DCS Corporation (United States)

<div class="section abstract"><div class="htmlview paragraph">As part of technology maturation efforts, the COAT Lab evaluated the impact of external audio on driving performance in simulated under amor environments. To do so, we conducted an Engineering Evaluation Test (EET) wherein participants were asked to drive a simulated military vehicle through a Slalom course (primary task) while monitoring for aerial threats (secondary task). Using a combination of objective and subjective metrics, this evaluation quantified participants’ ability to maneuver and detect threats while using external audio as an enabling technology. Evaluation results indicated external audio positively benefited driving performance and situation awareness. However, evaluation results also indicated that external audio was not sufficient in and of itself for detecting time-sensitive aerial threats. Together, these results suggest a development path forward in which external audio is combined with visual information to enhance crew situation awareness under armor.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Evaluation of integrated human-machine systems depends on having accurate human performance models. However, such models often provide only instantaneous snapshots of cognitive state and fail to account for ongoing dynamics. We argue that generative AI solutions can be used to alleviate this problem. Generative AI tools have been successful when applied to problems that have repeatable structure captured by a low-dimensional lexicon and associated with large amounts of training data. These properties apply to human performance modeling as well. Here, we introduce our Generative Cognitive Modeling Tool, a prototype human performance model developed using strategies from the generative AI community. We demonstrate the utility of our approach using simulated driving data. Our results show that cognitive states associated with driving errors are not randomized events but rather the outcome of continuous dynamics and are predictable up to 25 secs prior to the error event. We also find that the voluntary utilization of autonomous driving aids can be predicted, in part, by the disruption of ongoing dynamics. Overall, this underscores the importance of ongoing dynamics for human performance modeling and establish that generative AI approaches can provide one way to account for such factors.</div></div>

Cloud computing has significantly transformed the way organizations utilize computing resources by offering scalability, flexibility, and cost-effectiveness. Energy consumption has become a major research concern with the growing demand for cloud services these days. Adaptive energy optimization techniques are very much essential to minimize energy usage across cloud servers. The main objective of this research is to conserve energy across heterogeneous cloud servers in datacentres. The proposed adaptive energy optimization framework uses container orchestration utilizing Kubernetes framework by sharing the host system kernel and reducing resource overhead to maximize energy efficiency. The work also incorporates smart algorithms, including Ant Colony Optimization (ACO), Swarm Intelligence, and Dynamic Voltage and Frequency Scaling (DVFS) to enable the real-time, energy cognizant distribution of workloads according to the current demand, avoiding unnecessary consumption of resources. Kubernetes container orchestration to optimize energy usage, facilitating effective scaling and thus simplifying the management of applications. Two novel components, the Adaptive Resource Adjustment Algorithm (ARAA) and the Automated Resource-Aware Kubernetes Lifecycle Management (ARKLM), work together to dynamically adjust resource allocation and deactivate idle containers within the Kubernetes environment. Experimental results confirm the framework’s effectiveness, demonstrating that the DVFS algorithm achieved a low 0.94% average CPU usage, while the ACO algorithm delivered the fastest execution time. This dual-focus on energy efficiency and speed makes the framework a scalable and sustainable solution for modern cloud operations, aligning with UNSustainable Development Goals (7, 9, 12, 13, 17) by enabling energy-efficient, low-carbon, and collaborative cloud operations through intelligent automation.

<title>ABSTRACT</title> <p>In this paper, we discuss a neuroimaging experiment that employed a mission-based scenario (MBS) design, a new approach for designing experiments in simulated environments for human subjects [<xref rid="R1" ref-type="bibr">1</xref>]. This approach aims to enhance the realism of the Soldier-task-environment interaction by eliminating many of the tightly-scripted elements of a typical laboratory experiment; however, the absence of these elements introduces several challenges for both the experimental design and statistical analysis of the experimental data. Here, we describe an MBS experiment using a simulated, closed-hatch crewstation environment. For each experimental session, two Soldiers participated as a Commander-Driver team to perform six simulated low-threat security patrol missions. We discuss challenges faced while designing and implementing the experiment before addressing analysis approaches appropriate for this type of experimentation. We conclude by highlighting three example transition pathways from MBS experiments to enhanced Army capabilities using a class of neurotechnologies called Brain-Computer Interaction Technologies.</p>

Future operating environments will require humans to team with increasingly sophisticated and evolving technologies. These intelligent technologies will be required to keep pace with adversarial challenges. Operators and leaders in these domains will require increased technological aptitudes and skills to leverage their expertise and creativity to adapt technologies to evolving mission sets and avoid being overmatched by the adversary. This combination of aptitudes and skills is termed technological fluency (TF): the ability of operators to use and rapidly adapt new and intelligent technologies without formal training. With this new and evolving area of research, models of TF will be developed, as well as assessment approaches necessary to quantify TF in individuals for selection and assignment, and finally training protocols and in-field aids to enhance TF. These will modernize personnel selection and training methodologies, which will lead to a reduction in training costs, faster and more extensive technology fielding, and greater operational flexibility and effectiveness. To reach these goals, it is crucial to determine what necessary knowledge, skills, abilities, and other behaviors are required for individuals to become technologically fluent. This report outlines five preliminary categories relating to enhancing and maximizing TF within human technology interaction domains.

Cognitive pupillometry in the “wild” (outside laboratory control) faces the difficulty of low signal-to-noise due to relatively larger scale pupil size fluctuations induced by luminance variation. A proposed method to improve inference of cognitive processes in natural environments is to amplify the cognitive signal by methodologically controlling for, or statistically removing, the influence of the pupillary light reflex (PLR). Here we suggest an alternative method by purposefully inducing PLR’s and measuring variation in the shape of the PLR waveform to infer cognitive states. To explore this possibility, we induced the PLR in two different experiments while subjects performed a wide range of cognitive tasks. In one experiment participants navigated a stressful naturalistic virtual environment while searching for targets. During the experiment the screen flashed white to mimic a flashbang to induce a stress response and PLR. In the second experiment participants viewed simple flashing stimuli to induce the PLR before and after completing a battery of cognitive tests. First, our results show that aspects of the PLR vary over time and when concomitant with experimental manipulations of cognitive processes. Second, we demonstrate statistically significant and interesting relationships between aspects of the PLR (I.e., latency of the PLR, velocity and magnitude of the constriction) and behavioral performance. Together these results suggest that the PLR variation has the potential to be used reliably as a measure of cognitive processing and may be especially useful in naturalistic experimentation.

Numerical simulations of Rayleigh–Bénard (RB) convective flow are performed to determine the optical and mechanical turbulence properties and resulting index of refraction field in order to study the propagation characteristics of a laser beam carrying orbital angular momentum. The simulations are carried out in air using a computational fluid dynamics package which solves the Navier-Stokes equations. The computational domain is 0.1 m × 0.5 m × 0.5 m with a temperature difference of 1°K maintained between the top and bottom plate. The simulated temperature field is then converted into refractive index values. To study light propagation through the turbulent volume the split-step screen method is employed with screens at 0.025 m distance from each other along the propagation path which ranges from 0.5 m to 2.5 m. The structure of the phase screens, their spatio-temporal correlation with respect to up/down drafts, and phase changes across regions of interest are discussed. It was observed as the propagation distance increased, the orbital angular momentum carrying Laguerre–Gaussian beams wandered and the beam phase singularity distorted thus impacting the light intensity distribution. In addition, it was found that higher topological charge and order of Laguerre polynomial result in increased distortion of the beam vortex.

Two long-standing challenges for geophysical inverse problems are nonuniqueness (especially in the multiparameter setting) and the slow convergence of gradient-based optimization methods. We introduce shape-based inversion algorithms to address these challenges. The shape-based algorithms determine, based on the available geophysical data, the underlying subsurface structures (e.g., stratigraphic layers) and the spatially varying unknown physical parameters (e.g., velocity and density). Inverting simultaneously for geologic structures and physical parameters eliminates degeneracies between parameters and also provides a multiscale framework for decomposing the inverse problem. First, we provide a rigorous mathematical formulation of shape-based algorithms through diffuse interface approximations. Then, we demonstrate the applicability of these algorithms to a range of synthetic model problems in traveltime tomography, full-waveform inversion, gradiometry, and history matching of single-phase flow. These examples highlight the ability of shape-based inversion to mitigate nonuniqueness and improve the rate of convergence to a physically realistic solution.

We study nonequilibrium roughening during compressive plastic flow of initially flat ${\mathrm{Cu}}_{50}{\mathrm{Zr}}_{50}$ metallic glass using large-scale molecular dynamics simulations. Roughness emerges at atomically flat interfaces beyond the yield point of the glass. A self-affine rough topography is imprinted at yield and is reinforced during subsequent deformation. The imprinted topographies have Hurst exponents that decrease with increasing strain rate and temperature. After yield, the root-mean-square roughness amplitude grows as the square root of the applied strain with a prefactor that also drops with increasing strain rate and temperature. Our calculations reveal the emergence of spatial power-law correlations from homogeneous samples during plastic flow with exponents that depend on the rate of deformation and the temperature. The results have implications for interpreting and engineering roughness profiles.