Modern power systems are the backbone of our society, supplying electric energy for daily activities. With the integration of communication networks and high penetration of renewable energy sources (RESs), modern power systems have evolved into a cross-domain multilayer complex system of systems with improved efficiency, controllability, and sustainability. However, increasing numbers of unexpected events, including natural disasters, extreme weather, and cyberattacks, are compromising the functionality of modern power systems and causing tremendous societal and economic losses. Resilience, a desirable property, is needed in modern power systems to ensure their capability to withstand all kinds of hazards while maintaining their functions. This article presents a systematic review of recent power system resilience enhancement techniques and proposes new directions for enhancing modern power systems’ resilience considering their cross-domain multilayer features. We first answer the question, “what is power system resilience?” from the perspectives of its definition, constituents, and categorization. It is important to recognize that power system resilience depends on two interdependent factors: network design and system operation. Following that, we present a review of articles published since 2016 that have developed innovative methodologies to improve power system resilience and categorize them into infrastructural resilience enhancement and operational resilience enhancement. We discuss their problem formulations and proposed quantifiable resilience measures, as well as point out their merits and limitations. Finally, we argue that it is paramount to leverage higher order subgraph studies and scientific machine learning (SciML) for modern power systems to capture the interdependence and interactions across heterogeneous networks and data for holistically enhancing their infrastructural and operational resilience.

Academic makerspaces have continued to rise in popularity as research shows the diverse benefits they provide to students. More and more engineering curriculums are incorporating makerspaces and as such there is a need to better understand how their student users can best be served. Surveys administered to makerspace users at a public research university in the Southwest United States during Fall 2020, Spring 2021, Spring 2022, and Fall 2022 tracked student tool usage trends with academic career stages. The survey asked questions about prior experience, motivation, tool usage, and demographics. Analyzed results for mechanical engineering student users provide insight into how curriculum and class year affect the specific tools used and the percentage of student who used a particular tool. The survey results also create a bipartite network model of students and tools, mimicking plant-pollinator type mutualistic networks in ecology. The bipartite network models the student interactions with the tools and visualizes how students interact with the tools. This network modeling enables ecological network analysis techniques to identify key makerspace actors quantitatively. Ecological modularity, for example, identifies divisions in the student-tool makerspace network that highlight how students from different majors (here we investigate mechanical) utilize the makerspace's tools. Modularity is also able to identify “hub” tools in the space, defined as tools central to a student's interaction within the space, based on student-tool connectivity data. The analysis finds that tools commonly used for class by mechanical engineering students, such as the 3D printer or laser cutter, act as gateway tools that bring users into the space and help spark interest in the space's other tools. Using the combined insights from the survey results and the network analysis, ecological network metrics are shown here to be a promising route for informing makerspace policy, tool purchases, and curriculum development. The results can help ensure that the space is set up to give students the best learning opportunities.

How innovative are you? High levels of innovativeness are correlated with high-value creation, and today's rapid pace of change requires new skills for lifelong learning, curiosity, innovation, and continuous improvement. To what level can personal innovativeness be improved through curricular innovations and how do we improve students' practical skills for ongoing discovery? Innovation Mind and Skill Sets for Design and Research is a new course that focuses on providing STEM students the broader mind and skill sets that will help them connect their specialized work to the larger system and the society it impacts and effectively experiment, discover, and create value in the highly dynamic and uncertain environment of breakthrough innovation. This paper will discuss the development of the course which Hipwell based on both innovation literature and her own two decades of experience applying and improving innovation business processes with her teams in a fast-paced, high-tech industry. An educational class structure and learning outcomes are developed based upon the theory that innovation is itself a learning process and can be improved through the known learning science concepts of improvement of student metacognition about their innovation process and incorporation of intention and reflection in their learning cycles. Further, the model incorporates literature on teaching system thinking and Transformative Learning Theory for adults. Content is developed that teaches the students practical techniques that scaffold their innovation process and increase their metacognition. Movement up levels of Bloom's Taxonomy and long-term habits are developed through the practice of the innovation process in a team project with expert feedback from an experienced innovation practitioner. Learning is increased through reflection on the process during class presentations. Measurement of student improvement results compared to control design curricula using the Innovator Mindset® Assessment is also discussed.