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3,202 results for “maintenance”
Figure 3 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 3. Mean daily consumption of microalgae (mixture of Isochrysis galbana and Chaetoceros muelleri on trial I or only C. muelleri on trial II and III) estimated for each individual (cells.mL−1. clam−1) of yellow clam (Amarilladesma mactroides) in trials I, II and III.
Fig. 4 in Maintenance of Trypanosoma cruzi, T. evansi and Leishmania spp. by domestic dogs and wild mammals in a rural settlement in Brazil-Bolivian border
Fig. 4. Path analysis on the influences of contact and feeding on wild mammals in relation to infections of dogs surveyed at Urucum settlement, Corumbá, Mato Grosso do Sul, Brazil in 2015.
Fig. 3 in Maintenance of Trypanosoma cruzi, T. evansi and Leishmania spp. by domestic dogs and wild mammals in a rural settlement in Brazil-Bolivian border
Fig. 3. Path analysis on the influences of infections in relation to physical examination of dogs surveyed at Urucum settlement, Corumbá, Mato Grosso do Sul, Brazil in 2015.
Fig. 2 in Maintenance of Trypanosoma cruzi, T. evansi and Leishmania spp. by domestic dogs and wild mammals in a rural settlement in Brazil-Bolivian border
Fig. 2. Three-way Venn diagram illustrating coinfection, single infection or no infection of T. cruzi, T. evansi, and Leishmania spp. in 62 dogs from the Urucum settlement along the Brazil-Bolivia border. Total numbers and percentages are presented.
Fig. 1 in Maintenance of Trypanosoma cruzi, T. evansi and Leishmania spp. by domestic dogs and wild mammals in a rural settlement in Brazil-Bolivian border
Fig. 1. The Brazil-Bolivian border and Urucum settlement (Corumbá, MS) demonstrating the site of collections.
BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 5. Maintenance Views
<p>Beside the tabs and the areas, the database table FLD also contains the fields TABLENAME and FIELD, which suggest the related parameters, whom input may be updated at runtime. Through standard SAP functionality the tables in BASIS, respectively their fields are by default translated in the login language of the user. So in the fields TABLENAME and FIELD of the FLD table, we will obtain, in the user login language, the names of the tables and fields from BASIS via the foreign keys to the table DD03L for TABLENAME and to the table DD02L for FIELD. Beside the database tables, 3 maintenance views have been created, TABV, AREV and FLDV, for the tabs, areas and fields of the popup (figure 5). We have chosen maintenance views instead of database views to be able to use them in the view cluster.</p>
Dataset and Software for Abstraction Materialization Maintenance
<p>Abstraction Refinement is a technique which allows for reducing materialization of an ontology with a large ABox to materialization of a smaller (compressed) `abstraction' of this ontology. The corresponding conference paper shows how Abstraction Refinement can be adopted for incremental ABox materialization by combining it with the well-known DRed algorithm for materialization maintenance. The combination is non-trivial and to preserve correctness, already Horn ALCHI requires more complex abstractions. Nevertheless, significant benefits can be obtained for synthetic and real-world ontologies. This data set contains the source code for the implementation as well as the used test data and test runners to reproduce the results reported in the paper.</p>
Disk replacement log file examples from a very large RAID disk system for predictive maintenance analysis
<p>README.txt</p> <p>Maintenance example belonging to: </p> <p> The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance<br> Chapter 9, The Future of Maintenance (2019).<br> Lambert Schomaker, Michele Albano, Erkki Jantunen, Luis Lino Ferreira<br> River Publishers (DK)<br> ISBN: 9788793609853, e-ISBN: 9788793609846, https://doi.org/10.13052/rp-9788793609846</p> <p>The figure .pdf did not make it into the book. Here are the raw data, processed <br> logs and .gnu script to produce it.</p> <p>Data: event logs on disk failure in two racks of a huge RAID disk system (2009-2016).</p> <p>disks1.raw<br> disks2.raw</p> <p>Event logs to RC-filtered time series:<br> RC-filt-disks-log.c <br> do-RC-filter-to-make-spikes-more-visible (bash script)<br> --><br> disks1.log<br> disks2.log</p> <p>Constant (horizontal line) indicating the level where users experienced system-down time<br> Disrupted-operations-threshold</p> <p>disk-replacement-log.gnu<br> disk-replacement-log.pdf<br> </p>
Fig. 4 in Influence of rainfall regime in the Cerrado biome on the maintenance of traps built by Myrmeleon brasiliensis (Navás) (Neuroptera: Myrmeleontidae) larvae and the morphology of adults
Fig. 4. Size (mean ± SD) of Myrmeleon brasiliensis (Návas) larvae traps submitted to different rain freQuencies. (Treatment I: control, no rain; Treatment II: rain every 10 days and Treatment III: rain every 5 days).
Fig. 3 in Influence of rainfall regime in the Cerrado biome on the maintenance of traps built by Myrmeleon brasiliensis (Navás) (Neuroptera: Myrmeleontidae) larvae and the morphology of adults
Fig. 3. Percentage of live larvae, pupae, live adults and dead larvae of Myrmeleon brasiliensis (Návas, 1914) (Neuroptera: Myrmeleontidae) observed at the end of the experiments (Treatment I: control, without rain; Treatment II: rain every 10 days and Treatment III: rain every 5 days).
Fig. 1 in Influence of rainfall regime in the Cerrado biome on the maintenance of traps built by Myrmeleon brasiliensis (Navás) (Neuroptera: Myrmeleontidae) larvae and the morphology of adults
Fig. 1. Abundance of Myrmeleon brasiliensis (Návas, 1914) larvae traps and rainfall (mm), recorded between December 2018 and November 2019 in an area of Cerrado, AQuidauana, MS, Brazil.
Fig. 5 in Influence of rainfall regime in the Cerrado biome on the maintenance of traps built by Myrmeleon brasiliensis (Navás) (Neuroptera: Myrmeleontidae) larvae and the morphology of adults
Fig. 5. Mean and standard deviation of the adult body length of Myrmeleon brasiliensis (Návas, 1914) from larvae subjected to different rainfall freQuencies. (Treatment I: control, no rain; Treatment II: rain every 10 days and Treatment III: rain every 5 days).
Dataset 'Influence of bacteria on the maintenance of a yeast during Drosophila melanogaster metamorphosis'
<p>Dataset from the manuscript 'Influence of bacteria on the maintenance of a yeast during <em>Drosophila </em><em>melanogaster </em>metamorphosis'</p>
Fig. 1 in Cost of territorial maintenance by Parodon nasus (Osteichthyes: Parodontidae) in a Neotropical stream
Fig. 1. Study site showing location Camarinha Stream in Serra das Araras, State of Mato Grosso, Brazil.
Fig. 2 in Cost of territorial maintenance by Parodon nasus (Osteichthyes: Parodontidae) in a Neotropical stream
Fig. 2. Relationship between the number of attacks on intruders (a), the time foraging (b) and the time resting (c) with the size of the territory defended by Parodon nasus in a stream in Serra das Araras, State of Mato Grosso, Brazil (October 2008).
Fig. 1 in Fish passage ladders from Canoas Complex - Paranapanema River: evaluation of genetic structure maintenance of Salminus brasiliensis (Teleostei: Characiformes)
Fig. 1. Partial view of the Paranapanema River and its principal affluents (Tibagi and Cinzas Rivers). Featured are the two collection sites: HEP Canoas I and HEP Canoas II.
MetroPT2: A Benchmark dataset for predictive maintenance
<p><strong>Abstract</strong></p> <p>The MetroPT2 data set is an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.</p> <table> <tbody> <tr> <td> <p>Data Set Characteristics:</p> </td> <td> <p>Multivariate Time series</p> </td> <td> <p>Number of Instances:</p> </td> <td> <p>7116940</p> </td> </tr> <tr> <td> <p>Attribute Characteristics:</p> </td> <td> <p>Real</p> </td> <td> <p>Number of Attributes</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Associated Tracks:</p> </td> <td> <p>Classification, Regression</p> </td> <td> <p>Missing Values</p> </td> <td> <p>N/A</p> </td> </tr> </tbody> </table> <p><strong>Data Set Information:</strong></p> <p>The dataset was collected to support the development of predictive maintenance, anomaly detection, and remaining useful life (RUL) prediction models for compressors using deep learning and machine learning methods.</p> <p>It consists of multivariate time series data obtained from several analogue and digital sensors installed on the compressor of a train. The data span between 2022-04-28 and 2022-07-28 and includes 16 signals, such as pressures, motor current, oil temperature, flowmeter and electrical signals of air intake valves. The monitoring and logging of industrial equipment events, such as temporal behaviour and fault events, were obtained from records generated by the sensors. The data were logged at 1Hz by an onboard embedded device. You can find a schematic diagram of the air production unit of the compressor system in Figure 4 of the accompanying paper [1]. Also, the paper [2] provides a detailed examination of data collection and specifications of various types of potential failures in an air compressor system. </p> <p><strong>Relevant Papers:</strong></p> <p>[1]- Davari, N., Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). pp. 1–10. IEEE (2021) (DOI: <a href="https://doi.org/10.1109/DSAA53316.2021.9564181">10.1109/DSAA53316.2021.9564181</a>)</p> <p>[2] Veloso, B., Ribeiro, R.P., Pereira, P.M., Gama, J.: The MetroPT dataset for predictive maintenance. Scientific Data 9, no. 1 (2022): 764. (DOI: 10.1038/s41597-022-01877-3)</p> <p>[3]-Barros, M., Veloso, B., Pereira, P.M., Ribeiro, R.P., Gama, J.: Failure detection of an air production unit in the operational context. In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, pp. 61–74. Springer (2020) (DOI: 10.1007/978-3-030-66770-2_5)</p> <p><strong>Failure Information:</strong></p> <p>The dataset is unlabeled, but the failure reports provided by the company are available in the following table. This allows for evaluating the effectiveness of anomaly detection, failure prediction, and RUL estimation algorithms.</p> <p> </p> <table> <tbody> <tr> <td> <p>Nr.</p> </td> <td> <p>Start Time</p> </td> <td> <p>End Time</p> </td> <td>Failure</td> </tr> <tr> <td> <p>1</p> </td> <td> <p>2022-06-04 10:19:24.300</p> </td> <td> <p>2022-06-04 14:22:39.188</p> </td> <td>Air Leak</td> </tr> <tr> <td> <p>2</p> </td> <td> <p>2022-07-11 10:10:18.948</p> </td> <td> <p>2022-07-14 10:22:08.046</p> </td> <td>Oil Leak</td> </tr> </tbody> </table> <p> </p>
Effects of brain maintenance and cognitive reserve on age-related decline in three cognitive abilities
<p><strong>Objectives </strong></p> <p>Age-related cognitive changes can be influenced by both brain maintenance (BM), which refers to the relative absence over time of changes in neural resources or neuropathologic changes, and cognitive reserve (CR), which encompasses brain processes that allow for better-than-expected behavioral performance given the degree of life-course related brain changes. This study evaluated the effects of age, BM, and CR on longitudinal changes over two visits, 5 years apart, in three cognitive abilities that capture most of age-related variability.</p> <p><strong>Method </strong></p> <p>Participants included 254 healthy adults aged 20–80 years at recruitment. Potential BM was estimated using whole brain cortical thickness and white matter mean diffusivity at both visits. Education and IQ (estimated with AMNART) were tested as moderating factors for cognitive changes in the three cognitive abilities.</p> <p><strong> Results </strong></p> <p>Consistent with BM—after accounting for age, sex, and baseline performance—individual differences in the preservation of mean diffusivity and cortical thickness were independently associated with relative preservation in the three abilities. Consistent with CR—after accounting for age, sex, baseline performance, and structural brain changes—higher IQ, but not education, was associated with reduced 5-year decline in Reasoning (𝛽=0.387, p=0.002), and education was associated with reduced decline in Speed (𝛽=0.237, p=0.039). <strong> </strong></p> <p><strong>Discussion</strong></p> <p>These results demonstrate that both CR and BM can moderate cognitive changes in healthy aging and that the two mechanisms can make differential contributions to preserved cognition.</p>
High parasite virulence necessary for the maintenance of host outcrossing via parasite-mediated selection
<p>Biparental sex is widespread in nature, yet costly relative to uniparental reproduction. It is generally unclear why self-fertilizing or asexual lineages do not readily invade outcrossing populations. The Red Queen hypothesis predicts that coevolving parasites can prevent self-fertilizing or asexual lineages from invading outcrossing host populations. However, only highly virulent parasites are predicted to maintain outcrossing, which may limit the general applicability of the Red Queen hypothesis. Here, we tested whether the ability of coevolving parasites to prevent invasion of self-fertilization within outcrossing host populations was dependent on parasite virulence. We introduced wild-type <em>Caenorhabditis elegans</em> hermaphrodites, capable of both self-fertilization and outcrossing, into <em>C. elegans</em> populations fixed for a mutant allele conferring obligate outcrossing. Replicate <em>C. elegans</em> populations were exposed for 24 host generations to one of four strains of <em>Serratia marcescens</em> parasites that varied in virulence, under three treatments: a heat-killed (control, non-infectious) parasite treatment, a fixed-genotype (non-evolving) parasite treatment, and a copassaged (potentially coevolving) parasite treatment. As predicted, self-fertilization invaded <em>C. elegans</em> host populations in the control and fixed-parasite treatments, regardless of parasite virulence. In the copassaged treatment, selfing invaded host populations coevolving with low- to mid- virulent strains but remained rare in hosts coevolving with highly virulent bacterial strains. Therefore, we found that only highly virulent coevolving parasites can impede the invasion of selfing.</p>
A Study of Oral Ixazomib Maintenance Therapy in Participants With Newly Diagnosed Multiple Myeloma (NDMM) Not Treated With Stem Cell Transplantation (SCT)
ClinicalTrials.gov study NCT02312258. IPD Sharing: YES. Countries: 35. Publications: 2.
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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.