cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1627961
title: Determination of the sterile release rate for stopping growing age-structured populations

description: ABSTRACTA freely-growing age-structured population was modeled for growth and control by sterile male releases. Equilibrium populations yield critical sterile male release rates that would hold the population at equilibrium. It is shown here that these rates may be different from the release rates required to stop a growing population and bring it to an equilibrium. A computer simulation was constructed of this population and a parameter sensitivity analysis graphed the effects on the required sterile male release rate of fertility, mating delay in adult females, net juvenile survivorship, three adult survivorship curves, the time spent in the juvenile stages, and total life span. The adult survivorship curves had the greatest effect on the required sterile release rate for population elimination. The required release rate was also determined for <i>Ceratitis capitata</i> (Wiedemann) using survivorship and fertility data from a laboratory strain. The concepts of over-flooding ratio and release ratio were discussed and quantified for the cases above.
DOI: 10.6084/m9.figshare.1627961.v1
title: Determination of the sterile release rate for stopping growing age-structured populations

description: ABSTRACTA freely-growing age-structured population was modeled for growth and control by sterile male releases. Equilibrium populations yield critical sterile male release rates that would hold the population at equilibrium. It is shown here that these rates may be different from the release rates required to stop a growing population and bring it to an equilibrium. A computer simulation was constructed of this population and a parameter sensitivity analysis graphed the effects on the required sterile male release rate of fertility, mating delay in adult females, net juvenile survivorship, three adult survivorship curves, the time spent in the juvenile stages, and total life span. The adult survivorship curves had the greatest effect on the required sterile release rate for population elimination. The required release rate was also determined for <i>Ceratitis capitata</i> (Wiedemann) using survivorship and fertility data from a laboratory strain. The concepts of over-flooding ratio and release ratio were discussed and quantified for the cases above.
DOI: 10.6084/m9.figshare.1627958
title: Comprehensive rheological characterization of chopped and ground switchgrass

description: <b>Background:</b> In this work the impact of grinding and chopping on the rheological properties of four switchgrass materials (<i>panicum virgatum</i>) is investigated, comparing physical characterization to previously published feeding performance in bulk equipment in a high-tonnage facility, in which chopped materials exhibited dramatically better handling performance than corresponding ground materials. <b>Results:</b> Physical characterization included sieve analysis, automated digital image analysis of particle size and shape distributions, microscopy of particle microstructure, shear tests using two different sizes of ring shear testers, flow tests in a custom hopper with an adjustable outlet, and uniaxial compression and spring back tests. Shear tests failed to demonstrate consistent statistically significant differences between the shear strengths of the chopped and ground materials subjected to uniaxial compression. However, in laboratory-scale hopper flow tests, the chopped materials exhibited substantially better flow performance than the ground materials, in agreement with the previously published bulk handling tests. <b>Conclusion:</b> Chopped switchgrass materials exhibit substantially better flowability properties in large-scale bulk equipment and in laboratory-scale hopper feeding tests; however, shear tests were not able to reliably predict the flowability properties of bulk switchgrass particles, likely because uniaxial-compression shear tests do not capture the effects of compressibility and elasticity in multi-dimensional flow streams.
DOI: 10.6084/m9.figshare.1627958.v1
title: Comprehensive rheological characterization of chopped and ground switchgrass

description: <b>Background:</b> In this work the impact of grinding and chopping on the rheological properties of four switchgrass materials (<i>panicum virgatum</i>) is investigated, comparing physical characterization to previously published feeding performance in bulk equipment in a high-tonnage facility, in which chopped materials exhibited dramatically better handling performance than corresponding ground materials. <b>Results:</b> Physical characterization included sieve analysis, automated digital image analysis of particle size and shape distributions, microscopy of particle microstructure, shear tests using two different sizes of ring shear testers, flow tests in a custom hopper with an adjustable outlet, and uniaxial compression and spring back tests. Shear tests failed to demonstrate consistent statistically significant differences between the shear strengths of the chopped and ground materials subjected to uniaxial compression. However, in laboratory-scale hopper flow tests, the chopped materials exhibited substantially better flow performance than the ground materials, in agreement with the previously published bulk handling tests. <b>Conclusion:</b> Chopped switchgrass materials exhibit substantially better flowability properties in large-scale bulk equipment and in laboratory-scale hopper feeding tests; however, shear tests were not able to reliably predict the flowability properties of bulk switchgrass particles, likely because uniaxial-compression shear tests do not capture the effects of compressibility and elasticity in multi-dimensional flow streams.
DOI: 10.6084/m9.figshare.1627953
title: Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming

description: A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable, accounting for multiple comparisons through the existing methods yields overly conservative results when there are multiple outcome variables of interest. This stems from the fact that unmeasured confounding cannot affect the probability of assignment to treatment differently depending on the outcome being analyzed. Existing methods allow this to occur by combining the results of individual sensitivity analyses to assess whether at least one hypothesis is significant, which in turn results in an overly pessimistic assessment of a study's sensitivity to unobserved biases. By solving a quadratically constrained linear program, we are able to perform a sensitivity analysis while enforcing that unmeasured confounding must have the same impact on the treatment assignment probabilities across outcomes for each individual in the study. We show that this allows for uniform improvements in the power of a sensitivity analysis not only for testing the overall null of no effect, but also for null hypotheses on <i>specific</i> outcome variables while strongly controlling the familywise error rate. We illustrate our method through an observational study on the effect of smoking on naphthalene exposure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627954
title: Palaeoproterozoic A-type magmatism in northern Wuyishan terrane, Southeast China: petrogenesis and tectonic implications

description: ABSTRACTIn this article we present zircon U–Pb ages, Hf isotopes, and whole-rock geochemistry of the Longzhu rhyolite porphyry from the Cathaysia Block, Southeast China to constrain its petrogenesis and provide insights into the early Precambrian tectonic evolution of the Cathaysia Block. LA-ICP-MS zircon U–Pb dating of a representative sample yields a weighted mean <sup>206</sup>Pb/<sup>207</sup>Pb age of 1819 ± 16 Ma, interpreted as the crystallization age of the Longzhu rhyolite porphyry. Zircons from this sample have <i>ε</i><sub>Hf</sub>(<i>t</i>) values ranging from – 8.4 to – 2.2 and <i>T</i><sup>Hf</sup><i><sub>DM2</sub></i> model ages from 2.76 to 2.46 Ga. The whole-rock Nd isotopic data from the Longzhu rhyolite porphyries yield <i>ε</i><sub>Nd</sub>(<i>t</i>) values spanning – 6.3 to – 4.7 and <i>T</i><sup>Nd</sup><i><sub>DM2</sub></i> model ages from 2.81 to 2.69 Ga. The rhyolite porphyries have geochemical features similar to those of the typical A-type granites (rhyolites), with high SiO<sub>2</sub>, total alkali contents and FeO<sup>t</sup>/MgO ratios, and low CaO and MgO contents. Additionally, the rhyolite porphyries have high total rare earth element concentrations (627 ~ 760 ppm), high (La/Yb)<sub>N</sub> values (14.5 ~ 26.9), strongly negative Eu anomalies (δEu = 0.28 ~ 0.41), and display enrichments of Rb, Ga, Th, and U and depletions of Sr, Nb, Ta, Eu, and Ti. The geochemical and Nd-Hf isotopic features suggest that the Palaeoproterozoic Longzhu rhyolite porphyries were generated by partial melting of source rocks similar to those of the Badu Complex in an intra-plate extensional setting. The results from this study, when combined with existing geochronological data, further demonstrate that the Palaeoproterozoic rocks of Wuyishan terrane probably represent a remnant of the Columbia supercontinent.
DOI: 10.6084/m9.figshare.1627951
title: Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks

description: In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627954.v1
title: Palaeoproterozoic A-type magmatism in northern Wuyishan terrane, Southeast China: petrogenesis and tectonic implications

description: ABSTRACTIn this article we present zircon U–Pb ages, Hf isotopes, and whole-rock geochemistry of the Longzhu rhyolite porphyry from the Cathaysia Block, Southeast China to constrain its petrogenesis and provide insights into the early Precambrian tectonic evolution of the Cathaysia Block. LA-ICP-MS zircon U–Pb dating of a representative sample yields a weighted mean <sup>206</sup>Pb/<sup>207</sup>Pb age of 1819 ± 16 Ma, interpreted as the crystallization age of the Longzhu rhyolite porphyry. Zircons from this sample have <i>ε</i><sub>Hf</sub>(<i>t</i>) values ranging from – 8.4 to – 2.2 and <i>T</i><sup>Hf</sup><i><sub>DM2</sub></i> model ages from 2.76 to 2.46 Ga. The whole-rock Nd isotopic data from the Longzhu rhyolite porphyries yield <i>ε</i><sub>Nd</sub>(<i>t</i>) values spanning – 6.3 to – 4.7 and <i>T</i><sup>Nd</sup><i><sub>DM2</sub></i> model ages from 2.81 to 2.69 Ga. The rhyolite porphyries have geochemical features similar to those of the typical A-type granites (rhyolites), with high SiO<sub>2</sub>, total alkali contents and FeO<sup>t</sup>/MgO ratios, and low CaO and MgO contents. Additionally, the rhyolite porphyries have high total rare earth element concentrations (627 ~ 760 ppm), high (La/Yb)<sub>N</sub> values (14.5 ~ 26.9), strongly negative Eu anomalies (δEu = 0.28 ~ 0.41), and display enrichments of Rb, Ga, Th, and U and depletions of Sr, Nb, Ta, Eu, and Ti. The geochemical and Nd-Hf isotopic features suggest that the Palaeoproterozoic Longzhu rhyolite porphyries were generated by partial melting of source rocks similar to those of the Badu Complex in an intra-plate extensional setting. The results from this study, when combined with existing geochronological data, further demonstrate that the Palaeoproterozoic rocks of Wuyishan terrane probably represent a remnant of the Columbia supercontinent.
DOI: 10.6084/m9.figshare.1627951.v2
title: Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks

description: In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627951.v1
title: Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

description: In this paper we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data.
DOI: 10.6084/m9.figshare.1627953.v1
title: <b>Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming</b>

description: A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable, accounting for multiple comparisons through the existing methods yields overly conservative results when there are multiple outcome variables of interest. This stems from the fact that unmeasured confounding cannot affect the probability of assignment to treatment differently depending on the outcome being analyzed. Existing methods allow this to occur by combining the results of individual sensitivity analyses to assess whether at least one hypothesis is significant, which in turn results in an overly pessimistic assessment of a study’s sensitivity to unobserved biases. By solving a quadratically constrained linear program, we are able to perform a sensitivity analysis while enforcing that unmeasured confounding must have the same impact on the treatment assignment probabilities across outcomes for each individual in the study. We show that this allows for uniform improvements in the power of a sensitivity analysis not only for testing the overall null of no effect, but also for null hypotheses on <i>specific</i> outcome variables while strongly controlling the familywise error rate. We illustrate our method through an observational study on the effect of smoking on naphthalene exposure.
DOI: 10.6084/m9.figshare.1627953.v2
title: Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming

description: A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable, accounting for multiple comparisons through the existing methods yields overly conservative results when there are multiple outcome variables of interest. This stems from the fact that unmeasured confounding cannot affect the probability of assignment to treatment differently depending on the outcome being analyzed. Existing methods allow this to occur by combining the results of individual sensitivity analyses to assess whether at least one hypothesis is significant, which in turn results in an overly pessimistic assessment of a study's sensitivity to unobserved biases. By solving a quadratically constrained linear program, we are able to perform a sensitivity analysis while enforcing that unmeasured confounding must have the same impact on the treatment assignment probabilities across outcomes for each individual in the study. We show that this allows for uniform improvements in the power of a sensitivity analysis not only for testing the overall null of no effect, but also for null hypotheses on <i>specific</i> outcome variables while strongly controlling the familywise error rate. We illustrate our method through an observational study on the effect of smoking on naphthalene exposure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627944
title: Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis

description: Paradata refers here to data at unit level on an observed auxiliary variable, not usually of direct scientific interest, which may be informative about the quality of the survey data for the unit. There is increasing interest among survey researchers in how to use such data. Its use to reduce bias from nonresponse has received more attention so far than its use to correct for measurement error. This article considers the latter with a focus on binary paradata indicating the presence of measurement error. A motivating application concerns inference about a regression model, where earnings is a covariate measured with error and whether a respondent refers to pay records is the paradata variable. We specify a parametric model allowing for either normally or <i>t</i>-distributed measurement errors and discuss the assumptions required to identify the regression coefficients. We propose two estimation approaches that take account of complex survey designs: pseudo-maximum likelihood estimation and parametric fractional imputation. These approaches are assessed in a simulation study and are applied to a regression of a measure of deprivation given earnings and other covariates using British Household Panel Survey data. It is found that the proposed approach to correcting for measurement error reduces bias and improves on the precision of a simple approach based on accurate observations. We outline briefly possible extensions to uses of this approach at earlier stages in the survey process. Supplemental materials are available online.
DOI: 10.6084/m9.figshare.1627944.v1
title: Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis

description: Paradata refers here to data at unit level on an observed auxiliary variable, not usually of direct scientific interest, which may be informative about the quality of the survey data for the unit. There is increasing interest among survey researchers in how to use such data. Its use to reduce bias from nonresponse has received more attention so far than its use to correct for measurement error. This article considers the latter with a focus on binary paradata indicating the presence of measurement error. A motivating application concerns inference about a regression model, where earnings is a covariate measured with error and whether a respondent refers to pay records is the paradata variable. We specify a parametric model allowing for either normally or <i>t</i>-distributed measurement errors and discuss the assumptions required to identify the regression coefficients. We propose two estimation approaches that take account of complex survey designs: pseudo-maximum likelihood estimation and parametric fractional imputation. These approaches are assessed in a simulation study and are applied to a regression of a measure of deprivation given earnings and other covariates using British Household Panel Survey data. It is found that the proposed approach to correcting for measurement error reduces bias and improves on the precision of a simple approach based on accurate observations. We outline briefly possible extensions to uses of this approach at earlier stages in the survey process. Supplemental materials are available online.
DOI: 10.6084/m9.figshare.1627942
title: Sparse Approximate Inference for Spatio-Temporal Point Process Models

description: Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both nonlinear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and point-process observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary. Supplementary materials for this article are available online.