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11,506 results for “Dynamics”
Data for: "Simulation of uranium plasma plume dynamics in atmospheric oxygen produced via femtosecond laser ablation"
<p>Data generated by 2D reactive, compressible, multi-species fluid model of uranium femtosecond laser ablation in an atmospheric oxygen environment. The dataset consists of a series of plain text tabular data files recorded at several time points during the simulation run. The data files are numbered according to the simulation time in nanoseconds (FFF-0100.txt is the data at 100 ns) and are given at intervals of 50 ns for the first 500 ns of simulation time, and every 100 ns thereafter, up to the total simulation time of 10000 ns. The initial conditions are provided in the first data file (FFF-0000.txt). Each data file contains spatially-resolved values of the fluid moments along with the molar concentrations of each species considered in the model (total of 30 species), given in a column format delimited by spaces.</p> <p>For details on the model implementation and simulation conditions, please refer to the associated manuscript.</p>
Peculiar torsion dynamical response of spider dragline silk
<p>These data are related to our paper "Dabiao Liu, Longteng Yu, Yuming He*, Kai Peng, Jie Liu, Juan Guan, and D. J. Dunstan. Peculiar Torsion Dynamical Response of Spider Dragline Silk. Applied Physics Letters, 111 (2017), 013701." In this work, the torsional properties of spider dragline silks from Nephila edulis and Nephila pilipes spiders are investigated by using a torsion pendulum technique. A permanent torsional deformation is observed after even small torsional strain. This behaviour is quite different from that of the other materials tested here, i.e., carbon fiber, thin metallic wires, Kevlar fiber, and human hair. The spider dragline thus displays a strong energy dissipation upon the initial excitation (around 75% for small strains and more for a larger strain), which correspondingly reduces the amplitude of subsequent oscillations around the new equilibrium position. The variation of torsional stiffness in relaxation dynamics of spider draglines for different excitations is also determined. The experimental result is interpreted in the light of the hierarchical structure of dragline silk.</p> <p>This project has received funding from the EU’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 704292.</p>
Method to assess the functional role of noisy brain signals by mining envelope dynamics
<p>Preprocessed envelope EEG features based on a spatial filter approach. The features were computed across multiple within-trial SVIPT events for a large hyperparameter space on data of an exemplary subject.</p> <p>The file "components.bsv" contains the preprocessed envelope features of all investigated configurations and provides underlying parameters as well as a relative path for the key ``record_dir'' to additional component information. Specifically, for each configuration the spatial filter, spatial activity pattern and the time-resolved within-trial envelope signal is provided under "records/". </p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution
<p>When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 7. Application main form which includes route, bus stops and GA parameters
<p>For this study, the school bus routes within the Ankara Province were used as case studies. The school bus routes were recorded by using the Android application and instantaneous GPS monitoring method. The home address of each student was taken as a stopping point. At the end of each route, the distances between the beginning and end points were recorded. After transferring the obtained route data into the database, the ill-adapted points were eliminated. By means of the developed application, the existing school bus routes are dynamically optimized using GA. It was developed both as a mobile and desktop application. Using Android- based mobile software, the information regarding GPS locations of bus stops and school buses is transferred to the server on a real-time basis. Using the desktop software, where GAs are run, these coordinates are shown on a Google map, and the most suitable route is produced and sent to the school bus via server. This method provides the opportunity to dynamically reflect certain factors such as a different initial point for the school bus, some students being absent from the school on a particular day, and eventual changes on the existing roads on the route production process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 10. Obtained experimental results
<p>The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution
<p>VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))
<p>One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.</p>
An example of citation graphs showing year by year dynamics
<p>This is an example of citation graphs showing year by year dynamics. On x-axis there are years of publication while on y-axis the total number of citing articles for a given paper. Size of node also shows the total number of citing articles. This visualization is made with help of the Gephi application.</p>
Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
<p>This dataset is associated with "Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations", Daniele Coslovich, Misaki Ozawa, and Walter Kob, Eur. Phys. J. E 62, 41 (2018) [<a href="https://doi.org/10.1140/epje/i2018-11671-2">doi:10.1140/epje/i2018-11671-2</a> <a href="https://arxiv.org/abs/1804.04559">arXiv:1804.04559</a>]</p> <p>It includes scripts and data files to allow for the replication of the figures. EPS figures were generated using gnuplot version 5.0.</p> <p>Notes:</p> <ul> <li>Small differences in the dynamic data for the N=3600 dataset obtained with the MD protocol reflect additional statistics gathered since acceptance of the paper.</li> <li>Figure 6(b) in the published version of the manuscript was obtained using slightly incorrect values of the parameters J, T_0 entering equation 10. This minor issue has been fixed in this dataset.</li> </ul>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
Dynamic HD Model Relative Orography Corrections
<p>Relative orography corrections for MPI's Dynamic HD model version 3.0.</p>
Orbital dynamic admittance and earth shadow
<p>The effects of orbital dynamics on a velocity distribution from a point source --- for example, a fragmentation --- may be studied by Housen's method. This technique permits the computation of a spatial density due to the velocity distribution as the sum over all routes between the source point and any point in space of the velocity density divided by the absolute value of the Jacobian determinant of the propagation map. The determination of all routes constitute finding all Lambert (orbital two point boundary value problem) solutions for the two points over a given elapsed time. In order to understand the observed density structures better, the dynamic admittance is introduced. It is the sum of the reciprocal absolute Jacobian determinants spanning all possible routes and is independent of the initial velocity distribution. The bands, pinch point and other features seen in the dynamic admittance plots are analyzed. The effects of the earth in reducing the number of routes, thereby casting a dynamic shadow, are demonstrated.</p>
Structural dynamics of the E6AP/UBE3A-E6-p53 enzyme-substrate complex
<p>Deregulation of the ubiquitin ligase E6AP is causally linked to the development of human disease, including cervical cancer. In complex with the E6 oncoprotein of human papillomaviruses, E6AP targets the tumor suppressor p53 for degradation, thereby contributing to carcinogenesis. Moreover, E6 acts as a potent activator of E6AP by a yet unknown mechanism. However, structural information explaining how the E6AP-E6-p53 enzyme-substrate complex is assembled, and how E6 stimulates E6AP, is largely missing. We therefore developed and applied different approaches in structural mass spectrometry to show that binding of E6 induces conformational rearrangements in E6AP, which result in the positioning of E6 and p53 in the immediate vicinity of the catalytic centre of E6AP. Our data provides structural and functional insights into the dynamics of the full-length E6AP-E6-p53 enzyme-substrate complex and reveals how E6 can both stimulate the ubiquitin ligase activity of E6AP and facilitate the transfer of ubiquitin from E6AP onto p53.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.