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3,145 results for “Well Being”
DATASET J. Stat. Mech. (2022) 053209: "Virtual double-well potential for an underdamped oscillator created by a feedback loop"
<p><strong>Matlab Figures: </strong></p> <p>Fig_i.fig corresponds to the source file used to plot the figures of the article</p> <p><strong>Simulation and Model codes: Matlab script</strong></p> <p>Simu_and_Model.m is the code used to:</p> <p>1) Simulate trajectories of the underdamped cantilever in the virtual double well potential with hysteresis at the comparator switches.</p> <p>2) Compute from the simulation the steady state temperature and the crossing rate.</p> <p>2) Compute and plot the model's prediction regarding the steady state temperature and the crossing rate. </p> <p><strong>static_hyst_XX.mat : Matlab dataset</strong></p> <p>It is experimental data of the underdamped cantilever in vacuum trapped in a double well potential with hysteresis at the switches. It is not the data used in the article. Indeed:</p> <p>1) The velocity distributions of the article were the ones of the analogical implementation of the feedback loop with hysteresis. Here we propose as an example a more recent dataset from the numerical implementation of the feedback loop with a chosen hysteresis.</p> <p>2) Besides the in this dataset the cantilever is evolving in vacuum so that the cooling effect observed is much more important for a given hysteresis.</p> <p>But as the data treatment to obtain the velocity distribution and the cooling observed are similar to the one detailed in the article, this dataset should be enough for any person interested.</p> <p>Each data file contains in particular: x the position in sigma units (z in the article), X_1 the wells centers position in sigma units (z_1 in the article), x0 the barrier position in sigma units (z_0 in the article), sigma_m the position variance calibrated before the experiment and sigma_protocol the position variance calibrated before every data file. </p> <p> </p> <p><strong>Analyse_velocity_distribution.m : Matlab Script</strong></p> <p>It is the code to treat datasets such as the one provided here "static_hyst". The code recover the data from the files and compute the velocity distributions for different distances between the wells. The velocity variance is deduced from the velocity distributions and matches the variance computed using the velocity power spectrum. The velocity and position spectrums are also displayed.</p> <p> </p>
Impacts of oil well drilling and operating noise on abundance and productivity of grassland songbirds
<p>Anthropogenic noise from natural resource extraction may negatively impact many species, particularly those reliant on acoustic communication. To compare impacts of several types of noise resulting from oil extraction operations on habitat use and productivity of grassland songbirds, we designed and implemented a novel large-scale, spatially and temporally replicated experiment. <br>We recreated soundscapes produced by drilling and operating oil well noise, and compared impacts of noise-producing and quiet playback infrastructure, in twenty-nine 64.7-ha native prairie sites in Alberta, Canada, from 2013–2015. Drilling noise recordings played 24 hours/day for 10 days, twice during each breeding season, while oil well operating noise played continuously, 24 hours/day, throughout each ~90-day breeding season. <br>Despite the much shorter duration of drilling noise playbacks, drilling noise negatively impacted three of our four focal species, and had a much greater impact on habitat use and productivity than did well operating noise. Infrastructure also impacted Vesper Sparrows and Sprague's Pipits, even in the absence of noise. <br>Synthesis and applications: Acute oil drilling noise had a greater negative impact on breeding migratory birds when compared to chronic oil well noise, perhaps because drilling noise is unpredictable. While this study demonstrates that noise alone can negatively impact habitat use, nesting success, and nestling quality, it is also clear that effective mitigation strategies require both noise and above-ground infrastructure management to reduce impacts on wildlife.</p>
Well Sorted results from the UK Astronomy community
<p>Submissions to<a href="https://www.well-sorted.org/index.php"> Well Sorted</a> from the UK Astronomy community. Members were asked to highlight their scientific interests to use data from the SKA and pathfinder facilities using MID and mixed frequencies, and for LOW frequencies. These were used as a starting point for the breakout groups in <a href="https://doi.org/10.5281/zenodo.6616630">Workshop 1 Co-creating the UKSRC.</a></p> <pre> </pre>
Locations of GPS-collared moose and geographic correlates, as well as for random points within study area
<p>Moose are among the many species that are vulnerable to both direcdt and indirect effects of climate change. Habitat selection is one framework to assist investigators in disentangling the various factors (including weather) that ultimately dictate how animals respond to their environment. We investigated patterns of winter habitat selecdtion by adult female moose in southerwestern MOntana, USA, during 2007-2010, and how that selection was affected by snow (quantified by snow water equivalent) and winter temperatures. We used data from GPS colalrs and a suite of environmental covariates to quantify winter habitat selection at both study area (2nd order) and home range (3rd order) spatial scales using resource selection functions. Moose strongly select for the willow (<em>Salix</em> spp.) cover type, and against grassland cover. Moose use of conifer cover at the home range scale increased when either amount of snow or ambient temperature was higher, altough the latter only during periods of the day when conifer pathces were likely to have been cooler than cover types lacking a canopy. Wildlife conservatoin and management naturally focuses on preferred habitats, particularly those that fulfill essentially all forgaing requirements. However, habitats used preferentially under stresful weather conditions, even if used rarely overall, can also form a critical part of a species' overall needs.,</p>
Accompanying dataset to ACS Appl. Nano Mater. 2022, 5, 4, 5508–5515 : Nanoscale Mapping of Light Emission in Nanospade-Based InGaAs Quantum Wells Integrated on Si(100): Implications for Dual Light-Emitting Devices
<p>Accompanying dataset containing the raw data and analysis contained in the main publication. This comprises the fitted CL-SEM dataset, the CL-STEM dataset, the CL-SEM presented in the SI and the STEM-EDX linescans.</p>
Data from: How does sustainable water consumption in the shower relate to different dimensions of perceived well-being? Empirical evidence from university students
<p>Water scarcity is already a worrying issue and it is predicted to get worse in the future. This creates an imperative to use water efficiently and sustainably. In the domestic sphere, one of the main uses of water is showering, not only for hygiene reasons but also as a wellness activity. In order to gain insight into the implications of sustainable shower use, in this paper we analyse the relationship between subjective well-being and water consumption in the shower. We aim to answer the following questions: 1) How does shower water consumption relate to subjective well-being, 2) Does this relationship differ depending on showering habits (time spent in the shower, and number of showers per week), and 3) Does this relationship differ depending on the season (winter and summer). The dataset contains information on 937 students from different disciplines at the University of Granada, Spain. The different interpretations of subjective well-being considered are life satisfaction, affect, and vitality. Results suggest that there is a negative relationship between water consumption and subjective well-being, in line with the literature that identifies a well-being dividend from green behaviour (being pro-environmental helps the environment and increases happiness). All subjective well-being dimensions are negatively related to time spent in the shower, regardless of the season. In contrast, the frequency of showering is not significantly related to well-being. Therefore, it appears that higher water consumption does not translate into higher perceived well-being, indicating that there is no conflict between efficient shower water use and individual well-being.</p>
Naturally occurring metals in unregulated domestic wells in Nevada, USA
<p>The dominant source of drinking water in rural Nevada, United States, is privately-owned domestic wells. Because the water from these wells is unregulated with respect to government guidelines, it is the owner's responsibility to test their groundwater for heavy metals and other contaminants. Arsenic, lead, cadmium, and uranium have been previously measured at concentrations above Environmental Protection Agency (EPA) guidelines in Nevada groundwater. This is a public health concern because elevated levels of these metals are known to have negative health effects.</p> <p>We recruited individuals through a population health study, the Healthy Nevada Project, to submit drinking water samples from domestic wells for testing. Water samples were returned from 174 households with private wells. We found 22% had arsenic concentrations exceeding the EPA maximum contaminant level (MCL) of 10 mg/L. Additionally, federal, state, or health-based guidelines were exceeded for 8% of the households for uranium and iron, 6% for lithium and manganese, 4% for molybdenum, and 1% for lead. The maximum observed concentrations of arsenic, uranium, and lead were ~80, ~5, and ~1.5 times the EPA guideline values, respectively. 41% of households had a treatment system and submitted both pre- and post-treatment water samples from their well. The household treatments were shown to reduce metal concentrations, but concentrations above guideline values were still observed. Many treatment systems cannot reduce the concentration below guideline values because of water chemistry, treatment failure, or improper treatment techniques. These results show the pressing need for continued education and outreach on regular testing of domestic well waters, proper treatment types, and health effects of metal contamination. These findings are potentially applicable to other arid areas where groundwater contamination of naturally occurring heavy metals occurs.</p>
Thallium(I) exposure perturbs the gut microbiota and metabolic profile as well as the regional immune function of C57BL/6 J mice
<p><span>Intestinal microbes</span><span> regulate the development of diseases induced by environmental exposure. Thallium (Tl) is a highly toxic heavy metal</span><span>, </span><span>and its toxicity is rarely discussed in relation to gut microbes. Herein, we showed that Tl(I) exposure (10 ppm for two weeks) affected the alpha diversity of bacteria in </span><span>the ileum, colon, and </span><span>feces, but had little effect on the beta diversity of bacteria through 16S rRNA sequencing. LEfSe analysis revealed that Tl(I) exposure changed the abundance of intestinal microbiota along the digestive tract. Cecum metabolomic detection and analysis showed that Tl(I) exposure altered the abundance and composition of metabolites. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that Tl(I) exposure impaired amino acid, lipid, purine metabolism, and G protein-coupled receptor signalling pathways. A consistency test revealed a strong correlation, and a Pearson's correlation analysis showed an extensive interaction, between microorganisms and metabolites. Analysis of the intestinal immunity revealed that Tl(I) exposure suppressed the immune responses, which also had regional differences. These results identify the perturbation of the intestinal microenvironment by Tl exposure and provide a new explanation for Tl toxicity.</span></p>
IODP Expedition 336, Hole U1383C - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 310, Hole M0023B - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 310, Hole M0021B - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 336, Hole U1382A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
ODP Leg 110, Hole 671C - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
ODP Leg 199, Hole 1218A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
IODP Expedition 302, Hole M0004B - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
ODP Leg 209, Hole 1272A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
ODP Leg 202, Hole 1239A - Well Logging Data
<p>Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.</p>
Poroelastic response of water level in Tengchong well aquifer to dynamic stress
<p>Data used in manuscript named "Poroelastic response of water level in Tengchong well aquifer to dynamic stress"</p>
How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data
<p>The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields:</p> <p>S.name: the identification name of the rain gauge station</p> <p>S.vals_mm: series of hourly rainfall in millimeter</p> <p>S.time_utc: time steps series, in UTC time</p> <p>S.elev_m: elevation of the station, in m a.s.l.</p> <p>S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N</p>
Observed water level from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France
<p>This repository hosts the data used for drawing figure 8 from Jeannot et al.(submitted). The dataset is made of observed water levels from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France. The sampling time step is 2 hours.</p> <p><strong>Cited bibliography</strong></p> <p>Jeannot, B., Schaper, L., and Habets, F., submitted. Water Level in Observation Wells Simulated from Fracture and Matrix Water Heads Outputed by Dual-continuum Hydrogeological Models : POWeR-FADS. <em>Water Resources research</em></p>
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Allen Brain Atlas
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OpenNeuro
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