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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise. This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data. / Doctor of Philosophy / Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.

This thesis deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals and actuator saturation constraints. A new controller parameter optimization approach, which can be carried out experimentally with no knowledge of the plant model nor of the order of the system, is proposed. The approach has the advantage that controllers can be optimized by perturbing only the initial conditions of the servocompensator, and that the order of the resulting controller obtained can be specified by the designer. Implementation of the proposed controller design approach is described, and an experimental application study of the proposed method applied to a multivariable system with industrial sensor/actuator components is presented to illustrate the feasibility of the design method in an industrial environment.

This thesis deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals and actuator saturation constraints. A new controller parameter optimization approach, which can be carried out experimentally with no knowledge of the plant model nor of the order of the system, is proposed. The approach has the advantage that controllers can be optimized by perturbing only the initial conditions of the servocompensator, and that the order of the resulting controller obtained can be specified by the designer. Implementation of the proposed controller design approach is described, and an experimental application study of the proposed method applied to a multivariable system with industrial sensor/actuator components is presented to illustrate the feasibility of the design method in an industrial environment.

In industrial quality improvement and design studies, an important aim is to improve the product or process quality by determining factor levels that would result in satisfactory quality results. In these studies, quality characteristics that are qualitative are often encountered. Although there are many effective methods proposed for parameter optimization (robust design) with continuous responses, the methods available for qualitative responses are limited. In this study, a parameter optimization method for solving binary response robust design problems is proposed. The proposed method uses Mahalanobis Taguchi System to form a classification model that provides a distance function to separate the two response classes. Then, it finds the product/process variable settings that minimize the distance from the desired response class using quadratic programming. The proposed method is applied on two cases previously studied using Logistic Regression. The classification models are formed and the parameter optimization is conducted using the formed MTS models. The results are compared with those of the Logistic Regression. Conclusions and suggestions for future work are given.

In this thesis we combine quantitative measurements of mouse limb morphogenesis and computer modelling to test a well established theory about the cellular mechanisms promoting limb elongation. A distally directed gradient of cellular proliferation was believed to be the driving mechanism for limb outgrowth. We find that the empirically measured spatial proliferation pattern fails to promote normal development - a reverse engineering algorithm was applied and revealed a proliferation pattern that could indeed carry out normal development. The differences between those patterns is dramatic and suggests that isotopic cellular proliferation alone has very little impact on limb morphogenesis and other – non isotropic - mechanisms need to be involved. / En esta tesis tratamos de testar una bien establecida teor´ıa sobre los mecanismos celulares que promueven de la elongaci´on de las extremidades. Para eso combinamos mediciones cuantitativas del proceso morfogen ´etico de la extremidad del rat´on con modelos computacionales. Se cre´ıa que la fuerza conductora del crecimiento de las extremidades era un gradiente en sentido distal del incremento de la proliferaci´on celular. Descubrimos que el patr´on de proliferaci´on celular basado en medidas emp´ıricas no consegu´ıa promover un desarrollo normal, mientras que un algoritmo de ingenier´ıa inversa aplicado al proceso revel´o un patr´on que si podr´ıa. La diferencia entre estos dos patrones es inmensa y sugiere que la proliferaci´on celular isotropica por si sola tiene muy poco impacto sobre la morfog´enesis de las extremidades, indicando as´ı la necesidad de que otros procesos no isotr´opicos se hallen involucrados.

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Assessing the financial health of organizations remains a topic of great interest to economists, financial institutions, and invested stakeholders. For more than a century, research into financial distress has focused primarily on traditional applications of statistical analysis; however, modern advances in computational efficiency have created a significant opportunity for more sophisticated approaches. This thesis investigates the application of artificial intelligence on company bankruptcy prediction. The proposed neural network model is evaluated using the Polish Companies Bankruptcy dataset and yields a 5-year prediction accuracy of 96.5% and an AUC (area under receiver operating characteristic curve) measure of 92.4%.

Additive friction stir deposition (AFSD) is an innovative solid-state manufacturing process capable of producing parts with fine, equiaxed grains. However, due to the complexity of extensive plastic deformation and the viscoplastic behavior of metallic materials at elevated temperatures, the analysis of material flow and stress evolution during AFSD remains at a rudimentary stage. As a developing technology, gaining a deeper understanding of the underlying physical behaviors behind the processing is appreciable. This study comprises three objectives: investigating microstructure and stress-induced acoustic wave propagation behaviors, implementing non-contact in-situ monitoring in AFSD of aluminum alloy 6061 using a far-collimation acoustic metamaterial lens, and ex-situ analysis of parameter-dependent mechanics influences in AFSD of aluminum alloys 6061. To achieve this, a novel ultrasound in-situ monitoring method, along with ex-situ residual stress measurements, is facilitated by MD and FEA simulations and been experimentally verified. Real-time asymmetric property distribution and abnormal parameter-dependence acoustic wave phase change during the AFSD of aluminum alloy 6061 were identified through the in-situ monitoring and further investigated in detail through ex-situ inspection. A key parameter, effective viscosity, was introduced to the parameter windows selections, which can affect the thermo-fluidic mechanics during the process, thereby altering the physical aspects, mechanical properties, and microstructures.