Enrofloxacin was administered i.v. to five adult mares at a dose of 5 mg/kg. After administration, blood and endometrial biopsy samples were collected at regular intervals for 24 h. The plasma and tissue samples were analyzed for enrofloxacin and the metabolite ciprofloxacin by high-pressure liquid chromatography. In plasma, enrofloxacin had a terminal half-life (t(1/2)), volume of distribution (area method), and systemic clearance of 6.7 +/- 2.9 h, 1.9 +/- 0.4 L/kg, and 3.7 +/- 1.4 mL/kg/min, respectively. Ciprofloxacin had a maximum plasma concentration (Cmax) of 0.28 +/- 0.09 microg/mL. In endometrial tissue, the enrofloxacin Cmax was 1.7 +/- 0.5 microg/g, and the t(1/2) was 7.8 +/- 3.7 h. Ciprofloxacin Cmax in tissues was 0.15 +/- 0.04 microg/g and the t(1/2) was 5.2 +/- 2.0 h. The tissue:plasma enrofloxacin concentration ratios (w/w:w/v) were 0.175 +/- 0.08 and 0.47 +/- 0.06 for Cmax and AUC, respectively. For ciprofloxacin, these values were 0.55 +/- 0.13 and 0.58 +/- 0.31, respectively. We concluded that plasma concentrations achieved after 5 mg/kg i.v. are high enough to meet surrogate markers for antibacterial activity (Cmax:MIC ratio, and AUC:MIC ratio) considered effective for most susceptible gram-negative bacteria. Endometrial tissue concentrations taken from the mares after dosing showed that enrofloxacin and ciprofloxacin both penetrate this tissue adequately after systemic administration and would attain concentrations high enough in the tissue fluids to treat infections of the endometrium caused by susceptible bacteria.

Control rules for linked reservoirs meeting a common demand are evaluated for a two storage case. Draw‐off can be made from either of the reservoirs directly to supply, and transfers are permitted from the smaller to the larger reservoirs. Dynamic programming is effective in selecting the optimal control rules, for any stage of reservoir contents, given a defined objective of operation. The objective is expressed in monetary terms, relating to transmission, purification, or shortage costs, which are to be minimized in the long term. The case considered allows monthly inflows to the reservoirs to be treated as random variates; first order serial correlation of inflows is expressed by using ‘high’ or ‘low’ inflow distributions, according to whether the previous month's inflow was above or below its mean. Present worth factors, switch‐on costs, and costs of shortage that vary nonlinearly with total deficit can all be brought into the reckoning. The paper includes a numerical example of the dynamic programming calculation for a system of a finite surface reservoir and a full aquifer, the latter having limited pumpage. Also, the flow diagram for a computer program is given, which incorporates inflow, draw‐off, storage volumes, and operating costs as general parameters. Using this program, the convergence to optimal control rules has been obtained, for the most part within 5 years of iteration. Given the optimal control rules for an assumed reservoir system, it becomes possible to form transition matrices of contents, by an adaptation of Gould's method. The steady‐state solutions of the matrices show probabilities of each reservoir's contents in the long term. These lead to a long‐term operating cost for consideration at the design stage of the system.

This paper introduces a probabilistic method for short-term transmission congestion forecasting, which is recently developed by EPRI. The proposed method applies the sequential Monte Carlo Simulation (MCS) in a probabilistic load flow as the conceptual framework, adds all the significant uncertainties and their probability distributions to be modeled, develops the models, and most importantly specifies how to accurately model the key input assumptions in order to derive valid confidence levels of the forecasted congestion variables. The developed probabilistic method is successfully applied to the four-area WECC equivalent system. Focus is on the confidence levels of making such forecasts, so that a window of forecast-ability is defined, beyond which any forecast would be considered to contain little actionable information. Within the window of forecast-ability, the probabilistic forecasts of congestion would provide confidence limits and information for ranking the potential benefits of alleviating congestion at the various transmission bottlenecks.

This paper focuses on the empirical derivation of regret bounds for mobile systems that can vary their locations within a spatiotemporally varying environment in order to maximize performance. In particular, the paper focuses on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including a maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches.

This paper focuses on the empirical derivation of regret bounds for mobile systems that can optimize their locations in real time within a spatiotemporally varying renewable energy resource. The case studies in this paper focus specifically on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches. In addition, we use dimensional analysis to generalize the aforementioned results to other spatiotemporally varying environments, making the results applicable to a wider variety of renewably powered mobile systems. Finally, to deal with more general environmental mean models, we introduce a novel approach to modify calculable regret bounds to accommodate any mean model through what we term an "effective spatial domain."