Experimental study and characterization of bubble behaviors in the orifice-induced hydrodynamic cavitation

Experimental studies were performed to characterize the development process of orifice-induced cavitation and transitional bubble behaviors. The transition from non-cavitation to fully developed cavitation was carefully studied. Cavitation bubble clouds were observed at orifice, indicating the inception of cavitation. The number of bubbles produced were dramatically increased while the averaged sizes of bubble reduced when cavitation was initiated. Both orifice opening ratio and perimeter can affect the cavitation developing process. A long orifice perimeter promotes the production of fine bubbles. The orifice plates with the smallest opening ratio generated a desired gas-liquid interfacial area at the lowest required pressure. An orifice plate with multiple orifices is recommended in the design of orifice-based cavitation reactor for production of high cavitation intensity

Cosmic microwave background constraints on cosmological models with large-scale isotropy breaking

Several anomalies appear to be present in the large-angle cosmic microwave background (CMB) anisotropy maps of WMAP, including the alignment of large-scale multipoles. Models in which isotropy is spontaneously broken (e.g., by a scalar field) have been proposed as explanations for these anomalies, as have models in which a preferred direction is imposed during inflation. We examine models inspired by these, in which isotropy is broken by a multiplicative factor with dipole and/or quadrupole terms. We evaluate the evidence provided by the multipole alignment using a Bayesian framework, finding that the evidence in favor of the model is generally weak. We also compute approximate changes in estimated cosmological parameters in the broken-isotropy models. Only the overall normalization of the power spectrum is modified significantly.Comment: Accepted for publication in Phys. Rev.

Testing cosmological models with large-scale power modulation using microwave background polarization observations

We examine the degree to which observations of large-scale cosmic microwave background (CMB) polarization can shed light on the puzzling large-scale power modulation in maps of CMB anisotropy. We consider a phenomenological model in which the observed anomaly is caused by modulation of large-scale primordial curvature perturbations and calculate Fisher information and error forecasts for future polarization data, constrained by the existing CMB anisotropy data. Because a significant fraction of the available information is contained in correlations with the anomalous temperature data, it is essential to account for these constraints. We also present a systematic approach to finding a set of normal modes that maximize the available information, generalizing the well-known Karhunen-Loève transformation to take account of the constraints from the temperature data. A polarization map covering at least ∼60% of the sky should be able to provide a 3σ detection of modulation at the level favored by the temperature data. A significant fraction of the information in such a data set is contained in the single mode that optimally encapsulates the signal due to temperature-polarization correlation.National Science Foundation (U.S.) (Grants 0922748 and 1410133

Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.Comment: Accepted Paper in WWW'2

Physics-Informed Neural Operator for Learning Partial Differential Equations

Machine learning methods have recently shown promise in solving partial differential equations (PDEs). They can be classified into two broad categories: approximating the solution function and learning the solution operator. The Physics-Informed Neural Network (PINN) is an example of the former while the Fourier neural operator (FNO) is an example of the latter. Both these approaches have shortcomings. The optimization in PINN is challenging and prone to failure, especially on multi-scale dynamic systems. FNO does not suffer from this optimization issue since it carries out supervised learning on a given dataset, but obtaining such data may be too expensive or infeasible. In this work, we propose the physics-informed neural operator (PINO), where we combine the operating-learning and function-optimization frameworks. This integrated approach improves convergence rates and accuracy over both PINN and FNO models. In the operator-learning phase, PINO learns the solution operator over multiple instances of the parametric PDE family. In the test-time optimization phase, PINO optimizes the pre-trained operator ansatz for the querying instance of the PDE. Experiments show PINO outperforms previous ML methods on many popular PDE families while retaining the extraordinary speed-up of FNO compared to solvers. In particular, PINO accurately solves challenging long temporal transient flows and Kolmogorov flows where other baseline ML methods fail to converge

Ambient PM Toxicity is Correlated with Expression Levels of Specific MicroRNAs

Uncertainties regarding optimized air pollution control remain as the underlying mechanisms of city-specific ambient particulate matter (PM)-induced health effects are unknown. Here, water-soluble extracts of PMs collected from four global cities via automobile air-conditioning filters were consecutively injected three times by an amount of 1, 2, and 2 mg into the blood circulation of Wistar rats after filtration by a 0.45 μm pore size membrane. Acute health effects, such as immune and inflammatory responses and hemorrhage in alveoli, were observed right after the PM extraction injection. Significant differences between cities in biomarker tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCP-1) levels were detected following the second and third PM injections. Rats’ inflammatory responses varied substantially with the injections of city-specific PMs. Repeated PM extract exposure rendered the rats more vulnerable to subsequent challenges, and downregulation of certain microRNAs was observed in rats. Among the studied miRNAs, miR-125b, and miR-21 were most sensitive to the PM exposure, exhibiting a negative dose–response-type relationship with a source-specific PM (oxidative potential) toxicity (r² = 0.63 and 0.57; p-values < 0.05). The results indicated that city-specific PMs could induce different health effects by selectively regulating different miRNAs, and that certain microRNAs, e.g., miR-125b and miR-21, may be externally mediated to neutralize PM-related health damages

Mapmaking for Precision 21 cm Cosmology

In order to study the "Cosmic Dawn" and the Epoch of Reionization with 21 cm tomography, we need to statistically separate the cosmological signal from foregrounds known to be orders of magnitude brighter. Over the last few years, we have learned much about the role our telescopes play in creating a putatively foreground-free region called the "EoR window." In this work, we examine how an interferometer's effects can be taken into account in a way that allows for the rigorous estimation of 21 cm power spectra from interferometric maps while mitigating foreground contamination and thus increasing sensitivity. This requires a precise understanding of the statistical relationship between the maps we make and the underlying true sky. While some of these calculations would be computationally infeasible if performed exactly, we explore several well-controlled approximations that make mapmaking and the calculation of map statistics much faster, especially for compact and highly redundant interferometers designed specifically for 21 cm cosmology. We demonstrate the utility of these methods and the parametrized trade-offs between accuracy and speed using one such telescope, the upcoming Hydrogen Epoch of Reionization Array, as a case study.Comment: 28 pages, 14 figures. Slightly revised to match published Physical Review D versio

Ambient PM Toxicity is Correlated with Expression Levels of Specific MicroRNAs

Uncertainties regarding optimized air pollution control remain as the underlying mechanisms of city-specific ambient particulate matter (PM)-induced health effects are unknown. Here, water-soluble extracts of PMs collected from four global cities via automobile air-conditioning filters were consecutively injected three times by an amount of 1, 2, and 2 mg into the blood circulation of Wistar rats after filtration by a 0.45 μm pore size membrane. Acute health effects, such as immune and inflammatory responses and hemorrhage in alveoli, were observed right after the PM extraction injection. Significant differences between cities in biomarker tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCP-1) levels were detected following the second and third PM injections. Rats’ inflammatory responses varied substantially with the injections of city-specific PMs. Repeated PM extract exposure rendered the rats more vulnerable to subsequent challenges, and downregulation of certain microRNAs was observed in rats. Among the studied miRNAs, miR-125b, and miR-21 were most sensitive to the PM exposure, exhibiting a negative dose–response-type relationship with a source-specific PM (oxidative potential) toxicity (r² = 0.63 and 0.57; p-values < 0.05). The results indicated that city-specific PMs could induce different health effects by selectively regulating different miRNAs, and that certain microRNAs, e.g., miR-125b and miR-21, may be externally mediated to neutralize PM-related health damages