Multifaceted data are very common in the human sciences. For example, test takers' responses to essay items are marked by raters. If multifaceted data are analyzed with standard facets models, it is assumed there is no interaction between facets. In reality, an interaction between facets can occur, referred to as differential facet functioning. A special case of differential facet functioning is the interaction between ratees and raters, referred to as differential rater functioning (DRF). In existing DRF studies, the group membership of ratees is known, such as gender or ethnicity. However, DRF may occur when the group membership is unknown (latent) and thus has to be estimated from data. To solve this problem, in this study, we developed a new mixture facets model to assess DRF when the group membership is latent and we provided two empirical examples to demonstrate its applications. A series of simulations were also conducted to evaluate the performance of the new model in the DRF assessment in the Bayesian framework. Results supported the use of the mixture facets model because all parameters were recovered fairly well, and the more data there were, the better the parameter recovery.

Please check the README file for more information about the dataset.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines. Dataset SummaryPhenomenon Mapped: LandformsGeographic Extent: GlobalProjection: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereUnits: MetersCell Size: 231.91560581932 metersPixel Depth: 8-bit unsigned integerAnalysis: Restricted single source analysis. Maximum size of analysis is 30,000 x 30,000 pixels.Source: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/ In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS. The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plains Smooth plains with some local relief Irregular plains with moderate relief Irregular plains with low hills Scattered moderate hills Scattered high hills Scattered low mountains Scattered high mountains Moderate hills High hills Tablelands with moderate relief Tablelands with considerable relief Tablelands with high relief Tablelands with very high relief Low mountains High mountains To produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain"s texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class. The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them: What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through development of metrics and their demonstration on a hierarchy of downscaled climate datasets to quantify aspects of climate change projections that are credible, particularly for supporting decisions related to the energy-water-land nexus. As a part of this effort, we have produced a regional climate dataset using the Model for Prediction Across Scales coupled to the Community Atmosphere Model (CAM-MPAS). This global modeling framework is configured with variable-resolution meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The variable-resolution configurations allow fine-scale features to be better resolved inside the refinement and interact with global-scale circulations. The dataset includes multiple uniform- (240km and 120km) and variable-resolution (200-50km, 100-25km, and 46-12km) simulations that are designed to be compatible with other regional climate simulations that contribute to the hierarchy of downscaled climate datasets of the project. Furthermore, the dataset consists of simulations for both the present-day (1989-2010) and future (2079-2100) climate and post-processing of the model output has been coordinatedmore » across the project for consistency to facilitate common analysis across the hierarchy of datasets. Altogether, this CAM-MPAS model dataset provides a unique opportunity to assess the influence of resolutions and modeling framework on model credibility and climate change projection.« less

This resource contains the SWAT-MODFLOW model for the Santa Fe River of North Central Florida used in the Floridan Aquifer Collaborative Engagement for Sustainability (FACETS) project. The FACETS project was funded by the USDA National Institute of Food and Agriculture (Award Number: 2017-68007-26319) to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds (https://floridanwater.research.ufl.edu/). SWAT-MODFLOW couples the Soil and Water Assessment Tool (SWAT) to the U.S. Geological Survey modular finite-difference flow model (MODFLOW) to produce an integrated surface-groundwater model (https://swat.tamu.edu/software/swat-modflow/). Within SWAT-MODFLOW, SWAT handles most surface and soil processes, MODFLOW handles groundwater processes, and both models interact to simulate stream flows.

The SWAT portion of this model was developed using USGS digital elevation models, the 2017 Statewide Land Use / Land Cover map of the Florida Department of Environmental Protection (FDEP), Florida Department of Health septic tank data, STATSGO soil maps, the Public Land Survey System, and NLDAS weather data. Agricultural and silvicultural production land uses and management practices implemented within SWAT were co-developed with stakeholders in a participatory modeling process (PMP) and included row crops (corn-peanut and corn-carrot-peanut rotations) forage crops (bermudagrass hay and pasture), and production forestry (slash pine). Additional land uses implemented in SWAT included urban, low-density residential, septic tanks, rapid infiltration basins, fertilized lawns, natural grass, wetlands, and open water. The MODFLOW portion of the model was developed from the larger North Florida Southeast Georgia (NFSEG) MODFLOW model (version 1.0) as developed by the St John’s River and Suwannee River Water Management Districts. A detailed description of the complete model development process can be found in a document within this resource.

Calibration of the model was conducted using a Bayesian Sample-Importance-Resample method. Data used in the model calibration included: 1) USGS discharge data (Stations 02322500, 02322700, 02322800, and 02321500); 2) USGS operational Simplified Surface Energy Balance (SSEBop) actual evapotranspiration; and 3) Upper Floridan Aquifer potentiometric surfaces from FDEP. The calibration period of the model was 2010-2018 and the validation period was 1980-2009.

Background Talar fractures often require osteotomy during surgery to achieve reduction and screw fixation of the fractured fragments due to limited visualization and operating space of the talar articular surface. The objective of this study was to evaluate the horizontal approach to the medial malleolus facet by maximizing exposure through dorsiflexion and plantarflexion positions. Methods In dorsiflexion, plantarflexion, and functional foot positions, we respectively obtained the anterior and posterior edge lines of the projection of the medial malleolus on the medial malleolar facet. The talar model from Mimics was imported into Geomagic software for image refinement. Then Solidworks software was used to segment the medial surface of the talus and extend the edge lines from the three positions to project them onto the "semicircular" base for 2D projection. The exposed area in different positions, the percentage of total area it represents, and the anatomic location of the insertion..., DICOM-formatted CT-scan images of each patient were imported into Mimics software (21.0 ; Materialise, Leuven, Belgium). We removed the soft tissue and affected bones by the function of image segmentation, region growth and multiple slice editing of Mimics software, respectively. A total of 273 virtual foot and ankle models were created. In dorsiflexion, plantarflexion, and functional foot positions, we respectively obtained the anterior and posterior edge lines of the projection of the medial malleolus on the medial malleolar facet (Fig. 1). At the same time, we found that the medial malleolar facet had a shape resembling a “semicircle†, and regardless of whether the foot was in the functional position, dorsiflexed, or plantarflexed, the movement of the medial malleolus occurred within this "semicircular" region (Fig. 2A). Tracing the outline of the foot in the three positions on the talus, it is not difficult to observe that plantarflexion and dorsiflexion expand the exposure area on ..., , # Evaluation of the horizontal approach to the medial malleolar facet in sagittal talar fractures through dorsiflexion and plantarflexion positions

In the positions of foot dorsiflexion, plantarflexion, and functional, we respectively obtained the anterior and posterior edge lines of the projection of the medial malleolus on the medial malleolar facet. The talar model from Mimics was imported into Geomagic software for image refinement. Then Solidworks software was used to segment the medial surface of the talus and extend the edge lines from the three positions to project them onto the "semicircular" base for 2D projection. The exposed area in different positions, the percentage of total area it represents, and the anatomic location of the insertion point at the groove between the anteroposternal protrusions of the medial malleolus were calculated. The mean total area of the "semicircular" region on the medial malleolus surface of the talus was 542.10 ± 80.05 mm2. In the functional ...

The most parsimonious model accounts for data through adding distance and vehicular guidability features, and their multiplicative interaction. I-scores were explicative of distance sensitivities, but only in high influence blocks.

Objective structured clinical examinations (OSCEs) are widely used performance assessments for medical and dental students. A common limitation of OSCEs is that the evaluation results depend on the characteristics of raters and the scoring rubric. To overcome this limitation, item response theory (IRT) models such as the many-facet models have been proposed to estimate examinee abilities while accounting for the characteristics of raters and evaluation items in a rubric. However, conventional IRT models have two impractical assumptions: constant rater severity across all evaluation items in a rubric and an equal interval rating scale among evaluation items, which can decrease model fitting and ability measurement accuracy. To resolve this problem, we propose a new IRT model that relaxes these assumptions. We demonstrate the effectiveness of the proposed model by applying it to actual data collected from a medical interview test conducted at Tokyo Medical and Dental University as part of a post-clinical clerkship (PostCC) OSCE. The experimental results showed that the proposed model fit our OSCE data well and measured ability accurately. Furthermore, it provided abundant information on rater and item characteristics that conventional models cannot, helping us to better understand rater and item properties. This dataset includes the actual score data collected from the above-mentioned medical interview test in a PostCC OSCE, as well as the program for estimating the parameters of the proposed IRT model.

Reaction energies and atomic structures from first-principles electronic structure calculations.

To approximate the distribution of shrubland species based on their postfire reproductive strategy (resprouter, seeder, and facultative seeder) across Southern California, we created a raster layer subdividing the landscape into a number of different facet classes. This raster dataset is at 30 meters pixel resolution and contains 12 different landscape facet classes based on vegetation and physiography. Specifically, the facets included several different vegetation types based on the California Wildlife Habitat Relations (WHR) classification (three shrubland categories, annual grasslands, valley-foothill riparian woodland, and ‘other’ vegetation types) which were intersected with aspect (two classes: north or south facing) and topography (summit, ridges, slopes, valleys, flats, and depressions). The combination of factors is intended to capture warmer, more exposed vegetation types dominated by seeder species (occurring on south-facing slopes, summits and ridges) versus cooler, less exposed vegetation types associated with resprouter species (occurring on north-facing slopes, valleys, depressions, and flats).

The dataset is a key input into a tool developed for resource managers to aid in the prioritization of restoration activities in shrublands postfire. The tool is available at https://github.com/adhollander/postfire and described in the following technical guide:

Underwood, Emma C., and Allan D. Hollander. 2019. “Post-Fire Restoration Prioritization for Chaparral Shrublands Technical Guide.” https://github.com/adhollander/postfire/blob/master/Postfire_Restoration_Priorization_Tool_Technical_Guide.pdf

Methods The following are the GIS processing workflow steps used to create this dataset. A diagram illustrating this workflow is in the attached file collection (SoCal_Veg_Topo_Facets_Workflow.png).

1) Compile GIS layers. There were two input layers to the GIS workflow, a 30 meter digital elevation model for California (dem30) and a vegetation raster layer of the state from the California Department of Forestry and Fire Protection (fveg15). The 30 meter DEM was downloaded from the USGS National Map (https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map). The vegetation data is the FVEG dataset published in 2015 by the California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (https://frap.fire.ca.gov/media/10894/fveg15_1.zip). This is a 30 meter raster representation of statewide vegetation using the California Wildlife Habitat Relationships vegetation classification system (https://wildlife.ca.gov/Data/CWHR).

2) Import data into GIS. Both data layers were imported into GRASS 7 for further processing, using a mask of the Southern California study region (encompassing the Angeles, Cleveland, Los Padres, and San Bernardino National Forests) to filter processing to the study footprint.

3) Calculate aspect for elevation model. Using the command r.slope.aspect, we generated a raster layer (aspect) giving the topographic aspect (0-360 degrees) of slopes across the study region.

4) Generate north-south aspect layer. Using the command r.mapcalc, we subdivided the aspect layer into north and south-facing slopes through creating a raster layer (nsaspect) with two categories for north and south.

5) Generate geomorphons for study region. The geomorphon raster layer derives from the dem30m surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013) and was generated using the r.geomorphons extension for GRASS 7.

6) Merge geomorphons with north-south aspect layer. In this step we combined the north-south aspect layer with the geomorphons layer to create a layer entitled nsgeomorphon2a. In so doing we grouped the geomorphon types spurs, slopes, and hollows into a single “slope” category and assigned these to north-facing slopes and south-facing slopes depending upon the value of the north-south aspect layer.

7) Regroup merged layer into three groupings. In this step we took the merged nsgeomorphon2a layer and assigned the classes in it to three different physiographic groups, namely 1) flats 2) valleys, depressions, and north-facing slopes/spurs/hollows/footslopes/shoulders and 3) summits and ridges and south-facing slopes/spurs/hollows/footslopes/shoulders. This grouped layer was named nsgeomorphon2d.

8) Reclass vegetation layer to main habitat types. The vegetation layer fveg15 contains information about many details of the vegetation, including canopy size, canopy cover, and main habitat type. This reclass step extracts the main habitat type into a separate raster named fveg15whr.

9) Combine vegetation layer with physiography layer. Using the command r.cross, we combined the layers fveg15whr and nsgeomorphon2d into a new layer nsgeoxfvegwhr with a separate category for each combination of the raster values from the two input layers.

10) Reclass combined layer into small set of groupings. Taking the nsgeoxfvegwhr layer, we recategorized the 196 combinations of raster values into a set of 12 different combinations using the command r.reclass. This layer is named nsgeoxfvegnbclasses. The 12 different classes generated as an output are the following, with their raster values paired with their classes:

0 Annual grassland: south-facing slopes; summits; ridges

1 Annual grassland: north-facing slopes; valleys; depressions; flats

2 Chamise-redshanks chaparral: south-facing slopes; summits; ridges

3 Chamise-redshanks chaparral: north-facing slopes; valleys; depressions; flats

4 Mixed or montane chaparral: south-facing slopes; summits; ridges

5 Mixed or montane chaparral: north-facing slopes; valleys; depressions; flats

6 Valley-foothill riparian: south-facing slopes; summits; ridges

7 Valley-foothill riparian: north-facing slopes; valleys; depressions; flats

8 Coastal scrub: south-facing slopes; summits; ridges

9 Coastal scrub: north-facing slopes; valleys; depressions; flats

10 Other: south-facing slopes; summits; ridges

11 Other: north-facing slopes; valleys; depressions; flats

11) Export dataset. Using the command r.out.gdal, we exported the nsgeoxfvegnbclasses layer as the raster geotiff file SoCal_Veg_Topo_Facets.tif.

The GRASS commands used for these 11 steps are below:

r.in.gdal input="/home/adh/CARangelands/Vegetation/fveg15_11.tif" output="fveg15" memory=300 offset=0

r.proj input="dem1sec_calif" location="CAllnad83" mapset="statewide" output="dem30m" method="bilinear" memory=300 resolution=30

r.slope.aspect elevation=dem30m@statewide slope=slope aspect=aspect

r.mapcalc 'nsaspect = if(aspect <= 180, 1, 2)'

r.geomorphon --overwrite dem=dem30m@statewide forms=SoCalgeomorphons search=11 skip=4 flat=1 dist=0

r.mapcalc --overwrite 'nsgeomorphon = if((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 1, 11, if(((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 2), 12, SoCalgeomorphons@socalNF))'

r.reclass input=nsgeomorphon2a@socalNF output=nsgeomorphon2d rules=/home/adh/SantaClaraRiver/PostfireRestoration/jupyter/datasets/nsgeomorphon-reclass2d.lut

r.reclass input="fveg15@statewide" output="fveg15whr" rules="/home/adh/CARangelands/Vegetation/fveg15whr.lut"

r.cross --overwrite input=fveg15whr@statewide,nsgeomorphon2d@socalNF output=nsgeoxfvegwhr

r.reclass --overwrite input=nsgeoxfvegwhr@socalNF output=nsgeoxfvegnbclasses rules=/home/adh/SantaClaraRiver/PostfireRestoration/datasets/fvegwhrtonbclasses.lut

r.out.gdal --overwrite input=nsgeoxfvegnbclasses@socalNF output=SoCal_Veg_Topo_Facets.tif format=GTiff type=Byte createopt=COMPRESS=DEFLATE

β-Nickel oxyhydroxide (β-NiOOH) is a promising electrocatalyst for the oxygen evolution reaction (OER), which is the more difficult half-reaction involved in water splitting. In this study, we revisit the OER activities of the two most abundant crystallographic facets of pristine β-NiOOH, the (0001) and (1010) facets, which expose 6-fold-lattice-oxygen-coordinated and 5-fold-lattice-oxygen-coordinated Ni sites, respectively. To this end, we model various active sites on these two facets using hybrid density functional theory, which includes a fraction of the exact nonlocal Fock exchange in the electronic description of the system. By evaluating thermodynamic OER overpotentials, we show that the two active sites considered on each crystallographic facet demonstrate OER activities remarkably different from each other. However, the lowest OER overpotentials calculated for the two facets were found to be similar to each other and comparable to the overpotential for the 4-fold-lattice-oxygen-coordinated Ni site on the (1211) facet of β-NiOOH previously examined in J. Am. Chem. Soc. 2019, 141, 1, 693–705. This finding shows that all of the low-index facets investigated so far could be responsible for the experimentally observed OER activity of pristine β-NiOOH. However, the lowest overpotential active sites on these three crystallographic facets operate via different mechanisms, underscoring the importance of considering multiple OER pathways and intermediates on each crystallographic facet of a potential electrocatalyst. Specifically, our work demonstrates that consideration of previously overlooked active sites, transition-metal-ion oxidation states, reaction intermediates, and lattice-oxygen-stabilization are critical to reveal the lowest overpotential OER pathways on pristine β-NiOOH.

Descriptive and normative data of the factors of the FFMQ-SF.

Adaptation of communities to environmental fluctuations can emerge from different facets of biodiversity, Â which may impact ecosystem functioning differently. Previous work examined how ecosystem functions can be influenced by two sources of adaptive potential: sorting (i.e., changes in community composition due to fitness differences) can occur when multiple species or groups are present (richness), and trait adaptability (i.e., trait adjustments within species or functional groups) can emerge from genetic or phenotypic diversity. However, their effect is typically studied separately, and often in the context of only one trophic level. Therefore, we used a bitrophic trait-based model varying in richness and in the presence of trait adaptability at each trophic level, to investigate how sorting and trait adaptability, at one or two trophic levels, separately or jointly shape ecosystem functions. We found that the adaptive potential emerging from any facet of diversity-induced changes in..., The datasets were generated and not collected in the field and the laboratory. We briefly summarise the methods used, which are extensively explained in the associated Oikos article. We solved numerically the ordinary differential equations of an an extended Rosenzweig-MacArthur predator-prey model in C using the SUNDIALS CVODE solver 5.7.0}. Then, we used several packages in Python 3.10 among which NumPy, Pandas, and Matplotlib to analyse the biomass and trait dynamics, and to quantify ecosystem functions. We notably compared the temporal means and variation (coefficient of variation) of ecosystem functions and properties (e.g. total biomass, production, biomass-weighted mean trait, synchrony of prey and predators, and the ratio between prey losses due to predation and the sum of prey losses due to competition and predation) of food webs with different sources of adaptive potential., , # Scripts and data for: Integrating different facets of diversity into food web models: how adaptation among and within functional groups shape ecosystem functioning

https://doi.org/10.5061/dryad.ttdz08m4x

Note that biomasses are expressed in mg C m-3 and traits are unitless.

Tasks performed by the python and C scripts

Generating time series and computing ecosystem functions and properties

To generate biomass and trait time series of a particular food web, we executed the file get_timeseries_ecosystem_functions.py. This Python script performed several tasks: (a) setting the parametrisation of the model, (b) calling and executing the file get_timeseries_ecosystem_functions (produced by compiling get_timeseries_ecosystem_functions.c; cf details later), which solved the model's equations and generated the biomass and trait time series by using the parametrisation defined in python, and (c) computing the temporal means and the coefficie...

Digital interventions are increasingly recognised as cost-effective treatment solutions for a number of health concerns, but adoption and use of these interventions can be low, affecting outcomes. This research sought to identify how individual aesthetic facets and perceived trust may influence perceptions toward and intentions to use an online health intervention by building on the Technology Acceptance Model, where perceived attractiveness, perceived usefulness, perceived ease of use and perceived enjoyment are thought to predict behavioural intentions towards a website. An online questionnaire study assessed perceptions of nine stimuli varying in four aesthetic facets (simplicity, diversity, colour & craftsmanship), utilising a quasi-experimental within-subjects design with a repetition among three different groups: individuals from the general population who were shown stimuli referring to general health (GP-H) (N = 257); individuals experiencing an eating disorder and shown stimuli referring to eating disorders (ED-ED) (N = 109); and individuals from the general population who were shown stimuli referring to eating disorders (GP-ED) (N = 235). Linear mixed models demonstrated that perceptions of simplicity and craftsmanship significantly influenced perceptions of usefulness, ease of use, enjoyment and trust, which in turn influenced behavioural intentions. This study demonstrates that developing the TAM model to add a further construct of perceived trust could be beneficial for digital health intervention developers. In this study, simplicity and craftsmanship were identified as the aesthetic facets with the greatest impact on user perceptions of digital health interventions.

The Building Component Library (BCL) is the U.S. Department of Energy's comprehensive online searchable library of energy modeling building blocks and descriptive metadata. Novice users and seasoned practitioners can use the freely available and uniquely identifiable components to create energy models and cite the sources of input data, which will increase the credibility and reproducibility of their simulations.

The BCL contains components which are the building blocks of an energy model. They can represent physical characteristics of the building such as roofs, walls, and windows, or can refer to related operational information such as occupancy and equipment schedules and weather information. Each component is identified through a set of attributes that are specific to its type, as well as other metadata such as provenance information and associated files.

The BCL also contains energy conservation measures (ECM), referred to as measures, which describe a change to a building and its associated model. For the BCL, this description attempts to define a measure for reproducible application, either to compare it to a baseline model, to estimate potential energy savings, or to examine the effects of a particular implementation.

The BCL contains more than 30,000 components and measures. A faceted search mechanism has been implemented on the BCL that allows users to filter through the search results using various facets. Facet categories include component and measure types, data source, and energy modeling software type. All attributes of a component or measure can also be used to filter the results.

We provide the instances used in the paper “Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem”, by S. Raghavan and Rui Zhang. This repository contains the 100 instances used in the paper.

All the instances used in the paper are provided in a compressed archive. The accompanying data is contained in the following file: • PIDS_Instances.zip

Description: There is one main folder, which contains 100 instances based on 10 real-world graphs.

For graphs Gnutella, Anybeat, Advogato, Escorts, Hamster, Ning, and Delicious, the setting is as follows: For each instance file, there are m + 2 lines. The first m lines provide the edges in the graphs. Nodes are labeled from 0 to n where n is the largest number in the first m lines. The (m + 1)th line contains the weight (b) for each node. The (m + 2)th line contains the threshold value (g) for each node.

For graphs Flixster, Youtube, and Lastfm, the setting is as follows: Each real-world graph “G” is described by the file named “G_Graph.txt” which contains the edges in the graph. Nodes are labeled from 0 to n, where n is the largest number in the file. Each line provides the two end nodes of an edge. The 10 instances associated with each graph “G” are provided in the 10 files named “G_i.txt” for i in {0, 1, · · · , 9}. In each file, there are two lines. The first line contains the weight (b) for each node. The second line contains the threshold value (g) for each node.

The excel file “PIDS_Results.xlsx” reports, for each instance, the upper and lower bounds obtained in the paper.

Canopy structure is an important driver of the energy budget of the grassland ecosystem and is, at the same time, altered by plant diversity. Diverse plant communities typically have taller and more densely packed canopies than less diverse communities. With this, they absorb more radiation, have a higher transpiring leaf surface, and are better coupled to the atmosphere which leads to cooler canopy surfaces. However, whether plant diversity generally translates into a cooling potential remains unclear and lacks empirical evidence. Here, we assessed how functional identity, functional diversity, and species richness of grassland communities in the Jena Experiment predict the mean and variation of plant surface temperature mediated via effects of canopy structure. Using terrestrial laser scanning, we estimated canopy structure describing metrics of vertical structure (mean height, LAI), the distribution (evenness), and the highest allocation (center of gravity) of biomass along height strata. As metrics of horizontal structure, we considered community stands gaps, canopy surface variation, and emergent flowers. We measured surface temperature with a thermal camera. We used SEM models to predict biodiversity effects on the surface temperature during two seasonal peaks of biomass. Before the first cut in May, herb-dominated communities directly promoted lower leaf surface temperatures. However, communities with a lower center of gravity (mostly herb-dominated) also increased canopy surface temperatures compared with grass-dominated communities with higher biomass stored in the top canopy. Grass-dominated communities showed a smaller variation of surface temperatures, which was also positively affected by species richness via an increase in mean height. In August, mean surface temperature decreased with increasing community clumpiness and LAI. The variation of surface temperature was greater in herb-dominated than in grass-dominated communities and increased with plant species richness (direct effects). Synthesis: The mean and variation of canopy surface temperature were driven by differences in functional group composition (herbs- vs. grass dominance) and to a lesser extent by plant diversity. These effects were partly mediated by the metrics of canopy structure but also by direct effects unrelated to the structural metrics considered.

Methods Our field data was collected within the Trait-Based Biodiversity Experiment in 2014 (TBE; Ebeling et al., 2014) at the Jena Experiment site (Thuringia, Germany; 50°55´ N, 11°35`E, 130 m above sea level (Roscher et al., 2004; Weisser et al., 2017). We conducted this study in 92 plots of the Trait-Based experiment (3.5 m x 3.5 m size), comprehending the two species pools, with a gradient of plant species richness of 1 to 8 species. We performed a non-destructive measurement of plant community canopy structure at high resolution, we used a terrestrial laser scanner (TLS) Faro Focus 3D X330 (FARO Technologies Inc., 2011). We scanned 92 plots on April 31st (the first peak of biomass) and August 20th, 2014 (the second biomass peak). The TLS was mounted upside-down on a tripod that was elevated 3.35 m above ground level. The legs of the tripod were centered on permanent survey markers to guarantee identical scanning areas on both dates. We extracted an area of 3.75 m² (1.5 m x 2.5 m) in each plot below the scanner to reduce the effect of shadows within scans. The point clouds of the 92 plots were filtered using statistical outlier removal (SOR) and noise filter. We used the 3D point clouds from terrestrial laser scanning to calculate metrics characterizing vertical and horizontal dimensions of the community canopy structure. We produced height-based metrics from the point cloud of each community. We used mean height as the first vertical dimension metric. To characterize vertical space-filling properties, we calculated the evenness and the center of gravity of the point cloud. Evenness reports the homogeneity of the point cloud density in their vertical distribution, while the center of gravity identifies the height stratum (definition see below) with the highest density of points (Spehn et al., 2000; Barry et al., 2020). As a baseline for only these two vertical metrics (evenness and center of gravity), we calculated voxel grids from the 3D point cloud for each plot. For each scan, a voxel grid of 5 cm was created containing at least one laser return, and the volume was then calculated as the product of the cell area and the attributed height. We used the function ‘vox’ from the R package VoxR (Lecigne et al., 2014). We used the voxel grids to define five different strata of height (0.3 - 20 cm, 20 - 40 cm, 40 - 60 cm, 60 – 80 cm, and 80 - 100 cm). For every stratum, we applied the method ‘Sum of Voxel,’ which calculated the sum of all voxels separately for each of the five strata. As a result, we obtained volumetric data based on 3D point clouds for five different strata and the community canopy height. Based on this information, the evenness metric represents the mean proportion of filled voxels across strata of vegetation height, calculated as the sum of all five voxel strata volumes divided by 5. The center of gravity, in turn, used the volume of voxel grids per height strata to identify the location with the highest density of points. This location was measured in terms of the height-weighted average volume allocation of the community. We then calculated the center of gravity by multiplying each stratum's volume with the mean height of the strata and dividing by the total community volume. Center of gravity range from 1 to 5, in which 1 is the bottom layer (0-20 cm) and five the top canopy (80-100 cm). Further, the leaf area index was also measured at the same time in all 92 plots using the LAI-2000 plant canopy analyzer (LI-Cor, Inc, 2013). Ten random measurements were averaged to a mean of LAI value per plot. Hence, we used LAI as an additional vertical dimensional metric to characterize plant ground area covered by the plant community. To assess the horizontal heterogeneity of the plant community for each plot, we also calculated two horizontal metrics describing the canopy surface variation and clumpiness. We used the surface reconstruction method, which fits a mesh on the 3D point cloud density of each plot (the filtered point clouds and not voxel grids) (Attene & Spagnuolo, 2000). We applied the Poisson Surface Reconstruction method, which fits a mesh on all oriented points (perpendicular vectors to the tangential plane to the surface at that point) (Kazhdan & Hoppe, 2019). After producing the surface mesh for all plots, a surface area of the mesh in square meters was calculated and divided by the area of the plot (3.75 m²). The variation metric is a dimensionless ratio between the mesh surface area and the ground area. For clumpiness, we evaluated the size and distribution of clusters in the spatial arrangement of the point cloud into two dimensions based on the rasterized 3D point clouds. For this, we computed Geary´s index, an identifier of cluster points with similar attributes, assessed by the pixel spatial autocorrelation. We used the function Geary from the R package “raster”. The two response variables, the mean and the coefficient of variation (hereafter CV) of community surface temperature were obtained using the Testo 882 Thermal Imaging Camera, which also recorded RGB images of all 92 plots (for example, Figure 3). We obtained the thermal data and terrestrial laser scans within two days. All thermal measurements were carried out around noon (12:30 – 13:30), at 150 cm height, and facing north. The thermal camera settings controlled the canopy's emissivity as 0.95 with reflectance temperature at 20°C. The sensor detects long-wave infrared radiation in the spectral range from 7.5 to 14 μm and has a thermal sensitivity of 50 mK at +30°C and accuracy of ±2.0°C. With the thermal matrix (registered pixel temperature with an original resolution of 640 x 480), we computed the mean and the coefficient of variation of surface temperature for each plot. As flower heads are often 10 K warmer than the surrounding leaves of herbs and grasses, we calculated the Normalized Green-Red Difference Index (NGRDI, the difference between the green and red bands divided by their sum (Pérez et al., 2000). Further, we also calculated the Normalized Green-Blue Difference Index (NGBDI, the difference between the green and the blue bands divided by their sum (Wang Xiaoqin et al., 2015) better to distinguish the hot spots of inflorescences and green vegetation. We expected that biodiversity effects on mean canopy surface temperature are indirectly mediated by predictors related to vertical structure metrics, while biodiversity effects on temperature CV are mediated by the horizontal structure. To test these assumptions further, we constructed a more detailed formal hypothesis using linear mixed-effects models within a PiecewiseSEM (Lefcheck, 2016). we ran the initial SEM model as a list of causal relationships between canopy structure and biodiversity facets. The last linear model inside the SEM was between the mean and CV of surface temperature and biodiversity facets (functional identity, dispersion, and species richness) and all canopy structure metrics to test the fit of the model to the data. Second, we inspected this initial SEM model results for goodness-of-fit tests for both the full and causal relationships, we then added the predictors that significantly improved the model fit with P values higher than 0.05.

Data Set Information: The dataset provides patient reviews on specific drugs along with related conditions and a 10-star patient rating reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. The intention was to study

(1) sentiment analysis of drug experience over multiple facets, i.e. sentiments learned on specific aspects such as effectiveness and side effects, (2) the transferability of models among domains, i.e. conditions, and (3) the transferability of models among different data sources (see 'Drug Review Dataset (Druglib.com)').

The data is split into a train (75%) a test (25%) partition (see publication) and stored in two .tsv (tab-separated-values) files, respectively.

Machine learning has permeated nearly all fields and disciplines of study. One hot topic is using natural language processing and sentiment analysis to identify, extract, and make use of subjective information. The UCI ML Drug Review dataset provides patient reviews on specific drugs along with related conditions and a 10-star patient rating system reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. This data was published in a study on sentiment analysis of drug experience over multiple facets, ex. sentiments learned on specific aspects such as effectiveness and side effects (see the acknowledgments section to learn more).

The sky's the limit here in terms of what your team can do! Teams are free to add supplementary datasets in conjunction with the drug review dataset in their Kernel. Discussion is highly encouraged within the forum and Slack so everyone can learn from their peers.

Important notes:

When using this dataset, you agree that you 1) only use the data for research purposes 2) don't use the data for any commercial purposes 3) don't distribute the data to anyone else 4) cite us: https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29

This article showcases a systematic and generalized phase-field modeling approach for addressing the phenomenon of faceted crystal dissolution in different crystalline solids, in two and three dimensions. A thermodynamically consistent phase-field model was adapted to account for anisotropies in the surface energy and kinetic mobility associated with the crystal surface that evolves during dissolution. Two significant and novel aspects of this work are: (I) the proposed general prescription of anisotropy parameters and (II) quantitative process simulation, within the phase-field modeling framework. The prescription allows us to simulate dissolution in different crystal–liquid systems, where the crystal may exhibit arbitrary growth and dissolution facets. Moreover, the order of precedence and relative velocities of facets can be precisely controlled. To demonstrate the procedure of quantitative modeling, we considered the system of α-quartz in silica-undersaturated solution under the physical conditions from previous experiments and determined other input model parameters from the existing literature. Further, the missing anisotropy parameters were retrieved based on simulations of dissolving single crystals. Following the proposed prescription and the procedure of parameter-set generation, dissolution processes in other crystal–liquid systems under different physical conditions can be modeled. The general applicability, capabilities, and performance of this model in capturing diverse system-specific dissolution behavior are demonstrated through representative numerical examples.

To investigate the ability of coarse-grained molecular dynamics simulations to predict the relative growth rates of crystal facets of pharmaceutical molecules, we apply two coarse-graining strategies to two drug molecules, phenytoin and carbamazepine. In the first method, we map an atomistic model to a MARTINI-level coarse-grained (CG) force field that uses 2 or 3 heavy atoms per bead. This is followed by applying Particle Swarm Optimization (PSO), a global optimum searching algorithm, to the CG Lennard-Jones intermolecular potentials to fit the radial distribution functions of both the crystalline and melt structures. In the second, a coarser-grained method, we map 5 or more heavy atoms into one bead with the help of the Iterative Boltzmann Inversion (IBI) method to derive a tabulated longer-range force field (FF). Simulations using the FF’s derived from both strategies were able to stabilize the crystal in the correct structure and to predict crystal growth from the melt with modest computational resources. We evaluate the advantages and limitations of both methods and compare the relative growth rates of various facets of both drug crystals with those predicted by the Bravais–Friedel–Donnay–Harker (BFDH) and attachment energy (AE) theories. While all methods, except for the simulations conducted with the coarser-grained IBI-generated model, produced similarly good results for phenytoin, the finer-grained PSO-generated FF using MARTINI mapping rules outperformed the other methods in its prediction of the facet growth rates and resulting crystalline morphology for carbamazepine.