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As infrastructure confront rapidly changing environments, there is an immediate need to provide the flexibility to pivot resources and how infrastructures are prioritized. Yet infrastructures are often categorized based on static criticality framings. We describedynamic criticalityas the flexibility to reprioritize infrastructure resources during disturbances. We find that the most important prerequisite for dynamic criticality is organizational adaptive capacity characterized by flexible goals, structures, sensemaking, and strategies. Dynamic capabilities are increasingly important in the Anthropocene, where accelerating conditions, uncertainty, and growing complexity are challenging infrastructures. We review sectors that deployed dynamic management approaches amidst changing disturbances: leadership and organizational change, defense, medicine, manufacturing, and disaster response. We use an inductive thematic analysis to identify key themes and competencies and analyze capabilities that describe dynamic criticality. These competencies drive adaptive capacity and open up the flexibility to pivot what is deemed critical, depending on the particulars of the hazard. We map these competencies to infrastructure systems and describe how infrastructure organizations may build adaptive capacity toward flexible priorities.

 

Traditional infrastructure adaptation to extreme weather events (and now climate change) has typically been techno‐centric and heavily grounded in robustness—the capacity to prevent or minimize disruptions via a risk‐based approach that emphasizes control, armoring, and strengthening (e.g., raising the height of levees). However, climate and nonclimate challenges facing infrastructure are not purely technological. Ecological and social systems also warrant consideration to manage issues of overconfidence, inflexibility, interdependence, and resource utilization—among others. As a result, techno‐centric adaptation strategies can result in unwanted tradeoffs, unintended consequences, and underaddressed vulnerabilities. Techno‐centric strategies thatlock‐intoday's infrastructure systems to vulnerable future design, management, and regulatory practices may be particularly problematic by exacerbating these ecological and social issues rather than ameliorating them. Given these challenges, we develop a conceptual model and infrastructure adaptation case studies to argue the following: (1) infrastructure systems are not simply technological and should be understood as complex and interconnected social, ecological, and technological systems (SETSs); (2) infrastructure challenges, like lock‐in, stem from SETS interactions that are often overlooked and underappreciated; (3) framing infrastructure with aSETS lenscan help identify and prevent maladaptive issues like lock‐in; and (4) a SETS lens can also highlight effective infrastructure adaptation strategies that may not traditionally be considered. Ultimately, we find that treating infrastructure as SETS shows promise for increasing the adaptive capacity of infrastructure systems by highlighting how lock‐in and vulnerabilities evolve and how multidisciplinary strategies can be deployed to address these challenges by broadening the options for adaptation.

 

Abstract City governments and urban universities are well-positioned to play critical roles in advancing urban sustainability transformations. However, in partnering, cities and universities often focus efforts on discrete sustainability-related projects, neglecting the development of long-term relationships and deep, inter-organizational ties that can allow for collaboration on lasting and transformational change. Yet, at both cities and universities there are often individuals who are deeply interested in developing better partnerships that contribute to the sustainability and livability of their communities. This research develops and tests an evidence-based and facilitated process to guide sustainability researchers and municipal practitioners in the development of transformational City-university partnerships for sustainability. The Audacious Partnerships Process was tested by four City-university partnerships including Arizona State University and the City of Tempe, Dublin City University and the City of Dublin, King’s College London and the City of Westminster and the National Autonomous University of Mexico and Mexico City. The Audacious Partnerships Process as well as results from post-surveys and interviews following implementation are elaborated. We conclude with key lessons for modifying and implementing the process to contribute to transformative partnership development. 

There is little literature exploring the needs of engineering faculty and the resources available at engineering colleges to support faculty development. Engineering deans are key stakeholders within institutions well-positioned to discuss trends and practices in faculty development within engineering colleges, however their perspective has not been captured in the literature. The purpose of this exploratory qualitative study was to learn about the state of faculty development within engineering colleges through the perspective of engineering deans. A particular focus was placed on identifying salient faculty needs and resources available to support faculty development within engineering colleges. Semi-structured interviews were completed with 23 engineering deans representing three types of institutions: R1 public (n = 8), R1/R2 private (n = 6), and primarily undergraduate-focused (n = 9). A rigorous thematic analysis process was completed until a final codebook emerged with strong interrater agreement. According to the deans the primary needs for incoming faculty involved teaching, research, understanding expectations, time management, and connectivity. There were variances in the approaches and resources available at each institution especially in relation to mentorship. This study indicates that further investigating effectiveness of faculty development programs especially mentorship across the various stages of a faculty’s career would be fruitful contributions to the engineering education community. 

Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has some potential to increase the assessment’s validity and reliability. The primary objective of this study is to examine the feasibility of a novel approach for stealthily assessing literacy skills using games in an intelligent tutoring system (ITS) designed for reading comprehension strategy training. We investigated the degree to which learners’ game performance and enjoyment predicted their scores on standardized reading tests. Amazon Mechanical Turk participants (n = 211) played three games in iSTART and self-reported their level of game enjoyment after each game. Participants also completed the Gates–MacGinitie Reading Test (GMRT), which includes vocabulary knowledge and reading comprehension measures. The results indicated that participants’ performance in each game as well as the combined performance across all three games predicted their literacy skills. However, the relations between game enjoyment and literacy skills varied across games. These findings suggest the potential of leveraging serious games to assess students’ literacy skills and improve the adaptivity of game-based learning environments. 

The problem of adapting models from a source domain using data from any target domain of interest has gained prominence, thanks to the brittle generalization in deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic data augmentations in cases of limited target data availability. In this paper, we consider the challenging setting of single-shot adaptation and explore the design of augmentation strategies. We argue that augmentations utilized by existing methods are insufficient to handle large distribution shifts, and hence propose a new approach SiSTA (Single-Shot Target Augmentations), which first fine-tunes a generative model from the source domain using a single-shot target, and then employs novel sampling strategies for curating synthetic target data. Using experiments with a state-of-the-art domain adaptation method, we find that SiSTA produces improvements as high as 20% over existing baselines under challenging shifts in face attribute detection, and that it performs competitively to oracle models obtained by training on a larger target dataset. Our codes can be accessed at github.com/kowshikthopalli/SISTA.