Increasing Awareness of the HPV Vaccine

Increasing the public awareness and knowledge of HPV complications and prevention through vaccination is an effective intervention to increase vaccination rates and reduce overall public health cost and burden.https://scholarworks.uvm.edu/fmclerk/1141/thumbnail.jp

Measuring Strong and Weak Phases in Time-Independent B Decays

Flavor SU(3) symmetry implies certain relations among $B$-decay amplitudes to $\pi\pi$, $\pi K$ and $K {\bar K}$ final states, when annihilation-like diagrams are neglected. Using three triangle relations, we show how to measure the weak CKM phases $\alpha$ and $\gamma$ using time-independent rate measurements only. In addition, one obtains all the strong final-state phases and the magnitudes of individual terms describing tree (spectator), color-suppressed and penguin diagrams. Many independent measurements of these quantities can be made with this method, which helps to eliminate possible discrete ambiguities and to estimate the size of SU(3)-breaking effects.Comment: 2 figures available from the authors upon request, 12 pages,UdeM-LPN-TH-94-19

On the Design and Analysis of Multiple View Descriptors

We propose an extension of popular descriptors based on gradient orientation histograms (HOG, computed in a single image) to multiple views. It hinges on interpreting HOG as a conditional density in the space of sampled images, where the effects of nuisance factors such as viewpoint and illumination are marginalized. However, such marginalization is performed with respect to a very coarse approximation of the underlying distribution. Our extension leverages on the fact that multiple views of the same scene allow separating intrinsic from nuisance variability, and thus afford better marginalization of the latter. The result is a descriptor that has the same complexity of single-view HOG, and can be compared in the same manner, but exploits multiple views to better trade off insensitivity to nuisance variability with specificity to intrinsic variability. We also introduce a novel multi-view wide-baseline matching dataset, consisting of a mixture of real and synthetic objects with ground truthed camera motion and dense three-dimensional geometry

Hard Hat Safety by California Contractors

The hard hat is the quintessential item of PPE, and yet it often gets overlooked when ensuring safety standards are being met. We all expect our hard hat to protect us, but many never inspect it to ensure that it is properly fulfilling its purpose. This disregard to inspection and maintenance can lead to serious injury or death in the event of an accident. While organizations such as OSHA and the hard hat manufacturers themselves post information on this topic, it is unclear if this information is actually being received by the people involved in a construction project. This research is intended to discover which methods are currently being used by contractors in California to train their employees on the importance of using a properly maintained hard hat. An online survey was used to discover the qualities of hard hat safety by contractors throughout California. In addition, a survey was performed on-site to discover workers’ knowledge on hard hat usage and safety. The expectation is that all workers on a site should understand the basic importance of wearing a hard hat that is free from damage and defects

The significance of passive acoustic array-configurations on sperm whale range estimation when using the hyperbolic algorithm

In cetacean monitoring for population estimation, behavioural studies or mitigation, traditional visual observations are being augmented by the use of Passive Acoustic Monitoring (PAM) techniques that use the creature’s vocalisations for localisation. The design of hydrophone configurations is evaluated for sperm whale (Physeter macrocephalus) range estimation to meet the requirements of the current mitigation regulations for a safety zone and behaviour research. This thesis uses the Time Difference of Arrival (TDOA) of cetacean vocalisations with a three-dimensional hyperbolic localisation algorithm. A MATLAB simulator has been developed to model array-configurations and to assess their performance in source range estimation for both homogeneous and non-homogeneous sound speed profiles (SSP). The non-homogeneous medium is modelled on a Bellhop ray trace model, using data collected from the Gulf of Mexico. The sperm whale clicks are chosen as an exemplar of a distinctive underwater sound. The simulator is tested with a separate synthetic source generator which produced a set of TDOAs from a known source location. The performance in source range estimation for Square, Trapezium, Triangular, Shifted-pair and Y-shape geometries is tested. The Y-shape geometry, with four elements and aperture-length of 120m, is the most accurate, giving an error of ±10m over slant ranges of 500m in a homogeneous medium, and 300m in a non-homogeneous medium. However, for towed array deployments, the Y-shape array is sensitive to angle-positioning-error when the geometry is seriously distorted. The Shifted-pair geometry overcomes these limits, performing an initial accuracy of ±30m when the vessel either moves in a straight line or turns to port or starboard. It constitutes a recommendable array-configuration for towed array deployments. The thesis demonstrates that the number of receivers, the array-geometry and the arrayaperture are important parameters to consider when designing and deploying a hydrophone array. It is shown that certain array-configurations can significantly improve the accuracy of source range estimation. Recommendations are made concerning preferred array-configurations for use with PAM systems

On the Mobility of Small Aperture Telescopes for Initial Orbit Determination and Apparent Magnitude Derivation of Low Earth Satellites

Maintaining Space Domain Awareness (SDA) of satellites in low Earth orbit (LEO) requires effective methods of tracking and characterization. Optical measurements of these objects are generally sparse due to limited access intervals and high angular rates. Light pollution and geographic obstructions may also preclude consistent observations. However, a mobile small aperture telescope grants the ability to minimize such environmental effects, thereby increasing capture likelihoods for objects within this regime. By enhancing LEO satellite visibility in this way, extensive orbital and visual data are obtainable. An 8-inch Meade LX200GPS telescope equipped with a Lumenera SKYnyx2-0M CCD camera comprises the system that conducted observations of LEO. From 22 sessions spanning four months, 76 objects were imaged to provide a data set of 313 streak frames for initial orbit and photometric analyses. An Assumed Circular Orbit formulation provided considerable refinements in semimajor axis and eccentricity, up to one order of magnitude, when compared to a Gauss Extended method. Regarding the use of initial orbits for future pass predictions, the Assumed Circular Orbit angular positions indicated improvements up to 97.4% in accuracy and 65.7% in consistency over Gauss Extended. A photometric study placed the brightest observed visual magnitude at 3.60 mag, and the faintest visible at 9.47 mag. By converting brightness to a physical size, detected objects were approximately 23.8 meters at the largest and 40.6 centimeters at the smallest. Angles and brightness measurements of LEO satellites with mobile platforms may thus benefit the SDA effort

Denoising diffusion probabilistic models for probabilistic energy forecasting

Scenario-based probabilistic forecasts have become vital for decision-makers in handling intermittent renewable energies. This paper presents a recent promising deep learning generative approach called denoising diffusion probabilistic models. It is a class of latent variable models which have recently demonstrated impressive results in the computer vision community. However, to our knowledge, there has yet to be a demonstration that they can generate high-quality samples of load, PV, or wind power time series, crucial elements to face the new challenges in power systems applications. Thus, we propose the first implementation of this model for energy forecasting using the open data of the Global Energy Forecasting Competition 2014. The results demonstrate this approach is competitive with other state-of-the-art deep learning generative models, including generative adversarial networks, variational autoencoders, and normalizing flows.Comment: Version accepted to Powertech 2023. arXiv admin note: text overlap with arXiv:2106.09370, arXiv:2107.0103