Union exterior, Fall 1950

Irving Kane and Allen B. Pond, architects. Plans for the Union were on a scale unknown at the time for "club houses" in American colleges and universities: 250 feet long and 200 feet wide. Construction began in 1916 and owing to war time difficulties was not ready to be used by students until 1919.

Residence for Morse Dell Plain, Esq.; Hammond, Indiana

Howard Van Doren Shaw, architect. 56 cm. x 45 cm. Scale: 1/4"=1' [from photographic copy by Lance Burgharrdt]

Residence for Morse Dell Plain, Esq.; Hammond, Indiana

Howard Van Doren Shaw, architect. 56 cm. x 45 cm. Scale: 1/4"=1' [from photographic copy by Lance Burgharrdt]

Residence for Morse Dell Plain, Esq.; Hammond, Indiana

Howard Van Doren Shaw, architect. 56 cm. x 45 cm. Scale: 1/4"=1' [from photographic copy by Lance Burgharrdt]

Residence for Morse Dell Plain, Esq.; Hammond, Indiana

Howard Van Doren Shaw, architect. 56 cm. x 45 cm. Scale: 1/4"=1' [from photographic copy by Lance Burgharrdt]

Time scale analysis for fluidizedbedmeltgranulation-II: binder spreading rate

The spreading time of liquid binder droplet on the surface a primary particle is analyzed for Fluidized Bed Melt Granulation (FBMG). As discussed in the first paper of this series (Chua et al., in press) the droplet spreading rate has been identified as one of the important parameters affecting the probability of particles aggregation in FBMG. In this paper, the binder droplet spreading time has been estimated using Computational Fluid Dynamic modeling (CFD) based on Volume of Fluid approach (VOF). A simplified analytical solution has been developed and tested to explore its validity for predicting the spreading time. For the purpose of models validation, the droplet spreading evolution was recorded using a high speed video camera. Based on the validated model, a generalized correlative equation for binder spreading time is proposed. For the operating conditions considered here, the spreading time for Polyethylene Glycol (PEG1500) binder was found to fall within the range of 10-2 to 10-5 s. The study also included a number of other common binders used in FBMG. The results obtained here will be further used in paper III, where the binder solidification rate is discussed.

Time scale analysis for fluidizedbedmeltgranulation III: binder solidification rate

In series I and II of this study ([Chua et al., 2010a] and [Chua et al., 2010b]), we discussed the time scale of granule–granule collision, droplet–granule collision and droplet spreading in Fluidized Bed Melt Granulation (FBMG). In this third one, we consider the rate at which binder solidifies. Simple analytical solution, based on classical formulation for conduction across a semi-infinite slab, was used to obtain a generalized equation for binder solidification time. A multi-physics simulation package (Comsol) was used to predict the binder solidification time for various operating conditions usually considered in FBMG. The simulation results were validated with experimental temperature data obtained with a high speed infrared camera during solidification of ‘macroscopic’ (mm scale) droplets. For the range of microscopic droplet size and operating conditions considered for a FBMG process, the binder solidification time was found to fall approximately between 10-3 and 10-1 s. This is the slowest compared to the other three major FBMG microscopic events discussed in this series (granule–granule collision, granule–droplet collision and droplet spreading).

Time scale analysis for fluidized bed melt granulation I: granule-granule and granule-droplet collision rates

Fluidized bed spray granulators (FBMG) are widely used in the process industry for particle size growth; a desirable feature in many products, such as granulated food and medical tablets. In this paper, the first in a series of four discussing the rate of various microscopic events occurring in FBMG, theoretical analysis coupled with CFD simulations have been used to predict granule–granule and droplet–granule collision time scales. The granule–granule collision time scale was derived from principles of kinetic theory of granular flow (KTGF). For the droplet–granule collisions, two limiting models were derived; one is for the case of fast droplet velocity, where the granule velocity is considerable lower than that of the droplet (ballistic model) and another for the case where the droplet is traveling with a velocity similar to the velocity of the granules. The hydrodynamic parameters used in the solution of the above models were obtained from the CFD predictions for a typical spray fluidized bed system. The granule–granule collision rate within an identified spray zone was found to fall approximately within the range of 10-2–10-3 s, while the droplet–granule collision was found to be much faster, however, slowing rapidly (exponentially) when moving away from the spray nozzle tip. Such information, together with the time scale analysis of droplet solidification and spreading, discussed in part II and III of this study, are useful for probability analysis of the various event occurring during a granulation process, which then lead to be better qualitative and, in part IV, quantitative prediction of the aggregation rate.

Fall prevention among older adults: a quantitative and qualitative study

The purpose of this study was to investigate the effect of a fall prevention program on older adults as well as to explore the perceptions of older adults have on fall risk and the fall prevention program. This research was completed in Newfoundland and Labrador, Canada with participants above the age of 65. The 10-week fall prevention program focused on balance, strength, and flexibility and was followed by focus groups with the control and intervention groups. Pre and post-test measures (postural sway, TUG test, foam and dome test, ABC Scale, ESE Scale, FES Scale, SAFFE) were completed to determine if the fall prevention program decreased fall risk. The results of the quantitative portion of the study did not produce significant results however the qualitative portion was very informative. Five themes emerged from the focus group data: risk factor awareness, confidence, connectedness, quality of life, and program promotion. This research highlights the importance of fall prevention programs for older adults, not only to keep them healthy, but also for the personal and social benefits they facilitate.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Decadal-to-centennial scale variations during the Holocene reconstructed from sediment core D13902

Sea surface temperature (SST), marine productivity, and fluvial input have been reconstructed for the last 11.5 calendar (cal) ka B.P. using a high-resolution study of C37 alkenones, coccolithophores, iron content, and higher plant n-alkanes and n-alkan-1-ols in sedimentary sequences from the inner shelf off the Tagus River Estuary in the Portuguese Margin. The SST record is marked by a continuous decrease from 19C, at 10.5 and 7 ka, to 15C at present. This trend is interrupted by a fall from 18C during the Roman and Medieval Warm Periods to 16C in the Little Ice Age. River input was very low in the early Holocene but increased in the last 3 cal ka B.P. in association with an intensification of agriculture and deforestation and possibly the onset of the North Atlantic Oscillation/Atlantic Multidecadal Oscillation modes of variability. River influence must have reinforced the marine cooling trend relative to the lower amplitude in similar latitude sites of the eastern Atlantic. The total concentration of alkenones reflects river-induced productivity, being low in the early Holocene but increasing as river input became more important. Rapid cooling, of 1-2C occurring in 250 years, is observed at 11.1, 10.6, 8.2, 6.9, and 5.4 cal ka B.P. The estimated age of these events matches the ages of equivalent episodes common in the NE Atlantic- Mediterranean region. This synchronicity reveals a common widespread climate feature, which considering the twentieth century analog between colder SSTs and negative North Atlantic Oscillation (NAO), is likely to reflect periods of strong negative NAO.

Impact of a fall prevention program in the Internal Medicine wards of a tertiary care university hospital

Objective: To determine whether a falls prevention program reduces the incidence of falls within a hospital. Materials and methods: Each patient admitted to the Internal Medicine ward was classiied into a risk category (high, medium, low) according to the scale of J.H. Downton, and then various general and speciic measures were applied by risk group. Interventions included appointments, teaching materials, and training of medical staff and family. Furthermore, a registration system was developed that allowed adverse event fe edback to the program and identiicat ion of the causes of the fall. The SPSS version 20.0 was used for the data analysis. Descriptive analysis was used for quantitative variables, and qualitative variables were expressed as proportions. To compare the rate of pre- and post-program implementation falls, x 2 was used, with a  = 0.05 determining a signiicant statistical value. Results: Since the implementation of the program, the rate of falls per 1000 days/patient decreased from 1.9 in 2007 to 0.67 in the period 2008-2013, representing a decrease of the rate of falls of up to 70%, with a statistically signiicant difference (P=.02). Conclusions: The implementation of a falls prevention program is an effective tool and reduces the rate and complications associated with them.