Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY

A dynamic simulation model for the soil plant system is described. The model includes a number of main modules, viz., a hydrological model including a submodel for soil water dynamics, a soil temperature model, a soil nitrogen model including a submodel for soil organic matter dynamics, and a crop model including a submodel for nitrogen uptake. The soil part of the model has a one-dimensional vertical structure. The soil profile is divided into layers on the basis of physical and chemical soil characteristics. The simulation model was used to simulate soil nitrogen dynamics and biomass production in winter wheat grown at two locations at various levels of nitrogen fertilization. The simulated results were compared to experimental data including concentration of inorganic nitrogen in soil, crop yield, and nitrogen accumulated in the aboveground part of the crop. Based on this validation it is concluded that the overall performance of the model is satisfactory although some minor adjustments of the model may prove to be necessary.     

Ammoxidation of toluene over Y-Ba-Cu-Co-O perovskites

Ammoxidation of toluene over the perovskites YBa₂Cu₃O_6.1, YBa₂Cu₂CoO_6.7 and YBaCuCoO_4.9 was investigated at 400 °C. At low partial pressures of O₂ benzonitrile was selectively formed, while CO₂ was the main product at high pressures of O₂. Systematic differences in activity were observed for the three phases and are related to the crystal contents of Cu and Co. At low O₂ pressures, Cu-sites are active for nitrile formation, while Co-sites give CO₂. At high O₂ pressures, the activity for CO₂ of Cu-sites increases more than that of Co-sites due to filling of near-surface oxygen vacancies.     

Impact strength and melt viscosity of bisphenol-a polycarbonate and styrene-maleic-anhydride copolymer blends

Mixtures of 90, 80, and 70 percent by weight bisphenol-A-polycarbonate (PC) and 10, 20, and 30 percent by weight styrene maleic anhydride (SMA) copolymer were melt-blended in a single screw extruder. Differential scanning calorimetry (DCS) and scanning electron microscopy (SEM) were used to determine the miscibility of the blends. The viscosity, as a function of shear rate and temperature, was measured by an Instron capillary viscometer. The notched impact strength as a function of temperature was measured by an Izod impact tester. The results of DSC showed two glass transition temperatures which merged slightly towards each other, indicating marginal miscibility of these blends. There was a decrease in viscosity as the fraction of SMA copolymer was increased. The most significant decrease occurred with the initial addition of SMA copolymer. The viscosity also decreased with increases in temperature. The impact strength of the blends was also dependent on SMA copolymer content. The blends showed six to ten times lower impact strengths at room temperature than the 100 percent polycarbonate. SEM analysis helped to determine the reason why the impact strength was lower for the blends. High magnification showed the presence of SMA copolymer inclusions dispersed throughout the PC matrix. These inclusions, which increased in size as SMA copolymer content was increased, acted as defects in the system.     

Learning Read-Constant Polynomials of Constant Degree Modulo Composites

Boolean functions that have constant degree polynomial representation over a fixed finite ring form a natural and strict subclass of the complexity class ACC⁰. They are also precisely the functions computable efficiently by programs over fixed and finite nilpotent groups. This class is not known to be learnable in any reasonable learning model. In this paper, we provide a deterministic polynomial time algorithm for learning Boolean functions represented by polynomials of constant degree over arbitrary finite rings from membership queries, with the additional constraint that each variable in the target polynomial appears in a constant number of monomials. Our algorithm extends to superconstant but low degree polynomials and still runs in quasipolynomial time.     

Thermal conductivity of high polymers—the influence of molecular weight

Some earlier observations on the influence of molecular weight, plasticizer, and irradiation on the thermal conductivity of polymers is reviewed. Also some new data on polystyrene and some new calculations based on the theory of Hansen and Ho are presented. These data show almost a two fold increase in the thermal conductivity of polystyrene as the molecular weight goes from 900 to 100,000. Data on the moleculer weight effect on thermal conductivity appear to be consistent with the theoretical calculations.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

VISIM: Sequential simulation for linear inverse problems

Linear inverse Gaussian problems are traditionally solved using least squares-based inversion. The center of the posterior Gaussian probability distribution is often chosen as the solution to such problems, while the solution is in fact the posterior Gaussian probability distribution itself. We present an algorithm, based on direct sequential simulation, which can be used to efficiently draw samples of the posterior probability distribution for linear inverse problems. There is no Gaussian restriction on the distribution in the model parameter space, as inherent in traditional least squares-based algorithms.As data for linear inverse problems can be seen as weighed linear averages over some volume, block kriging can be used to perform both estimation (i.e. finding the center of the posterior Gaussian pdf) and simulation (drawing samples of the posterior Gaussian pdf). We present the kriging system which we use to implement a flexible GSLIB-based algorithm for solving linear inverse problems.We show how we implement such a simulation program conditioned to linear average data. The program is called VISIM as an acronym for Volume average Integration SIMulation. An effort has been made to make the program efficient, even for larger scale problems, and the computational efficiency and accuracy of the code is investigated.Using a synthetic cross-borehole tomography case study, we show how the program can be used to generate realizations of the a posteriori distributions (i.e. solutions) from a linear tomography problem. Both Gaussian and non-Gaussian a priori model parameter distributions are considered.     

Family psychosocial screening: should we focus on high-risk settings?

The objective of this study was to address the question of whether or not psychosocial screening should be focused on "high-risk" populations. A cross-sectional survey of mothers of young children was conducted in various clinics: 758 in teaching clinics, 444 in private practices, and 202 at a military clinic. The self-administered questionnaire covered demographic factors, problems in mothers family of origin, maternal depression, and substance abuse. Mothers in the teaching clinics were younger and had less education and lower incomes than mothers in private practices, with intermediate levels in the military clinic. However, a substantial proportion of mothers seen in all sites reported psychosocial problems. Approximately 20% of mothers in all sites reported a family history of alcoholism. Positive screens for maternal depression ranged from about 15% to 35%. Binge drinking was reported by 10% to 20% at different sites. Psychosocial problems were common even among families seen in "low-risk" settings. Focusing screening only on high-risk clinics would miss many families with psychosocial problems.