Coopetition as a lead generating mechanism: design, modelling and simulation

The aim of this research is to develop a tool that could allow to organize coopetitional relationships between organizations on the basis of two-sided Internet platform. The main result of current master thesis is a detailed description of the concept of the lead generating internet platform-based coopetition. With the tools of agent-based modelling and simulation, there were obtained results that could be used as a base for suggestion that the developed concept is able to cause a positive effect on some particular industries (e.g. web-design studios market) and potentially can bring some benefits and extra profitability for most companies that operate on this particular industry. Also on the basis of the results it can be assumed that the developed instrument is also able to increase the degree of transparency of the market to which it is applied.

Performance Degradation Modelling and Techno-Economic Analysis of Lithium-Ion Battery Energy Storage Systems

Transmission system operators and distribution system operators are experiencing new challenges in terms of reliability, power quality, and cost efficiency. Although the potential of energy storages to face those challenges is recognized, the economic implications are still obscure, which introduce the risk into the business models. This thesis aims to investigate the technical and economic value indicators of lithium-ion battery energy storage systems (BESS) in grid-scale applications. In order to do that, a comprehensive performance lithium-ion BESS model with degradation effects estimation is developed. The model development process implies literature review on lifetime modelling, use, and modification of previous study progress, building the additional system parts and integrating it into a complete tool. The constructed model is capable of describing the dynamic behavior of the BESS voltage, state of charge, temperature and capacity loss. Five control strategies for BESS unit providing primary frequency regulation are implemented, in addition to the model. The questions related to BESS dimensioning and the end of life (EoL) criterion are addressed. Simulations are performed with one-month real frequency data acquired from Fingrid. The lifetime and cost-benefit analysis of the simulation results allow to compare and determine the preferable control strategy. Finally, the study performs the sensitivity analysis of economic profitability with variable size, EoL and system price. The research reports that BESS can be profitable in certain cases and presents the recommendations.

Reliability of a new measure of motoneuron excitability

Objectlve:--This study examined the intraclass reliability· of different measures of the excitability of the Hoffmann reflex, derived from stimulus-response curves. The slope of the regression line of the H-reflex stimulus-response curve advocated by Funase et al. (1994) was also compared to the peak of the first derivative of the H-reflex stimulus-response curve (dHIdVmax), a new measure introduced in this investigation. A secondary purpose was to explore the possibility of mood as a covariate when measuring excitability of the H-reflex arc. Methods: The H-reflex amplitude at a stimulus intensity corresponding to 5% of the maximum M-wave (Mmax) is an established measure that was used as an additional basis of comparison. The H-reflex was elicited in the soleus for 24 subjects (12 males and 12 females) on five separate days. Vibration was applied to the Achilles tendon prior to stimulation to test the sensitivity of the measures on test day four. The means of five evoked potentials at each gradually increasing intensity, from below H-reflex threshold to above Mmax, were used to create both the H-reflex and M-wave stimulus response curves for each subject across test days. The mood of the subjects was assessed using the Subjective Exercise Experience Scale (SEES) prior to the stimulation protocol each day. Results: There was a modest decrease in all H-reflex measures from the first to third test day, but it was non-significant (P's>0.05). All measures of the H-reflex exhibited a profound reduction following vibration on test day four, and then returned to baseline levels on test day five (P's<0.05). The intraclass correlation coefficient (ICC) for H-reflex amplitude at 5% of Mmax was 0.85. The ICC for the slope of the regression line was 0.79 while it was 0.89 for dH/dVmax. Maximum M-wave amplitude had an ICC of 0.96 attesting to careful methodological controls. The SEES subscales of fatigue and psychological well-being remained unchanged IV across the five days. The psychological distress subscale (PO.05). Conclusions: The peak of the first derivative of the H-reflex stimulus-response curve (dH/dVmax) was shown to have comparable reliability and sensitivity to other more established measures of excitability. Psychological distress and the amplitude of the H-reflex at 5% Mmax follow similar trends across days, however there was no significant correlation between the two measures. Significance: The proposed method appears to be a more robust measure ofH-reflex excitability than the other methods tested. As such it would be an advantageous method to apply in clinical and investigative settings. Additionally, the results suggest that the relationship between psychological distress and H-reflex amplitude should be investigated further.

The reliability of an isometric test based on constant perception of effort

To date there is no documented procedure to extrapolate findings of an isometric nature to a whole body performance setting. The purpose of this study was to quantify the reliability of perceived exertion to control neuromuscular output during an isometric contraction. 21 varsity athletes completed a maximal voluntary contraction and a 2 min constant force contraction at both the start and end of the study. Between pre and post testing all participants completed a 2 min constant perceived exertion contraction once a day for 4 days. Intra-class correlation coefficient (R=O.949) and standard error of measurement (SEM=5.12 Nm) concluded that the isometric contraction was reliable. Limits of agreement demonstrated only moderate initial reliability, yet with smaller limits towards the end of 4 training sessions. In conclusion, athlete's na"ive to a constant effort isometric contraction will produce reliable and acceptably stable results after 1 familiarization sessions has been completed.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Reliability of muscle fiber conduction velocity in the tibialis anterior

This document could not have been completed without the hard work of a number of individuals. First and foremost, my supervisor, Dr. David Gabriel deserves the utmost recognition for the immense effort and time spent guiding the production of this document through the various stages of completion. Also, aiding in the data collection, technical support, and general thought processing were Lab Technician Greig Inglis and fellow members of the Electromyographic Kinesiology Laboratory Jon Howard, Sean Lenhardt, Lara Robbins, and Corrine Davies-Schinkel. The input of Drs. Ted Clancy, Phil Sullivan and external examiner Dr. Anita Christie, all members ofthe assessment committee, was incredibly important and vital to the completion of this work. Their expertise provided a strong source of knowledge and went to ensure that this project was completed at exemplary level. There were a number of other individuals who were an immense help in getting this project off the ground and completed. The donation of their time and efforts was very generous and much needed in order to fulfill the requirements needed for completion of this study. Finally, I cannot exclude the contributions of my family throughout this project especially that of my parents whose support never wavers.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Éffectivité des subventions à la recherche et au développement, évaluation circonstancielle à l'aide d'une micro-simulation.

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