908 resultados para Population growth model
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The population mixing hypothesis proposes that childhood leukaemia (CL) might be a rare complication of a yet unidentified subclinical infection. Large population influxes into previously isolated rural areas may foster localised epidemics of the postulated infection causing a subsequent increase of CL. While marked population growth after a period of stability was central to the formulation of the hypothesis and to the early studies on population mixing, there is a lack of objective criteria to define such growth patterns. We aimed to determine whether periods of marked population growth coincided with increases in the risk of CL in Swiss municipalities. We identified incident cases of CL aged 0-15 years for the period 1985-2010 from the Swiss Childhood Cancer Registry. Annual data on population counts in Swiss municipalities were obtained for 1980-2010. As exposures, we defined (1) cumulative population growth during a 5-year moving time window centred on each year (1985-2010) and (2) periods of 'take-off growth' identified by segmented linear regression. We compared CL incidence across exposure categories using Poisson regression and tested for effect modification by degree of urbanisation. Our study included 1500 incident cases and 2561 municipalities. The incident rate ratio (IRR) comparing the highest to the lowest quintile of 5-year population growth was 1.18 (95 % CI 0.96, 1.46) in all municipalities and 1.33 (95 % CI 0.93, 1.92) in rural municipalities (p value interaction 0.36). In municipalities with take-off growth, the IRR comparing the take-off period (>6 % annual population growth) with the initial period of low or negative growth (<2 %) was 2.07 (95 % CI 0.95, 4.51) overall and 2.99 (1.11, 8.05) in rural areas (p interaction 0.52). Our study provides further support for the population mixing hypothesis and underlines the need to distinguish take-off growth from other growth patterns in future research.
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Population growth, economic globalization, improving living standards and urbanization are causing important changes in the global food system and modifying the dietary habits in many parts of the world (Molden, 2007; Godfray et al., 2010). The nutritional transition (linked to the development of countries and the increasing wealth of its population) implies a shift away from traditional staple food such as roots and tuber vegetables and a rise in consumption of meat and milk products, refined and processed foods, as well as sugars, oils and fats (Ambler-Edwards et al., 2009). The contemporary food system puts significant pressure on natural resources, especially on land and water, because the growing food demand pushes the agricultural frontier beyond, causing large impacts on ecosystems (Ambler-Edwards et al. 2009: 11-18). Also, the trend towards richer diets in animal proteins and processed food adds further pressure on the environment, since it requires larger amount of water and land to be produced (Allan, 2011; Mekonnen and Hoekstra, 2012).
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In this paper we investigate the trade-off faced by regulators who must set a price for an intermediate good somewhere between the marginal cost and the monopoly price. We utilize a growth model with monopolistic suppliers of intermediate goods. Investment in innovation is required to produce a new intermediate good. Marginal cost pricing deters innovation, while monopoly pricing maximizes innovation and economic growth at the cost of some static inefficiency. We demonstrate the existence of a second-best price above the marginal cost but below the monopoly price, which maximizes consumer welfare. Simulation results suggest that substantial reductions in consumption, production, growth, and welfare occur where regulators focus on static efficiency issues by setting prices at or near marginal cost.
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We study memory effects in a kinetic roughening model. For d=1, a different dynamic scaling is uncovered in the memory dominated phases; the Kardar-Parisi-Zhang scaling is restored in the absence of noise. dc=2 represents the critical dimension where memory is shown to smoothen the roughening front (a=0). Studies on a discrete atomistic model in the same universality class reconfirm the analytical results in the large time limit, while a different scaling behavior shows up for t
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A nonlinear dynamic model of microbial growth is established based on the theories of the diffusion response of thermodynamics and the chemotactic response of biology. Except for the two traditional variables, i.e. the density of bacteria and the concentration of attractant, the pH value, a crucial influencing factor to the microbial growth, is also considered in this model. The pH effect on the microbial growth is taken as a Gaussian function G0e-(f- fc)2/G1, where G0, G1 and fc are constants, f represents the pH value and fc represents the critical pH value that best fits for microbial growth. To study the effects of the reproduction rate of the bacteria and the pH value on the stability of the system, three parameters a, G0 and G1 are studied in detail, where a denotes the reproduction rate of the bacteria, G0 denotes the impacting intensity of the pH value to microbial growth and G1 denotes the bacterial adaptability to the pH value. When the effect of the pH value of the solution which microorganisms live in is ignored in the governing equations of the model, the microbial system is more stable with larger a. When the effect of the bacterial chemotaxis is ignored, the microbial system is more stable with the larger G1 and more unstable with the larger G0 for f0 > fc. However, the stability of the microbial system is almost unaffected by the variation G0 and G1 and it is always stable for f0 < fc under the assumed conditions in this paper. In the whole system model, it is more unstable with larger G1 and more stable with larger G0 for f0 < fc. The system is more stable with larger G1 and more unstable with larger G0 for f0 > fc. However, the system is more unstable with larger a for f0 < fc and the stability of the system is almost unaffected by a for f0 > fc. The results obtained in this study provide a biophysical insight into the understanding of the growth and stability behavior of microorganisms.
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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Little is known about the pharmacokinetics of potassium canrenoate/canrenone in paediatric patients WHAT THIS STUDY ADDS • A population pharmacokinetic model has been developed to evaluate the pharmacokinetics of canrenone in paediatric patients who received potassium canrenoate as part of their therapy in the intensive care unit. AIMS To characterize the population pharmacokinetics of canrenone following administration of potassium canrenoate to paediatric patients. METHODS Data were collected prospectively from 23 paediatric patients (2 days to 10 years of age; median weight 4 kg, range 2.16–28.0 kg) who received intravenous potassium canrenoate (K-canrenoate) as part of their intensive care therapy for removal of retained fluids, e.g. in pulmonary oedema due to chronic lung disease and for the management of congestive heart failure. Plasma samples were analyzed by HPLC for determination of canrenone (the major metabolite and pharmacologically active moiety) and the data subjected to pharmacokinetic analysis using NONMEM. RESULTS A one compartment model best described the data. The only significant covariate was weight (WT). The final population models for canrenone clearance (CL/F) and volume of distribution (V/F) were CL/F (l h−1) = 11.4 × (WT/70.0)0.75 and V/F (l) = 374.2 × (WT/70) where WT is in kg. The values of CL/F and V/F in a 4 kg child would be 1.33 l h−1 and 21.4 l, respectively, resulting in an elimination half-life of 11.2 h. CONCLUSIONS The range of estimated CL/F in the study population was 0.67–7.38 l h−1. The data suggest that adjustment of K-canrenoate dosage according to body weight is appropriate in paediatric patients.
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Setting out from the database of Operophtera brumata, L. in between 1973 and 2000 due to the Light Trap Network in Hungary, we introduce a simple theta-logistic population dynamical model based on endogenous and exogenous factors, only. We create an indicator set from which we can choose some elements with which we can improve the fitting results the most effectively. Than we extend the basic simple model with additive climatic factors. The parameter optimization is based on the minimized root mean square error. The best model is chosen according to the Akaike Information Criterion. Finally we run the calibrated extended model with daily outputs of the regional climate model RegCM3.1, regarding 1961-1990 as reference period and 2021-2050 with 2071-2100 as future predictions. The results of the three time intervals are fitted with Beta distributions and compared statistically. The expected changes are discussed.