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Shoes or insoles embedded with carbon fiber materials to increase longitudinal stiffness have been shown to enhance running and walking performance in elite runners, and younger adults, respectively. It is unclear, however, if such stiffness modifications can translate to enhanced mobility in older adults who typically walk with greater metabolic cost of transport compared to younger adults. Here, we sought to test whether adding footwear stiffness via carbon fiber insoles could improve walking outcomes (eg, distance traveled and metabolic cost of transport) in older adults during the 6-minute walk test. 20 older adults (10 M/10 F; 75.95 [6.01] y) performed 6-minute walk tests in 3 different shoe/insole stiffnesses (low, medium, and high) and their own footwear (4 total conditions). We also evaluated participants’ toe flexor strength and passive foot compliance to identify subject-specific factors that influence performance from added shoe/insole stiffnesses. We found no significant group differences in distance traveled or net metabolic cost of transport (P ≥ .171). However, weaker toe flexors were associated with greater improvement in distance traveled between the medium and low stiffness conditions (P = .033,r = −.478), indicating that individual foot characteristics may help identify potential candidates for interventions involving footwear stiffness modifications. 

C–H functionalization of commodity polyolefins affords functional materials derived from a high‐volume, low‐cost resource. However, current postpolymerization modification strategies result in randomly distributed functionalization along the length of the polymer backbone, which has a negative impact on the crystallinity of the resultant polymers, and thus the thermomechanical properties. Here, we demonstrate an amidyl radical mediated C–H functionalization of polyolefins to access blocky microstructures, which exhibit a higher crystalline fraction, larger crystallite size, and improved mechanical properties compared to their randomly functionalized analogues. Taking inspiration from the site‐selective C–H functionalization of small molecules, we leverage the steric protection provided by crystallites and target polymer functionalization to amorphous domains in a semicrystalline polyolefin gel. The beneficial outcomes of blocky functionalization are independent of the identity of the pendant functional group that is installed through functionalization. The decoupling of functional group incorporation and crystallinity highlights the promise in accessing nonrandom microstructures through selective functionalization to circumvent traditional tradeoffs in postpolymerization modification, with potential impact in advanced materials and upcycling plastic waste. 

As the frequency of rocket launches increases, accurately predicting their noise is necessary to assess structural, environmental, and societal impacts. NASA’s Space Launch System (SLS) is a challenging vehicle to model because it has both solid-fuel rocket boosters and liquid-fueled engines that contribute to its thrust at launch. This paper discusses measured aeroacoustic properties of this super heavy-lift rocket in the context of supersonic jet theory and measurements of other rockets. Using four measured aeroacoustic properties: directivity, spectral peak frequency, maximum overall sound pressure level, and overall sound power level, an equivalent rocket based on merged plumes is created for SLS. With the constraint that the effective thrust and mass flow rates should match those of the actual vehicle, a method using weighted averages of the disparate plume parameters successfully reproduces SLS’s desired aeroacoustic properties, yielding a relatively simple model for the complex vehicle. 

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling 

Abstract Deciduous tree cover is expected to increase in North American boreal forests with climate warming and wildfire. This shift in composition has the potential to generate biophysical cooling via increased land surface albedo. Here we use Landsat-derived maps of continuous tree canopy cover and deciduous fractional composition to assess albedo change over recent decades. We find, on average, a small net decrease in deciduous fraction from 2000 to 2015 across boreal North America and from 1992 to 2015 across Canada, despite extensive fire disturbance that locally increased deciduous vegetation. We further find near-neutral net biophysical change in radiative forcing associated with albedo when aggregated across the domain. Thus, while there have been widespread changes in forest composition over the past several decades, the net changes in composition and associated post-fire radiative forcing have not induced systematic negative feedbacks to climate warming over the spatial and temporal scope of our study. 

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the ‘LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how ‘LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents. 

Houston Lightning Mapping Array (HLMA) post-processed (L2+) data, collected during the ESCAPE (Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment) field campaign IOP period (6 June - 31 July 2022), and the remaining DOE TRACER IOP period (July-Sep 2022). These data were processed uniformly with reasonable default quality control parameters and are available in NetCDF format.