Today many strategies of monitoring health status and well-being are done through measurementmethods that are connected to the body, e.g. sensors or electrodes. These are often complicatedand requires personal assistance in order to use, because of advanced hardware and attachmentissues. This paper proposes a new method of making it possible for a user to self-monitoring theirwell-being and health status over time by using a non-contact camera system. The camera systemextracts physiological parameters (e.g. Heart Rate (HR), Respiration Rate (RR), Inter-bit-Interval(IBI)) based on facial color variations, due to blood circulation in facial skin. By examining anindividual’s physiological parameters, one can extract measurements that can be used in order tomonitor their well-being. The measurements used in this paper is features of heart rate variability(HRV) that are calculated from the physiological parameter IBI. The HRV features included andtested in this paper is SDNN, RMSSD, NN50 and pNN50 from Time Domain and VLF, LF andLF/HF from Frequency Domain. Machine Learning classification is done in order to classifyan individual’s sleepiness from the given features. The Machine Learning classification modelwhich gave the best results, in forms of accuracy, were Support Vector Machines (SVM). The bestmean accuracy achieved was 84,16% for the training set and 81,67% for the test set for sleepinessdetection with SVM. This paper has great potential for personal health care monitoring and can befurther extended to detect other factors that could help a user to monitor their well-being, such asmeasuring stress level

With the advancement of technology, medical imaging has become a fast growing area of research. Some imaging questions require little physician analysis, such as diagnosing a broken bone, using a 2-D X-ray image. More complicated questions, using 3-D scans, such as computerized tomography (CT), can be much more difficult to answer. For example, estimating tumor growth to evaluate malignancy; which informs whether intervention is necessary. This requires careful delineation of different structures in the image. For example, what is the tumor versus what is normal tissue; this is referred to as segmentation. Currently, the gold standard of segmentation is for a radiologist to manually trace structure edges in the 3-D image, however, this can be extremely time consuming. Additionally, manual segmentation results can differ drastically between and even within radiologists. A more reproducible, less variable, and more time efficient segmentation approach would drastically improve medical treatment. This potential, as well as the continued increase in computing power, has led to computationally intensive semiautomated segmentation algorithms. Segmentation algorithms' widespread use is limited due to difficulty in validating their performance. Fusion models, such as STAPLE, have been proposed as a way to combine multiple segmentations into a consensus ground truth; this allows for evaluation of both manual and semiautomated segmentation in relation to the consensus ground truth. Once a consensus ground truth is obtained, a multitude of approaches have been proposed for evaluating different aspects of segmentation performance; segmentation accuracy, between- and within -reader variability. The focus of this dissertation is threefold. First, a simulation based tool is introduced to allow for the validation of fusion models. The simulation properties closely follow a real dataset, in order to ensure that they mimic reality. Second, a statistical hierarchical Bayesian fusion model is proposed, in order to estimate a consensus ground truth within a robust statistical framework. The model is validated using the simulation tool and compared to other fusion models, including STAPLE. Additionally, the model is applied to real datasets and the consensus ground truth estimates are compared across different fusion models. Third, a statistical hierarchical Bayesian performance model is proposed in order to estimate segmentation method specific accuracy, between- and within -reader variability. An extensive simulation study is performed to validate the model’s parameter estimation and coverage properties. Additionally, the model is fit to a real data source and performance estimates are summarized.

Workers in the agricultural industry exhibit higher rates of respiratory diseases than workers who are not employed in agriculture. Farm workers may be chronically exposed to organic dust, which is composed of molds, fungi, pesticides, herbicides, animal-derived particles, feed and bedding particles, and endotoxin. Exposure to organic dust has been linked to the development of various respiratory diseases. Research evidence has shown that variability in exposure to inhalable dust is present, and no studies have assessed variability in exposures to inhalable dust specifically among dairy parlor workers. A field-based study was conducted to assess exposures to inhalable dust exposure among individuals working as milkers or pushers on dairy farms in the Midwestern United States. A total of 62 dairy parlor workers participated, and 18 of these workers agreed to participate in repeat measurements and were sampled for two work shifts. Two, bilateral personal breathing zone samples were collected continuously from each worker during one work shift using inhalable samplers, amounting to 160 inhalable dust concentration measurements. The filters were weighed, and the TWA of inhalable airborne dust exposure was calculated for each subject and reported in mg/m3. Statistical analyses were used to examine exposure variability. The results of the statistical analyses did not indicate any statistically significant differences in the means of exposure to inhalable dust between paired sampler groups, with p-values of 0.793, 0.617, and 0.619. An ANOVA analysis of within-worker variance found no statistically significant differences, with p-values of 0.702 and 0.744 for sampler location and sampling day, respectively. Results of the simple linear regression analyses suggested that temperature and humidity levels contribute to less than ten percent of the variability in inhalable dust concentrations. Analyses of the study indicate that exposure to inhalable agricultural dust does not vary significantly (p-value of 0.05 or less) between the means of right-side and left-side collected exposures, as well as from day-to-day, among dairy parlor workers. The geometric mean of 0.54 mg/m3 (GSD 2.5 mg/m3) of the inhalable dust concentrations from this study align with geometric means found in previous studies of inhalable dust concentrations among dairy farm workers. These results support the hypothesis that using a sample of the dairy parlor worker population can provide an accurate estimate of exposure to inhalable agricultural dust among the general dairy farm worker population.

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With increasing pressures on sagebrush steppe ecosystems, the sagebrush defoliating Aroga moth, Aroga websteri Clarke (Lepidoptera: Gelechiidae) has become a critical organism of concern. Despite the cyclic nature of A. websteri outbreaks throughout the Great Basin, there is limited information on the moth’s population dynamics. The goal of this dissertation was to develop effective means of assessing and describing population trends of the Aroga moth across space and time, and potentially promoting biological control of the moth to prevent unnaturally large, prolonged and destructive outbreaks. Field studies were conducted to: 1) monitor and quantify activity of the Aroga moth and its damage to sagebrush across a montane landscape, 2) assess the effects of parasitoid and floral diversity on parasitism of the moth, and 3) develop a degree-day (D) model to describe the phenology of the insect, as well as field populations studied previously. North-facing stands of sagebrush, characterized by low values of solar radiation, appear to be especially suitable local habitats for the Aroga moth. High habitat suitability may result from favorable microclimate, both in its direct effects on the Aroga moth and in indirect effects tied to sagebrush plant community productivity and performance. Parasitoid and floral diversity differed strongly and predictably across space and time, with greatest overall parasitism occurring when three major parasitoid species were present. Field experiments revealed individual species of parasitoids differed substantially and complemented one another in their patterns of attack among local populations of the Aroga moth across the montane landscape. Differing responses to provision of floral resources and methyl salicylate (an herbivore- induced plant volatile) support the general hypothesis that over large scales of space and time, species diversity of natural enemies promotes suppression of insect herbivores. Lastly, degree-day models were developed and least variation among years in (D) phenology resulted with the single-sine method with base temperature of 5C. Years of historical Aroga moth outbreaks had characteristic seasonal patterns of D accumulation that were intermediate and characterized by high precipitation in June and July during late stage larval development. Thus, it appears that periodic outbreaks of the defoliator are due to favorable weather conditions.

Typically developing children learn from play. For example, play serves as a foundation for children to acquire early language and social skills. Children with autism tend to have deficits in play, and often engage in rigid or repetitive behaviors during play. Such rigid play behavior can limit opportunities for these children to learn from play. Researchers have shown that it is possible to increase the variety of play behaviors that children with autism engage in. But, research has not yet shown whether these gains in play behavior will transfer to other play environments and situations. Therefore, the purpose of this study was to investigate methods for promoting the transfer of varied and appropriate play to other play situations with three children with autism. In this study, we increased varied play behavior by providing rewards for playing in a varied manner (and not providing rewards for playing in an inappropriate or rigid manner). We did this with multiple different play situations to help the participants learn to engage in varied play in different situations. We then tested to see if the participants would vary their play with completely new play situations. We found that, following some modifications, our procedures were successful at increasing varied play behavior for all three participants, and that their varied play transferred to other play situations.

Repetitive behavior and delays in communication are core deficits of autism spectrum disorder. As a result, individuals with autism often engage in repetitive verbal behavior, and they may not vary their verbal behavior, even when the situation demands it. The purpose of this study was to investigate the effects of a script training and discrimination training procedure on mand variability in preschoolers with autism. Participants were taught to vary their vocal mands in the presence of written scripts, a green placemat, and Lag schedule of reinforcement. They were also taught to not vary their vocal mands in the presence of the same written scripts and a red placemat. When the scripts were removed, all three participants continued to engage in varied manding in the presence of the Lag schedule of reinforcement and the green placemat. All three participants also did not vary their mands in the presence of the red placemat. When the Lag schedule of reinforcement was removed, two participants continued to engage in varied responding in the presence of the green placemat and unvaried responding in the iv presence of the red placemat. One participant did not engage in varied responding when the Lag schedule of reinforcement was removed. However, when the Lag schedule of reinforcement was re-introduced, varied responding re-emerged. Finally, all three participants demonstrated mand variability during snack sessions when their peers were present, and they maintained their varied manding after a 2-week follow-up.

Research has shown that psychotherapy patients experience increased physiological responsivity to stress which might negatively impact their experience in psychotherapy and their overall progress and outcome. The purpose of the present study was to investigate the effect of a heart rate variability biofeedback intervention on the physiological stress responsiveness and the psychotherapy outcomes of participants in psychotherapy. Forty college students attending psychotherapy at their university counseling center were divided into an experimental group and a control group. The experimental group participated in a 6-week biofeedback intervention and we assessed their physiological stress reactivity before and after implementation of the intervention, compared to a matched control group. The Trier Social Stress Test (TSST) was administered pre- and post-intervention to induce a stress reaction. It was hypothesized that psychotherapy patients involved in the biofeedback intervention would show decreased physiological stress reactivity to and faster physiological recovery from a laboratory induced stressor post-intervention compared to psychotherapy patients in the matched control group. It was also hypothesized that these participants would demonstrate larger distress reduction after implementation of the intervention. Results of the study found no significant main effect of the TSST on systolic blood pressure, heart rate, and HRV. There however was a main effect on diastolic blood pressure. The only variable that significantly differed between groups was the LF/HF ration. The results also revealed no significant change from pre-intervention baseline to post-intervention heart rate, blood pressure, and HRV, suggesting that the HRV biofeedback intervention was not effective in changing the stress response over time. Regarding levels of distress, results also revealed no statistical between group differences post-intervention, although the biofeedback group appeared to report significantly lower levels of distress post-intervention.

Gravity Recovery and Climate Experiment (GRACE) mission has amplified the knowledge of both static and time-variable part of the Earth’s gravity field. Currently, GRACE maps the Earth’s gravity field with a near-global coverage and over a five year period, which makes it possible to apply statistical analysis techniques to the data. The objective of this study is to analyse the most dominant spatial and temporal variability of the Earth’s gravity field observed by GRACE using a combination of analytical and statistical methods such as Harmonic Analysis (HA) and Principal Component Analysis (PCA). The HA is used to gain general information of the variability whereas the PCA is used to find the most dominant spatial and temporal variability components without having to introduce any presetting. The latter is an important property that allows for the detection of anomalous or a-periodic behaviour that will be useful for the study of various geophysical processes such as the effect from earthquakes. The analyses are performed for the whole globe as well as for the regional areas of: Sumatra- Andaman, Australia, Africa, Antarctica, South America, Arctic, Greenland, South Asia, North America and Central Europe. On a global scale the most dominant temporal variation is an annual signal followed by a linear trend. Similar results mostly associated to changing land hydrology and/or snow cover are obtained for most regional areas except over the Arctic and Antarctic where the secular trend is the prevailing temporal variability. / Apart from these well-known signals, this contribution also demonstrates that the PCA is able to reveal longer periodic and a-periodic signal. A prominent example for the latter is the gravity signal of the Sumatra-Andaman earthquake in late 2004. In an attempt to isolate these signals, linear trend and annual signal are removed from the original data and the PCA is once again applied to the reduced data. For a complete overview of these results the most dominant PCA modes for the global and regional gravity field solutions are presented and discussed.

當我們探討的是兩組樣本的變異是否有所差異時，常見的方法有以ANOVA 為 基礎的檢定與秩檢定，傳統的秩檢定需要假設兩母體具有相同的中位數或知道 其差異。本研究採用Moses (1963) 提出的rank-like 檢定方法，此方法在處理兩組樣本的變異問題時，優點是不需要估計任何中心參數，也不需要假設母體中心參數相同，在資料偏態的情況下也表現得很穩健，我們試圖在樣本數極小的情況下對此方法作修正，將此檢定方法與以ANOVA 為基礎的檢定和秩檢定進行模擬比較，以能夠良好的控制型一誤差與檢定力作為評斷標準。由模擬的結果可得知，rank-like 檢定方法與修正後的方法在不同的分配下皆表現的穩健而修正後的方法特別適用於小樣本的情形。 / We consider the problem of detecting variability change in the two-sample case.Several classical variability tests are investigated, including the ANOVA based tests and the rank tests. Traditional two-sample rank tests assume that the location parameters for both samples are identical or of known difference. In this thesis, a modified version of the distribution-free rank-like test proposed by Moses (1963) is proposed. Moses’s test has several advantages. It does not require location parameter estimation, is applicable without assuming that location parameter are identical, and is robust for skewed data. However, Moses’s test has no power when each of the two samples has size 5 or less. The modified version of Moses’s test proposed in this thesis has some power when the sample sizes are small. Comparative simulation results are presented. According to these results, both Moses’s test and the proposed test are robust under all conditions, and the proposed test works better when the sample sizes are small.