Synthesis and characterization of poly(ethylene aspartate)-metal complexes

Poly(ethylene aspartate) [PEA] was synthesized by the melt condensation of D,L-aspartic acid and ethylene glycol. PEA containing pendent amino and carbonyl groups in its repeating chain was used as the polymeric ligand for complexation with transition metal ions, viz. Co(II), Ni(II), Cu(II), Mn(II), Zn(II), Cd(II), Ca(II), Mg(II), Pb(II) and Hg(II). Complexation was found to be most effective in DMSO. The resulting polyester-metal complexes were solid coloured materials which have been characterized by IR spectroscopy, elemental analysis and magnetic susceptibility measurements. The thermal stability of the polyester-metal complexes was investigated by thermogravimetric analysis (TGA). On the basis of the physico-chemical studies, an oxygen and nitrogen coordinated structure for the polyester-metal complexes is proposed.     

Cut off frequency and transit time analysis of lightly doped drain (LDD) MOSFETs

Analytical model for the transconductance, cut off frequency, transit time and fringing capacitance of LDD MOSFETs is presented with a simple approach. The analysis is carried out considering the LDD device as a conventional MOSFET with a series resistance [Z.-H. Liu et al., Threshold voltage model for submicrometer MOSFETs. IEEE Trans Electron Devices 1993; ED-40: 86–94] and a simple closed form expressions for cut off frequency and transit time is obtained. The total gate capacitance, i.e. the geometric and fringing capacitance, is calculated for both LDD and non-LDD devices and lower fringing capacitance is reported in LDD devices. Lower cut-off frequencies and higher transit time are reported in LDD devices for the same channel length.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.