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29 results for “Resource optimization”
Data from: Determining the optimal movement strategies in environments with heterogeneously distributed resources and toxicants
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Optimal resource allocation and prolonged dormancy strategies in herbaceous plants
<p>1. Understanding the fitness consequences of different life histories is critical for explaining their diversity and for predicting effects of changing environmental conditions. However, current theory on plant life histories relies on phenomenological, rather than mechanistic, models of resource production.</p> <p>2. We combined a well-supported mechanistic model of ontogenetic growth that incorporates differences in the size-dependent scaling of gross resource production and maintenance costs with a dynamic optimization model to predict schedules of reproduction and prolonged dormancy (plants staying below ground for ≥ 1 growing season) that maximize lifetime offspring production.</p> <p>3. Our model makes three novel predictions: First, maintenance costs strongly influence the conditions under which a monocarpic or polycarpic life history evolves and how resources should be allocated to reproduction by polycarpic plants. Second, in contrast to previous theory, our model allows plants to compensate for low survival conditions by allocating a larger proportion of resources to storage and thereby improving overwinter survival. Incorporating this ecological mechanism in the model is critically important because without it our model never predicts significant investment into storage, which is inconsistent with empirical observations. Third, our model predicts that prolonged dormancy may evolve solely in response to resource allocation tradeoffs.</p> <p>4. Significance: Our findings reveal that maintenance costs and the effects of resource allocation on survival are primary determinants of the fitness consequences of different life history strategies, yet previous theory on plant life history evolution has largely ignored these factors. Our findings also validate recent arguments that prolonged dormancy may be an optimal response to costs of sprouting. These findings have broad implications for understanding patterns of plant life history variation and predicting plant responses to changing environments.</p>
Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact
<p>This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.</p>
Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production
<p>These datasets were utilized to generate the figures and tables presented in the paper '<strong>Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production</strong><strong>'</strong>:</p> <p>(1) Fig.1.html: Code for generating Fig. 1.<br>(2) Fig.2.xlsx: Data for Fig. 2.<br>(3) Fig.3.xlsx: Data for Fig. 3.<br>(4) Fig.4.xlsx: Data for Fig. 4.<br>(5) Fig.5.xlsx: Data for Fig. 5.</p> <p>(6) Other .html files are Python code corresponding to different tables and figures.</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Data from: Integrating vital rates explains optimal worker size for resource return by bumble bee workers
1. Size-number trade-offs in reproduction are commonly observed in nature. Bumble bee (Bombus spp.) colonies produce workers that vary considerably in size. This variation suggests that colonies face potential size-number trade-offs when producing workers. 2. Here, we estimated size-based vital rates of Bombus vosnesenskii workers using colonies reared from wild caught queens. We conducted a mark-recapture study to estimate worker survival as a function of body size. We also collected data on pollen and nectar loads as well as foraging trips using a radio-frequency identification system to estimate daily resource return as a function of body size. We integrated survival and daily resource return to estimate lifetime resource collection and offset these estimates by the size-based worker production costs. 3. We found size-based trade-offs among workers of different sizes. Smaller workers had higher survival, but larger workers returned with more resources per day. The largest workers made slightly fewer foraging trips per day. 4. Overall, larger workers made the greatest lifetime contribution to both nectar and pollen collection. However, once the benefits of larger workers are offset by their higher production costs, intermediate-sized workers were the optimal for net resource contribution according to our models. Many previous studies have found that larger workers outperformed smaller workers with foraging and in-nest tasks, yet these studies have not integrated multiple fitness components or worker production costs to quantify net resource contribution towards colony growth. 5. Accounting for tradeoffs between costs and performance changed our conclusions about optimal body size from being large to being near the observed average. Similar approaches of integrating multiple vital rates may resolve apparently suboptimal life histories in other taxa.
Data from: Integrating vital rates explains optimal worker size for resource return by bumble bee workers
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Data from: Evaluating Sphagnum traits in the context of resource economics and optimal partitioning theories
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Optimal resource allocation and prolonged dormancy strategies in herbaceous plants
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Data from: Multi-objective optimization for plant germplasm collection conservation of genetic resources based on molecular variability
Germplasm collections play a significant role among strategies for conservation of diversity. It is common to select a core collection to represent the genetic diversity of a germplasm collection, in order to minimize the cost of conservation, while ensuring the maximization of genetic variation. We aimed to solve two main problems: (1) to select a set of individuals, from an in situ data set, that is genetically complementary to an existing germplasm collection, and (2) to define a core collection for a germplasm collection. We proposed a new multi-objective optimization (MOO) approach based on principles of systematic conservation planning (SCP) incorporating heterozygosity information; therefore, optimization takes genotypic diversity and variability patterns into account as well. As a case study, we used Dipteryx alata microsatellite loci information from two sources, an ex situ germplasm collection located at the Agronomy School of the Federal University of Goiás (UFG-AS), and an in situ data set composed of 642 sampled individual trees. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. We found that material from nine in situ individual trees are enough to complement the UFG-AS germplasm collection as it is, and that it is possible to define a core collection of 20 individual trees representing all studied genetic diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. As far as we know, this is the first time that principles of SCP and the MOO approach are applied to the problem of complementing a germplasm collection and of finding a core collection for a germplasm collection.
Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"
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Pareto-OptimaL allocatIon of Continuous IntEgration Resources
<p>Pareto-OptimaL allocatIon of Continuous IntEgration Resources (POLICIER) replication package</p>
Wyniki zawarte w Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>This resource contains the results included in Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</p>
Data from: Multi-objective optimization for plant germplasm collection conservation of genetic resources based on molecular variability
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Allocating resources for land protection using continuous optimization: an application to US biodiversity conservation
Spatial optimization approaches that were originally developed to help conservation organizations determine protection decisions over small spatial scales are now used to inform global or continental scale priority setting. However, the different decision contexts involved in large-scale resource allocation need to be considered. We present a continuous optimization approach in which a decision-maker allocates funding to regional offices. Local decision-makers then use these funds to implement habitat protection efforts with varying effectiveness when evaluated in terms of the funder's goals. We illustrate this continuous formulation by examining the relative priority that should be given to different counties in the coterminous United States (US) when acquiring land to establish new protected areas. If weighting all species equally, counties in the southwest US, where large areas can be bought cheaply, are priorities for protection. If focusing only on species of conservation concern, priorities shift to locations rich in such species, particularly near expanding exurban areas facing high rates of future habitat conversion (e.g., south-central Texas). Priorities for protection are sensitive to what is assumed about local ecological and decision-making processes. For example, decision-makers who doubt the efficacy of local land protection efforts should focus on a few key areas, while optimistic decision-makers should disperse funding more widely. Efforts to inform large-scale conservation priorities should reflect better the types of choice that decision-makers actually face when working over these scales. They also need to report the sensitivity of recommended priorities to what are often unstated assumptions about local processes affecting conservation outcomes.
GORA-PT: Gradient-based Optimization of Resource Allocation of progeny in breeding schemes with PyTorch
<p>This directory is a public directory to share the scripts, simulated datasets, and results for the paper "Differentiable breeding: Automatic differentiation enables efficient gradient-based optimization of breeding strategies". In the "BSP" folder, we shared the core codes for the differentiable breeding schemes implemented by the automatic differentiation function of PyTorch. In the "scripts" folder, we shared the codes to perform gradient-based and black-box-based optimizations. In the "midstream" folder, we shared the datasets simulated by R and all the results, including the optimized results by PyTorch.</p>
Data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal
<p>This data set contains the data for the paper 'A Novel Simulation Optimization Framework for Multi Scale Irrigation Water Distribution and Scheduling Considering Crop Growth Process' submitted to Water Resources Research, an AGU journal. <span>The settings and crop parameters are consist of maize</span><span><span>(</span></span><span><a title="Ran, 2018 #74" href="#_ENREF_44"><span>Ran et al., 2018</span></a></span><span>; </span><span><a title="Shirazi, 2021 #75" href="#_ENREF_48"><span>Shirazi et al., 2021</span></a></span><span>)</span><span></span><span>, flower</span><span><span>(</span></span><span><a title="Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018 #83" href="#_ENREF_45"><span>Reyhaneh alsadat Mousavi Zadeh Mojarad, 2018</span></a></span><span>; </span><span><a title="Karimi Avargani, 2023 #80" href="#_ENREF_24"><span>Karimi Avargani et al., 2023</span></a></span><span>)</span><span></span><span> and wheat</span><span><span>(</span></span><span><a title="Iqbal, 2014 #76" href="#_ENREF_17"><span>Iqbal et al., 2014</span></a></span><span>; </span><span><a title="Huang, 2022 #79" href="#_ENREF_15"><span>Huang et al., 2022</span></a></span><span>; </span><span><a title="Lyu, 2022 #99" href="#_ENREF_38"><span>Lyu et al., 2022</span></a></span><span>; </span><span><a title="Karimi Avargani, 2023 #80" href="#_ENREF_24"><span>Karimi Avargani et al., 2023</span></a></span><span>)</span><span></span><span>,</span><span> which used for initializing AquaCrop-OS . </span></p>
Dataset of the paper: "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"
<p>This repository contains the dataset of the paper "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"</p>
Low-Cost, Portable Flexible Nasopharyngoscope in Head & Neck Cancers in Low Resource Settings - Optimization Phase
ClinicalTrials.gov study NCT04905134. IPD Sharing: NO. Countries: 1. Publications: 0.
Resources for Optimizing Outcomes in Toddlers on the Spectrum
ClinicalTrials.gov study NCT07086781. IPD Sharing: YES. Countries: 1. Publications: 0.
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.