Variability in Crassulacean Acid Metabolism: A Survey of North Carolina Succulent Species

This is the publisher's version, also available electronically from: http://www.jstor.org/stable/10.2307/2474765.The correlation between succulence and Crassulacean acid metabolism (CAM) was investigated in 28 succulent species growing in various habitats throughout North Carolina. Three species (Opuntia compressa^ Agave virginica, and Tillandsia usneoides) exhibited diurnal fluctuations in tissue titratable acidity, nighttime uptake of 1 4C02 , and a high carbon isotope ratio (513C), all indicators of CAM. Seven species displayed one or two characteristics of CAM in situ yet yielded lower 513C values, indicating a partial or total restriction of atmospheric CO2 uptake to the C3 photosynthetic system: Yucca gloriosa, Sesuvium maritimum, Talinum terettfolium, Diamorpha smallii, Sedum pusillum, Sedum nevii, and Sedum telephioides. Several of these species were apparently capable of utilizing the CAM pathway to fix internal respiratory CO2. The results emphasize that one photosynthetic pathway does not characterize all succulents in North Carolina

Thermochemical Storage Conditions of Caldera Forming Magmatic Systems Revealed by Diffusion Chronometry

Large silicic magmatic systems are responsible for producing the largest explosive volcanic eruptions on earth. These phenomena, although infrequent (i.e., 1 per 100,000 years), impact the global climate, deposit ash over continent sized regions, and significantly alter landscapes. Silicic magmatism also plays important roles in the formation and ongoing evolution of continental crust. This makes understanding the processes that generate, transport, store, and erupt large volumes of silicic magma extremely important. In this dissertation, I present two case studies that explore the long-term thermal evolution of two caldera forming magmatic systems: Cerro Galán in NW Argentina and Toba in Sumatra. In them, I quantify the amount of time that the system experiences thermochemical conditions (i.e., temperatures > 750°C) sufficient to produce and store large volumes of eruptible magma (i.e., < ~50 % crystals and at a viscosity below the magma extrusion limit of 106 - 108 Pa·s). This eruptible time window, or thermal history, is recorded by the minerals (i.e., plagioclase) that crystallize within the reservoir and can be quantified by fitting forward models of trace element diffusion (i.e., Sr in plagioclase, Mg in plagioclase, Sr in hornblende) to the observed trace element profiles within the grain. We find that both the Cerro Galán and Toba magmatic systems experience relatively short time intervals (e.g., decades to centuries) during their overall history in which the thermal state of the magmatic system is sufficient to produce and store the large volumes of eruptible magma that constitute their eruptive products. This implies that these systems were dominantly stored in relatively cool, crystal-rich conditions and were only remobilized shortly before eruption. In addition, all the other processes that occur in the formation and evolution of large bodies of silicic magma – such as melting, recharge etc. also all need to fall within this “thermal budget”. This is also consistent with the dearth of geophysically resolved liquid-rich magma bodies under active volcanoes. A large part of the work described above uses the chemical variations within minerals to study magmatic processes, and by necessity much of this is done on 2D sections of minerals. However, the 3D distribution of elements in minerals is also important to evaluate, and thus the second part of this study investigates chemical zoning in sanidine, a common volcanic mineral, in 3D using micro computed tomography (microCT). We show that X-ray attenuation is largely a function of Ba concentration in sanidine and from this develop a method to quantify Ba zoning in 3D. We also show that this method can help mitigate problems in diffusion modeling caused by sectioning effects (i.e., apparent diffusion widths) by allowing for the true chemical gradient to be extracted from a 3D volume rather than interpreted from a random 2D section. Furthermore, we show that true zoning geometries can be observed in 3D, allowing for the interpretations that come from studying them to be more completely understood

Oaza zdravog razuma u duhovnoj pustinji (Suzana Marjanić: Topoi umjetnosti performansa: lokalna vizura, Durieux/ Hrvatska sekcija, AICA, 2018.)

Christian Democratic parties have been a stable feature of many western European party systems since the early 20th century. As Tim Immerzeel, Eva Jaspers and Marcel Lubbers write, however, support for Christian Democrats has fallen significantly in several European countries. Based on an analysis of voting patterns in Europe, they argue that radical right parties have the potential to win the votes of religious citizens who have traditionally supported Christian Democratic parties. This effect depends to a large extent on how threatened Christian voters feel by the presence of immigrants subscribing to other belief systems

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics - in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our machine learning model exhibits strong performance (R$^2$=0.9) when tested on extremely diverse real domains at multiple scales

Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundational models for interatomic potentials