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836 results for “Avoidance”
Vector Field Histogram (VFH) video results for vineyard navigation and collision avoidance
<p><strong>Video Results for PhD Thesis</strong>: This video demonstrates the implementation of Vector Field Histogram (VFH) navigation for collision avoidance within crop rows. The simulation is conducted in Gazebo, utilizing a Husky robot to showcase efficient path planning and obstacle avoidance in an agricultural setting.</p>
Supplementary files from: Improving inference and avoiding over-interpretation of hidden-state diversification models: Specialized plant breeding has no effect on diversification in frogs
<p>The hidden-state speciation and extinction (HiSSE) model helps avoid spurious results when testing whether a character affects diversification rates. However, care must be taken to optimally analyze models and interpret results. Recently, Tonini et al. (2020; TEA hereafter) studied anuran (frog and toad) diversification with HiSSE methods. They concluded that their focal state, breeding in phytotelmata, increases net diversification rates. Yet this conclusion is counterintuitive, because the state that purportedly increases net diversification rates is 14 times rarer among species than the alternative. Herein I revisit TEA's analyses and demonstrate problems with inferring model likelihoods, conducting post-hoc tests, and interpreting results. I also re-evaluate their top models and find that diverse strategies are necessary to reach the parameter values that maximize each model's likelihood. In contrast to TEA, I find no support for an effect of phytotelm breeding on net diversification rates in Neotropical anurans. In particular, even though the most highly supported models include the focal character, averaging parameter estimates over hidden states shows that the focal character does not influence diversification rates. Finally, I suggest ways to better analyze and interpret complex diversification models – both state-dependent and beyond – for future studies in other organisms.</p>
Upconverting Nanoparticles in Aqueous Media: Not a Dead-End Road. Avoiding Degradation by Using Hydrophobic Polymer Shells
<p>Dataset of https://zenodo.org/record/5793193#.YcCAcWjMJPY</p>
Avoiding obstacles while intercepting a moving target: A miniature fly's solution
<p>The miniature robber fly <i>Holcocephala fusca<i> </i></i>intercepts its targets using a system whose behaviour is approximated by the proportional navigation guidance law. During predatory trials, we challenged <em>Holcocephala</em>'s interception performance by placing a large object in its potential flight path. In response, <em>Holcocephala</em> deviated from the path predicted by pure-proportional navigation, but in many cases still eventually contacted the target. We show that such flight deviations can be explained as the output of two competing navigational systems; pure-proportional navigation and a simple obstacle avoidance algorithm. Obstacle avoidance by <em>Holcocephala</em> is here described by a simple feedback loop that uses the visual expansion of the approaching obstacle to mediate the magnitude of the turning-away response. We name the integration of this this steering law with pro-nav "Combined Guidance". The results demonstrate that predatory intent does not operate a monopoly on the fly's steering when attacking a target, and that simple guidance combinations can explain obstacle avoidance during interceptive tasks.</p>
Best Tips On How To Avoid Grammar Mistakes
<p>In order to improve your writing, it is important to understand the rules of grammar. According to the experts from the <a href="https://www.europeanbusinessreview.com/5-best-case-study-writing-services/">best case study writing services</a>, one of the most common grammar mistakes is the misuse of apostrophes. The incorrect use of the apostrophe can completely change the meaning of a sentence or word. For example, the incorrect use of 'its' is an obvious mistake, even though it's an accepted rule. In fact, the apostrophe is never placed at the end of a possessive pronoun.</p> <p>Another common grammar mistake in college is subject-verb agreement. This can be tricky, especially if a sentence contains several clauses. To avoid this, be sure to double-check your verbs and ensure that the subjects match. Lastly, <a href="https://papersforge.com/">PapersForge</a> writers recommend to be careful with citation style. While it may not seem that important, it's always a good idea to adhere to the correct APA, MLA, and Turabian citation styles. These differ according to the subject area and the Chicago Manual of Style.</p> <p>Listen to this audio to the end in order to learn about more about avoiding these mistakes.</p>
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
<p>Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise between wind energy development and wildlife protection due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially-explicit model to predict potential areas of conflict with future wind turbines deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply (presence of wild ungulates). Flight activity at potential risk of collision was generally high, concentrating on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially-explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.</p>
Vermilion flycatchers avoid singing during sudden peaks of anthropogenic noise
<p>Data from a playback experiment where we showed that vermilion flycatchers stop singing when experiencing an increase in sudden urban noise, and resume singing showing full vocal recovery after that. This could be an uncommon strategy to enhance information transfer in environments where noise amplitude fluctuates rapidly</p>
Functional traits and their plasticity shift from tolerant to avoidant under extreme drought
<p>Under climate change, extreme droughts will limit water availability for plants. However, the species-specific responses make it difficult to draw general conclusions. We hypothesized that changes in species' abundance in response to extreme drought can be best explained by a set of water economic traits under ambient conditions in combination with the ability to adjust these traits towards higher drought resistance. We conducted a four-year field experiment in temperate grasslands using rainout shelters with 30% and 50% rainfall reduction. We quantified the response as the change in species abundance between ambient conditions and the rainfall reduction. Abundance response to extreme drought was best explained by a combination of traits in ambient conditions and their functional adjustment, most likely reflecting plasticity. Smaller leaved species decreased less in abundance under drought. With increasing drought intensity, we observed a shift from drought tolerance, i.e. an increase leaf dry matter content, to avoidance, i.e. a less negative turgor loss point (TLP) in ambient conditions and a constancy in TLP under drought. We stress the importance of using a multidimensional approach of variation in multiple traits and the importance of considering a range of drought intensities to improve predictions of species' response to climate change.</p>
Avoiding growing pains in reproductive trait databases: the curse of dimensionality
<p><strong>Aim: </strong>Reproductive output features prominently in many trait databases, but the metrics describing it vary and are often untethered to temporal- and volumetric-dimensions (e.g., fecundity-per-bout). Using such ambiguous reproductive measures to make broadscale comparisons across taxonomic groups will only be meaningful if they show a 1:1 relationship with a reproductive measure that explicitly includes both a volumetric and temporal component (i.e., reproductive mass-per-year). We sought to map the prevalence of ambiguous and explicit reproductive measures across taxa, and explore their relationships with one another to determine the cross-compatibility and utility of reproductive metrics in trait databases.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1990-2021.</p> <p><strong>Major taxa studied:</strong> We searched for reproductive measures across all Metazoa, and identified 19,785 Chordata species, along with 440 species of Arthropoda, Cnidaria, or Mollusca.</p> <p><strong>Methods:</strong> We included 37 databases from which we summarised the commonality of reproductive metrics across taxonomic groups. We also quantified scaling relationships between ambiguous reproductive traits (fecundity-per-bout, fecundity-per-year and reproductive mass-per-bout) and an explicit measure (reproductive mass per-year) to assess their cross-compatibility.</p> <p><strong>Results: </strong>Most species were missing at least one temporal or volumetric dimension of reproductive output, such that reproductive mass-per-year could be reconstructed for only 4,786 vertebrate species. Ambiguous reproductive measures were poor predictors of reproductive mass-per-year – in no instance did these measures scale at 1:1.</p> <p><strong>Main Conclusions:</strong> Ambiguous measures systematically misestimate reproductive mass-per-year. Until more data are collected, we suggest authors use the clade-specific scaling relationships provided here to convert ambiguous reproductive measures to reproductive mass-per-year. </p>
Figure 19. Feeding Phidippus from southern Greenville County, South Carolina. 1, Penultimate female P in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 19. Feeding Phidippus from southern Greenville County, South Carolina. 1, Penultimate female P. audax with two leafhoppers. This spider held one leafhopper as it jumped and captured the second. 2, Adult female P. audax fedding on a large brachyceran fly. 3, Adult female P. princeps feeding on spider. 4, Adult female P. princeps feeding on a captured bug after wiping it against the surface. Each scale bar = 1.0 mm.
Figure 20 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 20. SEM of chemosensory setae (spondylae) associated with the pretarsus or foot of an adult male Phidippus audax from Iowa City, Iowa. 1-3, Ventral views of the distal end of right leg I at three levels of magnification. 2, A group of spondylae (inset) originates between the anterior and posterior plates of flattened tenent setae. 3, Detail showing the conical tip (arrows) at the end of three spondylae. These are surrounded by flattened tenent setae bearing, ventrally, regular rows of bifid filaments that adhere to a smooth surface. Each spondyla bears an open sensory pore at the apex of the cone.
Figure 4 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 4. Two female Phidippus texanus (sisters) reared from the same brood sac found in Lea County, New Mexico, in August of 1978 (on mesquite 21 miles W of Jal on SR 128). Half of the females in this brood had the typical texanus form with cream to white scales on a black background (1), and the other half had a similar dorsal pattern with the coloration of the related P. ardens, with rust-red scales covering much of the dorsal opisthosoma. Edwards (2004) placed P. ardens and P. texanus in the borealis clade of the purpuratus group within Phidippus, but kept the species separate in part because of their parapatric ranges. However, he did report both species from Lea County, New Mexico where this brood sac was found, and both live on mesquite. These are very large Phidippus, with females averaging 13-15 mm in body length.
Figure 3 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 3. Two views of an adult female Phidippus princeps captured in an old field in Ithaca, Tompkins County, New York (1978). In this area female P. princeps were tan in color, often with abundant white or cream-colored facial scales as shown here. At least as juveniles, they build their nests and hunt on herbaceous plants in old field habitats. They are common in eastern North America, from Minnesota southeast to northwestern South Carolina and northern Georgia. Further to the southeast, they are replaced by the closely related P. pulcherrimus Keyserling 1885, also an inhabitant of old fields (Edwards 2004). Note the distinctive 'hair' tufts on the carapace, a characteristic of most Phidippus jumping spiders.
Figure 2 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 2. Two adult female Phidippus audax captured in an old field in Ithaca, Tompkins County, New York (1978). Many local varieties of P. audax do not have the broad lateral band of opisthosomal scales shown here. P. audax appears to be a generalist with respect to habitat and it is widely distributed across much of North America, with many recent sightings in the far west. It can frequently be found living on herbaceous plants in old fields, but I have also found it near water, woodland margins, on trees, on fence posts, and even nesting on the ground under rocks.
Figure 18 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 18. Oncopeltus fasciatus aggregating on Asclepias leaves and seed pods in southern Greenville County, South Carolina. These insects pierce seed pods to feed on seeds. 1, Pair of immatures resting on top of an Asclepias leaf. 2, Two adults feeding on seed pod. 3, Aggregation of mating adult pairs. 4, Lateral view of adult showing long stylus. 5, Dorsal view of adult.
Figure 14 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 14. Recovery of tendency to attack by adult female Phidippus audax. Each spider was placed in a clean Petri dish with one adult Oncopeltus reared on Asclepias reared. After an initial attack (t=0), the behavior of 40 spiders (numbered at left) was charted through either the third sequential attack, or until 15 minutes had elapsed, whatever came first. Turns to face the bugs are shown as green circles, and attacks (jump and contact) are shown as red circles.
Figure 6 in Learned avoidance of the Large Milkweed Bug (Hemiptera: Lygaeidae: Oncopeltus fasciatus) by jumping spiders (Araneae: Salticidae: Dendryphantina: Phidippus)
Figure 6. Violent reaction of an adult female Phidippus princeps to fluids associated with an adult Oncopeltus fasciatus. 1, This spider first bit the bug on its head but held its legs and pedipalps far away from the prey. 2, Moments later, the spider dropped the fatally-bitten bug, and began to wipe its mouthparts against the surface, leaving a trail of fluid behind (fluid cannot be seen in these photographs).
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
<p>The video illustrates simulation and experimental results of a team of unmanned aerial vehicles executing Linear Temporal Logic (LTL) tasks. More specifically, given a certain LTL task over predefined regions of interest, each agent derives a high-level plan that satisfies the given task. Then, it executes the plan using a continuous controller that is based on decentralized navigation functions, which also guarantee inter-agent collision avoidance. The video shows one simulation and two experimental scenarios.</p>
Data supplementing the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal
<p>These data supplement the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal</p> <p>The directory contains the following files:</p> <p>1<strong>5 fastq files raw reads (5 mock communities, 3 replicates)</strong><strong>.rar </strong>- contains the 15 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>15 fastq files information.xlsx</strong> :</p> <p>- contains the information relative to the 15 fastq files corresponding to the PGM raw data of the 5 mock communities (sequenced with 3 replicates), including: the ID of the fastq files, the mock community name, the replicate number, the final sample Id and the number of raw reads per fastq file.</p> <p>- contains the information of the proportion of the 8 diatoms species (%) used to create the 5 mock communities (estimated from microscopy).</p>
How much methane removal is required to avoid overshooting 1.5°C?
<p>This repository reproduces the results from Smith & Mathison, submitted. Code is available from GitHub at <a href="https://github.com/chrisroadmap/methane-mitigation">https://github.com/chrisroadmap/methane-mitigation</a>. The Zenodo version includes the <code>results</code> directory, which are datasets that are too large for GitHub.</p> <h2>Reproduction steps</h2> <h3>Set up conda repository</h3> <p>This assumes that you are using <code>anaconda</code> and <code>python</code>. Currently, <code>fair</code> and <code>fair-calibrate</code> appear to be most stable with <code>python</code> versions 3.8, 3.9, 3.10 and 3.11. Others may work, but these ones are tested.</p> <p>1. Create your environment:</p> <p><code>$ conda env create -f environment.yml</code><br><br>2. If you want to make nice version-control friendly notebooks, which will remove all output and data upon committing, run</p> <p><code>$ nbstripout --install</code></p> <h3>Run and reproduce results</h3> <p>1. Fire up jupyter notebook</p> <p><code>$ jupyter notebook</code></p> <p>2. Inside <code>notebook</code>, navigate to <code>notebooks</code> directory. Run the notebooks in this order:<br> - <code>adaptive-removal-1.4.0.ipynb</code>: this does the data crunching. It will likely take between 6 and 24 hours, depending on your machine.<br> - <code>zec-1.4.0.ipynb</code>: calculate ZEC and carbon cycle metrics<br> - <code>analyse-1.4.0.ipynb</code>: produce the results and plots reported in the paper</p> <p>3. As a sensitivity case we run 10 MtCH4 removal steps (default 20); the results are almost identical but runtime is slower. These are in the files <code>adaptive-removal-1.4.0-10Mt.ipynb</code> and <code>analyse-1.4.0-10Mt.ipynb</code>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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