80 resultados para Switch allocation


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A standard problem within universities is that of teaching space allocation which can be thought of as the assignment of rooms and times to various teaching activities. The focus is usually on courses that are expected to fit into one room. However, it can also happen that the course will need to be broken up, or ‘split’, into multiple sections. A lecture might be too large to fit into any one room. Another common example is that of seminars or tutorials. Although hundreds of students may be enrolled on a course, it is often subdivided into particular types and sizes of events dependent on the pedagogic requirements of that particular course. Typically, decisions as to how to split courses need to be made within the context of limited space requirements. Institutions do not have an unlimited number of teaching rooms, and need to effectively use those that they do have. The efficiency of space usage is usually measured by the overall ‘utilisation’ which is basically the fraction of the available seat-hours that are actually used. A multi-objective optimisation problem naturally arises; with a trade-off between satisfying preferences on splitting, a desire to increase utilisation, and also to satisfy other constraints such as those based on event location and timetabling conflicts. In this paper, we explore such trade-offs. The explorations themselves are based on a local search method that attempts to optimise the space utilisation by means of a ‘dynamic splitting’ strategy. The local moves are designed to improve utilisation and satisfy the other constraints, but are also allowed to split, and un-split, courses so as to simultaneously meet the splitting objectives.

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Macrophage migration inhibitory factor (MIF), one of the first cytokines to be discovered, has recently been localized to the Leydig cells in adult rat testes. In the following study, the response of MIF to Leydig cell ablation by the Leydig cell-specific toxin ethane dimethane sulfonate (EDS) was examined in adult male rats. Testicular MIF mRNA and protein in testicular interstitial fluid measured by ELISA and western blot were only marginally reduced by EDS treatment, in spite of the fact that the Leydig cells were completely destroyed within 7 days. Immunohistochemistry using an affinity-purified anti-mouse MIF antibody localized MIF exclusively to the Leydig cells in control testes. At 7 days post-EDS treatment, there were no MIF immunopositive Leydig cells in the interstitium, although distinct MIF immunostaining was observed in the seminiferous tubules, principally in Sertoli cells and residual cytoplasm, and some spermatogonia. A few peritubular and perivascular cells were also labelled at this time, which possibly represented mesenchymal Leydig cell precursors. At 14 and 21 days, Sertoli cell MIF immunoreactivity was observed in only a few tubule cross-sections, while some peritubular and perivascular mesenchymal cells and the re-populating immature Leydig cells were intensely labeled. At 28 days after EDS-treatment, the MIF immunostaining pattern was identical to that of untreated and control testes. The switch in the compartmentalization of MIF protein at 7 days after EDS-treatment was confirmed by western blot analysis of interstitial tissue and seminiferous tubules separated by mechanical dissection. These data establish that Leydig cell-depleted testes continue to produce MIF, and suggest the existence of a mechanism of compensatory cytokine production involving the Sertoli cells. This represents the first demonstration of a hitherto unsuspected pattern of cellular interaction between the Leydig cells and the seminiferous tubules which is consistent with an essential role for MIF in male testicular function.

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This paper presents a new method for transmission loss allocation. The method is based on tracing the complex power flow through the network and determining the share of each load on the flow and losses through each line. Transmission losses are taken into consideration during power flow tracing. Unbundling line losses is carried out using an equation, which has a physical basis, and considers the coupling between active and reactive power flows as well as the cross effects of active and reactive power on active and reactive losses. A tracing algorithm which can be considered direct to a good extent, as there is no need for exhaustive search to determine the flow paths as these are determined in a systematic way during the course of tracing. Results of application of the proposed method are also presented.

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This paper presents a new method for transmission loss allocation in a deregulated electrical power market. The proposed method is based on physical flow through transmission lines. The contributions of individual loads to the line flows are used as basis for allocating transmission losses to different loads. With minimum assumptions, that sound to be reasonable and cannot be rejected, a novel loss allocation formula is derived. The assumptions made are: a number of currents sharing a transmission line distribute themselves over the cross section in the same manner; that distribution causes the minimum possible power loss. Application of the proposed method is straightforward. It requires only a solved power flow and any simple algorithm for power flow tracing. Both active and reactive powers are considered in the loss allocation procedure. Results of application show the accuracy of the proposed method compared with the commonly used procedures.