Body Image and Eating Patterns in Older Adults

The purpose of this study was to observe patterns in eating and body image within the older population. Body dissatisfaction has become a socially normative experience and older women, in particular, are pressured to alter their appearance to adhere to society’s beauty standards. Because of these feelings of dissatisfaction, older adults are at an increased risk of developing eating disorders and body dysmorphic disorder (Peat et al., 2008; Phillips, 2014). This study utilized a phenomenological approach to explore older adults’ lived experience with body image and eating patterns throughout the lifetime. Five participants, between the ages of 65-86, were interviewed related to body image and eating patterns. Two themes were identified: physical health and healthiness/wellbeing. Previous research has not emphasized physical health and overall healthiness as factors strongly influencing body image and eating factors, while this study found those to be the two most predominant factors impacting older adults’ lived experience with body image and eating patterns. These findings can positively influence the ways in which we support and advocate for clients within the counseling field

Adaptation and Innovation: Trends and Developments in American Judaism

Celebrating 10 years of Judaic Studies at Fairfield University… The Carl and Dorothy Bennett Center for Judaic Studies presents The Schurmacher Lecture in Judaic Studies. [Dr. David Ellenson] President of Hebrew Union College - Jewish Institute of Religion. Author of Between Tradition and Culture: the Dialectics of Jewish Religion and Identity in the Modern World (1994) and Tradition in Transition: Orthodoxy, Halakhah and the Boundaries of Modern Jewish Identity (1990).https://digitalcommons.fairfield.edu/bennettcenter-posters/1225/thumbnail.jp

Center Pivot Sprinkler Distribution Uniformity Impacts on the Spatial Variability of Evapotranspiration

Understanding variable evapotranspiration (ET) throughout a field can help maximize yield on a per-acre basis, as well as assist with proper irrigation scheduling. The results from this study indicate that irrigation system distribution uniformity (DU) has a significant effect on the uniformity of ET during water-stressed periods. The study site involved intensely managed forage (alfalfa and winter grain hay) irrigated by center pivots being supplied with reclaimed water near Palmdale, California. During spring and early summer 2007 the center pivots were operating under deficit irrigation. In 2010, after the installation of reservoirs, water was applied to meet full evapotranspiration (ETc) demands. Using remote sensing of actual evapotranspiration, the variability in ETcfor the same pivots with the same crop was quantified. During the non-water-stressed period, ET uniformity was significantly better than during the water-stressed period (2007). The difference in uniformity was found to be attributable to irrigation system distribution uniformity. For the 540 ha used in this study, irrigation system DU was found to explain 55% of the ET nonuniformity during deficit irrigation. A method to predict the nonuniformity in ET as a result of irrigation system DU and water-stress level is presented

The Application of Machine Learning Techniques on Marine Science Models

A variety of stakeholders require information about marine systems. In the open ocean, pilots of marine vessels require knowledge about environmental conditions for safe passage and route planning. On the coastline, communities rely on information about nearshore dynamics to increase safety from coastal hazards such as nearshore pollutants, coastal erosion, or dangerous recreational conditions (e.g., rip currents). Models provide information for environmental health and safety in the form of forecasts or general knowledge of the marine science systems. Large volumes of data from a variety of marine sensors are now available thanks to progress in computer processing and data storage. These data should be lever-aged to advance the boundaries of marine science knowledge. Herein, Machine Learning (ML) techniques are applied to improve different types of marine science models and increase the knowledge of marine science systems. Two different types of ML techniques are considered; traditional machine learning and deep learning. The techniques are applied in a transparent way, ensuring that the ML routine has made predictions with appropriate reasoning. Also, the transferability of the ML routines is assessed to determine the limits of ML routine generalizability. The thesis is organized in a manuscript format, where the first and last chapters serve as overall Introduction and Conclusions, respectively. The central three chapters are individual manuscripts. The second chapter applies a traditional ML technique called a decision tree to numerical wave model output. The decision tree predicts corrections of 24-hour time horizon significant wave height forecasts generated by a numerical wave model. The wave model output was located at buoy locations offshore of the United States Pacific Northwest coastline. The application of the decision tree increased wave model skill more for winter than for summer. The decision tree also made accurate predictions in a geospatial transfer experiment, where the decision tree predicted error for a location that was not used in training data. However, the decision tree predictions were less accurate when it was applied to a different time period. The transparent nature of the algorithm allowed for inspection of the algorithm’s architecture, finding consistent underestimations of significant wave height for data points associated with mid wave periods (6-12s). The third chapter develops an automated technique to recognize morphological shapes within coastal imagery using a Convolutional Neural Network (CNN). The morphological shapes are morphological patterns that occur frequently in the nearshore called beach states. The input to the CNN was coastal imagery from two different study sites and the output was beach state labels. The two different study sites were Narrabeen, Sydney, Australia and Duck, North Carolina, United States. Three ensembles of CNNs were trained; two single-site CNNs (trained at individual locations) and one multi-site CNN (trained at both locations). The CNNs were applied to both locations to determine skill at the location it was trained (the original location) in a self-test and skill at the location where it was not trained (the alternate location) in a transfer-test. For the self-tests, the CNN skill was comparable to inter-labeller agreement, with skill at Duck higher than skill at Narrabeen (F-scores of 0.8 for Duck and 0.59 for Narrabeen). The CNN skill was reduced in the transfer tests. However, if at least 25% of the training data came from the alternate location, the skill increased to within 10% of the skill at the original location. A visualization technique (Guided Grad-CAM) re-vealed areas of importance within input images for CNN decision making, and confirmed that the CNN identified the appropriate morphological characteristics (e.g., terraces or rip currents) for each classification. The fourth chapter builds off the third, and applies a CNN to a long (>20 years) dataset to detect alongshore variability of beach state quantified as a beach probability simplex, thereby advancing the beach state framework from discrete space to continuous space. The approach from the third chapter is modified to detect alongshore differences in beach state using a windowing technique. The CNN produced beach probability simplices from a 28-year dataset of images from Duck, NC, and results showed that most (67%) of the resulting beach probability simplices encompassed more than one state. The 28-year time series was dominated by an annual cycle, where simplices that encompassed onshore states occurred in summer, offshore states in winter, and intermediate states in fall or spring. The mean value of the beach probability simplex exhibited a strong relationship with significant wave height (28-year daily average R=0.77) and mean wave direction (28-year daily average R=0.84). The simplices that encompassed the highest number of states (three) were most likely to occur in fall, specifically the month of September

Weather and large waves along the Oregon coast: atmospheric controls on a numerical wave model

In this study, the effects of implementing different wind input or physics packages in a numerical wave model to recreate large wave conditions are explored. Three large wave events are simulated with WaveWatch III. The wind inputs which are compared are NCEP's Global Forecasting System (GFS) with 0.5 degree resolution and Climate Forecast System Reanalysis (CFSR) with 0.312 degree resolution, and the physics packages which are compared are ST2 (Tolman and Chalikov, 1996) and ST4 (Ardhuin et al, 2010). The modelled output, including spectral shape and bulk parameter time series, are compared with National Data Buoy Center buoy observations offshore of Newport, OR. The atmospheric conditions which generate these large waves include a wind feature called a coastal jet along with a distant cyclone. The energetic contribution of these simultaneously occurring atmospheric features results in a wave field characterized by bi-modal energy spectra for two events and uni-modal energy spectra for the third event. The analysis of model output includes evaluates bulk parameter time series significant wave height, mean period and mean wave direction derived from partitioned energy spectra. A consistent underestimation in wave energy emanating from the southwestern direction is found for the output associated with all model configurations. This wave energy is generated by the coastal jet. An overestimation in swell energy emanating from the northwest is also found for all model configurations. The model configuration which features the combination of CFSR winds with Ardhuin et al (2010) physics results in the best performance for the largest wave heights, with a reduction in error for overall bulk parameters as well as partitioned bulk parameters.Keywords: numerical wave model, large waves, wave

Biodiesel effects on particulate radiocarbon (14C) emissions from a diesel engine

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Aerosol Science 39 (2008): 667-678, doi:10.1016/j.jaerosci.2008.04.001.The relative amount of 14C in a sample of atmospheric particulate matter (PM), defined as percent modern carbon (pMC), allows EPA to infer the fraction of PM derived from anthropogenic pollution sources. With increased use of biofuels that contain 14C, the main assumption of the two-source model, that 14C is solely derived from biogenic sources, may become invalid. The goal of this study was to determine the 14C content of PM emitted from an off-highway diesel engine running on commercial grade biodiesel. Tests were conducted with an off-highway diesel engine running at 80% load fueled by various blends of soy-based biodiesel. A dilution tunnel was used to collect PM10 emissions on quartz filters that were analyzed for their 14C content using accelerator mass spectrometry. A mobility particle sizer and 5-gas analyzer provided supporting information on the particle size distribution and gas-phase emissions. The pMC of PM10 aerosol increased linearly with the percentage of biodiesel present in the fuel. Therefore, PM emissions resulting from increased combustion of biodiesel fuels will likely affect contemporary 14C apportionment efforts that attempt to split biogenic vs. anthropogenic emissions based on aerosol-14C content. Increasing the biodiesel fuel content also reduced emissions of total hydrocarbons (THC), PM10 mass, and particulate elemental carbon. Biodiesel had variable results on oxides of nitrogen (NOx) emissions