136879 research outputs found
Sort by
The Development of Texneut and Spectroscopy of 10Li Using Isobaric Analog States
No full textFor at least two decades there has been uncertainty in the description of the low-level structure of 10Li, including the J �� and energy of the ground state. The properties of the system are crucial for the understanding of the evolution of nuclear structure out beyond the drip line. The 9Li + n dynamics also play an important role in the 11Li system. 11Li has been experimentally shown to be a di-neutron halo nucleus whose structure, in part, is determined by the interaction of the 9Li + n system. Reliable experimental results on 10Li are also important to benchmark predictions of contemporary nuclear structure models.
Various experiments and calculations have been conducted that target the low-lying structure of 10Li. Many of the calculations, including ab initio, were performed. Past experiments have used different reactions to populate the 10Li nucleus. Yet, no clear understanding on the J �� of 10Li has been achieved. We propose a new way to study 10Li.
With the TexAT time-projection chamber, we measured the excitation function for 9Li + p elastic scattering populating T = 2 isobaric analog states (IAS) in 10Be. The presence of the T = 2 IAS is expected in 10Be just above the 9Li+p threshold. These resonances lead to enhancements in the elastic scattering cross section at the resonance energies. Comparing the experimental data for 9Li + p elastic scattering to R-matrix calculations we hoped to assess the spin-parity assignment for the T = 2 states in 10Be, and infer the low-lying level spin-parity of 10Li
Furthermore, we have developed a neutron detector array, TexNeut. The spectroscopy of fast neutrons opens up a wide range of experiments that can be performed with rare isotope beams (RIBs) at Texas A&M University. These experiments complement those performed using charged particles. TexNeut is comprised of small modular detector bars which fit compactly together to form a thick array. TexNeut has been characterized and shown to give excellent n/�� pulse shape discrimination (PSD), fast timing, and gives a discrete position spectrum with a resolution fixed to 2 �� 2 �� 2 cm3 . The commissioning experiment of TexNeut is a measurement of 9Li(p, n) 9Be reaction to study the same IASs in 10Be as previously mentioned. The development of the detector modules, construction of the array, and detector commissioning will be discussed in this work
Connecting the Dots: Improving Information Extraction by Modeling the Non-Sequential Dependencies of Entity Mentions
No full textInformation Extraction (IE) aims at automatically extracting structured information from unstructured and semi-structured documents. It is an important and challenging topic in Natural Language Processing (NLP) that plays a critical role in downstream applications such as Question Answering and Summarizing. It includes many sub-tasks like Named Entity Recognition (NER), Relation Extraction, Event Extraction, Table Annotations, etc.
Previous works on IE mainly focus on extracting knowledge from a document sentence by sentence and the text encoding models (e.g., RNNs, CNNs, and Transformers) regard the text as a linear sequence from the left to the right or from the right to the left. However, we humans do not always understand a document by sequentially reading it. We tend to connect the concepts across the whole document and then form structural knowledge in our brains. Motivated by the intuition, this work proposes to introduce the non-sequential connections within a document, and uses the relations among the entity mentions as the surrogates for the non-sequential conceptual connections to further improve the performance of the IE systems.
Firstly, I propose to connect the related entity mentions in a document and enforce information flow among them for consistent entity type predictions. Specifically, the work connects both the local dependency relations and global coreference relations for the entity mentions to build better entity mention representations. Experimental results show that applying Graph Neural Networks (GNNs) on the connections can improve the NER performance over strong baselines on two domain-specific datasets.
Secondly, I propose to connect the conceptually related regions in a document and encourage semantic interactions within and among regions. In particular, it builds the connections among the candidate role fillers (i.e., entity mentions from an event mention) for the event extraction task, and characterizes the connections based on different regional affiliations. Then edge-aware GNNs are applied to update the representations of the candidates for false positive filtering. Empirical results show that the proposed method can yield new state-of-the-art performance on two document-level event extraction datasets in two different languages.
Lastly, I propose to connect the entity mentions from semi-structured tables and incorporate the table structures into the table element representations, benefiting table annotation (knowledge extraction from tables) tasks. Specifically, the system will first build hyper-graphs for the cell values coming from the same row or column and then apply the hyper-graph Neural Networks to learn better table representations. The evaluation and analysis show the effectiveness of the structure-aware table representations in improving the table annotation tasks
Lip Position Preferences as They Relate to Convexity and Divergence
No full textIntroduction: The aim of the present study was to assess lip position preferences as they relate to convexity and divergence. Of secondary interest were how sex, ethnicity, and age influenced these preferences.
Methods: A sample of 1000 internet users were asked to rank the relative attractiveness of a series of modified 3D facial profiles with differing lip projections. One Caucasian male served as the model, and the model's facial scan underwent mandibular augmentation in the sagittal and vertical dimensions resulting in nine distinct facial profiles. Following these augmentations, the lips were adjusted anteriorly and posteriorly in 1.5 mm increments from baseline.
Results: Each of the nine facial profiles experienced significant within-group differences in preferred lip position. Females preferred more protrusive lips in all profiles than males. African Americans preferred more retrusive lips for nearly all profiles than Caucasians and Hispanics. Younger generations tolerated more retrusive lip positions compared with older generations for nearly all profiles.
Conclusions: Preferred lip positions are a function of convexity as opposed to divergence; facial types with vertical imbalances followed similar trends to those seen in other facial types with similar convexity. Sex, ethnicity, and age-related preferences persisted despite most facial imbalances
John Bickham field notebook: AK9501-AK10000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK10001-AK10500 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
The Impact of Preovulatory Estradiol on the Oviductal and Uterine Environments, and Profit per Pregnancy Associated with the Detection of Estrus
No full textThe expression of estrus and preovulatory estradiol concentrations influence pregnancy success in beef cattle; however, it is not clear how estrus and/or estradiol impact oviductal and uterine environments. The goals of this dissertation were to evaluate how preovulatory estradiol impacts oviductal gene and protein expression, how physiological estradiol exposure with/without estrus impacts pregnancy-associated factors, and how utilizing the detection of estrus with timed-artificial insemination (TAI) impacts expense and profit per pregnancy. In Chapter II, oviducts (n=6) were collected from synchronized beef cows with High (10.12��0.62 pg/ml, n=3) or Low (5.97��0.62 pg/ml, n=3) estradiol concentrations at fixed-time artificial insemination (FTAI), and differentially expressed genes were identified. Oviductal gene expression was hormonally regulated, with differential gene expression between High and Low cows. In Chapter III, oviducts and ovaries were collected from synchronized beef cows with High (���6.0 pg/mL; n=4) or Low (���4.5 pg/mL; n=5) estradiol concentrations. Oviducts were fixed and embedded for localization of prostaglandin E2 synthase (PTGES) and prostaglandin E2 receptor 2 (PTGER2) localization by immunofluorescence. There was no difference in immunoreactivity of PTGER2 protein; however, immunoreactivity of PTGES protein tended to be more intense in oviducts from High compared with Low cows. In Chapter IV, beef cows (n=603) were synchronized and grouped based on estrus by FTAI (day 0), or the administration of gonadotropin-releasing hormone (GnRH) and/or exogenous estradiol, and estrus by day 7. While estrus or physiological estradiol did not impact pregnancy or interferon-stimulated gene expression by day 19, pregnancy rate and pregnancy-associated glycoprotein abundance by day 24 differed between cows that did or did not express estrus by day 7. Differences in day 55 and 90 pregnancy were observed due to estrus by day 7, but not due to estradiol exposure without estrus. In Chapter V, beef cows and heifers were synchronized using a TAI protocol with (6d; n=437) or without (7d; n=429) estrus detection. Pregnancy rates did not differ; however, decreased expense and increased profit per pregnancy were observed with the 6d compared with the 7d protocol. These data indicate estrus supports pregnancy through maternal environment changes, and utilizing detection of estrus with TAI is more economical
Using X-Ray Computed Tomography to Quantify Pore Characteristics in a Shrink-Swell Clay
No full textShrink-swell soils are those which shrink when drying and swell when wetting. This creates cracks that may measure >10 cm in width and >1 m in depth when the soil is dry. These soils have low permeability when wet but allow rapid water movement through cracks when dry. Because of their dynamic nature, these soils can cause infrastructure damage, crush crop roots, and lead to unpredictable rates and concentrations of contaminant transport. Current numerical models are not able to accurately represent the dynamic pore characteristics, and often soil shrink-swell processes are not taken into consideration at all. To better model the potential impacts of dynamic pore networks in a shrink-swell soil, it is necessary to quantify changes in pore characteristics���size distribution, connectivity, and tortuosity���that accompany changes in soil water content. X-ray computed tomography (CT) scanning is a technology used to visualize the internal structure of an object and can be used to observe and quantify porosity in a soil sample. The goal of this project is to improve our understanding of dynamic porosity in shrink-swell soil, using X-ray CT scanning to quantify crack patterns and sizes in shrink-swell soils at multiple water contents. Three intact soil cores were saturated, scanned using Xray CT, then dried and scanned again. Dragonfly, ImageJ, and MATLAB software were used for image processing and analysis of structural and porosity changes within the cores. Our results show a higher number of pores with volumes >1 mm^3 and pores with lengths >5 mm in the dried cores compared to the saturated. A higher connectivity of these pores was observed in the dried cores compared to the saturated. The knowledge gained through this study regarding changes in porosity between saturated and dried cores will improve our understanding of shrink-swell soils, contributing to improvements in shrink-swell soil���s role in regulating hydrological processes
Atomic Boson Sampling in a Bose-Einstein Condensed Gas
No full textWe propose a multi-qubit Bose-Einstein-condensate (BEC) trap as a platform for studies of quantum statistical phenomena in many-body interacting systems. In particular, it could facilitate testing atomic boson sampling of the excited-state occupations and its quantum advantage over classical computing in a full, controllable and clear way. Contrary to a linear interferometer enabling Gaussian boson sampling of non-interacting non-equilibrium photons, the BEC trap platform pertains to an interacting equilibrium many-body system of atoms with established Bose���Einstein condensate. We discuss a basic model and the main features of such a multi-qubit BEC trap.
We describe boson sampling of interacting atoms from the noncondensed fraction of Bose-Einstein-condensed gas confined in a box trap with periodic boundary conditions. We explicitly show increasing apparent complexity of sampling probability patterns with changing the observational basis of excited atom states from the eigen-squeeze modes to more and more involved unitary mixtures of them. We calculate the characteristic function and statistics of atom numbers via newly found hafnian master theorem. Using Bloch-Messiah reduction, we find that interatomic interactions give rise to two equally important entities ��� eigen-squeeze modes and eigen-energy quasiparticles ��� whose interplay with sampling atom states determines quantum statistics of the BEC gas. We infer that two necessary ingredients of computational ���P-hardness, squeezing and interference, are self-generated in the BEC gas and, contrary to Gaussian boson sampling in linear interferometers, external sources of squeezed bosons are not required for observation of quantum advantage manifestations.
The focus of the dissertation is on the origin of the computational ���P-hard complexity and quantum advantage of atomic boson sampling due to interference and squeezing of the sampled atom states via their interplay with the eigen-squeeze modes and eigen-energy quasiparticles
Analyzing the Effects of Groundwater Flow Rates on the Recovery Efficiency of Aquifer Thermal Energy Storage Systems Using Numerical Modelling
No full textThis study looks at the effects of groundwater flow velocity on the recovery efficiency of Aquifer Thermal Energy Storage (ATES) systems and how modifying the pumping rate and storage time can optimize the efficiency of an ATES system located in an aquifer with low to high regional flow rates. By examining the processes that control the efficiency of these energy storage systems, we hope to improve their effectiveness and contribute to their use as an energy conservation technology.
The ATES investigation was done by applying principles of heat and solute transport to a numerical model that represents a low-temperature doublet ATES system installed in a shallow confined aquifer. The finite difference method modeling suite MODFLOW was used as the primary modeling software, with MT3DMS serving as the solute transport engine. The model includes six regional groundwater flow rates ranging from 0-50 m/yr, two pumping regimes of 200-400 m3 /d (injection and extraction), and two storage times of 0-3 months. The ATES wells operated on annual cycles, with injection temperatures ranging from 5-30��C.
We found that groundwater velocity is a primary control in the recovery efficiency of ATES systems, with an average drop of 27% from no groundwater flow to 50 m/yr. Additionally, we found that at groundwater flow velocities higher than 30 m/yr, the injection rate is the most important secondary parameter, with lower pumping and injection rates associated with lower recovery efficiencies. Inversely, at groundwater flow velocities lower than 20 m/yr, storage time is the most important secondary parameter, with longer storage times associated with lower recovery efficiencies
Geometric Deep Learning for Molecular Discoveries
No full textWith the rapid advancement of artificial intelligence (AI), its applications in scientific research have grown significantly, giving rise to the research area of AI for science (AI4Science). In this dissertation, we focus on AI for molecular science, such as small molecules and proteins, aiming to build efficient and effective methods to accelerate molecular discovery. we particularly focus on two fundamental tasks, molecular representation learning and molecule generation. Specifically, we model molecules as graphs and design geometric deep learning methods for molecules.
We first consider representation learning of 3D molecular graphs, where each node has its 3D coordinates. With an accurate representation learning model, we can reduce the computation time required for predicting molecular properties. In this dissertation, we provide an analysis in the spherical Coordinate System (SCS) for the complete identification of 3D graph structures and propose our SphereNet. SphereNet can distinguish similar molecular structures, such as two enantiomers that are mirror images of each other and reduce complexity from O(nk��) to O(nk��),
enabling it to perform efficiently on large-scale molecules. Here n and k denote the number of nodes and the average degree in the 3D graph, respectively.
While SphereNet presents advancements in accuracy and efficiency, it still can not incorporate 3D information completely. Furthermore, its complexity remains higher than some existing methods. We then propose ComENet to address these issues and incorporate 3D information completely and efficiently. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness with a complexity of O(nk).
SphereNet and ComENet are tailored for small molecules. Extending their application to proteins is challenging due to the large number of atoms in proteins and their inherent multi-level nature. Therefore, we further design our method ProNet specifically for proteins. ProNet completely captures three levels of protein structures, e.g., the amino acid, backbone, or all-atom levels and is more efficient than existing methods. ProNet can be applied on various downstream tasks, including protein fold and function prediction, protein-ligand binding affinity prediction, and protein-protein interaction prediction.
Lastly, we consider 3D molecule generation. The generation of novel molecules with desired properties is an important step in drug discovery. In this dissertation, we apply language models (LMs) for 3D molecule generation by introducing our canonical and SE(3)-invariant tokenizer, Geo2Seq. Experiments show that our new method can achieve promising results
Spatial Patterns, Drivers, and Impacts of the Urban Land Expansion in Nigeria from 1985 to 2015
No full textUrbanization is reshaping landscapes worldwide, posing considerable challenges to ecosystems, climate patterns, and resource utilization. This dissertation studies the spatio-temporal patterns of urban land change, its drivers, and its implications for biodiversity and flood exposure in Nigeria from 1985 to 2015. A pivotal aspect of the methodology is the evaluation of global urban land cover products to facilitate the understanding of urban land expansion in a context where specific national data on land cover is scarce or not available. This evaluation revealed each product's ability and limitations in accurately reflecting the reality of Nigeria's urban development. The specific findings of this research also reveal significant environmental implications, such as the loss and fragmentation of natural habitats and the exposure of urban land to flooding. Thus, the dissertation provides valuable insights that can inform urban management, policy formulation, and conservation strategies in developing urban landscapes, that are particularly tailored to Nigeria, but also indicative of other rapidly urbanizing countries across Sub-Saharan Africa