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Abstract As the global energy sector transitions towards a cleaner and more sustainable future, observational evidence suggests that many new energy technologies share a close relationship with well-established technologies. Yet, the topic of how closely technologies are related has not been addressed rigorously, rather it has been the purview of practitioner know-how and informal expert opinion. In this study, we propose a quantitative method to supplement practitioners’ subjective understanding of the relatedness between technology domains. The method uses patents to represent the position of a technology in knowledge space and calculates the Hausdorff distance between patent domains to proxy the relatedness between technologies. We apply this method to investigate the relatedness of offshore wind energy technology to two more mature domains: onshore wind energy technology and offshore oil and gas technology. We examine the technological relatedness of individual offshore wind components to these two technologies, and represent the changes in relatedness through time. The results confirm that offshore wind components such as foundations, installation, and maintenance are more related to the offshore oil and gas industry; while other components, such as rotors and nacelles, are more related to onshore wind energy. The results also suggest that many offshore wind energy components are becoming less related through time to both of these domains, possibly indicating increasing innovation. This method can provide quantitative parameters to improve the modeling of technological change and guide practitioners in strategic decision-making regarding the positioning of industries and firms within those industries. 

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults. 

The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm’s accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO 2 /CH 4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.