The major purpose of the study was to examine mood fluctuations associated with phases of the menstrual cycle. To assess the relative impact of the menstrual cycle on moods, other cycles hypothesized to influence moods also were assessed. Mood fluctuations in women and men were studied both prospectively and retrospectively to determine whether cyclic changes occur with the phases of the menstrual cycle, lunar cycle, and/or days of the week. Each participant (15 women using oral contraceptives, 12 normally cycling women, and 15 men) recorded their moods daily for 70 days (prospective data). A daily mean score was obtained for both pleasantness and arousal (each on a 9-point positive to negative scale). Mood stability/variability was recorded daily on a 4-point scale. At the end of the study, participants recalled (retrospective data) their mood over the previous 2 months for each day of the week and the phases of their menstrual cycle (women only). The focus on menstrual cycles was sucessfully camouflaged. Prospectively, there were no group differences and no menstrually-related mood fluctuations. The retrospective reports, however, indicated systematic bias. Women recalled more positive moods in the follicular phase and more negative moods in the premenstrual and menstrual phases than they had reported prospectively. All groups reported weekday mood changes — Monday lows and Friday/Saturday highs. Recollections of weekday mood fluctuations were similar to but more exaggerated than prospective reports. Prospective reports revealed no mood fluctuations over the lunar cycle. Together, these results indicate that stereotypes (both well- and ill-founded) influence recollections of mood, and are consistent with schematic processing theories. The importance for menstrual cycle research of obtaining information about positive as well as negative experiences, camouflaging the purpose of the study, collecting prospective data, and assessing results in the contexts of other cycles also is discussed. / Arts, Faculty of / Psychology, Department of / Graduate

College students participating in dyads played a game designed as an analog of early hunters whose survival, as a dyad and ultimately individually, depend on rabbits they hunt. Dyadic fitness was defined as both participants being able to hunt and it was measured by the proportion of trials in a condition that both participants hunted. The effects of scarcity (alternating rich and poor conditions) on dyadic fitness were examined in two experiments. First experiment results did not show a difference in dyadic fitness as a function of the independent variable. The second experiment increased the number of hunting seasons and also the discrepancy between scarcity in rich and poor seasons. Second experiment results show that dyads start fit in rich seasons and become increasingly fit in poor seasons. External variables could not be ruled out; therefore, additional experiments still need to be carried out to clarify results.

No description available.

<p>Ransomware, a form of malicious software, takes users’ files hostage via encryption and demands payment for their return. Since its inception, ransomware has branched into many different variants, some of which threaten users with scare tactics in order to evoke payment. For this study, four variants of ransomware were examined by presenting vignettes via an anonymous online survey. No actual malware was installed on any devices throughout this study. Their emotional responses were captured as well as their level of familiarity with information security. Responses to the survey after the simulated ransomware vignette were recorded to gauge how users would react to a ransomware attack. Data was analyzed to discover which types of ransomware evoked payment as well as if information security knowledge also had an effect on likelihood to pay. This data is intended to be used to develop better prevention methods and messaging, with an emphasis on promoting training on malware avoidance. The study found most individuals did not choose to pay, and this could be attributed to a distrust of the ransomware threat. Self-reported information security behavior appeared to decrease payment evocation, however, peer information security experience and prior exposure to malware appeared to increase payment evocation.</p>

<p>As the need for outdoor recreation grows, the profound impact of recreational activities upon wildlife is a major concern. For example, the presence of humans may increase risk-averse behavior by wildlife, restricting access to essential resources, and reducing foraging, thereby negatively impacting breeding. Ultimately, the impacts that recreationists have on wildlife include directly or indirectly altering population structure and community composition. Unfortunately, understanding the impacts of recreating humans upon wildlife is a complex challenge that is dependent upon wildlife species and human activity types. Our understanding of human-wildlife relationships can be improved by combining results from empirical studies with simulation models to extrapolate mechanisms to a broader range of circumstances and investigate their implications. Accordingly, we developed an ABM modeling framework, that enables both dynamic virtual human and wildlife agents to change their actions. These changes are based upon their state as a consequence of their interactions with their environment and other virtual agents. A unique aspect of the framework we developed is the explicit simulation of both wildlife and human agent behavior as emergent rather than imposed. We use this framework to model the disturbance of birds, in the Lawrence Creek Forest Unit (LCFU) of Fort Harrison State Park, IN, by human recreation. We parameterize the model with human recreation data collected through an intercept survey of recreationists at the park and bird data from published studies. We compare our modeling framework to a more traditional model type where human behavior is imposed while wildlife behavior is emergent. Our results indicate that the frequency of humans entering the park influences the rates of disturbance of birds more than model types. Examining simulation behavior within our new framework, the utility and off-trail options had the most influence across all scenarios. These comparisons illustrate that the use of a modeling framework that allows managers to explore factors altering wildlife disturbance rates. Despite the marginal influence of model type upon our results, our research elucidates the value of a model that allows emergent behavior for multiple agent types. The emergent human and wildlife responses of simulated interacting agents provides new insight when managing these relationships. <b></b></p>

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / This research proposes limit values of height and population density of buildings for a Prospective Management of Seismic Risk in residential areas. The analysis of an efficient evacuation of buildings to the street or refuge area was carried out, evaluating human behavior with models based on the agent, the influence of buildings and the urban parameters of the city with three-dimensional models (BIM) for a severe seismic scenario. The present article establishes that the maximum permissible height of buildings projected in a residential avenue is directly related to the width of the available refuge zone to guarantee the correct evacuation of said zone during a severe seismic event. In addition, an evaluation of a real existing scenario is made in a section of a residential avenue.

<p>The residential sector is responsible for approximately 21% of the total energy use in the U.S. As a result, there have been various programs and studies aiming to reduce energy consumption and utility burden on individual households. Among various energy efficiency strategies, behavior-based approaches have received considerable attention because they significantly affect operational energy consumption without requiring building upgrades. For example, up to 30% of heating and cooling energy savings can be achieved by having an efficient temperature setpoint schedule. Such approaches can be particularly beneficial for multi-family residential buildings because 88% of their residents are renters paying their own utility bills without being allowed to upgrade their housing unit.</p> <p>In this context, eco-feedback has emerged as an approach to motivate residents to reduce energy use by providing information (feedback) on human behavior and environmental impact. This research has gained significant attention with the development of new smart home technology such as smart thermostats and home energy management systems. Research on the design of effective eco-feedback focuses on how to motivate residents to change their behavior by identifying and notifying implementable actions in a timely manner via energy analytics such as energy prediction models, energy disaggregation, etc.</p> <p>However, unit-level energy analytics pose significant challenges in multi-family residential buildings tasks due to the inter-unit heat transfer, unobserved variables (e.g., infiltration, human body heat gain, etc.), and limited data availability from the existing infrastructure (i.e., smart thermostats and smart meters). Furthermore, real-time model inference can facilitate up-to-date eco-feedback without a whole year of data to train models. To tackle the aforementioned challenges, three new modeling approaches for energy analytics have been proposed in this Thesis is developed based on the data collected from WiFi-enabled smart thermostats and power meters in a multi-family residential building in IN, U.S.</p> <p>First, this Thesis presents a unit-level data-driven modeling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings. The proposed modeling approach provides normalized groups of units that have similar building characteristics to provide the relative evaluation of energy-related behaviors. The physics-informed approach begins from a heat balance equation to derive a linear regression model, and a Bayesian mixture model is used to identify normalized groups in consideration of the inter-unit heat transfer and unobserved variables. The probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data is available. The model finds distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.</p> <p>Second, this Thesis presents a real-time modeling approach to predict the HC energy consumption of individual units in a multi-family residential building. The model has a state-space structure to capture the building thermal dynamics, includes the setpoint schedule as an input, and incorporates real-time state filtering and parameter learning to consider uncertainties from unobserved boundary conditions (e.g., temperatures of adjacent spaces) and unobserved disturbances (i.e., window opening, infiltration, etc.). Through this real-time form, the model does not need to be re-trained for different seasons. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.</p> Finally, this Thesis presents a scalable and practical HC energy disaggregation model that is designed to be developed using data from smart meters and smart thermostats available in current advanced metering infrastructure (AMI) in typical residential houses without additional sensors. The model incorporates sequential Bayesian update whenever a new operation type is observed to learn seasonal parameters without long-term data for training. Also, it allows modeling the skewed characteristics of HC and non-HC power data. The results show that the model successfully predicts disaggregated HC power from 15-min interval data, and it shows less than 12% of error in weekly HC energy consumption. Finally, the model is able to learn seasonal parameters via sequential Bayesian update and gives good prediction results in different seasons.

This is a theoretical/philosophical paper which is intended to bring to the reader's attention an emerging literature and discussion which holds potentially productive consequences for the understanding of man. This thesis does not offer completed formulations or empirical groundings. The purpose is to create a basis for dialogue. This paper will initially specify a current conflict in psychology around the different metaphors used to define the image of man. A theoretical/philosophical basis for viewing the process of generating models of man and his behavior as essentially "metaphorical" is then presented. A specific category of human behavior known as the neurotic paradox (henceforth abbreviated NP) is defined and a review of literature on the root metaphorical interpretations of the NP is discussed. The prominent extant models of human behavior reviewed in this discussion are those based on the metaphors Spirit, Disease, Machine, and Seed. The limitations of each model will be discussed with regard to that model's adequacy to provide understanding of the four basic defining characteristics of the NP. This section constitutes the main body of the thesis. This evaluative discussion of the theoretical/philosophical inadequacies of each model is intended to bring to light the process and strategies (both explicit and implicit) which have evolved in the interpretation of the image of man.

On my good days, I find that I am often generous with my species. I let the driver in the Hummer merge into my lane with a wave, pick up litter, open doors for stragglers, and give loose change to beggars. On my bad days, I too easily curse (quietly) at the rude and the clueless, keep my hands in my pockets, my head down, and my shoulders hunched. My gait becomes slightly simian. That I can swing with graceful agility from one orientation to the other (often without anyone actually knowing) used to disturb me. I suppose I grew up believing I had to be one kind of person or the other. Not anymore. Perhaps I'm finally reconciled to the fact that I am human; that is, a creature with a consciousness, a being of sometimes-exhausting contradictions, a repository of living memories, an almost-virtuous and curious animal. I've landed, finally, in gray territory, which is where, as it turns out, I always hoped I would. The personal essays in this thesis explore this fluid nature of my (our) creatureliness. As a Catholic priest and a writer, my intention was to use the sacraments of the Catholic Church (Baptism, Eucharist, Confession, Confirmation, Holy Matrimony, Holy Orders, and Anointing of the Sick) as points of departure, and to allow each sacrament to provide me a lens and a perch through which (and from which) I might see our species anew. Aware that these seven sacraments mark the life--from birth to death--of a Catholic, I saw them as a useful and invisible thread that would give these essays thematic cohesion. Recognizing that this exploration was going to be more spelunking than mountain climbing, this sacramental thread became a rope with which I could jump down into some fascinating dark places without getting lost or stranded. In the end, these seven essays attempt to get at a few questions that have haunted me for years: Why have I not given up on my species yet? Why do I still believe in the innate goodness of human creatures? From where do I draw the strength to go spelunking into the darkest warrens of the human heart? These essays eschew the easy answers. They rather delight instead in dark places, illuminated, for a second, by one shaky candle.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 342-356. / by Ilan Salomon. / Ph.D.