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The effectiveness of model-based leak localization methods in water distribution systems (WDSs), including optimization-based and machine learning approaches, significantly depends on the quality and quantity of input data. Pressure data, easily accessible due to nonintrusive sensor installation and maintenance, are commonly used. However, economic constraints limit the number of sensors in WDSs, highlighting the need for strategic sensor placement to enhance data quality. This study introduces a novel, method-independent sensor placement strategy that integrates cluster definitions (leak resolution) with intuitive surrogates for localization performance, addressing the limitations of existing methods reliant on complex, nonintuitive metrics. We propose the Euclidean cluster-based optimal placement of sensors (ECOPS) approach, which employs sensitivity and uniqueness as fundamental signal properties to guide sensor placement. Validation tests within a comprehensive real-world WDS demonstrate that ECOPS outperforms existing surrogate-based approaches and improves the performance of current sensors installed for leak characterization. These findings provide compelling evidence of ECOPS’s potential for enhancing pressure sensor placement, thereby improving leak localization in WDS applications. 

Abstract Cryonotothenioids constitute a subgroup of notothenioid fishes endemic to the Southern Ocean that are specialized to exist in a narrow range of near-freezing temperatures. Due to the challenges of reliably collecting and maintaining larval cryonotothenioids in good condition, most thermal tolerance studies have been limited to adult and juvenile stages. With increasing environmental pressures from climate change in Antarctic ecosystems, it is important to better understand the impacts of a warming environment on larval stages as well. In this study, we determine the critical thermal maxima (CTmax) of cryonotothenioid larvae collected in pelagic net tows during three research cruises near the western Antarctic Peninsula. We sampled larvae of seven species representing three cryonotothenioid families—Nototheniidae, Channichthyidae, and Artedidraconidae. For channichthyid and nototheniid species, CTmax values ranged from 8.6 to 14.9 °C and were positively correlated with body length, suggesting that younger, less motile larvae may be especially susceptible to rapid warming events such as marine heatwaves. To our knowledge, this is the first published test of acute thermal tolerance for any artedidraconid, with CTmax ranging from 13.2 to 17.8 °C, which did not correlate with body length. Of the two artedidraconid species we collected,Neodraco skottsbergishowed remarkable tolerance to warming and was the only species to resume normal swimming following trials. We offer two hypotheses as to whyN. skottsbergihas such an elevated thermal tolerance: (1) their unique green coloration serves as camouflage within near-surface phytoplankton blooms, suggesting they occupy an especially warm near-surface niche, and (2) recent insights into their evolutionary history suggest that they are derived from taxa that may have occupied warm tide-pool habitats. Collectively, these results establishN. skottsbergiand larval channichthyids as groups of interest for future physiological studies to gain further insights into the vulnerability of cryonotothenioids to a warming ocean. 

We provide the first large-scale data collection of real-world approval-based committee elections. These elections have been conducted on the Polkadot blockchain as part of their Nominated Proof-of-Stake mechanism and contain around one thousand candidates and tens of thousands of (weighted) voters each. We conduct an in-depth study of application-relevant questions, including a quantitative and qualitative analysis of the outcomes returned by different voting rules. Besides considering proportionality measures that are standard in the multiwinner voting literature, we pay particular attention to less-studied measures of overrepresentation, as these are closely related to the security of the Polkadot network. We also analyze how different design decisions such as the committee size affect the examined measures. 

The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic. 

How ocean acidification (OA) interacts with other stressors is understudied, particularly for predators and prey. We assessed long-term exposure to decreased pH and low salinity on (1) juvenile blue crab Callinectes sapidus claw pinch force, (2) juvenile hard clam Mercenaria mercenaria survival, growth, and shell structure, and (3) blue crab and hard clam interactions in filmed mesocosm trials. In 2018 and 2019, we held crabs and clams from the Chesapeake Bay, USA, in crossed pH (low: 7.0, high: 8.0) and salinity (low: 15, high: 30) treatments for 11 and 10 wk, respectively. Afterwards, we assessed crab claw pinch force and clam survival, growth, shell structure, and ridge rugosity. Claw pinch force increased with size in both years but weakened in low pH. Clam growth was negative, indicative of shell dissolution, in low pH in both years compared to the control. Growth was also negative in the 2019 high-pH/low-salinity treatment. Clam survival in both years was lowest in the low-pH/low-salinity treatment and highest in the high-pH/high-salinity treatment. Shell damage and ridge rugosity (indicative of deterioration) were intensified under low pH and negatively correlated with clam survival. Overall, clams were more severely affected by both stressors than crabs. In the filmed predator-prey interactions, pH did not substantially alter crab behavior, but crabs spent more time eating and burying in high-salinity treatments and more time moving in low-salinity treatments. Given the complex effects of pH and salinity on blue crabs and hard clams, projections about climate change on predator-prey interactions will be difficult and must consider multiple stressors. 

Variable-pressure electron-beam lithography (VP-EBL) employs an ambient gas at subatmospheric pressures to reduce charging during electron-beam lithography. VP-EBL has been previously shown to eliminate pattern distortion and provide improved resolution when patterning poly(methyl methacrylate) (PMMA) on insulating substrates. However, it remains unknown how water vapor affects the contrast and clearing dose nor has the effect of water vapor on the negative-tone behavior of PMMA been studied. In addition, water vapor has recently been shown to alter the radiation chemistry of the VP-EBL process for Teflon AF. Such changes in radiation chemistry have not been explored for PMMA. In this work, VP-EBL was conducted on conductive substrates to study the effect of water vapor on PMMA patterning separately from the effects of charge dissipation. In addition, both positive and negative-tone processes were studied to determine the effect of water vapor on both chain scission and cross-linking. The contrast of PMMA was found to improve significantly with increasing water vapor pressure for both positive and negative-tone patterning. The clearing dose for positive-tone patterning increases moderately with vapor pressure as would be expected for electron scattering in a gas. However, the onset set dose for negative-tone patterning increased dramatically with pressure revealing a more significant change in the exposure mechanism. X-ray photoelectron spectra and infrared transmission spectra indicate that water vapor only slightly alters the composition of exposed PMMA. Also, electron scattering in water vapor yielded a much larger clear region around negative-tone patterns. This effect could be useful for increasing the range of the developed region around cross-linked PMMA beyond the backscattered electron range. Thus, VP-EBL for PMMA introduces a new means of tuning clearing/onset dose and contrast, while allowing additional control over the size of the cleared region around negative-tone patterns. 

The primary goal of the project is to leverage recent developments in smart water technologies to detect and reduce water leakages in large water distribution networks with the aid of neural networks. A cost effective non-invasive solution to detect leakages in transmission pipelines is needed by many water utilities as it will lead to significant water savings and reduced pipe breakage frequencies, especially in older infrastructure systems. The eventual goal of the project is to test the ANN model on a real network using field measured pressure and pipe breakage data after tuning and developing the model with simulated data. In this project we propose building a regression model, based on Multi-Layer Perceptron (MLP) algorithm, which is a class of feedforward Artificial Neural Networks (ANNs) to detect the leak locations within a proposed network. The model should be able to learn the structure, i.e. mapping of various leak nodes and sensor nodes in an area, such that it can detect the leak nodes based on the pressure values with significant accuracy. 

This work discusses the development, verification and performance assessment of a discontinuous Galerkin solver for the compressible Navier-Stokes equations using the Legion programming system. This is motivated by (i) the potential of this family of high-order numer- ical methods to accurately and efficiently realize scale-resolving simulations on unstructured grids and (ii) the desire to accommodate the utilization of emerging compute platforms that exhibit increased parallelism and heterogeneity. As a task-based programming model specifically designed for performance portability across distributed heterogeneous architectures, Legion represents an interesting lternative to the traditional approach of using Message Passing Interface for massively parallel computational physics solvers. Following detailed discussion of the implementation, the high-order convergence of the solver is demonstrated by a suite of canonical test cases and good strong scaling behavior is obtained. This work constitutes a first step towards a research platform that is able to be deployed and efficiently run on modern supercomputers.