We develop a class of models to represent the dynamics of a virus spreading in a cellphone network, employing a taxonomy that includes five key characteristics. Based on the resulting dynamics governing the spread, we present optimization models to rapidly detect the virus, subject to resource limitations. We consider two goals, maximizing the probability of detecting a virus by a time threshold and minimizing the expected time to detection, which can be applied to all spread models we consider. We establish a submodularity result for these two objective functions that ensures that a greedy heuristic yields a well-known constant-factor (63%) approximation. We relate the latter optimization problem, under a specific virus-spread mechanism from our class of models, to a classic facility-location model. Using data from a large carrier, we build several base cellphone contact networks of different scale. We then rescale these base networks using the so-called c-core decomposition that removes vertices of low degree in a recursive way. We further show that this down-sampling strategy preserves, in general, the topological properties of the base networks, based on testing several measures. For the objective that maximizes the probability that we detect a virus by a time threshold, we provide a sample-average optimization model that yields an asymptotically-optimal design for locating the detection devices, as the number of samples grows large. To choose a relevant time threshold, we perform a simulation for some spread models. We then test the performance of our proposed solution methods by solving the presented optimization models for some spread dynamics using some of the contact networks built after the c-core decomposition. The computational results show that the greedy algorithm is an efficient way to solve the corresponding sample-average approximation model, and the greedy solutions outperform some other simple solution approaches. / text

Protein mechanical responses play a critical role in a wide variety of biological phenomena, impacting events as diverse as muscle contraction and stem cell differentiation. Recent advances in both experimental and computational techniques have provided the opportunity to explore protein constitutive properties at the molecular level. However, despite these advances many questions remain about how proteins respond to applied mechanical forces, particularly as a function of load magnitude. In order to address these questions, relatively simple helical structures were computationally tested to determine the mechanisms and kinetics of unfolding at a range of physiologically relevant load magnitudes. Atomically detailed constant force molecular dynamics simulations combined with the Milestoning kinetic analysis framework revealed that the mean first passage time (MFPT) of the initiation of unfolding of long (~16nm) isolated helical domains was a non-monotonic function of the magnitude of applied tensile load. The unfolding kinetics followed a profile ranging from 2.5ns (0pN) to a peak of 3.75ns (20pN) with a decreasing MFPT beyond 40pN reflected by an MFPT of 1ns for 100pN. The application of the Milestoning framework with a coarse-grained network analysis approach revealed that intermediate loads (15pN-25pN) retarded unfolding by opening additional, slower unfolding pathways through non-native [pi]-helical conformations. Analysis of coiled-coil helical pairs revealed that the presence of the second neighboring helix delayed unfolding initiation by a factor of 20, with calculated MFPTs ranging from 55ns (0pN) to 85ns (25pN per helix) to 20ns (100pN per helix). The stability of the coiled-coil domains relative to the isolated helix was shown to reflect a decreased propensity to break flexibility restraining intra-helix hydrogen bonds, thereby delaying [psi] backbone dihedral angle rotation and unfolding. These results show for the first time a statistically determined profile of unfolding kinetics for an atomically detailed protein that is non-monotonic with respect to load caused by a change in the unfolding mechanism with load. Together, the methods introduced for analyzing the mechanical response of proteins as well as the timescales determined for the initiation of unfolding provide a framework for the determination of the constitutive properties of proteins and non-biological polymers with more complicated geometries. / text

Indoor air quality investigations often focus on air and settled dust samples to assess chemical and biological contamination. Although the information provided by these techniques is useful, HVAC filters represent a new option for investigating contaminants in the indoor environment. This dissertation explores the potential use of HVAC filters as long-term, passive samplers by investigating the contaminants found in HVAC dust and other indoor locations and by evaluating the likelihood that HVAC filters will capture indoor particles. A field investigation of heavy metal and culturable microbial contaminants found in air, settled dust and HVAC filter dust corroborated the hypothesis that HVAC filters hold promise as a sampling mechanism in residences. However, several factors including filter efficiency, HVAC cycling and particle size seemed to influence the results. Also, it was unclear how the composition of the microbial communities varied with sampling location. Subsequently, the bacterial and fungal communities present in several sampling locations within residences and in an unoccupied test house were investigated. In residences, the microbial communities encountered in HVAC filter dust were not different from those in high surface dust. High efficiency HVAC filters also seem to be a viable alternative to long-term air sampling. Occupants influence the composition of the microbial communities in residences and are viii associated with Actinobacteria and Firmicutes, while Proteobacteria dominate the air samples and might have an outdoor air origin. A fate analysis to assess the magnitude of the different particle removal mechanisms revealed that small and large particles are likely to deposit on surfaces, while intermediate sized particles stay suspended in air longer. HVAC filters can collect particulate matter over a broad size range and may be effective overall samplers of particle-bound contaminants. Nevertheless, filter efficiency and air recirculation rate are important parameters that influence the likelihood that filters will capture particles, while air exchange rate has little effect. The results from this study indicate that HVAC filters can be used as an alternative to traditional indoor sampling mechanisms for contaminants associated with particles.

Controlled auto-ignition (CAI) combustion in the gasoline engine has been extensively studied in the last several years due to its potential for simultaneous improvement in fuel consumption and exhaust emissions. At the same time, there has been increasing interest in the use of alternative fuels in order to reduce reliance on conventional fossil fuels. Therefore, this study has been carried out to investigate the effect of alcohol fuels on the combustion characteristics and in-cylinder processes of CAI combustion in a single cylinder gasoline engine. In order to study the effect of alcohol fuels, combustion characteristics were investigated by heat releases analysis in the first part. The combustion process was studied through flame structure and excited molecule by chemiluminescence imaging. Furthermore, in-cylinder gas composition was analysis by GC-MS to identify the auto-ignition reactions involved in the CAI combustion. In addition, the influence of spark-assisted ignition and injection timings were also studied. Alcohol fuels, in particular methanol, resulted in advanced auto-ignition and faster combustion than that of gasoline. In addition, their use could lead to substantially lower HC, NOX and CO exhaust emissions. Spark-assisted ignition assisted gasoline combustion by advancing ignition timing and initiating flame kernel at the centre of combustion chamber but it had marginal effect on alcohol fuels. Auto-ignition always took place at the perimeter of the chamber and occurred earlier with alcohol fuels. Fuel reforming reactions during the NVO period were observed and they had significant effect on alcohol combustion.

<p>The current study assessed the relationship between licensing and self-control maintenance. Previous research on licensing has found mixed results for the effect of perceived progress on goal pursuit. Some studies find evidence that progress increases commitment and motivation to a goal, making licensing less likely, whereas other studies have found that progress leads to coasting and feelings of earned licensing. Previous work on managing food consumption has demonstrated that using a mental budget in tandem with a salient avoidance goal is an effective means of monitoring and limiting overindulgence. The current study used a mixed event-contingent and fixed-interval experience sampling design to examine the role of licensing in the eating domain and its effect on goal pursuit. Participants in the experimental condition were prompted with personalized commitment devices each day to assess if they promoted goal pursuit and appropriate licensing. We found that licensing occurs infrequently, but when it does occur, goal progress and goal commitment increase. The use of commitment device has little impact on licensing or goal pursuit.</p> / Dissertation

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / Massive amounts of data are typically acquired in third generation full-waveform (FW) LIDAR systems to generate image-like depthmaps of a scene of acceptable quality. The sampling systems acquiring this data, however, seldom take into account the low information rate generally present in the FW signals and, consequently, they sample very inefficiently. Our main goal here is to compare two efficient sampling models and processes for the individual time-resolved FW signals collected by a LIDAR system. Specifically, we compare two approaches of sub-Nyquist sampling of the continuous-time LIDAR FW return pulses: (i) modeling FW signals as short-duration pulses with multiple bandlimited echoes, and (ii) modeling them as signals with finite rates of innovation (FRI).

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / Third generation LIDAR full-waveform (FW) based systems collect 1D FW signals of the echoes generated by laser pulses of wide bandwidth reflected at the intercepted objects to construct depth profiles along each pulse path. By emitting a series of pulses towards a scene using a predefined scanning patter, a 3D image containing spatial-depth information can be constructed. Unfortunately, acquisition of a high number of wide bandwidth pulses is necessary to achieve high depth and spatial resolutions of the scene. This implies the collection of massive amounts of data which generate problems for the storage, processing and transmission of the FW signal set. In this research, we explore the recovery of individual continuous-time FW signals at sub-Nyquist rates. The key step to achieve this is to exploit the sparsity in FW signals. Doing this allows one to sub-sample and recover FW signals at rates much lower than that implied by Shannon's theorem. Here, we describe the theoretical framework supporting recovery and present the reader with examples using real LIDAR data.

This paper presents numerical solutions to integration problems with bivariate integrands. Using equally spaced nodes in Adaptive Simpson's Rule as a base case, two ways of sampling the domain over which the integration will take place are examined. Drawing from Ouellette and Fiume, Voronoi sampling is used along both axes of integration and the corresponding points are used as nodes in an unequally spaced degree two Newton-Cotes method. Then the domain of integration is triangulated and used in the Triangular Prism Rules discussed by Limaye. Finally, both of these techniques are tested by running simulations over heavily oscillatory and monomial (up to degree five) functions over polygonal regions.

Thesis (MTech. degree in Chemistry)--Tshwane University of Technology, 2010. / Discusses an accurate and sensitive method for the determination of trace levels of mercury in environmental air samples may be established using cost effective technologies.

The efficacy of interventions to control HIV spread depends upon many features of the communities where they are implemented, including not only prevalence, incidence, and per contact risk of transmission, but also properties of the sexual or transmission network. For this reason, HIV epidemic models have to take into account network properties including degree distribution and mixing patterns. The use of sampled data to estimate properties of a network is a common practice; however, current network generation methods do not account for the uncertainty in the estimates due to sampling. In chapter 1, we present a framework for constructing collections of networks using sampled data collected from ego-centric surveys. The constructed networks not only target estimates for density, degree distributions and mixing frequencies, but also incorporate the uncertainty due to sampling. Our method is applied to the National Longitudinal Study of Adolescent Health and considers two sampling procedures. We demonstrate how a collection of constructed networks using the proposed methods are useful in investigating variation in unobserved network topology, and therefore also insightful for studying processes that operate on networks. In chapter 2, we focus on the degree to which impact of concurrency on HIV incidence in a community may be overshadowed by differences in unobserved, but local, network properties. Our results demonstrate that even after controlling for cumulative ego-centric properties, i.e. degree distribution and concurrency, other network properties, which include degree mixing and clustering, can be very influential on the size of the potential epidemic. In chapter 3, we demonstrate the need to incorporate information about degree mixing patterns in such modeling. We present a procedure to construct collections of bipartite networks, given point estimates for degree distribution, that either makes use of information on the degree mixing matrix or assumes that no such information is available. These methods permit a demonstration of the differences between these two network collections, even when degree sequence is fixed. Methods are also developed to estimate degree mixing patterns, given a point estimate for the degree distribution.