Paths to research-driven decision making in the realms of environment and water

Now more than ever it is critical for researchers and decision makers to work together to improve how we manage and preserve the planet\u27s natural resources. Water managers in the western U.S., as in many regions of the world, are facing unprecedented challenges including increasing water demands and diminishing or unpredictable supplies. The transfer of knowledge (KT) and technology (TT) between researchers and entities that manage natural resources can help address these issues. However, numerous barriers impede the advancement of such transfer, particularly between organizations that do not operate in a profit-oriented context and for which best practices for university-industry collaborative engagement may not be sufficient. Frameworks designed around environmental KT – such as the recently-developed Research-Integration-Utilization (RIU) model – can be leveraged to address these barriers. Here, we examine two examples in which NASA Earth science satellite data and remote-sensing technology are used to improve the management of water availability and quality. Despite differences in scope and outcomes, both of these case studies adopt KT and TT best practices and can be further understood through the lens of the RIU model. We show how these insights could be adopted by NASA through a conceptual framework that charts individual- and organizational-level integration milestones alongside technical milestones. Environmental organizations can learn from this approach and adapt it to fit their own institutional needs, integrating KT/TT models and best practices while recognizing and leveraging existing institutional logics that suit their organization\u27s unique history, technical capability and priorities

TrustHop: Building a Social Trust Network

Undergraduate thesis submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2022In recent online social networks, each user can often assign a value to their immediate friends level of trustworthiness. Understanding a social trust value between any two nodes in an online social network is beneficial in a range of applications, like online marketing and recommendation systems. However, assessing social trust between two members in an online social network is difficult and time-consuming. This is because existing work either created handcrafted rules based on specialized domain knowledge or required a large number of computational resources, limiting its scalability. Graph-based techniques have recently been proved to be effective at learning from graph data. Even though social trust may be represented as graph data, its advantages have a lot of potential for trust evaluation. Therefore, we begin by reviewing the characteristics of online social networks and the properties of trust. After which the two types of graph-simplification and graph-analogy methodologies would be compared and contrasted as well as their respective problems and obstacles. We then conduct a quick examination of its pre- and post-processes to present an integrated view of trust evaluation. Finally, we discuss some unresolved issues that all trust models face.Ashesi Universit