Technical Efficiency of the Danish Trawl fleet: Are the Industrial Vessels Better than Others?

Technical efficiency in the Danish trawl fishery in the North Sea is estimated for 1997 and 1998 by a stochastic production frontier model. This model allows noise when the frontier and the technical efficiency is found, which for fisheries is a reasonable assumption. The results show that the production frontier can be modelled by a translog function without time effects and a technical ineffi-ciency function. The type of fishery (industrial or consumption), size of vessel (greater or lesser than 60 GRT) and year give a good explanation for the ineffi-ciency in the fleet. The average technical efficiency is estimated to be 0.82. On average, industrial vessels have a higher technical efficiency than human con-sumption vessels, and smaller vessels have higher technical efficiency than lar-ger vessels. In sum, the analysis reveals that vessel larger than 60 GRT and fishing industrial species are the most efficient.Technical efficiency, stochastic production frontier, Danish trawl fishery

Transverse Instabilities of the LHC Proton Beam in the SPS

The availability from the injectors of the proton beam required for the LHC era has allowed studying its transverse behaviour in the SPS. Profile and beam oscillation measurements evidenced the existence of strong transverse instabilities developing along the batch and inducing an emittance blow-up of increasing importance from the head to the tail of the batch. An intensity threshold, comparable to that observed for the development of the beam induced electron cloud, has been found for the onset of the above phenomena. The results of the measurements and their possible interpretation are presented

Parallelization of the PC Algorithm

This paper describes a parallel version of the PC algorithm for learning the structure of a Bayesian network from data. The PC algorithm is a constraint-based algorithm consisting of fi ve steps where the first step is to perform a set of (conditional) independence tests while the remaining four steps relate to identifying the structure of the Bayesian network using the results of the (conditional) independence tests. In this paper, we describe a new approach to parallelization of the (conditional) independence testing as experiments illustrate that this is by far the most time consuming step. The proposed parallel PC algorithm is evaluated on data sets generated at random from five different real- world Bayesian networks. The results demonstrate that signi cant time performance improvements are possible using the proposed algorithm

4. The School Develops

Between 1947 and 1953, when M.P. Catherwood left the deanship to become New York’s industrial commissioner, the ILR School developed into a full fledged enterprise. These pages attempt to capture some of the excitement of this period of the school’s history, which was characterized by vigor, growth, and innovation. Includes: Alumni Recall Their Lives as Students; The Faculty Were Giants; Alice Cook: Lifelong Scholar, Consummate Teacher; Frances Perkins; Visits and Visitors; Tenth Anniversary: Reflection and Change; The Emergence of Departments at ILR; Development of International Programs and Outreach

Parameter learning algorithms for continuous model improvement using operational data

In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. The analysis is based on an object-oriented Bayesian network developed for failure diagnostics of a one-dimensional pick-and-place industrial robot developed by IEF-Werner GmbH.We consider four learning algorithms (batch Expectation-Maximization (EM), incremental EM, Online EM and fractional updating) for parameter updating in the object-oriented Bayesian network using a real operational dataset. Also, we evaluate the performance of the considered algorithms on a dataset generated from the model to determine which algorithm is best suited for recovering the underlying generating distribution. The object-oriented Bayesian network has been integrated into both the control software of the robot as well as into a software architecture that supports diagnostic and prognostic capabilities of devices in manufacturing systems. We evaluate the time performance of the architecture to determine the feasibility of online learning from operational data using each of the four algorithms. © Springer International Publishing AG 2017

Measurement of the electron cloud properties by means of a multi-strip detector in the CERN SPS

Electron cloud effects presently limit the performances of the CERN SPS with LHC type beams [1] and are of concern for the LHC itself [2]. Electron multipacting in the SPS produces dramatic dynamic pressure increases and strong transverse instabilities [3]. In the LHC the electron cloud is expected to significantly increase the heat load in the cryogenics system. Estimates of these effects are based on computer simulations of the electron cloud build-up and of its spatial distribution in field free regions and in strong magnetic fields. The accuracy of such simulations is therefore a key issue for component design and for the definition of the operating strategies for the LHC. In 2001 a multi-strip detector has been installed in the SPS to study the electron cloud and to provide experimental data to validate the models and to better constrain their input parameters. After a description of the monitor characteristics and of its associated electronics an overview of its performance and of the results of the measurements conducted with different proton beam parameters are presented. The measurements are compared with simulation results. Possible monitor upgrades are also discussed