Recycling of Asphalt Concrete From I-80 in Cass County, HR-1011, 1978

Approximately 40,000 tons of deteriorated asphalt concrete has been removed from Interstate 80 in Cass County and stockpiled. Laboratory tests indicate that this material has considerable value when upgraded with new aggregate and asphalt cement. This report documents the procedures used and results obtained on an experimental recycling project. It was demonstrated that present drum mixing-recycling equipment and procedures can be used to utilize this material with satisfactory results. Laboratory analyses of material components and mixtures were performed; these analyses indicate mixture can be produced that is uniform, stable, and very closely resembles mixture produced with all virgin material. A 1700 foot long test section was constructed on US 169 in Kossuth County wherein salvaged asphalt concrete from I-80 in Cass County was utilized. The salvaged mix was blended with virgin aggregate and recycled through a modified drum mixing plant, the reprocessed mixture was satisfactorily placed 1 1/2 inches thick as a resurfacing course on an old PCC pavement. An inspection of the test section was made in December of 1978 to evaluate the performance after one full year of service. There was no evidence of rutting or shoving from traffic. The surface does, however, have a very dry and somewhat ravelled appearance. This can be related to a low asphalt content in the mix and some temperature control problems which were difficult to get fully corrected on such a short project and with a short supply of readily available materials.

Managing Subsidiary Integration During Corporate Expansion in Russia

Kansallisten rajojen yli laajentuvat yritykset kohtaavat kasvavia paineita yhtenäistää eri yksiköiden toimintatapoja, prosesseja ja järjestelmiä. Hyvin toteutettuna organisaation sisäinen integrointi voi johtaa tytäryritysten tuottavuuden parantumiseen ja strategisiin mittakaavaetuihin, kun taas huonosti toteutettuna integrointi voi johtaa lisääntyviin konflikteihin ja emoyhtiön kontrollin katoamiseen. Integroinnin kannalta Venäjälle perustettavat tytäryritykset asettavat suuria haasteita. Kasvava ja kehittyvä kansantalous on jatkuvassa muutoksessa kohti edistyneempiä ja tuottavampia toimintamalleja, mutta toisaalta yritystoiminnantaustalla vaikuttaa edelleenkin Neuvostoajan perinnöt, jotka muokkaavat yritysten johtamisrakenteita ja prosesseja. Nämä taustavaikuttajat vaikeuttavat kansainvälisen yrityksen yhtenäistämistä, mutta toisaalta tarjoavat suuria mahdollisuuksia yrityksille, jotka oppivat elämään Venäjän markkinoiden ehdoilla. Tämä tutkimus käyttää apunaan konstruktiivista tutkimustyötä ratkoakseenYIT:n Venäjälle perustettavien yritysten integrointiin liittyviä ongelmia ja mahdollisuuksia. Työn lopputuloksena syntyy oppimiseen pohjautuva integraatiostrategia ja tätä strategiaa tukeva integroinnin johtamisjärjestelmä.

Incumbent audit firm-provided tax services and the clients with low financial reporting quality

This study investigates whether incumbent audit firm-provided tax services enhance or impair the likelihood of acknowledging client companies’ low financial reporting quality. In particular, we examine the association between tax-related fees and the likelihood of timely restatements, and internal control weakness disclosures among a sample of US companies that all have misstatements in financial information. The empirical findings indicate that companies paying higher tax-related fees are less likely to disclose SOX 404 internal control weakness disclosures, implying that underlying control problems are unacknowledged when incumbent audit firm provided tax-related fees are higher. However, the findings suggest that just providing both audit and tax-related services does not have an impact on audit quality per se, but rather it is the magnitude of the tax-related fees in particular that counts. We also find some evidence suggesting that companies paying higher tax-related fees have higher likelihood of restatement lags, whereas companies paying smaller tax-related fees to their audit firm restate financial statements in a timelier manner. Overall, the findings suggest that audit scrutiny of client companies with low quality financial reporting is weaker when the magnitude of tax-related fees is higher.

Kaksiakselisen välijäähdytyksellä, välipoltolla ja rekuperaattorilla varustetun mikrokaasuturbiinin osakuormahyötysuhteen säilyvyyden optimointi

Työssä pyrittiin etsimään differentiaalievoluutioalgoritmilla kaksiakseliselle, välijäähdytyksellä, välipoltolla ja rekuperaattorilla varustetulle mikrokaasuturbiinille sellaiset kompressorien painesuhteet ja rekuperaattorin rekuperaatioaste, että saavutettaisiin mandollisimman hyvä osakuormahyötysuhteen säilyvyys. Osakuormatehon säätömenetelmäksi oli valittu pyörimisnopeussäädön ja turbiinien sisääntulolämpötilan alentamisen yhdistelmä, jossa generaattorilla varustetun akselin pyörimisnopeus sekä molempien turbiinien sisääntulolämpötilat olivat toisistaan riippumatta vapaasti säädettävissä. Työssä löydettiin optimaalinen säätömenetelmien yhdistelmä, jolla saavutetaan parempi osakuormahyötysuhteen säilyvyys, kuin millään käytetyistä menetelmistä yksinään. Lisäksi havaittiin, ettei optimaalinen säätömenetelmä merkittävästi riipu koneikolle valituista suunnittelupisteen parametreista. Osakuormahyötysuhteen säilyvyyden kannalta optimaalinen koneikko ei merkittävästi poikennut suunnittelupisteen hyötysuhteen kannalta optimaalisesta.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Freedom of choice and bounded rationality: a brief appraisal of behavioral economists' plea for light paternalism

Behavioral economics has addressed interesting positive and normative questions underlying the standard rational choice theory. More recently, it suggests that, in a real world of boundedly rational agents, economists could help people to improve the quality of their choices without any harm to autonomy and freedom of choice. This paper aims to scrutinize available arguments for and against current proposals of light paternalistic interventions mainly in the domain of intertemporal choice. It argues that incorporating the notion of bounded rationality in economic analysis and empirical findings of cognitive biases and self-control problems cannot make an indisputable case for paternalism.

Qualité des jeux de bataille père-enfant et adaptation sociale de l'enfant d'âge préscolaire

Les pères s’impliquent aujourd’hui davantage qu’auparavant auprès de leurs enfants. À l’âge préscolaire, les jeux physiques (incluant les jeux de bataille) sont une caractéristique distinctive du style paternel d’interaction. Quelques études tendent à suggérer un lien entre ce type de jeu et l’adaptation sociale des enfants. Cependant,des contradictions se dégagent de la littérature, notamment quant au lien entre la quantité de jeu physique père-enfant et des mesures d’adaptation sociale, quant aux différentes opérationnalisations de la qualité du jeu physique, ainsi qu’en ce qui a trait au genre de l’enfant. Il y a également un débat entourant le degré optimal de contrôle ou de mutualité) au cours du jeu, de même qu’un nombre très limité d’études sur le lien entre le jeu physique père-enfant et l’anxiété/retrait. Dans ce contexte de divergences entre les chercheurs, la présente thèse vise quatre objectifs, soit : 1)vérifier si la quantité de jeux de bataille père-enfant est liée à l’adaptation sociale des enfants d’âge préscolaire (via des mesures de compétence sociale, d’agressivité/irritabilité, d’agression physique et d’anxiété/retrait); 2) tester si des mesures de mutualité ou de contrôle modèrent la relation entre la quantité de jeux de bataille père-enfant et les mesures d’adaptation sociale; 3) explorer le rôle potentiel d’autres indices de qualité du jeu de bataille; 4) clarifier le rôle du genre de l’enfant. L’échantillon est composé de 100 dyades père-enfant de Montréal et les environs. Les résultats des analyses corrélationnelles suggèrent que la fréquence et la durée de jeu de bataille ne sont pas reliées directement à l’adaptation sociale des enfants et mettent en lumière des variables qui pourraient jouer un rôle modérateur. Les régressions pour modèles modérateurs indiquent que la mutualité père-enfant dans les initiations au jeu de bataille et la peur exprimée par l’enfant au cours de ce type de jeu modèrent la relation entre la durée des jeux de bataille et la compétence sociale de l’enfant d’âge préscolaire. La mutualité modère également le lien entre la durée du jeu et l’agressivité/irritabilité de l’enfant. Les initiations autoritaires faites par le père modèrent le lien entre la durée du jeu et les agressions physiques, alors qu’aucune variable ne modère le lien entre la durée du jeu et l’anxiété/retrait des enfants. Les analyses post-hoc donnent davantage d’informations sur la nature des liens de modération. Bien que les pères rapportent ne pas faire davantage de jeux de bataille, ni jouer plus longtemps à se batailler avec leurs garçons qu’avec leurs filles, trois modèles modérateurs sur quatre demeurent significatifs uniquement pour les garçons. Ces données sont interprétées à la lumière des théories éthologique et développementale. Il est suggéré que plutôt que de traiter l’agression et la compétence sociale comme des variables opposées de l’adaptation, une mesure de compétition permettrait peut-être de réconcilier les deux mondes.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Controlling the transport of an ion: classical and quantum mechanical solutions

The accurate transport of an ion over macroscopic distances represents a challenging control problem due to the different length and time scales that enter and the experimental limitations on the controls that need to be accounted for. Here, we investigate the performance of different control techniques for ion transport in state-of-the-art segmented miniaturized ion traps. We employ numerical optimization of classical trajectories and quantum wavepacket propagation as well as analytical solutions derived from invariant based inverse engineering and geometric optimal control. The applicability of each of the control methods depends on the length and time scales of the transport. Our comprehensive set of tools allows us make a number of observations. We find that accurate shuttling can be performed with operation times below the trap oscillation period. The maximum speed is limited by the maximum acceleration that can be exerted on the ion. When using controls obtained from classical dynamics for wavepacket propagation, wavepacket squeezing is the only quantum effect that comes into play for a large range of trapping parameters. We show that this can be corrected by a compensating force derived from invariant based inverse engineering, without a significant increase in the operation time.

Optimizing Robust Quantum Gates in Open Quantum Systems

We are currently at the cusp of a revolution in quantum technology that relies not just on the passive use of quantum effects, but on their active control. At the forefront of this revolution is the implementation of a quantum computer. Encoding information in quantum states as “qubits” allows to use entanglement and quantum superposition to perform calculations that are infeasible on classical computers. The fundamental challenge in the realization of quantum computers is to avoid decoherence – the loss of quantum properties – due to unwanted interaction with the environment. This thesis addresses the problem of implementing entangling two-qubit quantum gates that are robust with respect to both decoherence and classical noise. It covers three aspects: the use of efficient numerical tools for the simulation and optimal control of open and closed quantum systems, the role of advanced optimization functionals in facilitating robustness, and the application of these techniques to two of the leading implementations of quantum computation, trapped atoms and superconducting circuits. After a review of the theoretical and numerical foundations, the central part of the thesis starts with the idea of using ensemble optimization to achieve robustness with respect to both classical fluctuations in the system parameters, and decoherence. For the example of a controlled phasegate implemented with trapped Rydberg atoms, this approach is demonstrated to yield a gate that is at least one order of magnitude more robust than the best known analytic scheme. Moreover this robustness is maintained even for gate durations significantly shorter than those obtained in the analytic scheme. Superconducting circuits are a particularly promising architecture for the implementation of a quantum computer. Their flexibility is demonstrated by performing optimizations for both diagonal and non-diagonal quantum gates. In order to achieve robustness with respect to decoherence, it is essential to implement quantum gates in the shortest possible amount of time. This may be facilitated by using an optimization functional that targets an arbitrary perfect entangler, based on a geometric theory of two-qubit gates. For the example of superconducting qubits, it is shown that this approach leads to significantly shorter gate durations, higher fidelities, and faster convergence than the optimization towards specific two-qubit gates. Performing optimization in Liouville space in order to properly take into account decoherence poses significant numerical challenges, as the dimension scales quadratically compared to Hilbert space. However, it can be shown that for a unitary target, the optimization only requires propagation of at most three states, instead of a full basis of Liouville space. Both for the example of trapped Rydberg atoms, and for superconducting qubits, the successful optimization of quantum gates is demonstrated, at a significantly reduced numerical cost than was previously thought possible. Together, the results of this thesis point towards a comprehensive framework for the optimization of robust quantum gates, paving the way for the future realization of quantum computers.

Implementación de la administración de costos de calidad en TGS : impresora de valores

Basándose en la necesidad manifiesta de la empresa Thomas Greg & Sons de Colombia por controlar los costos en los que incurre por adoptar un Sistema de Gestión de Calidad, se realizó una investigación de los procesos que realiza la empresa a nivel productivo y el esquema de costos que utiliza a nivel organizacional, con el fin de proponer y concebir un modelo de administración de costos de la calidad y de la no calidad que sea conforme a lo esperado por la alta gerencia y que contribuya significativamente al control de costos.

Análisis de sensibilidad para un modelo de medición de percepción de marca basado en simulación computacional por agentes

Se presenta el análisis de sensibilidad de un modelo de percepción de marca y ajuste de la inversión en marketing desarrollado en el Laboratorio de Simulación de la Universidad del Rosario. Este trabajo de grado consta de una introducción al tema de análisis de sensibilidad y su complementario el análisis de incertidumbre. Se pasa a mostrar ambos análisis usando un ejemplo simple de aplicación del modelo mediante la aplicación exhaustiva y rigurosa de los pasos descritos en la primera parte. Luego se hace una discusión de la problemática de medición de magnitudes que prueba ser el factor más complejo de la aplicación del modelo en el contexto práctico y finalmente se dan conclusiones sobre los resultados de los análisis.

Ecuaciones diferenciales estocásticas con condición final y soluciones de viscosidad de EDPS semilineales de segundo orden

El objetivo de este documento es recopilar algunos resultados clasicos sobre existencia y unicidad ´ de soluciones de ecuaciones diferenciales estocasticas (EDEs) con condici ´ on final (en ingl ´ es´ Backward stochastic differential equations) con particular enfasis en el caso de coeficientes mon ´ otonos, y su cone- ´ xion con soluciones de viscosidad de sistemas de ecuaciones diferenciales parciales (EDPs) parab ´ olicas ´ y el´ıpticas semilineales de segundo orden.

Automatic generation of active coordinates for quantum dynamics calculations: application to the dynamics of benzene photochemistry

A new practical method to generate a subspace of active coordinates for quantum dynamics calculations is presented. These reduced coordinates are obtained as the normal modes of an analytical quadratic representation of the energy difference between excited and ground states within the complete active space self-consistent field method. At the Franck-Condon point, the largest negative eigenvalues of this Hessian correspond to the photoactive modes: those that reduce the energy difference and lead to the conical intersection; eigenvalues close to 0 correspond to bath modes, while modes with large positive eigenvalues are photoinactive vibrations, which increase the energy difference. The efficacy of quantum dynamics run in the subspace of the photoactive modes is illustrated with the photochemistry of benzene, where theoretical simulations are designed to assist optimal control experiments

Enhanced convolution approach for CAC in ATM networks, an analytical study and implementation

The characteristics of service independence and flexibility of ATM networks make the control problems of such networks very critical. One of the main challenges in ATM networks is to design traffic control mechanisms that enable both economically efficient use of the network resources and desired quality of service to higher layer applications. Window flow control mechanisms of traditional packet switched networks are not well suited to real time services, at the speeds envisaged for the future networks. In this work, the utilisation of the Probability of Congestion (PC) as a bandwidth decision parameter is presented. The validity of PC utilisation is compared with QOS parameters in buffer-less environments when only the cell loss ratio (CLR) parameter is relevant. The convolution algorithm is a good solution for CAC in ATM networks with small buffers. If the source characteristics are known, the actual CLR can be very well estimated. Furthermore, this estimation is always conservative, allowing the retention of the network performance guarantees. Several experiments have been carried out and investigated to explain the deviation between the proposed method and the simulation. Time parameters for burst length and different buffer sizes have been considered. Experiments to confine the limits of the burst length with respect to the buffer size conclude that a minimum buffer size is necessary to achieve adequate cell contention. Note that propagation delay is a no dismiss limit for long distance and interactive communications, then small buffer must be used in order to minimise delay. Under previous premises, the convolution approach is the most accurate method used in bandwidth allocation. This method gives enough accuracy in both homogeneous and heterogeneous networks. But, the convolution approach has a considerable computation cost and a high number of accumulated calculations. To overcome this drawbacks, a new method of evaluation is analysed: the Enhanced Convolution Approach (ECA). In ECA, traffic is grouped in classes of identical parameters. By using the multinomial distribution function instead of the formula-based convolution, a partial state corresponding to each class of traffic is obtained. Finally, the global state probabilities are evaluated by multi-convolution of the partial results. This method avoids accumulated calculations and saves storage requirements, specially in complex scenarios. Sorting is the dominant factor for the formula-based convolution, whereas cost evaluation is the dominant factor for the enhanced convolution. A set of cut-off mechanisms are introduced to reduce the complexity of the ECA evaluation. The ECA also computes the CLR for each j-class of traffic (CLRj), an expression for the CLRj evaluation is also presented. We can conclude that by combining the ECA method with cut-off mechanisms, utilisation of ECA in real-time CAC environments as a single level scheme is always possible.