Improved Estimates of the Spatial Distribution and Temporal Trends of Water Quality Parameters Using Geostatistical Data Fusion Methods.

Strategies aimed at reducing the degradation of water quality and predicting future changes in surface waters resulting from natural and anthropogenic forcing rely on the ability to track water quality changes, and to accurately quantify the distribution of water quality attributes. The three components of this dissertation focus on developing geostatistical data fusion methods that make optimal use of the available monitoring data in the Passaic River, Lake Erie, and the Chesapeake Bay, respectively. The first component presents a method for accurately estimating the spatial distribution of the total organic carbon in the sediments of the Passaic River using a dataset with non-uniform resolution. Estimating the spatial distribution of water sediment attributes at a uniform spatial resolution is often required for site characterizations and the design of appropriate risk-based remediation alternatives. Using a pseudodata example, a noval geostatisitical downscaling approach is shown to yield better estimates with a more accurate assessment of uncertainties, relative to traditional kriging methods. When applied to the estimation of the distribution of total organic carbon, geostatistical downscaling shows that the uncertainty associated with the spatial distribution of attribute is higher than would have been assumed if a kriging approach had been applied. The second and third components explore the degradation of water quality in time and space. Specifically, hypoxia (low dissolved oxygen) has been observed in Lake Erie and Chesapeake Bay since the early 1900s, leading to negative impacts such as ecosystem habitat degradation, altered migration patterns, and decreased fishery production. The interannual variability in hypoxic extent since mid-1980s in these two systems is quantified by combining spatially explicit auxiliary data with in situ dissolved oxygen measurements. The significance of nutrient loading, weather patterns, and stratification in explaining hypoxia in these systems is also explored. This research points to strong meteorological controls on hypoxia, through impacts on stratification and nutrient loading, in addition to the impact of anthropogenic activities. Overall, the developed geostatistical data fusion methods are shown to provide a means for producing reliable estimates of water quality attributes along with their associated uncertainties.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98069/1/ytzhou_1.pd

Social Metaverse: Challenges and Solutions

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this paper, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on smart contracts and digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.Comment: Accepted by Internet of Things Magazine in 23-May 202

A Nutrient-Phytoplankton-Zooplankton Model for Classifying Estuaries Based in Susceptibility to Nitrogen Loads

Estuarine responses to nutrient loads can be remarkably different. Many driving variables including light, water residence time, physical stratification, and temperature are responsible for the diversity of the response. To classify estuaries based on their susceptibility to nutrient loads, a nutrient- phytoplankton- zooplankton (NPZ) model was developed and applied to river-dominated, well-mixed estuaries. Estuaries are classified as having low, medium, high and hyper eutrophic conditions by the model. The result of the model suggests that water residence time is an important controlling variable in the process of achieving a steady-state response to nutrient loads. Although phytoplankton responses to residence time vary under different loads, they have the same positive trend. Phytoplankton responses are almost linear with water residence time initially, then decrease, and eventually plateau.Master of ScienceNatural Resources and EnvironmentUniversity of Michigan, School of Natural Resources and Environmenthttp://deepblue.lib.umich.edu/bitstream/2027.42/36310/1/Thesis_Zhou.pd

Nutrient loading and meteorological conditions explain interannual variability of hypoxia in Chesapeake Bay

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109879/1/lno20145920373.pd

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table