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Endocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study
Not provided.
Application of sulfur SAD to small crystals with a large asymmetric unit and anomalous substructure
The application of sulfur single-wavelength anomalous dispersion (S-SAD) to determine the crystal structures of macromolecules can be challenging if the asymmetric unit is large, the crystals are small, the size of the anomalously scattering sulfur structure is large and the resolution at which the anomalous signals can be accurately measured is modest. Here, as a study of such a case, approaches to the SAD phasing of orthorhombic Ric-8A crystals are described. The structure of Ric-8A was published with only a brief description of the phasing process [Zeng et al. (2019), Structure , 27 , 1137–1141]. Here, alternative approaches to determining the 40-atom sulfur substructure of the 103 kDa Ric-8A dimer that composes the asymmetric unit are explored. At the data-collection wavelength of 1.77 Å measured at the Frontier micro-focusing Macromolecular Crystallography (FMX) beamline at National Synchrotron Light Source II, the sulfur anomalous signal strength, |Δ ano |/σΔ ano ( d ′′/sig), approaches 1.4 at 3.4 Å resolution. The highly redundant, 11 000 000-reflection data set measured from 18 crystals was segmented into isomorphous clusters using BLEND in the CCP 4 program suite. Data sets within clusters or sets of clusters were scaled and merged using AIMLESS from CCP 4 or, alternatively, the phenix.scale_and_merge tool from the Phenix suite. The latter proved to be the more effective in extracting anomalous signals. The HySS tool in Phenix , SHELXC / D and PRASA as implemented in the CRANK 2 program suite were each employed to determine the sulfur substructure. All of these approaches were effective, although HySS , as a component of the phenix.autosol tool, required data from all crystals to find the positions of the sulfur atoms. Critical contributors in this case study to successful phase determination by SAD included (i) the high-flux FMX beamline, featuring helical-mode data collection and a helium-filled beam path, (ii) as recognized by many authors, a very highly redundant, multiple-crystal data set and (iii) the inclusion within that data set of data from crystals that were scanned over large ω ranges, yielding highly isomorphous and highly redundant intensity measurements.
Comparing cross-platform performance via node-to-node scaling studies
Due to the increasing diversity of high-performance computing architectures, researchers and practitioners are increasingly interested in comparing a code’s performance and scalability across different platforms. However, there is a lack of available guidance on how to actually set up and analyze such cross-platform studies. In this paper, we contend that the natural base unit of computing for such studies is a single compute node on each platform and offer guidance in setting up, running, and analyzing node-to-node scaling studies. In conclusion, we propose templates for presenting scaling results of these studies and provide several case studies highlighting the benefits of this approach.
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
Abstract. Depressions – inwardly draining regions – are common to many landscapes. When there is sufficient moisture, depressions take the form of lakes and wetlands; otherwise, they may be dry. Hydrological flow models used in geomorphology, hydrology, planetary science, soil and water conservation, and other fields often eliminate depressions through filling or breaching; however, this can produce unrealistic results. Models that retain depressions, on the other hand, are often undesirably expensive to run. In previous work we began to address this by developing a depression hierarchy data structure to capture the full topographic complexity of depressions in a region. Here, we extend this work by presenting the Fill–Spill–Merge algorithm that utilizes our depression hierarchy data structure to rapidly process and distribute runoff. Runoff fills depressions, which then overflow and spill into their neighbors. If both a depression and its neighbor fill, they merge. We provide a detailed explanation of the algorithm and results from two sample study areas. In these case studies, the algorithm runs 90–2600 times faster (with a reduction in compute time of 2000–63 000 times) than the commonly used Jacobi iteration and produces a more accurate output. Complete, well-commented, open-source code with 97 % test coverage is available on GitHub and Zenodo.
Development of Representative Microgrids Test Cases and Study of Selected Operational Scenarios in the Island of Puerto Rico.
Abstract not provided.
Healthy Building Initiative: Fort Worth Pilot Study
This fact sheet describes a case study of a federal office building in Fort Worth, Texas, that was used as a test site for indoor environmental quality data collection and analysis.
Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps
Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.
Comparative Analysis of Report-Back of Research Results Strategies for Personal Chemical Exposure Data
Background. Report-back of research results (RBRR) is ethically supported and highly requested by participants yet lacks broadly transferable guidelines for RBRR. Effective RBRR must be responsive to target audience needs and may not be addressed by a ‘one-size-fits-all’ approach. Objective. Within a subset of our 19 studies on RBRR, we had the unique opportunity to carry out a comparative analysis of RBRR strategies across cohorts with similar development and evaluation methods, yet distinct in life stage, geography, number and type of chemicals assessed, and community contexts. Methods. We highlight key outcomes from three environmental health studies: an ongoing New York, NY cohort (Fair Start; n=486) and a Detroit, MI cohort (CLEAR; n=34) assessing exposure to ambient urban pollution during pregnancy, and a longitudinal cohort in Houston, TX (Houston-3H) following Hurricane Harvey (n=312). Focus group and survey data were analyzed to identify lessons learned and explore how RBRR supports understanding of environmental health. Results. Commonalities emerged in RBRR development, design, organization, and data visualization, as well as in how RBRR can contribute to an understanding of health-environment connections. Differences included preferences for individual versus community level findings, as well as distinguishable contextual considerations. For pregnancy cohorts, messaging was framed with cultural sensitivity, and to avoid unintended consequences of parental guilt due to prenatal exposures. In the post-disaster Houston-3H study, participants requested additional transparency regarding sampling design and study rationale. Significance. All RBRR case studies reported chemicals without known regulatory or health guidelines, so results were contextualized within the study population. Participants across cohorts requested multi-study comparisons to better understand their results beyond their communities. While foundational RBRR elements (e.g. plain language, graphic organizers) may supersede cohort-specific differences, RBRR should be personalized to encompass perceptions of health across different life-stage, cultural, and environmental contexts.
Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments
For many crystal-based products (e.g., pharmaceuticals, energy storage), the size uniformity is not only a key quality attribute, but sometimes also an indicator of other attributes such as solid purity. This article proposes a sequential selection approach to find a proper experimental setting that leads to high uniformity, or equivalently, small variance for crystal sizes, from the advanced slug flow reaction crystallization process of a model crystal, called manganese oxalate hydrate. The proposed sequential selection approach contains a Bayesian adaptive method to incorporate new uniformity measurements in each step and two design acquisition functions to improve the selection of the most promising experimental setting in terms of minimizing the variance. We study the performance of the proposed approach through multiple synthetic numerical studies, as well as a case study based on data from slug flow crystallization experiments. Throughout these studies, the proposed approach shows competitive performance in identifying the best experimental setting.
Frozen Hydrometeor Terminal Fall Velocity Dependence on Particle Habit and Riming as Observed by Vertically Pointing Radars
Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor ($V$ t = $aZ$$^{b}_{e}$) relations for frozen hydrometeor populations of different habits during snowfall events observed at Oliktok Point, Alaska, and at the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Case study events range from snowfall with highly rimed particles observed during periods with large amounts of supercooled liquid water path (LWP > 320 g m –2 ) to unrimed snowflakes including instances when pristine planar crystals were the dominant frozen hydrometeor habit. The prefactor a and the exponent b in the observed $V$ t –$Z$ e relations scaled to the sea level vary in the approximate ranges 0.5–1.4 and 0.03–0.13, respectively (reflectivities are in mm 6 m –3 and velocities are in m s –1 ). The coefficient a values are the smallest for planar crystals (a ~ 0.5) and the largest (a > 1.2) for particles under severe riming conditions with high LWP. There is no clear distinction between b values for high and low LWP conditions. The range of the observed $V$ t –$Z$ e relation coefficients is in general agreement with results of modeling using fall velocity–size (υ t = αD β ) relations for individual particles found in literature for hydrometeors of different habits, though there is significant variability in α and β coefficients from different studies even for a same particle habit. Correspondences among coefficients in the $V$ t –$Z$ e relations for particle populations and in the individual particle υ t –$D$ relations are analyzed. Furthermore, these correspondences and the observed $V$ t –$Z$ e relations can be used for evaluating different frozen hydrometeor fall velocity parameterizations in models.
Self-assembly of co-continuous nanostructured copolymer templates with compositional and architectural dispersity
In our previous DOE-supported project (DE-SC0016208), we demonstrated that a particular architecture dubbed ‘randomly end-linked copolymer networks’ (RECNs) led to especially robust self-assembly of disordered cocontinuous nanostructures. Such structures, wherein both microphases are fully percolating in three dimensions, are attractive for a wide-variety of energy-relevant applications including purification membranes, separators in batteries and fuel cells, catalyst supports, and high surface area electrodes. In the current project we sought to substantially extend our fundamental understanding of how different types of randomness in blocky copolymer architecture contribute to the robust formation of disordered cocontinuous nanostructures. To do so, we filled in the sizable gap between the previously studied cases of randomly linked linear multiblock copolymers and RECNs by studying two intermediate cases: (i) random miktoarm star polymers, which contain dispersity in the number of the two chemically-incompatible strands located at each junction and therefore the preferred interfacial curvature of each polymer, and (ii) randomly branched copolymers, which also possess bridging strands that stretch across domains and apply local ‘elastic’ forces to the interfaces. In addition, we studied a different network architecture, statistically crosslinked copolymer networks, that is more straightforward to prepare using chain growth polymerization methods, enabling more facile formation of cocontinuous nanostructures. Finally, we extended our approaches from model systems based on polystyrene and poly(lactic acid) to copolymer architectures containing either (i) polysulfone, an engineering polymer that provides excellent mechanical properties for membrane applications, or (ii) polyacrylonitrile, providing a convenient route to nanoporous carbon materials.
Solution and sensitivity analysis of nonlinear equations using a hypercomplex-variable Newton-Raphson method
Here, the classical Newton-Raphson (NR) method for solving nonlinear equations is enhanced in two ways through the use of hypercomplex variables and algebra. In particular, i) the Jacobian is computed in a highly accurate and automated way, and ii) the derivative of the solution to the nonlinear equations is computed with respect to any parameter contained within the system of equations. These advances provide two significant enhancements in that it is straightforward to provide an accurate Jacobian and to construct a reduced order model (ROM) of arbitrary order with respect to any parameter of the system. The ROM can then be used to approximate the solution for other parameter values without requiring additional solutions of the nonlinear equations. Several case studies are presented including 1D and 2D academic examples with fully functioning Python code provided. Additionally, a case of study of the catenary of an elastic cable subject to its own weight and a vertical point load. Derivatives up to 10th order were computed with respect to material, loading, and geometrical parameters. The derivatives were used to generate reduced order models of the cable deformation and reaction forces at its ends with respect to multiple input parameters. Results show that from a single hypercomplex evaluation of the cable under a single vertical point load, it is possible to generate an accurate reduced order model capable of predicting the cable deformation with 1.5 times the load in the opposite direction and with 3.5 times the load in the same direction without resolving the system of equations.
Techno-Economic Assessment of CHP Systems in Wastewater Treatment Plants
Wastewater treatment plant (WWTP) utilization of combined heat and power (CHP) systems allows for the efficient use of on-site biogas production, as well as increased annual savings in utility costs. In this paper, a review of biogas energy recovery options, CHP prime mover technologies, and the costs associated with biogas cleaning give a broad summary of the current state of CHP technology in WWTPs. Even though there are six different prime mover technologies, the main ones currently being implemented in WWTPs are micro turbines, fuel cells and reciprocating engines. Different prime movers offer varying efficiencies, installation costs, and biogas impurity (H2S, siloxanes, HCl) tolerances. To evaluate the long-term savings capabilities, a techno-economic assessment of a CHP installation at a case study WWTP shows the payback, annual savings, and initial costs associated with the installation of a CHP system. In this case, a study a payback of 5.7 years and a net present value of USD 709,000 can be achieved when the WWTP generates over 2,000,000 m3 of biogas per year and utilizes over 36,000 GJ of natural gas per year.
Flower‐Type Organized Trade‐Wind Cumulus: A Multi‐Day Lagrangian Large Eddy Simulation Intercomparison Study
Shallow cumulus cloud fields in subtropical marine trade wind environments, particularly over the tropical Atlantic Ocean, show distinct organizational patterns. Among these, Flower‐type clouds are characterized by expansive stratiform cloud patches surrounded by regions of scattered convection. The objectives of this study were (a) to construct a case study of a time period during the EUREC 4 A/ATOMIC field campaign when Flower‐type organization was observed, (b) to evaluate the fidelity of a multi‐model ensemble of large eddy simulations of that case, and (c) to analyze the interaction between cloud and precipitation processes and mesoscale organization in the simulations. The simulations follow a quasi‐Lagrangian trajectory, allowing mesoscale features to develop over time in a domain that follows the boundary‐layer airmass. The results show a broad agreement in simulated thermodynamic properties across different LES codes, with Flower‐type cloud patches appearing within hours of each other. The consensus among models is consistent with observations made during the EUREC 4 A/ATOMIC field campaign on the specific day of interest. The cloud structure reveals three distinct peaks in the joint probability densities of cloud base and cloud top height, with the dominant peak at any given time influenced by the stage of cloud organization. The simulated cloud system evolution reveals consistent occurrence of maxima in liquid water path and rain rate before Flower reaches its maximum length scale. Targeted sensitivity tests reveal a weak relationship between Cloud Droplet Number concentration and the extent/degree/type of organization.
Influence of Diffuse and Ground-Reflected Irradiance on the Spectral Modeling of Solar Reference Cells
Thermal Energy Storage (TES) is a key component for solar thermal applications to bridge the gap between the demand for thermal energy and the supply of solar energy, whose availability depends on the time of day and season. Thus, cost-effective packed-bed thermal containers filled with a solid storage medium have been proposed for high-temperature sensible heat storage as materials are abundant and relatively cheap. Thus, it is necessary to investigate their performance and temperature profiles during the charge-discharge cycle. Several models are available for this purpose. Typically, the more detailed a model, the greater the computational effort required to solve it, and hence a time-efficient model is needed to prevent excessively long computation times for long-term analysis. At the more basic level, the common Hughes E-NTU model and the less realistic simplified Infinite-NTU model are very important for their less time and computational effort. In this paper, the appropriateness of employing the Infinite-NTU model was evaluated to investigate the performance of a typical and scalable rock-bed TES as a case study. The results presented provide a methodology to quickly test the validity of the model and predict the temperature profile for the case under study. Accordingly, such simple charge-discharge cycle thermal performance predictions are important to plan, design, and rapidly deploy a reliable and economical solar thermal system for the supply of valuable heat to high-temperature demanding applications of power generation and industrial processes as part of a rapid shift towards non-polluting renewable energy. Keywords: Solar Thermal, TES, Packed-bed, NTU model, Temperature profile
Rubisco activity and activation state dictate photorespiratory plasticity in Betula papyrifera acclimated to future climate conditions
Plant metabolism faces a challenge of investing enough enzymatic capacity to a pathway without overinvestment. As it takes energy and resources to build, operate, and maintain enzymes, there are benefits and drawbacks to accurately matching capacity to the pathway influx. The relationship between functional capacity and physiological load could be explained through symmorphosis, which would quantitatively match enzymatic capacity to pathway influx. Alternatively, plants could maintain excess enzymatic capacity to manage unpredictable pathway influx. In this study, we use photorespiration as a case study to investigate these two hypotheses in Betula papyrifera. This involves altering photorespiratory influx by manipulating the growth environment, via changes in CO 2 concentration and temperature, to determine how photorespiratory capacity acclimates to environmental treatments. Surprisingly, the results from these measurements indicate that there is no plasticity in photorespiratory capacity in B. papyrifera, and that a fixed capacity is maintained under each growth condition. The fixed capacity is likely due to the existence of reserve capacity in the pathway that manages unpredictable photorespiratory influx in dynamic environments. Additionally, we found that B. papyrifera had a constant net carbon assimilation under each growth condition due to an adjustment of functional rubisco activity driven by changes in activation state. These results provide insight into the acclimation ability and limitations of B. papyrifera to future climate scenarios currently predicted in the next century.
Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method
Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.