Background: Policy, systems and environmental (PSE) approaches can sustainably improve opportunities for healthy eating (HE) and active transportation (AT). PSEs require cross-sector collaboration. Adopting and implementing PSEs is complex and not well understood. Methods: First, using a national probability survey dataset of US local health departments (LHD), inclusion of HE and AT PSE strategies in local community health improvement plans (CHIPs) was examined. Next, a content analysis of current CHIP documents provided data for multilevel latent class analyses to identify classes of CHIPs based on patterns of PSE-strategy alignment with six key activities that facilitate change. Lastly, semi-structured interviews informed a qualitative exploration of early stage Complete Streets policy implementation in Worcester, Massachusetts. Results: Less than half of US LHDs reported developing a CHIP containing any HE policy (32%) or AT (46%) strategies. Two classes of CHIPs were identified: CHIPs in Class A (HE: 71%; Physical Activity (PA): 79%) simply identified a PSE solution; Class B CHIPs (HE: 29%; PA 21%) mostly included PSE strategies that comprehensively addressed multiple key activities. Six themes emerged as factors for early Complete Streets implementation. Conclusions: This mixed methods study provides a novel understanding of the status, development and implementation of PSE strategies in relation to collaborative strategic health improvement planning efforts. CHIPs are underutilized to promote PSE strategies and few CHIPs in our study developed strategies that comprehensively address the process of PSE-change. Among other factors, CHIPs may provide a guiding structure for policy adoption and implementation.

Indiana University-Purdue University Indianapolis (IUPUI)

Indiana University-Purdue University Indianapolis (IUPUI) / With the rapid growth in wireless mobile communication technology, Vehicular Ad-hoc Network (VANET) has emerged as a promising method to effectively solve transportation-related issues. So far, most of researches on VANETs have been conducted with simulations as the real-world experiment is expensive. A core problem affecting the fidelity of simulation is the mobility model employed. In this thesis, a sophisticated traffic simulator capable of generating realistic vehicle traces is introduced. Combined with network simulator NS-2, we used this tool to evaluate the general performance of several routing protocols and studied the impact of intersections on simulation results. We show that static nodes near the intersection tend to become more active in packet delivery with higher transferred throughput.

Indiana University-Purdue University Indianapolis (IUPUI) / In systems biology study, biological networks were used to gain insights into biological systems. While the traditional approach to studying biological networks is based on the identification of interactions among genes or the identification of a gene set ranking according to differentially expressed gene lists, little is known about interactions between higher order biological systems, a network of gene sets. Several types of gene set network have been proposed including co-membership, linkage, and co-enrichment human gene set networks. However, to our knowledge, none of them contains directionality information. Therefore, in this study we proposed a method to construct a regulatory gene set network, a directed network, which reveals novel relationships among gene sets. A regulatory gene set network was constructed by using publicly available gene regulation data. A directed edge in regulatory gene set networks represents a regulatory relationship from one gene set to the other gene set. A regulatory gene set network was compared with another type of gene set network to show that the regulatory network provides additional information. In order to show that a regulatory gene set network is useful for understand the underlying mechanism of a disease, an Alzheimer's disease (AD) regulatory gene set network was constructed. In addition, we developed Pathway and Annotated Gene-set Electronic Repository (PAGER), an online systems biology tool for constructing and visualizing gene and gene set networks from multiple gene set collections. PAGER is available at http://discern.uits.iu.edu:8340/PAGER/. Global regulatory and global co-membership gene set networks were pre-computed. PAGER contains 166,489 gene sets, 92,108,741 co-membership edges, 697,221,810 regulatory edges, 44,188 genes, 651,586 unique gene regulations, and 650,160 unique gene interactions. PAGER provided several unique features including constructing regulatory gene set networks, generating expanded gene set networks, and constructing gene networks within a gene set. However, tissue specific or disease specific information was not considered in the disease specific network constructing process, so it might not have high accuracy of presenting the high level relationship among gene sets in the disease context. Therefore, our framework can be improved by collecting higher resolution data, such as tissue specific and disease specific gene regulations and gene sets. In addition, experimental gene expression data can be applied to add more information to the gene set network. For the current version of PAGER, the size of gene and gene set networks are limited to 100 nodes due to browser memory constraint. Our future plans is integrating internal gene or proteins interactions inside pathways in order to support future systems biology study.

Indiana University-Purdue University Indianapolis (IUPUI) / The purpose of this thesis is to use experimental methods to seek deeper understanding and better performance in the self-circulating self-regulating microfluidic gas generator initially developed in Dr. Zhu’s group, by changing the major features and dimensions in the reaction channel of the device. In order to effectively conduct experiments described above, a microfabrication method that is capable of making new microfluidic devices with low cost, short time period, as well as relatively high accuracy was needed first. Developing such a fabrication method is the major part of this thesis. We initially used patterned polymer films and glass slide, and bonded them together by sequentially aligning and stacking them into a microfluidic device with patterned double-sided tapes. Later we developed a more advanced microfabrication method that used only patterned polystyrene (PS) films. The patterned PS films were obtained from a digital cutter and they were bonded into a microfluidic device by thermopress bonding method that required no heterogeneous bonding agents. This new method did not need manual assembly which greatly improved its precision (~ 100 µm), and it used only PS as device material that has favorable surface wetting property for microfluidics applications. In order to find the optimized microfluidic channel design to improve gas generating performance, we've designed and fabricated microfluidic devices with different channel dimensions using the PS fabrication method. Based on the gas generation testing results of those devices, we were able to come up with the optimal dimensions for the reaction channel that had the best gas generation performance. To obtain a more fundamental understanding about the working mechanism of our device and its bubble dynamics, we have conducted ultrafast X-ray imaging test at Advanced Photon Source (APS), Argonne National Laboratory. High speed (100 KHz) phase contrast images were captured that allowed us to observe directly inside the reaction channel on the cross section view during the self-circulating catalytic reaction. The images provided us with lots of insightful information that in turn helped the dimensional improvement for the microchannel design. The 100 KHz high speed images also gave us useful information about the dynamics of bubble development on a catalyst bed, such as growth and merging of the bubbles.

Indiana University-Purdue University Indianapolis (IUPUI) / Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.

Indiana University-Purdue University Indianapolis (IUPUI) / In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.

Indiana University-Purdue University Indianapolis (IUPUI) / Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of information about the model of POMDP remains a highly anticipated feature. Learning with minimal information is desirable in complex systems as methods requiring complete information among decision makers are impractical in complex systems due to increase of problem dimensionality. In this thesis we address the problem of decentralized control of POMDPs with unknown transition probabilities and reward. We suggest learning in POMDP using a tree based approach. States of the POMDP are guessed using this tree. Each node in the tree has an automaton in it and acts as a decentralized decision maker for the POMDP. The start state of POMDP is known as the landmark state. Each automaton in the tree uses a simple learning scheme to update its action choice and requires minimal information. The principal result derived is that, without proper knowledge of transition probabilities and rewards, the automata tree of decision makers will converge to a set of actions that maximizes the long term expected reward per unit time obtained by the system. The analysis is based on learning in sequential stochastic games and properties of ergodic Markov chains. Simulation results are presented to compare the long term rewards of the system under different decision control algorithms.

Indiana University-Purdue University Indianapolis (IUPUI) / A Laboratory Information Management Systems (LIMS) is designed to manage laboratory processes and data. It has the ability to extend the core functionality of the LIMS through configuration tools and add-on modules to support the implementation of complex laboratory workflows. The purpose of this project is to demonstrate how laboratory data and processes from a complex workflow can be implemented using a LIMS. Genomic samples have become an important part of the drug development process due to advances in molecular testing technology. This technology evaluates genomic material for disease markers and provides efficient, cost-effective, and accurate results for a growing number of clinical indications. The preparation of the genomic samples for evaluation requires a complex laboratory process called the precision aliquotting workflow. The precision aliquotting workflow processes genomic samples into precisely created aliquots for analysis. The workflow is defined by a set of aliquotting scheme attributes that are executed based on scheme specific rules logic. The aliquotting scheme defines the attributes of each aliquot based on the achieved sample recovery of the genomic sample. The scheme rules logic executes the creation of the aliquots based on the scheme definitions. LabWare LIMS is a Windows® based open architecture system that manages laboratory data and workflow processes. A LabWare LIMS model was developed to implement the precision aliquotting workflow using a combination of core functionality and configured code.

Indiana University-Purdue University Indianapolis (IUPUI) / The recording and processing of bioelectric signals over the decades has led to the development of many different types of analog filtering and amplification techniques. Meanwhile, there have also been many advancements in the realm of digital signal processing that allow for more powerful analysis of these collected signals. The issues with present acquisition schemes are that (1) they introduce irreversible distortion to the signals and may ultimately hinder analyses that rely on the unique morphological differences between bioelectric signal events and (2) they do not allow the collection of frequencies in the signal from direct-current (DC) to high-frequencies. The project put forth aims to overcome these two issues and present a new scheme for bioelectric signal acquisition and processing. In this thesis, a system has been developed, verified, and validated with experimental data to demonstrate the ability to build an invertible universal amplifier and digital restoration scheme. The thesis is primarily divided into four sections which focus on (1) the introduction and background information, (2) theory and development, (3) verification implementation and testing, and (4) validation implementation and testing. The introduction and background provides pertinent information regarding bioelectric signals and recording practices for bioelectric signals. It also begins to address some of the issues with the classical and present methods for data acquisition and make the case for why an invertible universal amplifier would be better. The universal amplifier transfer function and architecture are discussed and presented along with the development and optimization of the characterization and the inversion, or restoration, filter process. The developed universal amplifier, referred to as the invertible universal amplifier (IUA), while the universal amplifier and the digital restoration scheme together are referred to as the IUA system. The IUA system is then verified on the bench using typical square, sine, and triangle waveforms with varying offsets and the results are presented and discussed. The validation is done with in-vivo experiments showing that the IUA system may be used to acquire and process bioelectric signals with percent error less than to 6% when post-processed using estimated characteristics of and when compared to a standard flat bandwidth high-pass cutoff amplifier.