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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
Remote and Proximal Imaging Methods for Cotton Nitrogen Status Estimation
No full textNitrogen (N) is an essential plant nutrient and also a major environmental pollutant. Plants require N for amino acid synthesis, chlorophyll health, canopy growth, and yield. In cotton, both N deficiency and excessive N applications affect plant growth and yield. Studies showed that the growth period between squaring and peak flowering corresponded to peak N uptake in cotton plants. Here we explored remote and proximal imaging methods to quantitatively estimate N status in cotton.
A multi-year N management field experiment was conducted to observe cotton development between vegetative growth and early flowering stages. Extraction of precise spatiotemporal features and robust modeling were the topics emphasized in this dissertation. Objective 1 determined the effect of exposure settings on image radiometric accuracy. The results favored the use of fixed exposure settings for UAV flights and the ideal exposure time and gain were empirically determined for the camera. The object-based empirical line calibration method was proposed for images acquired with fixed exposure settings. Objective 2 explored the systematic integration of the downwelling light sensor (DLS) to compensate for changing illumination conditions. The proposed DLS-based methods effectively removed radiometric errors due to illumination changes in fixed and auto-exposure images.
Objective 3 assimilated results from Objectives 1 and 2 to extract calibrated spectral and morphological cotton canopy features to quantify canopy N and predict stress levels. Plant biological parameters ��� plant N concentration, plant N uptake, dry biomass weight ��� were best estimated when spectral and morphological features were combined through random forest regression and gradient boosting regression models. Model estimated parameters were used to derive nitrogen nutrition index to predict stress levels with good precision and recall (F1 = 0.75). Objective 4 explored extracting spectral and morphological features from oblique ground-based images. The goal was to see if cotton had differences in spectral vegetation indices in the top and bottom canopy layers due to N stress. An algorithm was developed to correct perspective distortion in oblique images. The results showed scope for further exploration of oblique images for early detection of N stress
Three Essays on the Economics of Resource Conservation in Agriculture
No full textThis dissertation presents three essays that explore the economics of water and soil conservation in agriculture. The overarching focus of this work is on the impact and cost of conservation programs and policies aimed at conserving natural resources, as well as studying potential barriers to their implementation.
The first essay is an empirical study assessing the cost of reducing water use from irrigated agriculture in the Colorado River Basin. Utilizing basin-wide farm-level data into an econometric choice modeling framework, I simulate farmers��� crop choices under different hypothetical conservation prices paid for per acre-foot of water. I use the predicted crop shares to derive the abatement cost curve of water, which estimates the cost of an additional unit of water conserved in agriculture. My findings suggest that water abatement appears negligible at prices below $200 per acre-foot, but increases thereafter with a significant regional heterogeneity in the marginal cost of additional conserved water. Specifically, my results suggest that marginal abatement cost is smaller in the Lower Basin states of the river, suggesting opportunities for higher levels of conservation at relatively lower costs in this region.
The second essay studies the direct and spillover effect of an extension-based contest program in Arkansas, designed to promote the adoption of irrigation best management practices among pro-ducers. Specifically, I study how participation in the ���Most Crop per Drop��� irrigation yield contest, which ranks participants by water use efficiency and provides feedback on their performance, influences their adoption of irrigation practices. Results show that participation in the contest is positively correlated with a four percent increase in the average adoption of irrigation practices. Additionally, there appears to be an indirect positive spillover effect from the contest, which has influenced the behavior of those producers who were not participants of the program but were indirectly linked to it.
In the third essay, I investigate the difference in the adoption of conservation practices among different groups of producers based on their tenure status. I use operation-level data from the Census of Agriculture for all 48 contiguous states to test whether the enrollment in land retirement programs, and the use of on-farm conservation practices differ among full-owner, part-owner, and full-tenant operations. My results suggest that part-owners and full-tenants are more likely than full-owners to adopt practices such as reduced-tillage and using cover crops. Based on my findings, full-owners are using no-till practice at higher rates than other tenure categories, and are also enrolling higher shares of their operation into land retirement programs
An Examination of Texas Virtual Teachers' Understanding of Self-Efficacy and Perception of Students' Self-Efficacious Behaviors in a Digital Learning Environment
No full textSelf-efficacy, or one���s ability to achieve the desired result on a task, is a key factor in student
success, particularly in virtual learning contexts. Self-efficacy has been linked to students���
persistence, levels of effort, and goal-setting, as well as their self-regulatory behaviors, such as
time management. The purpose of this study was to explore what virtual middle school teachers
know about self-efficacy, what misconceptions they have, and how teachers believe virtual
students exhibit behaviors of persistence, effort, and goal-setting in asynchronous digital learning
environments. A case study of four teachers from a virtual school in Texas was conducted.
Through qualitative semi-structured interviews and review of lessons, thematic analysis revealed
that virtual teachers had nearly equal amounts of knowledge and misconceptions about self-efficacy. Teachers were able to correctly link self-efficacy to motivation and self-regulation, and they used methods to increase student self-efficacy consistent with the literature in their teaching practice. Teachers had misconceptions about what causes digital learners to develop self-efficacy and around the relationship between self-efficacy and other concepts. Finally, virtual teachers determined that their virtual students do demonstrate behaviors of persistence and effort but did not autonomously exhibit the behavior of goal-setting. This study provides recommendations for increased professional learning through instructional materials to increase teachers��� knowledge of self-efficacy, and therefore inform their practice
A mm-Wave Concurrent Dual-Band Dual-Beam Phased Array Receiver Front-End in 22 nm FDSOI CMOS
No full textA mm-Wave concurrent dual-band (28 and 39 GHz) dual-beam phased array multi-input���multi output (MIMO) receiver front end with mid-band rejection was designed and fabricated in 22 nm Fully Depleted Silicon on Insulator (FDSOI) CMOS process. This phased array receiver front end has 4 inputs and 2 output streams with fully connected configuration where takes advantage of sharing LNA and quadrature network and using a unique PS structure which allows power and area saving. The measured 3-dB gain bandwidth is from 23 to 30 GHz for the lower bandwidth a peak gain of 21 dB at 29 GHz, and 36 to 40 GHz for the upper bandwidth a peak gain of 18 dB at 38.5 GHz, and a NF minimum of 6 and 7 dB at 28 and 37 GHz, respectively. The 21 dB mid-band rejection at 33.5 GHz is provided by the LNA to attenuate the out-of-band unwanted interference helping the relaxing the linearity requirement. The entire single-channel receiver front end achieves IIP3 varying from -18 dBm to -11 dBm, and input 1-dB compression point varying from -25 dBm to -18 dBm. The front end has 5-bit phase control and 7-dB gain control achieving the RMS phase and gain errors less than 6 deg and 1.2 dB, respectively, enabling orthogonality. This array demonstrates the concurrent functionality, and carrier aggregation for over-the-air beam steering and EVM measurements. The chip has a length of 2738 ��m, a width of 1808 ��m, and an area of 4.95 mm2 including all DC, RF pads, and decoupling capacitors
An Examination of Texas Virtual Teachers' Understanding of Self-Efficacy and Perception of Students' Self-Efficacious Behaviors in a Digital Learning Environment
No full textSelf-efficacy, or one���s ability to achieve the desired result on a task, is a key factor in student
success, particularly in virtual learning contexts. Self-efficacy has been linked to students���
persistence, levels of effort, and goal-setting, as well as their self-regulatory behaviors, such as
time management. The purpose of this study was to explore what virtual middle school teachers
know about self-efficacy, what misconceptions they have, and how teachers believe virtual
students exhibit behaviors of persistence, effort, and goal-setting in asynchronous digital learning
environments. A case study of four teachers from a virtual school in Texas was conducted.
Through qualitative semi-structured interviews and review of lessons, thematic analysis revealed
that virtual teachers had nearly equal amounts of knowledge and misconceptions about self-efficacy. Teachers were able to correctly link self-efficacy to motivation and self-regulation, and they used methods to increase student self-efficacy consistent with the literature in their teaching practice. Teachers had misconceptions about what causes digital learners to develop self-efficacy and around the relationship between self-efficacy and other concepts. Finally, virtual teachers determined that their virtual students do demonstrate behaviors of persistence and effort but did not autonomously exhibit the behavior of goal-setting. This study provides recommendations for increased professional learning through instructional materials to increase teachers��� knowledge of self-efficacy, and therefore inform their practice
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
D-modules and the Cauchy-Kovlevskaya Theorem
No full textThis is an expository article served as an elementary introduction to the theory of algebraic D-modules. For the first part, we give the definition and basic properties of D-modules. We take advantage of the sheaf theory and compare the results of complex manifolds with those on affine spaces. We particularly focus on introducing the characteristic varieties of D-modules. For the second part, we introduce the classical Cauchy-Kovalevskaya theorem. Using the languages of category theory and homological algebra, we give a generalized version on affine spaces, which is a simplified version of the Cauchy-Kovalevskaya-Kashiwara theorem
Unfolding the Complexity of Soil Chemical Process and Remote Sensing for the Detection and Monitoring of Oil Contaminants Using AI Techniques
No full textThe rapid acceleration of global economic development has significantly increased energy demands, leading to severe environmental consequences, particularly soil contamination due to oil pollutants. This contamination not only alters soil's physical and chemical properties but also jeopardizes its ecological balance and human health. In response to these challenges, our research embarks on a comprehensive exploration of soil contamination by oil pollutants, emphasizing the need for a deep understanding of these contaminants within their ecosystems. We investigate the effectiveness of various remediation strategies, considering the intricate dynamics of soil ecosystems. Our study aims to contribute significantly to environmental science by identifying pollutants and deploying tailored remediation techniques that harmonize with soil ecosystem complexities.
First, our research utilizes an AI-assisted systematic review to understand the remediation of soils contaminated with Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals. By employing literature databases, text mining, and interactive data mining tools, we aim to offer a holistic view of soil contamination. Results indicate a prevalence of combined treatment techniques, with biological-biological approaches being most common, highlighting the challenges and potential strategies for effective remediation.
Secondly, our efforts are directed toward transforming soil remediation techniques with the introduction of Advanced Fenton-Photo Systems, complemented by the integration of deep-learning neural networks aimed at refining petrochemical degradation processes. The empirical evidence from our research indicates remarkable oxidation rates of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs), with degradation rates reaching up to 99% in mere minutes. This highlights a substantial leap forward in the efficiency of removing contaminants from soil.
In our third objective, we harness Artificial Intelligence (AI) to enhance the capabilities of remote sensing in accurately predicting oil contamination within the Al-Burgan oil field. Our findings, derived from the application of advanced neural network models and Sentinel-2 satellite data, have significantly improved oil contamination detection, achieving flawless accuracy in certain scenarios. This approach not only refines the detection and quantification of oil contamination but also showcases the transformative potential of AI in elevating environmental surveillance and monitoring practices.
Lastly, the research is aimed at advancing the monitoring and prediction of vegetation coverage in arid ecosystems, with a specific focus on Kuwait's Burgan field, through the application of AI-integrated remote sensing. Our analysis has unveiled notable vegetation recovery following remediation efforts, highlighted by interannual and seasonal changes in vegetation cover. The utilization of Soil-Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI), processed through neural networks, has provided deep insights into vegetative dynamics. This underscores the effectiveness of remote sensing in ecological assessments, driven by the development of innovative vegetation indices and the employment of advanced remote sensing techniques. These efforts are pivotal in offering profound insights into the health and dynamics of vegetation, underlining the critical role of ecological monitoring and management.
This research will result in improved strategies for environmental management and remediation, offering novel insights and methodologies that facilitate the sustainable management of contaminated soils. Through a multifaceted approach that integrates advanced technological solutions and ecological understanding, our study contributes to the advancement of environmental restoration efforts, ensuring healthier ecosystems for future generations
Complex Fluids Formed from Surfactant-Based Dynamic Binary Complexes
No full textThis work attempts to study the rheological properties and corresponding morphologies associated with complex fluids formed by novel surfactant-based dynamic binary complexes. Dynamic binary complexes refer to non-covalent supramolecular assemblies of surfactants and an appropriate complexing agent, giving rise to the potential for stimuli-responsiveness. The fundamental mechanisms that constitute viscosity modifier systems are an important point of discussion as well. Such chemistries are capable of interesting nanoarchitectures in aqueous suspension, that can have wide-ranging implications in several multi-billion dollar industries such as pharmaceuticals, personal care and the energy sector.
We examined an aqueous system of zwitterionic surfactant stearyl betaine with diethylenetriamine as the complexing agent, which displayed pH-responsive rheological properties and morphological changes. The surfactant was synthesized via a straightforward condensation reaction and then mixed with diethylenetriamine in an aqueous suspension at 2wt% to formulate the system. Notably, acidic conditions exhibited substantial steady-shear viscosities (up to 160 Pa.s) and viscoelastic behavior, decreasing with rising pH. A temperature analysis using zero-shear viscosities and differential scanning calorimetry unveiled thermodynamic transitions. Morphological insights from AFM and SAXS revealed distinctive bilayer nanotubules at low pH, transforming into bilayer sheets and then vesicles with increasing pH.
We then studied a system comprising of ��-cyclodextrin (��-CD) with three sulfonic surfactants: sodium hexadecylsulfate, sodium dodecylbenzenesulfonate, and myristyl sulfobetaine. Optimal axial growth and high viscosities in the suspensions were achieved with a ��-CD:surfactant ratio of 2:1. The complexation processes exhibited nanostructural phase behaviors and rheological properties sensitive to the molecular structure of sulfonic surfactants. DIC microscopy provided visual insights into the microstructure regarding sulfonate molecular architecture. Additionally, surface tension measurements, along with FTIR and NMR spectroscopies, deepened the understanding of interactions leading to complexation and nanostructural features. The proposed mechanics aimed to correlate supramolecular morphologies with rheological properties