We characterize the first 40 Myr of evolution of circumstellar disks through a unified study of the infrared properties of members of young clusters and associations with ages from 2 Myr up to similar to 40 Myr: NGC 1333, NGC 1960, NGC 2232, NGC 2244, NGC 2362, NGC 2547, IC 348, IC 2395, IC 4665, Chamaeleon I, Orion OB1a and OB1b, Taurus, the beta Pictoris Moving Group,. Ophiuchi, and the associations of Argus, Carina, Columba, Scorpius-Centaurus, and Tucana-Horologium. Our work features: (1) a filtering technique to flag noisy backgrounds; (2) a method based on the probability distribution of deflections, P(D), to obtain statistically valid photometry for faint sources; and (3) use of the evolutionary trend of transitional disks to constrain the overall behavior of bright disks. We find that the fraction of disks three or more times brighter than the stellar photospheres at 24 mu m decays relatively slowly initially and then much more rapidly by similar to 10 Myr. However, there is a continuing component until similar to 35 Myr, probably due primarily to massive clouds of debris generated in giant impacts during the oligarchic/chaotic growth phases of terrestrial planets. If the contribution from primordial disks is excluded, the evolution of the incidence of these oligarchic/chaotic debris disks can be described empirically by a log-normal function with the peak at 12-20 Myr, including similar to 13% of the original population, and with a post-peak mean duration of 10-20 Myr.

Contemporary cosmology is a vibrant field, with data and observations increasing rapidly. This allows for accurate estimation of the parameters describing our cosmological model. In this thesis we present new research based on two different types of cosmological observations, which probe the universe at multiple epochs. We begin by reviewing the current concordance cosmological paradigm, and the statistical tools used to perform parameter estimation from cosmological data. We highlight the initial conditions in the universe and how they are detectable using the Cosmic Microwave Background radiation. We present the angular power spectrum data from temperature observations made with the Atacama Cosmology Telescope (ACT) and the methods used to estimate the power spectrum from temperature maps of the sky. We then present a cosmological analysis using the ACT data in combination with observations from the Wilkinson Microwave Anisotropy Probe to constrain parameters such as the effective number of relativistic species and the spectral index of the primordial power spectrum, which we constrain to deviate from scale invariance at the 99% confidence limit. We then use this combined dataset to constrain the primordial power spectrum in a minimally parametric framework, finding no evidence for deviation from a power-law spectrum. Finally we present Bayesian Estimation Applied to Multiple Species, a parameter estimation technique using photometric Type Ia Supernova data to estimate cosmological parameters in the presence of contaminated data. We apply this algorithm to the full season of the Sloan Digital Sky Survey II Supernova Search, and find that the constraints are improved by a factor of three relative to the case where one uses a smaller, spectroscopically confirmed subset of supernovae.

In the past few decades, demand for construction in underground spaces has increased dramatically in urban areas with high population densities. However, the impact of the construction of underground structures on surrounding infrastructure raises concerns since movements caused by deep excavations might damage adjacent buildings. Unfortunately, the prediction of geotechnical behaviour is difficult due to uncertainties and lack of information of on the underground environment. Therefore, to ensure safety, engineers tend to choose very conservative designs that result in requiring unnecessary material and longer construction time. The observational method, which was proposed by Peck in 1969, and formalised in Eurocode 7 in 1987, provides a way to avoid such redundancy by modifying the design based on the knowledge gathered during construction. The review process within the observational method is recognised as back analysis. Supervised learning can aid in this process, providing a systematic procedure to assess soil parameters based on monitoring data and prediction of the ground response. A probabilistic model is developed in this research to account for the uncertainties in the problem. Sequential Bayesian inference is used to update the soil parameters at each excavation stage when observations are available. The accuracy of the prediction for future stages improves at each stage. Meanwhile, the uncertainty contained in the prediction decreases, and therefore the confidence on the corresponding design also increases. Moreover, the Bayesian method integrates subjective engineering experience and objective observations in a rational and quantitative way, which enables the model to update soil parameters even when the amount of data is very limited. It also allows the use of the knowledge learnt from comparable ground conditions, which is particularly useful in the absence of site-specific information on ground conditions. Four probabilistic models are developed in this research. The first two incorporate empirical excavation design methods. These simple models are used to examine the practicality of the approach with several cases. The next two are coupled with a program called FREW, which is able to simulate the excavation process, still in a relatively simplistic way. The baseline model with simple assumptions on model error and another one is a more sophisticated model considering measurement error and spatial relationships among the observations. Their efficiency and accuracy are verified using a synthetic case and tested based on a case history from the London Crossrail project. In the end, the models are compared and their flexibility in different cases is discussed.

One experiment was adminstered to address three issues central to identifying the processes that underlie our ability to learn through observation. One objective of the study was to identify the movement features (relative or absolute) extracted by an observer when demonstration acts as the training protocol. A second objective was to investigate how the performance level of the model (trial-to-trial variability in strategy selection) providing the demonstrations influences movement feature extraction. Lastly, a goal was to test whether or not visual perspective of the model by the observer (first-person or third-person) interacts with the aforementioned variables. The goal of the task was to trace two circles templates with a 90 degree relative phase offset between the two hands. Video recordings of two models practicing over three days were used to make three videos for the study; an expert performance, discovery performance, and instruction performance video. The discovery video portrayed a decrease in relative phase error and a transition from high trial-to-trial variability in the strategy selection to use of a single strategy. The instruction video also portrayed a decrease in relative phase error, but with no strategy search throughout practice. The expert video showed no strategy search with trial-to-trial variability within 5% of the goal relative phase of 90 across every trial. Observers watched one of the three video recordings from either a first-person or third-person perspective. In a retention test, the expert observers showed the most consistant capability (learning) in performing the goal phase. The instruction observers also showed learning, but to a lesser degree than the expert observers. The discovery group observers showed the least amount of learning of relative phase. The absolute feature of movement amplitude was not extracted by any observer group, results consistent with postulations by Scully and Newell (1985). Observation from the 1P perspective proved optimal in the expert and instruction observation groups, but the 3P perspective allowed for greater learning of of the goal relative phase (90 degree) in the discovery observation group. Hand lead, a relative feature of motion, was extracted by most obsevers, except those who observed the discovery model from the 3P perspective. It's concluded that the trial-to-trial variabiliy in terms of strategy selection interacted with the process of mental rotation, which prevented the extraction of hand lead in those observers that viewed the discovery model.

One experiment was adminstered to address three issues central to identifying the processes that underlie our ability to learn through observation. One objective of the study was to identify the movement features (relative or absolute) extracted by an observer when demonstration acts as the training protocol. A second objective was to investigate how the performance level of the model (trial-to-trial variability in strategy selection) providing the demonstrations influences movement feature extraction. Lastly, a goal was to test whether or not visual perspective of the model by the observer (first-person or third-person) interacts with the aforementioned variables. The goal of the task was to trace two circles templates with a 90 degree relative phase offset between the two hands. Video recordings of two models practicing over three days were used to make three videos for the study; an expert performance, discovery performance, and instruction performance video. The discovery video portrayed a decrease in relative phase error and a transition from high trial-to-trial variability in the strategy selection to use of a single strategy. The instruction video also portrayed a decrease in relative phase error, but with no strategy search throughout practice. The expert video showed no strategy search with trial-to-trial variability within 5% of the goal relative phase of 90 across every trial. Observers watched one of the three video recordings from either a first-person or third-person perspective. In a retention test, the expert observers showed the most consistant capability (learning) in performing the goal phase. The instruction observers also showed learning, but to a lesser degree than the expert observers. The discovery group observers showed the least amount of learning of relative phase. The absolute feature of movement amplitude was not extracted by any observer group, results consistent with postulations by Scully and Newell (1985). Observation from the 1P perspective proved optimal in the expert and instruction observation groups, but the 3P perspective allowed for greater learning of of the goal relative phase (90 degree) in the discovery observation group. Hand lead, a relative feature of motion, was extracted by most obsevers, except those who observed the discovery model from the 3P perspective. It's concluded that the trial-to-trial variabiliy in terms of strategy selection interacted with the process of mental rotation, which prevented the extraction of hand lead in those observers that viewed the discovery model.

Three experiments compared learning of relative and absolute timing of a sequential key-pressing task by physical and observational practice. Experiment 1 compared a task with a complex internal structure (goal proportions of 22.2, 44.4, 33.4 on the three movement segments) to one with a simpler structure (goal proportions of 33.3, 33.3, 33.4). Observers only learned the relative timing as well as physical practicers when the internal structure was simple, but learned the absolute timing in both conditions. Experiment 2 compared variable (700, 900, and 1100 ms overall time) with constant practice (900 ms overall time). Observers of constant practice models learned the relative timing better than no-practice control participants, but not as well as the models, while observers of variable practice models learned the relative timing no better than the control group. Observers in both practice conditions were able to produce the absolute timing as well as those who physically practiced. In Experiment 3 observers of an expert model were able to produce the relative timing as well as those who physically practiced the skill, while those who observed learning models were not. All observers and the physical practice participants were able to produce the overall duration as well as the expert model. The results of these three experiments support earlier findings that increasing stability during practice promotes better learning of relative timing, but that absolute timing can be learned under less-stable conditions (Lai, Shea, Wulf, & Wright, 2000b). These findings also have important implications on the limitations of Scully and Newells' (1985) prediction that relative timing, but not absolute timing, could be learned by observation. Experiments 1-3 along with earlier findings (Black & Wright, 2000) have consistently found that absolute timing could be learned by observers even as the nature of the task, practice schedule, and model are manipulated. Furthermore, the results suggest a limitation to the effectiveness of learning models (Adams, 1986; McCullagh & Caird, 1990).

Research on human behaviour during earthquake shaking has identified three main influences of behaviour: the environment the individual is located immediately before and during the earthquake, in terms of where the individual is and who the individual is with at the time of the earthquake; individual characteristics, such as age, gender, previous earthquake experience, and the intensity and duration of earthquake shaking. However, little research to date has systematically analysed the immediate observable human responses to earthquake shaking, mostly due to data constraints and/or ethical considerations. Research on human behaviour during earthquakes has relied on simulations or post-event, reflective interviews and questionnaire studies, often performed weeks to months or even years following the event. Such studies are therefore subject to limitations such as the quality of the participant's memory or (perceived) realism of a simulation. The aim of this research was to develop a robust coding scheme to analyse human behaviour during earthquake shaking using video footage captured during an earthquake event. This will allow systematic analysis of individuals during real earthquakes using a previously unutilized data source, thus help develop guidance on appropriate protective actions. The coding scheme was developed in a two-part process, combining a deductive and inductive approach. Previous research studies of human behavioral response during earthquake shaking provided the basis for the coding scheme. This was then iteratively refined by applying the coding scheme to a broad range of video footage of people exposed to strong shaking during the Canterbury earthquake sequence. The aim of this was to optimise coding scheme content and application across a broad range of scenarios, and to increase inter-coder reliability. The methodology to code data will enhance objective observation of video footage to allow cross-event analysis and explore (among others): reaction time, patterns of behaviour, and social, environmental and situational influences of behaviour. This can provide guidance for building configuration and design, and evidence-based recommendations for public education about injury-preventing behavioural responses during earthquake shaking.

We interpret distance measurements from nearby galaxies, type Ia supernovae, and gamma-ray bursts in the light of a cosmological model that incorporates a spatial averaging technique to account for the inhomogeneous distribution of structure in the late-epoch Universe and the consequent importance of the location of the observer. In the timescape cosmology it is suggested that dark energy is a misidentification of gravitational energy gradients---and consequently of the relative calibration of clocks and rulers---in a complex inhomogeneous structure. This model is consistent with the current supernova and gamma-ray burst data within the limits imposed by our understanding of the systematic uncertainties, to the extent that a Bayesian model comparison with the standard model yields a preference for the timescape model that is “not worth more than a bare mention”. In the spirit of the timescape model, of attempting to understand the astrophysics with as few cosmological assumptions as possible, we perform a model-independent analysis of galaxy distances in the local Universe. We find that the rest frame of the Local Group provides a more uniform Hubble expansion field than the rest frame of the CMB. We find that the dipole in the Hubble expansion field coincides with the dipole in the CMB temperature with a correlation coefficient of -0.92, and that this pattern is induced within 60 h⁻¹ Mpc, provided the variation in the distance-redshift relation due to the formation of structure is taken into account.

Thesis (Ph. D.)--Ohio State University, 1977. / Includes vita. Includes bibliographical references (leaves 200-209). Available online via OhioLINK's ETD Center.

Observational learning has been defined as the learning of new responses that occurs as a result of observing the responding of a model and the consequences that this responding produces (Catania, 2007; Taylor & DeQuinzio, 2012). The following study compared the effectiveness of a peer and staff model for teaching four adults with intellectual and developmental disabilities a new response chained task. An alternating treatment design, counterbalanced across subjects was used to evaluate the effects of each modeling condition. Results indicated that all four adults learned the skill with fewer sessions by observing the peer model. Following acquisition of each task, the degree of generalization and maintenance of responding was also evaluated. Implications and directions for future research are further discussed.