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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: von Stein, Meriel;

    This repo contains the tools, paper, and study data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443. Also available via https://github.com/MissMeriel/DeepManeuver/ .

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    Authors: Tourville, Jordon; Publicover, David; Dovciak, Martin;

    Remote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image. All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery. Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m). For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%). Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0). Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year. Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu). Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions. Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA. Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata. Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance. Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32). Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Associated csv's require R (or Excel) to be loaded and for data to be analyzed. Funding provided by: Edna Bailey Sussman FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006517Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1759724

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    Authors: Sanchez, Kevin;

    This is an aggregated dataset, consisting of timeseries with in-situ aircraft or ship campaign measurements from ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. CCN, CCN proxies and measurements necessary to identify particle physical and chemical properties and non-marine contributions to particle concentrations are included. All missing or invalid data flags are converted to 'Na'. Some datasets have already been filtered for inlet shattering in-cloud, and measurement contamination from ship exhausts; however, methods of filtering ship exhaust vary by campaign. For the NAAMES ship campaigns, the research ship exhaust was identified and filtered out based on the wind direction relative to the ship exhaust and total particle counts. For CAPRICORN2, wind direction, total particle counts, black carbon particle concentration, and CO and CO2 measurements were also utilized in filtering ship exhaust. Finally, the MARCUS ship exhaust contamination periods are identified and filtered using total particle counts and CO measurements. The aggregated dataset is further filtered to eliminate measurements influenced by in-cloud inlet shattering and averaged at 10 second intervals for aircraft measurements and 5-minute intervals for ship measurements (except for CAPRICOR2 which is only publicly available at hourly averaged intervals). In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates. The data files are in a .csv format and can be opened with many open-source softwares. The data from each campaign deployment is in a seprate .csv file. Some of the data files are stored on the Zenodo data repository due to licensing requirements (CC BY 4.0) and must be downloaded from https://doi.org/10.5281/zenodo.8135766.Funding provided by: Langley Research CenterCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006199Award Number:

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    Authors: Champlin, Lena; Woolfolk, Andrea; Oczkowski, Autumn; Rittenhouse, Audrey; +8 Authors

    To examine interrelationships between nitrogen pollution and anthropogenic sources over the past century, we parametrized a model of nitrogen inputs to the watershed. Our model was based on the Nitrogen Loading Model (NLM). We applied the NLM model to calculate watershed sources of nitrogen over time in decadal increments from 1930–2010. We compiled historical data on changes in human population from census data, atmospheric deposition, homes with wastewater treatment, the areal extent of cultivated and natural lands and impervious surface cover, and estimated changes in fertilizer application rates in the Elkhorn watershed (based on annual "Commercial Fertilizers" and "Fertilizing Materials" reports published by the California Department of Agriculture 1925–2012). Eighty-five ~ three-meter-deep sediment cores were collected during 2010 from the vertices of a 200 m x 200 m grid superimposed over the tidal and never-diked portions of the estuary. Most of the sediment cores were collected using a Russian peat borer to minimize compaction; in a few locations, a vibracorer was necessary to penetrate sands. Six focal cores were selected for high-resolution analyses and were collected using a piston corer with polycarbonate liners to obtain intact core sections for scanning and archiving. Focal cores were split into 1-cm sections; the remaining cores were sectioned into 10 cm intervals for 0–50 cm depths, and into 25-cm intervals for 50–100 cm depths. Core splits were archived at the LacCore repository at the University of Minnesota. Chronologies were created using downcore profiles of 210Pb, 137Cs, and 226Ra measured with a low-energy germanium multichannel gamma spectrometer. Historical geochemical markers included Pb concentrations measured using ICP-AES following four-acid extractions, AMS radiocarbon dating of fossil peat, and magnetic susceptibility and imaging using a Geotek Multi-Sensor core logger. The maximum depth of radiocesium was assigned an age of 1953, radiocesium peaks were assigned an age of 1963, and total lead concentration peaks were assigned an age of 1974. Lead-210, radiocesium, and radiocarbon dating were combined in an age-depth model using a Bayesian approach to construct chronologies for seven cores. The age model 210Pb Plum in R version 4.0.5 uses the same statistical approach as the previous model Bacon, but incorporates radionuclide dating including parameters of deposition of 210Pb, supported 210Pb, and accretion rates. The Plum model was selected because it can account for incremental 210Pb data over depth in the cores, as opposed to using the analytical approach of the continuous rate of supply model. Additionally, this model has been used previously for chronologies of estuarine sediments. Within Elkhorn Slough, sediment accumulation rates varied little from site to site over the past century and were similar to values reported previously; thus, to estimate ages for the 85 undated cores, we compiled a composite core chronology using the seven cores to represent mean age-date model for the entire estuary. This composite core chronology was then applied to the 85 undated cores, using the composite age-depth relationship to estimate dates for the depth segments utilized for isotopic and stoichiometric measurements. We report the mean year output of the model and 95% confidence intervals around the mean. For the six high-resolution focal sites, cores were analyzed at 1-cm increments (for 0 to 50 cm depths) for stable carbon and nitrogen isotopic composition using a Finnegan Delta Plus continuous flow isotope ratio mass spectrometer using standard methods, and for carbon and nitrogen concentration using a Flash 1112 EA. For the 85 coarser resolution cores, sediments were analyzed for carbon and nitrogen abundance and stable isotope ratios using a Vario Cube elemental analyzer interfaced to an Isoprime 100 IRMS. Isotope ratios for carbon and nitrogen are reported in permille notation as: where R is the abundance ratio of the less common (a) to more common isotope. The standard for nitrogen is atmospheric nitrogen gas; the standard for carbon is PeeDee Belemnite; by definition, standards have δ=0. Sediments were not pretreated to remove inorganic carbon, as acidification did not quantitatively shift ratios. Previous studies suggest little effect of diageneses on sediment δ15N ratios in coastal marine settings, but shifts of ~ -1.5‰ in δ13C ratios are expected and C/N ratios are thought to decrease over time. Furthermore, atmospheric δ13C ratios have declined by about -1.5‰ since 1850 associated with the Suess Effect – the release of lighter C from fossil fuel combustion. Whole estuary isoscape and stoichioscape maps were produced using sedimentary stable isotope (δ13C and δ15N) and molar nutrient stoichiometric (C/N) ratios interpolated from the 85 core locations using ordinary kriging in ArcGIS version 10.2.2 (ESRI, Redlands, CA, USA) to the spatial extent of cored areas in Elkhorn Slough. Maps were created for six depth intervals dated using the composite chronology (ca. 1726–1839, 1839–1885, 1885–1951, 1951–1963,1963–1981, and 1981–2010). Different interpolation variogram models including spherical, circular, exponential, Gaussian, linear interpolation with linear drift, and linear with quadratic drift were tested. Leave-one-out cross validation of 15% of the points was used to choose the model which yielded the smallest root mean square error between predicted and actual values. To ensure that historical differences in interpolation maps were a function of data differences rather than variogram methodology, the spherical kriging method was used for all timepoints. We also applied data from monthly water quality sampling at a network of (~26) stations across Elkhorn Slough since 1988. Monthly nitrate data from the sites were averaged during the full year of 1995 and mapped using ordinary kriging for comparison to spatial patterns of the isoscape and stoichioscape maps. Trends in isotopic and stoichiometric signatures since the 1850s were examined for the six high-resolution cores. Timeseries analysis of the high-resolution data investigated the statistical significance of trends during the period of increasing fertilizer application, as well as offsets in the signatures associated with the timing of marine inlet construction for the harbor. Statistically significant change points in the timeseries were determined using the Pettitt Test, a nonparametric test that identifies the year of a step change and assigns significance to the selection. Datasets of δ15N, δ13C, and C/N for each of the six high-resolution coring sites were separately tested for the period 1850–2010 (n = 45 time points each). Next, the timeseries were split at the significant step change points that were statistically identified, forming two datasets "before" and "after" the year of change. Trend analysis was performed using linear regression on the split datasets, to model the slope after the split as well as the difference of y-intercept at the step change year (Fig. S1 diagrams the slope and intercept of our statistical models). The difference of y-intercept at the step change year is interpreted as an offset in the timeseries, consistent with construction of the harbor inlet when the step occurred at the same time as the construction (1946 ± 10 years). The slope after this step change year is attributed to increasing fertilizer addition to the watershed from 1940–1980. To compare sediment isotope results to dissolved nutrient concentrations, we compared water quality monitoring data to the high-resolution sediment cores during a 20-year period. Monthly water sampling of parameters (including salinity) were measured at the sites, and water samples were also collected into brown Nalgene bottles; stored on ice; filtered; and analyzed for nutrients, including nitrate (NO3−), within 48 hours, or frozen for later analysis in accordance with standard methods. Three of the high-resolution sediment cores were collected at the same locations as water quality monitoring sites. For these three water quality sampling stations (Portero Road North, Kirby Park, and Hudsons Landing West), we compared annual mean water column dissolved NO3− (mM) and salinity (ppt) to sedimentary δ15N values during the same year from 1990–2010. Coastal eutrophication is a prevalent threat to the healthy functioning of ecosystems globally. While degraded water quality can be detected by monitoring oxygen, nutrient concentrations, and algal abundance, establishing regulatory guidelines is complicated by a lack of baseline data (e.g., pre-Anthropocene). We use historical carbon and nitrogen isoscapes from sediment cores to reconstruct spatial and temporal changes in nutrient dynamics for a central California estuary, where development and agriculture dramatically enhanced nutrient inputs over the past century. We found strong contrasts between current sediment stable isotopes and those from the recent past, demonstrating shifts exceeding those in previously studied eutrophic estuaries and substantial increases in nutrient inputs. Comparisons of contemporary with historical isoscapes also revealed that nitrogen sources shifted from a marine-terrestrial gradient to amplified denitrification at the head and mouth of the estuary. Geospatial analysis of historical data suggests that an increase in fertilizer application – rather than population growth or increases in the extent of cultivated land – is chiefly responsible for increasing nutrient loads during the 20th century. This study demonstrates the ability of isotopic and stoichiometric maps to provide important perspectives on long-term shifts and spatial patterns of nutrients that can be used to improve management of nutrient pollution. Excel, R, and a GIS software such as ArcGIS or QGIS.Funding provided by: National Oceanic and Atmospheric AdministrationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000192Award Number: NA06NOS4190167

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    Authors: Dmytro Humeniuk; Foutse Khomh; Giuliano Antoniol;

    To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain percentage of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. 

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    Authors: Robinson, Samuel; Schwinghamer, Timothy; Carcamo, Hector; Galpern, Paul;

    Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions is expensive and time-consuming to collect. Furthermore, relationships derived from small-scale plot data may not apply to ecosystem services operating at larger spatial scales (fields, landscapes). Precision yield data can be used to improve the accuracy and geographic range of ecosystem service studies, but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis. We provide examples of analysis using simulated and real precision yield data and outline two case studies of ecosystem services using precision yield data. Ecologists and agronomists should consider using precision yield data more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes. All scripts were written in RMarkdown (Allaire et al 2023) using R version 4.3.1 (R Core Team 2023).Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2023). rmarkdown: Dynamic Documents for R. R package version 2.22, https://github.com/rstudio/rmarkdown. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.Funding provided by: Natural Sciences and Engineering Research Council of CanadaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000038Award Number: The combined yield monitor data for Supplemental 1 was donated by Trent Clark (the absolution location of the spatial data has been anonymized for privacy). Supplemental 2 uses entirely generated data (see script for details). Supplemental 3 uses a correlation matrix created from unpublished yield data collected by Hector Cárcamo.

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    Authors: Lenders, Martine S.; Amsüss, Christian; Gündoğan, Cenk; Nawrocki, Marcin; +2 Authors

    In this paper, we present the design, implementation, and analysis of DNS over CoAP (DoC), a new proposal for secure and privacy-friendly name resolution of constrained IoT devices. We implement different design choices of DoC in RIOT, an open-source operating system for the IoT, evaluate performance measures in a testbed, compare with DNS over UDP and DNS over DTLS, and validate our protocol design based on empirical DNS IoT data. Our findings indicate that plain DoC is on par with common DNS solutions for the constrained IoT but significantly outperforms when additional standard features of CoAP are used such as caching. With OSCORE, we can save more than 10 kBytes of code memory compared to DTLS, when a CoAP application is already present, and retain the end-to-end trust chain with intermediate proxies, while leveraging features such as group communication or encrypted en-route caching. We also discuss a compression scheme for very restricted links that reduces data by up to 70%. If you use this software, please cite the article from preferred-citation in CITATION.cff.

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    Authors: Lenders, Martine; Amsüss, Christian; Gündoğan, Cenk; Nawrocki, Marcin; +2 Authors

    In this paper, we present the design, implementation, and analysis of DNS over CoAP (DoC), a new proposal for secure and privacy-friendly name resolution of constrained IoT devices. We implement different design choices of DoC in RIOT, an open-source operating system for the IoT, evaluate performance measures in a testbed, compare with DNS over UDP and DNS over DTLS, and validate our protocol design based on empirical DNS IoT data. Our findings indicate that plain DoC is on par with common DNS solutions for the constrained IoT but significantly outperforms when additional standard features of CoAP are used such as caching. With OSCORE, we can save more than 10 kBytes of code memory compared to DTLS, when a CoAP application is already present, and retain the end-to-end trust chain with intermediate proxies, while leveraging features such as group communication or encrypted en-route caching. We also discuss a compression scheme for very restricted links that reduces data by up to 70%.

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    Authors: EFSA Panel On Food Contact Materials, Enzymes, Processing Aids (CEP); Lambré, Claude; Barat Baviera, José Manuel; Bolognesi, Claudia; +19 Authors

    Food Enzyme Intake Model for the production of modified milk proteins. The Food Enzyme Intake Model (FEIM) is a tool for estimating chronic dietary exposure to food enzymes used in food processes. FEIM follows the methodology recommended in the Scientific Guidance for the submission of dossiers on Food Enzymes. It has been developed on the basis of summary statistics of food consumption data collected from Member States (stored in the EFSA Comprehensive European Food Consumption Database). Each release uses the most recent consumption data from the Comprehensive Database. FEIM comprises process-specific calculators, such as FEIM-baking or FEIM-brewing, which allow estimation of dietary exposure to food enzymes used in individual food manufacturing processes. Exposure results are reported at mean and high level for six population groups (e.g. infants, toddlers, adults, etc.) in different countries. {"references": ["EFSA CEP Panel (EFSA Panel on Food Contact Materials, Enzymes and Processing Aids), Lambr\u00e9 C, Barat Baviera JM, Bolognesi C, Cocconcelli PS, Crebelli R, Gott DM, GrobK, Lampi E, Mengelers M, Mortensen A, Riviere G, Steffensen I-L, Tlustos C, Van Loveren H, Vernis L,Zorn H, Glandorf B, Herman L, Aguilera J, Andryszkiewicz M, Gomes A, Kovalkovicova N, Liu Y, RainieriS and Chesson A, 2021. Scientific Guidance for the submission of dossiers on Food Enzymes. EFSAJournal 2021;19(10):6851, 37 pp.https://doi.org/10.2903/j.efsa.2021.6851"]} EU; xls; fip@efsa.europa.eu

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    Authors: EFSA Panel on Food Contact Materials, Enzymes, Processing Aids (CEP); Lambré, Claude; Barat Baviera, José Manuel; Bolognesi, Claudia; +19 Authors

    Food Enzyme Intake Model for the production of protein hydrolysates from plants and fungi. The Food Enzyme Intake Model (FEIM) is a tool for estimating chronic dietary exposure to food enzymes used in food processes. FEIM follows the methodology recommended in the Scientific Guidance for the submission of dossiers on Food Enzymes. It has been developed on the basis of summary statistics of food consumption data collected from Member States (stored in the EFSA Comprehensive European Food Consumption Database). Each release uses the most recent consumption data from the Comprehensive Database. FEIM comprises process-specific calculators, such as FEIM-baking or FEIM-brewing, which allow estimation of dietary exposure to food enzymes used in individual food manufacturing processes. Exposure results are reported at mean and high level for six population groups (e.g. infants, toddlers, adults, etc.) in different countries. {"references": ["EFSA CEP Panel (EFSA Panel on Food Contact Materials, Enzymes and Processing Aids), Lambr\u00e9 C, Barat Baviera JM, Bolognesi C, Cocconcelli PS, Crebelli R, Gott DM, GrobK, Lampi E, Mengelers M, Mortensen A, Riviere G, Steffensen I-L, Tlustos C, Van Loveren H, Vernis L,Zorn H, Glandorf B, Herman L, Aguilera J, Andryszkiewicz M, Gomes A, Kovalkovicova N, Liu Y, RainieriS and Chesson A, 2021. Scientific Guidance for the submission of dossiers on Food Enzymes. EFSAJournal 2021;19(10):6851, 37 pp.https://doi.org/10.2903/j.efsa.2021.6851"]} EU; xls; fip@efsa.europa.eu

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: von Stein, Meriel;

    This repo contains the tools, paper, and study data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443. Also available via https://github.com/MissMeriel/DeepManeuver/ .

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    Authors: Tourville, Jordon; Publicover, David; Dovciak, Martin;

    Remote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image. All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery. Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m). For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%). Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0). Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year. Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu). Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions. Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA. Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata. Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance. Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32). Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Associated csv's require R (or Excel) to be loaded and for data to be analyzed. Funding provided by: Edna Bailey Sussman FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006517Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1759724

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    Authors: Sanchez, Kevin;

    This is an aggregated dataset, consisting of timeseries with in-situ aircraft or ship campaign measurements from ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. CCN, CCN proxies and measurements necessary to identify particle physical and chemical properties and non-marine contributions to particle concentrations are included. All missing or invalid data flags are converted to 'Na'. Some datasets have already been filtered for inlet shattering in-cloud, and measurement contamination from ship exhausts; however, methods of filtering ship exhaust vary by campaign. For the NAAMES ship campaigns, the research ship exhaust was identified and filtered out based on the wind direction relative to the ship exhaust and total particle counts. For CAPRICORN2, wind direction, total particle counts, black carbon particle concentration, and CO and CO2 measurements were also utilized in filtering ship exhaust. Finally, the MARCUS ship exhaust contamination periods are identified and filtered using total particle counts and CO measurements. The aggregated dataset is further filtered to eliminate measurements influenced by in-cloud inlet shattering and averaged at 10 second intervals for aircraft measurements and 5-minute intervals for ship measurements (except for CAPRICOR2 which is only publicly available at hourly averaged intervals). In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates. The data files are in a .csv format and can be opened with many open-source softwares. The data from each campaign deployment is in a seprate .csv file. Some of the data files are stored on the Zenodo data repository due to licensing requirements (CC BY 4.0) and must be downloaded from https://doi.org/10.5281/zenodo.8135766.Funding provided by: Langley Research CenterCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006199Award Number:

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    Authors: Champlin, Lena; Woolfolk, Andrea; Oczkowski, Autumn; Rittenhouse, Audrey; +8 Authors

    To examine interrelationships between nitrogen pollution and anthropogenic sources over the past century, we parametrized a model of nitrogen inputs to the watershed. Our model was based on the Nitrogen Loading Model (NLM). We applied the NLM model to calculate watershed sources of nitrogen over time in decadal increments from 1930–2010. We compiled historical data on changes in human population from census data, atmospheric deposition, homes with wastewater treatment, the areal extent of cultivated and natural lands and impervious surface cover, and estimated changes in fertilizer application rates in the Elkhorn watershed (based on annual "Commercial Fertilizers" and "Fertilizing Materials" reports published by the California Department of Agriculture 1925–2012). Eighty-five ~ three-meter-deep sediment cores were collected during 2010 from the vertices of a 200 m x 200 m grid superimposed over the tidal and never-diked portions of the estuary. Most of the sediment cores were collected using a Russian peat borer to minimize compaction; in a few locations, a vibracorer was necessary to penetrate sands. Six focal cores were selected for high-resolution analyses and were collected using a piston corer with polycarbonate liners to obtain intact core sections for scanning and archiving. Focal cores were split into 1-cm sections; the remaining cores were sectioned into 10 cm intervals for 0–50 cm depths, and into 25-cm intervals for 50–100 cm depths. Core splits were archived at the LacCore repository at the University of Minnesota. Chronologies were created using downcore profiles of 210Pb, 137Cs, and 226Ra measured with a low-energy germanium multichannel gamma spectrometer. Historical geochemical markers included Pb concentrations measured using ICP-AES following four-acid extractions, AMS radiocarbon dating of fossil peat, and magnetic susceptibility and imaging using a Geotek Multi-Sensor core logger. The maximum depth of radiocesium was assigned an age of 1953, radiocesium peaks were assigned an age of 1963, and total lead concentration peaks were assigned an age of 1974. Lead-210, radiocesium, and radiocarbon dating were combined in an age-depth model using a Bayesian approach to construct chronologies for seven cores. The age model 210Pb Plum in R version 4.0.5 uses the same statistical approach as the previous model Bacon, but incorporates radionuclide dating including parameters of deposition of 210Pb, supported 210Pb, and accretion rates. The Plum model was selected because it can account for incremental 210Pb data over depth in the cores, as opposed to using the analytical approach of the continuous rate of supply model. Additionally, this model has been used previously for chronologies of estuarine sediments. Within Elkhorn Slough, sediment accumulation rates varied little from site to site over the past century and were similar to values reported previously; thus, to estimate ages for the 85 undated cores, we compiled a composite core chronology using the seven cores to represent mean age-date model for the entire estuary. This composite core chronology was then applied to the 85 undated cores, using the composite age-depth relationship to estimate dates for the depth segments utilized for isotopic and stoichiometric measurements. We report the mean year output of the model and 95% confidence intervals around the mean. For the six high-resolution focal sites, cores were analyzed at 1-cm increments (for 0 to 50 cm depths) for stable carbon and nitrogen isotopic composition using a Finnegan Delta Plus continuous flow isotope ratio mass spectrometer using standard methods, and for carbon and nitrogen concentration using a Flash 1112 EA. For the 85 coarser resolution cores, sediments were analyzed for carbon and nitrogen abundance and stable isotope ratios using a Vario Cube elemental analyzer interfaced to an Isoprime 100 IRMS. Isotope ratios for carbon and nitrogen are reported in permille notation as: where R is the abundance ratio of the less common (a) to more common isotope. The standard for nitrogen is atmospheric nitrogen gas; the standard for carbon is PeeDee Belemnite; by definition, standards have δ=0. Sediments were not pretreated to remove inorganic carbon, as acidification did not quantitatively shift ratios. Previous studies suggest little effect of diageneses on sediment δ15N ratios in coastal marine settings, but shifts of ~ -1.5‰ in δ13C ratios are expected and C/N ratios are thought to decrease over time. Furthermore, atmospheric δ13C ratios have declined by about -1.5‰ since 1850 associated with the Suess Effect – the release of lighter C from fossil fuel combustion. Whole estuary isoscape and stoichioscape maps were produced using sedimentary stable isotope (δ13C and δ15N) and molar nutrient stoichiometric (C/N) ratios interpolated from the 85 core locations using ordinary kriging in ArcGIS version 10.2.2 (ESRI, Redlands, CA, USA) to the spatial extent of cored areas in Elkhorn Slough. Maps were created for six depth intervals dated using the composite chronology (ca. 1726–1839, 1839–1885, 1885–1951, 1951–1963,1963–1981, and 1981–2010). Different interpolation variogram models including spherical, circular, exponential, Gaussian, linear interpolation with linear drift, and linear with quadratic drift were tested. Leave-one-out cross validation of 15% of the points was used to choose the model which yielded the smallest root mean square error between predicted and actual values. To ensure that historical differences in interpolation maps were a function of data differences rather than variogram methodology, the spherical kriging method was used for all timepoints. We also applied data from monthly water quality sampling at a network of (~26) stations across Elkhorn Slough since 1988. Monthly nitrate data from the sites were averaged during the full year of 1995 and mapped using ordinary kriging for comparison to spatial patterns of the isoscape and stoichioscape maps. Trends in isotopic and stoichiometric signatures since the 1850s were examined for the six high-resolution cores. Timeseries analysis of the high-resolution data investigated the statistical significance of trends during the period of increasing fertilizer application, as well as offsets in the signatures associated with the timing of marine inlet construction for the harbor. Statistically significant change points in the timeseries were determined using the Pettitt Test, a nonparametric test that identifies the year of a step change and assigns significance to the selection. Datasets of δ15N, δ13C, and C/N for each of the six high-resolution coring sites were separately tested for the period 1850–2010 (n = 45 time points each). Next, the timeseries were split at the significant step change points that were statistically identified, forming two datasets "before" and "after" the year of change. Trend analysis was performed using linear regression on the split datasets, to model the slope after the split as well as the difference of y-intercept at the step change year (Fig. S1 diagrams the slope and intercept of our statistical models). The difference of y-intercept at the step change year is interpreted as an offset in the timeseries, consistent with construction of the harbor inlet when the step occurred at the same time as the construction (1946 ± 10 years). The slope after this step change year is attributed to increasing fertilizer addition to the watershed from 1940–1980. To compare sediment isotope results to dissolved nutrient concentrations, we compared water quality monitoring data to the high-resolution sediment cores during a 20-year period. Monthly water sampling of parameters (including salinity) were measured at the sites, and water samples were also collected into brown Nalgene bottles; stored on ice; filtered; and analyzed for nutrients, including nitrate (NO3−), within 48 hours, or frozen for later analysis in accordance with standard methods. Three of the high-resolution sediment cores were collected at the same locations as water quality monitoring sites. For these three water quality sampling stations (Portero Road North, Kirby Park, and Hudsons Landing West), we compared annual mean water column dissolved NO3− (mM) and salinity (ppt) to sedimentary δ15N values during the same year from 1990–2010. Coastal eutrophication is a prevalent threat to the healthy functioning of ecosystems globally. While degraded water quality can be detected by monitoring oxygen, nutrient concentrations, and algal abundance, establishing regulatory guidelines is complicated by a lack of baseline data (e.g., pre-Anthropocene). We use historical carbon and nitrogen isoscapes from sediment cores to reconstruct spatial and temporal changes in nutrient dynamics for a central California estuary, where development and agriculture dramatically enhanced nutrient inputs over the past century. We found strong contrasts between current sediment stable isotopes and those from the recent past, demonstrating shifts exceeding those in previously studied eutrophic estuaries and substantial increases in nutrient inputs. Comparisons of contemporary with historical isoscapes also revealed that nitrogen sources shifted from a marine-terrestrial gradient to amplified denitrification at the head and mouth of the estuary. Geospatial analysis of historical data suggests that an increase in fertilizer application – rather than population growth or increases in the extent of cultivated land – is chiefly responsible for increasing nutrient loads during the 20th century. This study demonstrates the ability of isotopic and stoichiometric maps to provide important perspectives on long-term shifts and spatial patterns of nutrients that can be used to improve management of nutrient pollution. Excel, R, and a GIS software such as ArcGIS or QGIS.Funding provided by: National Oceanic and Atmospheric AdministrationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000192Award Number: NA06NOS4190167

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    Authors: Dmytro Humeniuk; Foutse Khomh; Giuliano Antoniol;

    To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain percentage of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. 

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    Authors: Robinson, Samuel; Schwinghamer, Timothy; Carcamo, Hector; Galpern, Paul;

    Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions is expensive and time-consuming to collect. Furthermore, relationships derived from small-scale plot data may not apply to ecosystem services operating at larger spatial scales (fields, landscapes). Precision yield data can be used to improve the accuracy and geographic range of ecosystem service studies, but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis. We provide examples of analysis using simulated and real precision yield data and outline two case studies of ecosystem services using precision yield data. Ecologists and agronomists should consider using precision yield data more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes. All scripts were written in RMarkdown (Allaire et al 2023) using R version 4.3.1 (R Core Team 2023).Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2023). rmarkdown: Dynamic Documents for R. R package version 2.22, https://github.com/rstudio/rmarkdown. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.Funding provided by: Natural Sciences and Engineering Research Council of CanadaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000038Award Number: The combined yield monitor data for Supplemental 1 was donated by Trent Clark (the absolution location of the spatial data has been anonymized for privacy). Supplemental 2 uses entirely generated data (see script for details). Supplemental 3 uses a correlation matrix created from unpublished yield data collected by Hector Cárcamo.

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    Authors: Lenders, Martine S.; Amsüss, Christian; Gündoğan, Cenk; Nawrocki, Marcin; +2 Authors

    In this paper, we present the design, implementation, and analysis of DNS over CoAP (DoC), a new proposal for secure and privacy-friendly name resolution of constrained IoT devices. We implement different design choices of DoC in RIOT, an open-source operating system for the IoT, evaluate performance measures in a testbed, compare with DNS over UDP and DNS over DTLS, and validate our protocol design based on empirical DNS IoT data. Our findings indicate that plain DoC is on par with common DNS solutions for the constrained IoT but significantly outperforms when additional standard features of CoAP are used such as caching. With OSCORE, we can save more than 10 kBytes of code memory compared to DTLS, when a CoAP application is already present, and retain the end-to-end trust chain with intermediate proxies, while leveraging features such as group communication or encrypted en-route caching. We also discuss a compression scheme for very restricted links that reduces data by up to 70%. If you use this software, please cite the article from preferred-citation in CITATION.cff.

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    Authors: Lenders, Martine; Amsüss, Christian; Gündoğan, Cenk; Nawrocki, Marcin; +2 Authors

    In this paper, we present the design, implementation, and analysis of DNS over CoAP (DoC), a new proposal for secure and privacy-friendly name resolution of constrained IoT devices. We implement different design choices of DoC in RIOT, an open-source operating system for the IoT, evaluate performance measures in a testbed, compare with DNS over UDP and DNS over DTLS, and validate our protocol design based on empirical DNS IoT data. Our findings indicate that plain DoC is on par with common DNS solutions for the constrained IoT but significantly outperforms when additional standard features of CoAP are used such as caching. With OSCORE, we can save more than 10 kBytes of code memory compared to DTLS, when a CoAP application is already present, and retain the end-to-end trust chain with intermediate proxies, while leveraging features such as group communication or encrypted en-route caching. We also discuss a compression scheme for very restricted links that reduces data by up to 70%.

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    Authors: EFSA Panel On Food Contact Materials, Enzymes, Processing Aids (CEP); Lambré, Claude; Barat Baviera, José Manuel; Bolognesi, Claudia; +19 Authors

    Food Enzyme Intake Model for the production of modified milk proteins. The Food Enzyme Intake Model (FEIM) is a tool for estimating chronic dietary exposure to food enzymes used in food processes. FEIM follows the methodology recommended in the Scientific Guidance for the submission of dossiers on Food Enzymes. It has been developed on the basis of summary statistics of food consumption data collected from Member States (stored in the EFSA Comprehensive European Food Consumption Database). Each release uses the most recent consumption data from the Comprehensive Database. FEIM comprises process-specific calculators, such as FEIM-baking or FEIM-brewing, which allow estimation of dietary exposure to food enzymes used in individual food manufacturing processes. Exposure results are reported at mean and high level for six population groups (e.g. infants, toddlers, adults, etc.) in different countries. {"references": ["EFSA CEP Panel (EFSA Panel on Food Contact Materials, Enzymes and Processing Aids), Lambr\u00e9 C, Barat Baviera JM, Bolognesi C, Cocconcelli PS, Crebelli R, Gott DM, GrobK, Lampi E, Mengelers M, Mortensen A, Riviere G, Steffensen I-L, Tlustos C, Van Loveren H, Vernis L,Zorn H, Glandorf B, Herman L, Aguilera J, Andryszkiewicz M, Gomes A, Kovalkovicova N, Liu Y, RainieriS and Chesson A, 2021. Scientific Guidance for the submission of dossiers on Food Enzymes. EFSAJournal 2021;19(10):6851, 37 pp.https://doi.org/10.2903/j.efsa.2021.6851"]} EU; xls; fip@efsa.europa.eu

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    Authors: EFSA Panel on Food Contact Materials, Enzymes, Processing Aids (CEP); Lambré, Claude; Barat Baviera, José Manuel; Bolognesi, Claudia; +19 Authors

    Food Enzyme Intake Model for the production of protein hydrolysates from plants and fungi. The Food Enzyme Intake Model (FEIM) is a tool for estimating chronic dietary exposure to food enzymes used in food processes. FEIM follows the methodology recommended in the Scientific Guidance for the submission of dossiers on Food Enzymes. It has been developed on the basis of summary statistics of food consumption data collected from Member States (stored in the EFSA Comprehensive European Food Consumption Database). Each release uses the most recent consumption data from the Comprehensive Database. FEIM comprises process-specific calculators, such as FEIM-baking or FEIM-brewing, which allow estimation of dietary exposure to food enzymes used in individual food manufacturing processes. Exposure results are reported at mean and high level for six population groups (e.g. infants, toddlers, adults, etc.) in different countries. {"references": ["EFSA CEP Panel (EFSA Panel on Food Contact Materials, Enzymes and Processing Aids), Lambr\u00e9 C, Barat Baviera JM, Bolognesi C, Cocconcelli PS, Crebelli R, Gott DM, GrobK, Lampi E, Mengelers M, Mortensen A, Riviere G, Steffensen I-L, Tlustos C, Van Loveren H, Vernis L,Zorn H, Glandorf B, Herman L, Aguilera J, Andryszkiewicz M, Gomes A, Kovalkovicova N, Liu Y, RainieriS and Chesson A, 2021. Scientific Guidance for the submission of dossiers on Food Enzymes. EFSAJournal 2021;19(10):6851, 37 pp.https://doi.org/10.2903/j.efsa.2021.6851"]} EU; xls; fip@efsa.europa.eu

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