The past oil crises have caused dramatic improvements in fuel efficiency in all industrial sectors. The aviation sector—aircraft manufacturers and airlines—has also made significant efforts to improve the fuel efficiency through more advanced jet engines, high-lift wing designs, and lighter airframe materials. However, the innovations in energy-saving aircraft technologies do not coincide with the oil crisis periods. The largest improvement in aircraft fuel efficiency took place in the 1960s while the high oil prices in the 1970s and on did not induce manufacturers or airlines to achieve a faster rate of innovation. In this paper, we employ a historical analysis to examine the socio-economic reasons behind the relatively slow technological innovation in aircraft fuel efficiency over the last 40 years. Based on the industry and passenger behaviors studied and prospects for alternative fuel options, this paper offers insights for the aviation sector to shift toward more sustainable technological options in the medium term. Second-generation biofuels could be the feasible option with a meaningful reduction in aviation’s lifecycle environmental impact if they can achieve sufficient economies of scale.

The extensive utilization of electronic health records (EHRs) and the growth of enormous open biomedical datasets has readied the area for applications of computational and machine learning techniques to reveal fundamental patterns. This study’s goal is to develop a medical treatment recommendation system using Korean EHRs along with the Markov decision process (MDP). The sharing of EHRs by the National Health Insurance Sharing Service (NHISS) of Korea has made it possible to analyze Koreans’ medical data which include treatments, prescriptions, and medical check-up. After considering the merits and effectiveness of such data, we analyzed patients’ medical information and recommended optimal pharmaceutical prescriptions for diabetes, which is known to be the most burdensome disease for Koreans. We also proposed an MDP-based treatment recommendation system for diabetic patients to help doctors when prescribing diabetes medications. To build the model, we used the 11-year Korean NHISS database. To overcome the challenge of designing an MDP model, we carefully designed the states, actions, reward functions, and transition probability matrices, which were chosen to balance the tradeoffs between reality and the curse of dimensionality issues.

Media publishers that have commonly relied on profits from advertisements and content sales to fund their operations are not only content providers (manufacturers) to media aggregator platforms (retailers) but are also competing service providers (rival retailers) in the digital video supply chain. Different from a traditional media supply chain, they can easily operate their own direct distribution channels in the Internet era. In the digital video supply chain, it is not clear whether commercialization of the direct distribution channel would be beneficial for the media publisher because it would decrease profits from advertisements. The choice about commercialization should be investigated thoroughly because it is closely related to the media publishers’ sustainability as a public medium in a digital environment. In this study, we analyze the impacts of commercializing the direct distribution channel based on a game-theoretic approach. Specifically, we compare the profits of a media publisher with and without the commercialization of the channel from analyses of sequentially defined games. Our results show that media publishers prefer to use a free service on their direct channel if the content they provide is not highly valued. They can also choose not to provide their content to the media aggregator with a paid service.