Contributors: UnaElsLive Natra, mdgmatest5 live
... RDM - File Type Support 21May2019 ElsCustomer Apart from .u3d all files preview [ .obj / .ply / .vtk / .stl / .ent / .brk / .pdb / .pse / .mol / .mol2 / .cif / .u3d / .dcm / .nii] - .pse is not supported
Data for: Thermodynamics of an austenitic stainless steel (AISI-348) through in situ TEM heavy ion irradiations
Contributors: Matheus A. Tunes, Graeme Greaves, Thomas M. Kremmer, Vladimir Vishnyakov, Philip Edmondson, Stefan Pogatscher, Stephen Donnelly, Claudio Schon
... This dataset contains processed and raw data necessary to reproduce the reported research investigation. The steps to reproduce this research are detailed described in the Materials and Method section in our paper.
Contributors: Timothy McDonald
... We were interested in predicting tree properties, particularly size, from the sound made by a feller-buncher hotsaw during felling. Included are 279 3-second clips of felling sounds, along with stem size (DBH) information. Cut duration was found to predict stem size moderately well (standard error of prediction = 4.6 cm), but not well enough to be useful in measuring properties of individual stems. Could be useful for tract-level, or per-hectare predictions.
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Data from: Behaviourally mediated predation avoidance in penguin prey: in situ evidence from animal-borne camera loggers
Contributors: Handley, Jonathan M, Thiebault, Andréa, Stanworth, Andrew, Schutt, David, Pistorius, Pierre
... Predator dietary studies often assume that diet is reflective of the diversity and relative abundance of their prey. This interpretation ignores species-specific behavioural adaptations in prey that could influence prey capture. Here, we develop and describe a scalable biologging protocol, using animal-borne camera loggers, to elucidate the factors influencing prey capture by a seabird, the gentoo penguin (Pygoscelis papua). From the video evidence, we show, for the first time, that aggressive behavioural defence mechanisms by prey can deter prey capture by a seabird. Furthermore, we provide evidence demonstrating that these birds, which were observed hunting solitarily, target prey when they are most discernible. Specifically, birds targeted prey primarily while ascending and when prey were not tightly clustered. In conclusion, we show that prey behaviour can significantly influence trophic coupling in marine systems because despite prey being present, it is not always targeted. Thus, these predator-prey relationships should be accounted for in studies using marine top predators as samplers of mid to lower trophic level species.
Contributors: Christian Döhler
... This is a recording of Maembu Kwozi who tells a hunting story that happened a few days earlier. The recording took place in Rouku village, Western Province, Papua New Guinea. The dataset includes: audio file (tci20130903v-03.wav) - extracted from the video for transcription video file (tci20130903v-03.mpeg) transcription file (tci20130903v-03.eaf) The material was recorded by Christian Döhler as part of a language documentation project for his PhD. The project was located at the School of Culture, History and Language at the Australian National University, Canberra. For the most part it was funded by the DOBES project of the Volkswagen Foundation.
Contributors: Christian Döhler
... This is a recording of Abia Bai talking about his mother in-law, Naimr, who lives with the family. As a child, Naimr surived a headhunting raid during which most her family was killed. The recording took place in Rouku village, Western Province, Papua New Guinea. The dataset includes: audio file (tci20131013v-02.wav) - extracted from the video file for transcription video file (tci20131013v-02.mpeg) transcription file (tci20131013v-02.eaf) The material was recorded by Christian Döhler as part of a language documentation project for his PhD. The project was located at the School of Culture, History and Language at the Australian National University, Canberra. For the most part it was funded by the DOBES project of the Volkswagen Foundation.
Data from: Flightin maintains myofilament lattice organization required for optimal flight power and courtship song quality in Drosophila
Contributors: Chakravorty, Samya, Tanner, Bertrand C. W., Foelber, Veronica Lee, Vu, Hien, Rosenthal, Matthew, Ruiz, Teresa, Vigoreaux, Jim O.
... The indirect flight muscles (IFMs) of Drosophila and other insects with asynchronous flight muscles are characterized by a crystalline myofilament lattice structure. The high-order lattice regularity is considered an adaptation for enhanced power output, but supporting evidence for this claim is lacking. We show that IFMs from transgenic flies expressing flightin with a deletion of its poorly conserved N-terminal domain (flnΔN62) have reduced inter-thick filament spacing and a less regular lattice. This resulted in a decrease in flight ability by 33% and in skinned fibre oscillatory power output by 57%, but had no effect on wingbeat frequency or frequency of maximum power output, suggesting that the underlying actomyosin kinetics is not affected and that the flight impairment arises from deficits in force transmission. Moreover, we show that flnΔN62 males produced an abnormal courtship song characterized by a higher sine song frequency and a pulse song with longer pulses and longer inter-pulse intervals (IPIs), the latter implicated in male reproductive success. When presented with a choice, wild-type females chose control males over mutant males in 92% of the competition events. These results demonstrate that flightin N-terminal domain is required for optimal myofilament lattice regularity and IFM activity, enabling powered flight and courtship song production. As the courtship song is subject to female choice, we propose that the low amino acid sequence conservation of the N-terminal domain reflects its role in fine-tuning species-specific courtship songs.
Contributors: Gonzalez-Franco, Mar
... Immersive stereoscopic footage of a Coordinate Response Measure (CRM) recorded from two actors. The audio-visual recorded corpus consists of 8 CALLs and 32 COMMANDs per actor. The CALLs and COMMANDs are to be combined at rendering time into full sentences that always follow the same structure: “ready CALL go to COMMAND now ”. The COMMANDs consists of one in four colors (blue, green, red or white) followed by one in eight numbers (1 to 8). This generates a full combinatorial of 256 individual sentences when combined with one of the 8 CALLS (arrow, baron, charlie, eagle, hopper, laker, ringo, tiger). Additionally the dataset also includes the UV positions to texturize the semi-spheres at the rendering time. These have been calculated from the intrinsic and extrinsic calibration parameters of the cameras to facilitate the correct rendering of the video footage. Our system for recording the actors consists of a custom wide-angle stereo camera system made of two Grasshopper 3 cameras with ﬁsheye Fujinon lenses (2.7mm focal length) reaching 185 degrees of Field of View (FoV). The cameras were mounted parallel to each other and separated by 65 mm distance (average human interpupillary distance39) to provide stereoscopic capturing. The video is encoded in H264 format reaching 28-30 frames per second encoding speed at 1600x1080 resolution per camera/eye. The audio was recorded through a near range microphone at a 44kHz sampling rate and 99kbps and both the audio and video are synchronized within 10ms range and saved in mp4 format. The recording room was equipped for professional recording with monobloc LED lighting and chromakey screen. The actor sat at 1 meter distance from the camera recording setup and read the corpus sentences when presented on the screen behind the cameras. The actors were recorded separately in two sessions, seating each at 30 degrees from the bisection, and their videos can be synthetically attached at the rendering time. In the post processing the audio was equalized for all words, and the video was stitched to combine the actors and generate the full the corpus. Sentences were band passed at 80Hz to 16kHz. The corpus sentences are temporally aligned within the range of 64ms in our case, which is below the described 200ms to be perceived. So two or more CRMs can be played synchronously generating an overlap.
Supplementary Material: A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer
Contributors: Shamsoddin, Sina, Porté-Agel, Fernando
... Supplementary material for Energies 2016, 9, 366; doi:10.3390/en9050366: Video S1: Normalized instantaneous streamwise velocity field both on a vertical plane (x-z) going through the center of the turbine and on a horizontal plane at the equator height of the turbine (Note: the physical time corresponding to this video is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes). Video S2: Normalized instantaneous streamwise velocity field on a horizontal plane at the equator height of the turbine for two cases: when the turbine starts to operate (top) and when the flow has reached statistically steady condition (bottom) (Note: the physical time corresponding to both videos is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).
Contributors: Lopez, Gerardo, Da Silva, David, Auzmendi, Iñigo, Favreau, Romeo, DeJong, Theodore
... L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008). The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard. In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008). Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning. The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock. What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references). Description of files Simulation 1: L-PEACH-d over three years of growth. Simulation 2: Detailed growth of leaves and fruit using L-PEACH. Simulation 3: Pruning L-PEACH-d to a v-system. Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h. Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h. Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.