Simulation techniques in energy analysis

Simulation is one of the most frequently used techniques in energy modelling. After some general remarks on the nature of simulation models, a more detailed description of a large scale dynamic energy simulation model for the Federal Republic of Germany is given. The paper continues with a discussion of some model results and concludes with some brief remarks on the limitations of the simulation approach

Improved high-temperature expansion and critical equation of state of three-dimensional Ising-like systems

High-temperature series are computed for a generalized $3d$ Ising model with arbitrary potential. Two specific ``improved'' potentials (suppressing leading scaling corrections) are selected by Monte Carlo computation. Critical exponents are extracted from high-temperature series specialized to improved potentials, achieving high accuracy; our best estimates are: $\gamma=1.2371(4)$, $\nu=0.63002(23)$, $\alpha=0.1099(7)$, $\eta=0.0364(4)$, $\beta=0.32648(18)$. By the same technique, the coefficients of the small-field expansion for the effective potential (Helmholtz free energy) are computed. These results are applied to the construction of parametric representations of the critical equation of state. A systematic approximation scheme, based on a global stationarity condition, is introduced (the lowest-order approximation reproduces the linear parametric model). This scheme is used for an accurate determination of universal ratios of amplitudes. A comparison with other theoretical and experimental determinations of universal quantities is presented.Comment: 65 pages, 1 figure, revtex. New Monte Carlo data by Hasenbusch enabled us to improve the determination of the critical exponents and of the equation of state. The discussion of several topics was improved and the bibliography was update

Surface phenotype analysis of CD16+ monocytes from leukapheresis collections for peripheral blood progenitors

In peripheral blood progenitor cell (PBPC) collections from patients with solid tumour or haematological malignancy, monocytes were separated into two subpopulations. The majority of monocytes expressed CD14 at a high density without CD16 antigen (the CD14+CD16− monocytes). The remaining monocytes co-expressed CD14 and CD16 (the CD14+CD16+ monocytes). These CD14+CD16+ monocytes amounted to 20.6 ± 15.8%, while those in peripheral blood (PB) obtained from healthy volunteers were 7.3 ± 3.1% (P < 0.05). When subdividing the CD14+CD16+ monocytes into CD14brightCD16dim and CD14dimCD16bright cells, both populations were found to be increased in PBPC collections. Since typical CD14+CD16+ monocytes are the CD14dimCD16bright population, we compared the additional surface antigens on CD14dimCD16bright monocytes with those of CD14+CD16−monocytes. In PBPC collections, the CD14dimCD16bright monocytes exhibited lower levels of CD11b, CD15, CD33 and CD38 expression and higher levels of CD4, CD11a, CD11c and MHC class II, and also revealed a higher percentage of CD4+ cells and a lower percentage of CD15+ cells and CD38+ cells, compared with the CD14+CD16− monocytes. When compared with the CD14dimCD16bright monocytes in PB, those in PBPC collections exhibited higher expression of CD4 and lower expression of CD11b, and also showed higher percentages of CD4+ cells and CD38+ cells and a lower percentage of CD11b+ cells. These results suggest that PBPC collections may be rich in the CD14+CD16+ monocytes in which the proportion of the immature population is increased. It is likely that these monocytes participate in the haematological and immune recovery after PBPC transplantation