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60 results for “Cost efficiency”
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics
<p>This record includes the raw data associated with the Lawrence Berkeley National Laboratory (LBNL) report "Grid-Interactive Efficient Building Technology Cost, Performance, and Lifetime Characteristics," which is published <a href="https://escholarship.org/uc/item/44t4c2v6">here</a>.</p> <p>Data were collected by Guidehouse under the objective of developing current and projected performance, cost, and lifetime characteristics for residential and commercial building technologies and equipment with the potential to provide grid services. The list of technologies was developed based on data gathered from the U.S. Department of Energy (DOE) Grid Interactive Efficient Buildings (<a href="https://www.energy.gov/eere/buildings/grid-interactive-efficient-buildings">GEB</a>) Technical Report Series, ENERGY STAR Connected Certified Products, and input from researchers at the U.S. national laboratories. For each technology, characteristics are provided for a typical case and a connected or grid-interactive case. Where data are available, current DOE appliance standard levels are given. Definitions vary by technology and are provided with each data table. Current data is provided for 2020 and projections are available for 2030, 2040, and 2050. </p> <p><strong>Note</strong>: enabling technologies for grid-interactive efficient buildings such as smart meters and distributed energy management software were out of scope for this report. Non-building technologies such as EV chargers and PV inverters were also out of scope for this report.</p>
Energy Efficiency Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the Energy Efficiency Technologies Costs data for the Starter Data Kits. </p>
Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures
<p>These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.</p> <p>The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.</p> <p>The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:</p> <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>
Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations
<p>This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations.</p> <p>The following files are available:</p> <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel:<br> Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul> <p> </p> <p> </p>
Cost Efficiency Assessment of Four Pressure Management Methods in Water Distribution Systems (WRENG-5984)
<p>EPANET_3-PMX input data files for Hadımköy Water Distribution System accessible from</p> <p><a href="https://github.com/MehmetMelihKosucu/EPANET_3-PMX/tree/v3.1.1">https://github.com/MehmetMelihKosucu/EPANET_3-PMX/tree/v3.1.1</a></p>
Trade-offs Between Performance Efficiency and Costs While Using Function as a Service for Machine Learning Tasks: A Systematic Literature Review
<p>This repository contains all artifacts that were used for the systematic literature review (SLR) related to a the cost and performance trade-offs when Function as a Service (FaaS) for machine learning (ML) tasks.</p> <p>We selected and analyzed 65 papers and publications that were optained via Scopus. The study took place in 2024 from January to August 2024.</p>
A Cost-efficiency Analysis of Primary Assessors for Patients With Knee Pain in Primary Care
ClinicalTrials.gov study NCT03822533. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks
Tiger sharks Galeocerdo cuvier are a keystone, top-order predator that are assumed to engage in cost-efficient movement and foraging patterns. To investigate the extent to which patterns of oscillatory diving by these animals conform to these patterns, we used a biologging approach to model their cost of transport. High-resolution biologging tags with tri-axial sensors were deployed on 21 tiger sharks at Ningaloo Reef for durations of 5-48 hours. Using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure, we modelled the cost of transport of oscillatory movements of varying geometries in both horizontal and vertical planes for tiger sharks. The cost of horizontal transport was minimized by descending at the lowest possible angle and ascending at an angle of 5-14°, meaning that vertical oscillations conserved energy compared to swimming at a level depth. Reduction of vertical travel costs occurred at steeper angles. The absolute dive angles of tiger sharks increased between inshore and offshore zones, presumably to reduce the cost of transport while continuously hunting for prey in both benthic and surface habitats. Oscillatory movements of tiger sharks conform to strategies of cost-efficient foraging, and shallow inshore habitats appear to be an important habitat for both hunting prey and conserving energy while travelling.
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
<p>The quality of human life increases with increased usage of the Internet of Things (<i>IoT</i>). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.</p> <p>Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.</p> <p>The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.</p>
Data from: Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation
Global syntheses of leaf trait scaling relationships report an increase in light interception costs or 'diminishing returns' with increase in leaf area. However, variation in light interception costs across ecological gradients and plant strategies to cope up with these costs are not adequately understood. We analyzed leaf area (A) - leaf dry mass (M), leaf water mass (W) - M and W - A scaling relationships in plants occurring in a high altitude region of western Himalaya across environmental gradients to understand changes in light interception cost and metabolic mass component. M represents light interception cost, whereas, W is considered as a proxy of metabolic mass component for liquid phase being the ultimate source of metabolic activity. Trait values were measured from 9278 leaves belonging to 136 dominant species occurring at different sites, slope aspects, elevations and habitat types. Overall, light interception cost increased with increasing A (scaling exponent (α) <1 in A-M relationship) and metabolic mass component increased disproportionately high with increasing M and A. We found significant differences in scaling exponents of leaf trait relationship between sites, elevations, slope aspects and habitat types, indicating that increase in light interception cost was more evident at higher elevations, southern slopes and open habitats. Further, with increase in light interception cost, metabolic mass component also increased (α>1 in W-M and W-A relationships). The changes in scaling exponents of various leaf trait relationships across ecological gradients indicated that vegetation of different regions have differences in light interception cost and metabolic mass component. Moreover, increasing light interception cost (increase in mechanical and hydraulic tissues) with increasing A and increasing metabolic mass (leaf thickness) with increasing A and M are favored in high altitude vegetation. This could be a key strategy of high altitude plants for efficient resource capture and use in harsh environments.
Supplementary Material for "Cost-Efficient Construction of Performance Models"
<p><strong>Overview</strong></p> <p>The main contribution of the paper is a novel approach to ease the performance modeling process by introducing a pre-processing step that automatically determines performance-irrelevant configuration options and removes them from the remaining modeling process. We show that our identification process can accurately identify performance-irrelevant options using Pace3D, a multi-physics simulation, as case study. </p> <p>As Pace3D is proprietary software to which we are granted access to, we cannot provide an executable. Therefore, it is not possible to repeat measurements of the software. We do, however, provide our collected measurement data, including logs of our conducted experiments and inputs used, as well as performance models created. The measurement data can be used to reproduce the learning of models using DECART. Furthermore, the evaluation data shows the runtime difference between configurations. </p> <p>DECART is available for download here: https://github.com/jmguo/DECART/</p> <p><strong>Structure</strong></p> <p>- training-data: Input configurations and measurement data used for training models.<br>- models: DECART models.<br>- evaluation-data: Input configurations and measurement data for evaluating the models.<br>- questionnaire: Filled questionnare by the Pace3D developer.</p>
Supplementary material 7 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S2. Baseline-corrected amplification curves (left half) and melting -curves (right half) for A & B) kick-net samples and C & D) malaise trap samples
Efficiency and Cost-effectiveness of a Culturally Adopted Lifestyle Intervention Program - the MEDIM Study.
ClinicalTrials.gov study NCT01420198. IPD Sharing: Not stated. Countries: 1. Publications: 17.
Comparison of Time and Cost Efficiency in Arthroscopic Shoulder Surgery Under Regional Anesthesia vs General Anesthesia
ClinicalTrials.gov study NCT05563025. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Costs, Efficiency and the Role of Management in HIV Prevention Interventions for Female Sex Workers in Nigeria
ClinicalTrials.gov study NCT03371914. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Respiratory Support and Treatment for Efficient and Cost-Effective Care
ClinicalTrials.gov study NCT05909566. IPD Sharing: NO. Countries: 1. Publications: 1.
E-CLAIR: Efficiency and Cost-effectiveness of Artificial Intelligence Based Diabetic Retinopathy Screening in Flanders
ClinicalTrials.gov study NCT05391659. IPD Sharing: NO. Countries: 1. Publications: 16.
Data from: A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing
Open the record for dataset details and reuse information.
Data from: “Direct PCR” optimization yields a rapid, cost-effective, non-destructive, and efficient method for obtaining DNA barcodes without DNA extraction
Open the record for dataset details and reuse information.
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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.
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.