On the measurement of the surface energy budget over a land surface during the summer monsoon

The measurement of surface energy balance over a land surface in an open area in Bangalore is reported. Measurements of all variables needed to calculate the surface energy balance on time scales longer than a week are made. Components of radiative fluxes are measured while sensible and latent heat fluxes are based on the bulk method using measurements made at two levels on a micrometeorological tower of 10 m height. The bulk flux formulation is verified by comparing its fluxes with direct fluxes using sonic anemometer data sampled at 10 Hz. Soil temperature is measured at 4 depths. Data have been continuously collected for over 6 months covering pre-monsoon and monsoon periods during the year 2006. The study first addresses the issue of getting the fluxes accurately. It is shown that water vapour measurements are the most crucial. A bias of 0.25% in relative humidity, which is well above the normal accuracy assumed the manufacturers but achievable in the field using a combination of laboratory calibration and field intercomparisons, results in about 20 W m(-2) change in the latent heat flux on the seasonal time scale. When seen on the seasonal time scale, the net longwave radiation is the largest energy loss term at the experimental site. The seasonal variation in the energy sink term is small compared to that in the energy source term.

Hybrid framework iron(II) phosphate-oxalates

Two inorganic-organic hybrid framework iron phosphate-oxalates, I, [N2C4H12](0.5)[Fe-2(HPO4)(C2O4)(1.5)] and II, [Fe-2(OH2)PO4(C2O4)(0.5)] have been synthesized by hydrothermal means and the structures determined by X-ray crystallography. Crystal Data: compound I, monoclinic, spacegroup = P2(1)/c (No. 14), a=7.569(2) Angstrom, b=7.821(2) Angstrom, c=18.033(4) Angstrom, beta=98.8(1)degrees, V=1055.0(4) Angstrom(3), Z=4, M=382.8, D-calc=2.41 g cm(-3) MoK alpha, R-F=0.02; compound II, monoclinic, spacegroup=P2(1)/c (No. 14), a=10.240(1) b=6.375(3) Angstrom, 9.955(1) Angstrom, beta=117.3(1)degrees, V=577.4(1) Angstrom(3), Z=4, M=268.7, D-calc=3.09 g cm(-3) MoK alpha, R-F=0.03. These materials contain a high proportion of three-coordinated oxygens and [Fe2O9] dimeric units, besides other interesting structural features. The connectivity of Fe2O9 is entirely different in the two materials resulting in the formation of a continuous chain of Fe-O-Fe in II. The phosphate-oxalate containing the amine, I, forms well-defined channels. Magnetic susceptibility measurements show Fen to be in the high-spin state (t(2g)(4)e(g)(2)) in II, and in the intermediate-spin state (t(2g)(5)e(g)(1)) in I.

The Parallel Complexity Of Propagation In Boolean Circuits

We consider the problem of deciding whether the output of a boolean circuit is determined by a partial assignment to its inputs. This problem is easily shown to be hard, i.e., co-Image Image -complete. However, many of the consequences of a partial input assignment may be determined in linear time, by iterating the following step: if we know the values of some inputs to a gate, we can deduce the values of some outputs of that gate. This process of iteratively deducing some of the consequences of a partial assignment is called propagation. This paper explores the parallel complexity of propagation, i.e., the complexity of determining whether the output of a given boolean circuit is determined by propagating a given partial input assignment. We give a complete classification of the problem into those cases that are Image -complete and those that are unlikely to be Image complete.

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.