981 resultados para R(infinity) property
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Committee of Planning Librarians continued by Council of Planning Librarians.
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Kept up to date by supplements through 1972.
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Stickiness is a common problem encountered in food handling and processing, and also during consumption. Stickiness is observed as adhesion of the food to processing equipment surfaces or cohesion within the food particulate or mass. An important operation where this undesirable behavior of food is manifested is drying. This occurs particularly during drying of high-sugar and high-fat foods. To date, the stickiness of foods during drying or dried powder has been investigated in relation to their viscous and glass transition properties. The importance of contact surface energy of the equipment has been ignored in many analyses, despite the fact that some drying operations have reported using low-energy contact surfaces in drying equipment to avoid the problems caused by stickiness. This review discusses the fundamentals of adhesion and cohesion mechanisms and relates these phenomena to drying and dried products.
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We discuss recent progress towards the establishment of important structure-property-function relationships in eumelanins-key functional bio-macromolecular systems responsible for photoprotection and immune response in humans, and implicated in the development of melanoma skin cancer. We focus on the link between eumelanin's secondary structure and optical properties such as broad band UV-visible absorption and strong non-radiative relaxation; both key features of the photo-protective function. We emphasise the insights gained through a holistic approach combining optical spectroscopy with first principles quantum chemical calculations, and advance the hypothesis that the robust functionality characteristic of eumelanin is related to extreme chemical and structural disorder at the secondary level. This inherent disorder is a low cost natural resource, and it is interesting to speculate as to whether it may play a role in other functional bio-macromolecular systems.
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Investors and developers are often faced with the task of determining the worth or value of a real estate entity that presently exists or is proposed for development. This article explains the process for determining the value of a proposed project and, subsequently, the maximum investment dollars the project can cover, while at the same time producing a reasonable return for the investor. A proposed 300-room hotel serves as the real estate entity to be analyzed.
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The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (R-factor) of the (R)USLE model in areas without good temporal data coverage. In mainland Spain, the Nature Conservation Institute (ICONA) determined the R-factor at few selected pluviographs, so simple estimates of the R-factor are definitely of great interest. The objectives of this study were: (1) to identify a readily available estimate of the R-factor for mainland Spain; (2) to discuss the applicability of a single (global) estimate based on analysis of regional results; (3) to evaluate the effect of record length on estimate precision and accuracy; and (4) to validate an available regression model developed by ICONA. Four estimators based on monthly precipitation were computed at 74 rainfall stations throughout mainland Spain. The regression analysis conducted at a global level clearly showed that modified Fournier index (MFI) ranked first among all assessed indexes. Applicability of this preliminary global model across mainland Spain was evaluated by analyzing regression results obtained at a regional level. It was found that three contiguous regions of eastern Spain (Catalonia, Valencian Community and Murcia) could have a different rainfall erosivity pattern, so a new regression analysis was conducted by dividing mainland Spain into two areas: Eastern Spain and plateau-lowland area. A comparative analysis concluded that the bi-areal regression model based on MFI for a 10-year record length provided a simple, precise and accurate estimate of the R-factor in mainland Spain. Finally, validation of the regression model proposed by ICONA showed that R-ICONA index overpredicted the R-factor by approximately 19%.