Mesocosm Studies: Polycyclic Aromatic Hydrocarbons (PAHS) Exposure, Concentrations and Removal Rates

Polycyclic Aromatic Hydrocarbons (PAHs) are toxic constituents found in crude oils, which can be removed from the water column through a combination of processes: evaporation, sedimentation, photo-oxidation and/or biodegradation, collectively termed weathering. Marine snow consists of many particles including bacteria, phytoplankton, mineral particles, fecal pellets and aggregates and plays an important role in the process of removing PAHs from the water column through sedimentation and enhanced biodegradation. The microbial community produces exopolymeric substances (EPS) in response to stresses including exposure to petroleum may lead to excess production of marine snow, therefore affecting the biodegredation and transport and fate of PAHs. This research hypothesizes that the PAH removal from the water column is enhanced by microbial activity in the presence of petroleum and petroleum plus Corexit. In this study, mesocosm experiments were used to investigate PAH half-lives when petroleum and dispersants are present. The first four mesocosm experiments were undertaken with water collected from near shore or off-shore locations in the Gulf of Mexico. As part of these studies, oil and oil plus dispersant mixtures known as WAF (water accommodated oil fraction) and CEWAF (chemically enhanced water accommodated fraction) were generated in 130 L baffled recirculation tanks, and ~80 L transferred to the mesocosms. A 1:10 dilution of the CEWAF (DCEWAF) was an additional mesocosm treatment. Control treatments with no oil or dispersant were used for comparison. Concentrated phytoplankton collected from Galveston Bay were added to all mesocosm tanks. In mesocosm 3 (M3) and 4 (M4) f/20 nutrient additions were made. Total scanning fluorescence (TSF) analysis was performed to determine estimate oil equivalents (EOE) concentrations at the start, during and at the end of the experiment. PAH composition and concentration were determined using Gas Chromatography/Mass Spectroscopy (GC/MS). The concentrations of EOE and PAH as well as changes in the PAH composition of the WAF, DCEWAF and CEWAF over time were determined. Biomarker data were measured in selected samples in order to investigate the biodegradation process. The mesocosm experiments were designed to: 1) simulate the conditions of DWH oil spill using WAF, DCEWAF and CEWAF generated from a baffled recirculating system; 2) establish a relationship between EOE measured by TSF that allows for real time oil concentration estimates in mesocosm experiments; 3) compare PAH removal pattern under different biological conditions and 4) examine the impact of Corexit addition in the removal half-lives of PAH, providing additional information for evaluation of future usage of Corexit during marine oil spills

Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the correntropy-based gradient descent (CGD) and the correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the mean squared error (MSE) criterion, making them vulnerable to external acceleration and magnetic interference. To address this issue, we demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function for maximum likelihood estimation (MLE) when the noise follows a type of heavy-tailed distribution. In certain situations, the estimation error of the MKCL is bounded even in the presence of arbitrarily large outliers. By replacing the standard MSE cost function with MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and reveal that the kernel bandwidth should be neither too small nor too big in the sense of the lowest upper error bound. Meanwhile, we find that the proposed MKC is related to a specific heavy-tail distribution, and the level of the heavy tail is controlled by the kernel bandwidth solely. Interestingly, this distribution becomes the Gaussian distribution when the bandwidth is set to be infinite, which allows one to tackle both Gaussian and non-Gaussian problems. We propose an expectation-maximization (EM) algorithm to estimate the parameter vectors and explore the kernel bandwidths alternatively. The results show that our algorithm is equivalent to the traditional linear regression under Gaussian noise and outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method

Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity.Comment: in IEEE Transactions on Automatic Control (2023

Exact conditions for antiUnruh effect in (1+1)-dimensional spacetime

Exact conditions for antiUnruh effect in (1+1)-dimensional spacetime are obtained. For detectors with Gaussian switching functions, the analytic results are similar to previous ones, indicating that antiUnruh effect occurs when the energy gap matches the characteristic time scale. However, this conclusion does not hold for detectors with square wave switching functions, in which case the condition turns out to depend on both the energy gap and the characteristic time scale in some nontrivial way. We also show analytically that there is no antiUnruh effect for detectors with Gaussian switching functions in (3+1)-dimensional spacetime.Comment: 16 page

Accelerating Unruh-DeWitt detectors coupled with a spinor field

The behavior of accelerating Unruh-DeWitt detectors coupled with a spinor field in (3+1)-dimensional spacetime is investigated. For a single point-like detector with Gaussian switching function, the transition probability increases with the acceleration and thus the antiUnruh effect effect cannot occur. Due to the spinor structure of the Dirac field, UV divergences are encountered in the calculation of the entanglement between the detectors. After introducing some UV cutoff $\Lambda$, the logarithmic negativity of detectors is shown to behave nonmonotonically with respect to the acceleration. Besides, the logarithmic negativity increases with the cutoff $\Lambda$ and decreases with the distance between the detectors. The mutual information between the two detectors is also discussed.Comment: 30 page