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Theoretical Relationships (theoretical + relationships)
Selected AbstractsDemand- and Supply-Side Agglomerations: Distinguishing between Fundamentally Different Manifestations of Geographic ConcentrationJOURNAL OF MANAGEMENT STUDIES, Issue 3 2009Brian T. McCann abstract Agglomeration research investigates the benefits that firms receive from locating in close geographic proximity. Despite a substantial surge in interest in this topic over the past 20 years, a lack of distinction among unique manifestations of spatial concentrations of similar firms threatens continuing progress in this stream of research. We argue that agglomerations of related firms that draw benefits from the supply-related externalities of increased access to specialized labour, specialized inputs, and knowledge spillovers are fundamentally different from those that draw benefits from heightened demand realized through reduction in consumer search costs. Extending agglomeration theory, we explicate the differences between these distinct phenomena, discuss how the nature of key theoretical relationships varies across these agglomeration types, and demonstrate significant implications for research. We discuss how the differences affect a host of theoretical relationships and empirical research decisions. [source] Formed and preformed metabolites: facts and comparisonsJOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 10 2008Professor K. Sandy Pang The administration of metabolites arising from new drug entities is often employed in drug discovery to investigate their associated toxicity. It is expected that administration of metabolites can predict the exposure of metabolites originating from the administration of precursor drug. Whether exact and meaningful information can be obtained from this has been a topic of debate. This communication summarizes observations and theoretical relationships based on physiological modelling for the liver, kidney and intestine, three major eliminating organs/tissues. Theoretical solutions based on physiological modelling of organs were solved, and the results suggest that deviations are expected. Here, examples of metabolite kinetics observed mostly in perfused organs that did not match predictions are provided. For the liver, discrepancies in fate between formed and preformed metabolites may be explained by the heterogeneity of enzymes, the presence of membrane barriers and whether transporters are involved. For the kidney, differences have been attributed to glomerular filtration of the preformed but not the formed metabolite. For the intestine, the complexity of segregated flows to the enterocyte and serosal layers and differences in metabolism due to the route of administration are addressed. Administration of the metabolite may or may not directly reflect the toxicity associated with drug use. However, kinetic data on the preformed metabolite will be extremely useful to develop a sound model for modelling and simulations; in-vitro evidence on metabolite handling at the target organ is also paramount. Subsequent modelling and simulation of metabolite data arising from a combined model based on both drug and preformed metabolite data are needed to improve predictions on the behaviours of formed metabolites. [source] Non-parametric habitat models with automatic interactionsJOURNAL OF VEGETATION SCIENCE, Issue 6 2006Bruce McCune Abstract Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non-parametric multiplicative regression (NPMR) was developed from the premises that: the response variable has a minimum of zero and a physiologically-determined maximum, species respond simultaneously to multiple ecological factors, the response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built-in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact. [source] Levy-funded research choices by producers and societyAUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 1 2004Julian M. Alston Commodity levies are used increasingly to fund producer collective goods such as research and promotion. In the present paper we examine theoretical relationships between producer and national benefits from levy-funded research, and consider the implications for the appropriate rates of matching government grants, applied with a view to achieving a closer match between producer and national interests. In many cases the producer and national optima coincide. First, regardless of the form of the supply shift, when product demand is perfectly elastic, or all the product is exported, domestic benefits and costs of levy-funded research all go to producers and they have appropriate incentives. Second, if research causes a parallel supply shift, the producer share of research benefits is the same as their share of costs of a levy, and their incentives are compatible with national interests. In such cases, a matching grant would cause an over-investment in research from a national perspective. However, if demand is less than perfectly elastic, and research causes a pivotal supply shift, the producer share of benefits is smaller than their share of costs of the levy, and they will under-invest in research from a national point of view. A matching grant can be justified in such cases, however the magnitude of the optimal grant is sensitive to market conditions. [source] | ||||||||||