<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.

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.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Includes bibliographies. / by David Damm. / Ph.D.

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Deep learning has become a widely used tool for inference and prediction in neuroscience research. Despite their differences, most neural network architectures convert raw input data into lower-dimensional vector representations that subsequent network layers can more easily process. Significant advancements have been made in improving latent representations in audiovisual problems. However, human neurophysiological data is often scarcer, noisier, and more challenging to learn from when integrated from multiple sources. The present work integrates neural, physiological, and behavioral data to improve human behavior, preference, and state prediction. Across five studies, we explore (i) how embeddings, or vectorized representations, can be designed to understand the context of input data better, (ii) how the attention mechanism found in transformer models can be adapted to capture crossmodal relationships in an interpretable way, and (iii) how humans make sensorimotor decisions in a realistic scenario with implications for designing automated systems. Part I focuses on improving the context for latent representations in deep neural networks. We achieve this by introducing a hierarchical structure in clinical data to predict cognitive performance in a large, longitudinal cohort study. In a separate study, we present a recurrent neural network that captures non-cognitive pupil dynamics by utilizing visual areas of interest as inputs. In Part II, we employ attention-based approaches for multimodal integration by learning to weigh modalities that differ in the type of information they capture. We show that our crossmodal attention framework can adapt to audiovisual and neurophysiological input data. Part III proposes a novel paradigm to study sensorimotor decision-making in a driving scenario and study brain connectivity in the context of pupil-linked arousal. Our findings reveal that embeddings that capture input data's hierarchical or temporal context consistently yield high performance across different tasks. Moreover, our studies demonstrate the versatility of the attention mechanism, which we show can effectively integrate various modalities such as text descriptions, perceived differences in video clips, and recognized objects. Our multimodal transformer, designed to handle neurophysiological data, improves the prediction of emotional states by integrating brain and autonomic activity. Taken together, our work advances the development of multimodal systems for predicting human behavior, preference, and state across domains.

Different types of nonprofit organizations including human service nonprofits like homeless shelters, public broadcasting organizations, and the like thrive on donations. Effective fundraising techniques are essential to a nonprofit’s existence. This research study explored a revised theory of planned behavior to include guilt and convenience in order to understand whether these factors are important in donors’ intentions to give. This study also examined the impact of two different kinds of guilt; anticipated guilt and existential guilt to determine if there was any difference between the types of guilt and the roles that they play as predicting factors in a revised TPB model. This study also explored how human service nonprofits and public broadcasting organizations compare in the factors that help better predict their donating intentions. An online survey was administered to a convenience sample, and hierarchical regression analysis was used to determine significant predicting factors within each revised TPB model. This study confirmed that the standard theory of planned behavior model was a significant predictor of intentions to donate for donors of both human service nonprofits and public broadcasting organizations. However, in both contexts, not all traditional factors of the TPB model contributed to the donation intentions. This study also provides further evidence that guilt can increase the predictive value of the standard TPB model for both types of nonprofits. Anticipated guilt more specifically, was a significant predicting factor for donors’ intentions to give to public broadcasting organizations. In contrast, convenience did not affect the explanatory power of the TPB model in either context. The TPB models for the two nonprofits are compared and theoretical and practical explanations are discussed.

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.