Parameter sensitivity for optimal design of 65 nm node SOI transistors

Mixed-mode simulation, where device simulation is embedded directly within a circuit simulator, is used for the first time to provide scaling guidelines to achieve optimal digital circuit performance for double gate SOI MOSFETs. This significant advance overcomes the lack of availability of SPICE model parameters. The sensitivity of the gate delay and on-off current ratio to each of the key geometric and technological parameters of the transistor is quantified. The impact of the source-drain doping profile on circuit performance is comprehensively investigated.

Optimal basis set for electronic structure calculations in periodic systems

An efficient method for calculating the electronic structure of systems that need a very fine sampling of the Brillouin zone is presented. The method is based on the variational optimization of a single (i.e., common to all points in the Brillouin zone) basis set for the expansion of the electronic orbitals. Considerations from k.p-approximation theory help to understand the efficiency of the method. The accuracy and the convergence properties of the method as a function of the optimal basis set size are analyzed for a test calculation on a 16-atom Na supercell.

Element partitioning and potential mobility within surface dusts on buildings in a polluted urban environment, Budapest.

A voluminous literature exists on the analysis of water-soluble ions extracted from gypsum crusts and patinas formed on building surfaces. However, less data is available on the intermediate dust layer and the important role its complex matrix and constituents play in crust/patina formation. To address this issue, surface dust samples were collected from two buildings in the city of Budapest. Substrate properties, different pollution levels and environmental variations were considered by collecting samples from a city centre granite building exposed to intense traffic conditions and from an oolitic limestone church situated in a pedestrian area outside and high above the main pollution zone. Selective extraction examines both water-soluble ions (Ca2+, Mg2+, Na+, K+, Cl-, NO3- SO42-) and selected elements (Fe, Mn, Zn, Cu, Cr, Pb, Ni) from the water-soluble, exchangeable/carbonate, amorphous Mn, amorphous Fe/Mn, crystalline Fe/Mn, organic and residual phases, their mobility and potential to catalyse heterogeneous surface reactions. Salt weathering processes are highlighted by high concentrations of water-soluble Ca2+, Na+, Cl- and SO42-- at both sites. Manganese, Zn and Cu and to a lesser extent Pb and Ni, are very mobile in the city centre dust, where 30%, 54%, 38%, 11% and 11% of their totals are bound by the water-soluble phase, respectively. Church dust shows a sharp contrast for Mn, Zn, Cu and Pb with only 3%, 1%, 12% and 3% of their totals being bound by the water-soluble phase respectively. This may be due to (a) different environmental conditions at the church e.g. lower humidity (b) continuous replenishment of salts under intensive city centre traffic conditions (c) enrichment in oxidisable organic carbon by a factor of 4.5 and a tenfold increase in acidity in the city centre dust.

Optimal Body Size and Energy Expenditure during Winter: Why are voles smaller in declining populations?

Winter is energetically challenging for small herbivores because of greater energy requirements for thermogenesis at a time when little energy is available. We formulated a model predicting optimal wintering body size, accounting for the scaling of both energy expenditure and assimilation to body size, and the trade-off between survival benefits of a large size and avoiding survival costs of foraging. The model predicts that if the energy cost of maintaining a given body mass differs between environments, animals should be smaller in the more demanding environments, and there should be a negative correlation between body mass and daily energy expenditure (DEE) across environments. In contrast, if animals adjust their energy intake according to variation in survival costs of foraging, there should be a positive correlation between body mass and DEE. Decreasing temperature always increases equilibrium DEE, but optimal body mass may either increase or decrease in colder climates depending on the exact effects of temperature on mass-specific survival and energy demands. Measuring DEE with doubly labeled water on wintering Microtus agrestis at four field sites, we found that DEE was highest at the sites where voles were smallest despite a positive correlation between DEE and body mass within sites. This suggests that variation in wintering body mass between sites was due to variation in food quality/availability and not adjustments in foraging activity to varying risks of predation.

Optimal Credit Rationing in Not-For-Profit Financial Institutions

We examine the dynamic optimization problem for not-for-profit financial institutions (NFPs) that maximize consumer surplus, not profits. We characterize the optimal dynamic policy and find that it involves credit rationing. Interest rates set by mature NFPs will typically be more favorable to customers than market rates, as any surplus is distributed in the form of interest rate subsidies, with credit rationing being required to prevent these subsidies from distorting loan volumes from their optimal levels. Rationing overcomes a fundamental problem in NFPs; it allows them to distribute the surplus without distorting the volume of activity from the efficient level.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.