Confirmatory Factor Analysis of the Patient Health Questionnaire‐9: A Study of the Participants From the Spinal Cord Injury Model Systems

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146986/1/pmr2533.pd

Improving Striping Operations through System Optimization - Phase 2

Striping operations generate a significant workload for MoDOT maintenance operations. The requirement for each striping crew to replenish its stock of paint and other consumable items from a bulk storage facility, along with the necessity to make several passes on most of the routes to stripe all the lines on that road, introduce the potential for inefficiencies in the form of deadhead miles that striping crew vehicles must travel while not actively applying pavement markings. These inefficiencies generate unnecessary travel, wasted time, and vehicle wear. Phase 2 updates a 2015 project that developed a decision support tool for scheduling and routing road striping operations. The updates presented in the final report improve the optimization model, which generates more user-friendly outputs

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split

We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\Lambda$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $\Omega^{\rm geo}_\mathrm{m}$ and growth $\Omega^{\rm growth}_\mathrm{m}$ dark matter densities in the so-called split $\Lambda$CDM parameterization. When trained with a few million samples, our emulator shows uniform accuracy across a wide range in an 18-dimensional parameter space. We provide a detailed comparison of three neural network designs, illustrating the importance of adopting state-of-the-art Transformer blocks. Our study also details their performance when computing Bayesian evidence for cosmic shear on three fiducial cosmologies. The transformers-based emulator is always accurate within PolyChord's precision. As an application, we use our emulator to study the degeneracies between dark energy models and growth geometry split parameterizations. We find that the growth-geometry split remains to be a meaningful test of the smooth dark energy assumption.Comment: 16 pages, 6 figure

Thermal history modeling of the H chondrite parent body

The cooling histories of individual meteorites can be empirically reconstructed by using ages from different radioisotopic chronometers with distinct closure temperatures. For a group of meteorites derived from a single parent body such data permit the reconstruction of the cooling history and properties of that body. Particularly suited are H chondrites because precise radiometric ages over a wide range of closure temperatures are available. A thermal evolution model for the H chondrite parent body is constructed by using all H chondrites for which at least three different radiometric ages are available. Several key parameters determining the thermal evolution of the H chondrite parent body and the unknown burial depths of the H chondrites are varied until an optimal fit is obtained. The fit is performed by an 'evolution algorithm'. Empirical data for eight samples are used for which radiometric ages are available for at least three different closure temperatures. A set of parameters for the H chondrite parent body is found that yields excellent agreement (within error bounds) between the thermal evolution model and empirical data of six of the examined eight chondrites. The new thermal model constrains the radius and formation time of the H chondrite parent body (possibly (6) Hebe), the initial burial depths of the individual H chondrites, the average surface temperature of the body, the average initial porosity of the material the body accreted from, and the initial 60Fe content of the H chondrite parent body.Comment: 16 pages, 7 figure

Dynamical Fine Tuning in Brane Inflation

We investigate a novel mechanism of dynamical tuning of a flat potential in the open string landscape within the context of warped brane-antibrane inflation in type IIB string theory. Because of competing effects between interactions with the moduli stabilizing D7-branes in the warped throat and anti-D3-branes at the tip, a stack of branes gives rise to a local minimum of the potential, holding the branes high up in the throat. As branes successively tunnel out of the local minimum to the bottom of the throat the potential barrier becomes lower and is eventually replaced by a flat inflection point, around which the remaining branes easily inflate. This dynamical flattening of the inflaton potential reduces the need to fine tune the potential by hand, and also leads to successful inflation for a larger range of inflaton initial conditions, due to trapping in the local minimum.Comment: 23 pages, 9 figures. v2: Updated D3-dependence in potential, small changes to numerical result

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Seagrass meadows as a globally significant carbonate reservoir

There has been growing interest in quantifying the capacity of seagrass ecosystems to act as carbon sinks as a natural way of offsetting anthropogenic carbon emissions to the atmosphere. However, most of the efforts have focused on the particulate organic carbon (POC) stocks and accumulation rates and ignored the particulate inorganic carbon (PIC) fraction, despite important carbonate pools associated with calcifying organisms inhabiting the meadows, such as epiphytes and benthic invertebrates, and despite the relevance that carbonate precipitation and dissolution processes have in the global carbon cycle. This study offers the first assessment of the global PIC stocks in seagrass sediments using a synthesis of published and unpublished data on sediment carbonate concentration from 403 vegetated and 34 adjacent un-vegetated sites. PIC stocks in the top 1 m of sediment ranged between 3 and 1660 Mg PIC ha(-1), with an average of 654 +/- 24 Mg PIC ha(-1), exceeding those of POC reported in previous studies by about a factor of 5. Sedimentary carbonate stocks varied across seagrass communities, with meadows dominated by Halodule, Thalassia or Cymodocea supporting the highest PIC stocks, and tended to decrease polewards at a rate of -8 +/- 2 Mg PIC ha(-1) per degree of latitude (general linear model, GLM; p \u3c 0.0003). Using PIC concentrations and estimates of sediment accretion in seagrass meadows, the mean PIC accumulation rate in seagrass sediments is found to be 126.3 +/- 31.05 g PIC m(-2) yr(-1). Based on the global extent of seagrass meadows (177 000 to 600 000 km(2)), these ecosystems globally store between 11 and 39 Pg of PIC in the top metre of sediment and accumulate between 22 and 75 Tg PIC yr(-1), representing a significant contribution to the carbonate dynamics of coastal areas. Despite the fact that these high rates of carbonate accumulation imply CO2 emissions from precipitation, seagrass meadows are still strong CO2 sinks as demonstrated by the comparison of carbon (PIC and POC) stocks between vegetated and adjacent un-vegetated sediments

Benthic silicon cycling in the Arctic Barents Sea: a reaction–transport model study

Over recent decades the highest rates of water column warming and sea ice loss across the Arctic Ocean have been observed in the Barents Sea. These physical changes have resulted in rapid ecosystem adjustments, manifesting as a northward migration of temperate phytoplankton species at the expense of silica-based diatoms. These changes will potentially alter the composition of phytodetritus deposited at the seafloor, which acts as a biogeochemical reactor and is pivotal in the recycling of key nutrients, such as silicon (Si). To appreciate the sensitivity of the Barents Sea benthic system to the observed changes in surface primary production, there is a need to better understand this benthic–pelagic coupling. Stable Si isotopic compositions of sediment pore waters and the solid phase from three stations in the Barents Sea reveal a coupling of the iron (Fe) and Si cycles, the contemporaneous dissolution of lithogenic silicate minerals (LSi) alongside biogenic silica (BSi), and the potential for the reprecipitation of dissolved silicic acid (DSi) as authigenic clay minerals (AuSi). However, as reaction rates cannot be quantified from observational data alone, a mechanistic understanding of which factors control these processes is missing. Here, we employ reaction–transport modelling together with observational data to disentangle the reaction pathways controlling the cycling of Si within the seafloor. Processes such as the dissolution of BSi are active on multiple timescales, ranging from weeks to hundreds of years, which we are able to examine through steady state and transient model runs. Steady state simulations show that 60 % to 98 % of the sediment pore water DSi pool may be sourced from the dissolution of LSi, while the isotopic composition is also strongly influenced by the desorption of Si from metal oxides, most likely Fe (oxyhydr)oxides (FeSi), as they reductively dissolve. Further, our model simulations indicate that between 2.9 % and 37 % of the DSi released into sediment pore waters is subsequently removed by a process that has a fractionation factor of approximately −2 ‰, most likely representing reprecipitation as AuSi. These observations are significant as the dissolution of LSi represents a source of new Si to the ocean DSi pool and precipitation of AuSi an additional sink, which could address imbalances in the current regional ocean Si budget. Lastly, transient modelling suggests that at least one-third of the total annual benthic DSi flux could be sourced from the dissolution of more reactive, diatom-derived BSi deposited after the surface water bloom at the marginal ice zone. This benthic–pelagic coupling will be subject to change with the continued northward migration of Atlantic phytoplankton species, the northward retreat of the marginal ice zone and the observed decline in the DSi inventory of the subpolar North Atlantic Ocean over the last 3 decades

Mosque-based emotional support among young Muslim Americans

Despite a growing literature on social support networks in religious settings (i.e., church-based social support), little is known about mosque-based support among Muslims. This study investigates the demographic and religious behavior correlates of mosque-based social support among a multi-racial and ethnic sample of 231 young Muslims from southeast Michigan. Several dimensions of mosque-based support are examined including receiving emotional support, giving emotional support, anticipated emotional support and negative interactions with members of one’s mosque. Results indicated that women both received and antic- ipated receiving greater support than did men. Higher educational attainment was associated with receiving and giving less support compared to those with the lowest level of educational attainment. Moreover, highly educated members reported fewer negative interactions than less educated members. Mosque attendance and level of congregational involvement positively predicted receiving, giving, and anticipated emotional support from congregants, but was unrelated to negative interactions. Overall, the study results converge with previously established correlates of church- based emotional support.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107410/1/art%3A10.1007%2Fs13644-013-0119-0(1).pd

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays