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En route airspace congestion, often due to convective weather, causes system-wide delays and disruption in the US National Airspace System (NAS). Present-day methods for managing congestion are mos...

The collaborative routing coordination tools concept demonstration and evaluation prototype is a tool to help the Federal Aviation Administration detect traffic flow problems in advance, generate reroute-based problem resolutions, and evaluate the resolution strategies. The tool does this by modeling four-dimensional aircraft trajectories and using them to predict demand for sector usage. The predictions are affected by various sources of uncertainty in the national airspace system. Therefore, it is useful to understand the contribution of this uncertainty to the tool's predictive error. A methodology was developed and used to assess the impact of predeparture uncertainty on the prototype tool's prediction performance. The tool's trajectory modeling and sector load prediction are described, the methodology used to assess the tool's prediction performance is presented, and the results of the predeparture uncertainty analysis are discussed. The analysis is based on tool predictions made for two air route traffic control centers. Tool runs were made against recordings of actual air traffic data on two good weather and two bad weather days. Analysis of run data showed that predeparture uncertainty is a greater contributor to sector prediction error than en route uncertainty. Because predeparture uncertainty is reflected in departure time estimate, research is recommended to identify methods to improve the accuracy of this estimate.

Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for coherent errors on data qubits under the repetition code. We analytically evaluate the logical error as a function of concatenation level and code distance. We find that coherent errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the error is negligible after two or more concatenation levels or at fewer than $\epsilon^{-(d-1)}$ error correction cycles, where $\epsilon \ll 1$ is the rotation angle error per cycle for a single physical qubit and $d$ is the code distance. These results lend support to the validity of modeling coherent errors using a Pauli channel under some minimum requirements for code distance and/or concatenation., Comment: 10 pages, 3 figures. Minor edits and corrections; added references

Secondary delay may be defined as the additional waiting time experienced when a flight is behind a flight subject to a departure flow control restriction. For example, given a queue for a departure runway, flight “A” may be the first in line, yet is not immediately cleared for take-off, due to a miles-in-trail or minutes-in-trail restriction. Flights behind A, if unable to pass, must likewise accrue this delay. In recent benefits analyses of a departure management prototype for the New York area, a simple queuing model was employed. Analysts suspected that the results of the model were understating total delay, since secondary delay was not represented. In this paper, we demonstrate that (1) flexibility in arranging queue order, facilitated by airport surface design, impacts delay, and (2) secondary delay is a real phenomenon and can be measured in operational data.

The accurate evaluation of diagonal unitary operators is often the most resource-intensive element of quantum algorithms such as real-space quantum simulation and Grover search. Efficient circuits have been demonstrated in some cases but generally require ancilla registers, which can dominate the qubit resources. In this paper, we point out a correspondence between Walsh functions and a basis for diagonal operators that gives a simple way to construct efficient circuits for diagonal unitaries without ancillas. This correspondence reduces the problem of constructing the minimal-depth circuit within a given error tolerance, for an arbitrary diagonal unitary $e^{if(\hat{x})}$ in the $|x>$ basis, to that of finding the minimal-length Walsh-series approximation to the function $f(x)$. We apply this approach to the quantum simulation of the classical Eckart barrier problem of quantum chemistry, demonstrating that high-fidelity quantum simulations can be achieved with few qubits and low depth., 8 pages, 7 figures

Research and development activities to create an automated capability for the generation of operationally-acceptable alternative air traffic routing continue to be actively pursued in industry and government. A key challenge is how to direct the reroute formulation algorithms to generate “good” routes. We describe here an evaluation using subject matter experts, to assess the validity of various numeric attributes of candidate alternate reroutes.

Accurate predictions of reductions in airspace (sector) capacity due to weather are needed to make effective Traffic Flow Management (TFM) decisions. Sector capacity is dependent on the complexity of the traffic flows within the sector, as well as the presence or absence of hazardous weather. It is well-accepted that the sector capacity must be reduced when an area of severe weather overlaps part of the sector. However, no accepted algorithms for calculating the capacity under severe weather impact have been developed. This paper discusses the three proposed methodologies, which translate 2D sector weather coverage, 3D weather avoidance altitude field coverage, and flow-based available sector capacity ratio to sector capacity reduction.

En route airspace congestion, often due to convective weather, causes system-wide delays and disruption in the U.S. National Airspace System (NAS). Present-day methods for managing congestion are mostly manual, based on uncertain forecasts of weather and traffic demand, and often involve rerouting or delaying entire flows of aircraft. A sequential decision-making approach is proposed, in which traffic and weather forecast prediction uncertainty is quantified and explicitly used to develop efficient congestion resolution actions. The method is based on Monte Carlo simulation of traffic and weather outcomes from a specific forecast. Candidate sequential decision strategies are evaluated against the range of outcomes to determine the best course of action. Decisions are made based on a quantitative evaluation of the expected delay cost distribution, and resolution actions are targeted at specific flights, rather than flows. A weather-induced airspace congestion scenario is explored using the simulation, and three different levels of weather forecast uncertainty are postulated. Weather forecast uncertainty was shown to affect when and how aggressively to act to solve the congestion problem. Forecast uncertainty also increases the mean and variance of the congestion resolution cost. The simulation can be used both to learn about solving airspace congestion problems, and to do several types of cost benefit analyses. It is also a prototype of a future, real-time, probabilistic decision support aid for tactical traffic management.

Sector capacity is dependent on the complexity of the traffic flows within the sector, as well as the presence or absence of hazardous weather. It is well-accepted that the sector capacity must be reduced when an area of severe weather overlaps part of the sector. However, no accepted algorithms for calculating the capacity under severe weather impact have been developed. This paper presents an approach to predict sector capacity under severe weather impact for airspace congestion management. The nominal sector capacity is predicted as a function of traffic flow patterns to capture the traffic complexity. Under severe weather impact, pilot deviation behavior models developed by Lincoln Laboratory are applied to identify the weather avoidance altitude field in the sector. Graph theory is applied to decide the available ratio of flow capacity. Given the predicted traffic flow pattern and the available capacity ratio of each flow, the nominal sector capacity is reduced based on the combination of the flows in the predicted traffic flow pattern. Results are presented for an example sector with example weather impact.

We demonstrate theoretically the possibility of producing a pure spin current in graphene by filtering the charge from a spin-polarized electric current. To achieve this effect, which is based on the recently predicted property of specular Andreev reflection in graphene, we propose two possible device structures containing normal-superconductor junctions.

This paper describes a methodology for airspace congestion management that uses genetic algorithms to optimize probabilistic metrics of National Airspace System (NAS) performance and throughput. Stochastic metrics of predicted congestion are generated using a previously developed model and then used by a genetic algorithm to generate preventive resolution options such as ground delays and alternative routes. The stochastic performance metrics attempt to balance throughput and congestion likelihood. Performance using this approach is compared to a heuristic algorithm that develops a non-optimal, flight-specific congestion solution based on a prioritized flight list. This solution can be used directly to resolve congestion, or as a basis for collaboration with NAS customers. Simulation results demonstrate that both algorithms are generally effective at solving a range of presented congestion scenarios of varying complexity. In the test cases examined, the genetic algorithm resolves congestion using a combination of reroutes and ground delays that affect fewer flights than does the heuristic algorithm. The results suggest that the benefits of an optimizing algorithm may be largest for complex congestion scenarios with many degrees of freedom that must be balanced to arrive at a solution.

En route sector congestion exists when the air traffic demand on an en route sector exceeds the sector capacity. A metric of sector capacity is needed that (1) is a good approximation of the amount of traffic that can be effectively handled in the sector, (2) can be predicted at look-ahead times of 30 minutes to several hours, and (3) can include the impact of convective weather on available capacity. This paper presents a novel approach to predicting sector capacity for airspace congestion management. First, a set of primary flow patterns for each sector of interest is identified through cluster analysis. Second, the sector capacity for each pattern is established based on observed system performance transition behavior. Finally, future sector capacity for a given prediction look-ahead time can be predicted through pattern recognition. Quantifying sector capacity as a function of traffic flow pattern also provides a basis for capturing weather impact on sector capacity.

In the U.S. National Airspace System (NAS) a function called traffic flow management (TFM) seeks a balance between resource capacities and the demands placed upon them by air traffic. In general, capacity cannot be manipulated, and it is necessary for demand to be altered to meet a reduced capacity. Typically, demand can be altered in time (via delay, i.e., slowing flights so that the number per unit time is reduced) or space (via rerouting, when specific airspace sector capacity is reduced, e.g., during severe en route weather). This paper discusses the use of probability modeling for assessing airspace capacity, and discusses comparison of three techniques for generating solutions to the problem of demand allocation during reduced airspace capacity caused by severe en route weather.

It is shown that the diagrammatic cluster expansion technique for equilibrium averages of spin operators may be straightforwardly extended to the calculation of time-dependent correlation functions of spin operators. We use this technique to calculate exactly the first two non-vanishing moments of the spin-spin and energy-energy correlation functions of the XY model with arbitrary couplings, in the long-wavelength, infinite temperature limit appropriate for spin diffusion. These moments are then used to estimate the magnetization and spin-spin energy diffusion coefficients of the model using a phenomenological theory of Redfield. Qualitative agreement is obtained with recent experiments measuring diffusion of dipolar energy in calcium fluoride., 28 pages, 15 embedded .eps figures, Elsevier preprint format

We develop a linear response formalism for nuclear spin diffusion in a dipolar coupled solid. The theory applies to the high-temperature, long-wavelength regime studied in the recent experiments of Boutis et al. [Phys. Rev. Lett. 92, 137201 (2004)], which provided direct measurement of interspin energy diffusion in such a system. A systematic expansion of Kubo's formula in the flip-flop term of the Hamiltonian is used to calculate the diffusion coefficients. We show that this approach is equivalent to the method of Lowe and Gade [Phys. Rev. 156, 817 (1967)] and Kaplan [Phys. Rev. B 2, 4578 (1970)], but has several calculational and conceptual advantages. Although the lowest orders in this expansion agree with the experimental results for magnetization diffusion, this is not the case for energy diffusion. Possible reasons for this disparity are suggested., 7 pages, REVTeX4; Published Version

TFM personnel are known as Traffic Management Coordinators (TMCs) or Traffic Management Specialists (TMSs), depending on the facility in which they work. The general term for these personnel is traffic managers. One of their primary responsibilities is to ensure that traffic at national airspace system (NAS) resources (e.g., airspace sectors, airports) does not exceed levels that can be safely managed by controllers. Traffic managers also endeavor to ensure fair and equitable treatment for all NAS users, i.e., operators of commercial, general aviation, military, and other aircraft. Air Traffic Flow Management (TFM) is the process of balancing demand for airspace and airport resources with the capacity of those resources, in order to achieve both safe and efficient traffic throughput. Demand is typically estimated by predicting flight trajectories, and comparing the predictions to capacity metrics for airports and airspace. The effectiveness of TFM decision-making depends on the accuracy of these predictions. This effectiveness can be improved not only by improving prediction accuracy, but by quantifying the uncertainty in those predictions. When the uncertainty is known, decision analysis and risk management techniques can be applied to improve decision-making performance. To support this goal, a novel method has been developed for measuring and simulating uncertainty in traffic demand predictions. This method employs empirical observations of traffic characteristics to develop statistical models of the error distributions in demand predictions, which in turn can be used for Monte-Carlo simulation of specific traffic scenarios. Preliminary statistical results are presented here, as well as a discussion of simulation applications for both analysis and real-time decision-support tasks.

Reciprocal space measurements of spin diffusion in a single crystal of calcium fluoride (CaF$_2$) have been extended to dipolar ordered states. The experimental results for the component of the spin diffusion parallel with the external field are $D_{D}^{||}=29 \pm 3 \times 10^{-12}$ cm$^{2}$/s for the [001] direction and $D_{D}^{||}=33 \pm 4 \times 10^{-12}$ cm$^{2}$/s for the [111] direction. The diffusion rates for dipolar order are significantly faster than those for Zeeman order and are considerably faster than predicted by simple theoretical models. It is suggested that constructive interference in the transport of the two spin state is responsible for this enhancement. As expected the anisotropy in the diffusion rates is observed to be significantly less for dipolar order compared to the Zeeman case., Comment: 4 pages, 2 figures. Resubmitted to PRL - new figure added / discussion expanded

Traffic flow management (TFM) in the U.S. is the process by which the Federal Aviation Administration (FAA), with the participation of airspace users, seeks to balance the capacity of airspace and airport resources with the demand for these resources. This is a difficult process, complicated by the presence of severe weather or unusually high demand. TFM in en-route airspace is concerned with managing airspace demand, specifically the number of flights handled by air traffic control (ATC) sectors; a sector is the volume of airspace managed by an air traffic controller or controller team. Therefore, effective decision-making requires accurate sector demand predictions. While it is commonly accepted that the sector demand predictions used by current and proposed TFM decision support systems contain significant uncertainty, this uncertainty is typically not quantified or taken into account in any meaningful way. The work described here is focused on measuring the uncertainty in sector demand predictions under current operational conditions, and on applying those measurements towards improving the performance and human factors of TFM decision support systems.

Although prior studies have supported the validity of measuring total muscarinic receptor binding in postmortem brain, there has not been a study of postmortem effects on muscarinic receptor subtypes, M1 and M2, defined by high and low affinity for pirenzepine, respectively. We have examined in rat brain the effect of postmortem delay at room temperature, storage at 4 degrees C and -20 degrees C, and multiple freeze/thaw cycles on total muscarinic binding, measured with [3H]quinuclidinylbenzilate ([3H]QNB) and on M1 muscarinic binding, measured with [3H]pirenzepine ([3H]Pir). We found that delay at room temperature up to 4 h, or storage at 4 degrees C for 24 h or at -20 degrees C for 4 weeks, or 3 freeze/thaw cycles had no effect on [3H]QNB or [3H]Pir binding. Exposure of brain to room temperature for 15 h, however, led to an increase in [3H]QNB binding, without change in [3H]Pir. Scatchard analysis showed an increase in binding sites without a change in affinity. We conclude that [3H]QNB and [3H]Pir are valid measures of total and M1 muscarinic binding, respectively, under these circumstances, but that caution must be used in the interpretation of indirect measures of M2 binding.