Interpreting and exploiting narrative as a sketch design generator for application in VE

The research in this paper focusses on how a narrative text can be the generator of an architectural drawing, or other architectural representation, such as an Architectural Virtual Environment. The drawn physical sketch has traditionally played that role. A particular approach to narrative has been important for some notable architects and their architecture. Ian Ritchie (2014), for instance, celebrates the use of poetry to describe the essential spirit of a scheme before any drawing is done. The work in the paper here describes the proposition to capture such narrative text in a systematic and structured way. We describe foundational work on how the captured narrative text has been translated into a contemporary, computer-mediated, design development environment. Different narrative accounts recalling a now demolished house form the focus case study. This case study is the vehicle through which the initial principles establishing how best to move from narrative to virtual representation are established and tested

A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission

Dust models are essential for understanding the impact of mineral dust on Earth’s systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework’s utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (us*) and common, poorly constrained grain-scale entrainment threshold (u*ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(us* > u*tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y− 1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u*ts needed restricted sediment supply and compatibility with areal us*. Failure to predict dust emission during observed events, was due to us* being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with us* across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(us*>u*tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u*ts is required to restrict sediment supply over space and time.</p

Elucidating hidden and enduring weaknesses in dust emission modeling

Large-scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust-climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo-based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.</p

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.</p

Additional file 1: of Randomised controlled trial of simvastatin treatment for autism in young children with neurofibromatosis type 1 (SANTA)

Supplementary Materials. Table S1. MRI sequence parameters and scan time duration for a complete imaging acquisition lasting approximately 45 min (including scout sequences and planning time). Table S2. Baseline descriptive data. Table S3. Baseline clinical findings. Table S4. Adverse events. Table S5. Week 4 intermediate outcomes. Table S6. Quantification of MAPK outcomes at baseline and endpoint. Table S7. A comparison of the mutation data in the SANTA sample to previously reported data from a clinic referred NF1 sample (see text). Figure S1. a) Spectrum obtained from 3 × 3 × 3 voxel placed in deep grey matter of a 5-year-old child using MEGA-PRESS suppression scheme at 3T (top, non-edited subspectrum; bottom, GABA-edited spectrum) showing signals from amino-acid protons (AA), choline-containing compounds (cho), creatine + phosphocreatine (cr), N-acetylaspartate (NAA), GABA and glutamate + glutamine (Glx). b) Figure depicting example output of AMARES Model fitting in jMRUI. Figure S2. Example locations of VOI (3 × 3 × 3 cm3) acquired from a) left fontal white matter and b) deep grey matter (including caudate, lentiform nucleus, thalamus and putamen). Figure S3. Example illustrating in sagittal view the position of the perfusion-imaging slices, which were planed above the ventricles and the labelling slab (150 mm) that was set 10 mm below the imaging slices. Figure S4. SANTA CONSORT flow diagram. (DOCX 914 kb