Driven by rapid adoption and sustained improvements of genetic technologies and agronomic management practices, agricultural productivity has experienced a substantial growth worldwide since the start of the green revolution in 1960s. This growth has enabled the humanity to escape from the well-known Malthusian Trap. With the success in agricultural productivity, however, comes what is also known as “the other inconvenient truth” (Foley 2009). That is, modern agriculture has become one of the major drivers of global environmental change and is pushing the earth system beyond its safe operating boundaries (Rockström et al. 2009). Further, even more challenges lie ahead, given the growing number of population and increasing diversion of foods to fuels.

In this dissertation, three topics related to US agricultural systems are explored. In the first chapter, the environmental properties of US corn and cotton production and implications of land cover change from cotton to corn are evaluated using state-specific data and life cycle impact assessment. The results show that U.S. cotton and corn productions per hectare on average generate roughly similar impacts for most impact categories such as eutrophication and smog formation. Life cycle water use and freshwater ecotoxicity impacts of corn per hectare on average are smaller than those of cotton. When marginal impact is analyzed, however, the results show that the shifts from cotton to corn in cotton-growing states aggravates most of the regional environmental impacts while relieving freshwater ecotoxicity impact. The differences in the two estimates are due mainly to underlying regional disparities in crop suitability that affects input structure and environmental emissions.

In the second chapter, the carbon payback time (CPT) of corn ethanol expansion is re-examined considering three aspects: (1) yields of newly converted lands (i.e., marginal yield), (2) technology improvements over time within the corn ethanol system, and (3) temporal sensitivity of climate change impacts. The results show that without technological advances, the CPT estimates for corn ethanol from newly converted Conservation Reserve Program (CRP) land exceed 100 years for all Marginal to Average (MtA) yield ratios tested except for the case where MtA yield ratio is 100 %. When the productivity improvements within corn ethanol systems since previous CPT estimates and their future projections are considered, the CPT estimates fall into the range of 15 years (100 % MtA yield ratio) to 56 years (50 % MtA yield ratio), assuming land conversion takes place in early 2000s. Incorporating diminishing sensitivity of GHG emissions to future emissions year by year, however, increases the CPT estimates to17 to 88 years. For 60 MtA yield ratio, the CPT is estimated to be 43 years, which is relatively close to previous CPT estimates (i.e., 40 to 48 years) but with very different underlying reasons.

In the third chapter, the trends and underlying drivers of changes in non-global environmental impacts of major crops in the U.S. are investigated. The results show that the impact per hectare corn and cotton generated on the ecological health of freshwater systems decreased by about 50% in the last decade. This change is associated with the use of genetically modified (GM) crops, which has reduced the application of insecticides and relatively toxic herbicides such as atrazine. However, the freshwater water ecotoxicity impact per hectare soybean produced increased by 3-fold, mainly because the spread of invasive species, soybean aphid, has resulted in an increasing use of insecticides. In comparison, other impact categories remained relatively stable. By evaluating the relative ecotoxicity potential of a large number of pesticides, our analysis offers new insight into the benefits associated with genetically modified (GM) crops. The finding that different impact categories show different degrees of changes suggests that agricultural inventory data can be updated selectively in LCA to maximize cost-effectiveness.

Photoelectrochemical (PEC) cell is a device generated hydrogen fuel through an environmentally friendly method. The earliest report should date back to 1972. Honda and Fujishima firstdemonstrated solar water splitting by using titanium dioxide as photoanode in the cell. Then extensive efforts have been devotedto improving the solar-to-hydrogen (STH) conversionefficiency and decreasing thecost. However, current the efficiency of PEC device was limited on finding out a suitable photoanode material. The ideal photoanode material should have a good bandgap, favorable bandgap position, chemically stable and low cost. Therefore, this thesis would focus on studying different photoanode materials including GaN, TiO2 and Fe2O3 to achieve high PECwater oxidation performance. In this thesis, I will first designed GaN nanowires on carbon cloth via a chemical vapor deposition (CVD) method and demonstrated significant photoactivity for photoelectrochemical water oxidation. In addition, our group used to report a facile and general strategy to fundamentally improve the performance of TiO2 nanowires for PEC water splitting. However, there are some concerns about the real effects under higher hydrogen treated temperature as well as the stability of oxygen vacancies in TiO2. Therefore I investigated the effect of hydrogenation temperature and the stability of oxygen vacancies in TiO2 photoanodes. Furthermore, there are few reports about the study on the long term stability of TiO2 photoanode even though most scholars used to think TiO2 belongs to one of the most stable photoanode materials. So I carried out the first long term photostability measurement on various phases TiO2 photoanodes including rutile, anatase and mixed phased and found TiO2 photoanodes were not stable as people expected. Then I investigated the mechanism of the instability of TiO2 and carried out two strategies to stabilize TiO2 materials in the PEC cell. Finally, I created a facile acid treated method on hematite to substantially enhance the PEC activity. I found the enhanced photocurrent is due to improved efficiency of charge separation as well as potential passivation of surface electron traps.

By exploiting a vast trove of underutilized original sources, including thousands of epitaphs, archaeological reports, ritual manuals, and anecdotes, and by using digital humanities tools to analyze this large pool of data from a multiregional perspective, this dissertation reconstructs funerary practices in Tang-era China, and thereby explores cultural standardization and the effect of sociocultural changes on death rituals. My research demonstrates that certain death ritual practices prevailed among Tang elites of various regions and social strata and remained stable throughout the entire Tang dynasty, suggesting the existence of a standardized way of commemorating death in medieval China. Furthermore, my research reveals significant regional variations and temporal changes, which I use to examine the mechanisms behind uniformity and variety.

This dissertation also makes an original contribution to the understanding of actual mortuary practices among Tang elites of various strata and regional backgrounds. My core research material, the many thousands of Tang-era epitaphs, allows me to get closer to the actual practices of “ordinary” elites rather than rely on descriptions of rites by the ritual specialists in charge of compiling prescriptive ritual manuals. Moreover, as each tomb epitaph text usually provides a glimpse of a person’s life, tomb epitaphs are often the most direct and personal accounts of individuals, and they offer a perspective on a greater range of elite society than do either dynastic-history biographies or the eulogies preserved in the literary collections of famous writers.

In the last decades, the commissioning of high-energy, third-generation synchrotronspresents new opportunities for research with brilliant coherent X-ray beams. It allows us to study various systems’ structure and dynamics at shorter length scale than laser via diffraction patterns. In the Chapter 3, we adopted the laser technique Photon Correlation Spectroscopy to study the dynamical correlations of the spin-glass transition. We have implemented this method to observe and accurately characterize the critical slowing down of the spin orientation fluctuations in the classic metallic spin glass alloy CuMn over time scales of 100 to 103 secs. In the Chapter 4, we adopted the diffraction imaging method to retrieve the phase of the speckle pattern produced by the scattering of a coherent X-ray beam to reconstruct the image of samples at nanometer length scales. We have extended the oversampling methods and iterative schemes to use the full Distorted-Wave Born Approximation (DWBA) expression for the speckle pattern. The results obtained from detailed computer simulations of the scattering and reconstruction are very encouraging in showing that the method works. Verification with real experiments is planned.

Efficiency and robustness are often the main concerns in model design and algorithm development. Nowadays a lot of algorithms have been proposed with emphasis on one or the other. This thesis provides several algorithms together with their applications to address these two needs.

The first part of the thesis discusses the efficiency concern in video compression and reconstruction. With the increasing demand in real-time data transmission and storage, these two problems are attracting more and more attention. In terms of video compression, classic models often use a fixed temporal compression rate, while there are many potential gains in developing systems and procedures incorporating adaptive temporal compression rate. In Chapter 2, an algorithm based on local patches and polynomial fitting is proposed to adaptively predicts the temporal compression rate given the behavior of a few previous compressed frames. As for the inverse model, Chapter 3 presents a fast total variation based method for reconstructing video compressive sensing data. The regularization in the model is imposed on both the spatial and temporal components, which provides a more consistent approximation of the connection between neighboring frames with little to no increase in model complexity.

The second part of the thesis covers a new technique named adaptive outlier pursuit for handling sparsely corrupted data. In many real world applications, noise is often unavoidable during data acquisition and transmission. Some noise can damage part of the data seriously and make it contain no useful information at all. Algorithms robust to this type of noise are strongly needed. The technique adaptive outlier pursuit is introduced to deal with outliers in the acquired measurements. Instead of detecting and removing the outliers before applying classic algorithms, it alternates between the outlier detection and the signal reconstruction task, hence iteratively approaches the true signal in a more accurate way. It is applied to robust 1-bit compressive sensing and exact matrix completion in Chapter 5 and Chapter 6 respectively.

Next-generation sequencing (NGS) technologies are able to sequence DNA or RNA molecules at unprecedented speed and with high accuracy. Recently, NGS technologies have been applied in a variety of contexts, e.g., whole genome sequencing, transcript expression profiling, chromatin immunoprecipitation sequencing, and small RNA sequencing, to accelerate genomic researches. The size of NGS data is usually gigantic such that the data analysis in these applications of NGS largely relies on efficient computational methods. Due to the critical demand for high performance computational algorithms, in the past few years, my research interest was focused on designing novel algorithms to address challenges in NGS data analysis. The main theme of this dissertation includes algorithmic solutions to three crucial problems in NGS data analysis, two arising from differential expression analysis using high-throughput mRNA sequencing (RNA-Seq) and the other from chromosome structure capture using high-throughput DNA sequencing (Hi-C). (1) In differential expression analysis of RNA-Seq data, long or highly expressed genes are more likely to be detected by most of existing computational methods. However, such bias against short or lowly expressed genes may distort down-stream data analysis at system biology level. To further improve the sensitivity to short or lowly expressed genes, we designed a new computational tool, called MRFSeq, to combine both gene coexpression and RNA-Seq data. The performance of MRFSeq was carefully assessed using simulated and real benchmark datasets and the experimental results showed that MRFSeq was able to provide more accurate prediction in calling differentially expressed genes than the other existing methods such that the distortion due to the bias against short and lowly expressed genes was significantly alleviated. (2) Most of the existing differential expression analysis tools are developed for comparing RNA-Seq samples between known biological conditions. Hoever, the differential expression analysis is also important to other biological researches where the predefined conditions of samples are not available as a priori. For example, differential expressed transcripts can be used as biomarkers to classify a cohort of cancer samples into subtypes such that better diagnosis and therapy methods can be developed for each subtype. So, the first computational method, called SDEAP, was proposed to identify differential expressed genes and their alternative splicing events without the requirement of the predefined conditions. SDEAP provided accurate predition in our experiments on simulated and real datasets. The utility of SDEAP was further demonstrated by classifying subtypes of breast cancer, cell types and the cycle phases of mouse cells. (3) Chromosome structures in nucleus play important roles in biological processes of cells. The Hi-C technology allows biology researchers to reconstruct the three dimensional structures of chromosomes in nucleus of cells on a genome-wide scale and thus serves as a vital component in studies of chromosome structures. During the experimental steps of Hi-C, systematic biases may be introduced into Hi-C data. Hence, eliminating the systematic biases is essential to all the applications using Hi-C data. We developed an improved bias reduction algorithm, called GDNorm. By taking advantages of a Poisson regression model that explicitly formulates the causal relationship of Hi-C data, systematic biases and spatial distances in chromosome structures, our experimental results showed that GDNorm was able to remove the biases from Hi-C data such that the corrected Hi-C data could lead to accurate reconstruction of chromosome structures. In the near future, with the rapid accumulation of NGS data, we expect these efficient computational methods to become valuable tools for discovering novel biological knowledge and benefit numerous genomic researches.