Evaluation of health effects of air pollution in the Chestnut Ridge area : preliminary analysis

This project involves several tasks designed to take advantage of (1) a very extensive air pollution monitoring system that is operating ..n the Chestnut Ridge.region of Western Pennsylvania and (2) -the very well developed analytic dispersion models that have been previously fine-tuned to this particular area.. The major task in this project is to establish, through several distinct epidemiolopic approaches, health data to be used to test hypotheses about relations of air pollution exposures to morbidity and mortality rates in this region. Because the air quality monitoring network involves no expense to this contract this project affords a very cost-effective 6pportunity-for state-of-the-art techniques to be used in both costly areas of air pollution and health -effects data col1 ection. . The closely spaced network of monitors, plus the dispersion modeling capabilities,.allow for the investigation- of health impacts of. various pollutant gradients in neighboring geographic areas, thus minimizing -the confounding effects of social, ethnic, and economic factors. The pollutants that are monitored in this network include total gaseous sulfur, sulfates, total suspended particulates, NOx, NO, ozone/oxidants, and coefficient of haze. In addition to enabling the simulation of exposure profiles between monitors, the air quality2 modeling, along with extensive source and background inventories, will allow for upgrading the quality of the monitored data. as well as simulating the exposure levels for about 25 additional air pollutants. Another important goal of this project is to collect and test the many available models for associating.health effects with air pollution, to determine their predictive validity and their usefulness in the choice and siting of future energy facilities

Dynamic Energy Management

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar

NBS monograph

From Introduction Statement of Objectives: "The purpose of this publication is to provide assistance to the practicing engineer who is faced with the problem of making dynamic measurements of rapidly changing pressures.

Constructing seasonally adjusted data with time-varying confidence intervals

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time series [BSM] model. The usual premise of the BSM is that the variance of each of the components is constant. In this paper we address the possibility that the variance of the trend component in a macro-economic time series in some way depends on the business cycle. One reason for doing so is that one can expect that there is more uncertainty in recession periods. We extend the BSM by allowing for a business-cycle dependent variance in the level equation. Next we show how this affects the confidence intervals of seasonally adjusted data. We apply our extended BSM to monthly US unemployment and we show that the estimated confidence intervals for seasonally adjusted unemployment change with past changes in the oil price

Adiabatic Pair Creation

We give here the proof that pair creation in a time dependent potentials is possible. It happens with probability one if the potential changes adiabatically in time and becomes overcritical, that is when an eigenvalue enters the upper spectral continuum. The potential may be assumed to be zero at large negative and positive times. The rigorous treatment of this effect has been lacking since the pioneering work of Beck, Steinwedel and Suessmann in 1963 and Gershtein and Zeldovich in 1970.Comment: 53 pages, 1 figure. Editorial changes on page 22 f

A combined dynamic economic emission dispatch and time of use demand response mathematical modelling framework

In this paper, we integrate a Demand Response (DR) program into the multi-objective dynamic economic emission dispatch (DEED) optimization problem. The resulting optimization problem is termed DR-DEED. The DR program is a time based program known as the Time of Use DR program. The DR program has been developed using the customers’ Price Elasticity Matrices, which models the customer behavior under different conditions. An interactive control strategy between utility and consumers is proposed for the combined DR-DEED model, which determines the optimal power to be generated by minimizing fuel, emissions, and DR costs and also the optimal price. The customer in light of the utility’s optimal price minimizes its electricity cost and optimally schedules power consumption. Obtained results indicate that DR programs are mutually beneficial to utility and consumers alike and can bring about desired demand reduction in the power system.http://scitation.aip.org/content/aip/journal/jrsehb201

Relativistic nuclear recoil corrections to the energy levels of hydrogen-like and high ZZ lithium like atoms in all orders in αZ\alpha Z

The relativistic nuclear recoil corrections to the energy levels of low-laying states of hydrogen-like and high $Z$ lithium-like atoms in all orders in $\alpha Z$ are calculated. The calculations are carried out using the B-spline method for the Dirac equation. For low $Z$ the results of the calculation are in good agreement with the $\alpha Z$ -expansion results. It is found that the nuclear recoil contribution, additional to the Salpeter's one, to the Lamb shift ($n=2$) of hydrogen is $-1.32(6)\,kHz$. The total nuclear recoil correction to the energy of the $(1s)^{2}2p_{\frac{1}{2}}-(1s)^{2}2s$ transition in lithium-like uranium constitutes $-0.07\,eV$ and is largely made up of QED contributions.Comment: 19 pages, latex, accepted for publication in Phys. Rev.