Browsing by Subject "Sampling"

Now showing 1 - 6 of 6
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    Advancing Estimation Of Timber Products Output In The Lake States Region Of The Northern United States
    (2019-05) Young, John
    The Forest Inventory and Analysis (FIA) – Timber Products Output (TPO) program has chosen to implement a sampling design for collecting information from primary wood-using facilities across the US. Sample-based approaches are often clear alternatives to surveys, as they offer estimates with increased precision and quantifiable error at lower costs and greater speed. Coulston et al. (2018) has selected a unique stratified random sampling design, which separates mills into a “certainty” or “uncertainty” sample based on a measure of size (MOS). However, the new design is in its early stages and needs for developing the efficiency of the design have been identified. This research assesses the advancement of two key areas: the selection of an effective MOS and the identification of a threshold for allocating mills into a “certainty” sample. When sampling highly-skewed populations, a few large units may account for large portions of the mean estimated and incorrectly accounting for these units can negatively impact the precision of estimation. Systematic identification of a certainty threshold was assessed through methods inspired by the work of Glasser (1962) and Hidiroglou (1985). Estimates produced by these methods were analyzed against historic TPO data to assess for overall impact. MOS also alter the precision, and as the correlation between the variable of interest and the MOS increases the level of uncertainty tends to decrease. Sources for gathering auxiliary mill metrics were explored and relevant attributes were combined to create MOS using three separate techniques: correlation comparison, simple linear regression, and multiple regression. The implementation of different MOS and threshold identification techniques, their impact on sampling efficiency, and potential areas of further research are assessed.
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    Improved Automatic Sampling for Suspended Solids
    (Minnesota Department of Transportation, 2010-11) DeGroot, Greg; Gulliver, John S.
    Portable automatic field samplers have been found to overestimate concentrations of suspended sediment larger than 88 micrometers (coarse silts and sands) when sampling using existing methods. Samplers configured with intake manifolds were also found to substantially oversample coarse silts and sands. This research improves the performance of automatic water samplers for sampling coarse silts and sands. A sampling intake was developed that extracts samples from multiple locations in the cross-section. The new sampling intake increases the range of sediment size where sampling accuracy is within +/- 10% to sand particles less than an equivalent diameter of 250 micrometers. The sampling intake also performs with a predicted bias at larger sediment sizes. The new intake thus demonstrates improved sampling accuracy and precision.
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    Influenza In Pigs Prior To Weaning: Sampling Strategies, Transmission Pathways And Approaches To Reduce Prevalence
    (2020-01) Garrido Mantilla, Jorge Eduardo
    Influenza in pigs prior to weaning: sampling strategies, transmission pathways and approaches to reduce prevalence General abstract Influenza is an important disease of swine and it represents a threat to public health because it is caused by influenza A virus (IAV), a zoonotic virus with pandemic potential. One of the objectives that producers and veterinarians have is to control influenza in breed-to-wean (BTW) farms by weaning IAV negative pigs. Pigs prior to weaning play an important role in influenza epidemiology because they can maintain endemic infections in BTW farms and they can disseminate IAV to other farms and regions at weaning. Unfortunately, there is limited information regarding the transmission pathways that lead to piglet infections, nor it is known what effect specific pig-rearing practices and farm management procedures may have on these pathways. To properly detect, isolate and characterize IAV genetically and antigenically, it is necessary to have sampling approaches that accurately define disease status yet are cost-effective to conduct. However, IAV detection and isolation can be challenging in endemic situations. Endemicity and transmission of IAV in pig populations can be affected by certain management practices that are necessary in production and do not allow IAV control. Even though, control of influenza is difficult, it is possible. Vaccination is one of the most common strategies to control influenza transmission and sow vaccination can help to reduce IAV prevalence in piglets. However, the diversity of IAV found in farms requires the use of vaccines that antigenically match the wild-type virus circulating in the pigs in order to provide good cross-protection against the field strains. In an effort to increase vaccine efficacy, custom-made vaccines that include viral strains identified in farms are used to help to control influenza in BTW farms. However, despite the widespread use of custom-made vaccines, there is limited data on the long term effectiveness of using custom-made vaccines in farms. Finally, in this thesis, I aimed to address some of the questions that are central to the transmission and control of IAV in BTW farms and reduce the prevalence of IAV in piglets at weaning. Specifically, I aimed to: 1) identify the best sampling strategy to detect and isolate IAV in weaned and growing pigs, 2) determine the role of nurse sows in the transmission and perpetuation of IAV in pigs prior to weaning, and 3) assess the impact of custom-made vaccines in reducing IAV prevalence in an integrated swine production system over time. The results obtained during my studies determined that IAV detection rates are higher when group and environmental sampling strategies are used compared to samples collected from individual pigs. Nevertheless, individual samples may still be needed to obtain a viral isolate or perform genetic sequencing and virus characterization. As part of my work, I developed the udder skin wipe technique to detect IAV from lactating sows and isolate IAV from litters prior to weaning. Furthermore, I identified that management practices such as the use of nurse sows can transmit IAV between litters thereby perpetuating IAV infection in pigs prior to weaning. Finally, our multi-year evaluations of custom-vaccine usage on BTW farms demonstrated that on-going surveillance and characterization of IAV isolates facilitate vaccine updates with custom-made epidemiologically-relevant strains. In addition to selecting epidemiologically-relevant strains, the strain selection criteria should also include the identification of strains with similar HA antigenic properties, e.g. those with an overall HA protein identity of 95% or more and having identical or nearly identical amino acid motifs. Once selected, these strains can be included in the updated vaccines used to immunize sows and reduce IAV prevalence in their pigs at weaning. The findings on my thesis contribute to the understanding of IAV transmission in pigs before weaning and point to specific strategies to improve surveillance and disease control. Nevertheless, more studies are necessary to elucidate strategies to limit IAV infections and transmission in BTW farms.
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    Machine Learning Description of Excited State Dynamics in Small Organic Molecules
    (2024-06) Johannesen, Andrew
    Machine learning offers a method to assess systems at a highly accurate level comparable to electronic structure methods for a fraction of the computational cost. This work focuses on the sampling of molecular potential energy surfaces for the creation of data sets to train machine learning models. Chapter 2 seeks to model equilibrium between species in the nitric oxide formation reaction and use grand canonical Monte Carlo to model this reaction. While nitrogen and oxygen molecules were successfully sampled, discontinuities in the density functional theory and complete active space self-consistent field potential energy surfaces prohibited successful modeling of nitric oxide. Chapter 3 seeks to model pathway-based intramolecular reactivity between ethylene and ethylidene in their first excited state. This was approached by using normal mode sampling along nudged elastic band paths, along with configurations from network-driven molecular dynamics simulations selected via query-by-committee combined with a relative energy cutoff. It was found that these techniques were a useful supplementary data-gathering technique that successfully described reaction barrier energies to within 1.5 kcal/mol, but were unable to sample relevant regions of phase space required to reproduce correct molecular motion. Chapter 4 uses a classical force field in molecular dynamics simulations to provide theoretical insight into thermodynamic drives of a modified histidine substrate for Histidine Kinase that would be able to probe enzyme activity directly. Findings supported proteomics surveys indicating glutamate residue 253 provides the most thermodynamically accessible target for the modified histidine in diazirine form.
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    Minnesota Taconite Workers Health Study: Environmental Study of Airborne Particulate Matter in Mesabi Iron Range Communities and Taconite Processing Plants - Mesabi Iron Range Community Particulate Matter Collection and Gravimetric Analysis
    (University of Minnesota Duluth, 2019-12) Monson Geerts, Stephen D; Hudak, George J; Marple, Virgil; Lundgren, Dale; Zanko, Lawrence M; Olson, Bernard
    The Minnesota Taconite Workers Health Study (MTWHS) was initiated in 2008 and included a multicomponent study to further understand taconite worker health issues on the Mesabi Iron Range (MIR) in northeastern Minnesota. Approximately $4.9 million funding was provided by the Minnesota Legislature to conduct five separate studies related to this initiative, including:  An Occupational Exposure Assessment, conducted by the University of Minnesota School of Public Health (SPH);  A Mortality (Cause of Death) study, conducted by the University of Minnesota SPH;  Incidence studies, conducted by the University of Minnesota SPH;  A Respiratory Survey of Taconite Workers and Spouses, conducted by the University of Minnesota SPH; and  An Environmental Study of Airborne Particulate Matter, conducted by the Natural Resources Research Institute (NRRI) at the University of Minnesota Duluth (UMD). NRRI’s “Environmental Study of Airborne Particulate Matter” comprises a multi-faceted characterization of size-fractionated airborne particulate matter (PM) from MIR community “rooftop” locations, background sites, and all taconite processing facilities active between 2008 and 2014. Characterization includes gravimetric determinations, chemical characterization, mineralogical characterization, and morphological characterization. This report specifically discusses the methods and gravimetric results of multiple aerosol PM sample collections from five communities located within the MIR, as well as three background locations. The samples were collected between 2008 and 2011.
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    Optimization and Sampling using Iterative Algorithms
    (2022-07) Bhattacharya, Riddhiman
    Sampling and optimization are considered as the two key pillars in statistics with applications in many other fields like machine learning, physics, chemistry just to name a few. Although there has been a recent boom in literature for both optimization and sampling algorithms, some key questions on the properties of the aforementioned topics still remain unanswered. In this thesis we study some of these algorithms in detail and address some of the key questions. The common aspect that connects all the algorithms considered by us is that they are iterative in nature. We study the Langevin Monte Carlo (LMC) algorithm with incorrect gradient and establish asymptotic results. We also consider the Multiplicative Stochastic Gradient Descent Algorithm (M-SGD) and establish that the error term is asymptotically Gaussian. Also, we exhibit that the M-SGD algorithm is close to a certain diffusion, irrespective of the weights used, in order of the step size under suitable assumptions. Next, we establish the convergence of the algorithm and a CLT around the optimum in the regime of strong convexity. Lastly, we consider the preconditioned LMC algorithm and exhibit non-asymptotic bounds in the regime of strong convexity.