A Possible and Necessary Consistency Proof

After Gödel's incompleteness theorems and the collapse of Hilbert's programme Gerhard Gentzen continued the quest for consistency proofs of Peano arithmetic. He considered a finitistic or constructive proof still possible and necessary for the foundations of mathematics. For a proof to be meaningful, the principles relied on should be considered more reliable than the doubtful elements of the theory concerned. He worked out a total of four proofs between 1934 and 1939. This thesis examines the consistency proofs for arithmetic by Gentzen from different angles. The consistency of Heyting arithmetic is shown both in a sequent calculus notation and in natural deduction. The former proof includes a cut elimination theorem for the calculus and a syntactical study of the purely arithmetical part of the system. The latter consistency proof in standard natural deduction has been an open problem since the publication of Gentzen's proofs. The solution to this problem for an intuitionistic calculus is based on a normalization proof by Howard. The proof is performed in the manner of Gentzen, by giving a reduction procedure for derivations of falsity. In contrast to Gentzen's proof, the procedure contains a vector assignment. The reduction reduces the first component of the vector and this component can be interpreted as an ordinal less than epsilon_0, thus ordering the derivations by complexity and proving termination of the process.

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations-uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Den medeltida svenska hustrun och lagen : En studie i förmyndarskap, begränsningar och möjligheter 1350-1442

Denna studie behandlar kvinnans juridiska handlingsutrymme under Magnus Erikssons lagar 1350-1442. Det har länge antagits att den gifta kvinnan under medeltiden var omyndig, eftersom hon enligt lagen skulle ha en målsman (på fornsvenska malsman). Senare tids forskning har dock börjat påpeka att kvinnan inte var omyndig i dagens bemärkelse, men inte desto djupare utvecklat frågan om hennes rättigheter. Genom att jämföra lagtexterna med praxis har jag undersökt hur lagutrymmet som stadgade att en make skulle vara sin hustrus malsman påverkade kvinnans rättigheter. Under den undersökta tidsperioden tycks malsmanskapet inte i någon större utsträckning ha juridiskt begränsat kvinnan, men praxis visar att tradition och praktikaliteter bjöd att maken i allmänhet representerade sin hustru i juridiska göromål. Studien visar att representation inte nödvändigtvis indikerade myndighet, och att det faktum att hustrun representerades av sin make inte medförde omyndighet. Männens konstanta kontakt med den juridiska världen och kvinnornas synnerligen begränsade dito kom ändå på sikt att minska hennes juridiska medvetenhet, och därmed hennes möjlighet att påverka. Den viktigaste slutsatsen är likväl denna begränsning inte var lagstadgad. I studien tar jag också ställning till olika genusteoretiska förklaringsmodeller, nämligen enkönsmodellen och teorin om skilda sfärer. Enligt enkönsmodellen, som utvecklades av Laqueur i början av 1990-talet, sågs inte kvinnan och mannen som två olika kön, utan som olika grader av en man. Kvinnan skulle enligt det vara en mindre utvecklad variant av mannen. Resultaten i studien tyder dock snarare på att kvinnor och män sågs som två olika kön, men att de verkade inom olika sfärer. Den avgörande faktorn var förmågan att föda barn, där de som födde barn tillhörde en privat sfär, med hemmet i centrum, och de som inte födde barn tillhörde en offentlig sfär, där juridik, politik och ekonomi ingick. Därför argumenterar jag för att män förväntades sköta de juridiska göromålen även för sin hustru, eftersom det tillhörde hans sfär. Hustrun hade dock möjligheten, både enligt lagen och enligt praxis, att själv sköta exempelvis försäljningar och upprätta testamenten, och begränsades snarare av sitt kön än av genusstrukturer. Studien lyfter också fram problematiken med det diversifierade svenska medeltida samhället, och ifrågasätter traditionen med en indelning i endast två genus, en manlighet och en kvinnlighet. Resultaten tyder på att det var stora lokala skillnader beträffande en hustrus begränsningar och möjligheter, och att lagarna mer fungerade som riktlinjer än som faktiska regler. På grund av källmaterialets beskaffenhet går det inte att avgöra på vilket sätt social status spelade in, även om det är rimligt att anta att det fanns betydande skillnader mellan rikets övre och lägre skikt. Däremot finns det inget som indikerar att hustruns malsman skulle ha varit liktydig med det nutida målsman någonstans i riket, eller i någon samhällsgrupp.

Computational Methods for Studies of Quantum Dots and Boson Condensates

This thesis presents ab initio studies of two kinds of physical systems, quantum dots and bosons, using two program packages of which the bosonic one has mainly been developed by the author. The implemented models, \emph{i.e.}, configuration interaction (CI) and coupled cluster (CC) take the correlated motion of the particles into account, and provide a hierarchy of computational schemes, on top of which the exact solution, within the limit of the single-particle basis set, is obtained. The theory underlying the models is presented in some detail, in order to provide insight into the approximations made and the circumstances under which they hold. Some of the computational methods are also highlighted. In the final sections the results are summarized. The CI and CC calculations on multiexciton complexes in self-assembled semiconductor quantum dots are presented and compared, along with radiative and non-radiative transition rates. Full CI calculations on quantum rings and double quantum rings are also presented. In the latter case, experimental and theoretical results from the literature are re-examined and an alternative explanation for the reported photoluminescence spectra is found. The boson program is first applied on a fictitious model system consisting of bosonic electrons in a central Coulomb field for which CI at the singles and doubles level is found to account for almost all of the correlation energy. Finally, the boson program is employed to study Bose-Einstein condensates confined in different anisotropic trap potentials. The effects of the anisotropy on the relative correlation energy is examined, as well as the effect of varying the interaction potential.}

Blivande socionomers svar på ett yrkesetiskt dilemma - Hur klienten bemöts och hur dilemmat löses

Syftet med denna studie är att genom data-analys triangulation undersöka socionomstuderandes svar på ett yrkesetiskt dilemma av omsorgsetisk natur. Samplet består av 32 socionomer i början av sina studier som har svarat på ett hypotetiskt dilemma om hur de skulle bemöta en ung kvinna som ber om råd i en mycket svår situation. De huvudsakliga teoretiska utgångspunkterna för detta arbete är ECI (Ethic of Care Interview) som utvecklats av Eva Skoe som metod för att undersöka omsorgsetik, samt Osers och Althofs teori om diskursiva problemlösningsmetoder bland professionella. Som grundläggande teorier för all modern forskning om människans moralutveckling, presenteras också Carol Gilligans och Lawrence Kohlbergs teorier samt den huvudsakliga kritiken dessa bemött. Carol Gilligan är den som ursprungligen presenterade tanken om att det finns två olika typer av moraliskt tänkande där omsorgsetik är mer typisk för kvinnor och rättviseetik är mer typisk för män. Den första delen av analysen är en innehållsanalys där svaren på det yrkesrelaterade dilemmat på olika ECI stadium jämförs med varandra. Poängsättningen i ECI har varit grunden för denna analys. Den andra delen av analysen är en deduktiv teoribunden analys, där jag undersökt i fall Osers och Althofs modell om problemlösningsstrategier även går att tillämpa på ett yrkesetiskt dilemma. Slutligen tar jag också ställning till dessa två teoriers kompatibilitet. Resultatet visar att eleverna har svarat aningen sämre på det yrkesetiska dilemmat än vad deras allmänna ECI stadium är. Detta kan bero på att de är i början av sina studier men också på det allmänna klimat som råder inom socialbranschen. Teorin om diskursiva problemlösningsstrategier går inte heller att tillämpa på detta yrkesetiska dilemma, eftersom den hypotetiska klientens självbestämmanderätt gör en diskursiv lösning omöjlig. Till följd av detta har jag skapat en ny modell som baserar sig på 6 kategorier utgående från de faktorer de intervjuade lyfter fram som de viktigaste i den professionellas möte med klienten. Eftersom den nya modellen inte är hierarkisk, kan de två teorierna inte jämföras med varandra på så sätt att högre ECI nivå skulle innebära en viss typ av problemlösningsstrategi.

Det grekiska dramat. En Studie av euroländeras agerande i Greklandskrisen.

Jag granskar i min avhandling pro gradu den ekonomiska krisen i Grekland som kulminerade under vären 2010 när Grekland vände sig tili de övriga medlemsländerna i Ekonomiska och monetära Unionen (EMU) med en förfrägan om ekonomisk hjälp i formav län. Syftet med avhandlingen är att undersöka hur de övriga EMU-medlemmarna fattade sitt beslut om att ekonomiskt stöda Grekland efter att landets kreditvärdighet sänkts av de internationella kreditvärderingsinstituten. Jag granskar Greklandskrisen och dess utveckling, de lösningar som man gick in för inom ramen för valutaunionen, hur besluten om stödpaketet fattades och vilka faktorer som päverkade besluten. Jag tar avstamp i Optimum Currency Area-teorin (OCA-teorin) och teorier om europeisk ekonomisk integration. Dessutom för jag en diskussion kring solidariteten mellan EUländerna, som ocksä använts som argument för stödpaketet tili Grekland. Jag klassificerar euroländerna utgäende för hur det nationella beslutet om Greklandspaketet fattats och gör därefter en agglomerativ klusteranalys, med ambitionen att förklara vilka faktorer som päverkat besluten. Syftet med klusteranalysen är att klargöra huruvida politiska faktorer, som härrör sig tili regeringen och dess sammansättning, eller ekonomiska faktorer, som bclyser statsfinansernas tillständ, bäst förklarar hur ett land fattat sitt beslut. Resultatet visar att de politiska variablerna har päverkat ländernas beslut mer an de ekonomiska, men förklaringsgraden är relativt lag i bägge fallen. Jag för vidare en diskussion om resultatet ur ett OCA-perspektiv, kriterierna för ett optimalt valutaomräde samt EMU:s utveckling i dito riktning. Jag avslutar avhandlingen med en diskussion kring EMU:s framtid.

Multiple UAV Coalitions for a Search and Prosecute Mission

Unmanned aerial vehicles (UAVs) have the potential to carry resources in support of search and prosecute operations. Often to completely prosecute a target, UAVs may have to simultaneously attack the target with various resources with different capacities. However, the UAVs are capable of carrying only limited resources in small quantities, hence, a group of UAVs (coalition) needs to be assigned that satisfies the target resource requirement. The assigned coalition must be such that it minimizes the target prosecution delay and the size of the coalition. The problem of forming coalitions is computationally intensive due to the combinatorial nature of the problem, but for real-time applications computationally cheap solutions are required. In this paper, we propose decentralized sub-optimal (polynomial time) and decentralized optimal coalition formation algorithms that generate coalitions for a single target with low computational complexity. We compare the performance of the proposed algorithms to that of a global optimal solution for which we need to solve a centralized combinatorial optimization problem. This problem is computationally intensive because the solution has to (a) provide a coalition for each target, (b) design a sequence in which targets need to be prosecuted, and (c) take into account reduction of UAV resources with usage. To solve this problem we use the Particle Swarm Optimization (PSO) technique. Through simulations, we study the performance of the proposed algorithms in terms of mission performance, complexity of the algorithms and the time taken to form the coalition. The simulation results show that the solution provided by the proposed algorithms is close to the global optimal solution and requires far less computational resources.

Accurate Parameter Estimation of Contaminant Transport Inverse Problem using Paartaicle Swarm Optimization

Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

Swam Intelligent Based Inverse Model for Laboratory Double Reservoir Diffusion Experiments

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

Pickup and delivery problem using metaheuristics techniques

Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

Robust solver based on modified particle swarm optimization for improved solution of diffusion transport through containment facilities

Accurate estimation of mass transport parameters is necessary for overall design and evaluation processes of the waste disposal facilities. The mass transport parameters, such as effective diffusion coefficient, retardation factor and diffusion accessible porosity, are estimated from observed diffusion data by inverse analysis. Recently, particle swarm optimization (PSO) algorithm has been used to develop inverse model for estimating these parameters that alleviated existing limitations in the inverse analysis. However, PSO solver yields different solutions in successive runs because of the stochastic nature of the algorithm and also because of the presence of multiple optimum solutions. Thus the estimated mean solution from independent runs is significantly different from the best solution. In this paper, two variants of the PSO algorithms are proposed to improve the performance of the inverse analysis. The proposed algorithms use perturbation equation for the gbest particle to gain information around gbest region on the search space and catfish particles in alternative iterations to improve exploration capabilities. Performance comparison of developed solvers on synthetic test data for two different diffusion problems reveals that one of the proposed solvers, CPPSO, significantly improves overall performance with improved best, worst and mean fitness values. The developed solver is further used to estimate transport parameters from 12 sets of experimentally observed diffusion data obtained from three diffusion problems and compared with published values from the literature. The proposed solver is quick, simple and robust on different diffusion problems. (C) 2012 Elsevier Ltd. All rights reserved.

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem. (c) 2012 Elsevier Ltd. All rights reserved.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Clustering using Levy Flight Cuckoo Search

In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.