Management Retention Factors in the School Foodservice Industry

Management retention in the school foodservice industry has been a growing concern for school district decision makers due to the large numbers of managers reaching retirement age and the shortage of qualified people to fill the positions. As with other foodservice positions, turnover rates and the shortage of service employees will continue to be challenges well into the 21st centery. The current study employed by a self-administered questionnaire and asked 101 school foodservice managers in Central Florida to rate their perceived importance of and their perceived experience with 20 employment characteristics of their job. There were significant differences in 17 of the 20 characteristics thus highlighting significant gaps between perceived importance and perceived actual experience on the job and what would keep them from changing jobs. Management and human resources implications are discussed.

Cinema studies : Women's Experimental Cinema edited by Robin Blaetz

Women’s Experimental Cinema provides lively introductions to the work of fifteen avant-garde women filmmakers, some of whom worked as early as the 1950s and many of whom are still working today. In each essay in this collection, a leading film scholar considers a single filmmaker, supplying biographical information, analyzing various influences on her work, examining the development of her corpus, and interpreting a significant number of individual films. The essays rescue the work of critically neglected but influential women filmmakers for teaching, further study, and, hopefully, restoration and preservation. Just as importantly, they enrich the understanding of feminism in cinema and expand the terrain of film history, particularly the history of the American avant-garde.

Worldwide outdoor round robin study of organic photovoltaic devices and modules

Accurate characterization and reporting of organic photovoltaic (OPV) device performance remains one of the important challenges in the field. The large spread among the efficiencies of devices with the same structure reported by different groups is significantly caused by different procedures and equipment used during testing. The presented article addresses this issue by offering a new method of device testing using “suitcase sample” approach combined with outdoor testing that limits the diversity of the equipment, and a strict measurement protocol. A round robin outdoor characterization of roll-to-roll coated OPV cells and modules conducted among 46 laboratories worldwide is presented, where the samples and the testing equipment were integrated in a compact suitcase that served both as a sample transportation tool and as a holder and test equipment during testing. In addition, an internet based coordination was used via plasticphotovoltaics.org that allowed fast and efficient communication among participants and provided a controlled reporting format for the results that eased the analysis of the data. The reported deviations among the laboratories were limited to 5% when compared to the Si reference device integrated in the suitcase and were up to 8% when calculated using the local irradiance data. Therefore, this method offers a fast, cheap and efficient tool for sample sharing and testing that allows conducting outdoor measurements of OPV devices in a reproducible manner.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Room-temperature dual-wavelength erbium-doped fibre laser based on a sampled fibre Bragg grating and a photonic Robin Hood

With the assistance of a kind of photonic Robin Hood that is originated from four-wave mixing in a dispersion-flattened high-nonlinearity photonic-crystal fibre, a novel dual-wavelength erbium-doped fibre (EDF) laser is proposed and demonstrated by using a sampled fibre Bragg grating. The experiments show that, due to the contribution of the photonic Robin Hood, the proposed fibre laser has the advantage of excellent uniformity, high stability and stable operation at room temperature. Our dual-wavelength EDF laser has the unique merit that the wavelength spacing remains unchanged when tuning the two wavelengths of laser, and this laser is simpler and more stable than the laser reported by Liu et al. [Opt. Express, 13 142 (2005)].

James Evans : inventor of the syllabic system of the Cree language / by John McLean ... (Robin Rustler).

http://www.archive.org/details/jamesevans00maclrich

Simulating one of the CIB W14 round robin test cases using the SMARTFIRE fire field model

Numerical predictions produced by the SMARTFIRE fire field model are compared with experimental data. The predictions consist of gas temperatures at several locations within the compartment over a 60 min period. The test fire, produced by a burning wood crib attained a maximum heat release rate of approximately 11MW. The fire is intended to represent a nonspreading fire (i.e. single fuel source) in a moderately sized ventilated room. The experimental data formed part of the CIB Round Robin test series. Two simulations are produced, one involving a relatively coarse mesh and the other with a finer mesh. While the SMARTFIRE simulations made use of a simple volumetric heat release rate model, both simulations were found capable of reproducing the overall qualitative results. Both simulations tended to overpredict the measured temperatures. However, the finer mesh simulation was better able to reproduce the qualitative features of the experimental data. The maximum recorded experimental temperature (12141C after 39 min) was over-predicted in the fine mesh simulation by 12%. (C) 2001 Elsevier Science Ltd. All rights reserved.

Reviews: Private Passions with Robin Wilson [Radio review]

Tony Mann reviews Robin Wilson's appearance as Michael Berkeley's guest on Private Passions. Broadcast on BBC Radio 3, on 9 September 2007.

The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situRrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.

CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters.

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.