With 25% share in world"s fossil fuel consumption, the transportation sector is contributing about 29% of worldwide greenhouse gas (GHG) emissions [1]. Furthermore, about 23% of global carbon dioxide (CO2) emissions are attributed to the transportation sector [2]. Other transportation related pollutants include nitric oxides, sulphur dioxide and particulate matter (PM) dust in urban settings. According to WHO report, transportation related ambient air pollution has resulted in 4.2 million premature deaths worldwide in 2016 [3]. About 91% of these deaths are born by low and middle-income countries [3]. With increasing urbanization, traffic related health issues will exasperate further, resulting in increased lung inflammation, cardiovascular, pulmonary and respiratory diseases [3−5].

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Physical activity is a major part of the user's context for wearable computing applications. The System should be able to acquire the user's physical activities using body worn sensors. The authors propose developing a personal activity recognition system that is practical, reliable, and can be used for healthcare related applications. They propose to use the wearable device which is a readymade, light weight, small and easy to use device for identifying physical activities (i.e. lying, sitting, walking, standing, cycling, running, ascending stairs and descending stairs), fitness studio activities (i.e. using elliptical trainer, butterfly, bench-press and pull down) and swimming techniques (i.e., dolphin, backstroke , breast-stroke and free-style) using machine learning algorithms. In this chapter, the authors present an approach to build a system that exhibits this property and provides evidence based on user studies. Their results indicate that the system has a good accuracy rate.

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Improper execution of modern code-designed structures in many developing countries have resulted in significant deficient building stock; low strength of concrete, reduced reinforcement, inappropriate detailing of beam-column members, and lack of lateral ties in joint panels. Observations based on earthquake-induced damages and experimental studies conducted on such buildings have revealed significant vulnerability of beam-column joints of bare moment-resisting frame structures. Shake table tests were conducted on selected three 1 : 4 reduced-scale three-story reinforced concrete (RC) moment-resisting frames, including one bare RC frame and two masonry-infilled RC frames, having relatively lower bay width-to-height ratio. The models were tested under multilevels of seismic excitations using natural acceleration time history of 1994 Northridge and also free vibration tests, to acquire the models’ dynamic characteristics, i.e., frequencies and elastic viscous damping, and seismic response parameters, i.e., roof displacement, interstory drift and interstory shear, and seismic response curves, in order to understand the role of masonry infill in the selected frames under moderate seismic actions. The inclusion of masonry infill avoided joint shear hinging of the frame. Additionally, the infill provided energy dissipation to the structure through masonry sliding over multiple cracks. This enabled the structure to control seismic displacement demand and resist relatively higher ground motions, yet limiting structural damages.