Generalizable turbulent flow forecasting for adaptive optics control.

In this work, we characterize the capability of artificial neural network predictive models for generalizable turbulence forecasting, particularly for use in predictive adaptive optics (AO) applications. Predictive AO control, which utilizes future state predictions of an optical wavefront propagated through a turbulent medium to drive correction, is a promising technology for optical propagation in high-disturbance and low-signal environments. The dynamics describing the evolution of turbulent flow can vary greatly. Accordingly, a generalizable approach to turbulence forecasting has key benefits in allowing for prediction across a range of conditions, thus enabling continuous predictive AO operation in dynamic environments and having reduced sensitivity to changes in conditions. We present a model for generalizable turbulence forecasting, which demonstrated consistent high performance over a range of compressible flow conditions outside those included in the training sample, with only a minimal increase in prediction error compared with a hypothetical baseline model, which assumes perfect a priori characterization. These results demonstrate a clear ability to extract useful dynamics from a limited domain of turbulent conditions and apply these appropriately for forecasting, which could inform future design of predictive AO systems.

Dynamic mode decomposition based predictive model performance on supersonic and transonic aero-optical wavefront measurements.

Air density variations around an airborne directed energy system distort a beam's wavefront, resulting in degraded performance after propagation into the far field. Adaptive optics (AO) can be used to correct for these rapidly evolving aero-optical aberrations; however, in some conditions, the inherent latency between measurement and correction in state-of-the-art AO systems results in significantly reduced performance. Predictive AO control methods utilize future state predictions to compensate for rapidly evolving distortions and are promising techniques for mitigating this limitation. This paper demonstrates an application of the dynamic mode decomposition (DMD) method on turbulent boundary layer wavefront data from supersonic and transonic wind tunnel flow from the Air Force Research Laboratory's Aero-Effects Laboratory. DMD is a lightweight algorithm used to isolate spatiotemporal patterns in a dataset into physically meaningful modes with associated dynamics, which were used to predict future states from a given wavefront. This method showed notable improvements in simulated wavefront correction, providing a reduction of residual wavefront distortion, measured as root mean square over the aperture, by up to 25.4% over a simulated latency model, which could accordingly result in higher laser system performance.

Antifungal Packaging Film to Maintain Quality and Control Postharvest Diseases in Strawberries.

Strawberries are a highly perishable crop with postharvest losses than reach up to 40%. Cost-effective and sustainable technologies in the form of active packing films can provide a solution. Antimicrobial packaging films were produced from pullulan polymer and Solid Lipid Nanoparticles (SLN) containing 1% w/w cinnamaldehyde. Strawberries were stored at 3 °C for 10 days and 12 °C for 6 days. Microbial and physical quality parameters were evaluated during storage. A reduction of approximately 2 Log CFU/g in yeast and mold population was observed for treated strawberries stored at 3 °C as compared to the control (p < 0.05). Yeast and molds counts were significantly lower on day 2 and 4 at 12 °C for treated samples. Strawberries packaged with the active films demonstrated lower respiration rates and the retention of bright red color at both storage temperatures. Active pullulan films were effective in maintaining the desired strawberry quality and reducing fungal decay during refrigerated storage.

Evaluation of heating effects on the morphology and membrane structure of Escherichia coli using electron paramagnetic resonance spectroscopy.

Bacterial cell characteristics, such as size, morphology, and membrane integrity, are affected by environmental conditions. Thermal treatment results in related structural changes, extent of which is determined by the microorganism's survival skills and inactivation kinetics. The objective of this study was to characterize changes in cell structure of Escherichia coli during heating using the combined analysis of dynamic light scattering (DLS), electron paramagnetic resonance (EPR) spectroscopy, and transmission electron microscopy (TEM) techniques. The size of E. coli cells increased from 2.3 µm to 3.0 µm with heating up to 50 °C followed by a shrinkage with further heating up to 70 °C. The morphological changes were verified using transmission electron microscopy. Related changes in membrane integrity was quantified via the mobility of 16-doxylstearic acid (16-DSA) spin probe using EPR spectroscopy. Two order parameters S1 and S2 defined on x- and y-axes, respectively, decreased with increasing temperature indicating loss of membrane integrity. The combined techniques as in this study can be used to further understand factors that play role in survival behavior of microorganisms.

Effects of different moisture and temperature levels on Salmonella survival in poultry fat.

Fat products have been historically thought to have too low water activity to harbor pathogens. However, it has been recently reported that high moisture levels in fats may lead to Salmonella presence and growth. Limited research on strategies to eliminate pathogens in these environments is available, and the mechanisms contributing to microbial presence and growth are not yet well understood. The purpose of this research was to evaluate the effects of moisture levels and storage temperatures on the growth and survival of Salmonella in poultry fat. Samples were stored for 7 d at 48°C or 76°C and remaining Salmonella was evaluated. When poultry fat was challenged with a wet high inoculum, more than a 4 log CFU/mL difference in Salmonella population was observed with 1% and 3% moisture levels at 48°C after 5 d (P < 0.05). No differences between moisture levels (P > 0.05) were observed when samples were tested with a wet low inoculum. Counts below detectable limits were observed after 24 h in samples challenged at 76°C, regardless of inoculum level. When poultry fat was stored at 48°C and inoculated with low levels of Salmonella spp., bacterial growth was influenced only by time (P < 0.05) and not affected (P > 0.05) by moisture level. However, when poultry fat was stored at 48°C and inoculated with high levels of Salmonella spp., bacterial decrease was easier (P < 0.05) in samples containing greater moisture. This research suggests that residual moisture in containers during transportation of poultry fat largely does not affect Salmonella spp. growth.

Stochastic mirage phenomenon in a random medium.

In the framework of geometric optics, we consider the problem of characterizing the ray trajectory in a random medium with a mean refractive index gradient. Such a gradient results in the mirage phenomenon where an object's observed location is displaced from its actual location. We derive formulas for the mean ray path in both the situation of isotropic stochastic fluctuations and an important anisotropic case. For the isotropic model, the mean squared displacement is also given by a simple formula. Our results could be useful for applications involving the propagation of electromagnetic waves through the atmosphere, where larger-scale mean gradients and smaller-scale stochastic fluctuations are both present.

Stratonovich-to-Itô transition in noisy systems with multiplicative feedback.

Intrinsically noisy mechanisms drive most physical, biological and economic phenomena. Frequently, the system's state influences the driving noise intensity (multiplicative feedback). These phenomena are often modelled using stochastic differential equations, which can be interpreted according to various conventions (for example, Itô calculus and Stratonovich calculus), leading to qualitatively different solutions. Thus, a stochastic differential equation-convention pair must be determined from the available experimental data before being able to predict the system's behaviour under new conditions. Here we experimentally demonstrate that the convention for a given system may vary with the operational conditions: we show that a noisy electric circuit shifts from obeying Stratonovich calculus to obeying Itô calculus. We track such a transition to the underlying dynamics of the system and, in particular, to the ratio between the driving noise correlation time and the feedback delay time. We discuss possible implications of our conclusions, supported by numerics, for biology and economics.