Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

On the momentum balance in linear-combination models for the transition zone

The momentum balance of the linear-combination integral model for the transition zone is investigated for constant pressure flows. The imbalance is found to be small enough to be negligible for all practical purposes. [S0889-504X(00)00703-0].

Prediction of inplane damping from deterministic and stochastic models

This paper reviews computational reliability, computer algebra, stochastic stability and rotating frame turbulence (RFT) in the context of predicting the blade inplane mode stability, a mode which is at best weakly damped. Computational reliability can be built into routine Floquet analysis involving trim analysis and eigenanalysis, and a highly portable special purpose processor restricted to rotorcraft dynamics analysis is found to be more economical than a multipurpose processor. While the RFT effects are dominant in turbulence modeling, the finding that turbulence stabilizes the inplane mode is based on the assumption that turbulence is white noise.

Studies directed towards the synthesis of schisanartane and related complex nortriterpenoids: construction of models of the peripheral ABC and FGH segments of rubrifloradilactone C

A conceptually unifying and flexible approach to the ABC and FGH segments of the nortriterpenoid rubrifloradilactone C, each embodying a furo[3,2-b]furanone moiety, from the appropriate Morita-Baylis-Hillman adducts is delineated. (C) 2010 Elsevier Ltd. All rights reserved.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Development of mass balance models for lakes

Yhteenveto: Järvien ainetasemallien kehittäminen.

Snow cover models in operational watershed forecasting

Yhteenveto: Lumimallit vesistöjen ennustemalleissa

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Numerically Exact Study of Polarizabilities and Hyperpolarizabilities of Correlated Conjugated Organic Models

Polarizabilities and Hyperpolarizabilities of conjugated organic chains are calculated using correlated model Hamiltonians. While correlations reduce the Polarizabilities and extend the range of linear response, the Hyperpolarizabilities essentially are unaffected by the same. This explains the apparently large Hyperpolarizabilities of conjugated electronic systems.

Ordering near the percolation threshold in models of two-dimensional interacting bosons with quenched dilution

Randomly diluted quantum boson and spin models in two dimensions combine the physics of classical percolation with the well-known dimensionality dependence of ordering in quantum lattice models. This combination is rather subtle for models that order in two dimensions but have no true order in one dimension, as the percolation cluster near threshold is a fractal of dimension between 1 and 2: two experimentally relevant examples are the O(2) quantum rotor and the Heisenberg antiferromagnet. We study two analytic descriptions of the O(2) quantum rotor near the percolation threshold. First a spin-wave expansion is shown to predict long-ranged order, but there are statistically rare points on the cluster that violate the standard assumptions of spin-wave theory. A real-space renormalization group (RSRG) approach is then used to understand how these rare points modify ordering of the O(2) rotor. A new class of fixed points of the RSRG equations for disordered one-dimensional bosons is identified and shown to support the existence of long-range order on the percolation backbone in two dimensions. These results are relevant to experiments on bosons in optical lattices and superconducting arrays, and also (qualitatively) for the diluted Heisenberg antiferromagnet La-2(Zn,Mg)(x)Cu1-xO4.

World parliament – fact or fiction? On the feasibility of world parliamentary models

Cosmopolitan ideals have been on the philosophical agenda for several millennia but the end of the Cold War started a new discussion on state sovereignty, global democracy, the role of international law and global institutions. The Westphalian state system in practice since the 17th century is transforming and the democracy deficit needs new solutions. An impetus has been the fact that in the present world, an international body representing global citizens does not exist. In this Master’s thesis, the possibility of establishing a world parliament is examined. In a case analysis, 17 models on world parliament from two journals, a volume of essays and two other publications are discussed. Based on general observations, the models are divided into four thematic groups. The models are analyzed with an emphasis on feasible and probable elements. Further, a new scenario with a time frame of thirty years is proposed based on the methodology of normative futures studies, taking special interest in causal relationships and actions leading to change. The scenario presents three gradual steps that each need to be realized before a sustainable world parliament is established. The theoretical framework is based on social constructivism, and changes in international and multi-level governance are examined with the concepts of globalization, democracy and sovereignty. A feasible, desirable and credible world parliament is constituted gradually by implying electoral, democratic and legal measures for members initially from exclusively democratic states, parliamentarians, non-governmental organizations and other groups. The parliament should be located outside the United Nations context, since a new body avoids the problem of inefficiency currently prevailing in the UN. The main objectives of the world parliament are to safeguard peace and international law and to offer legal advice in cases when international law has been violated. A feasible world parliament is advisory in the beginning but it is granted legislative powers in the future. The number of members in the world parliament could also be extended following the example of the EU enlargement process.