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Improving Ocean Surface Albedo Parameterization and Evaluating Its Responses to Arctic Surface Temperature and Zonal-Mean Tropical Atmospheric Circulation in CESM2
No full textOcean surface albedo (OSA), the ratio of the upward to the downward radiation just above the air-sea interface, is of primary importance in quantifying the solar energy exchange between the atmosphere and the ocean. In current climate models, many OSA schemes depend only on the solar zenith angle (SZA) or both SZA and wind speed, are only valid for broadband, and commonly ignore considering ocean surface-layer optical properties. Oversimplified OSA in most climate models leads to less accurate estimation of the net shortwave energy in the coupled atmosphere-ocean system.
This dissertation develops an improved OSA algorithm by considering multiple influential factors, including additional oceanic light attenuation effects and appropriate treatment of spectral variations of the reflective properties for incident direct and diffuse solar beams at the sea surface. The proposed OSA method shows robust performance compared to the in-situ measurements from the Clouds and the Earth's Radiant Energy System (CERES) Ocean Validation Experiment on a regional scale, and the CERES OSA products on a global scale.
The new OSA is then added in the fully coupled mode of the Community Earth System Model2 to explore responses of Arctic surface temperature and the subsiding edges of the Hadley Circulation (HC) due to the OSA changes.
Incorporation of light attenuation in OSA increases absorbed solar radiation and warms the ocean, enhancing seasonal heat storage and release across the Arctic Ocean, and increasing sea ice reduction and other positive climate feedback that amplify Arctic surface warming. Surface air warming is induced primarily through positive heating anomalies of vertical advection, latent heat release, and longwave radiative forcing. Warmer skin temperature is driven predominantly by increased downward longwave radiation, another positive surface albedo feedback.
The warming effect not only occurs at the surface, but it also extends through most of the atmosphere, subsequently setting up corresponding atmospheric feedback affecting the HC. The increased meridional air temperature gradient intensifies the zonal wind and the baroclinic instability in the subtropics, which enhances equatorward Rossby waves, and alters the derivative of the horizontal eddy momentum flux, causing reduced meridional velocity, and the HC subsiding edge ultimately moves equatorward
Compositional Changes in MnO2-PLGA Nanoparticles and their Effects on Sustained Oxygen Production and Immune Activity in Cancer Spheroids
Get PDFIntroduction: Hypoxia is a common characteristic of the tumor microenvironment (TME) that allows it to effectively suppress anti-tumor immune responses and evade killing by cytotoxic cells and. Altering these hypoxic conditions has been a considerable interest in cancer therapeutics, specifically by oxygenating the TME through nanoparticle (NP) delivery. In particular, there is a desire to provide sustained oxygen release over an extended period of time to mitigate the immunosuppression caused by localized hypoxia while lengthening the therapeutic response. Manganese dioxide (MnO2) NPs degrade tumor prevalent hydrogen peroxide (H2O2) to decrease hypoxia. However, the MnO2 NPs produce burst of oxygen when exposed H2O2 and do not provide the sustained oxygen production necessary for modifying the TME. Previously, this has been addressed by polymeric nanoparticle encapsulation. Our group has shown that encapsulating MnO2 NP in poly (lactic-co-glycolic) acid (PLGA) resulted in modified kinetics of O2 production for encapsulated MnO2 compared to Pegylated MnO2 NPs. Therefore, PLGA encapsulation is a promising tool that can be exploited to control oxygen release profiles. To further investigate the kinetics of PLGA, different compositions of PLGA with varying hydrophobicity were investigated to determine their role in O2 kinetics and toxicity. Methods: MnO2 nanoparticles were encapsulated into poly (lactic-co-glycolic) acid (PLGA) of different lactic and glycolic ratios, including 50:50 (PLGA), 75:25 (PLgA), and 100:0 (PLA), by double emulsion method. Once synthesized, Mn-PLGA-NPs were characterized by size, surface charge, and oxygen production. Size and surface charge were ascertained via dynamic light scatter (Malvern Zetasizer). Mn content and cytotoxicity of nanoparticles were assessed using ICP-MS and MTS assay, respectively. Hypoxia reduction in spheroids was visualized using ImageIT Hypoxia Green stain under a fluorescent microscope. Results and discussion: It was found that increasing lactic acid content yielded larger NPs. Additionally, 75:25 PLgA-MnO2 NPs seemed to have contained lower amounts of Mn vs PLGA NPs with 3 percent wt Mn vs 4 percent. When examining the toxicity of the MnO2 particles, it was found that PLgA exhibited the greatest toxicity at high concentrations followed by PLA, then PLGA. This goes against the idea that increasing hydrophobicity may lead to favorable toxicity since it may slow the reaction between MnO2 particles and hydrogen peroxide, so further evaluation into the effect of hydrophobicity on toxicity is required. Oxygenation studies showed that PLGA exhibited the best oxygen release profile, followed by PLgA, then PLA, possibly due to the hydrophobicity slowing down the diffusion of MnO2 into the TME. Hypoxia reduction studies exhibited the best reduction of hypoxia within PLGA, followed by PLgA, and then no statistically significant decrease in hypoxia for PLA. Conclusion: Future studies will explore changes in TME modulations, such as tumor hypoxia, with differing lactic acid character
Bioactive, Self-fitting Scaffolds Prepared from Siloxane-based Shape Memory Polymers
Get PDFThermoresponsive shape memory polymer (SMP) scaffolds afford conformal ���self-fitting��� into irregularly shaped craniomaxillofacial (CMF) bone defects. Grunlan and co-workers previously reported SMP scaffolds based on biodegradable poly(��-caprolactone) (diacrylate) (PCL-DA). Later, to enhance the rate of degradation, semi-interpenetrating network (semi-IPN) scaffolds were formed with PCL-DA and thermoplastic poly(L-lactic acid) (PLLA) (75:25 wt%, respectively). Bioactivity (i.e., the ability to induce the formation of a layer of hydroxyapatite, HAp), a property integral to promoting bone regeneration, was imparted by coating scaffolds with polydopamine (PD). However, as the scaffolds erode, the PD coating is lost as is bioactivity. Furthermore, the impact of ethylene oxide (EtO) sterilization on such PD-coated scaffolds was not assessed. Grunlan and co-workers have previously observed that hydrogels containing siloxane-based polymers were bioactive. While PCL-based scaffolds had been previously prepared with a siloxane-based co-macromer, the bioactivity was not assessed.
In the first study, PD-coated PCL-DA and PCL-DA/PLLA semi-IPN scaffolds were EtO sterilized. Morphological features, in vitro bioactivity, PCL crystallinity, PLLA crystallinity, and crosslinking were all preserved. Subsequently, shape memory properties, compressive moduli, and in vitro degradation behaviors were also unchanged.
In the second study, to achieve self-fitting scaffolds with innate bioactivity, PCL/polydimethylsiloxane (PDMS) co-matrices were formed with three types of macromers to systematically alter PMDS content and crosslink density. PCL90-DA was combined with a linear-PDMS66-dimethacrylate (DMA) macromer, and a star-PDMS66-tetramethacrylate (TMA) macromer at 90:10, 75:25, and 60:40 wt % ratios. Scaffolds were also prepared with an acrylated (AcO) triblock macromer (AcO-PCL45-b-PDMS66-b-PCL45-OAc) (65:35 wt % ratio). All PCL/PDMS scaffolds displayed bioactivity in vitro, leading to significant increases in moduli. Furthermore, degradation rates increased with PDMS content.
Lastly, the impact of siloxane polymer hydrophobicity on the bioactivity of PCL-based scaffolds was investigated. Scaffolds were prepared by combining PCL90-DA with either with linear macromers: PDMS66-DMA or polymethylhydrosiloxane66-dimethacrylate (PMHS66-DMA) (90:10, 75:25, and 60:40 wt % ratios). These PMHS-containing scaffolds exhibited further increased degradation and mineralized in just two weeks. Scaffolds were also cultured with human mesenchymal stem cells (hMSCs) to assess osteoinductivity. Compared to PCL-DA scaffolds, both PCL-DA/PDMS-DMA and PCL-DA/PMHS-DMA scaffolds had increased cell viability and proliferation as well as expressed higher osteogenic protein markers
Magic and Random Matrices used for the Computational Results in the paper "p-adic Lifting with Early Termination (PALET): Expediting Dixon's Method for Solving Linear Systems"
No full textMagic and Random Matrices used for the Computational Results in the paper "p-adic Lifting with Early Termination (PALET): Expediting Dixon's Method for Solving Linear Systems"Magic and Random Matrices used for the Computational Results in the paper "p-adic Lifting with Early Termination (PALET): Expediting Dixon's Method for Solving Linear Systems
Discussion Guide for Lila and the Lost Robot
No full textDiscussion GuideThis guide was designed for parents and teachers to help them discuss themes about AI technology and ethics raised by the book "Lila and the Lost Robot," written by Heidi A Campbell
Machine Learning-Based Production Forecasting for Multiphase Reservoirs
No full textProduction forecasting plays a pivotal role in field development, but conventional techniques such as numerical simulations, reduced-order modeling, and decline curve analysis can be time-consuming and computationally challenging, especially with large and high-resolution geomodels. To address these limitations, a novel machine-learning-assisted modeling technique has been developed for rapid and accurate production forecasting.
The innovative technique begins with extreme geomodel compression, reducing the complexity of the geomodel by over 18,000 times. This compression is followed by machine-learning-based forecasting. A multi-attribute, multi-layer shale geomodel compression technique is employed, involving dimensionality reduction. This process condenses large shale geomodels into low-dimensional representations. Subsequently, a neural network model is trained to process the low-dimensional representation, completions, and production parameters to predict monthly condensate and gas production rates over a 5-year decline period for hydraulically fractured shale wells.
The performance of this new production forecasting method was rigorously evaluated using two distinct datasets. The first dataset featured 4,000 realizations of shale wells based on a simple layered reservoir, while the second dataset comprised 3,000 realizations based on a complex and geo-statistically accurate heterogeneous reservoir. The model achieved impressive results, with average mean absolute error (NMAE) and mean absolute percentage error (MAPE) of 0.005 and 1.84% for gas rate predictions in the simple dataset and 0.023 and 3.18% for the complex dataset. Moreover, this method significantly outpaces traditional commercial software, taking only 0.1 seconds for forecasting after model training.
Beyond performance validation, the study explores scenarios with limited data in new fields. When input data in the new field aligns with the pre-trained model, the pre-trained model is directly deployed, yielding an average MAPE of 4.4% and 5.8% for condensate and gas rate predictions. In cases where the new field introduces novel input parameters, the pre-trained model serves as a feature extraction layer. The extracted features, combined with the new parameters, are then used as input for a neural network, proving more accurate and adaptable than training models from scratch, especially with limited training data.
In summary, this research introduces a groundbreaking approach that combines intensive geomodel compression with a multi-layer neural network model to enhance production forecasting. This method offers valuable applications in history matching and production forecasting for both conventional and unconventional assets. Its efficiency, accuracy, and adaptability make it a promising tool for economic evaluation in the oil and gas industry