Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
205
datasets available to search
ShareScore release 0.9.0
Dataset results
205 results for “Intensification”
Bat community response to intensification of biomass production for bioenergy across the southeastern United States
Human demand for food, fiber, and space is accelerating the rate of change of land cover and land use. Much of the world now consists of a matrix of natural forests, managed forests, agricultural cropland, and urbanized plots. Expansion of domestic energy production efforts in the United States is one driver predicted to influence future land-use and land management practices across large spatial scales. Favorable growing conditions make the southeastern United States an ideal location for producing a large portion of the country's renewable bioenergy. We investigated patterns of bat occurrence in two bioenergy feedstocks commonly grown in this region (corn, Zea mays, and pine, Pinus taeda and P. elliottii). We also evaluated potential impacts of the three major pathways of woody biomass extraction (residue removal following clearcut harvest, short-rotation energy plantations, and mid-rotation forest thinning) to bat occurrence through a priori land-use contrasts. We acoustically sampled bat vocalizations at 84 sites in the Southeastern Plains and Southern Coastal Plains of the southeastern United States across three years. We found that mid-rotation thinning resulted in positive effects on bat occurrence, and potential conversion of unmanaged (reference) forest to managed forest for timber and/or bioenergy harvest resulted in negative effects on bat occurrence when effects were averaged across all species. The effects of short-rotation energy plantations, removal of logging residues from plantation clearcuts, and corn were equivocal for all bat species examined. Our results suggest that accelerated production of biomass for energy production through either corn or intensively managed pine forests is not likely to have an adverse effect on bat communities, so long as existing older unmanaged forests are not converted to managed bioenergy or timber plantations. Beyond bioenergy crop production, mid-rotation thinning of even-aged pine stands intended for timber production, increases to the duration of plantation rotations to promote older forest stands, arranging forest stands and crop fields to maximize edge habitat, and maintaining unmanaged forests could benefit bat communities by augmenting roosting and foraging opportunities.
Data from: Comparative landscape genetics of two frugivorous bats in a biological corridor undergoing agricultural intensification
Agricultural intensification in tropical landscapes poses a new threat to the ability of biological corridors to maintain functional connectivity for native species. We use a landscape genetics approach to evaluate impacts of expanding pineapple plantations on two widespread and abundant frugivorous bats in a biological corridor in Costa Rica. We hypothesize that the larger, more mobile Artibeus jamaicensis will be less impacted by pineapple than the smaller Carollia castanea. In 2012 and 2013, we sampled 735 bats in 26 remnant forest patches surrounded by different proportions of forest, pasture, crops and pineapple. We used 10 microsatellite loci for A. jamaicensis and 16 microsatellite loci for C. castanea to estimate genetic diversity and gene flow. Canonical correspondence analyses indicate that land cover type surrounding patches has no impact on genetic diversity of A. jamaicensis. However, for C. castanea, both percentage forest and pineapple surrounding patches explained a significant proportion of the variation in genetic diversity. Least-cost transect analyses (LCTA) and pairwise G″st suggest that for A. jamaicensis, pineapple is more permeable to gene flow than expected, while as expected, forest is the most permeable land cover for gene flow of C. castanea. For both species, LCTA indicate that development may play a role in inhibiting gene flow. The current study answers the call for landscape genetic research focused on tropical and agricultural landscapes, highlights the value of comparative landscape genetics in biological corridor design and management and is one of the few studies of biological corridors in any ecosystem to implement a genetic approach to test corridor efficacy.
Data from: Plant demographic and functional responses to management intensification: a long-term study in a Mediterranean rangeland
1. Understanding how functional traits, which are key for plant functioning, relate to demographic parameters of populations is central to tackle pending issues in plant ecology such as the forecast of the fate of populations and communities in a changing world, the quantification of community assembly processes or the improvement of species distribution models. We addressed this question in the case of species from a Mediterranean rangeland of southern France. 2. Changes in species abundance in response to management intensification (fertilization and increased grazing pressure) were followed over a 28-year period. Probabilities of presence, and elasticities of the changes in the probability of space occupancy to colonization and survival, which are analogues of demographic parameters, were calculated for 53 species from the time series of abundance data using a space occupancy model. Nine quantitative traits pertaining to resource use, plant morphology, regeneration and phenology were measured on these species and related to demographic parameters. 3. The long-term dynamics of species in response to management intensification was associated with major changes in functional traits and strategies. Changes in the probability of occurrence – analogous to population growth rate - were correlated with traits describing the fast-slow continuum of leaf functioning. The elasticity of population growth rate to colonization was significantly related to reproductive plant height and seed mass, and to a lower extent, to leaf carbon isotopic ratio. 4. Synthesis. The functional response of species to management intensification corresponds to a shift along the second axis of a recently identified global spectrum of plant form and function, which maps, to some extent, onto the fast-slow continuum of life-history strategies. By contrast, the elasticity of colonization relates to the global spectrum axis capturing the size of organs. Seed mass contributes to this axis and is assumed to relate to one of the important traits structuring the reproductive strategy axis of life histories as well, namely net reproductive rate. While this mapping between functional and life-history traits is appealing, further tests in contrasting types of communities are required to assess its degree of generality.
Data and code for: "Large reductions in tropical bird abundance attributable to heat extreme intensification "
<p>Data and code necessary to reproduce results of paper:</p> <p>Large reductions in tropical bird abundance attributable to heat extreme intensification</p> <p>By Maximilan Kotz, Tatsuya Amanon and James Watson.</p> <p>Please contact maximilian.kotz@bsc.es / maxkotz@pik-potsdam.de for questions.</p> <p> </p>
Data from: Urbanisation and agricultural intensification modulate plant-pollinator network structure and robustness
<p>Land use change is a major pressure on pollinator abundance, diversity, and plant-pollinator interactions. Far less is known about how land use alters the structure of plant-pollinator networks and their robustness to plant-pollinator coextinctions.</p> <p>We analyzed the structure of plant-pollinator networks sampled in 12 landscapes along an urbanisation and agricultural intensity gradient, from early spring to late summer 2021, and used a stochastic coextinction model to correlate plant-pollinator coextinction risk with network structure (species and network-level metrics) and landscape context.</p> <p>Networks in intensively managed (i.e. agricultural and urban) landscapes had a lower risk of initiating a coextinction cascade, while networks in less-intensively managed landscapes may be less robust. Network structure modulated the frequency and severity of coextinctions and species loss, while the strength of species interactions increased robustness.</p> <p>Urban networks were more species-rich and symmetrical due to the high diversity of ornamental plants, while intensively managed agricultural landscapes had smaller, more tightly connected, and nested networks.</p> <p>Network structure modulated the frequency of extinctions, which was decreased by greater linkage density, interaction asymmetry, and interaction dependence in the networks, while once an extinction occurred, nestedness and linkage density propagated the degree of the coextinction cascade and species loss. At the species level, species strength was inversely correlated with extinction risk, implying that generalist species with a high number of interactions with specialists had the lowest extinction risk.</p>
CESM2 Mechanically Decoupled (MD) for "Summer westerly wind intensification weakens Southern Ocean seasonal cycle under global warming" - submitted to Geophysical Research Letters
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MD in paper)</li> <li>FC = fully coupled model (referred to as FC in paper)</li> </ul> <p>Decoding file names:</p> <ul> <li>ensmean refers to ensemble mean</li> <li>trend refers to linear trend over 1950-2014</li> </ul> <p>Variables:</p> <ul> <li>SST = sea surface temperature</li> <li>HMXL = mixed layer depth</li> <li>WSPDSRFAV = horizontal total wind speed average at the surface</li> </ul>
TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images
<p>This is the official repository for the paper TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images. We use the publicly available dataset from Taiwan University (Bai et al., 2019) as experimental data, consisting of four channels of TC satellite images with a temporal resolution of 3 hours, whose preprocessing method is also publicly available. We use infrared and passive microwave TC satellite image sequences for our experiments, each divided into 24-hour segments (8 infrared and 8 passive microwave satellite images, 16 in total), preprocessing and enhancing data as noted above, so there is no experimental error due to different data preprocessing methods. In this study, the 2003–2017 TC dataset from various global basins was divided into training (1097 TCs, 43528 events), validation (188 TCs, 7884 events), and test sets (94 TCs, 3196 events).</p>
Process intensification in a chemical reactor with enhanced mass transfer for hydrogen production
<p><strong>Process intensification in a chemical reactor with enhanced mass transfer for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Chemical synthesis is concerned with the construction of complex chemical compounds from simpler ones. A synthesis usually is undertaken for one of three reasons. The first reason is to meet an industrial demand for a product. For example, ammonia is synthesized from nitrogen and hydrogen and is used to make, among other things, ammonium sulfate, employed as a fertilizer; vinyl chloride is made from ethylene and is used in the production of polyvinyl chloride plastic. In general, a vast range of chemical compounds are synthesized for applications. Second, an enormous number of compounds of considerable molecular complexity occur naturally. The syntheses of these natural products have usually been undertaken in the context of the determination of the structures of the compounds; if a material is deduced to have a particular structure on the basis of its chemical reactions and physical properties, then the discovery that a compound synthesized by an unambiguous method for this structure is identical to the natural product provides confirmation of the validity of the assigned structure. Third, a synthesis may be carried out to obtain a compound of specific structure that does not occur naturally and has not previously been made. This type of synthesis is performed in order to examine the properties of the compound and thereby test theories of chemical structure and reactivity.</p> <p>Streamwise distance (millimeters), Wall centerline temperature (degrees kelvin)</p> <p>0 501.211</p> <p>0.00025 501.158</p> <p>0.0005 500.999</p> <p>0.00075 500.714</p> <p>0.001 500.254</p> <p>0.00125 499.764</p> <p>0.0015 499.441</p> <p>0.00175 499.3</p> <p>0.002 499.307</p> <p>0.00225 499.312</p> <p>0.0025 499.195</p> <p>0.00275 498.964</p> <p>0.003 498.605</p> <p>0.00325 498.22</p> <p>0.0035 497.936</p> <p>0.00375 497.767</p> <p>0.004 497.696</p> <p>0.00425 497.643</p> <p>0.0045 497.535</p> <p>0.00475 497.367</p> <p>0.005 497.127</p> <p>0.00525 496.875</p> <p>0.0055 496.695</p> <p>0.00575 496.597</p> <p>0.006 496.57</p> <p>0.00625 496.556</p> <p>0.0065 496.505</p> <p>0.00675 496.414</p> <p>0.007 496.275</p> <p>0.00725 496.132</p> <p>0.0075 496.044</p> <p>0.00775 496.017</p> <p>0.008 496.045</p> <p>0.00825 496.079</p> <p>0.0085 496.078</p> <p>0.00875 496.046</p> <p>0.009 495.978</p> <p>0.00925 495.91</p> <p>0.0095 495.887</p> <p>0.00975 495.916</p> <p>0.01 495.987</p> <p>0.01025 496.057</p> <p>0.0105 496.093</p> <p>0.01075 496.099</p> <p>0.011 496.076</p> <p>0.01125 496.056</p> <p>0.0115 496.073</p> <p>0.01175 496.134</p> <p>0.012 496.231</p> <p>0.01225 496.323</p> <p>0.0125 496.38</p> <p>0.01275 496.409</p> <p>0.013 496.413</p> <p>0.01325 496.421</p> <p>0.0135 496.462</p> <p>0.01375 496.541</p> <p>0.014 496.652</p> <p>0.01425 496.755</p> <p>0.0145 496.824</p> <p>0.01475 496.865</p> <p>0.015 496.886</p> <p>0.01525 496.912</p> <p>0.0155 496.967</p> <p>0.01575 497.057</p> <p>0.016 497.176</p> <p>0.01625 497.286</p> <p>0.0165 497.362</p> <p>0.01675 497.414</p> <p>0.017 497.448</p> <p>0.01725 497.486</p> <p>0.0175 497.553</p> <p>0.01775 497.65</p> <p>0.018 497.773</p> <p>0.01825 497.887</p> <p>0.0185 497.968</p> <p>0.01875 498.026</p> <p>0.019 498.069</p> <p>0.01925 498.117</p> <p>0.0195 498.191</p> <p>0.01975 498.292</p> <p>0.02 498.416</p> <p>0.02025 498.53</p> <p>0.0205 498.614</p> <p>0.02075 498.677</p> <p>0.021 498.727</p> <p>0.02125 498.781</p> <p>0.0215 498.859</p> <p>0.02175 498.96</p> <p>0.022 499.08</p> <p>0.02225 499.19</p> <p>0.0225 499.272</p> <p>0.02275 499.334</p> <p>0.023 499.385</p> <p>0.02325 499.44</p> <p>0.0235 499.514</p> <p>0.02375 499.608</p> <p>0.024 499.718</p> <p>0.02425 499.818</p> <p>0.0245 499.892</p> <p>0.02475 499.948</p> <p>0.025 499.992</p> <p>0.02525 500.039</p> <p>0.0255 500.1</p> <p>0.02575 500.178</p> <p>0.026 500.268</p> <p>0.02625 500.349</p> <p>0.0265 500.407</p> <p>0.02675 500.448</p> <p>0.027 500.477</p> <p>0.02725 500.506</p> <p>0.0275 500.545</p> <p>0.02775 500.596</p> <p>0.028 500.656</p> <p>0.02825 500.708</p> <p>0.0285 500.741</p> <p>0.02875 500.757</p> <p>0.029 500.759</p> <p>0.02925 500.755</p> <p>0.0295 500.754</p> <p>0.02975 500.757</p> <p>0.03 500.759</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport
<p><strong>Process intensification in a methanol steam reforming chemical reactor with enhanced momentum transport</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Consideration must be given to the rate at which each step in the planned sequence occurs. In many instances, a desired reaction is possible in principle but in practice takes place so slowly as to be ineffective. It is then necessary to investigate whether the rate can be increased to a practicable level by altering the conditions of the reaction, for example, by raising the temperature or by adding an extra species, called a catalyst, that increases the rate without altering the course of the reaction.</p> <p>Streamwise distance (millimeters), Oxidation channel centerline temperature (degrees kelvin)</p> <p>0 376.499</p> <p>0.00025 391.55</p> <p>0.0005 411.347</p> <p>0.00075 432.857</p> <p>0.001 451.349</p> <p>0.00125 465.437</p> <p>0.0015 475.536</p> <p>0.00175 482.591</p> <p>0.002 487.466</p> <p>0.00225 490.805</p> <p>0.0025 493.063</p> <p>0.00275 494.556</p> <p>0.003 495.508</p> <p>0.00325 496.074</p> <p>0.0035 496.366</p> <p>0.00375 496.469</p> <p>0.004 496.424</p> <p>0.00425 496.278</p> <p>0.0045 496.077</p> <p>0.00475 495.832</p> <p>0.005 495.555</p> <p>0.00525 495.26</p> <p>0.0055 494.955</p> <p>0.00575 494.646</p> <p>0.006 494.337</p> <p>0.00625 494.033</p> <p>0.0065 493.735</p> <p>0.00675 493.445</p> <p>0.007 493.164</p> <p>0.00725 492.894</p> <p>0.0075 492.635</p> <p>0.00775 492.386</p> <p>0.008 492.15</p> <p>0.00825 491.925</p> <p>0.0085 491.712</p> <p>0.00875 491.511</p> <p>0.009 491.323</p> <p>0.00925 491.146</p> <p>0.0095 490.98</p> <p>0.00975 490.825</p> <p>0.01 490.682</p> <p>0.01025 490.549</p> <p>0.0105 490.427</p> <p>0.01075 490.314</p> <p>0.011 490.212</p> <p>0.01125 490.119</p> <p>0.0115 490.036</p> <p>0.01175 489.96</p> <p>0.012 489.893</p> <p>0.01225 489.833</p> <p>0.0125 489.781</p> <p>0.01275 489.737</p> <p>0.013 489.699</p> <p>0.01325 489.668</p> <p>0.0135 489.643</p> <p>0.01375 489.624</p> <p>0.014 489.61</p> <p>0.01425 489.603</p> <p>0.0145 489.6</p> <p>0.01475 489.601</p> <p>0.015 489.607</p> <p>0.01525 489.618</p> <p>0.0155 489.632</p> <p>0.01575 489.651</p> <p>0.016 489.673</p> <p>0.01625 489.699</p> <p>0.0165 489.728</p> <p>0.01675 489.76</p> <p>0.017 489.796</p> <p>0.01725 489.834</p> <p>0.0175 489.875</p> <p>0.01775 489.918</p> <p>0.018 489.964</p> <p>0.01825 490.012</p> <p>0.0185 490.063</p> <p>0.01875 490.115</p> <p>0.019 490.169</p> <p>0.01925 490.225</p> <p>0.0195 490.283</p> <p>0.01975 490.343</p> <p>0.02 490.404</p> <p>0.02025 490.467</p> <p>0.0205 490.531</p> <p>0.02075 490.597</p> <p>0.021 490.663</p> <p>0.02125 490.731</p> <p>0.0215 490.8</p> <p>0.02175 490.869</p> <p>0.022 490.94</p> <p>0.02225 491.011</p> <p>0.0225 491.083</p> <p>0.02275 491.155</p> <p>0.023 491.228</p> <p>0.02325 491.301</p> <p>0.0235 491.374</p> <p>0.02375 491.448</p> <p>0.024 491.521</p> <p>0.02425 491.595</p> <p>0.0245 491.668</p> <p>0.02475 491.741</p> <p>0.025 491.813</p> <p>0.02525 491.885</p> <p>0.0255 491.955</p> <p>0.02575 492.025</p> <p>0.026 492.094</p> <p>0.02625 492.161</p> <p>0.0265 492.228</p> <p>0.02675 492.292</p> <p>0.027 492.355</p> <p>0.02725 492.415</p> <p>0.0275 492.474</p> <p>0.02775 492.529</p> <p>0.028 492.583</p> <p>0.02825 492.633</p> <p>0.0285 492.68</p> <p>0.02875 492.724</p> <p>0.029 492.764</p> <p>0.02925 492.801</p> <p>0.0295 492.832</p> <p>0.02975 492.863</p> <p>0.03 492.881</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Process intensification in a chemical reactor with enhanced heat exchange
<p><strong>Process intensification in a chemical reactor with enhanced heat exchange</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>A reactor, in chemical engineering, is a device or vessel within which chemical processes are carried out for experimental or manufacturing purposes. Reactors range in size and complexity from small, open kettles fitted with simple stirrers and heaters to large, elaborate vessels equipped with jackets or internal coils for heating or cooling, nozzles or ports for adding and removing materials, sources of ultraviolet radiation or electrical energy, specially designed agitators or scrapers, and strong walls and tight seals to permit operation at high or low pressures. In many cases, these reactors are provided with instruments that measure temperature, pressure, pH, or other properties of the contents. They are constructed of materials selected for strength and resistance to attack by the substances being processed. For certain processes that are particularly adaptable to continuous operation, reactors are devised so that the chemical reaction may continue for weeks or months while starting materials are introduced continuously or intermittently and the finished product is removed in a steady stream or in periodic discharges. Scale of temperature is a methodology of calibrating the physical quantity temperature in metrology. Empirical scales measure temperature in relation to convenient and stable parameters, such as the freezing and boiling point of water. Absolute temperature is based on thermodynamic principles: using the lowest possible temperature as the zero point, and selecting a convenient incremental unit.</p> <p>Streamwise distance (millimeters), Wall centerline temperature (degrees kelvin)</p> <p>0 498.195</p> <p>0.00025 498.22</p> <p>0.0005 498.261</p> <p>0.00075 498.266</p> <p>0.001 498.214</p> <p>0.00125 498.105</p> <p>0.0015 497.946</p> <p>0.00175 497.745</p> <p>0.002 497.508</p> <p>0.00225 497.244</p> <p>0.0025 496.958</p> <p>0.00275 496.655</p> <p>0.003 496.34</p> <p>0.00325 496.017</p> <p>0.0035 495.69</p> <p>0.00375 495.362</p> <p>0.004 495.035</p> <p>0.00425 494.711</p> <p>0.0045 494.392</p> <p>0.00475 494.081</p> <p>0.005 493.776</p> <p>0.00525 493.48</p> <p>0.0055 493.194</p> <p>0.00575 492.917</p> <p>0.006 492.651</p> <p>0.00625 492.395</p> <p>0.0065 492.151</p> <p>0.00675 491.917</p> <p>0.007 491.694</p> <p>0.00725 491.483</p> <p>0.0075 491.283</p> <p>0.00775 491.094</p> <p>0.008 490.917</p> <p>0.00825 490.751</p> <p>0.0085 490.596</p> <p>0.00875 490.451</p> <p>0.009 490.318</p> <p>0.00925 490.194</p> <p>0.0095 490.081</p> <p>0.00975 489.978</p> <p>0.01 489.883</p> <p>0.01025 489.798</p> <p>0.0105 489.721</p> <p>0.01075 489.652</p> <p>0.011 489.591</p> <p>0.01125 489.538</p> <p>0.0115 489.492</p> <p>0.01175 489.453</p> <p>0.012 489.421</p> <p>0.01225 489.395</p> <p>0.0125 489.374</p> <p>0.01275 489.36</p> <p>0.013 489.35</p> <p>0.01325 489.346</p> <p>0.0135 489.347</p> <p>0.01375 489.353</p> <p>0.014 489.362</p> <p>0.01425 489.376</p> <p>0.0145 489.394</p> <p>0.01475 489.415</p> <p>0.015 489.44</p> <p>0.01525 489.469</p> <p>0.0155 489.5</p> <p>0.01575 489.534</p> <p>0.016 489.571</p> <p>0.01625 489.612</p> <p>0.0165 489.655</p> <p>0.01675 489.7</p> <p>0.017 489.748</p> <p>0.01725 489.798</p> <p>0.0175 489.85</p> <p>0.01775 489.904</p> <p>0.018 489.96</p> <p>0.01825 490.017</p> <p>0.0185 490.076</p> <p>0.01875 490.138</p> <p>0.019 490.2</p> <p>0.01925 490.264</p> <p>0.0195 490.33</p> <p>0.01975 490.396</p> <p>0.02 490.464</p> <p>0.02025 490.533</p> <p>0.0205 490.603</p> <p>0.02075 490.674</p> <p>0.021 490.745</p> <p>0.02125 490.818</p> <p>0.0215 490.891</p> <p>0.02175 490.964</p> <p>0.022 491.038</p> <p>0.02225 491.112</p> <p>0.0225 491.187</p> <p>0.02275 491.262</p> <p>0.023 491.336</p> <p>0.02325 491.411</p> <p>0.0235 491.485</p> <p>0.02375 491.56</p> <p>0.024 491.633</p> <p>0.02425 491.706</p> <p>0.0245 491.778</p> <p>0.02475 491.85</p> <p>0.025 491.92</p> <p>0.02525 491.988</p> <p>0.0255 492.056</p> <p>0.02575 492.122</p> <p>0.026 492.187</p> <p>0.02625 492.249</p> <p>0.0265 492.309</p> <p>0.02675 492.367</p> <p>0.027 492.422</p> <p>0.02725 492.474</p> <p>0.0275 492.524</p> <p>0.02775 492.57</p> <p>0.028 492.612</p> <p>0.02825 492.651</p> <p>0.0285 492.686</p> <p>0.02875 492.717</p> <p>0.029 492.742</p> <p>0.02925 492.764</p> <p>0.0295 492.779</p> <p>0.02975 492.789</p> <p>0.03 492.793</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Data from: Influence of agricultural intensification on pollinator pesticide exposure, food acquisition and diversity
<p>Pollinators are essential for maintaining sustainable crop production, while the decline of pollinators is a widespread concern. Agricultural intensification is one of the primary drivers of the decline of insect pollinators. Agricultural intensification usually involves a decreasing of non-crop semi-natural habitat and an increasing of pesticide exposure for pollinators. However, causal links between agricultural intensification, increased pesticide exposure, and reduced pollinator's food sources and pollinator diversity remain underexplored.</p> <p>We assessed pollinator diversity across a landscape gradient where the proportion of rice ranged from 11% to 85% in South China. We placed honeybee (<em>Apis mellifera</em>) and mason bee (<em>Osmia excavata</em>) in these landscapes and investigated pesticide exposure in honeybee foragers and pollen, and in mason bee pollen and nesting materials. We also assessed the acquisition of food by mason bees.</p> <p>We found a higher frequency of pesticide detection in honeybee foragers and honeybee pollen samples in areas with a higher proportion of rice fields. There was a strong positive relationship between mason bee food acquisition and the proportion of semi-natural habitats, while no significant effects of pesticide exposure on pollinator diversity were found in addition to the effect of semi-natural habitat.</p> <p><em>Synthesis and applications</em>: Our results suggest that pollinator communities could be at an increased risk of pesticide exposure due to intensified agriculture, while the negative impact on pollinator diversity mainly results from the loss of habitat and/or reduced food sources. This study highlights the importance of conserving semi-natural habitat to mitigate the causes of decline in pollinator diversity. We also recommend long-term, multi-year studies to further understand the mechanisms behind the loss of pollinators in farming landscapes.</p>
Part 2 of real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"
<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season. Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible. Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper. Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties. The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors). These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
Real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"
<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season. Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible. Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper. Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties. The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors). These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
Agricultural intensification erodes taxonomic and functional diversity in Mediterranean olive groves by filtering out rare species
<p><span>1. Agri-Environmental Schemes (AES) have been proposed to mitigate the impact of agriculture on both taxonomic and functional biodiversity. However, a better knowledge of the mechanisms involved in the loss of agrobiodiversity is needed to implement efficient AES. An unbalanced effort on research towards arable lands compared to permanent crops, and on fauna relative to plants, is patent, which limits the generalization of AES effectiveness. </span></p> <p><span>2. We evaluated the effects of agricultural management and landscape simplification on taxonomic and functional diversity of the ground herb cover of 40 olive groves. We use a recently developed approach based on Hill numbers (rare, common and dominant species based) to analyze taxonomic and functional dissimilarity between farms with contrasting agricultural practices, and its potential attenuation by landscape complexity. We further explore the filtering effect of agricultural intensification on functional traits, and the relationship between functional and species richness across landscapes.</span></p> <p><span>3. We found that taxonomic and functional dissimilarity of herb assemblages between intensively and low-intensively managed fields was mainly due to rare species. Dissimilarity decreased as landscape complexity increased, evidencing that complex landscapes attenuate the impact of agriculture intensification on herb assemblage composition. Agricultural intensification favoured more functionally homogeneous assemblages and disfavoured the herbs pollinated by insects, while it did not seem to affect wind-pollinated species. </span></p> <p><span>4. Overall, functional richness increased exponentially with species richness across landscapes, but the latter was insufficient to drive any clear enhancement in functional richness in simple landscapes. In contrast, high species richness accelerated the enhancement in functional richness in intermediate and complex landscapes. These results highlight the functional filtering that intensive agriculture has generated for decades in homogeneous olive-dominated landscapes. </span></p> <p><span>5. Herb cover is essential to support the fauna of permanent croplands and their sustainable production. Hence, AES in these croplands should promote management practices favouring the diversity and functionality of herb assemblages. Such AES should be particularly prioritized in homogeneous landscapes, where ground herb cover composition and function has long been homogenized to a great extent. </span></p>
Hydrogen production in microchannel reactors by process intensification
<p><strong>Hydrogen production in microchannel reactors by process intensification</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>In reactions under normal laboratory conditions, matter is neither created nor destroyed, and elements are not transformed into other elements. Therefore, equations depicting reactions must be balanced; that is, the same number of atoms of each kind must appear on opposite sides of the equation. The balanced equation for the iron-sulfur reaction shows that one iron atom can react with one sulfur atom to give one formula unit of iron sulfide. Chemists ordinarily work with weighable quantities of elements and compounds. For example, in the iron-sulfur equation the symbol Fe represents 55.845 grams of iron, S represents 32.066 grams of sulfur, and FeS represents 87.911 grams of iron sulfide. Because matter is not created or destroyed in a chemical reaction, the total mass of reactants is the same as the total mass of products. If some other amount of iron is used, say, one-tenth as much (5.585 grams), only one-tenth as much sulfur can be consumed (3.207 grams), and only one-tenth as much iron sulfide is produced (8.791 grams). If 32.066 grams of sulfur were initially present with 5.585 grams of iron, then 28.859 grams of sulfur would be left over when the reaction was complete. The ratio of reactants and products in a chemical reaction is called chemical stoichiometry. Stoichiometry depends on the fact that matter is conserved in chemical processes, and calculations giving mass relationships are based on the concept of the mole. One mole of any element or compound contains the same number of atoms or molecules, respectively, as one mole of any other element or compound. Energy plays a key role in chemical processes. According to the modern view of chemical reactions, bonds between atoms in the reactants must be broken, and the atoms or pieces of molecules are reassembled into products by forming new bonds. Energy is absorbed to break bonds, and energy is evolved as bonds are made. In some reactions the energy required to break bonds is larger than the energy evolved on making new bonds, and the net result is the absorption of energy. Such a reaction is said to be endothermic if the energy is in the form of heat. The opposite of endothermic is exothermic; in an exothermic reaction, energy as heat is evolved. The more general terms exoergic (energy evolved) and endoergic (energy required) are used when forms of energy other than heat are involved. A great many common reactions are exothermic. The formation of compounds from the constituent elements is almost always exothermic. Formation of water from molecular hydrogen and oxygen and the formation of a metal oxide such as calcium oxide from calcium metal and oxygen gas are examples. Among widely recognizable exothermic reactions is the combustion of fuels. Not all reactions are exothermic (or exoergic). A few compounds, such as nitric oxide and hydrazine, require energy input when they are formed from the elements. The decomposition of limestone to make lime is also an endothermic process; it is necessary to heat limestone to a high temperature for this reaction to occur. The decomposition of water into its elements by the process of electrolysis is another endoergic process. Electrical energy is used rather than heat energy to carry out this reaction. Generally, evolution of heat in a reaction favours the conversion of reactants to products. However, entropy is important in determining the favourability of a reaction. Entropy is a measure of the number of ways in which energy can be distributed in any system. Entropy accounts for the fact that not all energy available in a process can be manipulated to do work. A chemical reaction will favour the formation of products if the sum of the changes in entropy for the reaction system and its surroundings is positive. An example is burning methane. Methane has a low entropy. When methane burns, it produces ash as well as the high-entropy substances carbon dioxide gas and water vapour. The entropy of the reacting system increases during combustion. Just as important, the heat energy transferred by the combustion to its surroundings increases the entropy in the surroundings. The total of entropy changes for the substances in the reaction and the surroundings is positive, and the reaction is product-favoured.</p> <p>Streamwise distance (meter), Heterogeneous reaction rate along the length of the reactor (mole per square meter per second)</p> <p>0 14.1419</p> <p>0.00025 14.1567</p> <p>0.0005 14.1167</p> <p>0.00075 13.9733</p> <p>0.001 13.7817</p> <p>0.00125 13.5773</p> <p>0.0015 13.3834</p> <p>0.00175 13.2078</p> <p>0.002 13.0467</p> <p>0.00225 12.8884</p> <p>0.0025 12.7233</p> <p>0.00275 12.549</p> <p>0.003 12.3695</p> <p>0.00325 12.193</p> <p>0.0035 12.0273</p> <p>0.00375 11.8751</p> <p>0.004 11.7343</p> <p>0.00425 11.5992</p> <p>0.0045 11.4645</p> <p>0.00475 11.3284</p> <p>0.005 11.1919</p> <p>0.00525 11.0595</p> <p>0.0055 10.9352</p> <p>0.00575 10.8199</p> <p>0.006 10.7123</p> <p>0.00625 10.6092</p> <p>0.0065 10.5073</p> <p>0.00675 10.4052</p> <p>0.007 10.3035</p> <p>0.00725 10.2049</p> <p>0.0075 10.1114</p> <p>0.00775 10.0239</p> <p>0.008 9.94127</p> <p>0.00825 9.86107</p> <p>0.0085 9.78104</p> <p>0.00875 9.70044</p> <p>0.009 9.61993</p> <p>0.00925 9.54112</p> <p>0.0095 9.46564</p> <p>0.00975 9.39386</p> <p>0.01 9.32487</p> <p>0.01025 9.25686</p> <p>0.0105 9.1882</p> <p>0.01075 9.11849</p> <p>0.011 9.04832</p> <p>0.01125 8.97901</p> <p>0.0115 8.91153</p> <p>0.01175 8.84618</p> <p>0.012 8.78242</p> <p>0.01225 8.71871</p> <p>0.0125 8.65378</p> <p>0.01275 8.58747</p> <p>0.013 8.5204</p> <p>0.01325 8.45357</p> <p>0.0135 8.38782</p> <p>0.01375 8.3233</p> <p>0.014 8.25958</p> <p>0.01425 8.19529</p> <p>0.0145 8.12949</p> <p>0.01475 8.06216</p> <p>0.015 7.99387</p> <p>0.01525 7.92554</p> <p>0.0155 7.8578</p> <p>0.01575 7.79065</p> <p>0.016 7.72352</p> <p>0.01625 7.65551</p> <p>0.0165 7.58549</p> <p>0.01675 7.51373</p> <p>0.017 7.44118</p> <p>0.01725 7.36834</p> <p>0.0175 7.29539</p> <p>0.01775 7.22242</p> <p>0.018 7.1493</p> <p>0.01825 7.07505</p> <p>0.0185 6.999</p> <p>0.01875 6.92134</p> <p>0.019 6.84253</p> <p>0.01925 6.76289</p> <p>0.0195 6.683</p> <p>0.01975 6.60311</p> <p>0.02 6.5227</p> <p>0.02025 6.44094</p> <p>0.0205 6.35734</p> <p>0.02075 6.27217</p> <p>0.021 6.18588</p> <p>0.02125 6.09878</p> <p>0.0215 6.01121</p> <p>0.02175 5.92324</p> <p>0.022 5.8344</p> <p>0.02225 5.74426</p> <p>0.0225 5.65277</p> <p>0.02275 5.56008</p> <p>0.023 5.4663</p> <p>0.02325 5.37179</p> <p>0.0235 5.27682</p> <p>0.02375 5.18138</p> <p>0.024 5.08523</p> <p>0.02425 4.988</p> <p>0.0245 4.88969</p> <p>0.02475 4.79046</p> <p>0.025 4.69054</p> <p>0.02525 4.59019</p> <p>0.0255 4.48973</p> <p>0.02575 4.38924</p> <p>0.026 4.28848</p> <p>0.02625 4.18738</p> <p>0.0265 4.08615</p> <p>0.02675 3.98492</p> <p>0.027 3.88396</p> <p>0.02725 3.78374</p> <p>0.0275 3.68429</p> <p>0.02775 3.58597</p> <p>0.028 3.48836</p> <p>0.02825 3.38251</p> <p>0.0285 3.28598</p> <p>0.02875 3.18522</p> <p>0.029 3.08588</p> <p>0.02925 2.98803</p> <p>0.0295 2.89361</p> <p>0.02975 2.794388</p> <p>0.03 2.694288</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Data for: Simulating effects of agricultural intensification and climate change: Nitrogen fertilization and drought stress decrease insect herbivore performance
<p>Biodiversity is globally under pressure, and the current decline in insect biomass and diversity is likely caused by human activities. Key drivers of biodiversity loss include agricultural intensification and anthropogenic climate change. Nevertheless, a thorough understanding of potential interactions between both factors and the mechanisms underlying insect declines in general is still lacking.</p> <p>Here, we investigate the combined effects of nitrogen fertilization and drought, as applied to host plants, on the preference and performance of the butterfly <em>Lycaena tityrus</em>.</p> <p>Individuals performed best on plants having received medium nitrogen levels, while performance was reduced by either a lack of or strong fertilization, the former potentially caused by nitrogen limitation and the latter by increased concentrations of toxic allelochemicals. Female oviposition preference though was positively related to nitrogen fertilization, resulting in a mismatch between preference and offspring performance at high nitrogen levels. Plant drought stress additionally reduced herbivore performance, and females appeared to suffer more from low-quality food than males.</p> <p>Our results indicate that increasing nitrogen fertilization, as applied in intensive agriculture, may substantially reduce host-plant quality for insect herbivores, which may be exaggerated in the course of climate change due to the more frequent occurrence of droughts. Our study thus contributes to a better understanding of the mechanisms underlying human-driven insect declines in agricultural landscapes and beyond.</p>
Data from: The fate of nitrogen during agricultural intensification in East Africa: nitrogen budgets in contrasting agroecosystems
<p>The intensification of agricultural systems in sub-Saharan Africa (SSA) is necessary to reduce poverty and improve food security but requires increased nutrient applications to smallholder systems. To avoid the negative consequences of intensification that result from many large-scale agricultural systems (e.g., eutrophication), we must better understand how diverse soil systems in SSA will respond to increased fertilizer applications. We tracked nitrogen (N) inputs and outputs in fertilizer trials at two maize (Zea mays)-agroecological regions of contrasting soil type. We measured maize biomass, grain yields, N leaching, and N gaseous losses from a clayey soil in Yala, Kenya, and a sandy soil in Tumbi, Tanzania, with application rates of 0, 50, 75, 100, 150, and 200 kg N ha<sup>-1</sup> yr<sup>‑1</sup> over multiple years. Using measurements of NO, N<sub>2</sub>O, NO<sub>3</sub><sup>-</sup>, biomass N, and an <sup>15</sup>N enrichment experiment, we show that N budgets in Yala were nearly always negative, meaning more N was exported in yields or lost from the system than was added in fertilizer. In Tumbi, however, N budgets were negative at lower fertilizer levels (0 and 50 kg N ha<sup>-1</sup>), but positive at higher fertilizer levels (75 and 200 kg N ha<sup>-1</sup>). At both sites, most of the N was lost through maize biomass/grain removal and N leaching (over 96% of total losses). Gas losses were a minor component of N budgets. These results highlight the importance of tailoring fertilizer recommendations to a farm's specific soil and climatic conditions. However, on these two contrasting sites, fertilizer additions at or below 50 kg N ha<sup>-1</sup> do not lead to major losses of N (via gaseous or leaching) and may be recommended at a range of sites across SSA soils in maize agroecosystems.</p>
Pac_TIW_Intensification
<p>The repository provides Matlab codes (.m) and data (.mat) for a revised (Review round1) manuscript, "Intensification of Pacific Tropical Instability Waves over the recent three decades". They includes codes and data for each figures (5 main and 9 extended data figures, and 3 figures in Reply to Review), and also a diagnostic model for equatorial surface currents.</p>
Interferon Alpha 2b Intensification in HIV-Positive Individuals on Antiretroviral Therapy
ClinicalTrials.gov study NCT01295515. IPD Sharing: NO. Countries: 1. Publications: 4.
Preservation and Expansion of T-cell Subsets Following HAART De-intensification to Atazanavir/Ritonavir (ATV/r)
ClinicalTrials.gov study NCT00491556. IPD Sharing: Not stated. Countries: 2. Publications: 1.
ScienceDex guides
Understand access before you commit
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.