Drosophila Flight Stabilization Depends on Apparent Distances in Optic Flow Field

To perform daily flight tasks, insects rely heavily on their visual perception of a dynamic environment. They must process visual signals quickly and accurately and update their behavior. Flies are vulnerable to environmental disturbances, such as gusts of wind blowing them off course, but they may use the altered visual field to compensate and regain their original course. In studies using Drosophila melanogaster, it has been shown that their corrective responses can be analyzed by measuring changes in their wing beats. By enclosing a tethered fly in a cuboidal visual arena displaying a computerized optic flow field, it is possible to calculate the change in wing beat amplitudes from an infrared shadow of its wings using photodiodes and a custom wing beat analyzer. In this experiment, manipulations ofthe optic flow field are used to create a field where points have varying relative forward speed, to study how the insect performs corrective maneuvers. The results show that Drosophila have a stronger corrective response to the quickly moving, apparently near points compared to the slower moving, apparently distant points. This implies the flies are distinguishing points based on their relative speeds, inferring distance, and adjusting their corrective actions with this information.

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Movement Effects on the Flow Physics and Nutrient Delivery in Engineered Valvular Tissues

Mechanical conditioning has been shown to promote tissue formation in a wide variety of tissue engineering efforts. However the underlying mechanisms by which external mechanical stimuli regulate cells and tissues are not known. This is particularly relevant in the area of heart valve tissue engineering (HVTE) owing to the intense hemodynamic environments that surround native valves. Some studies suggest that oscillatory shear stress (OSS) caused by steady flow and scaffold flexure play a critical role in engineered tissue formation derived from bone marrow derived stem cells (BMSCs). In addition, scaffold flexure may enhance nutrient (e.g. oxygen, glucose) transport. In this study, we computationally quantified the i) magnitude of fluid-induced shear stresses; ii) the extent of temporal fluid oscillations in the flow field using the oscillatory shear index (OSI) parameter, and iii) glucose and oxygen mass transport profiles. Noting that sample cyclic flexure induces a high degree of oscillatory shear stress (OSS), we incorporated moving boundary computational fluid dynamic simulations of samples housed within a bioreactor to consider the effects of: 1) no flow, no flexure (control group), 2) steady flow-alone, 3) cyclic flexure-alone and 4) combined steady flow and cyclic flexure environments. We also coupled a diffusion and convention mass transport equation to the simulated system. We found that the coexistence of both OSS and appreciable shear stress magnitudes, described by the newly introduced parameter OSI-t , explained the high levels of engineered collagen previously observed from combining cyclic flexure and steady flow states. On the other hand, each of these metrics on its own showed no association. This finding suggests that cyclic flexure and steady flow synergistically promote engineered heart valve tissue production via OSS, so long as the oscillations are accompanied by a critical magnitude of shear stress. In addition, our simulations showed that mass transport of glucose and oxygen is enhanced by sample movement at low sample porosities, but did not play a role in highly porous scaffolds. Preliminary in-house in vitro experiments showed that cell proliferation and phenotype is enhanced in OSI-t environments.

Dynamic job quoting system for printed circuit board fabrication

This research deals with the development of a dynamic job quotation system for printed circuit board (PCB) fabrication, which can estimate the price and completion time of a job based on customer preference and current capacity of the shop floor. The primary purpose of building a dynamic quotation system is to maximize the company's profit by quoting optimum lead-time and competitive price for the day-to-day orders received from different customers and original equipment manufacturers. The system was developed using MS-Access relational database. Evaluating the output of the system it was observed that the dynamic system provided more reliable estimation of the lead-time needed for fabricating new jobs. The overall price quoted by the system was competitive with higher profit margin when compared to traditional static systems. This system would therefore provide a vital link between the job quoting and scheduling system of the firm enabling better utilization of the available resources.

Variable Speed Limit Strategies to Reduce the Impacts of Traffic Flow Breakdown at Recurrent Freeway Bottlenecks

Variable Speed Limit (VSL) strategies identify and disseminate dynamic speed limits that are determined to be appropriate based on prevailing traffic conditions, road surface conditions, and weather conditions. This dissertation develops and evaluates a shockwave-based VSL system that uses a heuristic switching logic-based controller with specified thresholds of prevailing traffic flow conditions. The system aims to improve operations and mobility at critical bottlenecks. Before traffic breakdown occurrence, the proposed VSL’s goal is to prevent or postpone breakdown by decreasing the inflow and achieving uniform distribution in speed and flow. After breakdown occurrence, the VSL system aims to dampen traffic congestion by reducing the inflow traffic to the congested area and increasing the bottleneck capacity by deactivating the VSL at the head of the congested area. The shockwave-based VSL system pushes the VSL location upstream as the congested area propagates upstream. In addition to testing the system using infrastructure detector-based data, this dissertation investigates the use of Connected Vehicle trajectory data as input to the shockwave-based VSL system performance. Since the field Connected Vehicle data are not available, as part of this research, Vehicle-to-Infrastructure communication is modeled in the microscopic simulation to obtain individual vehicle trajectories. In this system, wavelet transform is used to analyze aggregated individual vehicles’ speed data to determine the locations of congestion. The currently recommended calibration procedures of simulation models are generally based on the capacity, volume and system-performance values and do not specifically examine traffic breakdown characteristics. However, since the proposed VSL strategies are countermeasures to the impacts of breakdown conditions, considering breakdown characteristics in the calibration procedure is important to have a reliable assessment. Several enhancements were proposed in this study to account for the breakdown characteristics at bottleneck locations in the calibration process. In this dissertation, performance of shockwave-based VSL is compared to VSL systems with different fixed VSL message sign locations utilizing the calibrated microscopic model. The results show that shockwave-based VSL outperforms fixed-location VSL systems, and it can considerably decrease the maximum back of queue and duration of breakdown while increasing the average speed during breakdown.

Surface Mean Flow and Turbulence Structure in Tropical Cyclone Winds

Hurricanes are one of the deadliest and costliest natural hazards affecting the Gulf coast and Atlantic coast areas of the United States. An effective way to minimize hurricane damage is to strengthen structures and buildings. The investigation of surface level hurricane wind behavior and the resultant wind loads on structures is aimed at providing structural engineers with information on hurricane wind characteristics required for the design of safe structures. Information on mean wind profiles, gust factors, turbulence intensity, integral scale, and turbulence spectra and co-spectra is essential for developing realistic models of wind pressure and wind loads on structures. The research performed for this study was motivated by the fact that considerably fewer data and validated models are available for tropical than for extratropical storms. Using the surface wind measurements collected by the Florida Coastal Monitoring Program (FCMP) during hurricane passages over coastal areas, this study presents comparisons of surface roughness length estimates obtained by using several estimation methods, and estimates of the mean wind and turbulence structure of hurricane winds over coastal areas under neutral stratification conditions. In addition, a program has been developed and tested to systematically analyze Wall of Wind (WoW) data, that will make it possible to perform analyses of baseline characteristics of flow obtained in the WoW. This program can be used in future research to compare WoW data with FCMP data, as gust and turbulence generator systems and other flow management devices will be used to create WoW flows that match as closely as possible real hurricane wind conditions. Hurricanes are defined as tropical cyclones for which the maximum 1-minute sustained surface wind speeds exceed 74 mph. FCMP data include data for tropical cyclones with lower sustained speeds. However, for the winds analyzed in this study the speeds were sufficiently high to assure that neutral stratification prevailed. This assures that the characteristics of those winds are similar to those prevailing in hurricanes. For this reason in this study the terms tropical cyclones and hurricanes are used interchangeably.

A mathematical resolution to log transformations and the binning effect in applied processing of data in flow cytometry

This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as "histogram binning" inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation.

Numerical groundwater flow modeling in the Wakal River basin, India

Increasing dependence on groundwater in the Wakal River basin, India, jeopardizes water supply sustainability. A numerical groundwater model was developed to better understand the aquifer system and to evaluate its potential in terms of quantity and replenishment. Potential artificial recharge areas were delineated using landscape and hydrogeologic parameters, Geographic Information System (GIS), and remote sensing. Groundwater models are powerful tools for recharge estimation when transmissivity is known. Proper recharge must be applied to reproduce field-measured heads. The model showed that groundwater levels could decline significantly if there are two drought years in every four years that result in reduced recharge, and groundwater withdrawal is increased by 15%. The effect of such drought is currently uncertain however, because runoff from the basin is unknown. Remote sensing and GIS revealed areas with slopes less than 5%, forest cover, and Normalized Difference Vegetative Index greater than 0.5 that are suitable recharge sites.

Dynamic fault tree analysis with PEWMA modeling of event rates and Bayesian updating of material balance parameters using MCMC

This research explores Bayesian updating as a tool for estimating parameters probabilistically by dynamic analysis of data sequences. Two distinct Bayesian updating methodologies are assessed. The first approach focuses on Bayesian updating of failure rates for primary events in fault trees. A Poisson Exponentially Moving Average (PEWMA) model is implemnented to carry out Bayesian updating of failure rates for individual primary events in the fault tree. To provide a basis for testing of the PEWMA model, a fault tree is developed based on the Texas City Refinery incident which occurred in 2005. A qualitative fault tree analysis is then carried out to obtain a logical expression for the top event. A dynamic Fault Tree analysis is carried out by evaluating the top event probability at each Bayesian updating step by Monte Carlo sampling from posterior failure rate distributions. It is demonstrated that PEWMA modeling is advantageous over conventional conjugate Poisson-Gamma updating techniques when failure data is collected over long time spans. The second approach focuses on Bayesian updating of parameters in non-linear forward models. Specifically, the technique is applied to the hydrocarbon material balance equation. In order to test the accuracy of the implemented Bayesian updating models, a synthetic data set is developed using the Eclipse reservoir simulator. Both structured grid and MCMC sampling based solution techniques are implemented and are shown to model the synthetic data set with good accuracy. Furthermore, a graphical analysis shows that the implemented MCMC model displays good convergence properties. A case study demonstrates that Likelihood variance affects the rate at which the posterior assimilates information from the measured data sequence. Error in the measured data significantly affects the accuracy of the posterior parameter distributions. Increasing the likelihood variance mitigates random measurement errors, but casuses the overall variance of the posterior to increase. Bayesian updating is shown to be advantageous over deterministic regression techniques as it allows for incorporation of prior belief and full modeling uncertainty over the parameter ranges. As such, the Bayesian approach to estimation of parameters in the material balance equation shows utility for incorporation into reservoir engineering workflows.

Dynamic safety analysis of managed pressure drilling operations

The exploration and development of oil and gas reserves located in harsh offshore environments are characterized with high risk. Some of these reserves would be uneconomical if produced using conventional drilling technology due to increased drilling problems and prolonged non-productive time. Seeking new ways to reduce drilling cost and minimize risks has led to the development of Managed Pressure Drilling techniques. Managed pressure drilling methods address the drawbacks of conventional overbalanced and underbalanced drilling techniques. As managed pressure drilling techniques are evolving, there are many unanswered questions related to safety and operating pressure regimes. Quantitative risk assessment techniques are often used to answer these questions. Quantitative risk assessment is conducted for the various stages of drilling operations – drilling ahead, tripping operation, casing and cementing. A diagnostic model for analyzing the rotating control device, the main component of managed pressure drilling techniques, is also studied. The logic concept of Noisy-OR is explored to capture the unique relationship between casing and cementing operations in leading to well integrity failure as well as its usage to model the critical components of constant bottom-hole pressure drilling technique of managed pressure drilling during tripping operation. Relevant safety functions and inherent safety principles are utilized to improve well integrity operations. Loss function modelling approach to enable dynamic consequence analysis is adopted to study blowout risk for real-time decision making. The aggregation of the blowout loss categories, comprising: production, asset, human health, environmental response and reputation losses leads to risk estimation using dynamically determined probability of occurrence. Lastly, various sub-models developed for the stages/sub-operations of drilling operations and the consequence modelling approach are integrated for a holistic risk analysis of drilling operations.

Estimation of sea surface temperatures and salinity of ODP Site 177-1089 (Appendix A)

ODP Site 1089 is optimally located in order to monitor the occurrence of maxima in Agulhas heat and salt spillage from the Indian to the Atlantic Ocean. Radiolarian-based paleotemperature transfer functions allowed to reconstruct the climatic history for the last 450 kyr at this location. A warm sea surface temperature anomaly during Marine Isotope Stage (MIS) 10 was recognized and traced to other oceanic records along the surface branch of the global thermohaline (THC) circulation system, and is particularly marked at locations where a strong interaction between oceanic and atmospheric overturning cells and fronts occurs. This anomaly is absent in the Vostok ice core deuterium, and in oceanic records from the Antarctic Zone. However, it is present in the deuterium excess record from the Vostok ice core, interpreted as reflecting the temperature at the moisture source site for the snow precipitated at Vostok Station. As atmospheric models predict a subtropical Indian source for such moisture, this provides the necessary teleconnection between East Antarctica and ODP Site 1089, as the subtropical Indian is also the source area of the Agulhas Current, the main climate agent at our study location. The presence of the MIS 10 anomaly in the delta13C foraminiferal records from the same core supports its connection to oceanic mechanisms, linking stronger Agulhas spillover intensity to increased productivity in the study area. We suggest, in analogy to modern oceanographic observations, this to be a consequence of a shallow nutricline, induced by eddy mixing and baroclinic tide generation, which are in turn connected to the flow geometry, and intensity, of the Agulhas Current as it flows past the Agulhas Bank. We interpret the intensified inflow of Agulhas Current to the South Atlantic as responding to the switch between lower and higher amplitude in the insolation forcing in the Agulhas Current source area. This would result in higher SSTs in the Cape Basin during the glacial MIS 10, due to the release into the South Atlantic of the heat previously accumulating in the subtropical and equatorial Indian and Pacific Ocean. If our explanation for the MIS 10 anomaly in terms of an insolation variability switch is correct, we might expect that a future Agulhas SSST anomaly event will further delay the onset of next glacial age. In fact, the insolation forcing conditions for the Holocene (the current interglacial) are very similar to those present during MIS 11 (the interglacial preceding MIS 10), as both periods are characterized by a low insolation variability for the Agulhas Current source area. Natural climatic variability will force the Earth system in the same direction as the anthropogenic global warming trend, and will thus lead to even warmer than expected global temperatures in the near future.

Stopping rules in Bayesian adaptive threshold estimation

Threshold estimation with sequential procedures is justifiable on the surmise that the index used in the so-called dynamic stopping rule has diagnostic value for identifying when an accurate estimate has been obtained. The performance of five types of Bayesian sequential procedure was compared here to that of an analogous fixed-length procedure. Indices for use in sequential procedures were: (1) the width of the Bayesian probability interval, (2) the posterior standard deviation, (3) the absolute change, (4) the average change, and (5) the number of sign fluctuations. A simulation study was carried out to evaluate which index renders estimates with less bias and smaller standard error at lower cost (i.e. lower average number of trials to completion), in both yes–no and two-alternative forced-choice (2AFC) tasks. We also considered the effect of the form and parameters of the psychometric function and its similarity with themodel function assumed in the procedure. Our results show that sequential procedures do not outperform fixed-length procedures in yes–no tasks. However, in 2AFC tasks, sequential procedures not based on sign fluctuations all yield minimally better estimates than fixed-length procedures, although most of the improvement occurs with short runs that render undependable estimates and the differences vanish when the procedures run for a number of trials (around 70) that ensures dependability. Thus, none of the indices considered here (some of which are widespread) has the diagnostic value that would justify its use. In addition, difficulties of implementation make sequential procedures unfit as alternatives to fixed-length procedures.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.