University of Notre Dame
  • South Bend, Indiana, United States
Recent publications
Background Spatial repellents are widely used for prevention of mosquito bites and evidence is building on their public health value, but their efficacy against malaria incidence has never been evaluated in Africa. To address this knowledge gap, a trial to evaluate the efficacy of Mosquito Shield™, a spatial repellent incorporating transfluthrin, was developed for implementation in Busia County, western Kenya where long-lasting insecticidal net coverage is high and baseline malaria transmission is moderate to high year-round. Methods This trial is designed as a cluster-randomized, placebo-controlled, double-blinded clinical trial. Sixty clusters will be randomly assigned in a 1:1 ratio to receive spatial repellent or placebo. A total of 6120 children aged ≥6 months to 10 years of age will be randomly selected from the study clusters, enrolled into an active cohort (baseline, cohort 1, and cohort 2), and sampled monthly to determine time to first infection by smear microscopy. Each cohort following the implementation of the intervention will be split into two groups, one to estimate direct effect of the spatial repellent and the other to estimate degree of diversion of mosquitoes and malaria transmission to unprotected persons. Malaria incidence in each cohort will be estimated and compared (primary indicator) to determine benefit of using a spatial repellent in a high, year-round malaria transmission setting. Mosquitoes will be collected monthly using CDC light traps to determine if there are entomological correlates of spatial repellent efficacy that may be useful for the evaluation of new spatial repellents. Quarterly human landing catches will assess behavioral effects of the intervention. Discussion Findings will serve as the first cluster-randomized controlled trial powered to detect spatial repellent efficacy to reduce malaria in sub-Saharan Africa where transmission rates are high, insecticide-treated nets are widely deployed, and mosquitoes are resistant to insecticides. Results will be submitted to the World Health Organization Vector Control Advisory Group for assessment of public health value towards an endorsement to recommend inclusion of spatial repellents in malaria control programs. Trial registration ClinicalTrials.gov NCT04766879 . Registered February 23, 2021.
The initial-boundary value problem (ibvp) for the m-th order dispersion Korteweg-de Vries (KdV) equation on the half-line with rough data and solution in restricted Bourgain spaces is studied using the Fokas Unified Transform Method (UTM). Thus, this work advances the implementation of the Fokas method, used earlier for the KdV on the half-line with smooth data and solution in the classical Hadamard space, consisting of function that are continuous in time and Sobolev in the spatial variable, to the more general Bourgain spaces framework of dispersive equations with rough data on the half-line. The spaces needed and the estimates required arise at the linear level and in particular in the estimation of the linear pure ibvp, which has forcing and initial data zero but non-zero boundary data. Using the iteration map defined by the Fokas solution formula of the forced linear ibvp in combination with the bilinear estimates in modified Bourgain spaces introduced by this map, well-posedness of the nonlinear ibvp is established for rough initial and boundary data belonging in Sobolev spaces.
Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using the multigroup discrete ordinates (SN) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.
Aerosol transport of enteric microbiota including fecal pathogens and antimicrobial resistance genes (ARGs) has been documented in a range of settings but remains poorly understood outside indoor environments. We conducted a systematic review of the peer-reviewed literature to summarize evidence on specific enteric microbiota including enteric pathogens and ARGs that have been measured in aerosol samples in urban settings where the risks of outdoor exposure and antibiotic resistance (AR) spread may be highest. Following PRISMA guidelines, we conducted a key word search for articles published within the years 1990–2020 using relevant data sources. Two authors independently conducted the keyword searches of databases and conducted primary and secondary screenings before merging results. To be included, studies contained extractable data on enteric microbes and AR in outdoor aerosols regardless of source confirmation and reported on qualitative, quantitative, or viability data on enteric microbes or AR. Qualitative analyses and metric summaries revealed that enteric microbes and AR have been consistently reported in outdoor aerosols, generally via relative abundance measures, though gaps remain preventing full understanding of the role of the aeromicrobiological pathway in the fate and transport of enteric associated outdoor aerosols. We identified remaining gaps in the evidence base including a need for broad characterization of enteric pathogens in bioaerosols beyond bacterial genera, a need for greater sampling in locations of high enteric disease risk, and a need for quantitative estimation of microbial and nucleic acid densities that may be applied to fate and transport models and in quantitative microbial risk assessment.
In this paper, we present an innovative sine-helical heat exchanger whose channel geometry consists in a combination of a helically coiled geometry and a sine-wave geometry. Laminar flow is considered in the present study, with the Reynolds numbers ranging from 100 to 1400. The flow structure and the convective heat transfer in the novel heat exchanger are analyzed numerically using the finite volume method. The results are compared to those obtained with a classical helically coiled heat exchanger. It is shown that the periodic change of the centrifugal forces in the sine-helical channel breaks the flow symmetry and leads to chaotic particle trajectories. The effect of chaotic advection on the enhancement of mixing efficiency is evidenced. The coefficient of variation of the outlet temperature in the sine-helical flow is decreased by about 100% relative to that in the helical channel, highlighting thus a better temperature homogeneity. Moreover, the thermal enhancement factor, measuring the convective heat transfer coefficient at same pumping power, is also increased between 5.5 and 20.7% in the since-helical flow relative to the helical channel. Consequently, the proposed novel heat exchanger is very promising for many applications with laminar flow regimes.
Coastal wetlands of the Laurentian Great Lakes are diverse and productive ecosystems that provide many ecosystem services, but are threatened by anthropogenic factors, including nutrient input, land-use change, invasive species, and climate change. In this study, we examined one component of wetland ecosystem structure – phytoplankton biomass – using the proxy metric of water column chlorophyll-a measured in 514 coastal wetlands across all five Great Lakes as part of the Great Lakes Coastal Wetland Monitoring Program. Mean chlorophyll-a concentrations increased from north-to-south from Lake Superior to Lake Erie, but concentrations varied among sites within lakes. To predict chlorophyll-a concentrations, we developed two random forest models for each lake – one using variables that may directly relate to phytoplankton biomass (“proximate” variables; e.g., dissolved nutrients, temperature, pH) and another using variables with potentially indirect effects on phytoplankton growth (“distal” variables; e.g., land use, fetch). Proximate and distal variable models explained 16–43% and 19–48% of variation in chlorophyll-a, respectively, with models developed for lakes Erie and Michigan having the highest amount of explanatory power and models developed for lakes Ontario, Superior, and Huron having the lowest. Land-use variables were important distal predictors of chlorophyll-a concentrations across all lakes. We found multiple proximate predictors of chlorophyll-a, but there was little consistency among lakes, suggesting that, while chlorophyll-a may be broadly influenced by distal factors such as land use, individual lakes and wetlands have unique characteristics that affect chlorophyll-a concentrations. Our results highlight the importance of responsible land-use planning and watershed-level management for protecting coastal wetlands.
The b-Novikov equation is a one-parameter family of Camassa–Holm-type equations with cubic nonlinearities that possess multipeakon traveling wave solutions and for b=3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b=3$$\end{document} gives the well known Novikov equation, which is integrable. Here, using appropriate two-peakon solutions, instability and nonuniqueness for the initial value problem of the b-Novikov equation is studied when the initial data belong in Sobolev spaces Hs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H^s$$\end{document}, s<3/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s < 3/2$$\end{document}, on both the line and the circle. The rectangular region of the bs-plane defined by b>2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b>2$$\end{document} and s<3/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s<3/2$$\end{document} is divided into three subregions. The subregion that is below the line segment s=2-b4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s = 2-\frac{b}{4}$$\end{document}, 2<b<4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2<b < 4$$\end{document}, is characterized by the phenomenon of nonuniqueness. Then, to the right of this subregion the phenomenon of norm inflation occurs, which leads to instability and discontinuity of the solution map. However, on the segment s=2-b4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s = 2-\frac{b}{4}$$\end{document}, 2<b<4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2<b < 4$$\end{document}, either nonuniqueness or discontinuity may occur. All these are proved by constructing appropriate two-peakon solutions with arbitrary small initial size data that collide in arbitrary small time T. These solutions may become arbitrarily large near T. For b≤2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b\le 2$$\end{document}, the two-peakon solutions do not work since there is no collision. Finally, it is well known that for s>3/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s>3/2$$\end{document} there is well-posedness no matter what is the value of b.
In this paper, we show how one property of an average affects perceptions of the variance of the distribution that the average is derived from. Specifically, we find that when people view average ratings compatible with a possible input they perceive these ratings to come from less variable distributions—even when this is statistically less likely. Six experiments and four supplemental studies (total N = 16,988) document evidence for this effect: People perceive less dispersion in the distributions of “compatible average ratings” (i.e., averages matching a possible input; e.g., 4; 4.0; 4.00 on a discrete scale from 1 to 5 stars) compared to those of “non-compatible average ratings” (i.e., averages that do not match a possible input; e.g., 4.01 and 4.10). We argue that this error can be explained by a compatibility principle which states that the weighting of an input increases with its degree of compatibility with the output. People rely on the perceived compatibility between an output and input when forming judgments about the frequency of the input, affecting their assessment of the dispersion associated with the average. For instance, people recognize that a 4.0 average matches a 4 and thus perceive this average to be comprised of more 4s and indicative of less dispersion. We close with a discussion of consequences of this perception for choice and search.
Hurricanes cause significant building damages, whose losses are aggregated by existing hurricane regional loss estimation frameworks, compromising model granularity and fidelity. The need to reduce mounting losses through targeted mitigation investments instead demands tools that enable site-specific, building-specific, and component-level loss estimations. Delivering such granularity in high-fidelity loss modeling creates new challenges in efficiently assembling and managing spatial and geometric data of thousands of constructed buildings. In response, this paper offers two interrelated contributions: (1) a conceptual data model that leverages open data for efficient integration and querying of component- to site-level information across the loss estimation workflow and (2) the implementation of this data model in Python to tractably generate building models within existing open-source loss modeling workflows. The accompanying case study and scenario-based verifications demonstrate how the data model meets stated design requirements in its generation of building models.
The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of “prediction error neurons.” This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply.
Designing privacy-preserving distributed algorithms for stochastic aggregative games is urgent due to the privacy issues caused by information exchange between players. This paper proposes two differentially private distributed algorithms seeking the Nash equilibrium in stochastic aggregative games. By adding time-varying random noises, the input and output-perturbation methods are given to protect each player’s sensitive information. For the case of output-perturbation, utilizing mini-batch methods, the algorithm’s mean square error is inversely proportional to the privacy level ε and the number of samples. For the case of input-perturbation, a differentially private distributed stochastic approximation-type algorithm is developed to achieve almost sure convergence and (ε,δ)-differential privacy. Under suitable consensus time conditions, the algorithm’s convergence rate is rigorously presented for the first time, where the optimal convergence rate O(1/k) in a mean square sense is obtained. Then, utilizing mini-batch methods, the influence of added privacy noise on the algorithm’s performance is reduced, and the convergence rate of the algorithm is improved. Specifically, when the batch sizes and the number of consensus times at each iteration grow at a suitable rate, an exponential rate of convergence can be achieved with the same privacy level. Finally, a simulation example demonstrates the algorithms’ effectiveness.
Medieval churches constructed of unreinforced masonry (URM) represent critical assets of Italian architectural heritage. In order to preserve these churches against earthquakes, obtaining robust information regarding their material mechanical characteristics is necessary as part of a reliable structural analysis and strengthening intervention program. Given the drawbacks of semi-destructive or destructive testing of heritage material, non-destructive testing (NDT) is the most viable approach to obtain data regarding the mechanical characteristics of the material composing the structure of the churches. However, there are several uncertainties inherent within NDT techniques based on the current state of the art. Thus, two different NDT techniques (i.e., rebound hammer testing, and pulse velocity testing) and two expert judgment-based investigation techniques (i.e., masonry quality index, and mechanical properties ranges based on the Commentary to the Italian building code) were applied to 170 specimens belonging to the walls of 72 URM Italian medieval churches to assess the quality of the URM and its components. The surveyed churches walls, although highly variable in geometry, materials, and conditions, can be classified into four URM types: a) irregular stone masonry with pebbles, erratic and irregular stone units; b) roughly cut stone with good bond; c) ashlar masonry with regular squared blocks and mortar joints; and d) solid fired clay bricks with lime mortar. Subsequently, using the SonReb technique, predictive equations that aggregate the two NDT techniques and the correlation coefficient specific for each URM type were developed to define some of the critical mechanical properties of the URM (i.e., compressive strength, Young’s modulus, and shear modulus). The mechanical properties determined via predictive equations were then plotted and compared with the predictions of the two well-established expert judgment-based investigation techniques to evaluate the accuracy of the approach. Finally, a partial validation based on NDT and destructive testing techniques of six URM prisms was performed to evaluate the accuracy of the proposed predictive equations. Ultimately, three equations to determine the compressive strength, the Young’s modulus, and the shear modulus were developed. The developed equations offer to engineering practitioners a rapid NDT technique to assess URM properties that do not solely rely on the judgment and expertise of the practitioner.
We study a free boundary problem modeling multilayer tumor growth with a small time delay τ, representing the time needed for the cell to complete the replication process. The model consists of two elliptic equations which describe the concentration of nutrient and the tumor tissue pressure, respectively, an ordinary differential equation describing the cell location characterizing the time delay and a partial differential equation for the free boundary. In this paper, we establish the well‐posedness of the problem; namely, first, we prove that there exists a unique flat stationary solution (σ∗,p∗,ρ∗,ξ∗) for all μ>0. The stability of this stationary solution should depend on the tumor aggressiveness constant μ. It is also unrealistic to expect the perturbation to be flat. We show that, under non‐flat perturbations, there exists a threshold μ∗>0 such that (σ∗,p∗,ρ∗,ξ∗) is linearly stable if μ<μ∗ and linearly unstable if μ>μ∗. Furthermore, the time delay increases the stationary tumor size. These are interesting results with mathematical and biological implications.
Identifying the genetic basis of adaptation is a central goal of evolutionary biology. However, identifying genes and mutations affecting fitness remains challenging because a large number of traits and variants can influence fitness. Selected phenotypes can also be difficult to know a priori , complicating top–down genetic approaches for trait mapping that involve crosses or genome-wide association studies. In such cases, experimental genetic approaches, where one maps fitness directly and attempts to infer the traits involved afterwards, can be valuable. Here, we re-analyse data from a transplant experiment involving Timema stick insects, where five physically clustered single-nucleotide polymorphisms associated with cryptic body coloration were shown to interact to affect survival. Our analysis covers a larger genomic region than past work and revealed a locus previously not identified as associated with survival. This locus resides near a gene, Punch ( Pu ) , involved in pteridine pigments production, implying that it could be associated with an unmeasured coloration trait. However, by combining previous and newly obtained phenotypic data, we show that this trait is not eye or body coloration. We discuss the implications of our results for the discovery of traits, genes and mutations associated with fitness in other systems, as well as for supergene evolution. This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.
The effect of pulsed direct current (PDC) on solid-state diffusion in the Ni–Al binary system was investigated. Two experimental schemes were employed: in the presence and absence of an electric field. The diffusion couples were heat-treated for 1.5, 3, and 5 h at 803, 833, and 863 K. Under the investigated conditions, only two intermetallic phases (NiAl3 and Ni2Al3) formed at the boundary of the metals. It was shown that the PDC passing through the diffusion couple significantly enhanced the growth rates of both phases. The apparent reaction–diffusion coefficients were DNiAl3=4.0×10−9exp(−7.6×104RT) and DNi2Al3=9.7×10−9exp(−8.4×104RT) in the field-assisted scheme, whereas their corresponding values in the field-insulated scheme were DNiAl3=6.1×10−5exp(−1.55×105RT) and DNi2Al3=3.7×10−4exp(−1.71×105RT). The results directly imply that the effective activation energy of diffusion decreases by approximately two times when the PDC passes through the media.
In this paper, we propose a convolutional neural network for the removal of spatially varying motion blur from captured images with the assistance of inertial sensor data. In the proposed system, both the image and motion data are captured simultaneously and passed to a network for processing. The proposed network adopts three parallel nets to extract image features and a per-pixel concatenation to tightly integrate motion homographies estimated from inertial sensor data with the input degraded image. This unique network design facilitates the use of homographies which describes the motion blur kernel more accurately. Compared to the recently proposed image deblurring networks, the proposed network is found to produce restored images that have fewer artifacts and provide quantifiable and subjective improvement.
In this paper it is shown that the Hartogs triangle T\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{T}}$$\end{document} in C2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{C}}^2$$\end{document} is a uniform domain. This implies that the Hartogs triangle is a Sobolev extension domain. Furthermore, the weak and strong maximal extensions of the Cauchy-Riemann operator agree on the Hartogs triangle. These results have numerous applications. Among other things, they are used to study the Dolbeault cohomology groups with Sobolev coefficients on the complement of T\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{T}}$$\end{document}.
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Ronald Metoyer
  • Department of Computer Science and Engineering
Zoltan Toroczkai
  • Department of Physics
Omar Lizardo
  • Department of Sociology
Ramin Rajaei
  • Department of Computer Science and Engineering
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