Abstract Because the number of sharks is decreasing , the problem has been concerned in recent years. In this paper , I conduct a research about the shark bycatch problem¡Ataking the target specie of bigeye tunas and the bycatch specie of sharks as the research objects . Initially I collect from 2000 to 2007 Atlantic bigeye tuna and shark catches datas for resource assessment.and then making comparasion with resource equilibrium values of open access fishery model and net present value maximization fishery model. it is found during the period it has showed a upward trend for bigeye tuna resources¡Abut it has not showed the bigeye tuna resources achive the optimal level. It has showed a diminishing trend for shark resources, the result shows the resources will face extinction crisis if the fishery is not controlled well. Then, doing sensitivity analysis to understand the effects of exogenous parameters to bigeye tuna and shark catches, resources and efforts. Finally, facing with the sharks bycatch problem , discussing the effect of improved fishing gears on sharks bycatch control by doing the sensitivity analysis of fishing hook cost and bycatch coefficient to shark resources and catches¡OFrom the results , it shows that no matter affecting the fishing hook cost or bycatch coefficient , the amount of shark can be reduced effectively by adopting improved fishing gears.

Mathematical modeling is a key component of various disciplines in science and engineering. A mathematical model which represents important behavior of a real system can be used as a substitute for the real process for many analysis and synthesis tasks. The performance of model based techniques, e.g. system analysis, computer simulation, controller design, sensor development, state filtering, product monitoring, and process optimization, is highly dependent on the quality of the model used. Therefore, it is very important to be able to develop an accurate model from available experimental data. Parameter estimation is usually formulated as an optimization problem where the parameter estimate is computed by minimizing the discrepancy between the model prediction and the experimental data. If a simple model and a large amount of data are available then the estimation problem is frequently well-posed and a small error in data fitting automatically results in an accurate model. However, this is not always the case. If the model is complex and only sparse and noisy data are available, then the estimation problem is often ill-conditioned and good data fitting does not ensure accurate model predictions. Many challenges that can often be neglected for estimation involving simple models need to be carefully considered for estimation problems involving complex models. To obtain a reliable and accurate estimate from sparse and noisy data, a set of techniques is developed by addressing the challenges encountered in estimation of complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental design for collecting information-rich data by setting optimal experimental conditions; (3) regularization of estimation problem which solves the ill-conditioned large-scale optimization problem by reducing the number of parameters; (4) nonlinear estimation and filtering which fits the data by various estimation and filtering algorithms; (5) model verification by applying statistical hypothesis test to the prediction error. The developed methods are applied to different types of models ranging from models found in the process industries to biochemical networks, some of which are described by ordinary differential equations with dozens of state variables and more than a hundred parameters.

The objective of this thesis is to develop and implement an algorithm for inverting the sound speed profile via estimation of the parameters embedded in a geoacoustic model. The environmental model inscribes a continuously-varying marine sediment layer with density and sound speed distributions represented by the generalized-exponential and inverse-square functions, respectively. Based upon a forward problem of plane-wave reflection from a non-uniform sediment layer overlying a uniform elastic basement, an inversion procedure for estimating the sound speed profile from the reflected sound field under the influence of noise is established and numerically implemented. The inversion invokes a probabilistic approach quantified by the posterior probability density for measuring the uncertainties of the estimated parameters from synthetic noisy data. Preliminary analysis on the solution of the forward problem and the sensitivity of the model parameters is first conducted, leading to a determination of the parameters chosen for inversion in the ensuing study. The parameter uncertainties referenced 1-D and 2-D marginal posterior probability densities are then examined, followed by the statistical estimation for the sound speed profile in terms of 99 % credibility interval. The effects of, the signal-to-noise ratio (SNR), the dimension of data vector, the region in which the data sampled, on the statistical estimation of sound speed profile are demonstrated and discussed.

In this thesis, sensitivity and error analysis of a differential rectification method were performed by using digital images taken by a frame camera onboard BILSAT and pushbroom scanner on ASTER. Three methods were implemented for Sensitivity and Uncertainty analysis: Monte Carlo, covariance analysis and FAST (Fourier Amplitude Sensitivity Test). A parameter estimation procedure was carried out on the basis of so called Mixed Model extended by some suitable additional regularization parameters to stabilize the solution for improper geometrical conditions of the imaging system. The effectiveness and accuracy of the differential rectification method were compared with other rectification methods and the results were analyzed. Furthermore the differential method is adapted to the pushbroom scanners and software which provides rectified images from raw satellite images was developed.

In this study, hydrogeological modeling of the Somme river basin situated in the north of France was made with special emphasis on the stream-aquifer interaction. The coupled model developed at Ecole des Mines de Paris was used. Geographic Information Systems (GIS) tools were incorporated during all the stages of modeling process for both preparation of input data and visualization of the results of simulations. Initially, the process began with Digital Elevation Model (DEM) analysis. Afterwards, the surface and aquifer grids were generated by using nested grid generators and refinement was made on the stream network and subcatchment boundaries in order to increase the accuracy of numerical solution. In order to run the surface model, meteorological forcing, land use and soil type data were acquired. Surface model was used to partition the precipitation into evapotranspiration, infiltration and surface runoff components. A steady-state piezometric head distribution was computed by the groundwater model to serve as an initial condition to the coupled model. The flow in the unsaturated zone was simulated by using Nash cascade model. The unsteady groundwater and surface flow simulations were performed by taking into consideration the stream-aquifer interaction on a daily time step. The calibration and validation were realized by using the streamflow and piezometric head measurements distributed around the basin. The strong groundwater influence on the hydrology of the basin is well represented by the model. Comparisons of predicted flooded areas in year 2001 were made with other models and a satellite derived image. In the end, several sensitivity analyses were performed for several parameters concerning the groundwater flow.

Accuracy of the finite difference sensitivity calculations are improved by calculating the optimum finite difference interval sizes. In an aerodynamic inverse design algorithm, a compressor cascade geometry is perturbed by shape functions and finite differences sensitivity derivatives of the flow variables are calculated with respect to the base geometry flow variables. Sensitivity derivatives are used in an optimization code and a new airfoil is designed verifying given design characteristics. Accurate sensitivities are needed for optimization process. In order to find the optimum finite difference interval size, a method is investigated. Convergence error estimation techniques in iterative solutions and second derivative estimations are investigated to facilitate this method. For validation of the method, analytical sensitivity calculations of Euler equations are used and several applications are performed. Efficiency of the finite difference sensitivity calculations is improved by parallel computing. Finite difference sensitivity calculations are independent tasks in an inverse aerodynamic design algorithm and can be computed separately. Sensitivity calculations are performed on parallel processors and computing time is decreased.

The Flux Jacobian matrices, the elements of which are the derivatives of the flux vectors with respect to the flow variables, are needed to be evaluated in implicit flow solutions and in analytical sensitivity analyzing methods. The main motivation behind this thesis study is to explore the accuracy of the numerically evaluated flux Jacobian matrices and the effects of the errors in those matrices on the convergence of the flow solver, on the accuracy of the sensitivities and on the performance of the design optimization cycle. To perform these objectives a flow solver, which uses exact Newton&rsquo / s method with direct sparse matrix solution technique, is developed for the Euler flow equations. Flux Jacobian is evaluated both numerically and analytically for different upwind flux discretization schemes with second order MUSCL face interpolation. Numerical flux Jacobian matrices that are derived with wide range of finite difference perturbation magnitudes were compared with analytically derived ones and the optimum perturbation magnitude, which minimizes the error in the numerical evaluation, is searched. The factors that impede the accuracy are analyzed and a simple formulation for optimum perturbation magnitude is derived. The sensitivity derivatives are evaluated by direct-differentiation method with discrete approach. The reuse of the LU factors of the flux Jacobian that are evaluated in the flow solution enabled efficient sensitivity analysis. The sensitivities calculated by the analytical Jacobian are compared with the ones that are calculated by numerically evaluated Jacobian matrices. Both internal and external flow problems with varying flow speeds, varying grid types and sizes are solved with different discretization schemes. In these problems, when the optimum perturbation magnitude is used for numerical Jacobian evaluation, the errors in Jacobian matrix and the sensitivities are minimized. Finally, the effect of the accuracy of the sensitivities on the design optimization cycle is analyzed for an inverse airfoil design performed with least squares minimization.

Groundwater can be a critical issue to be considered in civil engineering, mining engineering and interdisciplinary fields. Karstic structures and aquifers enclosing groundwater are potential risks in case they are not studied in detail. Enclosed groundwater can result in floods at pit bottom or can cause instabilities of permanent pit slopes. This study is about analyses of flooding possibility at the pit bottom and possible instabilities of pit slopes in the Elbistan-&Ccedil / &ouml / llolar open cast coal mine due to the presence of a karstic aquifer under the lignite formation. Thickness and permeability of the bottom clay formation under the lignite bed are necessary critical parameters for investigating a possible water rush from a confined aquifer in limestone formation underneath the bottom clay. These parameters were changed, and water flow quantities towards the pit bottom were determined by finite element models. Critical values of these parameters were investigated considering the lack of accurate site investigation information regarding the thickness and permeability of bottom clay. Possible strength loss, fracturing, and thus permeability increase in bottom clay due to a confined aquifer were studied. In flooding and slope stability analyses Phase2 software based on finite element method is used. Results of analyses showed that as reported thickness of bottom clay is around 120 m at the pit bottom and permeability values are in orders of magnitudes of 10-8 m/s, no serious flooding problems are expected to occur unless the thickness of bottom clay layer drops down to around 20 m, and the permeability of this layer reaches an order of magnitude of 10-5 m/s. Mechanical effects of confined aquifer on slopes and bottom clay displacements were investigated, and thus fracturing and failure possibilities of bottom clay and permanent slope were assessed. Slope and pit bottom displacements increased to meter levels for less than 60 m bottom clay thicknesses. Whereas 50-60 m bottom clay thickness can be critical for cracking, 20 m bottom clay thickness was found to be critical for water rush to the pit bottom. With reported bottom clay thickness of 120 m and with 25o slope angle permanent slope factor of safety was found to be 1.2, and this value was not effected unless clay layer thickness drops below 70 m levels. Higher than 32o overall slope angle there will be a risk of slope failure for permanent and production slopes, reflected by safety factors less than one, in the stability analyses.

The topographical variations of the seas around Taiwan are great, which make the tides complicated. Taiwan is located in the juncture of the tropical and subtropical area. Geographically, it is located within the region of northwestern Pacific typhoon path. These seasonal and geographical situations causing Taiwan frequently threaten by typhoons during summer and autumn. In addition to natural disasters, the coastal area is over developed for the last few decades, which destroys the balance between nature and man. Storms and floods constantly threaten the lowland areas along the coast. An accurate and efficient storm surge model can be used to predict tides and storm surges. The model can be calibrated and verified with the field observations. Data measured by instruments at the tidal station constituting daily tidal variations and storm surge influences during typhoons. The model can offer both predictions to the management institutions and to the general public as pre-warning system and thus taking disaster-prevention measures. This study implements the numerical model, developed by Yu (1993) and Yu et al. (1994) to calculate the hydrodynamic in the seas around Taiwan. The main purpose of this study is to make a calibration and sensitivity analysis of the model parameters. Tidal gauge data around Taiwan coastal stations collected from June to October 2005 are used for the analysis and the comparison between the modeled data and the observations. Two steps have been taken for the model calibration and sensitivity analysis. First step is to calibrate the model for accurate prediction of the astronomical tide, and then the compound tide with meteorological influences. For the calibration of the astronomical tides, sensitivity analysis has been carried out by adjusting the horizontal diffusion coefficient and the bottom friction coefficients used in the model. The sensitivity of the time-step size used in the model and model grids fitted to coastlines are also checked. A depth dependent Chézy numbers are used in the model to describe bottom friction. The model has a better result when the Chézy value varied within 65 to 85. Modifying grids fitted to the coastline has improved the model results significantly. By improving the dynamic phenomenon brought about by the land features, the model calculation fits the real tidal phenomenon better. The analysis has shown that the model is less sensitive to the horizontal diffusion coefficient. Data from 22 tidal stations around Taiwan have been used for the comparisons. The maximum RMSE (root-mean-square error) is about 10 cm at WAi-Pu, whereas the minimum RMSE is about 1 cm for the stations along eastern coast. The calibration of the compound tide is divided into three cases. The first case is to calibrate the forecasted wind field. This has been done by comparing the forecasted wind field from the Central Weather Bureau with the satellite data obtained from QuikSCAT¡XLevel 3. The satellite wind speed has been applied to adjust the forecasted wind speed. The adjusted forecast wind field has shown improvement to the model predictions in the tidal stations south of Taichung, slightly improved in the eastern coast. The second case is tuning the drag coefficient on sea surface used by the hydrodynamic model. Several empirical formulas to describe the sea surface drag have been tested. The model result has shown little influence using various drag formulations. The third case is to single the influences by the meteo-inputs, i.e. the wind field and the atmospheric pressure. The tidal level is more sensitive to the variation of the atmospheric pressure through out the tests carried out during typhoon periods. The model simulation for 2006 using the best selected parameters has shown that the model is consisted with good stability and accuracy for both stormy and calm weather conditions.

The dynamics of biological neural networks are of great interest to neuroscientists and are frequently studied using conductance-based compartmental neuron models. For speed and ease of use, neuron models are often reduced in morphological complexity. This reduction may affect input processing and prevent the accurate reproduction of neural dynamics. However, such effects are not yet well understood. Therefore, for my first aim I analyzed the processing capabilities of 'branched' or 'unbranched' reduced models by collapsing the dendritic tree of a morphologically realistic 'full' globus pallidus neuron model while maintaining all other model parameters. Branched models maintained the original detailed branching structure of the full model while the unbranched models did not. I found that full model responses to somatic inputs were generally preserved by both types of reduced model but that branched reduced models were better able to maintain responses to dendritic inputs. However, inputs that caused dendritic sodium spikes, for instance, could not be accurately reproduced by any reduced model. Based on my analyses, I provide recommendations on how to construct reduced models and indicate suitable applications for different levels of reduction. In particular, I recommend that unbranched reduced models be used for fast searches of parameter space given somatic input output data. The intrinsic electrical properties of neurons depend on the modifiable behavior of their ion channels. Obtaining a quality match between recorded voltage traces and the output of a conductance based compartmental neuron model depends on accurate estimates of the kinetic parameters of the channels in the biological neuron. Indeed, mismatches in channel kinetics may be detectable as failures to match somatic neural recordings when tuning model conductance densities. In my first aim, I showed that this is a task for which unbranched reduced models are ideally suited. Therefore, for my second aim I optimized unbranched reduced model parameters to match three experimentally characterized globus pallidus neurons by performing two stages of automated searches. In the first stage, I set conductance densities free and found that even the best matches to experimental data exhibited unavoidable problems. I hypothesized that these mismatches were due to limitations in channel model kinetics. To test this hypothesis, I performed a second stage of searches with free channel kinetics and observed decreases in the mismatches from the first stage. Additionally, some kinetic parameters consistently shifted to new values in multiple cells, suggesting the possibility for tailored improvements to channel models. Given my results and the potential for cell specific modulation of channel kinetics, I recommend that experimental kinetic data be considered as a starting point rather than as a gold standard for the development of neuron models.