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281 results for “controlled conditions”
Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The complete agents “environment” is shown in figure 6. Each agent receives the necessary<br> environmental or plant condition measurements. While each agent keeps its autonomy and has its<br> own “personal” goals, it interacts with the other agents through some specific communication<br> language, in the “Agent discussion area”, where the overall goal is taken into account. This<br> interaction takes place under the presence of information from the existing models and the target<br> goals. At that point, each agent has decided on its best strategy.</p>
Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>Figure 2 illustrates how the sensor<br> nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a<br> better understanding of the microclimate layers which typically exist in the greenhouse, and to<br> figure out what kind of differences occur in the climate between lower and upper flora.</p>
Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The negotiations between agents are subject to optimization based on “knowledge” that is<br> derived from complete production models, yield models or even sparse models as expressed in<br> fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models<br> provide additional information useful to the design of a successful strategy for optimal management<br> [15] (illustrated in Figure 5).</p>
Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The multi agent method creates a non-structured environment using agents, these agents, in<br> order to reach the global optimum, must be capable of performing transactions between each other,<br> achieving several “deals”, which is performed though a common vocabulary, a finite set of<br> information exchange, a finite set of possible actions, penalties, etc. The power of each agent<br> depends on the degree of contribution of its represented process to the final output of the entire<br> system. Each agent communicates with the environment and adapts to its own internal state as well<br> as to the state of the entire system. In this systems, for the realization of the multi agent architecture<br> the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The greenhouse protects the plants from the extreme weather conditions. However, if the<br> period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting<br> allows the grower to extend the growing season. It enables a year-round producing of plants or<br> makes it possible for the grower to start sowing in early spring and continue season till the first<br> frost. Plants need about 10-12 hours light to improve growth. When the plants are producing<br> flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the<br> photosynthetic activity in different wavelengths of light radiation [16].</p>
BRAIN Journal-Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance-Figure 2. Energy-VAS subjective measures for the participants (n=12) under two conditions (control and exercise involved cognitive task).
<p>As shown in Figure 2, it was found that there was a statistically significant interaction in the<br> percentage of mental fatigue between the condition type and time-on-task factor times (F(6, 22) =<br> 492.19, p < 0.001) as well as there was a significant main effect of time-on-task (time5 to time30)<br> (F(5, 22) = 463.794, p < 0.001). In addition, there was also a significant main effect in the condition type (F (1, 22) = 713.133, p < 0.001) which represented a large effect size. For the physical fatigue<br> subjective measure, there was a significant difference between the two experimental conditions (p <<br> 0.001), and within the subject test times (p < 0.001). However, there was no significant difference<br> between the means of the concentration visual analogue scale for these two experimental conditions<br> (p = 0.057) despite a significant difference (p < 0.001) in the time-on-task repeated measures.</p>
Fig. 2 in Oviposition of Aedes aegypti Linnaeus, 1762 and Aedes albopictus Skuse, 1894 (Diptera: Culicidae) under laboratory and field conditions using ovitraps associated to different control agents, Manaus, Amazonas, Brazil
Fig. 2. Total Aedes aegypti and Aedes albopictus adults obtained through egg collection in ovitraps containing grass infusion (control) and associated with different control agents. Legend: *(gI + Bti) – grass infusion + Bacillus thuringiensis israelensis; *(gI + Ss) – grass infusion + Saccharopolyspora spinosa; *(dW + Th) – distilled water + Toxorhynchites haemorrhoidalis; *(gI + P) – grass infusion + Pyriproxyfen;* (gI) – grass infusion (control).
Fig. 1 in Oviposition of Aedes aegypti Linnaeus, 1762 and Aedes albopictus Skuse, 1894 (Diptera: Culicidae) under laboratory and field conditions using ovitraps associated to different control agents, Manaus, Amazonas, Brazil
Fig. 1. Ovitraps Positivity Index (OPI) and Egg Density Index (EDI) with association of grass infusion with different control agents using ovitraps in the period ranging from June to July 2015, Manaus – Amazonas – Brazil. Legend: *(gI + Bti) – grass infusion + Bacillus thuringiensis israelensis; * (gI + Ss) – grass infusion + Saccharopolyspora spinosa; * (dW + Th) – distilled water + Toxorhynchites haemorrhoidalis; *(gI + P) – grass infusion + Pyriproxyfen; * (gI) – grass infusion (control).
Fig. 2. Adult spotted wing drosophila emergence from berries 2 in Efficacy of entomopathogenic fungal products for biological control of spotted wing drosophila (Diptera: Drosophilidae) under laboratory conditions
Fig. 2. Adult spotted wing drosophila emergence from berries 2 wk afer re- moval from the arenas. Bars with the same letter are not significantly different from each other (P> 0.05). Error bars represent standard error of the mean.
Fig. 1 in Efficacy of entomopathogenic fungal products for biological control of spotted wing drosophila (Diptera: Drosophilidae) under laboratory conditions
Fig. 1. Percent mortality of spotted wing drosophila afer 24, 48, 72, and 168 h in each treatment (Untrt = deionized water treated control, BotL = BotaniGard low rate, BotH = BotaniGard high rate, PFRL = PFR 97 low rate, and PFRH = PFR 97 high rate). Bars with the same letter are not significantly different from each other (P> 0.05). N. S. = no significant differences (P> 0.05). Error bars represent standard error of the mean.
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, RNAseq Control Condition Sequencing Data
<p><strong>Arabidopsis stranded 150 bp paired end RNA sequencing data (Illumina) of plants that were grown under control conditions for the manuscript "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong><strong>Plant growth conditions</strong></strong></p> <p>Sequenced F4 seeds were sterilized for 10 minutes in 10% bleach, rinsed, and stratified at 4°C for four days in the dark before being sown on 0.5x Murashige & Skoog media (Du<em>schefa cat# M0222</em>) and transferred to growth chambers under long day conditions (16h of light at 24°C followed by 8h of darkness at 21°C; 20 seeds per plate, 6 replicate plates). Ten days after sowing, plants were subjected to 6°C for 24 hours and control plants were returned to normal long day growing conditions for 24 hours before harvesting (3 replicate plates per condition).</p> <p><strong><strong>RNA extraction and sequencing</strong></strong></p> <p>Seedlings were harvested and RNA extractions were done on pools of 5 plants. RNA extractions were performed for 3 biological replicate samples for each line in each condition (n=96) using the Macherey-Nagel NucleoSpin RNA kit (cat# 740955.50). Samples were sent to Novogene for Illumina 150bp paired-end sequencing using a stranded poly-A library.</p> <p><strong>RNAseq sample descriptions of the plants grown under control conditions</strong></p> <p>wt_control: wild-type plants.</p> <p>wtHS_control: wild-type plants that have been submitted to heat stress in a previous generation.</p> <p>wtAZ_control: wild-type plants that have been submitted to epigenetic drug treatments (alpha-amanitin and zebularine) in a previous generation.</p> <p>htLine#: plants carrying additional <em>ONSEN</em> transposable element insertions.</p> <p>Files description: Forward and reverse strand RNA seq data are combined in one file. The numbering at the end ("_1") denominates the biological replicate number.</p>
Figure 4 in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Figure 4. – Female (in front) with 3 males in the current just before the expulsion of ova.
Figure 7 in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Figure 7. – Health of gills. A: Early signs of mycosis on gills; B: Healthy gills.
Combining transinfected Wolbachia and a genetic sexing strain to control Aedes albopictus in laboratory-controlled conditions
<p>The global expansion of <em>Aedes albopictus</em> has stimulated the development of environmental-friendly methods aiming at controlling disease transmission through the suppression of natural vector populations. Sterile male release programs are currently being deployed worldwide, and are challenged by the availability of an efficient sex separation that can be achieved mechanically at the pupal stage and/or by artificial intelligence at the adult stage, or through genetic sexing, which allows separating males and females at an early development stage. In this study, we combined the genetic sexing strain (GSS) previously established based on the linkage of dieldrin resistance to the male <em>locus</em> with a <em>Wolbachia</em> transinfected line. For this, we introduced either the <em>w</em>Pip-I or the <em>w</em>Pip-IV strain from <em>Culex pipiens </em>in an asymbiotic-Wolbachia-free <em>Ae</em>. <em>albopictus</em> line. We then measured the penetrance of CI and life history traits of both transinfected lines, selected the <em>w</em>Pip-IV line, and combined it with the GSS. Population suppression experiments demonstrated a 90% reduction in population size and a 50% decrease in hatching rate. Presented results showed that such a combination has a high potential in terms of vector control but also highlighted associated fitness costs, which should be reduced before large-scale field assay.</p>
Re-assessment of the climatic controls on the carbon and water fluxes of a boreal aspen forest over 1996-2016: changing sensitivity to long-term climatic conditions
<p>Recent evidence suggests that the relationships between climate and boreal tree growth are generally non-stationary; however, it remains uncertain whether the relationships between climate and carbon (C) fluxes of boreal forests are stationary or have changed over recent decades. In this study, we used continuous eddy-covariance and microclimate data over 21 years (1996-2016) from a 100-year-old trembling aspen stand in central Saskatchewan, Canada to assess the relationships between climate and ecosystem C and water fluxes. Over the study period, the most striking climatic event was a severe, 3-year drought (2001-2003). Gross ecosystem production (GEP) showed larger interannual variability than ecosystem respiration (<em>R</em><sub>e</sub>) over 1996-2016, but <em>R</em><sub>e</sub> was the dominant component contributing to the interannual variation in net ecosystem production (NEP) during post-drought years. The inter-annual variations in evapotranspiration (ET) and C fluxes were primarily driven by temperature and secondarily by water availability. Two-factor linear models combining precipitation and temperature performed well in explaining the inter-annual variation in C and water fluxes (<em>R</em><sup>2</sup>>0.5). The temperature dependence of all three C fluxes (NEP, GEP and <em>R</em><sub>e</sub>) declined over 1996-2015 (<em>p</em><0.05), and as a result, the phenological controls on annual NEP weakened. The decreasing temperature sensitivity of the C fluxes over 1996-2015 may reflect changes in forest structure, related to the over-maturity of the aspen stand at 100-years of age and exacerbated by high tree mortality following the severe 2001-2003 drought. These results may provide an early warning signal of driver shift or even an abrupt status shift of aspen forest dynamics. They may also imply a universal weakening in the relationship between temperature and GEP as forests become over-mature, associated with the structural and compositional changes that accompany forest ageing.</p>
Support data for conference paper "Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions"
<p>Support data for article</p> <p>V. Kovtun, O. Kovtun, K. Grochla, and K. Połys, “Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions,” Electronics, vol. 13, no. 7. MDPI AG, p. 1180, Mar. 22, 2024. doi: 10.3390/electronics13071180.</p> <div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p> </div>
Data from a controlled drought experiment in greenhouse conditions on saplings of Prunus spinosa
<p>Dataset with measurements and observations on saplings of Prunus spinosa in a common garden setting (three European populations are included), that were treated with water withholding in the summer of 2021 in greenhouse conditions.</p> <p>One dataset contains the data on all 274 saplings that are included in the experiment. The second dataset contains measurements and counts of stomata on a subset of 57 saplings. The two datasets can be connected by the plant ID.</p>
Controlling rheology via boundary conditions in dense granular flows
<p>Boundary shape, particularly roughness, strongly controls the amount of wall slip in dense granular flows. We aim to quantify and understand which aspects of a dense granular flow are controlled by the boundary conditions and to incorporate these observations into a cooperative nonlocal model characterizing slow granular flows. To examine the influence of boundary properties, we perform experiments on a quasi-2D annular shear cell with a rotating inner wall and a fixed outer wall; the latter is selected among 6 walls with various roughnesses, local concavity, and compliance. Measuring flow field and stress field throughout the material in experimental studies of granular materials is an ongoing challenge due to the complex nature of these materials. Here, we use innovative techniques to quantify stress field and flow field when different boundaries were used. We find that we can successfully capture the full flow profile using a single set of empirically determined model parameters, with only the wall slip velocity set by direct observation. Through the use of photoelastic particles, we observe how the internal stresses fluctuate more for rougher boundaries, corresponding to a lower wall slip, and connect this observation to the propagation of nonlocal effects originating from the wall. Our measurements indicate a universal relationship between dimensionless fluidity and velocity.</p>
Helping People With Health Conditions Make Birth Control Decisions
ClinicalTrials.gov study NCT03153644. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
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.