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POLITICAL AND GENDER DIALECTICS IN THREE NINETEENTH-CENTURY SPANISH NOVELS OF THE BOURBON RESTORATION (1874-1931)
Get PDFThis investigation aims to show that three of the most iconic Spanish novels of the nineteenth century are connected to each other as a dialogue between their authors about politics and gender in Spain. These novels are Doña Perfecta by Benito Pérez Galdós, Los pazos de Ulloa by Emilia Pardo Bazán, and La Regenta by Clarín. Galdós’ novel presents a liberal perspective that is highly misogynistic. Pardo Bazán’s novel presents a conservative perspective that is also feminist because it decries the lack of autonomy that women have in nineteenth-century Spain. Clarín’s novel shares this feminism, but does so from the leftist perspective of a disillusioned radical
Evolution and Taxonomy of the Dryadoideae
Get PDFDryadoideae is one of three subfamilies within Rosaceae (the rose family) whose genera largely preside in the western part of North America. It contains mountain mahogany (Cercocarpus), mountain misery (Chamaebatia), cliff-rose (Purshia), and mountain avens (Dryas); many of these taxa house nitrogen-fixing bacteria belonging to the genus Frankia. There is also a long fossil record of these plants dating back to the Tertiary period. Morphological variation within Cercocarpus is of particular interest due to the presence of many high-quality leaf fossils. The precise relationships among and within the genera within Dryadoideae are not well-understood due to limitations in previous genetic data used and their conflicting phylogenetic hypotheses. The aim of this study is to resolve phylogenetic relationships within the subfamily, with an emphasis on Cercocarpus and Purshia, using hundreds of highly conserved and low-copy nuclear loci through target-capture (HybSeq) sequencing using the Angiosperm 353 baits and to morphologically compare leaves of extant and extinct species of Cercocarpus. Overall, in both genetic and morphological analyses, C. mojadensis and C. pringlei are most closely related. The subfamily Dryadoideae is strongly supported, and Purshia, Cercocarpus, and Chamaebatia were monophyletic. More data is needed to parse out how exactly the species in both Cercocarpus and Purshia are related. The morphological analyses offer some insight on how the Cercocarpus species relate to each other morphologically but would benefit from increased sampling
Survivor Magazine : v.1:no.1(1999)
No full textThis magazine’s main goal is to call attention to women as the main victims of rape. More than that its goal is to help these women through the healing process after surviving such a traumatic event. Survivor is written and illustrated in a way that gives power back to survivors and reminds them it’s not their fault. This magazine is largely a collection of poems and illustrations that cover themes of rape, child molestation, power, control, and survival. The ending of this issue lists biographies of all contributors that are throughout this source. Another important theme throughout this text is that of victim blaming.
Next_Gen Proppant Cleanout Operations_ Machine Learning for Bottom_Hole Pressure Prediction
Get PDFIn proppant cleanout operations, it's crucial to utilize the optimum bottom-hole pressure to achieve enough annular velocity in the wellbore to lift solids to the surface, make sure no skin damage is created due to excess fluid losses, and avoid stuck-pipe situations. Machine learning models, which offer real-time on-site prediction of bottom-hole pressure, can be used to achieve this. The main goal of this study is to create machine learning-driven models capable of predicting bottom-hole pressure at the coiled tubing nozzle's exit while pumping nitrified fluids in cleanout operations.
Nine machine learning and deep learning models were developed using readily available parameters typically gathered during cleanout operations, which include coiled tubing depth and inside diameter, bottom hole temperature at the coiled tubing nozzle, gel rate, nitrogen rate, and coiled tubing pressure at the surface as inputs. These models are trained utilizing measured bottom-hole pressure data acquired from deployed memory gauges, which serve as the model's outputs. Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbor (KNN), Linear Regression, Neural Network, and Stochastic Gradient Descent (SGD) are machine learning algorithms that were meticulously developed and optimized using an extensive data set derived from 48 wells. 33,453 data points make up this dataset, which was carefully divided into two subsets: 80% (26,763 data points) were used to train the algorithms, while 20% (6,690 data points) were used to test their predictive abilities. In addition, the performance of machine learning models is evaluated using the K-fold and random sampling validation procedures.
When comparing predictions of coiled tubing nozzle outlet pressure to actual measurements, the results of the top-performing machine learning models, specifically Neural network, AdaBoost, Random Forest, K- Nearest Neighbor and Gradient Boosting show remarkably low mean absolute percent error (MAPE) values. These MAPE values are, in order, 1.7%, 1.6%, 2%, 2.5%, and 3.2%. Furthermore, these models have remarkably high correlation coefficients (R2), with respective values of 0.947, 0.943, 0.929, 0.918, and 0.878. Moreover, machine learning models offer a distinct advantage over conventional vertical lift performance correlations, as they do not necessitate routine calibration. Beyond this, these models demonstrated their ability to accurately predict bottom-hole pressure across a wide range of cleanout parameters.
This paper introduces novel insights by demonstrating how using a machine learning model for predicting coiled tubing nozzle outlet pressure while pumping nitrified fluids in cleanout operations can enhance ongoing cleanout operations. Utilizing machine learning models offers a more efficient, rapid, real-time, and cost-effective alternative to calibrated vertical lift performance correlations and deployed memory gauges. Furthermore, these models excel at accommodating a wide spectrum of cleanout parameters and coiled tubing configurations. This was a challenge for single vertical lift performance correlation.N
Genetic evaluation of bile salt resistance and sensitivity in a subculture of Escherichia coli K-12 substrain JC3272
Get PDFThough most Escherichia coli strains are adapted to survival in environments with high concentrations of bile salts, some mutant strains lack this ability. Prior work has reported that a spontaneous mutant of the K-12 strain JC3272 is sensitive to bile salts. To test this, cultures of this mutant (JC3272I) and a non-mutant strain (JC3272F) were evaluated on both solid and liquid media containing bile salts. Amplification of the lapA and lapB genes—which are hypothesized to affect bile salt sensitivity—was achieved in both strains via polymerase chain reaction. A combination of Sanger and Illumina sequencing was then used to compare the amplified genes and to produce whole-genome sequences. Culturing confirmed a difference in sensitivity to bile salts between the two strains. However, sequencing did not reveal any differences between the strains in the lapAB region. Furthermore, no mutations found via whole-genome sequencing had known roles in bile salt resistance. These results may imply the existence of an unpublished mechanism of bile salt resistance in E. coli
2024-2025 Undergraduate Catalog
Get PDFAnnual publication of degrees offered and their requirements for all undergraduate students enrolled at the University of Central Oklahoma
Moral Foundations and Public Perceptions of Carbon Capture and Storage with Induced Seismicity
Get PDFMoral foundations held by the public significantly influence attitudes towards energy transition policies like carbon capture and storage (CCS). This study examined the relationships between moral foundations and public perception of CCS with induced seismicity risks in a nationally representative survey of Americans, while controlling for political party and orientation. The binding moral foundation of Loyalty and the individualizing foundation of Care were associated with support for CCS, despite the risk of small earthquakes. In contrast, the individualizing foundation of Fairness and the binding foundations of Authority and Purity were correlated with opposition to CCS when considering the possibility of induced seismicity. An interaction effect was observed between the moral foundation of Loyalty and political orientation. Liberals and moderates tended to increase their support for CCS with the risk of earthquakes as their in-group loyalty increased, while conservatives' support remained unchanged with increasing in-group loyalty.
These findings suggest effective energy transition strategies should consider moral foundation dynamics in policy design and public messaging, particularly for climate mitigation aspects involving seismicity risks. Tailoring approaches to align with distinct moral concerns of different population segments could enhance public acceptance of carbon-mitigating energy solutions. Policymakers and communicators should address underlying moral foundations shaping public attitudes to develop more targeted strategies for building support, especially for methods with inherent risks.N
