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5,573 results for “Optimality”
Design-of-Experiments In Vitro Transcription Yield Optimization of Self-Amplifying RNA
<p>This dataset contains original data generated from optimizing the IVT process for saRNAs that are approximately 9 kb in size through a design of experiment (DoE) approach to produce a maximal RNA yield.</p>
Equitable Access to Residential (EQUATOR) EV Charging: Optimal Investments in EV charging
<p>This folder comprises Julia codes produced to determine optimal investments in EV charging for Equitable Access to Residential (EQUATOR) EV Charging project. The file descriptions are below:</p> <p> </p> <p>1. Generator.csv: contains generator data for all the generators in the Manhattan power network</p> <p>2. Node.csv: contains the load data for each bus in the Manhattan power network</p> <p>3. Line.csv: contains technical line parameters for all the lines in the Manhattan power network</p> <p>4. NetworkDataType.jl and NetworkLoad.jl: Julia files to process csv data and design the Manhattan power grid</p> <p>5. Justice_LL.jl: Justice modeling of the power utility</p> <p>6. Justice_Case1_fixed.jl: Julia file for determining optimal investments in EV charging in Manhattan.</p>
Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
<p>We provide here the underlying data of the publication "Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose". Please find the abstract below.</p> <p>Mechanism-based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic-empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase-catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α-glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans-glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full-time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window-of-operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from <em>Leuconostoc mesenteroides</em> and <em>Bifidobacterium adolescentis</em>), we reveal the hybrid model as a powerful tool of “process decision making” to guide rational selection of the best-suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development.</p>
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </p>
Optimizing parametric factors in CIELAB and CIEDE2000 color-difference formulas for 3D printed spherical objects
<p>Forty-five spherical samples were printed using a Stratasys J750 3D color printer, and 82 pairs of 3D samples were produced to investigate the human color perception of the lightness, chroma and hue differences of 3D spherical objects, and to optimize the current CIELAB and CIEDE2000 color-difference formulas. This file contains the CIELAB values of the 45 spherical samples and the calculated colour differences as well as visual colour-difference data of 82 pairs of 3D samples. Optimizations of parametric factors in CIELAB and CIEDE2000 colour-difference formulas were performed based on the colour-difference data provided.</p>
Lignin biorefinery experimental optimization using BOSS
<p>This dataset was employed in the manuscript:</p> <p> [1] Löfgren J, Tarasov D, Koitto T, Rinke P, Balakshin M, Todorović M.<br> Machine Learning Optimization of Lignin Properties in Green Biorefineries.<br> ChemRxiv. Cambridge: Cambridge Open Engage; 2022.</p> <p>This dataset contains the results of optimizing the green AquaSolv Omni biorefinery concept using the Bayesian optimization code BOSS. The input data represents the experimental processing conditions for each sample, namely the reactor temperature and P-factor (reaction severity). For each sample, the lignin yield was measured and the number of ß-O-4 linkages, ratio of syringyl and guaiacyl units, and total carbohydrate content were quantified using a 2D NMR HSQC method. The lignin yield is given in weight percent of the original biomass and the NMR-quantified moieties are measured in terms of number of units per 100 aromatic (Ar) units.</p> <p>The data found in this record comes in two different versions: one for the Combined Acquisitions (CA) and one for the Pure Acquisitions (PA) strategy described in [1]. For each strategy, there is one data file for the training data and one for the test data.</p> <p>For additional details on the experimental processing and Bayesian optimization, please refer to [1].</p>
Data from: AgMate: an optimal mating software versus other mate pair designing methods on long-term breeding of Pinus taeda L
<p>Breeding objectives aim to optimize two crucial but contrasting goals of maximizing genetic gain while managing genetic diversity. In advanced generations, this becomes a challenge in monoecious conifer tree species breeding programs because they suffer from inbreeding. Developing an algorithm that maximizes genetic gain while maintaining genetic diversity for monoecious species is imperative. While methods and algorithms for animal breeding are well-established, an efficient algorithm suited to monoecious species remains elusive. Towards this goal, we have adopted an evolutionary genetic algorithm, the Differential Evolution algorithm, to optimize mate pair designing in <em>Pinus taeda</em> (loblolly pine), a widely planted pine species in the southern USA. AgMate, an optimal mating for monoecious species software, is a multi-functional, completely automated optimization software. It utilizes genetic relationships and breeding values as input to create an optimal mating list. AgMate maximizes the genetic gain and minimizes the increase in average coancestry and inbreeding in the proposed progeny. AgMate was more effective in optimizing mating lists than positive assortative mating and random mating in short-term and long-term settings. AgMate mating list resulted in an average 93% genetic gain each cycle for ten cycles while simultaneously minimizing the increase in coancestry to 0.086. The framework and methods adapted for Pinus taeda are also relevant to the breeding of other monoecious species.</p>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"
<p>There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.</p>
Dataset: Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer
<p>Dataset of Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer</p> <p><a href="https://doi.org/10.1016/j.clet.2021.100142">https://doi.org/10.1016/j.clet.2021.100142</a></p> <p><a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology">Cleaner Engineering and Technology</a> <a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology/vol/4/suppl/C">Volume 4</a>, October 2021, 100142</p>
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Environmental Flows Turbine Optimization
<p>This is a dataset of the results from determining the capacity (MW) of potential minimum flow turbines based on available environmental flows. This includes the turbine capacity from two methods of data processing, as well as the iterative screening envelopes to determine locations of likely opportunities for implementing these environmental flow turbines. </p> <p>Cite this dataset as well as the manuscript by the authors that is available through Renewable and Sustainable Energy Reviews.</p>
Optimal vaccination at high reproductive numbers: sharp transitions and counterintuitive allocations
Optimization of vaccine allocations among different segments of a heterogeneous population is important for enhancing the effectiveness of vaccination campaigns in reducing the burden of epidemics. Intuitively, it would seem that allocations designed to minimize infections should prioritize those with the highest risk of being infected and infecting others. This prescription is well supported by vaccination theory, e.g. when the vaccination campaign aims to reach herd immunity. In this work, we show, however, that for vaccines providing partial protection (leaky vaccines) and for sufficiently high values of the basic reproduction number, intuition is overturned: the optimal allocation minimizing the number of infections prioritizes the vaccination of those who are least likely to be infected. The work combines numerical investigations, asymptotic analysis for a general model, and complete mathematical analysis in a two-group model. The results point to important considerations in managing vaccination campaigns for infections with high transmissibility.
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to a paper published in MDPI Machines 2021
<p>The datasets contain the data related to the experiment presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Proximity sensor for thin wire recognition and manipulation,” Machines, vol. 9, no. 9, art. 188, Sept. 2021. (DOI: 10.3390/machines9090188)</p>
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to IMM2021 publication
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>Costanzo, M., Pirozzi, S., “Optical Force/Tactile Sensors for Robotic Applications” (2021) IEEE Instrumentation and Measurement Magazine, 24 (5), art. no. 9491003, pp. 28-35. DOI: 10.1109/MIM.2021.9491003</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
BRAIN Journal - Lamport's algorithm - Figure 2 from paper "Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time"
<p>Figure 2. Lamport’s algorithm</p> <p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message). If two events, 1 a and 2 a , are produced in different processes that do not exchange messages (neither directly nor indirectly), then it is not certain if 1 2 a → a or 2 1 a → a . In this case it is said that these events are competitive, which means that it is not known which one happened first (and it is not a must-know thing either).</p>
Figure 3. The multi-agent system-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>The authority is given to a single agent<br> that is symbolically situated on the top level; this agent would be considered the root of an<br> arborescent structure; but the stages of the process are provided by groups of agents, respectively by<br> the relations of communication between them.<br> Thus, we would deal with a ZERO AGENT and several groups of agents. Such a group is<br> made up of several agents, each and one of these agents accomplishing a certain role in the process<br> of synchronization (figure 3).</p>
Figure 2. Lamport's algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened<br> before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it<br> means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took<br> place. This relation can be directly observed in two situations (figure 2):<br> 1. If two events happen during the same process, the order of the happening is indicated by<br> the common clock;<br> 2. When two processes communicate through a message, the event that corresponds to<br> sending the precise message always happens before the event of receiving it (i.e. the<br> message).</p>
Figure 1. Cristian's Algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>Cristian’s Algorithm (figure 1) is a method for clock synchronization which can be used in<br> many fields of distributive computer science. It suffers, though, in implementations using a single<br> server, making it unsuitable for many distributive applications where redundancy may be crucial.</p>
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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.