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Rail and bridge infrastructure assets are critical elements of Canada’s transportation network and their continued efficient and safe operation is necessary to ensure the nation’s economic livelihood. Monitoring technologies that can detect changes in performance as well as precursors to failure are an important element of ensuring this efficient and safe operation. Digital Image Correlation (DIC) is a monitoring technology that has the potential to provide critical data for infrastructure assessment and to replace various conventional sensors with one integrated monitoring solution. In this research, the accuracy of DIC is evaluated using numerical, laboratory and field-based experiments. The sources of error of particular relevance to dynamic measurement using DIC are identified as (i) bias error in the sub-pixel interpolation scheme, (ii) the ratio of sample rate to the frequency of the signal being monitoring and (iii) the signal to noise ratio. It is also shown that the chosen sub-pixel interpolation scheme can greatly affect the accuracy of dynamic measurements. The use of DIC was investigated for field monitoring of both horizontal and vertical railway displacements at sites with good and poor subgrade conditions under dynamic train loading. It is shown that there is a significant benefit to using an absolute displacement measurement system rather than a relative displacement measurement system as the former can capture irrecoverable rail displacements in both the vertical and horizontal directions. Finally, DIC was also used for field monitoring of a very stiff reinforced concrete bridge during static and dynamic load tests. It is shown that when using DIC for deflection monitoring, corrections may have to be made to compensate for errors such as camera jitter and drift to acquire the most accurate results. Two potential correction methods were the use of a fixed reference point and generating composite images using average pixel intensity values from multiple images. It was found that using a fixed reference point was the optimal choice in this bridge test. It is concluded that DIC can be used as an effective displacement measurement tool for bridge assessment because it shows excellent correlation with linear potentiometer results and it can allow measurements to be taken without having to close the bridge. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-09-26 15:40:16.744

In the literature on contests, punishments have received much less attention than prizes. One possible reason is that punishing the bottom player(s) in a contest where all contestants are not allowed to quit, while effective in increasing contestants' total effort, often violates individual rationality constraints. But what will happen in an open contest where all potential contestants can choose whether or not to participate? In chapter 1, we study a model of this type and allow the contest designer to punish the bottom participant according to their performances. We conclude that punishment is often not desirable (optimal punishment is zero) when the contest designer wants to maximize the expected total effort, while punishment is often desirable (optimal punishment is strictly positive) when the contest designer wants to maximize the expected highest individual effort. In the literature on imperfectly discriminating contests, researchers normally assume that the contest designer has a certain level of accuracy in choosing the winner, which can be represented by the discriminatory power r in the Power Contest Success Function (the Power CSF, proposed by Tullock in 1980). With symmetric contestants, it is well known that increasing accuracy (r) always increases total effort when the pure-strategy equilibrium exists. In chapter 2, we look at the cases where the contestants are heterogeneous in ability. We construct an equilibrium set on r > 0, where a unique pure-strategy equilibrium exists for any r below a critical value and a mixed-strategy equilibrium exists for any r above this critical value. We find that if the contestants are sufficiently different in ability, there always exists an optimal accuracy level for the contest designer. Additionally, as we increase the difference in their abilities, the optimal accuracy level decreases. The above conclusions provide an explanation to many phenomena in the real world and may give guidance in some applications. In chapter 3, we propose the Power Contest Defeat Function (the Power CDF)which eliminates one player out at a time over successive rounds. We show that the Power CDF has the same good qualities as the Power Contest Success Function (the Power CSF) and is more realistic in some cases. We look at both the Power CSF mechanism (selecting winners in sequence) and the Power CDF mechanism (selecting losers in sequence) and show that punishments increase expected total e¤orts signi cantly. More interestingly, we also find that when the contestants' effort levels are different, the Power CDF mechanism is more accurate in finding the correct winner (the one who makes the greatest effort) and the Power CSF mechanism is more accurate in finding the correct loser (the one who makes the smallest effort).

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / Benefiting from the Automotive world, Micro-Machined Sensor Technology moves into the Military arena with greater accuracy at a reduced price tag. Advances in Micro-Machining have produced silicone cantilever beam Sensors which meet or exceed some Military environmental specifications while providing a higher overall accuracy, compared to traditional cantilever beam designs. There are several companies such as Motorola, Analog Devices, Sensym, Silicone Designs, and NovaSensor to name a few who have established product lines in Accelerometers and Pressure Transducers. This paper describes an experience utilizing micro-technology Accelerometers which were designed to replace older technology sensors.

Antibiotics that target the bacterial ribosome make up about half of all clinically used antibiotics. We have studied two ribosome targeting drugs: Fusidic acid and Viomycin. Fusidic acid inhibits bacterial protein synthesis by binding to elongation factor G (EF-G) on the ribosome, thereby inhibiting translocation of the bacterial ribosome. Viomycin binds directly to the ribosome and inhibits both the fidelity of mRNA decoding and translocation. We found that the mechanisms of inhibition of these two antibiotics were unexpectedly complex. Fusidic acid can bind to EF-G on the ribosome during three separate stages of translocation. Binding of the drug to the first and most sensitive state does not lead to stalling of the ribosome. Rather the ribosome continues unhindered to a downstream state where it stalls for around 8 seconds. Dissociation of fusidic acid from this state allows the ribosome to continue translocating but it soon reaches yet another fusidic acid sensitive state where it can be stalled again, this time for 6 seconds. Viomycin inhibits translocation by binding to the pre-translocation ribosome in competition with EF-G. If viomycin binds before EF-G it stalls the ribosome for 44 seconds, much longer than a normal elongation cycle. Both viomycin and fusidic acid probably cause long queues of ribosomes to build up on the mRNA when they bind. Viomycin inhibits translational fidelity by binding to the ribosome during initial selection. We found that the concentration of viomycin required to bind to the ribosome with a given probability during decoding is proportional to the accuracy of the codon∙anticodon pair being decoded. This demonstrated that long standing models about ribosomal accuracy cannot be correct. Finally, we demonstrated that a common viomycin resistance mutation increases the drug binding rate and decreases its dissociation rate. Our results demonstrate that ribosome targeting drugs have unexpectedly complex mechanisms of action. Both fusidic acid and viomycin preferentially bind to conformations of the ribosome other than those that they stabilize. This suggests that determining the structures of stable drug-bound states may not give sufficient information for drug design.

With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing scenarios, the measurements generated by sensor networks are noisy and usually annotated with some measure of uncertainty. The problem we address in this thesis is how to estimate the accuracy of the sensor systems based on the probabilistic measurements they provide. This problem is increasingly common in many settings, such as multiple sensing services are competing for the same group of users, detecting faults in large scale networks, or establishing trustworthiness of different individuals in social sensing. It is also challenging in many ways, for instance, the ground truth of the monitored states is absent, the users often lack a clear view of the implementation details of the sensor systems, and the reported accuracy can be misleading. To address theses challenges, in this thesis we formulate the problem of estimating the accuracy of sensor systems in a general manner that applies to a broad spectrum of sensing scenarios. We then propose an accuracy estimation framework that breaks the problem into layers, which can be implemented in different ways. We present a novel inference-based accuracy estimation approach, which assesses the accuracy of sensor systems by comparing the reported measurements with the states inferred with the probabilistic measurements from all systems and available prior knowledge. We also propose a new learning-based approach for accuracy estimation, which employs novel parameter learning techniques. The learned parameters are either used to improve estimating the accuracy of sensor measurements, or to derive the accuracy of sensor systems directly in certain cases. We perform a systematic experimental evaluation on two datasets collected from real-world sensor deployments, where an array of different approaches are juxtaposed and compared extensively. We discuss how they trade accuracy for computation cost, and how this trade-off largely depends on the knowledge of the sensing scenarios. We also show that the proposed approaches outperform the competing ones in estimating accuracy and ranking the sensor systems.

Master of Science / Department of Psychological Sciences / Patrick A. Knight / Despite the functional importance of performance appraisals in organizational settings, rating inaccuracies persist and have been a widely researched topic for decades. Contemporary efforts to explore the problem have turned to components of accuracy to foster a more detailed understanding of the influence of situational factors and individual biases. In particular, a great deal of research has examined the role of rating purpose (e.g., administrative, developmental) on subsequent accuracy, consistently revealing greater leniency for administrative ratings than for developmental ratings. On the basis of spreading activation theory, rating purpose was conceptualized as a priming event, and in combination with rating strategy priming, was expected to prompt predictable enhancements to specific components of accuracy. Participants for this experimental study were 160 undergraduate students. Participants were randomly assigned a rating purpose with “real-world” implications, and exposed to a strategy priming task designed to promote specific rating cognitions. Students viewed video-recorded competitive marching band performances, and rated them. Participants’ ratings were compared to those made by experienced raters to compute accuracy estimates. Results were largely non-significant, but in the directions expected. Limitations and future research opportunities are discussed.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / John English / This research builds upon previous efforts to explore the use of Statistical Process Control (SPC) in lieu of cycle counting. Specifically a three pronged effort is developed. First, in the work of Huschka (2009) and Miller (2008), a mixture distribution is proposed to model the complexities of multiple Stock Keeping Units (SKU) within an operating department. We have gained access to data set from a large retailer and have analyzed the data in an effort to validate the core models. Secondly, we develop a recursive relationship that enables large samples of SKUs to be evaluated with appropriately with the SPC approach. Finally, we present a comprehensive set of type I and type II error rates for the SPC approach to inventory accuracy monitoring.

The popularity of using UAV in image-taking for the production of 3D models and orthophotos has increased over time. Karlskoga Municipality has recently acquired an UAV to produce their own 3D models and orthophotos. This project paper aims to study the geospatial accuracy of the orthophotos and DEM files produced using the images taken with their UAV. The flight takes only a few minutes but a considerable time is spent in the production processes. Difficulty is experienced in determining the right center point for most GCPs. Produced orthophotos in the software Photoscan have a resolution from 1.7 to 2.4 centimeters while DEM files have a resolution from 3.4 to 4.8 centimeters. Four orthophotos and four DEM files are produced where GCPs are used and not used and at two different flight heights, 76 and 105 meters. The spatial data of the ten GCPs are identified on the orthophotos and DEM files in ArcMap and compared with GNSS NRTK measurements and Lantmäteriet's data. A visual control in terms of completeness of data, alignment, residual tilt and scale is also done. Standard deviations in plane for orthophotos there GCPs are not used are greater than 2 meters, while there GCPs are used are around 0.7 meters. Standard deviations for DEM files are observed at 0.8 meters.

Bachelor Thesis, Program of Master of Business and Economics, 15 hp School of Business and Economics – Linnaeus University in Växjö 2FE30E:3 Spring, 2017 Authors: Sofie Areskoug and Niklas Karlén Supervisor: Damai Nasution Examiner: Natalia Semenova Keywords: Financial Analyst, Gender, Determinants of forecast accuracy, Sweden Background: The search of finding analysts who make the best forecasts has been an ongoing process since the 1930's. Determinants that can help predict the forecast accuracy of the analysts are in the interest of both investors and brokerage houses. Newer research in this area has taken gender of the analyst into consideration. Women are widely under-represented in the analyst occupation and there is evidence that investors are apprehensive toward women in the financial sector. Purpose: The aim of this thesis is to examine determinants of forecast accuracy regarding analysts covering Swedish companies. The authors have confidence in the research to benefit investors in their decisions on the Swedish stock market. In addition, the authors aim to shed light on the unequal gender representation of female analysts. Method: This thesis has examined 519 individual scores of forecast accuracy from 284 financial analysts covering stocks on the Swedish Index OMXS30. The forecasts are from the years 2016 and 2017. This study has a quantitative strategy and the data have been tested by an OLS estimates regression. Results: The empirical evidence shows that being a female analyst have a statistically significant positive effect on forecast accuracy. Female analysts covering Swedish stocks seem to outperform their male colleagues. Furthermore, insignificant results were found for firm complexity, industry complexity, brokerage house and analyst experience.