Mori-Zwanzig formalism based reduced-order modeling for decision-making in marine autonomy

This thesis first considers the reduced-order modeling and path planning of a deterministic system, an example application of which is the AUV deterministic planning problem in the ocean flow field. We develop a data-driven reduced-order model of the flow dynamics. The proposed algorithm partitions the flow field into piece-wise constant flow speed, and leverages the real-time AUV measurements to learn the parameters in the reduced-order model. Such abstraction transforms the infinite dimensional planning problem into a mixed integer optimization problem (MIP). We develop an interleaved MIP solution with guaranteed completeness and optimality, and the computation cost is shown to be lower than the existing AUV planning methods. To further reduce the computation cost of solving the MIP, a bounded cost search algorithm is developed to compute a bounded cost sub-optimal solution of the MIP, with proved completeness. Further, we consider the reduced-order modeling and belief space planning of a continuous-state POMDP (partially observable Markov decision process), and develops a belief abstraction method to facilitate symbolic planning in the belief space. As unmodeled residual state has impact on the reduced-order dynamics, the abstracted belief dynamics is non-Markovian. Hence we identify the abstracted belief dynamics using a recurrent neural network, and develops a reduced-order Bayesian law based on the Mori-Zwanzig formalism. Both theoretical analysis and simulation results show that the proposed method provides high fidelity approximation of the belief dynamics. Further, we show through simulation that the belief abstraction reduces the computation cost in solving the planning problem in belief space.Ph.D

High-dimensional Optimal Density Control with Wasserstein Metric Matching

We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable agents whose behaviors can be collectively modeled as a time-evolving probability distribution. The goal is to steer the agents from an initial distribution to reach (or approximate) a given target distribution within a fixed time horizon at minimum cost. To tackle this problem, we propose to model the drift as a nonlinear reduced-order model, such as a deep network, and enforce the matching to the target distribution at terminal time either strictly or approximately using the Wasserstein metric. The resulting saddle-point problem can be solved by an effective numerical algorithm that leverages the excellent representation power of deep networks and fast automatic differentiation for this challenging high-dimensional control problem. A variety of numerical experiments were conducted to demonstrate the performance of our method.Comment: 8 pages, 4 figures. Accepted for IEEE Conference on Decision and Control 202

Anomaly Detection of Underwater Gliders Verified by Deployment Data

This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.Comment: 10 pages, 16 figures, accepted by the International Symposium on Underwater Technology (UT23

Real-time Autonomous Glider Navigation Software

Underwater gliders are widely utilized for ocean sampling, surveillance, and other various oceanic applications. In the context of complex ocean environments, gliders may yield poor navigation performance due to strong ocean currents, thus requiring substantial human effort during the manual piloting process. To enhance navigation accuracy, we developed a real-time autonomous glider navigation software, named GENIoS Python, which generates waypoints based on flow predictions to assist human piloting. The software is designed to closely check glider status, provide customizable experiment settings, utilize lightweight computing resources, offer stably communicate with dockservers, robustly run for extended operation time, and quantitatively compare flow estimates, which add to its value as an autonomous tool for underwater glider navigation.Comment: OCEANS 2023 Limeric

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Abstract Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes—C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)—emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients

Fe-CGS Effectively Inhibits the Dynamic Migration and Transformation of Cadmium and Arsenic in Soil

The iron-modified coal gasification slag (Fe-CGS) material has excellent performance in purifying heavy-metal-contaminated water due to its good surface properties and adsorption capacities. However, it is unclear whether it can provide long-term simultaneous stabilization of Cd and As in composite-contaminated soils in extreme environments. This study investigated the long-term stabilization of Cd and As in acidic (JLG) and alkaline (QD) soils by simulating prolonged heavy rainfall with the addition of Fe-CGS. Multiple extraction methods were used to analyze the immobilization mechanisms of Cd and As in soil and their effects on bioavailability. The results indicate that the stabilization efficiency was related to the dosage of Fe-CGS. The concentrations of Cd and As in the JLG soil leachate were reduced by 77.6% (2.0 wt%) and 87.8% (1.0 wt%), respectively. Additionally, the availability of Cd and As decreased by 46.7% (2.0 wt%) and 53.0% (1.0 wt%), respectively. In the QD soil leachate, the concentration of Cd did not significantly change, while the concentration of As decreased by 92.3% (2.0 wt%). Furthermore, the availability of Cd and As decreased by 22.1% (2.0 wt%) and 40.2% (1.0 wt%), respectively. Continuous extraction revealed that Fe-CGS facilitated the conversion of unstable, acid-soluble Cd into oxidizable Cd and acid-soluble Cd. Additionally, it promoted the transformation of both non-specifically and specifically adsorbed As into amorphous iron oxide-bound and residual As. Fe-CGS effectively improved the soil pH, reduced the bioavailability of Cd and As, and blocked the migration of Cd and As under extreme rainfall leaching conditions. It also promoted the transformation of Cd and As into more stable forms, exhibiting satisfactory long-term stabilization performance for Cd and As

Engineered/designer hierarchical porous carbon materials for organic pollutant removal from water and wastewater:A critical review

Hierarchical porous carbon (HPC) materials have found advanced applications in energy storage, adsorption, and catalysis in recent years. The HPC can be synthesized from a vast range of inexpensive carbon precursors, and contain unique structural features, such as nano-scale dimension, high porosity, high surface area, and tunable pore surfaces. These materials hold immense potential for removing contaminants from water and wastewater. However, this area is severely under-explored yet. In this review, we have discussed the recent advances of synthesis, modification, and application of HPC for the removal of pollutants from water, especially focusing on organic pollutants. Owing to their intrinsic hydrophobic nature and unique interconnected porous structure, HPC demonstrates a high affinity to hydrophobic organic contaminants, which can be enhanced many folds by target-specific chemical activation. Successful high-performance removal of contaminants by pristine and modified HPC includes plastic-derived (e.g. bisphenol A), pharmaceutical (e.g. antibiotics), dye (e.g. methylene blue) and pesticide micro-pollutants. Future research is warranted to find optimal and effective HPC synthesis and modification methods for further improving their ability to remove aqueous organic contaminants as a low-cost and energy-inexpensive remediation technology

Characterization of Flavonoids and Transcripts Involved in Their Biosynthesis in Different Organs of <i>Cissus rotundifolia</i> Lam

Cissus rotundifolia Lam. is used as a medicinal herb and vegetable. Flavonoids are the major components for the therapeutic effects. However, flavonoids constituents and expression profiles of related genes in C. rotundifolia organs are unknown. Colorimetric assay showed the highest flavonoid concentration in roots compared to the stem and leaf. Widely target-based metabolome analysis allowed tentative identification of 199 compounds in three organs. Flavonols and flavones were the dominant flavonoids subclasses. Among the metabolites, 171 were common in the three organs. Unique accumulation profile was observed in the root while the stem and leaf exhibited relatively similar patterns. In the root, six unique compounds (jaceosidin, licoagrochalcone D, 8-prenylkaempferol, hesperetin 7-O-(6″malonyl) glucoside, aureusidin, apigenin-4′-O-rhamnoside) that are used for medicinal purposes were detected. In total, 18,427 expressed genes were identified from transcriptome of the three organs covering about 60% of annotated genes in C. rotundifolia genome. Fourteen gene families, including 52 members involved in the main pathway of flavonoids biosynthesis, were identified. Their expression could be found in at least one organ. Most of the genes were highly expressed in roots compared to other organs, coinciding with the metabolites profile. The findings provide fundamental data for exploration of metabolites biosynthesis in C. rotundifolia and diversification of parts used for medicinal purposes