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Urban expansion and the increasing frequency and intensity of extreme precipitation events bring new challenges to stormwater collection systems. One underrecognized issue is the occurrence of transient flow conditions that lead to adverse multiphase flow interactions (AMFI): essentially, the formation, collapse, and uncontrolled release of air pockets within stormwater system flows. While the fundamental physics of AMFI have been evaluated in laboratory experiments and idealized modeling studies, much less is known about their development in real or simulated stormwater networks, and about the roles played by rainfall and network properties. A necessary precursor to AMFI is the development of pressurized flow conditions within a network. The goal of this study is to understand how spatiotemporal rainfall variability affects the occurrence of pressurized conditions in a stormwater drainage network in the Richmond district of San Francisco, California. High-resolution bias-corrected radar rainfall fields for 24 recent storms were used as the independent variable of EPA-SWMM simulations. Model analyses indicate that the incidence of pressurized flow increases with storm intensity and is more sensitive to rainfall temporal variability than spatial variability. This research provides a reference for analyzing AMFI precursors in other networks and may have important implications for the improvement of stormwater infrastructures. 

Accurate prediction of parallel application performance in HPC systems is essential for efficient resource allocation and system design. Classical performance models estimate of speedup based on theoretical assumptions, but their applicability is limited by parameter estimation, data acquisition, and real-world system issues such as latency and network congestion. This paper describes performance prediction using classical performance models boosted by a trainable machine learning framework. Domain-informed machine-learning models estimate the overhead of an application for a given problem size and resource configuration as a coefficient of the estimated speedup provided by performance laws. We evaluate this approach on two HPC mini-applications and two full applications with varying patterns of computation and communication and also evaluate the prediction accuracy on runs with varying processors-per-node configurations. Our results show that this method significantly improves the accuracy of performance predictions over standard analytical models and black-box regressors, while remaining robust even with limited training data. 

The investigation aimed to determine whether altering metal microstructure by introducing special grain boundaries through annealing could reduce the corrosion damage observed in the presence of pyruvate. Oxygen-free pure copper coupons were annealed at 325°C, 475°C and 950°C for varying durations to optimize the formation of ∑3 special boundaries. Samples annealed at 475°C for 30 min had the highest yield of such boundaries, thus, were selected for testing. Annealed and as-received, untreated, copper specimens were exposed under stagnant conditions to an aqueous oxic solution of sodium pyruvate for 30 days. Microscopy, spectroscopy, and electrochemical methods were employed to characterize the specimens prior to and following pyruvate exposure. Pyruvate caused localized corrosion of copper seen as micro pitting, irrespective of the specimen treatment. Reduced pitting severity and a decrease in the corrosion rate by 32 % were recorded for annealed coupons when compared to as-received ones. It is proposed that the difference in thickness and morphology of the oxide layer between annealed and as-received coupons, evidenced through electrochemical techniques, is the likely contributor to the improved corrosion resistance of annealed coupons. 

Richard E. Mayer has made major contributions to Educational Psychology since the 1970s, including work on learning in mathematics, creativity, interest, measurement, problem solving, and especially multimedia learning, defined as learning from instructional material that includes information in both verbal and visual form. In a 2024 reflection, Mayer called for identifying boundary conditions—i.e., moderators of effects—of his multimedia design principles. In an effort to identify these, we meta-analyzed Mayer’s corpus of multimedia research. We searched Google Scholar, PsycINFO, and the Cambridge Handbook of Multimedia Learning 3rd Ed. for peer-reviewed articles on multimedia learning with Mayer as an author published 1990-2022 and located 92 articles reporting on 181 studies reporting on 591 separate effects. We coded for 9 moderators: multimedia design principle, multimedia type, age, academic domain, country/continent, treatment duration, dependent variable type, year, and authorship order. We analyzed the Hedge’s g effect sizes using a multilevel regression approach in the metafor package in R. The overall effect was g = 0.37, which was significantly moderated by all moderators, including a small decline in effect size per year. Mean effects by multimedia design principle were uneven, with the largest significant effects for removing seductive detail, modality principle, personalization, multimedia principle, sentence-level coherence, and self-explanation. Medium significant overall effects were found for the testing effect, scaffolding, cueing, and embodiment. Large, consistent effects were found for text + diagrams across factual, inferential, and transfer outcomes. Less-consistent effects were found for animation, games, and simulations, with smaller effects on factual learning and on average larger effects on inferential and transfer outcomes, but no significant effects for virtual reality. We identified two boundary conditions in tests of design principle x DV type interactions and Multimedia type x DV type interactions. We close by interpreting various findings in phases of Mayer’s work, characterized by collaborators and educational technologies. We also contextualize Mayer’s findings within recent meta-analyses of the larger published research on various design principles. 

Abstract Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10), and the noise can be treated as a perturbation. We study optical neural networks where all layers except the last are operated in the limit that each neuron can be activated by just a single photon, and as a result the noise on neuron activations is no longer merely perturbative. We show that by using a physics-based probabilistic model of the neuron activations in training, it is possible to perform accurate machine-learning inference in spite of the extremely high shot noise (SNR  ~ 1). We experimentally demonstrated MNIST handwritten-digit classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to just 0.038 photons per multiply-accumulate (MAC) operation. Our physics-aware stochastic training approach might also prove useful with non-optical ultra-low-power hardware.