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The railway track system is the platform by which loads from moving trains are transferred to the underlying soil or supporting infrastructure such as bridges. The most common type of railway track system is ballasted track, which has been in use for over a century. Ballasted track has proved versatile. It can be constructed using locally available materials and with modifications to the rails and sleepers, crossings transferring trains from one route to another can be created. The structure of a ballasted track system consists of two main parts. The upper portion, termed the superstructure, comprises the rails, fastenings and sleepers. It is formed of components whose shape, stiffness and strength are designed and closely controlled. Below the superstructure is the substructure, which comprises the ballast and sub-ballast. Although the materials used in the substructure may have been specified, their engineering properties and geometric placement are less well controlled. In this chapter, we will explore how a typical ballasted track system transfers load to the ground and the ways in which the track form deteriorates, requiring maintenance and eventually renewal.

The mid-1990s marked a paradigm shift in the way physical activity is promoted, and walking is now considered the most suitable type of physical activity for widespread promotion. Accurate measurement underpins public health practice, hence the aims of this chapter are to: (1) provide a typology for the measurement of walking; (2) review methods to assess walking; (3) present challenges in defining walking measures; (4) identify issues in selecting instruments for the evaluation of walking and (5) discuss current efforts to overcome measurement challenges and methodological limitations. The taxonomy of walking indicates that secondary purpose walking is a more complex set of behaviours than primary purpose walks. It has many purposes and no specific domain or intensity, may lack regularity, and therefore poses greater measurement challenges. Objective measurement methods, such as accelerometers, pedometers, smartphones and other electronic devices, have shown good approximation for walking energy expenditure, but are indirect methods of walking assessment. Global Positioning System technology, the ‘Smartmat’ and radio-frequency identification tags are potential objective methods that can distinguish walkers, but also require complex analysis, are costly, and still need their measurement properties corroborated. Subjective direct methods, such as questionnaires, diaries and direct observation, provide the richest information on walking, especially short-term diaries, such as trip records and time use records, and are particularly useful for assessing secondary purpose walking. A unifying measure for health research, surveillance and health promotion would strongly advance the understanding of the impact of walking on health.

An accelerometer is a device that measures force due to gravity or a change in speed or direction of travel. This paper describes accelerometers and their application in other disciplines and, by way of an example, explores the utility of accelerometers for studying aggression. We end with a discussion of additional ways accelerometers might be used in group processes research.

We first review the use of accelerometers in other disciplines. We then present the results of four studies that demonstrate the use of accelerometers to measure aggression. Study 1 establishes the measure’s concurrent validity. Study 2 concerns its stability and representative reliability. Study 3 seeks to establish the measure’s predictive validity by associating it with an existing measure. Study 4 demonstrates the ability of accelerometers to address a sociological research question.

In Studies 1 and 2, we find that accelerometers can be used to differentiate between distinct levels of aggression. In Study 3, we find that men’s average peak acceleration correlates with a previously validated measure of aggression. Study 4 uses accelerometers to reproduce a well-established finding in the aggression literature.

We conclude that accelerometers are a flexible tool for group processes’ researchers and social scientists more broadly. Our findings should prove useful to social scientists interested in measuring aggression or in employing accelerometers in their work.

Health scientists and urban planners have long been interested in the influence that the built environment has on the physical activities in which we engage, the environmental hazards we face, the kinds of amenities we enjoy, and the resulting impacts on our health. However, it is widely recognized that the extent of this influence, and the specific cause-and-effect relationships that exist, are still relatively unclear. Recent reviews highlight the need for more individual-level data on daily activities (especially physical activity) over long periods of time linked spatially to real-world characteristics of the built environment in diverse settings, along with a wide range of personal mediating variables. While capturing objective data on the built environment has benefited from wide-scale availability of detailed land use and transport network databases, the same cannot be said of human activity. A more diverse history of data collection methods exists for such activity and continues to evolve owing to a variety of quickly emerging wearable sensor technologies. At present, no “gold standard” method has emerged for assessing physical activity type and intensity under the real-world conditions of the built environment; in fact, most methods have barely been tested outside of the laboratory, and those that have tend to experience significant drops in accuracy and reliability. This paper provides a review of these diverse methods and emerging technologies, including biochemical, self-report, direct observation, passive motion detection, and integrated approaches. Based on this review and current needs, an integrated three-tiered methodology is proposed, including: (1) passive location tracking (e.g., using global positioning systems); (2) passive motion/biometric tracking (e.g., using accelerometers); and (3) limited self-reporting (e.g., using prompted recall diaries). Key development issues are highlighted, including the need for proper validation and automated activity-detection algorithms. The paper ends with a look at some of the key lessons learned and new opportunities that have emerged at the crossroads of urban studies and health sciences.

We do have a vision for a world in which people can walk to shops, school, friends' homes, or transit stations; in which they can mingle with their neighbors and admire trees, plants, and waterways; in which the air and water are clean; and in which there are parks and play areas for children, gathering spots for teens and the elderly, and convenient work and recreation places for the rest of us. (Frumkin, Frank, & Jackson, 2004, p. xvii)

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs suffering from OA, sarcoma, and oral mucositis (a side effect of chemotherapy and radiation treatments). The R programming language and SAS JMP are used to clean data, generate ANOVA, LSR regression, decision tree, and nominal logistic regression models to predict changes in activity levels associated with the progression of arthritis. The predictive models provide a diagnostic basis for determining the degree of disease in a dog (based on demographics and activity levels) and provide forecasts that assist in establishing appropriate medication dosages for suffering dogs.

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.