Doctor of Philosophy

dissertationSin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (>50%). The primary virus host is deer mice, and greater deer mice abundance has been shown to increase the human risk of HPS. There is a great need in understanding the nature of the virus host, its temporal and spatial dynamics, and its relation to the human population with the purpose of predicting human risk of the disease. This research studies SNV dynamics in deer mice in the Great Basin Desert of central Utah, USA using multiyear field data and integrated geospatial approaches including remote sensing, Geographic Information System (GIS), and a spatially explicit agent-based model. The goal is to advance our understanding of the important ecological and demographic factors that affect the dynamics of deer mouse population and SNV prevalence. The primary research question is how climate, habitat disturbance, and deer mouse demographics affect deer mouse population density, its movement, and SNV prevalence in the sagebrush habitat. The results show that the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) can be good predictors of deer mouse density and the number of infected deer mice with a time lag of 1.0 to 1.3 years. This information can be very useful in predicting mouse abundance and SNV risk

An Application Study on AI Educational Robots in Spoken English Exercises of Chinese Primary Schools

China’s primary schools offer limited English courses, and the society lacks the environment for naturally acquiring English in everyday life. Students typically have weak spoken English abilities and inadequate application of English. With the aim of addressing this issue, 79 fifth-graders from China’s Hangzhou L elementary school participated in a one-semester AI-assisted English-speaking practice experiment. The control class practiced spoken English by reading English texts aloud, whereas the experimental class practiced for 30 minutes a day using the “AI educational robots + graded picture books + role play” approach. According to the results of the experiment’s post-test, Chinese primary school students regarded the experimental class’s acquisition mode to be highly appealing, this approach was well accepted by both students and parents and brought enthusiasm and good effect of spoken English exercise. The experimental class’s average daily reading time for English role-play reading grew by about 30 minutes, the amount of reading increased by five times, the amount of time spent watching cartoons and playing video games fell by nearly 28 minutes, and the spoken English score climbed by 37 points, representing an increase of 82% when compared to the control class; Additionally, the standard level of pronunciation and intonation has increased by two grades, from “poor” to “good,” and the English final exam scores have increased by roughly 8%. However, there has not been a considerable change in the aforementioned control class indicators. This AI-assisted second language practice technique is affordable, efficient, and helpful and has good implications for second language acquisition in other countries

Surface roughness effects on the broadband reflection for refractory metals and polar dielectrics

Random surface roughness and surface distortions occur inevitably because of various material processing and fabrication techniques. Tailoring and smoothing the surface roughness can be especially challenging for thermomechanically stable materials, including refractory metals, such as tungsten (W), and polar dielectrics, such as silicon carbide (SiC). The spectral reflectivity and emissivity of surfaces are significantly impacted by surface roughness effects. In this paper, we numerically investigated the surface roughness effects on the spectral reflectivity and emissivity of thermomechanically stable materials. Based on our results, we determined that surface roughness effects are strongly impacted by the correlation length of the Gaussian surface. In addition, our results indicate that surface roughness effects are stronger for the materials at the epsilon-near-zero region. Surface roughness effects are stronger between the visible and infrared spectral region for W and around the wavelength of 12 mu m for SiC, where plasma frequency and polar resonance frequency are located

Comparative analysis of the phytocyanin gene family in 10 plant species: a focus on Zea mays

Phytocyanins (PCs) are plant-specific blue copper proteins, which play essential roles in electron transport. While the origin and expansion of this gene family is not well investigated in plants. Here, we investigated their evolution by undertaking a genome-wide identification and comparison in 10 plants: Arabidopsis, rice, poplar, tomato, soybean, grape, maize, Selaginella moellendorffii, Physcomitrella patens and Chlamydomonas reinhardtii. We found an expansion process of this gene family in evolution. Except PCs in Arabidopsis and rice, which have described in previous researches, a structural analysis of PCs in other eight plants indicated that 292 PCs contained N-terminal secretion signals and 217 PCs were expected to have glycosylphosphatidylinositol-anchor signals. Moreover, 281 PCs had putative arabinogalactan glycomodules and might be AGPs. Chromosomal distribution and duplication patterns indicated that tandem and segmental duplication played dominant roles for the expansion of PC genes. In addition, gene organization and motif compositions are highly conserved in each clade. Furthermore, expression profiles of maize PC genes revealed diversity in various stages of development. Moreover, all nine detected maize PC genes (ZmUC10, ZmUC16, ZmUC19, ZmSC2, ZmUC21, ZmENODL10, ZmUC22, ZmENODL13, and ZmENODL15) were down-regulated under salt treatment, and five PCs (ZmUC19, ZmSC2, ZmENODL10, ZmUC22, and ZmENODL13) were down-regulated under drought treatment. ZmUC16 was strongly expressed after drought treatment. This study will provide a basis for future understanding the characterization of this family

Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.Comment: 5 pages, Submitted to IEEE MIPR 201

Reinforcement Learning for Generative AI: A Survey

Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer vision. The major paradigm to train a generative model is maximum likelihood estimation, which pushes the learner to capture and approximate the target data distribution by decreasing the divergence between the model distribution and the target distribution. This formulation successfully establishes the objective of generative tasks, while it is incapable of satisfying all the requirements that a user might expect from a generative model. Reinforcement learning, serving as a competitive option to inject new training signals by creating new objectives that exploit novel signals, has demonstrated its power and flexibility to incorporate human inductive bias from multiple angles, such as adversarial learning, hand-designed rules and learned reward model to build a performant model. Thereby, reinforcement learning has become a trending research field and has stretched the limits of generative AI in both model design and application. It is reasonable to summarize and conclude advances in recent years with a comprehensive review. Although there are surveys in different application areas recently, this survey aims to shed light on a high-level review that spans a range of application areas. We provide a rigorous taxonomy in this area and make sufficient coverage on various models and applications. Notably, we also surveyed the fast-developing large language model area. We conclude this survey by showing the potential directions that might tackle the limit of current models and expand the frontiers for generative AI

Dynamic response analysis of the rubber shock absorber in the artillery vibration system

The complex impulse environment of the artillery firing process brings very tough vibration attenuation requirement of the artillery-mounted equipment. The experiment is designed to decrease an artillery mounted instrument container’s vibration. However, after being equipped with a certain rubber shock absorber, the vibration of this instrument container wasn’t being controlled. Thus, based on the theoretical analysis and dynamic simulation, we summarized that this phenomenon was caused by the rigid collision between two parts of the rubber shock absorber while reaching elastic limit. The result provides the necessary theory of choosing the appropriate artillery shock absorber. By using the Genetic Algorithm optimization design method, we found the best independent variable H

The Composite Face Effect Between Young and Older Chinese Adults Remains Stable

Holistic face perception is often considered to be a cornerstone of face processing. However, the development of the ability to holistically perceive faces in East Asian individuals is unclear. Therefore, we measured and compared holistic face processing in groups of Chinese children, young adults, and older adults by employing the complete composite face paradigm. The results demonstrate a similar magnitude of the composite effect in all three groups although face recognition performance in the task was better in young adults than in the two other groups. These findings suggest that holistic face perception in Eastern individuals is stable from late childhood to at least age 60, whereas face memory may be subject to later development and earlier decline.Peer Reviewe

Ranolazine recruits muscle microvasculature and enhances insulin action in rats: Ranolazine, microvasculature and insulin action

Ranolazine, an anti-anginal compound, has been shown to significantly improve glycaemic control in large-scale clinical trials, and short-term ranolazine treatment is associated with an improvement in myocardial blood flow. As microvascular perfusion plays critical roles in insulin delivery and action, we aimed to determine if ranolazine could improve muscle microvascular blood flow, thereby increasing muscle insulin delivery and glucose use. Overnight-fasted, anaesthetized Sprague-Dawley rats were used to determine the effects of ranolazine on microvascular recruitment using contrast-enhanced ultrasound, insulin action with euglycaemic hyperinsulinaemic clamp, and muscle insulin uptake using 125I-insulin. Ranolazine's effects on endothelial nitric oxide synthase (eNOS) phosphorylation, cAMP generation and endothelial insulin uptake were determined in cultured endothelial cells. Ranolazine-induced myographical changes in tension were determined in isolated distal saphenous artery. Ranolazine at therapeutically effective dose significantly recruited muscle microvasculature by increasing muscle microvascular blood volume (∼2-fold, P < 0.05) and increased insulin-mediated whole body glucose disposal (∼30%, P= 0.02). These were associated with an increased insulin delivery into the muscle (P < 0.04). In cultured endothelial cells, ranolazine increased eNOS phosphorylation and cAMP production without affecting endothelial insulin uptake. In ex vivo studies, ranolazine exerted a potent vasodilatatory effect on phenylephrine pre-constricted arterial rings, which was partially abolished by endothelium denudement. In conclusion, ranolazine treatment vasodilatates pre-capillary arterioles and increases microvascular perfusion, which are partially mediated by endothelium, leading to expanded microvascular endothelial surface area available for nutrient and hormone exchanges and resulting in increased muscle delivery and action of insulin. Whether these actions contribute to improved glycaemic control in patients with insulin resistance warrants further investigation