950 resultados para General Electric Company


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At head of title: Space Sciences Laboratory. Aerophysics Section.

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Mode of access: Internet.

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Cover title.

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Faced with a future of rising energy costs there is a need for industry to manage energy more carefully in order to meet its economic objectives. A problem besetting the growth of energy conservation in the UK is that a large proportion of energy consumption is used in a low intensive manner in organisations where they would be responsibility for energy efficiency is spread over a large number of personnel who each see only small energy costs. In relation to this problem in the non-energy intensive industrial sector, an application of an energy management technique known as monitoring and targeting (M & T) has been installed at the Whetstone site of the General Electric Company Limited in an attempt to prove it as a means for motivating line management and personnel to save energy. The objective energy saving for which the M & T was devised is very specific. During early energy conservation work at the site there had been a change from continuous to intermittent heating but the maintenance of the strategy was receiving a poor level of commitment from line management and performance was some 5% - 10% less than expected. The M & T is concerned therefore with heat for space heating for which a heat metering system was required. Metering of the site high pressure hot water system posed technical difficulties and expenditure was also limited. This led to a ‘tin-house' design being installed for a price less than the commercial equivalent. The timespan of work to achieve an operational heat metering system was 3 years which meant that energy saving results from the scheme were not observed during the study. If successful the replication potential is the larger non energy intensive sites from which some 30 PT savings could be expected in the UK.

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"These studies were conducted by the General Electric Company, Reentry Systems Department, for the Stability and Control Section of the Flight Dynamics Laboratory of the Air Force Research and Technology Division."

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The site of present-day St. Catharines was settled by 3000 United Empire Loyalists at the end of the 18th century. From 1790, the settlement (then known as "The Twelve") grew as an agricultural community. St. Catharines was once referred to Shipman's Corners after Paul Shipman, owner of a tavern that was an important stagecoach transfer point. In 1815, leading businessman William Hamilton Merritt abandoned his wharf at Queenston and set up another at Shipman's Corners. He became involved in the construction and operation of several lumber and gristmills along Twelve Mile Creek. Shipman's Corners soon became the principal milling site of the eastern Niagara Peninsula. At about the same time, Merritt began to develop the salt springs that were discovered along the river which subsequently gave the village a reputation as a health resort. By this time St. Catharines was the official name of the village; the origin of the name remains obscure, but is thought to be named after Catharine Askin Robertson Hamilton, wife of the Hon. Robert Hamilton, a prominent businessman. Merritt devised a canal scheme from Lake Erie to Lake Ontario that would provide a more reliable water supply for the mills while at the same time function as a canal. He formed the Welland Canal Company, and construction took place from 1824 to 1829. The canal and the mills made St. Catharines the most important industrial centre in Niagara. By 1845, St. Catharines was incorporated as a town, with the town limits extending in 1854. Administrative and political functions were added to St. Catharines in 1862 when it became the county seat of Lincoln. In 1871, construction began on the third Welland Canal, which attracted additional population to the town. As a consequence of continual growth, the town limits were again extended. St. Catharines attained city status in 1876 with its larger population and area. Manufacturing became increasingly important in St. Catharines in the early 1900s with the abundance of hydro-electric power, and its location on important land and water routes. The large increase in population after the 1900s was mainly due to the continued industrialization and urbanization of the northern part of the city and the related expansion of business activity. The fourth Welland Canal was opened in 1932 as the third canal could no longer accommodate the larger ships. The post war years and the automobile brought great change to the urban form of St. Catharines. St. Catharines began to spread its boundaries in all directions with land being added five times during the 1950s. The Town of Merritton, Village of Port Dalhousie and Grantham Township were all incorporated as part of St. Catharines in 1961. In 1970 the Province of Ontario implemented a regional approach to deal with such issues as planning, pollution, transportation and services. As a result, Louth Township on the west side of the city was amalgamated, extending the city's boundary to Fifteen Mile Creek. With its current population of 131,989, St. Catharines has become the dominant centre of the Niagara region. Source: City of St. Catharines website http://www.stcatharines.ca/en/governin/HistoryOfTheCity.asp (January 27, 2011)

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A letter from Second V.P. Frederic Nicholls of Canadian General Electric Co. to W.B. Rankine in regards to transformers. The Canadian Niagara Power Co. has agreed to an order of nine transformers (1250 Killowatt) to be installed in the Power House at Niagara Falls. The transformers will cost the Canadian Niagara Power Company forty three thousand two hundred dollars.

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La obsolescencia programada es el deseo de tener algo un poco más nuevo, un poco mejor, un poco más rápido de lo necesario. El texto estudia este fenómeno a la luz del Estatuto del Consumidor – Ley 1480 de 2011 para determinar si el consumidor colombiano está suficientemente protegido con él.

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This investigation proposes to explore the existing link between a strategic conception of philanthropy and innovation. Indeed, the nature of the research question relies on an unexplored field in the CSR and Innovation management academic literature. It starts with the interest to know which the benefits are for a firm encouraged to invest strategically in philanthropy. In this regard, the analysis contributes in fitting this gap by following different objectives in an exploratory perspective. Throughout the research it will be analyzed the concept and the current and past contributions on the different branches of innovation (product innovation, managerial innovation, technological innovation), to accentuate the relation between an accurate strategic approach to philanthropy and the impact on the organizational value. Indeed, analyzing philanthropic innovation may provide insights about business opportunities and notions related to social investments and profit. That aspect includes the link between those strategic decisions that a firm can use to maximize those investments as it was part of their core business. It also proves the existing link between CSR and innovation, and the possibilities that the enterprises have towards this subject.

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This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

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The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

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This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

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Objective To assess several baseline risk factors that may predict patellofemoral and tibiofemoral cartilage loss during a 6-month period. Methods For 177 subjects with chronic knee pain, 3T magnetic resonance imaging (MRI) of both knees was performed at baseline and followup. Knees were semiquantitatively assessed, evaluating cartilage morphology, subchondral bone marrow lesions, meniscal morphology/extrusion, synovitis, and effusion. Age, sex, and body mass index (BMI), bone marrow lesions, meniscal damage/extrusion, synovitis, effusion, and prevalent cartilage damage in the same subregion were evaluated as possible risk factors for cartilage loss. Logistic regression models were applied to predict cartilage loss. Models were adjusted for age, sex, treatment, and BMI. Results Seventy-nine subregions (1.6%) showed incident or worsening cartilage damage at followup. None of the demographic risk factors was predictive of future cartilage loss. Predictors of patellofemoral cartilage loss were effusion, with an adjusted odds ratio (OR) of 3.5 (95% confidence interval [95% CI] 1.39.4), and prevalent cartilage damage in the same subregion with an adjusted OR of 4.3 (95% CI 1.314.1). Risk factors for tibiofemoral cartilage loss were baseline meniscal extrusion (adjusted OR 3.6 [95% CI 1.310.1]), prevalent bone marrow lesions (adjusted OR 4.7 [95% CI 1.119.5]), and prevalent cartilage damage (adjusted OR 15.3 [95% CI 4.947.4]). Conclusion Cartilage loss over 6 months is rare, but may be detected semiquantitatively by 3T MRI and is most commonly observed in knees with Kellgren/Lawrence grade 3. Predictors of patellofemoral cartilage loss were effusion and prevalent cartilage damage in the same subregion. Predictors of tibiofemoral cartilage loss were prevalent cartilage damage, bone marrow lesions, and meniscal extrusion.