averages of these quantities based on local electric current density. Evidently, kinetic scale plasma, like magnetohydrodynamics, becomes intermittent due to current sheet formation, leading to the expectation that heating and dissipation in astrophysical and space plasmas may be highly nonuniform. Comparison with previous results from 2D kinetic simulations, as well as high frequency solar wind observational data, are discussed.

Abstract The magnetosheath contains the shocked solar wind and behaves as a natural filter to the solar wind plasma before it reaches the magnetosphere. The redistribution of kinetic energy at the bow shock results in significant thermalization of the solar wind plasma, resulting in a magnetosheath temperature profile which is highly nonhomogeneous and nonisotropic and differs between the dawn and dusk flanks. The present study attempts to study the spatial distribution of magnetosheath ion temperature as a function of upstream solar wind conditions. We pay particular attention to the dawn/dusk asymmetry in which we attempt to quantify using experimental data collected over a 7 year period. We also compare these data to simulated data from both the Block‐Adaptive‐Tree‐Solarwind‐Roe‐Upwind‐Scheme (BATS‐R‐US) MHD code and a kinetic hybrid model. We present evidence that the dawn flank is consistently hotter than the dusk flank for a variety of upstream conditions. Our statistical data also suggest a dependency on solar wind speed such that the level of asymmetry increases with faster speeds. We conclude that the dawn‐favored asymmetry of the magnetosheath seed population is insufficient to explain the dawn asymmetry (30–40%) of cold component ions in the cold, dense plasma sheet, and therefore, other mechanisms are likely required.

Self-adaptive discrete-event simulation is a general paradigm for time integration of discretized partial differential equations and particle models. This novel approach enables local time steps for equations describing time evolution of grid-based elements (fluids, fields) and macro-particles on arbitrary grids while preserving underlying conservation laws. The solution-adaptive integration ensures robustness (stability) and efficiency (speed) of complex nonlinear simulations. Using this technique we achieved a breakthrough in simulations of multiscale plasma systems. A new particle-in-cell simulation tool, HYPERS (Hybrid Particle Event-Resolved Simulator), which solves a set of strongly coupled Maxwell's equations, electron fluid equations and ion particle equations of motion, is presented as the first multi-dimensional application of this technology. We discuss its parallel implementation and demonstrate first results from three-dimensional simulations of compact plasma objects that have been out of reach of conventional codes. Potential applications of the new methodology to other scientific and engineering domains are also discussed.

Using particle-in-cell (PIC) simulations with a Monte Carlo treatment of the Coulomb collision operator, we study the transition in electron dynamics of magnetic reconnection for various levels of collisionality. The weakly collisional cases considered all fall into the so-called Hall or kinetic regime. Nevertheless, collisions may still alter the electron kinetic physics characteristic of collisionless reconnection, where adiabatic trapping energizes the electrons and leads to strong anisotropy of the electron velocity distribution and pressure. This anisotropy can support extended current sheets, associated with secondary island formation and turbulent flux rope interactions in three dimensional systems. The collisional simulations demonstrate how weak collisions may modify or eliminate these electron structures in the kinetic regimes. While the reconnection rate is not sensitive to the collisionality in the range studied, we find that increasing collisionality reduces the level of electron energization near the reconnection site. Finally, the results provide guidance for new laboratory reconnection experiments that will access the weakly collisional regimes.

We discuss basic trade-offs in the application of unconditionally-stable time-updating schemes for the finite-element time-domain solution of Maxwell's equations. Particular attention is given to the case of large Courant-Friedrichs-Lewy number, where the conventional thought holds that unconditionally stable schemes provide marked advantage over conditionally stable ones.

Three-dimensional kinetic simulations of magnetic reconnection for parameter regimes relevant to the magnetopause current layer feature the development of turbulence, driven by the magnetic and velocity shear, and dominated by coherent structures including flux ropes, current sheets, and flow vortices. Here, we propose a new approach for computing the global reconnection rate in the presence of this complexity. The mixing of electrons originating from separate sides of the magnetopause layer is used as a proxy to rapidly identify the magnetic topology and track the evolution of magnetic flux. The details of this method are illustrated for an asymmetric current layer relevant to the subsolar magnetopause and for a flow shear dominated layer relevant to the lower latitude magnetopause. While the three-dimensional reconnection rates show a number of interesting differences relative to the corresponding two-dimensional simulations, the time scale for the energy conversion remains very similar. These results suggest that the mixing of field lines between topologies is more easily influenced by kinetic turbulence than the physics responsible for the energy conversion.

The concept of reconnection is found in many fields of physics with the closest analogue to magnetic reconnection being the reconnection of vortex tubes in hydrodynamics. In plasmas, magnetic reconnection plays an important role in release of energy associated with the magnetic shear into particle energy. Although most studies to date have focused on 2D reconnection, the availability of 3D petascale kinetic simulations have brought the complexity of 3D reconnection to the forefront in collisionless reconnection studies. Here we briefly review the latest advances in 2D and compare and contrast the results with recent 3D studies that address role of anomalous transport in reconnection, effects of turbulence on the rate and structure, among others. Another outcome of recent research is the realization of a deeper link between turbulence and reconnection where the common denominator is the generic formation of electron scale sheets which dissipate the energy through reconnection. Finally, we close the review by listing some of the major outstanding problems in reconnection physics.