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299 results for “water analysis”
Table 1 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 1:</b> Production of Red Snapper (<i>Lutjanus sp</i>) in Fishing Equipment Uni</p><table><tbody><tr><th><b>No</b></th><th><b>Months</b></th><th><b>Fishing Equipmen Hand Line Long Line</b></th><th><b>Production quantity (Kg)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>96.4</td><td>78.3</td><td>174.7</td></tr><tr><th>2</th><td>Februari</td><td>98.2</td><td>57.4</td><td>155.6</td></tr><tr><th>3</th><td>Maret</td><td>112.1</td><td>102.4</td><td>214.5</td></tr><tr><th>4</th><td>April</td><td>195.2</td><td>144.5</td><td>339.7</td></tr><tr><th>5</th><td>Mei</td><td>135.2</td><td>118.7</td><td>253.9</td></tr><tr><th>6</th><td>Juni</td><td>174.2</td><td>154.2</td><td>328.4</td></tr><tr><th>7</th><td>Juli</td><td>213.3</td><td>215.4</td><td>428.7</td></tr><tr><th>8</th><td>Agustus</td><td>325.4</td><td>222.1</td><td>547.5</td></tr><tr><th>9</th><td>September</td><td>221.1</td><td>155.9</td><td>377</td></tr><tr><th>10</th><td>Oktober</td><td>154.2</td><td>124.2</td><td>278.4</td></tr><tr><th>11</th><td>November</td><td>89.3</td><td>130</td><td>219.3</td></tr><tr><th>12</th><td>Desember</td><td>89.5</td><td>55.4</td><td>144.9</td></tr><tr><th>TOTAL</th><td>1904.1</td><td>1558.5</td><td>3462.6</td></tr></tbody></table><p>t</p>
Table 2 in Analysis of catch results per effort of catching red snapper (Lutjanus sp) in the waters of Lewalu village, Northwest Alor, Alor Regency
<p><b>Table 2:</b> CPUE of Red Snapper</p><table><tbody><tr><th><b>No Month</b></th><th><b>Production (kg)</b></th><th><b>Standar Effort (Trip)</b></th><th><b>Standar CPUE (Kg/Trip)</b></th></tr></tbody><tbody><tr><th>1</th><td>Januari</td><td>174.7</td><td>24</td><td>7.279</td></tr><tr><th>2</th><td>Februari</td><td>155.6</td><td>24</td><td>6.483</td></tr><tr><th>3</th><td>Maret</td><td>214.5</td><td>27</td><td>7.944</td></tr><tr><th>4</th><td>April</td><td>339.7</td><td>30</td><td>11.323</td></tr><tr><th>5</th><td>Mei</td><td>253.9</td><td>27</td><td>9.404</td></tr><tr><th>6</th><td>Juni</td><td>328.4</td><td>27</td><td>12.163</td></tr><tr><th>7</th><td>Juli</td><td>428.7</td><td>27</td><td>15.878</td></tr><tr><th>8</th><td>Agustus</td><td>547.5</td><td>21</td><td>26.071</td></tr><tr><th>9</th><td>September</td><td>377</td><td>23</td><td>16.391</td></tr><tr><th>10</th><td>Oktober</td><td>278.4</td><td>26</td><td>10.708</td></tr><tr><th>11</th><td>November</td><td>219.3</td><td>25</td><td>8.772</td></tr><tr><th>12</th><td>Desember</td><td>144.9</td><td>26</td><td>5.573</td></tr><tr><th></th><td>Amount</td><td>3462.6</td><td>307</td><td>137.990</td></tr><tr><th></th><td>Average</td><td>288.550</td><td>26</td><td>11.499</td></tr></tbody></table>
Table II in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
<p>Table II. – AfriBasin size, species and endemics per AfriBasin and different proxies of sampling effort based on 42022 georeferenced records from the RMCA fish collection and GBIF.</p><table><tbody><tr><th>Subbasin</th><th>Size (km 2)</th><th>Species</th><th>Species / 1000 km 2</th><th>Endemics</th><th>Endemics / 1000 km 2</th><th>Records</th><th>Records / 1000 km 2</th><th>Sampling localities</th><th>Sampling loc. / 1000 km 2</th><th>Sampling days</th><th>Sampling days / 1000 km 2</th><th>Expeditions</th><th>Expeditions / 1000 km 2</th></tr></tbody><tbody><tr><th>Middle Congo</th><td>75000</td><td>344</td><td>4.59</td><td>3</td><td>0.04</td><td>3622</td><td>48.29</td><td>172</td><td>2.29</td><td>495</td><td>6.60</td><td>154</td><td>2.05</td></tr><tr><th>Kasai</th><td>290000</td><td>259</td><td>0.89</td><td>23</td><td>0.08</td><td>1630</td><td>5.62</td><td>121</td><td>0.42</td><td>239</td><td>0.82</td><td>138</td><td>0.48</td></tr><tr><th>Pool Malebo</th><td>9500</td><td>254</td><td>26.74</td><td>10</td><td>1.05</td><td>4455</td><td>468.95</td><td>101</td><td>10.63</td><td>518</td><td>54.53</td><td>200</td><td>21.05</td></tr><tr><th>Ubangi</th><td>240000</td><td>248</td><td>1.03</td><td>19</td><td>0.08</td><td>1705</td><td>7.10</td><td>119</td><td>0.50</td><td>164</td><td>0.68</td><td>61</td><td>0.25</td></tr><tr><th>Lower Congo</th><td>50000</td><td>248</td><td>4.96</td><td>53</td><td>1.06</td><td>2233</td><td>44.66</td><td>264</td><td>5.28</td><td>366</td><td>7.32</td><td>131</td><td>2.62</td></tr><tr><th>Lualaba</th><td>323000</td><td>247</td><td>0.76</td><td>18</td><td>0.06</td><td>4354</td><td>13.48</td><td>388</td><td>1.20</td><td>645</td><td>2.00</td><td>226</td><td>0.70</td></tr><tr><th>Aruwimi</th><td>127500</td><td>233</td><td>1.83</td><td>12</td><td>0.09</td><td>1455</td><td>11.41</td><td>150</td><td>1.18</td><td>213</td><td>1.67</td><td>69</td><td>0.54</td></tr><tr><th>Ruki</th><td>177000</td><td>231</td><td>1.31</td><td>9</td><td>0.05</td><td>2211</td><td>12.49</td><td>81</td><td>0.46</td><td>328</td><td>1.85</td><td>103</td><td>0.58</td></tr><tr><th>Itimbiri</th><td>55000</td><td>228</td><td>4.15</td><td>4</td><td>0.07</td><td>1548</td><td>28.15</td><td>26</td><td>0.47</td><td>108</td><td>1.96</td><td>50</td><td>0.91</td></tr><tr><th>Upper Lualaba</th><td>144000</td><td>219</td><td>1.52</td><td>28</td><td>0.19</td><td>3449</td><td>23.95</td><td>371</td><td>2.58</td><td>644</td><td>4.47</td><td>160</td><td>1.11</td></tr><tr><th>Sangha</th><td>180000</td><td>215</td><td>1.19</td><td>8</td><td>0.04</td><td>2012</td><td>11.18</td><td>181</td><td>1.01</td><td>195</td><td>1.08</td><td>72</td><td>0.40</td></tr><tr><th>Uélé</th><td>122000</td><td>195</td><td>1.60</td><td>7</td><td>0.06</td><td>1104</td><td>9.05</td><td>85</td><td>0.70</td><td>184</td><td>1.51</td><td>83</td><td>0.68</td></tr><tr><th>Lindi-Tshopo</th><td>54000</td><td>182</td><td>3.37</td><td>1</td><td>0.02</td><td>931</td><td>17.24</td><td>86</td><td>1.59</td><td>128</td><td>2.37</td><td>47</td><td>0.87</td></tr><tr><th>Lomami</th><td>74000</td><td>181</td><td>2.45</td><td>1</td><td>0.01</td><td>738</td><td>9.97</td><td>151</td><td>2.04</td><td>161</td><td>2.18</td><td>44</td><td>0.59</td></tr><tr><th>Marine Lower Congo</th><td>10000</td><td>164</td><td>16.40</td><td>8</td><td>0.80</td><td>1421</td><td>142.10</td><td>104</td><td>10.40</td><td>192</td><td>19.20</td><td>123</td><td>12.30</td></tr><tr><th>Mweru</th><td>94000</td><td>163</td><td>1.73</td><td>29</td><td>0.31</td><td>3376</td><td>35.91</td><td>327</td><td>3.48</td><td>569</td><td>6.05</td><td>155</td><td>1.65</td></tr><tr><th>Léfini-Likouala</th><td>110000</td><td>140</td><td>1.27</td><td>10</td><td>0.09</td><td>2278</td><td>20.71</td><td>128</td><td>1.16</td><td>195</td><td>1.77</td><td>50</td><td>0.45</td></tr><tr><th>Sankuru</th><td>130000</td><td>119</td><td>0.92</td><td>10</td><td>0.08</td><td>659</td><td>5.07</td><td>33</td><td>0.25</td><td>80</td><td>0.62</td><td>34</td><td>0.26</td></tr><tr><th>Tumba</th><td>6500</td><td>110</td><td>16.92</td><td>3</td><td>0.46</td><td>357</td><td>54.92</td><td>12</td><td>1.85</td><td>67</td><td>10.31</td><td>23</td><td>3.54</td></tr><tr><th>Bangweulu</th><td>110000</td><td>103</td><td>0.94</td><td>1</td><td>0.01</td><td>1325</td><td>12.05</td><td>172</td><td>1.56</td><td>190</td><td>1.73</td><td>70</td><td>0.64</td></tr><tr><th>Kwilu</th><td>85000</td><td>86</td><td>1.01</td><td>5</td><td>0.06</td><td>130</td><td>1.53</td><td>19</td><td>0.22</td><td>34</td><td>0.40</td><td>26</td><td>0.31</td></tr><tr><th>Kwango</th><td>195000</td><td>84</td><td>0.43</td><td>10</td><td>0.05</td><td>299</td><td>1.53</td><td>24</td><td>0.12</td><td>33</td><td>0.17</td><td>18</td><td>0.09</td></tr><tr><th>Mai-Ndombe</th><td>40000</td><td>72</td><td>1.80</td><td>8</td><td>0.20</td><td>89</td><td>2.23</td><td>27</td><td>0.68</td><td>16</td><td>0.40</td><td>25</td><td>0.63</td></tr><tr><th>Lukenie</th><td>76500</td><td>51</td><td>0.67</td><td>2</td><td>0.03</td><td>275</td><td>3.59</td><td>18</td><td>0.24</td><td>32</td><td>0.42</td><td>21</td><td>0.27</td></tr><tr><th>Mongala</th><td>45000</td><td>41</td><td>0.91</td><td>0</td><td>0</td><td>93</td><td>2.07</td><td>9</td><td>0.20</td><td>17</td><td>0.38</td><td>14</td><td>0.31</td></tr><tr><th>Lulonga</th><td>67000</td><td>20</td><td>0.30</td><td>0</td><td>0</td><td>80</td><td>1.19</td><td>13</td><td>0.19</td><td>17</td><td>0.25</td><td>12</td><td>0.18</td></tr><tr><th>Kotto</th><td>71000</td><td>19</td><td>0.27</td><td>1</td><td>0.01</td><td>126</td><td>1.77</td><td>15</td><td>0.21</td><td>12</td><td>0.17</td><td>16</td><td>0.23</td></tr><tr><th>Bomu</th><td>130000</td><td>14</td><td>0.11</td><td>1</td><td>0.01</td><td>67</td><td>0.52</td><td>11</td><td>0.08</td><td>14</td><td>0.11</td><td>9</td><td>0.07</td></tr></tbody></table>
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
Dataset of paper "Kinetic and mechanistic analysis of membrane fouling in microplastics removal from water by dead-end microfiltration"
<p>Dataset of paper "Kinetic and mechanistic analysis of membrane fouling in microplastics removal from water by dead-end microfiltration":</p> <ul> <li>Microplastics and membrane characterisation: micro-FTIR and ATR spectra.</li> <li>Particle Size Distribution.</li> <li>Identifying the main successive fouling mechanisms using Hermia’s equation.</li> <li>The kinetic constants as a function of the microplastic type and the operating parameters.</li> <li>Permeate flux decline during microplastics filtration.</li> <li>Water contact angle and profilometry measured before and after the filtration of MPs.</li> </ul>
Calcium fortification of water during pregnancy for the prevention of preeclampsia in a low-income setting: a cost-effectiveness analysis
<p>This database contains the model and the parameters of the study. It's is important to highlight that this is the first version of the model, and thus modifications are forthcoming. With the current database, the abstract is the following:</p> <p>Fortification of water with calcium during pregnancy is proposed as a suitable and promising intervention to increase calcium intake and prevent preeclampsia and eclampsia (PE/E). However, the evidence of the cost-effectiveness of this type of intervention is scarce. We conducted a model-based cost-effectiveness analysis to estimate the incremental cost-effectiveness ratio (ICER) of calcium pills and bottled water fortified with calcium, compared to the standard of care (defined as the use of MgSO4 as the standard treatment for PE/E), in the context of Nepal. Outcome measures were years of life gained (YLG) by mothers and newborns and healthcare costs. We considered a lifetime time horizon and health benefits were discounted at 3%. The cost-effectiveness threshold was set at the 2019 Nepal gross domestic product per capita (USD 1071). For calcium pills versus standard of care, ICER was USD 63 per YLG. By switching from calcium pills to bottled water fortified with calcium, the resulting ICER was USD 1702 per YLG. In conclusion, the bottle of water fortified with calcium was found not cost-effective at the defined threshold for Nepal. Complementary economic evidence adapted to other low-and middle income contexts is required to guide novel preventive interventions to improve maternal and child health based on the efficiency principle</p>
Numerical Output for Analysis of Atlantic Water Pathways in Fram Strait
<p>This dataset contains data for the publication: Shifts of the Recirculation Pathways in central Fram Strait drive Atlantic Intermediate Water Variability on Northeast Greenland shelf (McPherson et al., JGR Oceans).</p>
Quantum chemical investigation of the predominant conformation of the antibiotic azithromycin in water and DMSO solutions: an integrated thermodynamic and NMR analysis
<p><span>Azithromycin (AZM) is a macrolide-type antibiotic used to prevent and treat serious infection</span><span>s (mycobacteria or MAC) that significantly inhibit bacterial growth. Knowledge of the predominant conformation in solution is of fundamental importance for advancing our understanding of the intermolecular interactions of AZM with biological targets. We report an extensive density functional theory (DFT) study of plausible AZM structures in solution considering implicit and explicit solvent effects. The best match between the experimental and theoretical nuclear magnetic resonance (NMR) profiles was used to assign the preferred conformer in solution, which was supported by the thermodynamic analysis. Among the 15 distinct AZM structures, conformer M14, having a short intramolecular C6-OH…N H-bond, is predicted to be dominant in water and DMSO solutions. The results indicated that the X-ray structure backbone is mostly conserved in solution, showing that large flexible molecules with several possible conformations may assume a preferential spatial orientation in solution, which is the molecular structure that ultimately interacts with biological targets.</span></p>
Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis
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Balancing water yield and water use efficiency between planted and natural forests: A global analysis
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Quantum chemical investigation of the predominant conformation of the antibiotic azithromycin in water and DMSO solutions: an integrated thermodynamic and NMR analysis
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Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
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Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
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Systematic review and meta-analysis: water type and temperature affect environmental DNA decay metadata
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‘Sowing and harvesting water’: revisiting forest restoration in the Peruvian Andes through a multi-stakeholder analysis
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Deep eutectic solvent-based emulsification liquid-liquid microextraction coupled with HPLC-UV for the analysis of phenoxy acid herbicides in paddy field water samples
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FIGURES 6A–C in Taxonomy of Venezuelan water beetles in the genus Hydrochus Leach, 1817, and an analysis of male genitalia morphology (Coleoptera: Hydrochidae)
FIGURES 6A–C. (A) Male genitalia of H. ducalis Knisch, with parameres spread, showing shape of aedeagus; (B) Male genitalia of H. variabilis Knisch and (C) H. n. sp. from Peru, showing the articulation point of the paramere dorsal basal lobe with the aedeagus dorsal basal lobe.
FIGURE 5 in Taxonomy of Venezuelan water beetles in the genus Hydrochus Leach, 1817, and an analysis of male genitalia morphology (Coleoptera: Hydrochidae)
FIGURE 5. Examples of morphological, species specific, variation in the form of the aedeagus basal dorsal lobe (adbl) of Venezuelan Hydrochus species.
FIGURES 4A–B in Taxonomy of Venezuelan water beetles in the genus Hydrochus Leach, 1817, and an analysis of male genitalia morphology (Coleoptera: Hydrochidae)
FIGURES 4A–B. Male genitalia characters of (A) H. pseudosecretus Oliva (ventral); (B) H. sagittarius n. sp. (ventral; diagrammatical, showing locations of muscles).
Analysis of well water level response to atmospheric loading from low- to high-frequency band
<p>Data used in manuscript "Analysis of well water level response to atmospheric loading from low- to high-frequency band"</p>
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Allen Brain Atlas
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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
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