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Sediment profiling imaging (SPI) is a versatile and widely used method to visually assess the quality of seafloor habitats (e.g., around fish farms and oil and gas rigs) and has been developed and used by both academics and consultancy companies over the last 50 years. Previous research has shown that inserting the flat viewport of an SPI camera into the sediment can have an impact on particle displacement pushing oxygenated surface sediments to deeper sediment depths and making anthropogenically-disturbed sediment appear healthier than they may actually be. To investigate the particle displacement that occurs when a flat plate is inserted into seafloor sediments, a testing device, termed the SPI purpose-built sediment chamber (SPI-PUSH) was designed and used in a series of experiments to quantify smearing where luminophores were used to demonstrate the extent of particle displacement caused by a flat plate being pushed into the sediment. Here, we show that the plate of the SPI-PUSH caused significant smearing, which varied with sediment type and the luminophore grain size. The mean particle smearing measured directly behind the inserted plate was 2.9 ± 1.5 cm for mud sediments with sand-like luminophores, 4.3 ± 2.5 cm for fine sand sediments with sand-like luminophores and 1.9 ± 1.1 cm for medium sand sediments with mud-like luminophores. When the mean depth of particle smearing was averaged over a larger sediment volume (11 cm3) next to the inserted plate, substantial differences were seen between the plate-insertion experiments and controls highlighting the potential extent of smearing artefacts that may be produced when a SPI camera penetrates the seafloor. This experimental data shows that future studies using the SPI camera, or any other periscope-like device (e.g., planar optodes) need to acknowledge that smearing may be significant. Furthermore, it highlights that a correction factor may need to be applied to these data (e.g., the depth of apparent redox potential discontinuity layer) to correctly interpret SPI camera images and better determine the effect of anthropogenic impacts on seafloor habitats.

Multivariate Statistical Process Control (MSPC) based on Principal Component Analysis (PCA) is a well-known methodology in chemometrics that is aimed at testing whether an industrial process is under Normal Operation Conditions (NOC). As a part of the methodology, once an anomalous behaviour is detected, the root causes need to be diagnosed to troubleshoot the problem and/or avoid it in the future. While there have been a number of developments in diagnosis in the past decades, no sound method for comparing existing approaches has been proposed. In this paper, we propose such a procedure and use it to compare several diagnosis methods using randomly simulated data and from realistic data sources. This is a general comparative approach that takes into account factors that have not previously been considered in the literature. The results show that univariate diagnosis is more reliable than its multivariate counterpart.

We address a number of technical problems with the popular Practitioner Black-Scholes (PBS) method for valuing options. The method amounts to a two-stage procedure in which fitted values of implied volatilities (IV) from a linear regression are plugged into the Black-Scholes formula to obtain predicted option prices. Firstly we ensure that the prediction from stage one is positive by using log-linear regression. Secondly, we correct the bias that results from the transformation applied to the fitted values (i.e., the Black-Scholes formula) being a highly non-linear function of implied volatility. We apply the smearing technique in order to correct this bias. An alternative means of implementing the PBS approach is to use the market option price as the dependent variable and estimate the parameters of the IV equation by the method of non-linear least squares (NLLS). A problem we identify with this method is one of model incoherency: the IV equation that is estimated does not correspond to the set of option prices used to estimate it. We use the Monte Carlo method to verify that (1) standard PBS gives biased option values, both in-sample and out-of-sample, (2) using standard (log-linear) PBS with smearing almost completely eliminates the bias, (3) NLLS gives biased option values, but the bias is less severe than with standard PBS. We are led to conclude that, of the range of possible approaches to implementing PBS, log-linear PBS with smearing is preferred on the basis that it is the only approach that results in valuations with negligible bias.

The dependence of the overlap Dirac operator on the Wilson-mass regulator parameter is studied through calculations of the overlap topological charge densities at a variety of Wilson-mass values. In this formulation, the Wilson-mass is used in the negative mass region and acts as a regulator governing the scale at which the Dirac operator is sensitive to topological aspects of the gauge field. We observe a clear dependence on the value of the Wilson-mass and demonstrate how these values can be calibrated against a finite number of stout-link smearing sweeps. The overlap topological charge density is also computed using a pre-smeared gauge field for the input kernel. We show how applying the overlap operator leads to further filtering of the gauge field. The results suggest that the freedom typically associated with smearing algorithms, through the variable number of sweeps, also exists in the overlap operator, through the variable Wilson-mass parameter., Comment: 7 pages, 4 figures; Added some references, modified the formatting, revised some sections, results unchanged