A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.
Compliance monitoring is used to evaluate and confirm the adequacy of assumptions, data, parameterizations, and analyses used to demonstrate performance of a given geologic repository site. Repository performance demonstration is accomplished via a performance assessment methodology. Performance assessment provides a reasonable expectation of long-term repository performance with quantified uncertainty. In this paper, the linkage between compliance monitoring and performance assessment is explored. The U.S. Waste Isolation Pilot Plant and the suspended Yucca Mountain site are used to illustrate the discussion.
Experiments have shown that pitting corrosion can develop in aluminum surfaces at potentials > − 0.5 V relative to the standard hydrogen electrode (SHE). Until recently, the onset of pitting corrosion in aluminum has not been rigorously explored at an atomistic scale because of the difficulty of incorporating a voltage into density functional theory (DFT) calculations. We introduce the Quantum Continuum Approximation (QCA) which self-consistently couples explicit DFT calculations of the metal-insulator and insulator-solution interfaces to continuum Poisson-Boltzmann electrostatic distributions describing the bulk of the insulating region. By decreasing the number of atoms necessary to explicitly simulate with DFT by an order of magnitude, QCA makes the first-principles prediction of the voltage of realistic electrochemical interfaces feasible. After developing this technique, we apply QCA to predict the formation energy of chloride atoms inserting into oxygen vacancies in Al(111)/α-Al2O3 (0001) interfaces as a function of applied voltage. We predict that chloride insertion is only favorable in systems with a grain boundary in the Al2O3 for voltages > − 0.2 V (SHE). Our results roughly agree with the experimentally demonstrated onset of corrosion, demonstrating QCA’s utility in modeling realistic electrochemical systems at reasonable computational cost.
White, Devin A.; Bradshaw, Corey J.A.; Crabtree, Stefani A.; Ulm, Sean; Bird, Michael I.; Williams, Alan N.; Saltre, Frederik
Reconstructing the patterns of Homo sapiens expansion out of Africa and across the globe has been advanced using demographic and travel-cost models. However, modelled routes are ipso facto influenced by migration rates, and vice versa. We combined movement ‘superhighways’ with a demographic cellular automaton to predict one of the world's earliest peopling events — Sahul between 75000 and 50000 years ago. Novel outcomes from the superhighways-weighted model include (i) an approximate doubling of the predicted time to continental saturation (∼10,000 years) compared to that based on the directionally unsupervised model (∼5000 years), suggesting that rates of migration need to account for topographical constraints in addition to rate of saturation; (ii) a previously undetected movement corridor south through the centre of Sahul early in the expansion wave based on the scenarios assuming two dominant entry points into Sahul; and (iii) a better fit to the spatially de-biased, Signor-Lipps-corrected layer of initial arrival inferred from dated archaeological material. Our combined model infrastructure provides a data-driven means to examine how people initially moved through, settled, and abandoned different regions of the globe.
Increase in the number and frequency of widespread outages in recent years has been directly linked to drastic climate change necessitating better preparedness for outage mitigation. Severe weather conditions are experienced more frequently and on larger scales, challenging system operation and recovery time after an outage. The impact is more evident and concerning than before, considering the increased dependency on electricity in all aspects of our lives.
In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.
Dornheim, Tobias; Moldabekov, Zhandos A.; Ramakrishna, Kushal; Tolias, Panagiotis; Baczewski, Andrew D.; Kraus, Dominik; Preston, Thomas R.; Chapman, David A.; Bohme, Maximilian P.; Doppner, Tilo; Graziani, Frank; Bonitz, Michael; Cangi, Attila; Vorberger, Jan
Matter at extreme temperatures and pressures - commonly known as warm dense matter (WDM) - is ubiquitous throughout our Universe and occurs in astrophysical objects such as giant planet interiors and brown dwarfs. Moreover, WDM is very important for technological applications such as inertial confinement fusion and is realized in the laboratory using different techniques. A particularly important property for the understanding of WDM is given by its electronic density response to an external perturbation. Such response properties are probed in x-ray Thomson scattering (XRTS) experiments and are central for the theoretical description of WDM. In this work, we give an overview of a number of recent developments in this field. To this end, we summarize the relevant theoretical background, covering the regime of linear response theory and nonlinear effects, the fully dynamic response and its static, time-independent limit, and the connection between density response properties and imaginary-time correlation functions (ITCF). In addition, we introduce the most important numerical simulation techniques, including path-integral Monte Carlo simulations and different thermal density functional theory (DFT) approaches. From a practical perspective, we present a variety of simulation results for different density response properties, covering the archetypal model of the uniform electron gas and realistic WDM systems such as hydrogen. Moreover, we show how the concept of ITCFs can be used to infer the temperature from XRTS measurements of arbitrary complex systems without the need for any models or approximations. Finally, we outline a strategy for future developments based on the close interplay between simulations and experiments.
Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using a few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly used optimization techniques for variational algorithms.
The outbreak of SARS-CoV-2 has emphasized the need for a deeper understanding of infectivity, spread, and treatment of airborne viruses. Bacteriophages (phages) serve as ideal surrogates for respiratory pathogenic viruses thanks to their high tractability and the structural similarities tailless phages bear to viral pathogens. However, the aerosolization of enveloped SARS-CoV-2 surrogate phi6 usually results in a .3-log10 reduction in viability, limiting its usefulness as a surrogate for aerosolized coronavirus in “real world” contexts, such as a sneeze or cough. Recent work has shown that saliva or artificial saliva greatly improves the stability of viruses in aerosols and microdroplets relative to standard dilution/storage buffers like suspension medium (SM) buffer. These findings led us to investigate whether we could formulate media that preserves the viability of phi6 and other phages in artificially derived aerosols. Results indicate that SM buffer supplemented with bovine serum albumin (BSA) significantly improves the recovery of airborne phi6, MS2, and 80a and outperforms commercially formulated artificial saliva. Particle sizing and acoustic particle trapping data indicate that BSA supplementation dose-dependently improves viral survivability by reducing the extent of particle evaporation. These data suggest that our viral preservation medium may facilitate a lower-cost alternative to artificial saliva for future applied aerobiology studies. IMPORTANCE We have identified common and inexpensive lab reagents that confer increased aerosol survivability on phi6 and other phages. Our results suggest that soluble protein is a key protective component in nebulizing medium. Protein supplementation likely reduces exposure of the phage to the air-water interface by reducing the extent of particle evaporation. These findings will be useful for applications in which researchers wish to improve the survivability of these (and likely other) aerosolized viruses to better approximate highly transmissible airborne viruses like SARS-CoV-2.
