The University of Colorado (CU) is a system of public universities in Colorado consisting of four campuses: University of Colorado Boulder, University of Colorado Colorado Springs, University of Colorado Denver in downtown Denver and at the Anschutz Medical Campus in Aurora. It is governed by the elected, nine-member Board of Regents of the University of Colorado.
Abstract There is growing demand for river-network models that can estimate sediment transport at fine temporal resolution. Although coupling physically based sediment modules with hydrological and hydrodynamic models is a logical path forward, these approaches require intensive calibration and substantial computing power, limiting their practical use. This study develops a physics-informed neural network (PINN) that embeds sediment transport and advection-dispersion equations directly into a neural-network architecture, boosting predictive accuracy while reducing the need for large observational datasets. We introduce a novel interconnected PINN scheme in which PINNs for separate river reaches are coupled through loss functions at their boundaries, creating a cohesive model for the river network. The resulting framework predicts suspended-sediment concentrations reliably throughout the catchment during flood events. We evaluated three configurations of the PINN model and compared their performance against a purely data-driven machine learning model and an optimized physically based model. Trained on a subset of flood events and tested on unseen events, the interconnected PINN framework (root mean squared error = 5.8 kt) outperformed both the data-driven (33.8 kt) and physical (27.8 kt) models in estimating event loads. This novel PINN framework is transferable and can be applied to other catchment and river-network modeling, supporting simulations of both hydrology and flow-transported constituents. It is among the first to leverage comprehensive real-world data, using observations from the Manawatū River in New Zealand for both model training and validation. Requiring only standard river-network descriptors and hydrological time series, the approach can be transferred readily to other catchments with similar datasets.
Cellulose aerogel-based phase change materials (CA-PCMs) combine the latent heat storage capability of phase change materials (PCMs) with the hierarchical porous architecture, low density, and structural stability of cellulose aerogels (CA), providing a promising platform for sustainable thermal energy storage. This review critically examines recent advances in CA-PCMs from a materials-by-design perspective by correlating fabrication strategies, thermophysical properties, and structure-property-performance relationships. A comparative assessment of impregnation, in situ polymerization, encapsulation, and direct mixing establishes the influence of fabrication route on PCM loading, latent heat retention, thermal conductivity, mechanical stability, and cycling durability. Furthermore, graphene-, MXene-, CNT-, polymer-, and other cellulose-based hybrid systems are critically compared, demonstrating that conductive nanofillers can enhance thermal conductivity by 50-200% while introducing important trade-offs between heat transfer, latent heat storage, multifunctionality, and manufacturing scalability. Unlike previous reviews, this work integrates comparative performance analysis, commercialization perspectives, and emerging computational approaches into a unified materials design strategy for rational CA-PCM development. Finally, the review identifies key challenges, including long-term durability, scalable manufacturing, sustainable processing, and multifunctional integration, and proposes a research roadmap to accelerate the industrial translation of next-generation CA-PCMs.
Early in language learning, learners may know the meanings of only a small subset of the words they encounter. We refer to these meaningful words as semantic seeds. In three experiments, we asked how this sparse semantic knowledge shapes what learners extract from distributional experience and whether the resulting semantic expectations facilitate word learning. Adults were first introduced to Seed pseudowords. Half of participants learned meanings for these Seeds, whereas the other half were only familiarized with Seed forms. All participants were then exposed to an artificial language with Subject-Object-Verb-like structure, containing Seed and additional, novel Test pseudowords. In Experiments 1a and 1b, learners with meaningful Seeds developed category-consistent semantic expectations for previously meaningless Test words based on their distributional histories. These expectations depended on systematic alignment between Seed meanings and distributional categories: disrupting that alignment eliminated the corresponding semantic preferences. However, these expectations did not reliably accelerate subsequent cross-situational learning of specific word-referent mappings. Experiment 2 showed that Seed meanings generalized to Test words that had never directly co-occurred with Seeds, indicating that semantic properties became associated with distributionally defined categories and were propagated throughout the language. While meaningful Seeds affected the semantic interpretation of distributional categories, they did not affect the acquisition of distributional structure itself; distributional category learning occurred even when Seeds were meaningless. Together, these findings suggest that although learners can acquire distributional structure without semantic knowledge, sparse word meanings can reveal semantic properties of that structure and support meaning hypotheses for otherwise unfamiliar words.
Abstract While humanitarian engineering (HE) programs increasingly emphasize preparing students to engage in social change—from addressing inequitable access to infrastructure and technology in marginalized communities to challenging biases in the workplace and examining norms in engineering organizations that perpetuate inequality—there remains a limited understanding of how these experiences build students’ confidence to engage in social justice activism. This study investigates how HE graduate education influences students’ social justice self-efficacy (SJSE)—their perceived ability to create positive social change across personal, interpersonal, community, and institutional dimensions. Drawing from 88 interviews with 22 students across six HE graduate programs during their first year, we identify a distinct three-phase developmental pattern. Specifically, students often experience (1) a critical exposure phase in fall term coursework that dramatically increased personal SJSE while often decreasing community and institutional dimensions; (2) an integration phase in spring term experiences where students developed more balanced SJSE by identifying specific approaches to social impact while maintaining critical awareness; and (3) a testing phase during summer fieldwork where students confronted the challenges of applying their developing SJSE in real-world contexts. Rather than developing uniformly, students experienced nonlinear, asymmetric growth across SJSE dimensions, often maintaining optimism in one area despite challenges in others. These findings suggest HE education may benefit from providing targeted support during these critical transitions, encouraging specific, more focused engagement in social justice advocacy rather than system-wide transformation, creating opportunities for social justice advocacy practice, and having more of a focus on students’ interpersonal SJSE. This research advances understanding of how HE programs can foster advocates for making engineering more equitable, inclusive, and responsive to marginalized communities’ needs.
