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Disequilibrium Melting of the Continental Crust During Emplacement of the Mt. Princeton Batholith, Central Colorado Volcanic Field
No full textAssimilation and crystallization are difficult to constrain at magmatic boundaries, including the interactions of magma with the surrounding country rock. The assumption of the relationship between a magma and what it is intruding upon is relegated to homogenous bodies or epizonal plutons. Realistically, wall rock influences chemical heterogeneity and isotopic variance at the outcrop scale and changes depending on distance from the magma-wall rock interface. Here, we present a case study of the 35 Ma Mt. Princeton Batholith and the host Precambrian rocks of the Central Colorado Volcanic Field. We assess chemical heterogeneity by whole rock and mineral trace element and isotope geochemistry, U-Pb zircon geochronology, and mineral assemblages in thin section at varying distances from the interface of the batholith with the wall rock. Two transect locations were selected that provide the best-defined contacts between the wall rock and the Mt. Princeton Batholith containing one sample from the host rock and 7-10 samples from the granite. We present a quantitative model for disequilibrium melting versus crystallization during magma emplacement. We suggest the percentage of zircons with Precambrian age cores included in the granitic body versus magmatic age cores represents the volume of magma affected by the wall rock melting. Granites range in age from 34.1 ± 0.6 Ma to 34.7 ± 0.3 Ma. Precambrian rocks contain two zircon intercept ages, averaging 1600 Ma, and 35 Ma showing evidence of partial resetting, metamorphism, and discordance. Zircon ranges in U-Th ratios from 0.097 - 40.3. Potassium feldspars in the host rock contain Sr contents from 3.9 ppm to 528 ppm and Rb from 4 ppm to 326 ppm. In granites, Sr ranges from 59.4 ppm to 723.9 ppm and Rb from 11.5 ppm to 174.6 ppm. Granite samples nearest to the wall rock contain K-spar inherited from the host where, as distance from the interface increases, these crystals become less prevalent. Modeling trace elements contents and Sr isotopic ratios of potassium feldspar, along with U-Pb zircon geochronology quantifies disequilibrium melting of Precambrian wall rock during emplacement of granitoid composition magmas. These data provide insight into the thermal state of the crust, where a magma partially melted the wall rock, creating a mixing zone at the interface
Public Perceptions of Human Physical Interactions, Exhibition, and Conservation of Tigers and Cheetahs
No full textTigers and cheetahs are common in the pet trade, public exhibits, and hands-on encounters with the public. Poor regulation of these experiences has resulted in numerous incidents worldwide in which a person was seriously injured or killed by captive big cats. Additionally, concerns for animal welfare have been raised by industry professionals. Prior research on primates has demonstrated exposing people to photographs of cats in different backgrounds can influence their attitudes about animals in captivity, but no study has addressed whether visual images affect human attitudes toward big cats. I used a survey that asked a series of questions about the suitability of keeping tigers and cheetahs in captivity, with each survey accompanied by a picture of tiger or cheetah in one of several backgrounds. Adult tigers were assessed as least happy when pictured in a circus, naturalistic zoo exhibit, or hard-surface exhibit, and adult cheetahs were evaluated as least happy when pictured on a leash or zoo background. In addition to the influence of the image characteristics, survey responses identified many significant trends in public perceptions of big cats as it relates to human physical interactions, exhibition, and conservation. For example, the majority of respondents believed that: scenarios where human interaction is permitted with tigers or cheetahs are unsafe, tigers and cheetahs are not appropriate to keep as pets, it should not be legal to own a big cat as a pet, and it is inappropriate for a tiger to perform tricks for the public’s entertainment. Although our findings show limited influence of image characteristics on attitudes about tigers or cheetahs, the compiled survey results indicate that the public is concerned about the safety of people and welfare of big cats both in captivity and the wild
Mining-Contaminated Sediment and Metal Storage in Channel Deposits in Turkey Creek, Tri-State Mining District, Missouri and Kansas
No full textThe Joplin subdistrict, within the Tri-State Mining District (TSMD), was a major producer of lead (Pb) and zinc (Zn) ore between the 1880s to 1920s. Metalliferous mining wastes are still stored in the channel deposits of local streams raising environmental and health concerns. This study quantifies the volume, sediment size, and metal concentrations in channel bed, bar, bench, and chute deposits to quantify the spatial variability of contaminated sediment storage in Turkey Creek (119 km2). Sample reaches (n=14) contained metal concentrations elevated above the Tri-State Mining District specific probable effects concentration at every site with mean concentrations in fine sediment (\u3c2 \u3emm) ranging from 229 to 996 mg kg-1 Pb and 4,946 to 5,819 mg kg-1 Zn. Mean concentrations in powdered coarse sediments (2 – 16 mm) ranged from 60 to 86 mg kg-1 Pb and 1,660 to 2,488 mg kg-1 Zn. Metal contamination levels were typically highest in the fine sediment fraction with the greatest metal concentrations observed near mining impaired sites. Regression modeling using distance downstream estimated that 127,000 kg Pb and 2,160,000 kg Zn are stored within a 18.8 km main channel segment of Turkey Creek. Fine sediment represented only 19% of in-channel sediment but stored the greatest mass of metal for both Pb and Zn representing 61% and 52% of total metal storage, respectively. Bar deposits stored the most Pb (69%) and Zn (74%).Volumetric storage of contaminated sediment (m3/m channel length) was positively related to active channel width (R2 = 0.97), bankfull width (R2 = 0.96), distance downstream (R2 = 0.74), slope (R2 = 0.70), average bed depth (R2 = 0.68), and drainage area (R2 = 0.83). These geomorphic variables can be used to estimate the total sediment volume and mass as well as metal mass by reach for Turkey Creek. Metal concentrations and storage rates are still relatively high below remediated mine sites in the main channel and some tributaries
Aucanquilcha Volcanic Cluster Magma Evolution and Magma Plumbing System Architecture During the Gordo Stage (6-4 Ma)
No full textAucanquilcha Volcanic Cluster (AVC) is an 11 m.y. volcanic system in the central Andes that is evolving over four distinct stages of activity. Stages include the Aloncha (11-8 Ma), Gordo (6-4 Ma), Polán (4-2 Ma), and Aucanquilcha (Ma) stages. The AVC evolved from a series of magmatic underpinnings during the Aloncha Stage to a larger zone of melting, assimilation, storage, and homogenization (MASH) by the Polán Stage. The transition from smaller underpinnings to MASH zones began during the Gordo Stage. At ~5-2 Ma the AVC reached thermal maturity before beginning its volcanic death during the Aucanquilcha Stage. This study focuses on elucidating the magma plumbing system architecture during the Gordo Stage to better understand the AVC’s evolving magmatic processes from 6-4 Ma. Magma evolution was determined by analyzing major and trace element chemistry of whole rock composition, plagioclase phenocrysts, and pyroxene phenocrysts. Populations of plagioclase include plagioclase phenocrysts with variation between Sr and Ba (Type 1), re-equilibrated Sr but not Ba (Type 2), and no variation in Sr or Ba (Type 3). The three types are further split into textural categories with patchy resorption, sieved rims, abundant patchy resorption in the core, or abundant sieving in the cores. All plagioclases have oscillatory zoning throughout and some normal zoning profiles in the outer mantle and rim. Molar % An ranges from ~30-90 and does not have a correlation with Sr/Ba values. Although some variation still exists, An composition across core-to-rim transects becomes more homogenous over time after multiple eruptions, except for one likely isolated eruption. In general pyroxene phenocrysts have MG# values ranging from ~0.45-0.75. Pyroxene populations include clinopyroxenes (Type 1) and orthopyroxenes (Type 2). Pyroxene population types are further classified based on if they have normal zoning, reverse zoning, oscillatory zoning and if they are phenocrysts, antecrysts, or xenocrysts. In addition to determining overlapping plagioclase phenocryst and pyroxene phenocryst populations, the magma plumbing system architecture was determined by analyzing rare earth element (REE) trends. REE trends for plagioclases and pyroxenes represent an open system for AVC magmas containing trends indicative of crystals residing in equilibrium, going through fractional crystallization, and being included from magma mingling. REE core patterns have multiple groupings of different trends that indicate AVC magmas have an interactive system of different source reservoirs in the crust. REE rim patterns have less variation indicating the magmas homogenized, but variation between the cores, mantles, and rims suggest a final magma mixing event triggered eruptions at the AVC. In summary, the AVC is long-lived volcanic system that began to evolve from a series of magmatic underpinnings from ~6-4 Ma to a series of connected, developing MASH reservoirs in the crust at ~15-30 km deep
Vision-Based Human Fall Detection in Smart Homes
No full textFalling is one of the leading causes of death from unintentional injuries in older adults. They are more common in people over the age of 65. Wearable sensor-based solutions are commercially available, but they possess limitations like recharging the sensors, and wearing them can be intrusive to the user. Consequently, vision-based fall detection approaches offer a feasible alternative due to the ever-increasing presence of cameras in smart homes. This thesis presents a novel two-stage human fall detection system for smart homes. The proposed approach uses humans as a sensor. It is a vision-based two-stage process where Stage-1 is dedicated to detecting fall-like events at the edge of the network, and Stage-2 is hosted in the cloud to confirm the fall. I propose a template matching technique in the first stage and a model based on LiteFlowNet and LRCN in the second stage. The proposed deployment reduces the workload on cloud servers while ensuring service availability at the edge level if the cloud service is inaccessible. I evaluated this approach using publicly available datasets and real-time videos. I have also compared the model performance with existing state-of-the-art vision-based fall detection systems that used the same publicly available dataset. Results accumulated from the experiments show the efficacy of the proposed approach for smart home deployment
Book Review of Operation Chaos: The Trump Coup Attempt and the Campaign to Erode Democracy
No full textIn Operation Chaos: The Trump Coup Attempt and the Campaign to Erode Democracy, Kevin James Shay tells the story of the January 6, 2021, insurrection on the United States Capitol. The book begins by describing certain events of the day, and takes readers down a path of political violence, dirty tricks, and political schemes aimed at keeping President Donald J. Trump in power. The story covers previous coup attempts, ways that President Trump began his plot years before January 6th and describes how leaders can escape justice and accountability
Enhanced Convolutional Neural Network for Image-Based Steganalysis in Spatial Domain Using Spatial Rich Model and 2d Gabor Filters
No full textDuring the past decade, many methods have been introduced to handle the image-based steganalysis problem. Traditional steganalysis methods are based on the two-step machine learning mechanism that consists of extracting and classifying phases. Most recent solutions are based on deep convolution neural networks (CNNs), which combine feature extraction and classification in one step. CNN-based steganalysis methods provide superior performance. These CNNs are designed to improve the detection rate by using a set of predefined filters for the pre-processing phase. In this thesis, I propose a CNN model that consists of two convolution layers for pre-processing and features extraction, and four fully connected layers for classification. The pre-processing layer relies on some of the well-known and efficient filters that were used in previous studies in addition to the various instances of the 2D Gabor filter based on selected parameters. I conducted experiments using grayscale cover images from a popular and publicly available BOSSbase_1.01 database with a consideration for two different image sizes. The results showed that the proposed CNN model outperforms many of the state-of-the-art studies in two out of three well-known adaptative spatial domain steganography algorithms (S-UNIWARD, HUGO) and provides a close result for (WOW) algorithm when using the database with resized images. On the other hand, the proposed model outperforms many of the state-of-the-art studies in the three algorithms when using the database with the original size. Moreover, the experiments illustrated that training the model with algorithm mismatched dataset can improve the detection accuracy significantly in many cases
The Comparison of Three Different Fecal Egg Counting Techniques and Their Ability to Perform a Fecal Egg Count Reduction Test
No full textA comprehensive gastrointestinal parasite control program includes an understanding of common parasites, application of chemotherapeutic agents, as well as frequent and appropriate diagnostic testing. An effective control program is essential for facilities such as animal shelters, that deal with large populations of transient canines with unknown parasite exposure and deworming history. The identification of a sensitive flotation method to evaluate anthelmintic efficacy is critical in monitoring parasite populations for drug resistance. The objective of the current study was to compare three different fecal egg counting technologies and their ability to perform a fecal egg reduction test. The flotation techniques evaluated include a Modified McMasters, a Modified Wisconsin, and the Mini-FLOTAC. Canine fecal samples were obtained from Polk County Humane Society in Bolivar, MO. When possible, three samples were submitted for each canine: the first stool eliminated in the shelter, a sample 7 days after shelter deworming, and a sample 14 days after deworming. Each sample was divided into aliquots of 2 grams for each of the three different diagnostic techniques. Upon examination all ova detected were identified, counted, and the appropriate multiplication factor applied to yield an egg per gram (EPG) result. Where follow up samples were collected, the FEC results were used to preform reduction tests to determine percent ova reduction after anthelmintic treatment. One way ANOVA results determined that the three flotation methods were not different in mean EPG or EPG level for roundworms (p=0.284), whipworms(p=0.130), or coccidia(p=0.315). One way ANOVA found a difference between total Nematode EPG (p=0.002) and hookworm EPG (p=0.033), where the Mini-FLOTAC yielded a higher average EPG than the Modified McMasters or the Modified Wisconsin. No difference was found in FECRT between flotation methods
Measuring Gender-Related Biases and Exploring Methods to Diminish Bias by Targeting Relations for Defusion
No full textBiases related to gender are an important area of empirical attention in the United States due to social challenges related to prejudice, stereotyping, and discrimination based on gender. The purpose of this study is to evaluate potential bias related to binary and nonbinary gender using a measure of relational responding rooted in Relational Density Theory (RDT) (Belisle & Dixon, 2020). Mass and volume of networks in terms of gendered stereotypical relations are assessed to further examine binary gendered stereotypes and to examine relations regarding nonbinary genders in the context of traditionally masculine and feminine labels. Implicit biases regarding male and female genders have been examined, however less research on nonbinary gender biases and stereotypes is available. As the number of individual’s identifying as nonbinary increases, (estimated 1.2 million) it is of particular importance to examine this population. Using an RDT approach, binary gender stereotypes were expected to tightly cluster, but become less dense after employing an Acceptance and Commitment Therapy (ACT) technique to weaken stereotypical relations that create bias. A brief 10-minute defusion procedure was utilized to elaborate relational networks, using an approach adapted from previous research (Belisle et al., 2019). Participants randomized into the control group that did not complete the defusion task were expected to see little to no change in relational responding. In the empirical investigation of the data, using a multidimensional scaling procedure (MDS), three distinct classes emerged where ‘woman’ tightly clustered with feminine descriptors, ‘man’ tightly clustered with masculine terms, and ‘nonbinary person’ appeared in its own class between the other two gendered terms. When comparing the two groups between both MDS procedures administered to measure the effects of the defusion procedure on gendered stereotypical relational responding, no changes were observed between the control group (G1) and the experimental group (G2). Relational distance (Rd) was measured between gendered terms, yielding like distances between all gendered terms. The greatest change observed in Rd occurred comparing both groups at time two of the MDS procedure. Implications and avenues for future interventions to diminish unhelpful bias and stereotypical responding are discussed in terms of this empirical investigation
Applications of a Combined Approach of Kinetic Monte Carlo Simulations and Machine Learning to Model Atomic Layer Deposition (ALD) of Metal Oxides
No full textMetal-oxides such as ZnO or Al2O3 synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used as descriptors in the subsequent analyses via Machine Learning algorithms. The simulated results of a combined approach of kMC and ML were then compared to the experimental results