Scheduling forest road maintenance using the analytic hierarchy process and heuristics

Abstract

Reconstruction of networks from their betweenness centrality

In this paper we study the reconstruction of a network topology from the values of its betweenness centrality, a measure of the influence of each of its nodes in the dissemination of information over the network. We consider a simple metaheuristic, simulated annealing, as the combinatorial optimization method to generate the network from the values of the betweenness centrality. We compare the performance of this technique when reconstructing different categories of networks –random, regular, small-world, scale-free and clustered–. We show that the method allows an exact reconstruction of small networks and leads to good topological approximations in the case of networks with larger orders. The method can be used to generate a quasi-optimal topology fora communication network from a list with the values of the maximum allowable traffic for each node.

The efficiency of indicator groups for the conservation of amphibians in the Brazilian Atlantic Forest

The adequate selection of indicator groups of biodiversity is an important aspect of the systematic conservation planning. However, these assessments differ in the spatial scale, in the methods used and in the groups considered to accomplish this task, which generally produces contradictory results. The quantification of the spatial congruence between species richness and complementarity among different taxonomic groups is a fundamental step to identify potential indicator groups. Using a constructive approach, the main purposes of this study were to evaluate the performance and efficiency of eight potential indicator groups representing amphibian diversity in the Brazilian Atlantic Forest. Data on the geographic range of amphibian species that occur in the Brazilian Atlantic Forest was overlapped to the full geographic extent of the biome, which was divided into a regular equal-area grid. Optimization routines based on the concept of complementarily were applied to verify the performance of each indicator group selected in relation to the representativeness of the amphibians in the Brazilian Atlantic Forest as a whole, which were solved by the algorithm"simulated annealing", through the use of the software MARXAN. Some indicator groups were substantially more effective than others in regards to the representation of the taxonomic groups assessed, which was confirmed by the high significance of data (F = 312.76; p < 0.01). Leiuperidae was considered as the best indicator group among the families analyzed, as it showed a good performance, representing 71% of amphibian species in the Brazilian Atlantic Forest (i.e. 290 species), which may be associated with the diffuse geographic distribution of its species. This study promotes understanding of how the diversity standards of amphibians can be informative for systematic conservation planning on a regional scale.

Heteropentámeros (etanol)4-agua: estudio estructural y termodinámico

Stochastic exploration of the potential energy surface of (ethanol)4-water heteropentamers through simulated annealing calculations was used to find probable structures of these clusters. Subsequent geometry optimization with the B3LYP/6-31+G(d) approach of these initial structures led to 13 stable heteropentamers. The strength of the hydrogen bonds of the type O"H-O (primary) and their spatial arrangements seem to be responsible for the geometric preferences and the high stability of these heteropentamers. This result is a consequence of the presence of the cooperative effects among such interactions. There is no significant influence of the secondary hydrogen bonds (C"H-O) on the stability of the heteropentamers.

Um modelo de regulação florestal e suas implicações na formulação e solução de problemas com restrições de recobrimento

Este trabalho teve como objetivo avaliar uma estratégia utilizada para geração de alternativas de manejo na formulação e solução de problemas de planejamento florestal com restrições de recobrimento. O problema de planejamento florestal foi formulado via modelo I e modelo II, assim denominados por Johnson E Scheurman (1977), resultando em problemas de programação linear inteira com 63 e 42 alternativas de manejo, respectivamente. Conforme esperado, no problema formulado via modelo I não houve violação das restrições de recobrimento, enquanto no problema formulado via modelo II algumas unidades de manejo foram fracionadas, fato já esperado, uma vez que essa formulação não assegura a integridade das unidades de manejo. Na formulação via modelo II, para assegurar a integridade das unidades de manejo foi necessário reformular o problema como um problema de programação não-linear inteira, problema esse de solução ainda mais complexa do que os de programação linear inteira. As soluções eficientes dos problemas de programação não-linear inteira esbarram nas limitações de eficiências dos principais algoritmos de solução exata e na carência de aplicações dos algoritmos aproximativos na solução desse tipo de problema, a exemplo das metaeurísticas simulated annealing, busca tabu e algoritmos genéticos, tornando-se, portanto, um atrativo para pesquisas nessa área.

Polyglutamine monomer structure and its implications for molecular self-assembly

Polyglutamine is a naturally occurring peptide found within several proteins in neuronal cells of the brain, and its aggregation has been implicated in several neurodegenerative diseases, including Huntington's disease. The resulting aggregates have been demonstrated to possess ~-sheet structure, and aggregation has been shown to start with a single misfolded peptide. The current project sought to computationally examine the structural tendencies of three mutant poly glutamine peptides that were studied experimentally, and found to aggregate with varying efficiencies. Low-energy structures were generated for each peptide by simulated annealing, and were analyzed quantitatively by various geometry- and energy-based methods. According to the results, the experimentally-observed inhibition of aggregation appears to be due to localized conformational restraint placed on the peptide backbone by inserted prolines, which in tum confines the peptide to native coil structure, discouraging transition towards the ~sheet structure required for aggregation. Such knowledge could prove quite useful to the design of future treatments for Huntington's and other related diseases.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Étude fonctionnelle du cotransporteur Na+/glucose (hSGLT1) : courant de fuite, vitesse de cotransport et modélisation cinétique

Les résultats présentés dans cette thèse précisent certains aspects de la fonction du cotransporteur Na+/glucose (SGLT1), une protéine transmembranaire qui utilise le gradient électrochimique favorable des ions Na+ afin d’accumuler le glucose à l’intérieur des cellules épithéliales de l’intestin grêle et du rein. Nous avons tout d’abord utilisé l’électrophysiologie à deux microélectrodes sur des ovocytes de xénope afin d’identifier les ions qui constituaient le courant de fuite de SGLT1, un courant mesuré en absence de glucose qui est découplé de la stoechiométrie stricte de 2 Na+/1 glucose caractérisant le cotransport. Nos résultats ont démontré que des cations comme le Li+, le K+ et le Cs+, qui n’interagissent que faiblement avec les sites de liaison de SGLT1 et ne permettent pas les conformations engendrées par la liaison du Na+, pouvaient néanmoins générer un courant de fuite d’amplitude comparable à celui mesuré en présence de Na+. Ceci suggère que le courant de fuite traverse SGLT1 en utilisant une voie de perméation différente de celle définie par les changements de conformation propres au cotransport Na+/glucose, possiblement similaire à celle empruntée par la perméabilité à l’eau passive. Dans un deuxième temps, nous avons cherché à estimer la vitesse des cycles de cotransport de SGLT1 à l’aide de la technique de la trappe ionique, selon laquelle le large bout d’une électrode sélective (~100 μm) est pressé contre la membrane plasmique d’un ovocyte et circonscrit ainsi un petit volume de solution extracellulaire que l’on nomme la trappe. Les variations de concentration ionique se produisant dans la trappe en conséquence de l’activité de SGLT1 nous ont permis de déduire que le cotransport Na+/glucose s’effectuait à un rythme d’environ 13 s-1 lorsque le potentiel membranaire était fixé à -155 mV. Suite à cela, nous nous sommes intéressés au développement d’un modèle cinétique de SGLT1. En se servant de l’algorithme du recuit simulé, nous avons construit un schéma cinétique à 7 états reproduisant de façon précise les courants du cotransporteur en fonction du Na+ et du glucose extracellulaire. Notre modèle prédit qu’en présence d’une concentration saturante de glucose, la réorientation dans la membrane de SGLT1 suivant le relâchement intracellulaire de ses substrats est l’étape qui limite la vitesse de cotransport.

Étude structure/fonction des cotransporteurs Na+/glucose

Cette thèse porte sur l’étude de la relation entre la structure et la fonction chez les cotransporteurs Na+/glucose (SGLTs). Les SGLTs sont des protéines membranaires qui se servent du gradient électrochimique transmembranaire du Na+ afin d’accumuler leurs substrats dans la cellule. Une mise en contexte présentera d’abord un bref résumé des connaissances actuelles dans le domaine, suivi par un survol des différentes techniques expérimentales utilisées dans le cadre de mes travaux. Ces travaux peuvent être divisés en trois projets. Un premier projet a porté sur les bases structurelles de la perméation de l’eau au travers des SGLTs. En utilisant à la fois des techniques de modélisation moléculaire, mais aussi la volumétrie en voltage imposé, nous avons identifié les bases structurelles de cette perméation. Ainsi, nous avons pu identifier in silico la présence d’une voie de perméation passive à l’eau traversant le cotransporteur, pour ensuite corroborer ces résultats à l’aide de mesures faites sur le cotransporteur Na/glucose humain (hSGLT1) exprimé dans les ovocytes. Un second projet a permis d’élucider certaines caractéristiques structurelles de hSGLT1 de par l’utilisation de la dipicrylamine (DPA), un accepteur de fluorescence dont la répartition dans la membrane lipidique dépend du potentiel membranaire. L’utilisation de la DPA, conjuguée aux techniques de fluorescence en voltage imposé et de FRET (fluorescence resonance energy transfer), a permis de démontrer la position extracellulaire d’une partie de la boucle 12-13 et le fait que hSGLT1 forme des dimères dont les sous-unités sont unies par un pont disulfure. Un dernier projet a eu pour but de caractériser les courants stationnaires et pré-stationaires d’un membre de la famille des SGLTs, soit le cotransporteur Na+/myo-inositol humain hSMIT2 afin de proposer un modèle cinétique qui décrit son fonctionnement. Nous avons démontré que la phlorizine inhibe mal les courants préstationnaires suite à une dépolarisation, et la présence de courants de fuite qui varient en fonction du temps, du potentiel membranaire et des substrats. Un algorithme de recuit simulé a été mis au point afin de permettre la détermination objective de la connectivité et des différents paramètres associés à la modélisation cinétique.

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

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.