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(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Figure 7: Cultural equipment dynamics modeling-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The multi-template modelling can be used to model cultural equipment<br> dynamics as described in figure 7. On this figure, we associate a queen to each<br> cultural center (cinema, theatre, ...). Each queen will emit many pheromon<br> templates, each template is associated to a specific criterium (according to age,<br> sex, ...). Initially, we put the material in the residential place. Each material<br> has some characteristics, corresponding to the people living in this residential<br> area. The simulation shows the self-organization processus as the result of the<br> set of the attractive effect of all the centers and all the templates.</p>
Figure 4: Complexity of geographical space with respect of emergent organizations-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The applications we focus on in the models that we will propose in the<br> following, concerns specifically the multi-center (or multi-organizational) phenomona<br> inside urban development. As an artificial ecosystem, the city development<br> has to deal with many challenges, specifically for sustainable development,<br> mixing economical, social and environmental aspects. The decentralized<br> methodology proposed in the following allows to deal with multi-criteria problems,<br> leading to propose a decision making assistance, based on simulation<br> analysis.</p>
Figure 1: Complex spatial organizational model-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On Figure 1, we describe a two-level model of spatial self-organizations with<br> interactions in both directions between these two levels: the emergence of organizations<br> from entities interactions but also the feed-back process describing<br> how organizations are regulating their own entities.</p>
The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation
<p>The model code and necessary data: NESMv3_gmd.tar.gz.</p> <p>The model manual :Using NESM v3 model.pdf</p> <p>The reference: Reference.tar.gz</p>
Accurate modeling and efficient QoS analysis of scalable adaptive systems under bursty workload
<p>The datasets include the traces used for the research and experiments on modelling and analyzing systems that execute under bursty workload:</p> <ul> <li>numReq10secondsfrom360000to660000-Paris contains a summary of the requests traces published in <a href="http://ita.ee.lbl.gov/html/contrib/WorldCup.html">http://ita.ee.lbl.gov/html/contrib/WorldCup.html</a> , by grouping into a single count the number of requests that servers in Paris region received every 10 seconds .</li> <li>mawi10seconds contains a summary of the traces published in <a href="http://mawi.wide.ad.jp/mawi/ditl/ditl2009/">http://mawi.wide.ad.jp/mawi/ditl/ditl2009</a> , by grouping the number of packets every 10 seconds into a single count. </li> </ul>
Figure 2. Catadioptric projection modelled by the unit sphere-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the beginning of UAV, the map building was one of the most addressed problems by<br> researchers. Several researchers used Omni directional vision for robot navigation and map<br> building. Because of the wide field of view in Omni directional sensors, the robot does not need to<br> look around using moving parts (cameras or mirrors) or turning the moving parts. The global view<br> offered by Omni directional vision is especially suitable for highly dynamic environments. The<br> Omni directional vision system consists of a hyperbolic mirror, a USB color digital camera<br> (Logitech C905) and a regulation device.</p>
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></p>
Data from: The PDI model system for parameterizing soil hydraulic properties
<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>
Figure 3 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 3: Temperature shift experiment from 18 °C to 30 °C. (A) After the temperature shift, the longitudinal growth of the propagules of equal length in three different tripartite communities was measured with ImageJ software and compared with the established model system for
Figure 2 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 2: Bioassay for morphogenetic activity performed at 18 °C. Using a tripartite community with Ulva mutabilis, the morphogenetic activity of the thallusin-releasing bacteria Maribacter sp. was complemented by one out of the four strains isolated from the surface of Ulva ohnoi. Under standard conditions, axenic gametes (A) were cultivated in the tissue culture flask with the tested bacteria alone (B–E), in the presence of Roseovarius sp. (G–J) or with Maribacter sp. MS6 (L–O) in comparison to the controls (F and K). Arrows with closed heads indicate protrusion formation due to the lack of thallusin released by Maribacter sp. Arrows with open heads indicate rhizoid formation in the presence of Maribacter sp. Magnification bar = 100 µm.
Figure 1 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 1: Workflow. Selected bacteria IH2, IH18, IH25, and G8 were collected from the surface of Ulva ohnoi, phenocopying the activity of Roseovarius sp. MS2 and forming a tripartite community with Maribacter sp. MS6 and the gametophyte of Ulva mutabilis (morphotype "slender"; strain FSU-UM5-1). Ulva mutabilis (25 mg dry weight) was cultivated with the two bacterial strains (OD620 = 0.001) under standard conditions (Wichard and Oertel 2010). Propagules of equal length were stressed by a temperature shift from 18 °C to 30 °C using continuous light (80 µmol photon m−2 s−1) to avoid chronobiological effects. Axenic cultures and tripartite communities were prepared according to Spoerner et al. (2012). exo-Metabolomics and multivariate analysis of the metabolite profiling of the supernatant (150 mL) of four tripartite communities were performed according to Alsufyani et al. (2017) and Ghaderiardakani et al. (2022). Drawings of Ulva were taken from Wichard (2023) under the terms of CC BY 4.0. Created with BioRender.com.
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
Supplementary online material for KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern. Eclipse modelling of the triple and spectroscopic analysis
<p>Additional figures and data supplementary to the published (or soon-to-be-published) paper KIC 4150611: A quadruply eclipsing heptuple star system with a g-mode period-spacing pattern Eclipse modelling of the triple and spectroscopic analysis.</p> <p> </p>
Fig. 1 in Echinoderm model systems, homology, and phylogenetic inference: comment and reply to Paul (2021)
Fig. 1. Tree comparison between two phylogenetic inference methods with bootstrap support at the nodes. A. Phylogenetic hypothesis from Paul (2021) inferred via maximum parsimony. B. Phylogenetic hypothesis inferred via maximum likelihood.
Auxiliary data for Moustakis et al. 2024 "Temperature overshoot responses to ambitious forestation in an Earth System Model"
<p>The netcdf file "Moustakis_et_al_2024_Data.nc" contains all the key variables presented in the figures of the manuscript of Moustakis et al. 2024: "Temperature overshoot responses to ambitious forestation in an Earth System Model".</p> <p>Please read the README.txt file for more information on the variables included.</p> <p>For any further queries please refer to the corresponding author, Yiannis Moustakis: <br>yiannis.moustakis@geographie.uni-muenchen.de</p> <p> </p>
CDR deployment in Europe, NEGEM-scenario results from Pan-European TIMES-VTT energy system modelling as reported in Markkanen et al. (2024)
<p>This dataset includes cumulative and yearly carbon dioxide removal (CDR) deployment in NEGEM-scenarios for Europe.</p> <p>The results originate from Pan-European TIMES-VTT energy system model and are published in Markkanen et al. (2024), manuscript submitted to Environmental Research Letters, Focus issue on Carbon Dioxide Removals on 31/05/2024. </p> <p>Regional coverage: EU-31. Temporal coverage: until 2060. </p> <p>Cumulative values are reported for the period 2025-2050. Yearly values are reported for 2010, 2020, 2030, 2040, 2050 and 2060.</p> <p>Negative emission technologies and practises (NETPs) included: bioenergy with carbon capture and storage (BECCS), biochar, direct air carbon capture and storage (DACCS), enhanced weathering (EW), forestry (A/R; afforestation and reforestation) and soil carbon sequestration (SCS). Additionally, sum of total CDR is reported, which is the sum of NETPs. For the yearly data, absolute CO2 emissions and net CO2 emissions are reported. </p> <p>Data covers six (6) NEGEM-scenarios, TEC, ENV and SEC, and their limited variants, which exclude the use of EW and SCS. Storylines and main assumptions for NEGEM-scenarios are reported in NEGEM Deliverable 8.2 Quantifying the NEGEM pathways and impact assessments with global TIMES-VTT and PET-VTT IAMs by <a href="https://www.negemproject.eu/wp-content/uploads/2023/11/NEGEM_D8.2_NEGEM-scenarios.pdf" target="_blank" rel="noopener">Lehtilä et al. (2023).</a></p>
Figure 1. Experimental unit model built with a 150 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 1. Experimental unit model built with a 150 mm PVC pipe and coupling, used in the maintenance of yellow clam (Amarilladesma mactroides) broodstock in the laboratory.
BRAIN Journal-Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical- Chemical System-Figure 1. Two compartments containing solution separated by a membrane.
<p>In fact, this equation will be found first if one consults physical or chemical textbooks for<br> diffusion. Also one may be able to find already-existing diffusion simulators to see vivid images of<br> the process.</p>
Figure 1. The Statechart for the Player movement and the Navigation System-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
ScienceDex guides
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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.