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."

Welcome to the 12th Workshop on Fault-Tolerance for HPC at eXtreme Scale (FTXS 2022), organized in conjunction with SC22. Addressing failures in extreme-scale systems remains a significant challenge for exascale and post-exascale systems. As a result, robust and efficient fault tolerance techniques are critical to obtaining acceptable application performance on these powerful new machines. Additionally, it is imperative that we develop an understanding of trends in hardware devices that may affect the reliability of future systems. The growing importance of hardware heterogeneity (e.g., GPUs, FPGAs, and other types of accelerators), the continued growth in the storage hierarchy, and the development of non-von Neumann devices (e.g., quantum and neuromorphic processors) will all impact fault tolerance on nextgeneration systems. These design trends coupled with increases in the number, variety, and complexity of components required to compose future extreme-scale systems mean that these systems will experience significant increases in aggregate fault rate, fault diversity, and the complexity of isolating root causes.Further complications arise from the fact that many of these hardware trends also mean that the likelihood of undetected errors, e.g., silent data corruption, is growing. Power limitations mean that future systems may not have room in their power budgets to deploy powerful hardware correction mechanisms (e.g., chipkill). Less protective (and more power-efficient) mechanisms have been shown to be more susceptible to undetected errors. As a result, application developers are increasingly less confident that they can rely on hardware devices to produce "correct" results.Based on these trends in extreme-scale systems, FTXS attracts work from scientists and engineers studying fault tolerance around the world. We would like to thank all the authors who submitted their work to FTXS 2022. The quality of the submissions that we received is a testament to the excellent work that is currently being done on fault tolerance for extreme-scale systems. We would also like to acknowledge the hard work of the members of our Program Committee in reviewing these submissions. Our workshop's program consists of a featured speaker and five papers that examine several important aspects of fault tolerance. Harish Dixit of Facebook will be our featured speaker. He will discuss work at Facebook to characterize the silent errors affecting their data centers. The topics addressed by the five papers that we accepted include: techniques for enabling algorithm-based fault tolerance, system anomaly detection, characterizing fault susceptibility in mixed-precision applications, and fault handling in communication libraries. FTXS 2022 will mark a return to normal after two pandemic-disrupted years. We hope that it will provide an opportunity for experts and novices alike to engage each other in thought-provoking discussions of new and innovative ideas on fault tolerance for next-generation extreme-scale systems.