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Grazed cover cropping : Drew Leitch
No full textDrew Leitch, whose farm in Nezperce, Idaho receives between 17 and 23 inches of precipitation annually, has been experimenting with using cover crops in his dryland wheat rotations and has achieved promising early results by integrating spring-seeded cover crops into his cow-calf operation. This case study is part of the Farmer-to-Farmer Case Study project, which explores innovative approaches regional farmers are using that may increase their resilience in the face of a changing climate. Case study information presented is based on growers’ experiences and expertise and should not be considered as university recommendations
WITHDRAWAL ASSESSMENT FOR ALCOHOL WITHDRAWAL AND COMORBID TRAUMATIC BRAIN INJURY
No full textEach year, the incidence of traumatic brain injuries (TBI) in the United States is reported to be ~1.7 million total cases with approximately 50% of all those cases being related to alcohol consumption. The medical staff during intake to the Neurosurgical Intensive Care Unit (NICU) using the Clinical Institute’s Withdrawal Assessment tool are challenged to differentiate the symptoms related to alcohol withdrawal to those of an acute phase TBI. This study is to test the validity of the CIWA-Ar tool in use on TBI patients testing alcohol positive to those testing alcohol negative upon intake to the Neuro ICU.
The study at a Western United States level-one Trauma hospital employed a retrospective two-group comparison design (alcohol positive, alcohol negative) with measurements obtained one to three consecutive days for group comparisons of CIWA-Ar total and CIWA-Ar 10 individual item scores. Patient data for 124 participants: alcohol positive (n= 31) and alcohol negative (n=84), were collected across the two TBI alcohol groups.
Multiple Linear Regression results of the CIWA-Ar total mean score showed that the alcohol group membership (= 0.21, p = 0.03) significantly predicted the mean CIWA-Ar total score for alcohol positive participants when controlled for age and gender. When examining each CIWA-Ar item, only CIWA-Ar item 2 (tremors) was predicted by alcohol ( = 0.29, p =0.03) and age ( = 0.21, p =0.03), where TBI alcohol-positive participants and older participants had higher CIWA-Ar 2 scores. The results of the exploratory aim showed that having a frontal contusion had significantly different scores on agitation, traumatic subarachnoid hemorrhage for paroxysmal sweats, and intraparenchymal hemorrhage for nausea / vomiting, agitation, and headache / fullness in head in comparison to the other types of TBI encountered.
Results of the analysis showed the importance in the clinical setting to require prior alcohol testing and administration of a substance withdrawal protocol at the NICU to enhance the specificity of the patients’ treatment plan. Proper identification of symptoms at this crucial time is critical for positive patient outcomes. This study provides a foundation that warrants further research to develop a specific CIWA-TBI tool.Washington State University, Nursin
Continuous in-situ measurements of HCHO and other VOCs by PTR-MS in nine homes in eastern WA
No full textExposure of volatile organic compounds (VOCs) can have negative impacts on human health but the range of indoor levels, along with variations and relationship with influencing factors are poorly understood. In this dissertation, the measurements of indoor and outdoor levels of formaldehyde and other pollutants, and influencing factors including temperature and air change rate (ACH) in nine single-family homes in eastern Washington State were presented. Proton transfer reaction mass spectrometer (PTR-MS) was applied to track the variation of indoor VOC levels. The results showed that for homes where infiltration dominated the ACH, the indoor VOC levels showed a diurnal variation, which suggests that the steady-state assumption regarding whole house emissions may not be applicable. For most homes measured, indoor formaldehyde levels ranged from 8 to 44 ppbv. Summertime indoor formaldehyde levels measured in homes with only infiltration as ACH displayed a linear increase with indoor temperature and the slopes ranged from 3.0 ppbv per °C to 4.5 ppbv per °C, and this increase results from the interaction of whole house emissions sensitivity to temperature and the impact of indoor temperature on infiltration rates. The data measured in a net-zero energy house were analyzed to examine the levels and trends of VOCs under multiple ventilation settings. For indoor temperatures of approximately 25 oC, formaldehyde decay coefficient was determined to be 0.50 ± 0.09 hr-1 and its emission rates were determined to be 90 ± 10 μg m-2 hr-1 under 0.49 hr-1 ACH, compared to 81 ± 16 μg m-2 hr-1 at natural infiltration rates estimated as 0.05 hr-1. The results showed only a 10% difference in emission rates observed for formaldehyde at these two very different ACHs. Our time-dependent mass balance model showed that this lack of sensitivity to ACH is due to the relatively large decay coefficient for formaldehyde. In comparison, for other VOCs, the whole house emission rates increased by a factor up to 3 when ACH increased from 0.05 hr-1 to 0.49 hr-1. For these VOCs, the decay coefficients are near zero so that the emission rates are much more dependent upon ACH.Washington State University, Engineering Scienc
A Modified Chang-Wilson-Wolff Inequality Via the Bellman Function
No full textWe produce the optimal constant in an inequality bounding the exponential integral
of a function by the exponential integral of its dyadic square function. This work is
motivated by a well known result due to Chang, Wilson, and Wolff which controls the
exponential integral of a function in terms of the essential supremum of its dyadic square
function.
Perhaps more interesting than the result itself is the method of proof. We establish
our inequality and find the optimal constant using a Bellman function argument. This
type of argument was pioneered in the 1980s by Donald Burkholder in his work on
martingale transforms.
Along the way, we trace the origin of the Bellman function technique back to Richard
Bellman’s development of dynamic programming in the 1950s. Doing so provides some
historical context and lends insight into the genesis of Burkholder’s ideas.Washington State University, Mathematic
Modeling and Simulation of Microstructure Evolution and Deformation in an Irradiated Environment
No full textThe ability to predict the behavior of structural components in a nuclear power plant is critical to the nuclear industry. Structural metals in the primary loop of nuclear power plants must endure challenges such as irradiation and mechanical and thermal loading, and these structural metal components must continue to function in potential transient and accident conditions throughout the operational lifetime of the power plant. This extreme operational environment changes the metal microstructure by creating additional defects. The physical interactions of dislocations with these defects govern how the metal will respond to future conditions. Therefore predicting the mechanical response of these metals requires a set of physically based and reliable models of dislocation and defect interactions. These microstructure elements include glide mobile and immobile dislocations, geometrically necessary dislocations, twinning dislocations, irradiation defects, and thermal aging defects. We present here a continuum dislocation dynamics crystal plasticity framework to capture the interaction mechanisms of these dislocations and defects, verified with a combination of benchmark problems and comparisons with experimental data for two different types of structural metals: alpha iron and nickel-based alloys. In our simulations of alpha iron we highlight the advantages of applying a Monte Carlo stochastic model of cross slip dislocation motion and show the importance of capturing the 3D nature of glide dislocation and self-interstitial atom loop radiation defect interactions. We demonstrate coupling of glide dislocations with geometrically necessary dislocations to capture the influence of lattice bending, including the sensitivity of the geometrically necessary dislocations to changes in the grain boundary angle. We further examine the interaction of glide dislocations with the twin dislocations and thermally aged defects which have been observed in a nickel-based alloy with additional models. Finally we assess the reliability of this crystal plasticity framework by comparing two dislocation glide velocity models across the range of normal operation temperatures. In successfully applying our crystal plasticity framework to multiple metals, we provide further evidence of the reliability of our approach. The results of this mechanism-based continuum dislocation dynamics crystal plasticity framework can be used to inform engineering scale models throughout the nuclear industry.Washington State University, Mechanical Engineerin
A study of convex hull optimization and null-stream-based chi squared discrimination statistics for gravitational-wave signal analysis
No full textWe develop data analysis methods to improve the sensitivity of searches for gravitational-wave signals from compact object binaries in networks of ground-based detectors, such as LIGO, Virgo and KAGRA. These are targeted for two different aspects of gravitational-wave data analysis. One focuses on blind searches in the sky, while the other improves the ability to veto triggers arising from spurious noise in detectors. The convex hull optimization focuses on maximizing the search statistic over the sky location parameters. This is done by bounding the search statistic by a convex function. This allows an all-sky search to effectively become a search over the convex set of detector data in the time delay parameter. We cut down on needed operations by searching the boundary (termed its convex hull) of this set of points, effectively searching over a smaller parameter space. We give the efficiency of such an algorithmic approach by comparing number of compute operations done to search the sky between current methods and our convex hull method. Our simulations show a gain in efficiency by a factor of seven or more, depending on the detector network used to perform a sky search. We also develop a veto for discriminating noise transients from compact binary coalescence signals. In some cases noise transients known as glitches match a signal search template well enough to generate a false trigger. We develop a network based statistic that distinguishes between gravitational-wave signal and noise triggers. This is done by combining a well known Chi-Squared statistic for single detectors with a null stream constructed veto for networks of detectors. The null stream is a linear combination of detector data constructed to remove any gravitational-wave signal content from it, leaving only detector noise. We then use this construction to compare data to signal template over smaller frequency windows in the detector band. Doing so allows for a more accurate test on how well a template matches over a given frequency range. Using simulated data, we show support for our null stream based statistic to perform as well, or better, than previous vetoing methods.Washington State University, Physic
IMPROVING SENSOR NETWORK PREDICTIONS THROUGH THE IDENTIFICATION OF GRAPHICAL FEATURES
No full textWe propose a framework that represents sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features to be used by a classifier for prediction tasks in a particular sensor network. The purpose of this graph-based framework is to provide a generic tool for sensor network application builders and practitioners to improve prediction task performance in general through the use of inherent graph structure that exists in sensor networks and through the use of generic graph-based features. We apply our graphical feature based approach to three different kinds of sensor network applications with different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone.
For smart home activity recognition, our graphical feature-based approach using Support Vector Machine outperformed three widely used methods, Naïve Bayes, Hidden Markov Model and Conditional Random Fields and other previous graph-based approaches on three different datasets from three smart apartments. For demographic prediction from smart phone sensors, we evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. In activity recognition using smart phone sensors, we find that adding graph-based features using GPS to basic smart phone sensor data improves activity recognition accuracy compared to using only basic non-graphical features with existence of nodes performing the best. Adding selected edges as features reduced error for some activities.
We can conclude that the graphical feature-based framework based on sensor categorization, node and edges as features, and feature selection techniques provides promising results compared to non-graph-based features.Washington State University, Computer Scienc