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The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

US Navy warships are capital-intensive national defense assets that require periodic depot and intermediate level maintenance availabilities (periods). Oftentimes, ship maintenance is deferred or forgone altogether due to geopolitical strife or fiscal challenges. The impacts of missed maintenance are not only a burden on ships’ crews, but they also have a deleterious effect on current and future readiness. It is a difficult task to strike a balance between current and future readiness when insufficient resources are available to sustain a fleet of warships. This paper draws from multi-attribute utility theory (MAUT) to develop a ship maintenance decision-making model that considers attributes from the current and life cycle readiness cohorts. Using the current maintenance plans for two DDG 51-class ships entering availabilities in same fiscal year, this model determines which ship is more capable of absorbing a loss of maintenance and planned modernizations relative to the context of the decision environment. Five attributes are considered for the overall decision: mandatory maintenance, non-mandatory maintenance, mission impact from maintenance, mission impact from planned modernizations, and maintenance backlog. The model presented here is generalizable to a number of U.S. Navy ships and watercraft and can be used to inform decision-makers of the short- and long-term impacts of deferring critical maintenance.

Los Angeles, California, is facing record drought conditions. As a result, there is interest in all things related to building and maintaining water capabilities. Leaks in the infrastructure can lead to costly losses of water resources. Accordingly, attention increasingly is being devoted to water leak management. Using data available through the City of Los Angeles’ open data movement, the number of leaks is analyzed in order to study both the impact of temperature and whether the number of leaks is decreasing over time. Three different approaches for modeling the number of leaks, including regression, time series, and neural networks, are compared.

This chapter explores the opportunities and challenges for Western firms that wish to engage in manufacturing operations in Iran, and particularly in the automotive industry. Although Iran has a long and fruitful history of embracing foreign investment, collaboration with foreign firms suffered in the aftermath of the Islamic Revolution in 1979. The imposition of UN sanctions in 2012, following the disagreements between Iran and leading Western powers over Iran’s nuclear policy, has resulted in a further exodus of foreign manufacturers from Iran, hurting the production quality, adoption of up-to-date technology and alignment to international standards for manufacturing, such as vehicle safety and engine emissions in Iran.

The removal of sanctions, contingent on the success of nuclear negotiations between Iran and leading world powers, could provide Iran with an opportunity to recommence manufacturing collaboration with Western firms. The case of the automotive industry discussed in this chapter indicates some of the challenges that Iran is likely to face if it once again wants to become a player in international markets.