kyuhank/MackerelStateSpace: State-space LBAM

<p>12 Dec 2023</p&gt

State-space stock assessment models for data-moderate fisheries

Fish populations are subject to natural growth, environmental pressures, and natural mortality. In addition, they may experience pressure from anthropic fishing mortality. Management of fish stocks requires the collection of suitable data from which population models can be built. State-space models (SSMs) are one modelling approach, and this project investigates their application to data-moderate stocks. We define data-moderate stocks as those for which there are no survey data, no information on age composition, and fisheries-dependent data are the only available source of information. We find that many existing state-space models are either too simple (e.g., state-space surplus production models) or too complex (e.g., state-space age-structured models) for these stocks, although many fisheries around the world face data-moderate situations. A state-space model is becoming a favoured choice in modelling fish population dynamics, as it allows one to incorporate both measurement and process errors. However, several studies have found that separation of the two sources of variability can result in estimability problems even in simple state-space models. Using a state-space surplus production model as an example, we found that such estimability problems can occur even in a simple stock assessment model, especially when measurement error is large relative to process error. This problem even exists when constraints are imposed on most of the model parameters. Such findings suggest the limitations of SSMs and the importance of model diagnostics. Using data collected from South Korean fish stocks as application examples, we developed two stock assessment models in state-space form. The first model is a state-space two-life stage-structured production model which can be applied to stocks where juvenile and adult fish have been separately exploited by different fisheries. The key feature of the model is that in the absence of any composition data (e.g., age and size), demographic relationships between juvenile and adult populations are incorporated using abundance indices collected from different fisheries, each of which selectively targets the two different life stages of fish. The second model is a state-space length-based age-structured model. This integrated model is developed to utilise length composition data to inform the age structure of a population. Such data are often available in many data-moderate stocks, instead of a direct measure of age composition, such as catch-at-age data. Separating age groups based on length compositions is not a new concept, but most existing models do not allow process error. Thus, the development of such a model in state-space form could provide a more reliable assessment tool for many data-moderate stocks. This thesis research contributes to the better understanding of potential estimability issues in SSMs for fish stock assessments, as well as development of the two new state-space models for data-moderate fisheries. We also identified several issues associated with our findings which could be useful for future research.</p

Analysis of Wave-Induced Current Using Digital Image Correlation Techniques

Recently, advancement of digital image techniques and communications technology has enabled the application of existing images for scientific purposes. Furthermore, both quantitative and qualitative analyses of images have become possible through image processing such as transmit/storage of digital image data and image rectification. In this study, a coast having representative characteristics of east coast of Korea was selected with having erosion in winter, and the sedimentation in summer takes place repeatedly. Three-dimensional hydraulic model test was conducted to analyze its outcomes by a digital image correlation technique in order to understand the wave-induced current affecting the sediment transport. For this study, images filmed by the high-sensitive and high-resolution video camera were converted into stopped images of regular intervals and then those converted images were used for the following procedure to analyze flow and velocity into digital coordinates. The outcomes from interpretation of images filmed by the high-sensitive and high-resolution video camera can be utilized as a very useful analysis method for appreciating the generation mechanism and movement route of longshore current and rip current

Hyper-cross-linked polymers with controlled multiscale porosity: Via polymerization-induced microphase separation within high internal phase emulsion

We report the preparation of hierarchically porous polymers containing fully interconnected and controlled micro-, meso-, and macropores, where a hyper-cross-linked microporous polymer skeleton forms a reticulating mesoporous wall that supports a highly porous macropore framework. These materials provide high specific surface area and >90% porosity, useful for rapid sorption of organic molecules. © 2018 The Royal Society of Chemistr

Predicting movie success with machine learning techniques: ways to improve accuracy

Previous studies on predicting the box-office performance of a movie using machine learning techniques have shown practical levels of predictive accuracy. Their works are technically- and methodologically-oriented, focusing mainly on what algorithms are better at predicting the movie performance. However, the accuracy of prediction model can also be elevated by taking other perspectives such as introducing unexplored features that might be related to the prediction of the outcomes. In this paper, we examine multiple approaches to improve the performance of the prediction model. First, we develop and add a new feature derived from the theory of transmedia storytelling. Such theory-driven feature selection not only increases the forecast accuracy, but also enhances the interpretability of a prediction model. Second, we use an ensemble approach, which has rarely been adopted in the research on predicting box-office performance. As a result, the proposed model, Cinema Ensemble Model (CEM), outperforms the prediction models from the past studies that use machine learning algorithms. We suggest that CEM can be extensively used for industrial experts as a powerful tool for improving decision-making process

Effect of cholesterol nanodomains on monolayer morphology and dynamics.

At low mole fractions, cholesterol segregates into 10- to 100-nm-diameter nanodomains dispersed throughout primarily dipalmitoylphosphatidylcholine (DPPC) domains in mixed DPPC:cholesterol monolayers. The nanodomains consist of 6:1 DPPC:cholesterol complexes that decorate and lengthen DPPC domain boundaries, consistent with a reduced line tension, λ. The surface viscosity of the monolayer, ηs, decreases exponentially with the area fraction of the nanodomains at fixed surface pressure over the 0.1- to 10-Hz range of frequencies common to respiration. At fixed cholesterol fraction, the surface viscosity increases exponentially with surface pressure in similar ways for all cholesterol fractions. This increase can be explained with a free-area model that relates ηs to the pure DPPC monolayer compressibility and collapse pressure. The elastic modulus, G, initially decreases with cholesterol fraction, consistent with the decrease in λ expected from the line-active nanodomains, in analogy to 3D emulsions. However, increasing cholesterol further causes a sharp increase in G between 4 and 5 mol% cholesterol owing to an evolution in the domain morphology, so that the monolayer is elastic rather than viscous over 0.1-10 Hz. Understanding the effects of small mole fractions of cholesterol should help resolve the controversial role cholesterol plays in human lung surfactants and may give clues as to how cholesterol influences raft formation in cell membranes

Application of Depletion Attraction in Mineral Flotation: I. Theory

We investigate the role of depletion interactions in the particle–bubble interactions that determine the attachment capability of particles on the bubble surface in flotation. In this article, we propose a theoretical model that explains how this attractive interaction could enhance flotation efficiency. Two optimum conditions are determined for the concentration and molecular weight of the depletion agent. The optimum concentration can be determined through the extent of surface activity of the depletion agents. The magnitude of the depletion attraction increases as the concentration increases; however, an increase in the concentration simultaneously enhances its surface concentration. The bubble surface adsorption of the depletion agent results in polymer brushes on the bubble surface that produce a large repulsive interaction. In contrast, the optimal molecular weight of the depletion agents is given by the interaction between the depletion agent sizes, which is determined by its molecular weight and Debye length which is determined by the solution ionic strength. We demonstrate that exploiting this depletion interaction could significantly enhance the flotation efficiency and in principal could be used for any particle system