A two-step approach Bayesian network model selection

It is often desirable to show relationships between unstructured, potentially related data elements, or features, composing a knowledge database (KD). By understanding the interaction between these elements, we may gain insight into the underlying process from which the KD is derived, and as a result, we can often model the process. Bayesian Belief Networks (BBN) in particular, are adept at modeling knowledge databases for two reasons. The first is that BBNs give a structural representation of data elements through a directed acyclic graph (DAG). This ability may make BBNs invaluable in areas such as data mining, where statistical relationships between the features of a traditional database are not apparent. An accurate BBN will clearly show features exerting influences on each other. The second strength of the BBN model is its ability to encode conditional expectations between knowledge database features. This ability facilitates using BBNs as inference engines. Given a set of instantiated elements, BBNs allow us to derive the most statistically likely instantiation of states for elements whose state is unknown. These qualities lend themselves to BBNs being proficient in applications ranging from computer vision to risk-assessment. In this thesis, two frameworks for BBN structure learning, or model selection, will be compared. The first is the asymptotically correct structure learning algorithm which shows efficient search space exploration characteristics. The second takes permutations of global structures in an elitist elimination heuristic search and shows precise search space exploitation characteristics. Comparisons between techniques will be presented, paying particular attention to computational complexity versus model precision. In the elitist elimination technique, comparisons between the Minimum Description Length (MDL) scoring heuristic and the Database probability given Model (DGM) scoring heuristic, will be provided. A comparison between naïve and non-naïve structure learning will be made along with an analysis of the infeasibility of naïve BBN model selection. Finally, an efficient and precise algorithm tor learning BBNs, which utilizes both frameworks, will be proposed

Discrimination of Disposable Vapes from Batteries Using the Magnetic Polarizability Tensor

Disposable vapes pose an environmental and fire hazard to waste streams when disposed of incorrectly. The lithium battery inside disposable vapes can produce an exothermic reaction when the lithium inside the battery is inadvertently exposed to air and moisture. New sensing technologies may be needed to screen waste streams for these vape hazards and this paper considers the potential of inductive techniques based on the magnetic polarisability tensor (MPT) representation. The MPT can be described by three complex components based on a target regardless of orientation. In this paper, the rank 2 MPT is measured and calculated for 10 vapes and 37 batteries for 28 logarithmically spaced frequencies from 119 Hz to 95.4 KHz. The 168 features of each object are reduced down to 2 features using principal component analysis (PCA) and linear discriminant analysis. The reduction of the features allows for the visualisation and grouping of the objects. Three clear groups of objects can be seen when the maximum feature scales the measurement and a two-component PCA transform is applied. The first group is the vapes, which are grouped away from the other batteries. The second is the batteries, which are grouped by size. Finally, zinc batteries are grouped away from the rest due to their case material.<br/

Youth Single-Sport Specialization in Professional Baseball Players.

Background: An increasing number of youth baseball athletes are specializing in playing baseball at younger ages. Purpose: The purpose of our study was to describe the age and prevalence of single-sport specialization in a cohort of current professional baseball athletes. In addition, we sought to understand the trends surrounding single-sport specialization in professional baseball players raised within and outside the United States (US). Study Design: Cross-sectional study; Level of evidence, 3. Methods: A survey was distributed to male professional baseball athletes via individual team athletic trainers. Athletes were asked if and at what age they had chosen to specialize in playing baseball at the exclusion of other sports, and data were then collected pertaining to this decision. We analyzed the rate and age of specialization, the reasons for specialization, and the athlete\u27s perception of injuries related to specialization. Results: A total of 1673 professional baseball athletes completed the survey, representing 26 of the 30 Major League Baseball (MLB) organizations. Less than half (44.5%) of professional athletes specialized in playing a single sport during their childhood/adolescence. Those who reported specializing in their youth did so at a mean age of 14.09 ± 2.79 years. MLB players who grew up outside the US specialized at a significantly earlier age than MLB players native to the US (12.30 ± 3.07 vs 14.89 ± 2.24 years, respectively; Conclusion: This study challenges the current trends toward early youth sport specialization, finding that the majority of professional baseball athletes studied did not specialize as youth and that those who did specialize did so at a mean age of 14 years. With the potential cumulative effects of pitching and overhead throwing on an athlete\u27s arm, the trend identified in this study toward earlier specialization within baseball is concerning

RELATIONSHIP BETWEEN LOBLOLLY PINE SMALL CLEAR SPECIMENS AND DIMENSION LUMBER TESTED IN STATIC BENDING

Prior to the 1980s the allowable stresses for lumber in North America were derived from testing of small clear specimens but the procedures changed because these models were found to be inaccurate.  Nevertheless, small clear testing continues to be used around the world for allowable stress determinations and in studies that examine forest management impacts on wood quality.  Using small clears and nondestructive technologies is advantageous because of the ease of obtaining and testing small clear specimens compared to lumber.  The objective of this study was to compare the mechanical properties in bending of small clear specimens with lumber specimens for loblolly pine.  Eight hundred and forty-one pieces of lumber in the No. 1 to No. 3 grades and 2×4 to 2×10 sizes were collected from a forest-thru-mill study and tested in static bending.  A small clear specimen (25 x 25 x 410 mm) was prepared from each piece of lumber and tested in static bending.  The effect of growth ring orientation was explored and overall samples tested on the radial or rift face did a better job of explaining the variation in lumber than samples tested on the tangential face; however, the relationships were generally poor for the modulus of elasticity (MOE) (R2 = 0.22) and modulus of rupture (MOR) (R2 = 0.11) pooled data.  A lumber-based multiple regression model explained 44% and 37% of the variability for MOE and MOR, respectively; whereas a stand-based multiple regression model explained 41% and 29% of the variability for MOE and MOR, respectively

Improved non-contact 3D field and processing techniques to achieve macrotexture characterisation of pavements

Macrotexture is required on pavements to provide skid resistance for vehicle safety in wet conditions. Increasingly, correlations between macrotexture measurements captured using non-contact techniques and tyre-pavement contact friction are being investigated in order to enable more robust and widescale measurement and monitoring of skid resistance. There is a notable scarcity of research into the respective accuracy of the non-contact measurement techniques at these scales. This paper compares three techniques: a laser profile scanner, Structure from Motion photogrammetry and Terrestrial Laser Scanning (TLS). We use spectral analysis, areal surface texture parameters and 2D cross-correlation analysis to evaluate the suitability of each approach for characterising and monitoring pavement macrotexture. The results show that SfM can produce successful measures of the areal root mean square height (Sq), which represents pavement texture depth and is positively correlated with skid resistance. Significant noise in the TLS data prevented agreement with the laser profiler but we show that new filtering procedures result in significantly improved values for the peak density (Spd) and the arithmetic peak mean curvature (Spc), which together define the shape and distribution of pavement aggregates forming macrotexture. However, filtering the TLS data results in a trade-off with vertical accuracy, thus altering the reliability of Sq. Finally, we show the functional areal parameters Spd and Spc are sensitive to sample size. This means that pavement specimen size of 150 mm × 150 mm or smaller, when used in laboratory or field observations, are inadequate to capture the true value of areal surface texture parameters. The deployment of wider scale approaches such as SfM and spectrally filtered TLS are required in order to successfully capture the functional areal parameters (Spc and Spd) for road surfaces

Quantifying long-term rates of texture change on road networks

Texture is required on pavements to provide safe and comfortable ride performance for users. This paper provides the first meaningful analysis of a long-term study of texture data obtained using TRACS (TRAffic Speed Condition Survey) at a site in the UK. TRACS data were collected annually, over a 2 km stretch of motorway from 1995 to 2019. A new data analysis approach utilising time series data with spectral analysis and spatial filtering procedures is presented. The results reveal that the approach enables legacy TRACS laser profile Sensor Measured Texture Depth (SMTD) data to be used to determine long term rates of change in road surface macrotexture. Thus, the technique has unlocked the potential for SMTD data collected annually for 7000 km of the Strategic Road Network in the UK, to inform road maintenance programmes by extrapolation. Additionally, results expose a systematic periodicity occurring each year within the SMTD data studied, corresponding to longitudinal oscillations with wavelengths between 33 and 62 m. The time-invariant periodicity of these oscillations suggests that it is ‘imprinted’ in the early life of the pavement. ‘Imprinting’ may theoretically arise with cyclic tyre loading applied by the suspension systems of heavy vehicles or during road construction

Seasonal Signals Observed in Non-Contact Long-Term Road Texture Measurements

Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The “rate of change” is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning. In the summer, this signal is evident as a transient peak in the “rate of change” of texture greater than 0.04 mm, and in the winter as a reduction. The transient changes in texture corresponded to above average rainfall occurring in the week prior to SMTD measurement. The signal observed demonstrates an inverse pattern to the classically understood seasonal variation of skid resistance in the UK, where values are low in the summer and high in the winter. The findings demonstrate for the first time that texture measurements experience a seasonal signal, and provide compelling evidence pointing toward surface processes (such as polishing and the wetting and drying of surface contaminants) causing changes to texture that are affecting seasonal variation in skid resistance

Precision near-infrared radial velocity instrumentation II: Non-Circular Core Fiber Scrambler

We have built and commissioned a prototype agitated non-circular core fiber scrambler for precision spectroscopic radial velocity measurements in the near-infrared H band. We have collected the first on-sky performance and modal noise tests of these novel fibers in the near-infrared at H and K bands using the CSHELL spectrograph at the NASA InfraRed Telescope Facility (IRTF). We discuss the design behind our novel reverse injection of a red laser for co-alignment of star-light with the fiber tip via a corner cube and visible camera. We summarize the practical details involved in the construction of the fiber scrambler, and the mechanical agitation of the fiber at the telescope. We present radial velocity measurements of a bright standard star taken with and without the fiber scrambler to quantify the relative improvement in the obtainable blaze function stability, the line spread function stability, and the resulting radial velocity precision. We assess the feasibility of applying this illumination stabilization technique to the next generation of near-infrared spectrographs such as iSHELL on IRTF and an upgraded NIRSPEC at Keck. Our results may also be applied in the visible for smaller core diameter fibers where fiber modal noise is a significant factor, such as behind an adaptive optics system or on a small < 1 meter class telescope such as is being pursued by the MINERVA and LCOGT collaborations.Comment: Proceedings of the SPIE Optics and Photonics Conference "Techniques and Instrumentation for Detection of Exoplanets VI" held in San Diego, CA, August 25-29, 201

Tracking the nature and trajectory of social support in Facebook mutual aid groups during the COVID-19 pandemic.

At the onset of the COVID-19 pandemic, thousands of mutual aid groups were established on social media and operated as platforms through which people could offer or request social support. Considering the importance of Facebook mutual aid groups during the early stages of the COVID-19 pandemic in the United Kingdom but also the lack of empirical research regarding the trajectories and types of social support rendered available through the groups, our aims in this paper are threefold; first, to examine the trajectory of social support-related activity during the period between March-December 2020; second, to compare offers and requests of support during the peaks of the first and second waves; third to provide a rich analysis of the types of social support that were offered or requested through the online mutual aid groups. Quantitative findings suggest that online social support activity declined soon after the peak of the first pandemic wave and, at least in Facebook mutual aid groups, did not reach the levels observed during the first wave. Also, the number of offers of support during the first wave was higher compared to offers during the second wave, and similar was the case for requests for support. Additionally, offers for support were higher compared to requests for support during both the first and second waves. Finally, qualitative analysis showed that people used the Facebook mutual aid groups to offer and request various types of practical, emotional, and informational support. Limitations as well as implications of our study are considered. [Abstract copyright: © 2022 The Authors.