Non-destructive evaluation of railway trackbed ballast

The “green agenda” combined with highway congestion has accelerated the demand for increased freight and passenger travel on the world’s railways. These increases have driven demand for more efficient and rapid investigation of trackbed ballast. Network Rail and other rail infrastructure operators spend significant financial sums on inspecting, tamping, adjusting, cleaning, and replacing trackbed ballast. Such maintenance is often to the detriment of normal network operation. Industry requires a method of ballast evaluation that is non-intrusive, cheap, can appraise long stretches of track in a short period of time, and give a fingerprinting result from which time-to-maintenance can be calculated and planned. Thus, the aim was to develop evaluation methods using non-destructive testing techniques. A 10-year old full-scale trackbed composed of variously fouled ballast was re-visited and used for experimentation. The condition of the ballast was calculated using the Ionescu Fouling Index. Earlier research at the University of Edinburgh enabled researchers worldwide to characterise ballast using ground penetrating radar (GPR). This research was repeated, validated and taken forward in a series of GPR experiments on the trackbed using a range of antennas from 500MHz to 2.6GHz. New "scatter" metrics were developed to determine ballast condition from the GPR waveforms. These metrics were then used to predict the Ionescu Fouling Index with a correlation coefficient greater than 0.9. One of the current approaches to evaluating the stiffness of railway ballast is to use a Falling Weight Deflectometer (FWD). The viability of using a Prima 100 mini-FWD on railways to measure stiffness was determined and deemed to be ineffective on ballast. The applicability of the impulse response technique on railways was determined. An instrumented hammer was used to excite the ballast, with a geophone measuring the response. The Frequency Response Function of this was successfully correlated with the Ionescu Fouling Index with a correlation coefficient also greater than 0.9. Finally, using GPR data and measured stiffness data collected by Banverket, Sweden, a numerical model to successfully relate radar responses to stiffness was developed

The daily association between affect and alcohol use: a meta-analysis of individual participant data

Influential psychological theories hypothesize that people consume alcohol in response to the experience of both negative and positive emotions. Despite two decades of daily diary and ecological momentary assessment research, it remains unclear whether people consume more alcohol on days they experience higher negative and positive affect in everyday life. In this preregistered meta-analysis, we synthesized the evidence for these daily associations between affect and alcohol use. We included individual participant data from 69 studies (N = 12,394), which used daily and momentary surveys to assess affect and the number of alcoholic drinks consumed. Results indicate that people are not more likely to drink on days they experience high negative affect, but are more likely to drink and drink heavily on days high in positive affect. People self-reporting a motivational tendency to drink-to-cope and drink-to-enhance consumed more alcohol, but not on days they experienced higher negative and positive affect. Results were robust across different operationalizations of affect, study designs, study populations, and individual characteristics. These findings challenge the long-held belief that people drink more alcohol following increases in negative affect. Integrating these findings under different theoretical models and limitations of this field of research, we collectively propose an agenda for future research to explore open questions surrounding affect and alcohol use.The present study was funded by the Canadian Institutes of Health Research Grant MOP-115104 (Roisin M. O’Connor), Canadian Institutes of Health Research Grant MSH-122803 (Roisin M. O’Connor), John A. Hartford Foundation Grant (Paul Sacco), Loyola University Chicago Research Support Grant (Tracy De Hart), National Institute for Occupational Safety and Health Grant T03OH008435 (Cynthia Mohr), National Institutes of Health (NIH) Grant F31AA023447 (Ryan W. Carpenter), NIH Grant R01AA025936 (Kasey G. Creswell), NIH Grant R01AA025969 (Catharine E. Fairbairn), NIH Grant R21AA024156 (Anne M. Fairlie), NIH Grant F31AA024372 (Fallon Goodman), NIH Grant R01DA047247 (Kevin M. King), NIH Grant K01AA026854 (Ashley N. Linden-Carmichael), NIH Grant K01AA022938 (Jennifer E. Merrill), NIH Grant K23AA024808 (Hayley Treloar Padovano), NIH Grant P60AA11998 (Timothy Trull), NIH Grant MH69472 (Timothy Trull), NIH Grant K01DA035153 (Nisha Gottfredson), NIH Grant P50DA039838 (Ashley N. Linden-Carmichael), NIH Grant K01DA047417 (David M. Lydon-Staley), NIH Grant T32DA037183 (M. Kushner), NIH Grant R21DA038163 (A. Moore), NIH Grant K12DA000167 (M. Potenza, Stephanie S. O’Malley), NIH Grant R01AA025451 (Bruce Bartholow, Thomas M. Piasecki), NIH Grant P50AA03510 (V. Hesselbrock), NIH Grant K01AA13938 (Kristina M. Jackson), NIH Grant K02AA028832 (Kevin M. King), NIH Grant T32AA007455 (M. Larimer), NIH Grant R01AA025037 (Christine M. Lee, M. Patrick), NIH Grant R01AA025611 (Melissa Lewis), NIH Grant R01AA007850 (Robert Miranda), NIH Grant R21AA017273 (Robert Miranda), NIH Grant R03AA014598 (Cynthia Mohr), NIH Grant R29AA09917 (Cynthia Mohr), NIH Grant T32AA07290 (Cynthia Mohr), NIH Grant P01AA019072 (P. Monti), NIH Grant R01AA015553 (J. Morgenstern), NIH Grant R01AA020077 (J. Morgenstern), NIH Grant R21AA017135 (J. Morgenstern), NIH Grant R01AA016621 (Stephanie S. O’Malley), NIH Grant K99AA029459 (Marilyn Piccirillo), NIH Grant F31AA022227 (Nichole Scaglione), NIH Grant R21AA018336 (Katie Witkiewitz), Portuguese State Budget Foundation for Science and Technology Grant UIDB/PSI/01662/2020 (Teresa Freire), University of Washington Population Health COVID-19 Rapid Response Grant (J. Kanter, Adam M. Kuczynski), U.S. Department of Defense Grant W81XWH-13-2-0020 (Cynthia Mohr), SANPSY Laboratory Core Support Grant CNRS USR 3413 (Marc Auriacombe), Social Sciences and Humanities Research Council of Canada Grant (N. Galambos), and Social Sciences and Humanities Research Council of Canada Grant (Andrea L. Howard)

The ATLAS Data Acquisition and High-Level Trigger: Concept, Design and Status

The Trigger and Data Acquisition system (TDAQ) of the ATLAS experiment at the CERN Large Hadron Collider is based on a multi-level selection process and a hierarchical acquisition tree. The system, consisting of a combination of custom electronics and commercial products from the computing and telecommunication industry, is required to provide an online selection power of 105 and a total throughput in the range of Terabit/sec. This paper introduces the basic system requirements and concepts, describes the architecture of the system, discusses the basic measurements supporting the validity of the design and reports on the actual status of construction and installation

The ATLAS trigger - high-level trigger commissioning and operation during early data taking

The ATLAS experiment is one of the two general-purpose experiments due to start operation soon at the Large Hadron Collider (LHC). The LHC will collide protons at a centre of mass energy of 14~TeV, with a bunch-crossing rate of 40~MHz. The ATLAS three-level trigger will reduce this input rate to match the foreseen offline storage capability of 100-200~Hz. This paper gives an overview of the ATLAS High Level Trigger focusing on the system design and its innovative features. We then present the ATLAS trigger strategy for the initial phase of LHC exploitation. Finally, we report on the valuable experience acquired through in-situ commissioning of the system where simulated events were used to exercise the trigger chain. In particular we show critical quantities such as event processing times, measured in a large-scale HLT farm using a complex trigger menu

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe