THE USEFUL SIDE OF AMBIVALENCE: Ambivalent attitudes can be adaptive and socially valued

En s'inspirant de la littérature récente qui a dépeint l'ambivalence comme étant adaptative et en lien avec des comportements stratégiques, cette thèse examine le versant utile des attitudes ambivalentes. Elle met tout d'abord en évidence que son expression peut-être sciemment contrôlée et mise en avant pour des raisons d'auto-présentation. De plus, elle démontre que les individus peuvent présenter une attitude ambivalente afin de gagner l'approbation sociale sur un objet d'attitude controversé alors que l'inverse a été observé sur des objets consensuels (Première ligne de recherche). Cette thèse a également révélé que l'expression d'attitudes ambivalentes pouvait amener à être valorisé socialement. En effet, contrairement à des attitudes plus tranchées (pro-normatives ou contre-normatives), les attitudes ambivalentes ont été évaluées de façon plus importante sur la dimension de l'utilité sociale (une dimension qui indique la compétence d'autrui ou encore la propension à évoluer dans la hiérarchie sociale). La valorisation de l'ambivalence n'est apparue que sur la dimension de l'utilité sociale et non sur la dimension de la désirabilité sociale (une dimension qui indique la sympathie d'autrui ainsi que la propension à être apprécié socialement). De plus, ce résultat a été observé sur des thèmes controversés et non sur des thèmes consensuels (Seconde ligne de recherche). Dans l'ensemble cette thèse soutient une approche de l'ambivalence comme donnant lieu à des bénéfices. Elle peut également ouvrir la voie à l'étude de l'ambivalence en lien avec la pensée critique. - Drawing on the recent literature that portrayed ambivalence as being adaptive and linked with strategic behaviors, this thesis examines the useful side of ambivalent attitudes. It first revealed that the expression of ambivalent attitudes could be controlled and purposely displayed for self-presentational concerns. Furthermore, it demonstrated that people could put ambivalence forward to gain social approval when expressing it on controversial social issues, whereas the opposite was true on consensual social issues (First line of research). The thesis also revealed that the expression of ambivalent attitudes could lead to be socially valued. Indeed, contrary to clear-cut attitudes (either pro-normative or counter-normative attitudes), ambivalent attitudes have been evaluated the highest on the social utility dimension (a dimension indicating people's competence as well as the extent to which they are likely to climb in social hierarchy). The valorization of ambivalent attitudes only appeared on social utility and not on social desirability (a dimension indicating people's niceness as well as the extent to which they are likely to be socially appreciated). This effect was true on controversial social issues but not on consensual ones (Second line of research). Overall, this thesis provides support for an approach that conceives attitudinal ambivalence as leading to benefits. It also may open avenues for the study of ambivalence in relation with critical thinking.

A method for decision making with the OWA operator

A new method for decision making that uses the ordered weighted averaging (OWA) operator in the aggregation of the information is presented. It is used a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index is based on distance measures and other techniques that are useful for decision making. By using the OWA operator in the IMAM, we form a new aggregation operator that we call the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. The main advantage is that it provides a parameterized family of aggregation operators between the minimum and the maximum and a wide range of special cases. Then, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. A further extension of this approach is presented by using hybrid averages and Choquet integrals. We also develop an application of the new approach in a multi-person decision-making problem regarding the selection of strategies.

Vertical Clearance Restrictions on Primary Roads and Primary Road Extensions: Standard U.S. Measurements [Vertical Clearance Log], May 1, 2015

The Vertical Clearance Log is prepared for the purpose of providing vertical clearance restrictions by route on the primary road system. This report is used by the Iowa Department of Transportation’s Motor Carrier Services to route oversize vehicles around structures with vertical restrictions too low for the cargo height. The source of the data is the Geographic Information Management System (GIMS) that is managed by the Office of Research & Analytics in the Performance & Technology Division. The data is collected by inspection crews and through the use of LiDAR technology to reflect changes to structures on the primary road system. This log is produced annually.

Decision making techniques with similarity measures and OWA operators

We analyse the use of the ordered weighted average (OWA) in decision-making giving special attention to business and economic decision-making problems. We present several aggregation techniques that are very useful for decision-making such as the Hamming distance, the adequacy coefficient and the index of maximum and minimum level. We suggest a new approach by using immediate weights, that is, by using the weighted average and the OWA operator in the same formulation. We further generalize them by using generalized and quasi-arithmetic means. We also analyse the applicability of the OWA operator in business and economics and we see that we can use it instead of the weighted average. We end the paper with an application in a business multi-person decision-making problem regarding production management

Lattices for 3-Dimensional Fuzzy Data generated by Fuzzy Galois Connections

Vagueness and high dimensional space data are usual features of current data. The paper is an approach to identify conceptual structures among fuzzy three dimensional data sets in order to get conceptual hierarchy. We propose a fuzzy extension of the Galois connections that allows to demonstrate an isomorphism theorem between fuzzy sets closures which is the basis for generating lattices ordered-sets

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Motor Grader Operator Maintenance of Granular Surfaced Roads : Tips from Iowa Operators, 1991

This booklet is a compilation of notes taken during motor grader operators workshops held at some 20 different locations throughout Iowa during the last two years. It is also the advice of 16 experienced motor grader operators and maintenance foremen (from 14 different counties around Iowa), who serve as instructors and assistant instructors at the "MoGo" workshops. The instructors have all said that they learn as much from the operators who attend the workshops as they impart. Motor grader operators from throughout Iowa have shown us new, innovative and better ways of maintaining gravel roads. This booklet is an attempt to pass on some of these "tips" that we have gathered from Iowa operators. It will need to be revised, corrected, and added to based on the advice we get from you, the operators who do the work here in Iowa.

Monte Carlo study of a kinetic lattice model with random diffusion of disorder

We report Monte Carlo results for a nonequilibrium Ising-like model in two and three dimensions. Nearest-neighbor interactions J change sign randomly with time due to competing kinetics. There follows a fast and random, i.e., spin-configuration-independent diffusion of Js, of the kind that takes place in dilute metallic alloys when magnetic ions diffuse. The system exhibits steady states of the ferromagnetic (antiferromagnetic) type when the probability p that J>0 is large (small) enough. No counterpart to the freezing phenomena found in quenched spin glasses occurs. We compare our results with existing mean-field and exact ones, and obtain information about critical behavior.

Reaction-diffusion lattice gas: Theory and computer results

We report on the study of nonequilibrium ordering in the reaction-diffusion lattice gas. It is a kinetic model that relaxes towards steady states under the simultaneous competition of a thermally activated creation-annihilation $(reaction$) process at temperature T, and a diffusion process driven by a heat bath at temperature T?T. The phase diagram as one varies T and T, the system dimension d, the relative priori probabilities for the two processes, and their dynamical rates is investigated. We compare mean-field theory, new Monte Carlo data, and known exact results for some limiting cases. In particular, no evidence of Landau critical behavior is found numerically when d=2 for Metropolis rates but Onsager critical points and a variety of first-order phase transitions.

Ising critical behavior of a non-Halmiltonian lattice system

We study steady states in d-dimensional lattice systems that evolve in time by a probabilistic majority rule, which corresponds to the zero-temperature limit of a system with conflicting dynamics. The rule satisfies detailed balance for d=1 but not for d>1. We find numerically nonequilibrium critical points of the Ising class for d=2 and 3.

Fuzzy Principal Components Analysis

Due to the large number of characteristics, there is a need to extract the most relevant characteristicsfrom the input data, so that the amount of information lost in this way is minimal, and the classification realized with the projected data set is relevant with respect to the original data. In order to achieve this feature extraction, different statistical techniques, as well as the principal components analysis (PCA) may be used. This thesis describes an extension of principal components analysis (PCA) allowing the extraction ofa finite number of relevant features from high-dimensional fuzzy data and noisy data. PCA finds linear combinations of the original measurement variables that describe the significant variation in the data. The comparisonof the two proposed methods was produced by using postoperative patient data. Experiment results demonstrate the ability of using the proposed two methods in complex data. Fuzzy PCA was used in the classificationproblem. The classification was applied by using the similarity classifier algorithm where total similarity measures weights are optimized with differential evolution algorithm. This thesis presents the comparison of the classification results based on the obtained data from the fuzzy PCA.

Ground-state order and spin-lattice coupling in tetrahedral spin systems Cu2Te2O5X2 (X = Br or Cl)

High-resolution ac susceptibility and thermal conductivity measurement on Cu2Te2O5X2 (X=Br,Cl) single crystals are reported. For Br-sample, sample dependence prevents one from distinguishing between possibilities of magnetically ordered and spin-singlet ground states. In Cl-sample a three-dimensional transition at 18.5 K is accompanied by almost isotropic behavior of susceptibility and almost switching behavior of thermal conductivity. Thermal conductivity studies suggest the presence of a tremendous spin-lattice coupling characterizing Cl- but not Br-sample. Below the transition Cl-sample is in a complex magnetic state involving AF order but also the elements consistent with the presence of a gap in the excitation spectrum.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Adding similarity-based reasoning capabilities to a Horn fragment of possibilistic logic with fuzzy constants

PLFC is a first-order possibilistic logic dealing with fuzzy constants and fuzzily restricted quantifiers. The refutation proof method in PLFC is mainly based on a generalized resolution rule which allows an implicit graded unification among fuzzy constants. However, unification for precise object constants is classical. In order to use PLFC for similarity-based reasoning, in this paper we extend a Horn-rule sublogic of PLFC with similarity-based unification of object constants. The Horn-rule sublogic of PLFC we consider deals only with disjunctive fuzzy constants and it is equipped with a simple and efficient version of PLFC proof method. At the semantic level, it is extended by equipping each sort with a fuzzy similarity relation, and at the syntactic level, by fuzzily “enlarging” each non-fuzzy object constant in the antecedent of a Horn-rule by means of a fuzzy similarity relation.