The dissertation details work aimed toward the development and implementation of a 3-D impact fragmentation module to perform rock fall analysis by taking into account impact fragmentation. This fragmentation module is based on a database of a large set of impact simulations using a fully calibrated discrete element model (DEM), and is employed to predict impact fragmentation processes in rockfall analysis by either training a neural network model or linearly interpolating the database. A DEM was employed to model impact fragmentation in the study. A DEM code was developed from scratch. The model was first calibrated and verified with experimental results to demonstrate the capability of modeling both quasi-static and dynamic material behavior. Algorithms to calibrate the model’s micro-parameters against triaxial tests on rocks were presented. Sensitivity analyses were used to identify the deformability micro-parameters by obtaining relationships between microscopic and macroscopic deformability properties. The strength model parameters were identified by a global optimization process aimed at minimizing the difference between computed and experimental failure envelopes. When applied to the experimental results of tested granite, this calibration process produced a good agreement between simulated and experimental results for both deformability and strength properties. Dynamic compression and SHPB tests were performed to verify the dynamic model. A strain-rate-dependent dynamic strength was observed in the experimental results. This strain-rate-dependent dynamic strength was also confirmed by the numerical results. No rate-dependent constitutive model was used in the DEM to simulate dynamic behavior. This simulated rate-dependent dynamic strength can be attributed to material inertia because the inertia inhibits crack growth. Some fundamental mechanisms of impact fragmentation associated with rockfalls were then numerically investigated. The developed DEM code was coupled with a simplified impact model inspired by the theory of dynamic foundations. It has been shown that the magnitude of impact velocity, the angle of the incidence, the ground condition all play very important roles in impact fragmentation. Several case studies were performed to validate the developed impact fragmentation module in rock fall analysis. It has been demonstrated that the developed fragmentation module can reasonably predict impact fragmentation and perform some risk analysis in rock fall analysis. / text

The relationships among counseling expectations, attitudes toward seeking psychological help, psychological distress, and intention to seek counseling have only been examined in one previous study (Vogel, Wester, Wei, & Boysen, 2005). The primary purpose of the current study was to replicate and address the limitations of the Vogel et al. (2005) study. First, a mediation analysis of attitudes on the relationship of expectations and intention to seek therapy was performed. Next, path analyses were used to test a model of the relationship among counseling expectations, attitudes toward seeking psychological help, psychological distress, and the intent to seek counseling, for men and women separately. In the hypothesized model, two separate paths were predicted to impact intentions to seek psychological help. First, three distinct expectations about counseling (personal commitment, facilitative conditions, and counselor expertise) were expected to influence attitudes toward seeking psychological help, which in turn, predicted intention to seek counseling. Second, psychological distress was expected to relate to the intent to seek therapy. Because the hypothesized model for both genders did not fit the data, exploratory path analyses were completed. In the final path model for men, additional paths from the expectancy factors personal commitment and counselor expertise to intent to seek therapy resulted in a well-fitting model. For women, an additional path between psychological distress and attitudes improved the model significantly. Impact of these findings for research and practice are discussed. / Department of Counseling Psychology and Guidance Services

abstract: In the present research, two interventions were developed to increase sun protection in young women. The purpose of the study was to compare the effects of intervention content eliciting strong emotional responses to visual images depicting photoaging and skin cancer, specifically fear and disgust, coupled with a message of self-efficacy and benefits of sun protection (the F intervention) with an intervention that did not contain an emotional arousal component (the E intervention). Further, these two intervention conditions were compared to a control condition that contained an emotional arousal component that elicited emotion unrelated to the threat of skin cancer or photoaging (the C control condition). A longitudinal study design was employed, to examine the effects of condition immediately following the intervention, and to examine sun protection behavior 2 weeks after the intervention. A total of 352 undergraduate women at Arizona State University were randomly assigned to one of the three conditions (F n = 148, E n = 73, C n = 131). Several psychosocial constructs, including benefits of sun protection, susceptibility to and severity of photoaging and sun exposure, self-efficacy beliefs of making sun protection a daily habit, and barriers to sun protection were measured before and immediately following the intervention. Sun protection behavior was measured two weeks later. Those in the full intervention reported higher self-efficacy and severity of photoaging at immediate posttest than those in the efficacy only and control conditions. The fit of several path models was tested to explore underlying mechanisms by which the intervention affected sun protection behavior. Experienced emotion, specifically fear and disgust, predicted susceptibility and severity, which in turn predicted anticipated regret of failing to use sun protection. The relationship between this overall threat component (experienced emotion, susceptibility, severity, and anticipated regret) and intentions to engage in sun protection behavior was mediated by benefits. The present research provided evidence of the effectiveness of threat specific emotional arousal coupled with a self-efficacy and benefits message in interventions to increase sun protection. Further, this research provided additional support for the inclusion of both experienced and anticipated emotion in models of health behavior. / Dissertation/Thesis / Ph.D. Psychology 2011

This dissertation presents a unique approach to human behavior analysis based on expert guidance and intervention through interactive construction and modification of behavior models. Our focus is to introduce the research area of behavior analysis, the challenges faced by this field, current approaches available, and present a new analysis approach: Interactive Relevance Search and Modeling (IRSM). More intelligent ways of conducting data analysis have been explored in recent years. Ma- chine learning and data mining systems that utilize pattern classification and discovery in non-textual data promise to bring new generations of powerful "crawlers" for knowledge discovery, e.g., face detection and crowd surveillance. Many aspects of data can be captured by such systems, e.g., temporal information, extractable visual information - color, contrast, shape, etc. However, these captured aspects may not uncover all salient information in the data or provide adequate models/patterns of phenomena of interest. This is a challenging problem for social scientists who are trying to identify high-level, conceptual patterns of human behavior from observational data (e.g., media streams). The presented research addresses how social scientists may derive patterns of human behavior captured in media streams. Currently, media streams are being segmented into sequences of events describing the actions captured in the streams, such as the interactions among humans. This segmentation creates a challenging data space to search characterized by non- numerical, temporal, descriptive data, e.g., Person A walks up to Person B at time T. This dissertation will present an approach that allows one to interactively search, identify, and discover temporal behavior patterns within such a data space. Therefore, this research addresses supporting exploration and discovery in behavior analysis through a formalized method of assisted exploration. The model evolution presented sup- ports the refining of the observer\'s behavior models into representations of their understanding. The benefit of the new approach is shown through experimentation on its identification accuracy and working with fellow researchers to verify the approach\'s legitimacy in analysis of their data. / Ph. D.

No description available.

A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.

The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns. / text

Durant ce doctorat financé par l'observatoire B2V des mémoires, nous avons réalisé une modélisation mathématique du comportement dans trois tâches distinctes (avec des sujets humains, des sujets singes et des rongeurs), mais qui supposent toutes une coordination entre systèmes de mémoire. Dans la première expérience, nous avons reproduit le comportement de sujets humains (choix et temps de réaction) en combinant les modèles mathématiques d'une mémoire de travail et d'une mémoire inflexible. Nous avons associé pour un sujet son comportement au meilleur modèle possible en comparant des modèles génériques de coordination de ces deux mémoires issues de la littérature actuelle ainsi que notre propre proposition d'une interaction dynamique entre les mémoires. Au final, c'est notre proposition d'une interaction au lieu d'une séparation stricte qui s'est avérée la plus efficace dans la majorité des cas pour expliquer le comportement des sujets. Dans une deuxième expérience, les mêmes modèles de coordination ont été testés dans une tâche chez le singe. Considérée comme un test de transférabilité, cette expérience démontre principalement la nécessité de coordination de mémoires pour expliquer le comportement de certains singes. Dans une troisième expérience, nous avons modélisé le comportement d'un groupe de souris confronté à l'apprentissage d'une séquence d'action motrice dans un labyrinthe sans indices externes. En comparant avec deux autres stratégies d'apprentissages (intégration de chemin et planification dans un graphe), la combinaison d'une mémoire épisodique avec une mémoire inflexible s'est révélée être le meilleur modèle pour reproduire le comportement des souris. / During this PhD funded by the B2V Memories Observatory, we performed a mathematical modeling of behavior in three distinct tasks (with human subjects, monkeys and rodents), all involving coordination between memory systems. In the first experiment, we reproduced the behavior of human subjects (choice and reaction time) by combining the mathematical models of working memory and procedural memory. For each subject, we associated their behavior to the best possible model by comparing generic models of coordination of these two memories from the current literature as well as our own proposal of a dynamic interaction between memories. In the end, it was our proposal of an interaction instead of a strict separation which proved most effective in the majority of cases to explain the behavior of the subjects. In a second experiment, the same coordination models were tested in a monkey task. Considered as a transferability test, this experiment mainly demonstrates the need for coordination of memories to explain the behavior of certain monkeys. In a third experiment, we modeled the behavior of a group of mice confronted with the learning of a motor action sequence in a labyrinth without visual cues. Comparing with two other learning strategies (path integration and graph planning), the combination of an episodic memory with a procedural memory proved to be the best model to reproduce the behavior of mice.

Bakalauro baigiamajame darbe nagrinėjama Šiaulių miesto rankų darbo aksesuarų vartotojų elgsenos tema. Tiriamą problemą apibūdina klausimas: kokie pagrindiniai vidiniai ir išoriniai vartotojų elgsenos veiksniai skatina įsigyti rankų darbo aksesuarus? Teorinėje darbo dalyje pateikiama vartotojų elgsenos samprata, vidiniai ir išoriniai veiksniai, vartotojų sprendimo pirkti priėmimo procesas, alternatyvų identifikavimas bei marketingo komplekso elementų įtaka vartotojų elgsenai. Empirinėje darbo dalyje apklausus 100 Šiaulių miesto gyventojų – moterų, identifikuotos esminės priežastys, įtakojančios rankų darbo aksesuarų vartotojų elgseną; išsiaiškinti pagrindiniai tokio tipo aksesuarų pasirinkimo motyvai ir kriterijai, vartotojų nuomonės; giluminio interviu metu pagrįsta kokybinio tyrimo metu gauta informacija. Remiantis gautais kiekybinio ir kokybinio tyrimo rezultatais, sudarytas rankų darbo aksesuarų vartotojų elgsenos modelis. / Undergraduate thesis examined Šiauliai handmade accessories, consumer behavior topic. The problem under study describes the issue: what are the key internal and external factors are driving consumer behavior to purchase handmade accessories? The theoretical part of the consumer behavior on the concept of internal and external factors, consumer purchase decision process, the identification of alternatives, and marketing mix elements influence consumer behavior. Empirical survey of 100 working in Šiauliai city's population - women, identified the root causes that influence handmade accessories, consumer behavior, to clarify the main reasons for the choice of the type of accessories and the criteria for consumer satisfaction, depth interviews during the qualitative analysis based on the information received. Based on quantitative and qualitative results of the study, consisting of handmade accessories, consumer behavior model.

When practicing secure programming, it is important to understand the restrictive influence programmed entities have on the propagation of authority in a program. To precisely model authority propagation in patterns of interacting entities, we present a new formalism Knowledge Behavior Models (KBM). To describe such patterns, we present a new domain specific declarative language SCOLL (Safe Collaboration Language), which semantics are expressed by means of KBMs. To calculate the solutions for the safety problems expressed in SCOLL, we have built SCOLLAR: a model checker and solver based on constraint logic programming. SCOLLAR not only indicates whether the safety requirements are guaranteed by the restricted behavior of the relied-upon entities, but also lists the different ways in which their behavior can be restricted to guarantee the safety properties without precluding their required functionality and (re-)usability. How the tool can help programmers to build reliable components that can safely interact with partially or completely untrusted components is shown in elaborate examples.