Quantitative risk assessment of groundwater quality utilizing GIS technology and coupled groundwater models

The thesis presents a two-dimensional Risk Assessment Method (RAM) where the assessment of risk to the groundwater resources incorporates both the quantification of the probability of the occurrence of contaminant source terms, as well as the assessment of the resultant impacts. The approach emphasizes the need for a greater dependency on the potential pollution sources, rather than the traditional approach where assessment is based mainly on the intrinsic geo-hydrologic parameters. The risk is calculated using Monte Carlo simulation methods whereby random pollution events were generated to the same distribution as historically occurring events or a priori potential probability distribution. Integrated mathematical models then simulate contaminant concentrations at the predefined monitoring points within the aquifer. The spatial and temporal distributions of the concentrations were calculated from repeated realisations, and the number of times when a user defined concentration magnitude was exceeded is quantified as a risk. The method was setup by integrating MODFLOW-2000, MT3DMS and a FORTRAN coded risk model, and automated, using a DOS batch processing file. GIS software was employed in producing the input files and for the presentation of the results. The functionalities of the method, as well as its sensitivities to the model grid sizes, contaminant loading rates, length of stress periods, and the historical frequencies of occurrence of pollution events were evaluated using hypothetical scenarios and a case study. Chloride-related pollution sources were compiled and used as indicative potential contaminant sources for the case study. At any active model cell, if a random generated number is less than the probability of pollution occurrence, then the risk model will generate synthetic contaminant source term as an input into the transport model. The results of the applications of the method are presented in the form of tables, graphs and spatial maps. Varying the model grid sizes indicates no significant effects on the simulated groundwater head. The simulated frequency of daily occurrence of pollution incidents is also independent of the model dimensions. However, the simulated total contaminant mass generated within the aquifer, and the associated volumetric numerical error appear to increase with the increasing grid sizes. Also, the migration of contaminant plume advances faster with the coarse grid sizes as compared to the finer grid sizes. The number of daily contaminant source terms generated and consequently the total mass of contaminant within the aquifer increases in a non linear proportion to the increasing frequency of occurrence of pollution events. The risk of pollution from a number of sources all occurring by chance together was evaluated, and quantitatively presented as risk maps. This capability to combine the risk to a groundwater feature from numerous potential sources of pollution proved to be a great asset to the method, and a large benefit over the contemporary risk and vulnerability methods.

Integrated component scheduling models for chip shooter machines

This paper focuses on minimizing printed circuit board (PCB) assembly time for a chipshootermachine, which has a movable feeder carrier holding components, a movable X–Y table carrying a PCB, and a rotary turret with multiple assembly heads. The assembly time of the machine depends on two inter-related optimization problems: the component sequencing problem and the feeder arrangement problem. Nevertheless, they were often regarded as two individual problems and solved separately. This paper proposes two complete mathematical models for the integrated problem of the machine. The models are verified by two commercial packages. Finally, a hybrid genetic algorithm previously developed by the authors is presented to solve the model. The algorithm not only generates the optimal solutions quickly for small-sized problems, but also outperforms the genetic algorithms developed by other researchers in terms of total assembly time.

Inference by replication in densely connected systems

An efficient Bayesian inference method for problems that can be mapped onto dense graphs is presented. The approach is based on message passing where messages are averaged over a large number of replicated variable systems exposed to the same evidential nodes. An assumption about the symmetry of the solutions is required for carrying out the averages; here we extend the previous derivation based on a replica-symmetric- (RS)-like structure to include a more complex one-step replica-symmetry-breaking-like (1RSB-like) ansatz. To demonstrate the potential of the approach it is employed for studying critical properties of the Ising linear perceptron and for multiuser detection in code division multiple access (CDMA) under different noise models. Results obtained under the RS assumption in the noncritical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in the critical regime one observes a first-order transition line that ends in a continuous phase transition point. Finite size effects are also observed. While the 1RSB ansatz is not required for the original problems, it was applied to the CDMA signal detection problem with a more complex noise model that exhibits RSB behavior, resulting in an improvement in performance. © 2007 The American Physical Society.

A Mathematical Apparatus for Domain Ontology Simulation. Logical Relationship Systems

* This paper was made according to the program of fundamental scientific research of the Presidium of the Russian Academy of Sciences «Mathematical simulation and intellectual systems», the project "Theoretical foundation of the intellectual systems based on ontologies for intellectual support of scientific researches".

ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results

On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.

Mathematical Modeling and Simulation of Radial Temperature Profile of Strontium Bromide Lasers

For metal and metal halide vapor lasers excited by high frequency pulsed discharge, the thermal effect mainly caused by the radial temperature distribution is of considerable importance for stable laser operation and improvement of laser output characteristics. A short survey of the obtained analytical and numerical-analytical mathematical models of the temperature profile in a high-powered He-SrBr2 laser is presented. The models are described by the steady-state heat conduction equation with mixed type nonlinear boundary conditions for the arbitrary form of the volume power density. A complete model of radial heat flow between the two tubes is established for precise calculating the inner wall temperature. The models are applied for simulating temperature profiles for newly designed laser. The author’s software prototype LasSim is used for carrying out the mathematical models and simulations.

Continuous property measurement techniques and physics based mathematical model for frost growth control

The development of a new set of frost property measurement techniques to be used in the control of frost growth and defrosting processes in refrigeration systems was investigated. Holographic interferometry and infrared thermometry were used to measure the temperature of the frost-air interface, while a beam element load sensor was used to obtain the weight of a deposited frost layer. The proposed measurement techniques were tested for the cases of natural and forced convection, and the characteristic charts were obtained for a set of operational conditions. ^ An improvement of existing frost growth mathematical models was also investigated. The early stage of frost nucleation was commonly not considered in these models and instead an initial value of layer thickness and porosity was regularly assumed. A nucleation model to obtain the droplet diameter and surface porosity at the end of the early frosting period was developed. The drop-wise early condensation in a cold flat plate under natural convection to a hot (room temperature) and humid air was modeled. A nucleation rate was found, and the relation of heat to mass transfer (Lewis number) was obtained. It was found that the Lewis number was much smaller than unity, which is the standard value usually assumed for most frosting numerical models. The nucleation model was validated against available experimental data for the early nucleation and full growth stages of the frosting process. ^ The combination of frost top temperature and weight variation signals can now be used to control the defrosting timing and the developed early nucleation model can now be used to simulate the entire process of frost growth in any surface material. ^

Trust based security mechanisms for wireless sensor networks

Wireless sensor networks are emerging as effective tools in the gathering and dissemination of data. They can be applied in many fields including health, environmental monitoring, home automation and the military. Like all other computing systems it is necessary to include security features, so that security sensitive data traversing the network is protected. However, traditional security techniques cannot be applied to wireless sensor networks. This is due to the constraints of battery power, memory, and the computational capacities of the miniature wireless sensor nodes. Therefore, to address this need, it becomes necessary to develop new lightweight security protocols. This dissertation focuses on designing a suite of lightweight trust-based security mechanisms and a cooperation enforcement protocol for wireless sensor networks. This dissertation presents a trust-based cluster head election mechanism used to elect new cluster heads. This solution prevents a major security breach against the routing protocol, namely, the election of malicious or compromised cluster heads. This dissertation also describes a location-aware, trust-based, compromise node detection, and isolation mechanism. Both of these mechanisms rely on the ability of a node to monitor its neighbors. Using neighbor monitoring techniques, the nodes are able to determine their neighbors’ reputation and trust level through probabilistic modeling. The mechanisms were designed to mitigate internal attacks within wireless sensor networks. The feasibility of the approach is demonstrated through extensive simulations. The dissertation also addresses non-cooperation problems in multi-user wireless sensor networks. A scalable lightweight enforcement algorithm using evolutionary game theory is also designed. The effectiveness of this cooperation enforcement algorithm is validated through mathematical analysis and simulation. This research has advanced the knowledge of wireless sensor network security and cooperation by developing new techniques based on mathematical models. By doing this, we have enabled others to build on our work towards the creation of highly trusted wireless sensor networks. This would facilitate its full utilization in many fields ranging from civilian to military applications.

Trust Based Security Mechanisms for Wireless Sensor Networks

Wireless sensor networks are emerging as effective tools in the gathering and dissemination of data. They can be applied in many fields including health, environmental monitoring, home automation and the military. Like all other computing systems it is necessary to include security features, so that security sensitive data traversing the network is protected. However, traditional security techniques cannot be applied to wireless sensor networks. This is due to the constraints of battery power, memory, and the computational capacities of the miniature wireless sensor nodes. Therefore, to address this need, it becomes necessary to develop new lightweight security protocols. This dissertation focuses on designing a suite of lightweight trust-based security mechanisms and a cooperation enforcement protocol for wireless sensor networks. This dissertation presents a trust-based cluster head election mechanism used to elect new cluster heads. This solution prevents a major security breach against the routing protocol, namely, the election of malicious or compromised cluster heads. This dissertation also describes a location-aware, trust-based, compromise node detection, and isolation mechanism. Both of these mechanisms rely on the ability of a node to monitor its neighbors. Using neighbor monitoring techniques, the nodes are able to determine their neighbors’ reputation and trust level through probabilistic modeling. The mechanisms were designed to mitigate internal attacks within wireless sensor networks. The feasibility of the approach is demonstrated through extensive simulations. The dissertation also addresses non-cooperation problems in multi-user wireless sensor networks. A scalable lightweight enforcement algorithm using evolutionary game theory is also designed. The effectiveness of this cooperation enforcement algorithm is validated through mathematical analysis and simulation. This research has advanced the knowledge of wireless sensor network security and cooperation by developing new techniques based on mathematical models. By doing this, we have enabled others to build on our work towards the creation of highly trusted wireless sensor networks. This would facilitate its full utilization in many fields ranging from civilian to military applications.

Mathematical modelling of integrin-like receptors systems

Nel presente lavoro, ho studiato e trovato le soluzioni esatte di un modello matematico applicato ai recettori cellulari della famiglia delle integrine. Nel modello le integrine sono considerate come un sistema a due livelli, attivo e non attivo. Quando le integrine si trovano nello stato inattivo possono diffondere nella membrana, mentre quando si trovano nello stato attivo risultano cristallizzate nella membrana, incapaci di diffondere. La variazione di concentrazione nella superficie cellulare di una sostanza chiamata attivatore dà luogo all’attivazione delle integrine. Inoltre, questi eterodimeri possono legare una molecola inibitrice con funzioni di controllo e regolazione, che chiameremo v, la quale, legandosi al recettore, fa aumentare la produzione della sostanza attizzatrice, che chiameremo u. In questo modo si innesca un meccanismo di retroazione positiva. L’inibitore v regola il meccanismo di produzione di u, ed assume, pertanto, il ruolo di modulatore. Infatti, grazie a questo sistema di fine regolazione il meccanismo di feedback positivo è in grado di autolimitarsi. Si costruisce poi un modello di equazioni differenziali partendo dalle semplici reazioni chimiche coinvolte. Una volta che il sistema di equazioni è impostato, si possono desumere le soluzioni per le concentrazioni dell’inibitore e dell’attivatore per un caso particolare dei parametri. Infine, si può eseguire un test per vedere cosa predice il modello in termini di integrine. Per farlo, ho utilizzato un’attivazione del tipo funzione gradino e l’ho inserita nel sistema, valutando la dinamica dei recettori. Si ottiene in questo modo un risultato in accordo con le previsioni: le integrine legate si trovano soprattutto ai limiti della zona attivata, mentre le integrine libere vengono a mancare nella zona attivata.

Seleção de características para identificação de diferentes proporções de tipos de fibras musculares por meio da eletromiografia de superfície

Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case.

Mathematical Modeling of Perifusion Cell Culture Experiments

In perifusion cell cultures, the culture medium flows continuously through a chamber containing immobilized cells and the effluent is collected at the end. In our main applications, gonadotropin releasing hormone (GnRH) or oxytocin is introduced into the chamber as the input. They stimulate the cells to secrete luteinizing hormone (LH), which is collected in the effluent. To relate the effluent LH concentration to the cellular processes producing it, we develop and analyze a mathematical model consisting of coupled partial differential equations describing the intracellular signaling and the movement of substances in the cell chamber. We analyze three different data sets and give cellular mechanisms that explain the data. Our model indicates that two negative feedback loops, one fast and one slow, are needed to explain the data and we give their biological bases. We demonstrate that different LH outcomes in oxytocin and GnRH stimulations might originate from different receptor dynamics. We analyze the model to understand the influence of parameters, like the rate of the medium flow or the fraction collection time, on the experimental outcomes. We investigate how the rate of binding and dissociation of the input hormone to and from its receptor influence its movement down the chamber. Finally, we formulate and analyze simpler models that allow us to predict the distortion of a square pulse due to hormone-receptor interactions and to estimate parameters using perifusion data. We show that in the limit of high binding and dissociation the square pulse moves as a diffusing Gaussian and in this limit the biological parameters can be estimated.

Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).

Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output

Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the interactions and synthesis of mathematical models, computer experiments, statistics, field/real experiments, and probability theory, with a particular emphasize on the large-scale simulations by computer models. The challenges not only come from the complication of scientific questions, but also from the size of the information. It is the focus in this thesis to provide statistical models that are scalable to massive data produced in computer experiments and real experiments, through fast and robust statistical inference.

Chapter 2 provides a practical approach for simultaneously emulating/approximating massive number of functions, with the application on hazard quantification of Soufri\`{e}re Hills volcano in Montserrate island. Chapter 3 discusses another problem with massive data, in which the number of observations of a function is large. An exact algorithm that is linear in time is developed for the problem of interpolation of Methylation levels. Chapter 4 and Chapter 5 are both about the robust inference of the models. Chapter 4 provides a new criteria robustness parameter estimation criteria and several ways of inference have been shown to satisfy such criteria. Chapter 5 develops a new prior that satisfies some more criteria and is thus proposed to use in practice.

Numerical Modelling of Eddy Current Probes for CANDU® Fuel Channel Inspection

Multi-frequency Eddy Current (EC) inspection with a transmit-receive probe (two horizontally offset coils) is used to monitor the Pressure Tube (PT) to Calandria Tube (CT) gap of CANDU® fuel channels. Accurate gap measurements are crucial to ensure fitness of service; however, variations in probe liftoff, PT electrical resistivity, and PT wall thickness can generate systematic measurement errors. Validated mathematical models of the EC probe are very useful for data interpretation, and may improve the gap measurement under inspection conditions where these parameters vary. As a first step, exact solutions for the electromagnetic response of a transmit-receive coil pair situated above two parallel plates separated by an air gap were developed. This model was validated against experimental data with flat-plate samples. Finite element method models revealed that this geometrical approximation could not accurately match experimental data with real tubes, so analytical solutions for the probe in a double-walled pipe (the CANDU® fuel channel geometry) were generated using the Second-Order Vector Potential (SOVP) formalism. All electromagnetic coupling coefficients arising from the probe, and the layered conductors were determined and substituted into Kirchhoff’s circuit equations for the calculation of the pickup coil signal. The flat-plate model was used as a basis for an Inverse Algorithm (IA) to simultaneously extract the relevant experimental parameters from EC data. The IA was validated over a large range of second layer plate resistivities (1.7 to 174 µΩ∙cm), plate wall thickness (~1 to 4.9 mm), probe liftoff (~2 mm to 8 mm), and plate-to plate gap (~0 mm to 13 mm). The IA achieved a relative error of less than 6% for the extracted FP resistivity and an accuracy of ±0.1 mm for the LO measurement. The IA was able to achieve a plate gap measurement with an accuracy of less than ±0.7 mm error over a ~2.4 mm to 7.5 mm probe liftoff and ±0.3 mm at nominal liftoff (2.42±0.05 mm), providing confidence in the general validity of the algorithm. This demonstrates the potential of using an analytical model to extract variable parameters that may affect the gap measurement accuracy.