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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.

It is doubtful whether Max Weber would have been appreciative of his current status as the father of organisation theory. Weber did not develop the concept of bureaucracy as part of a quest to advance a science of organisations, or in order to do a microanalysis of the internal structure of particular organisational units. The concept of bureaucracy was an ideal-typical concept developed as a point of departure for comparisons across historical periods and geographic settings. Weber’s research was motivated by macroscopic and historical questions such as ‘why did capitalism develop in the West’ and, ‘how do persons in the West and other civilizations attach meaning to their activities?’ Unlike consultants and organisation theorists that make use of him today, it was not a major concern for Weber to develop criteria for the most efficient kinds of organisations. Rather, his concern was to identify variations in administrative and bureaucratic cultures and patterns by the means of the bureaucratic ideal type. It is maintained in modern textbooks in organisation theory that there has been a development from a closed and rationalistic paradigm towards an understanding of organisations as open and natural systems, and Max Weber’s theory of bureaucracy is taken as a point of departure for this kind of narrative. This classification of Weber as an example of a rational and closed approach is highly questionable. The cross-societal and historical approach used so effectively by Weber, is put on a sidetrack in such mainstream narratives. It would be more in the spirit of Weber to focus on organising as an activity, bureaucracy as an ethos and to study organisations within their particular political and cultural contexts.

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.

After briefly covering Bill Freudenburg’s early years, this essay reviews his major scholarly contributions and professional accomplishments while a faculty member at Washington State University, the University of Wisconsin, and the University of California-Santa Barbara. Bill’s unique strengths – especially his keen sociological imagination – and crucial conceptual, theoretical and empirical contributions are highlighted, as well as his commitment to providing valuable mentoring for students and colleagues. The enduring importance of his work is ensured by the continuing application and extension of his ideas by other scholars.

This chapter narratively examines the value of scholarship grants to seven underrepresented science, technology, engineering, and mathematics (STEM) students who attended the same research comprehensive university. The scholarships that the students in our convenience sample received were awarded by six National Science Foundation grant programs in the United States. A literature review tracing the effects of scholarships, instrumentalism, and the core purpose of education sets the context for this narrative investigation. The four pillars comprising the theoretical framework are value, experience, story, and identity. The seven stories of impact that emerged from the narrative inquiry reveal multiperspectival insights into the value of scholarships to students' lives, careers, and selves. Moreover, we also explore how scholarship recipients established their sense of value in autonomous and committed ways while promoting their personal welfare and seeking the common good of others. All of these important considerations contribute to the national and international literature relating to diversity, higher education, STEM careers, and the power of scholarship grants to transcend instrumentalism privileging workforce demands.

The ongoing anthropological transformation urges the rethinking of education, underlining the inadequacy of our schools and universities in dealing with hypercomplexity, that is, with the global extension of all political, social, and cultural processes and with their indeterminacy, interdependence, and interconnection. The idea that educational processes are questions of a purely technical/technological nature, solely a problem of skills and know-how, is the “great mistake” of the hypertechnological society, based on the illusion of being able to measure and quantify everything, to eliminate error and unpredictability, and to achieve total control and rationality. It is necessary to rethink education radically because the extraordinary scientific discoveries and the dynamics of the new technologies have completely overturned the complex interaction between biological and cultural evolution, doing away with the borders between the natural and the artificial. Emergence and emergency themselves are structural features of complex systems (living, social, and human systems), rendered hypercomplex through today’s acceleration and virality, regarding not only education and socialization but also the representations and perceptions of all systemic processes. The merging of fields of knowledge and an epistemology of error become essential for the analysis and interpretation of this hypercomplexity and the unpredictability that distinguishes it.

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.