The West Tidewater earthflow, one of the largest in Oregon's history, occurred in December of 1994. The earthflow is located approximately 15 km north of Jewel, Oregon near the summit ofthe Northern Oregon Coast Range Mountains. The earthflow is 900 m long and 250 m wide, giving it a surface area of 9 ha, or 22 acres. Volume is 3.5 million m3. The earthflow occurred in low strength, well-bedded, tuffaceous, carbonaceous, micaceous, clay-rich mudstone, and very fine-grained, feldspathic, clay-rich siltstone of the lower Miocene age Northrup Creek Formation. The soil clay fractions contain up to 90% smectite with indications ofhalloysite. This earthflow is a reactivation ofa 650-year-old landslide (C-14 dating of uncovered buried trees). The failure mode is examined using a Janbu slope analysis and includes double wedge failure near the headscarp. High soil pore water pressure is one of the major causes of this slope failure. Rainfall levels for October, November, and December of 1994 were twice the previous five-year average. Present day groundwater level within the basin is less than one meter below ground surface. The earthflow is partially controlled by two faults of regional extent that dissect the basin near the headscarp in NW-SE and NE-SW directions. The Inceptisol soils in the basin remain moist below 20 cm year around. Soil in the basin may have been further weakened due to loss of root strength following timber harvest on the site in 1991. Soil liquid limits range from 42% to 95%, with PI values ranging from 2% to 77%. Soil clay content ranges between 18% and 30%. Direct shear tests on the mudstone and siltstone bedrock in both drained and undrained conditions produced internal friction angles of 14-18°, with cohesion values of 4 - 8 kPa. Back calculation of study area soil strength using the modified Bishop method results in a residual friction angle of 20.7°. The failure mode ofthe earthflow is from the headscarp downward and is modeled using Janbu methods. The study includes a detailed topographic map and a failure analysis of the earthflow basin.

LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.

A current meter has been designed, intended for surveillance of low-power battery driven devices with a large dynamic range in their current consumption. Among such devices may, for example, be nodes in a wireless sensor network (WSN). The large dynamic range originates in the nodes’ states from an active mode for data collection and communication to a sleep mode for preserving very limited energy and prolonging the lifetime of the device. The active mode draws current up to hundreds of milliamperes whilst the sleep mode’s current may be as low as 22 nA. This makes the dynamic range larger than 6 orders of magnitude. To perform measurements that prevail over noise sources and resolution of involved components, an increase in the SNR must be made. If the current is measured with a shunt resistor, this may be done by varying its resistance as required. The resistance should be as high as possible to maximize the SNR, but low enough such that the burden voltage of the current meter is kept within tolerances of the device. A literature study was done, and with the gained knowledge a circuit was designed and simulated in LTSpice. Then a printed circuit board (PCB) of the circuit was designed and implemented using Altium Designer (an ECAD program), manufactured and hand-soldered. The final PCB was tested but shown to have some design flaws that deteriorated the performance, for example too frequent switching of the current range.

In modern world sensors play important role where they help to acquire information about the procecess, such as temperature, velocity,distance, etc. Based on this information acquired from the sensorsdecisions can be made, for example to increase heating in the buildingor accelerate the car.In many cases, a single sensor type cannot provide enough information for complex decision making, for example, when the physicalproperties of the process are outside of the measurement range of thesensor. As a result, in order to achieve desired performance levels, acombination of sensors should be used in an integrated manner.Sensor generated data need to be processed into information throughthe use of appropriate decision making models in order to improveoverall performance. Here we compare two sensors which are shortrange and long-range sensor. We use a short-range and long-rangesensor, and calculates the distance from both sensors to the same object by using Arduino UNO microcontroller. The sensors that we usein our work have overlapping or common interval in their measurementranges. Therefore we investigated how we can make a decision aboutthe distance to an object when the acquired data from both sensors isin that common range.

A 2-yr grazing study quantified pasture and animal responses of four forage systems (FS) grazed at two stocking rates (SR; 3 or 6 animals ha-1). Using „Marshall? annual ryegrass (Lolium multiflorum Lam.) and „Durana? white clover (Trifolium repens L.), FS treatments included spatially separated grass and legumes within the same paddock (SS), monoculture grass (MG), monoculture legume (ML), and a binary grass and legume mixture (MIX). Annual herbage mass (HM) was similar among FS at high SR (1900 kg ha-1), but at low SR, grass plots had greater HM (2900 vs. 2000 kg ha-1) than plots of legume monocultures. Animals on SS (1.12 kg) had greater average daily gain (ADG) than ML (0.97 kg), but neither was different from MG (1.08 kg) or MIX (1.00 kg). Low SR animals had greater ADG than high SR (1.09 vs. 0.99 kg). These results indicate that SS grazing system can improve pasture productivity.

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This work aims to develop a model capable of calculating the energy consumption of an electric car. The goal is a method of calculating the energy needs for a certain trip and presenting recommendations for a more efficient driving mode. The purpose of the work is to create a tool to help drivers use less energy and identify problems when developing such a tool. The model is based on a power equation which considers air resistance, rolling resistance, height differences and acceleration. A model of regenerative brakes is also developed in order to account for regained kinetic energy. Using map data, a routing tool is developed to allow input of a trip which the model can calculate energy needs for. The model shows for an example car Porsche Taycan 4S over a specific test-trip an energy consumption of 128.65 Wh/km and a 3% energy savings in relation to energy efficient driving. When evaluating the model against the Urban Dynamometer Driving Schedule (UDDS) and the New European Driving Cycle (NEDC) estimated driving ranges corresponding to 493.3 km and 521.2 km respectively, in relation to official statistics of 396 km. Finally, findings during development, problems with the model and recommendations are discussed.

The U.S. Coast Guard maintains a network structure to connect its nation-wide assets. This paper analyzes and models four highly aggregate traces of the traffic to/from the Coast Guard Data Network ship-shore nodes, so that the models may be used to predict future system demand. These internet traces (polled at 5â 40â intervals) are shown to adhere to a Gaussian distribution upon detrending, which imposes limits to the exponential distribution of higher time-resolution traces. Wavelet estimation of the Hurst-parameter is shown to outperform estimation by another common method (Sample-Variances). The First Differences method of detrending proved problematic to this analysis and is shown to decorrelate AR(1) processes where 0.65< phi1 <1.35 and correlate AR(1) processes with phi1 <-0.25. The Hannan-Rissanen method for estimating (phi,theta) is employed to analyze this series and a one-step ahead forecast is generated. / Master of Science

Species range movement due to changing ocean conditions is occurring around the world. As species move, they build new interaction networks as they shift from or into new ecological communities. Typically, species ranges are modeled individually, but biotic interactions have been shown to be important to creating more realistic modeling outputs for species. Kelp forests are known to be one of the most productive ecosystems in the world. To understand the importance of consumer interactions in Eastern Pacific kelp forest species distributions, I used a Maxent framework to model a key foundation species, giant kelp (Macrocystis pyrifera), and a dominant herbivore, purple sea urchins (Strongylocentrotus purpuratus). With neither species having previously been modeled in the Eastern Pacific, I found evidence for M. pyrifera expansion in the northern section of its range, with no projected contraction at the southern range edge. Despite its known co-occurrence with M. pyrifera, models of S. purpuratus showed a non-concurrent southern range contraction and a co-occurring northern range expansion. While the co-occurring movement may lead to increased spatial competition for suitable substrate, this non-concurrent contraction could result in community wide impacts such as herbivore release, tropicalization, or ecosystem restructuring. When looking at species distributions, an aspect that is often overlooked is species abundance. Drops in species abundance could lead to a species being functionally extinct while still being labeled as present in presence/absence distribution maps. Alternatively, rises in abundance could lead to trophic imbalance without being labeled as an area undergoing change. To understand how species abundance might change in Eastern Pacific kelp forests, I used a Generalized Additive Model framework to understand the relationship between abundance and environmental conditions for a suite of echinoderm species representing a variety functional groups. I then created a series of abundance based distribution models across climate change scenarios using these relationships. These models provide further clarity to how ocean warming may impact several ecologically important marine species as well as giving us a better understanding of where communities may be more vulnerable to restructuring due to altered abundance patterns. In addition to range movement, thermal stress is also expected to alter interaction strength. Thermal anomalies occur in both short-term heatwaves and long-term increases to annual mean temperatures. In ectothermic organisms, increased temperatures should directly increase organism metabolism, but studies looking at metabolic change after heatwaves have shown conflicting results to long-term climate change studies leaving questions as to how species will be impacted on short-term and long-term scales. In eastern Pacific kelp forests, red sea urchins (Mesocentrotus franciscanus) and purple sea urchins (Strongylocentrotus purpuratus) are the dominant kelp forest herbivores consuming kelp from Baja California to Alaska. I tested the hypothesis that moderate experimental warming would increase herbivory rates over time, with a more pronounced increase occurring during long-term warming in comparison to simulated short-term heatwaves. After simulating both short-term and long-term warming events I found that feeding rates and thermal maxima varied by species and the length of warming event. While short-term heatwave events showed no increase in herbivory rates, longer-term warming led to increased herbivory in both species. Purple urchins demonstrated a lower thermal maximum than red urchins despite purple urchins being generally more thermally tolerant. This study provides valuable insights into the differences in species interaction strength under varying global change scenarios highlighting variation in physiological response to the same thermal stress imposed at different timescales. Integration of these studies allows us to create more holistic predictions about ecosystem wide change—providing information not just on changes to organisms’ biogeographic range but also how interactions and trophic dynamics may change within that range. / Biology