Gathering support from rivals: the two rivals case

Two voters must choose between two alternatives. Voters vote in a fixed linear order. If there is not unanimity for any alternative, the procedure is repeated. At every stage, each voter prefers the same alternative to the other, has utilities decreasing with stages, and has an impatience degree representing when it is worth voting for the non-preferred alternative now rather than waiting for the next stage and voting for the preferred alternative. Intuition suggests that the more patient voter will get his preferred alternative. I found that in the unique solution of the sequential voting procedure obtained by backward induction, the first voter get his preferred alternative at the first stage independently from his impatience rate. Keywords: sequential voting, impatience rate, multi-stage voting, unanimity

Customer Driven Order-to-Delivery Process

This thesis was made for a large forest industry company’s business segment. The purpose of the study was to improve the performance of the order-to-delivery process of the business segment. The study proceeded in three phases. The first phase was to define customer expectations in the market. The second phase was to analyse the performance and the operations of the order-to-delivery process, and to define any challenges or problems in serving the customers. The third and final phase was improving the performance of the order-to-delivery process, within the scope defined by the first two phases. The analysis showed that the delivery reliability is an essential but a challenging issue in the case company’s markets. On delivery reliability standpoint, the most challenging factors were the detected information flow distortions within the company as well as in the whole supply chain, and the lack of horizontal control over the multi-stage process.

Hydroksihappojen erotus mustalipeästä nanosuodatuksella

Hydroksihapoille on olemassa useita käyttömahdollisuuksia teollisuudessa, joten niiden hyödyntäminen sellunvalmistuksen sivuvirrasta eli mustalipeästä on suuren kiinnostuksen kohteena. Tässä työssä selvitettiin, onko hydroksihappojen erotus ja puhdistus mustalipeästä mahdollista nanosuodatuksella. Kokeellisessa osassa suodatettiin emäksisen mustalipeän lisäksi hapotettua ja jäähdytyskiteytettyä mustalipeää, johon oli lisätty liuotinta. Mustalipeäsuodatuksissa käytettiin viittä erilaista nanosuodatusmembraania (Microdyn Nadir® NP010 ja NP030, Dow Chemical Company NF-90, Woongjin Chemical NE-70 sekä Ge-Osmonics Desal 5 DK). Kirjallisuusosassa käsiteltiin puun sisältämien yhdisteiden kemiallista koostumusta, sellun valmistuksen pääperiaatteita, mustalipeän ja hydroksihappojen ominaisuuksia sekä hydroksihappojen käyttömahdollisuuksia. Lisäksi tarkasteltiin erilaisia hydroksihappojen erotusmenetelmiä, nanosuodatuksen teoriaa ja prosessiin sopivan membraanin valintakriteerejä. Työn kokeellisessa osassa tutkittiin emäksisen mustalipeän monivaiheisen nanosuodatuksen tehokkuutta hydroksihappojen erotuksessa. Hapotetun ja jäähdytyskiteytetyn mustalipeän suodatuskokeissa tutkittiin erityyppisten membraanien erotuskykyä sekä syötön liuotinlisäyksen vaikutusta hydroksihappojen erottumiseen. Lisäksi tarkasteltiin membraanien kestävyyttä ja foulaantumista suodatusolosuhteissa. Työn tulokset osoittivat, että hydroksihappoja voidaan fraktioida mustalipeästä nanosuodatuksella. Hydroksihappojen fraktiointiin vaikuttaa merkittävästi mustalipeässä käytetyn liuottimen läsnäolo sekä suodatuspaine. Lisäksi koetulosten perusteella havaittiin, että monivaiheisella nanosuodatuksella hydroksihapot läpäisevät membraanin ja permeaattiin saavutetaan puhtaampi happojae.

Estimating the most important criteria for hatching eggs as function of broiler breeders age

This research aimed to compare two female broiler breeder ages during the incubation period regarding management using the Analytic Hierarchy Process method (AHP). This method is characterized by the possibility of analyzing a multicriteria problem and assists a decision making. This study was carried out on a commercial hatchery located in São Paulo, Brazil. Two ages of broiler breeder (42 and 56 weeks) were compared relative to production rate. Production index data were the same in both ages and were submitted to multicriteria decision analysis using the AHP method. The results indicate that broiler breeders of 42 weeks presented better performance than those of 56 week-old. The setter phase (incubation) is more critical than the hatcher. The AHP method was efficient for this analysis and can serve as a methodological basis for future studies to improve the hatchability of broilers eggs.

Comparison of sustainability of technologies for water desalination

The aim of this work is to perform an in-depth overview on the sustainability of several major commercialized technologies for water desalination and to identify the challenges and propose suggestions for the development of water desalination technologies. The overview of those technologies mainly focuses on the sustainability from the viewpoint of total capital investment, total product cost, energy consumption and global warming index. Additionally, a systematic sustainability assessment methodology has been introduced to validate the assessment process. Conclusions are:1) Reverse osmosis desalination (RO) plants are better than multi-stage flash distillation (MSF) desalination plants and multiple-effect distillation (MED) desalination plants from the viewpoint of energy consumption, global warming index and total production cost; 2)Though energy intensive, MSF plants and MED plants secure their advantages over RO plants by lower total capital investment, wider applicability and purer water desalted and they are still likely to flourish in energy-rich area;3) Water production stage and wastewater disposal stage are the two stages during which most pollutant gases are emitted. The water production stage alone contributes approximately 80~90% of the total pollutant gases emission during its life cycle; 4)The total capital cost per m3 desalted water decreases remarkably with the increasing of plant capacity. The differences between the capital cost per m3 desalted water of RO and other desalination plants will decrease as the capacity increases; 5) It is found that utilities costs serve as the major part of the total product cost, and they account for 91.16%, 85.55% and 71.26% of the total product cost for MSF, MED and RO plants, respectively; 6) The absolute superiority of given technology depends on the actual social-economic situation (energy prices, social policies, technology advancements).

Dynamic carbon dioxide liquidization model

In literature CO 2 liquidization is well studied with steady state modeling. Steady state modeling gives an overview of the process but it doesn’t give information about process behavior during transients. In this master’s thesis three dynamic models of CO2 liquidization were made and tested. Models were straight multi-stage compression model and two compression liquid pumping models, one with and one without cold energy recovery. Models were made with Apros software, models were also used to verify that Apros is capable to model phase changes and over critical state of CO 2. Models were verified against compressor manufacturer’s data and simulation results presented in literature. From the models made in this thesis, straight compression model was found to be the most energy efficient and fastest to react to transients. Also Apros was found to be capable tool for dynamic liquidization modeling.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Brain tumor and brain endothelial cells' response to ionizing radiation and phytochemical treatments

Le glioblastome multiforme (GBM) représente la tumeur cérébrale primaire la plus agressive et la plus vascularisée chez l’adulte. La survie médiane après le diagnostic est de moins d’un an en l’absence de traitement. Malheureusement, 90% des patients traités avec de la radiothérapie après la résection chirurgicale d’un GBM développent une récidive tumorale. Récemment, le traitement des GBM avec radiothérapie et témozolomide, un agent reconnu pour ses propriétés antiangiogéniques, a permis de prolonger la survie médiane à 14,6 mois. Des efforts sont déployés pour identifier des substances naturelles capables d’inhiber, de retarder ou de renverser le processus de carcinogenèse. Epigallocatechin-3-gallate (EGCG), un polyphénol retrouvé dans le thé vert, est reconnu pour ses propriétés anticancéreuses et antiangiogéniques. L’EGCG pourrait sensibiliser les cellules tumorales cérébrales et les cellules endothéliales dérivées des tumeurs aux traitements conventionnels. Le chapitre II décrit la première partie de ce projet de doctorat. Nous avons tenté de déterminer si l’EGCG pourrait sensibiliser la réponse des GBM à l’irradiation (IR) et si des marqueurs moléculaires spécifiques sont impliqués. Nous avons documenté que les cellules U-87 étaient relativement radiorésistantes et que Survivin, une protéine inhibitrice de l’apoptose, pourrait être impliquée dans la radiorésistance des GBM. Aussi, nous avons démontré que le pré-traitement des cellules U-87 avec de l’EGCG pourrait annuler l’effet cytoprotecteur d’une surexpression de Survivin et potentialiser l’effet cytoréducteur de l’IR. Au chapitre III, nous avons caractérisé l’impact de l’IR sur la survie de cellules endothéliales microvasculaires cérébrales humaines (HBMEC) et nous avons déterminé si l’EGCG pouvait optimiser cet effet. Bien que les traitements individuels avec l’EGCG et l’IR diminuaient la survie des HBMEC, le traitement combiné diminuait de façon synergique la survie cellulaire. Nous avons documenté que le traitement combiné augmentait la mort cellulaire, plus spécifiquement la nécrose. Au chapitre IV, nous avons investigué l’impact de l’IR sur les fonctions angiogéniques des HBMEC résistantes à l’IR, notamment la prolifération cellulaire, la migration cellulaire en présence de facteurs de croissance dérivés des tumeurs cérébrales, et la capacité de tubulogenèse. La voie de signalisation des Rho a aussi été étudiée en relation avec les propriétés angiogéniques des HBMEC radiorésistantes. Nos données suggèrent que l’IR altère significativement les propriétés angiogéniques des HBMEC. La réponse aux facteurs importants pour la croissance tumorale et l’angiogenèse ainsi que la tubulogenèse sont atténuées dans ces cellules. En conclusion, ce projet de doctorat confirme les propriétés cytoréductrices de l’IR sur les gliomes malins et propose un nouveau mécanisme pour expliquer la radiorésistance des GBM. Ce projet documente pour la première fois l’effet cytotoxique de l’IR sur les HBMEC. Aussi, ce projet reconnaît l’existence de HBMEC radiorésistantes et caractérise leurs fonctions angiogéniques altérées. La combinaison de molécules naturelles anticancéreuses et antiangiogéniques telles que l’EGCG avec de la radiothérapie pourrait améliorer l’effet de l’IR sur les cellules tumorales et sur les cellules endothéliales associées, possiblement en augmentant la mort cellulaire. Cette thèse supporte l’intégration de nutriments avec propriétés anticancéreuses et antiangiogéniques dans le traitement des gliomes malins pour sensibiliser les cellules tumorales et endothéliales aux traitements conventionnels.

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.

Elastic constants of Ni-Mn-Ga magnetic shape memory alloys

We have measured the adiabatic second order elastic constants of two Ni-Mn-Ga magnetic shape memory crystals with different martensitic transition temperatures, using ultrasonic methods. The temperature dependence of the elastic constants has been followed across the ferromagnetic transition and down to the martensitic transition temperature. Within experimental errors no noticeable change in any of the elastic constants has been observed at the Curie point. The temperature dependence of the shear elastic constant C' has been found to be very different for the two alloys. Such a different behavior is in agreement with recent theoretical predictions for systems undergoing multi-stage structural transitions.

An evaluation of Primary health care system in Kerala

The thesis entitled An Evaluation of Primary Health Care System in Kerala. The present study is intended to examine the working of primary health care system and its impact on the health status of people. The hypothesis tested in the thesis includes, a. The changes in the health profile require reallocation of resources of primary health care system, b. Rate of utilization depends on the quality of services provided by primary health centers, and c. There is a significant decline in the operational efficiency of the primary health care system. The major elements of primary health care stated in the report of AlmaAta International Conference on Primary Health Care (WHO, 1994)” is studied on the basis of the classification of the elements in to three: Preventive, Promotive, and Curative measures. Preventive measures include Maternal and Child Health Care including family Planning. Provision of water and sanitation is reviewed under promotive measures. Curative measures are studied using the disease profile of the study area. Collection of primary data was done through a sample survey, using pre-tested interview schedule of households of the study area. Multi stage random sampling design was used for selecting the sample. The design of the present study is both descriptive and analytical in nature. As far as the analytical tools are concerned, growth index, percentages, ratios, rates, time series analysis, analysis of variance, chi square test, Z test were used for analyzing the data. Present study revealed that no one in these areas was covered under any type of health insurance. Conclusion states that considering the present changes in the health profile, traditional pattern of resource allocation should be altered to meet the urgent health care needs of the people. Preventive and promotive measures like health education for giving awareness among people to change health habits, diet pattern, life style etc. are to be developed. Proper diagnosis and treatment of the disease at the beginning of the stage itself may help to cure majority of disease. For that, Public health policy must ensure the primary health care as enunciated at Alma- Ata international Conference. At the same time Public health is not to be treated as the sole responsibility of the government. Active community participation is an essential means to attain the goals.

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Reinforcement Learning approaches to Economic Dispatch problem

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems