A Multiple Goal Perspective on Eating Behavior

Although people frequently pursue multiple goals simultaneously, these goals often conflict with each other. For instance, consumers may have both a healthy eating goal and a goal to have an enjoyable eating experience. In this dissertation, I focus on two sources of enjoyment in eating experiences that may conflict with healthy eating: consuming tasty food (Essay 1) and affiliating with indulging dining companions (Essay 2). In both essays, I examine solutions and strategies that decrease the conflict between healthy eating and these aspects of enjoyment in the eating experience, thereby enabling consumers to resolve such goal conflicts.

Essay 1 focuses on the well-established conflict between having healthy food and having tasty food and introduces a novel product offering (“vice-virtue bundles”) that can help consumers simultaneously address both health and taste goals. Through several experiments, I demonstrate that consumers often choose vice-virtue bundles with small proportions (¼) of vice and that they view such bundles as healthier than but equally tasty as bundles with larger vice proportions, indicating that “healthier” does not always have to equal “less tasty.”

Essay 2 focuses on a conflict between healthy eating and affiliation with indulging dining companions. The first set of experiments provides evidence of this conflict and examine why it arises (Studies 1 to 3). Based on this conflict’s origins, the second set of experiments tests strategies that consumers can use to decrease the conflict between healthy eating and affiliation with an indulging dining companion (Studies 4 and 5), such that they can make healthy food choices while still being liked by an indulging dining companion. Thus, Essay 2 broadens the existing picture of goals that conflict with the healthy eating goal and, together with Essay 1, identifies solutions to such goal conflicts.

Completion strategy or emphasis manipulation? Task support for teaching information problem solving

While most students seem to solve information problems effortlessly, research shows that the cognitive skills for effective information problem solving are often underdeveloped. Students manage to find information and formulate solutions, but the quality of their process and product is questionable. It is therefore important to develop instruction for fostering these skills. In this research, a 2-h online intervention was presented to first-year university students with the goal to improve their information problem solving skills while investigating effects of different types of built-in task support. A training design containing completion tasks was compared to a design using emphasis manipulation. A third variant of the training combined both approaches. In two experiments, these conditions were compared to a control condition receiving conventional tasks without built-in task support. Results of both experiments show that students' information problem solving skills are underdeveloped, which underlines the necessity for formal training. While the intervention improved students’ skills, no differences were found between conditions. The authors hypothesize that the effective presentation of supportive information in the form of a modeling example at the start of the training caused a strong learning effect, which masked effects of task support. Limitations and directions for future research are presented.

U.S. wildfire governance as social-ecological problem

There are fundamental spatial and temporal disconnects between the specific policies that have been crafted to address our wildfire challenges. The biophysical changes in fuels, wildfire behavior, and climate have created a new set of conditions for which our wildfire governance system is poorly suited to address. To address these challenges, a reorientation of goals is needed to focus on creating an anticipatory wildfire governance system focused on social and ecological resilience. Key characteristics of this system could include the following: (1) not taking historical patterns as givens; (2) identifying future social and ecological thresholds of concern; (3) embracing diversity/heterogeneity as principles in ecological and social responses; and (4) incorporating learning among different scales of actors to create a scaffolded learning system.

Uma abordagem evolucionária e espacial para o jogo da autorregulação de processos de trocas sociais em sistemas multiagentes

Interações sociais são frequentemente descritas como trocas sociais. Na literatura, trocas sociais em Sistemas Multiagentes são objeto de estudo em diversos contextos, nos quais as relações sociais são interpretadas como trocas sociais. Dentre os problemas estudados, um problema fundamental discutido na literatura e a regulação¸ ao de trocas sociais, por exemplo, a emergência de trocas equilibradas ao longo do tempo levando ao equilíbrio social e/ou comportamento de equilíbrio/justiça. Em particular, o problema da regulação de trocas sociais e difícil quando os agentes tem informação incompleta sobre as estratégias de troca dos outros agentes, especificamente se os agentes tem diferentes estratégias de troca. Esta dissertação de mestrado propõe uma abordagem para a autorregulacao de trocas sociais em sistemas multiagentes, baseada na Teoria dos Jogos. Propõe o modelo de Jogo de Autorregulacão ao de Processos de Trocas Sociais (JAPTS), em uma versão evolutiva e espacial, onde os agentes organizados em uma rede complexa, podem evoluir suas diferentes estratégias de troca social. As estratégias de troca são definidas através dos parâmetros de uma função de fitness. Analisa-se a possibilidade do surgimento do comportamento de equilíbrio quando os agentes, tentando maximizar sua adaptação através da função de fitness, procuram aumentar o numero de interações bem sucedidas. Considera-se um jogo de informação incompleta, uma vez que os agentes não tem informações sobre as estratégias de outros agentes. Para o processo de aprendizado de estratégias, utiliza-se um algoritmo evolutivo, no qual os agentes visando maximizar a sua função de fitness, atuam como autorregulares dos processos de trocas possibilitadas pelo jogo, contribuindo para o aumento do numero de interações bem sucedidas. São analisados 5 diferentes casos de composição da sociedade. Para alguns casos, analisa-se também um segundo tipo de cenário, onde a topologia de rede é modificada, representando algum tipo de mobilidade, a fim de analisar se os resultados são dependentes da vizinhança. Alem disso, um terceiro cenário é estudado, no qual é se determinada uma política de influencia, quando as medias dos parâmetros que definem as estratégias adotadas pelos agentes tornam-se publicas em alguns momentos da simulação, e os agentes que adotam a mesma estratégia de troca, influenciados por isso, imitam esses valores. O modelo foi implementado em NetLogo.

SOCIAL AND ECOLOGICAL FACTORS INFLUENCING COLLECTIVE BEHAVIOR IN GIANT DANIO

A fundamental problem in biology is understanding how and why things group together. Collective behavior is observed on all organismic levels - from cells and slime molds, to swarms of insects, flocks of birds, and schooling fish, and in mammals, including humans. The long-term goal of this research is to understand the functions and mechanisms underlying collective behavior in groups. This dissertation focuses on shoaling (aggregating) fish. Shoaling behaviors in fish confer foraging and anti-predator benefits through social cues from other individuals in the group. However, it is not fully understood what information individuals receive from one another or how this information is propagated throughout a group. It is also not fully understood how the environmental conditions and perturbations affect group behaviors. The specific research objective of this dissertation is to gain a better understanding of how certain social and environmental factors affect group behaviors in fish. I focus on two ecologically relevant decision-making behaviors: (i) rheotaxis, or orientation with respect to a flow, and (ii) startle response, a rapid response to a perceived threat. By integrating behavioral and engineering paradigms, I detail specifics of behavior in giant danio Devario aequipinnatus (McClelland 1893), and numerically analyze mathematical models that may be extended to group behavior for fish in general, and potentially other groups of animals as well. These models that predict behavior data, as well as generate additional, testable hypotheses. One of the primary goals of neuroethology is to study an organism's behavior in the context of evolution and ecology. Here, I focus on studying ecologically relevant behaviors in giant danio in order to better understand collective behavior in fish. The experiments in this dissertation provide contributions to fish ecology, collective behavior, and biologically-inspired robotics.

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Spectral graph theory with applications to quantum adiabatic optimization

In this dissertation I draw a connection between quantum adiabatic optimization, spectral graph theory, heat-diffusion, and sub-stochastic processes through the operators that govern these processes and their associated spectra. In particular, we study Hamiltonians which have recently become known as ``stoquastic'' or, equivalently, the generators of sub-stochastic processes. The operators corresponding to these Hamiltonians are of interest in all of the settings mentioned above. I predominantly explore the connection between the spectral gap of an operator, or the difference between the two lowest energies of that operator, and certain equilibrium behavior. In the context of adiabatic optimization, this corresponds to the likelihood of solving the optimization problem of interest. I will provide an instance of an optimization problem that is easy to solve classically, but leaves open the possibility to being difficult adiabatically. Aside from this concrete example, the work in this dissertation is predominantly mathematical and we focus on bounding the spectral gap. Our primary tool for doing this is spectral graph theory, which provides the most natural approach to this task by simply considering Dirichlet eigenvalues of subgraphs of host graphs. I will derive tight bounds for the gap of one-dimensional, hypercube, and general convex subgraphs. The techniques used will also adapt methods recently used by Andrews and Clutterbuck to prove the long-standing ``Fundamental Gap Conjecture''.

'A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem'

Abstract- A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Synthesis and behavior of Belite-Alite-Ye’elimite (BAY) cement

Ye’elimite based cements have been studied since 70’s years in China, due to the irrelevant characteristics from a hydraulic and environmental point of view. One of them is the reduced fuel consumption, related to the lower temperature reaction required for this kind of cement production as compared to Ordinary Portland Cement (OPC), another characteristic is the reduced requirement of carbonates as a typical raw material, compared to OPC, with the consequent reduction in CO2 releases (~22%)from combustion. Thus, Belite-Ye’elimite-Ferrite (BYF) cements have been developed as potential OPC substitutes. BYF cements contain belite as main phase (>50 wt%) and ye´elimite as the second content phase (~30 wt%). However, an important technological problem is associated to them, related to the low mechanical strengths developed at intermediate hydration ages (3, 7 and 28 days). One of the proposed solutions to this problem is the activation of BYF clinkers by preparing clinkers with high percentage of coexisting alite and ye'elimite. These clinkers are known Belite-Alite-Ye’elimite (BAY) cements. Their manufacture would produce ~15% less CO2 than OPC. Alite is the main component of OPC and is responsible for early mechanical strengths. The reaction of alite and ye´elimite with water will develop cements with high mechanical strengths at early ages, while belite will contribute to later curing times. Moreover, the high alkalinity of BAY cement pastes/mortars/concretes may facilitate the use of supplementary cementitious materials with pozzolanic activity which also contributes to decrease the CO2 footprint of these ecocements. The main objective of this work was the design and optimization of all the parameters evolved in the preparation of a BAY eco-cement that develop higher mechanical strengths than BYF cements. These parameters include the selection of the raw materials (lime, gypsum, kaolin and sand), milling, clinkering conditions (temperature, and holding time), and clinker characterization The addition of fly ash has also been studied. All BAY clinker and pastes (at different hydration ages) were mineralogically characterized through laboratory X-ray powder diffraction (LXRPD) in combination with the Rietveld methodology to obtain the full phase assemblage including Amorphous and Crystalline non-quantified, ACn, contents. The pastes were also characterized through rheological measurements, thermal analyses (TA), scanning electronic microscopy (SEM) and nuclear magnetic resonance (NMR). The compressive strengths were also measured at different hydration times and compared to BYF.

A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

A novel methodology for simulating contact-line behavior in capillary-driven flows

Despite the wide swath of applications where multiphase fluid contact lines exist, there is still no consensus on an accurate and general simulation methodology. Most prior numerical work has imposed one of the many dynamic contact-angle theories at solid walls. Such approaches are inherently limited by the theory accuracy. In fact, when inertial effects are important, the contact angle may be history dependent and, thus, any single mathematical function is inappropriate. Given these limitations, the present work has two primary goals: 1) create a numerical framework that allows the contact angle to evolve naturally with appropriate contact-line physics and 2) develop equations and numerical methods such that contact-line simulations may be performed on coarse computational meshes.

Fluid flows affected by contact lines are dominated by capillary stresses and require accurate curvature calculations. The level set method was chosen to track the fluid interfaces because it is easy to calculate interface curvature accurately. Unfortunately, the level set reinitialization suffers from an ill-posed mathematical problem at contact lines: a ``blind spot'' exists. Standard techniques to handle this deficiency are shown to introduce parasitic velocity currents that artificially deform freely floating (non-prescribed) contact angles. As an alternative, a new relaxation equation reinitialization is proposed to remove these spurious velocity currents and its concept is further explored with level-set extension velocities.

To capture contact-line physics, two classical boundary conditions, the Navier-slip velocity boundary condition and a fixed contact angle, are implemented in direct numerical simulations (DNS). DNS are found to converge only if the slip length is well resolved by the computational mesh. Unfortunately, since the slip length is often very small compared to fluid structures, these simulations are not computationally feasible for large systems. To address the second goal, a new methodology is proposed which relies on the volumetric-filtered Navier-Stokes equations. Two unclosed terms, an average curvature and a viscous shear VS, are proposed to represent the missing microscale physics on a coarse mesh.

All of these components are then combined into a single framework and tested for a water droplet impacting a partially-wetting substrate. Very good agreement is found for the evolution of the contact diameter in time between the experimental measurements and the numerical simulation. Such comparison would not be possible with prior methods, since the Reynolds number Re and capillary number Ca are large. Furthermore, the experimentally approximated slip length ratio is well outside of the range currently achievable by DNS. This framework is a promising first step towards simulating complex physics in capillary-dominated flows at a reasonable computational expense.

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence.

The Notion of the “Concept Instance”: Problems in Modeling Concept Change in SKOS

The U.S. National Science Foundation metadata registry under development for the National Science Digital Library (NSDL) is a repertory intended to manage both metadata schemes and schemas. The focus of this draft discussion paper is on the scheme side of the development work. In particular, the concern of the discussion paper is with issues around the creation of historical snapshots of concept changes and their encoding in SKOS. Through framing the problem as we see it, we hope to find an optimal solution to our need for a SKOS encoding of these snapshots. Since what we are seeking to model is concept change, it is necessary at the outset to make it clear that we are not talking about changes to a concept of such a nature that would require the declaration a new concept with its own URI.In the project, we avoid the use of the terms “version” and “versioning” with regard to changes in concepts and reserve their use to the significant changes of schemes as a whole. Significant changes triggering a new scheme version might include changes in scheme documentation that express a significant shift in the purpose, use or architecture of the scheme. We use the term “snapshot” to denote the state of a scheme at identifiable points in time. Thus, snapshots are identifiable views of a scheme that record the incremental changes that have occurred to concepts, relationships among concepts, and scheme documentation since the last snapshot. Aspects of concept change occur that we need to capture and make available both through the registry and through potentially in transmission of a scheme to other registries. We call these capturings “concept instances.”

Intimate partner violence in pregnancy: Communicate better to unveil the problem.

The intimate partner violence (IPV) is defined as any behavior within an intimate relationship that causes physical, psychological or sexual damage to members of relationship (Organización Panamericana de la Salud, Oficina Regional para las Américas de la Organización Mundial de la Salud 2003). Exposure to the IPV during pregnancy leads to a number of risk factors with significant impact on the physical, mental and social well-being of women, as well in perinatal outcomes. The prevalence rates, existing throughout the world, have demonstrated the importance of further enhance the attention given to the woman / couple / family, from prenatal care to the postpartum. (World Health Organization, WHO Collaborating Centre for Violence Prevention 2010), challenges the health professionals to monitor the phenomenon of IPV and compare national and international indicators in order to adjust and qualify interventions. This requires awareness of health professionals in the early identification of these indicators using appropriate communication strategies and safe environments.

Scalar turbulent behavior in the roughness sublayer of an Amazonian forest.

An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of Monin?Obukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40 m), measured at 39.4 and 81.6 m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0°  <  |Z|  <  20°. We conjecture that this improvement is due to the relationship between sunlight incidence and the ?activation?deactivation? of scalar sinks and sources vertically distributed in the forest. Finally, we evaluated the relaxation coefficient of relaxed eddy accumulation: it is also affected by zenith angle, with considerable improvement in the range 0°  <  |Z|  <  20°, and its values fall within the range reported in the literature for the unstable surface layer. In general, our results indicate the possibility of better stability-derived flux estimates for low zenith angle ranges.