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102 results for “hops”
Dataset: An Empirical Analysis of Pool Hopping Behavior in the Bitcoin Blockchain
<p>We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale. </p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Tapping to hip-hop: Effects of cognitive load, arousal, and musical meter on time experiences
<p>This upload contains the data set for the study published in Attention, Perception and Psychophysics.</p>
Figure 1. A in Aleyrodid (Hemiptera) diversity in hop varieties grown in Brazil
Figure 1. A - abaxial face of Humulus lupulus leaf (Chinook variety) infested by Aleurodicus pulvinatus adults on 9/27th/2019; B - abaxial face of H. lupulus leaves (Nugget variety) infested by A. pulvinatus nymphs on 10/21st/2019; C - abaxial face of H. lupulus leaf (Cascade variety) infested by A. pulvinatus nymphs near the petiole on 10/21st/2019; D - approximate view of 4th instar A. pulvinatus nymphs ('pupae') on the abaxial face of H. lupulus leaf (Nugget variety).
Figure 1 in First Report of Meloidogyne morocciensis Infecting Hops (Humulus lupulus)
Figure 1: (A) Hop plants with leaf wilt symptoms; (B) hop root with symptoms of swelling and galls on roots; (C) juvenile (J2) of Meloidogyne morocciensis, reference scale: 50 μm; (D) crosssection of infected roots with galls; (E) root galls formed by M. morocciensis; (F) PAGE analysis of esterase isoenzyme phenotype, with one female in each cavity (J3 = M. javanica and 1–8: females extracted from hop roots); (G) perineal region of a female of M. morocciensis.
Figure 1 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 1. Explants of a hop genotype, grown in different culture media (yellow, blue and pink). Each medium provided different development behavior for the number of nodal segments. The larger the number of nodal segments, the more new plants will be obtained.
Figure 2 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 2. Estimates of the direct and indirect effects of the variables root length (RL), shoot height (SH) and number of shoots (NS) on the variable number of nodal segments (NNS) (main variable).
Data for Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'
<p>The results for <a href="https://arxiv.org/abs/1806.01465">https://arxiv.org/abs/1806.01465</a></p> <p>"Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'",</p> <p>including both manuscript and supplemental material.</p>
Fig. 1 in Pests of Florida hops: preliminary observations
Fig. 1. Mean number (± SEM) of spider mite eggs and motiles collected from 1.5 and 5 m above the ground from hops (cv. 'Cascade'), fall 2019, Gulf Coast Research and Education Center, Wimauma, Florida, USA. About 10 P. persimilis per plant were released 10 Sep (5 at 1.5 m and 5 at 5 m) and about 5 P. persimilis per plant were released in the lower canopy 17 Sep. An asterisk (*) indicates that numbers of spider mite eggs at 1.5 and 5 m were statistically different using Tukey's mean separation test (p <0.05); a cross (+) indicates that numbers of spider mite motiles at 1.5 and 5 m were statistically different.
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops version 0.0
<p><strong>Benchmark for Pairs of Papers in Semantic Scholar: 1 hop vs. 2-4 hops (version 0.0)</strong></p> <p>There are two files: valid.txt and test.txt; both files use the same format.</p> <p>Columns 2 and 3 are corpus ids from Semantic Scholar.</p> <p>Column 1 is the distance between the two papers in the citation index.</p> <p>Columns 4 and 5 are the bins of the two paper, respectively. The bin is a number between 0 and 100. Papers are sorted by publication date. There are about 2M papers per bin, with the oldest papers in bin 0, and the newest papers in bin 99.</p> <p>Bin 100 is a catch-all for papers with unknown publication dates.</p> <p>head valid.txt</p> <p>1 248518397 1041744 97 51</p> <p>2 248518397 23848439 97 21</p> <p>3 248518397 4235810 97 12</p> <p>4 248518397 82079949 97 11</p> <p>1 3374228 140728989 79 0</p> <p>1 68334187 36144275 58 34</p> <p>2 68334187 7008060 58 4</p> <p>1 205881482 94036919 77 72</p> <p>2 205881482 95069173 77 53</p> <p>3 205881482 53480264 77 52</p> <p>Each row is assigned to a bin, B, where B = max(col4, col5).</p> <p> </p> <p><strong>Task</strong>: the task is to distinguish pairs of papers with distance == 1 from pairs of papers with distance > 1.</p> <p><strong>Test/Train splits</strong>: For all thresholds, 0 <= T_{train} <= 99, train a model on rows in bins between 0 and T_{train} (inclusively). Test these models on rows in all bins 0 <= T_{test} <= 99. Report average accuracy for all combinations of T_{train} and T_{test}.</p> <p> </p> <p>Average Accuracy is defined as: mean(Predict(row) == 1, Gold(row) == 1)</p> <p>The means are computed over rows in a test bin.</p> <p> </p>
Research data accompanying the paper 'Exploiting Sparsity in Free Energy Basin-Hopping'
<p>Input and output files for the results presented in Tables 1-5 of the paper 'Exploiting Sparsity in Free Energy Basin-Hopping'. Further information on these files can be found in the README files within the tar file. </p> <p> </p>
Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing
<p>Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing</p> <p>A detailed description can be found in the <em>README.md</em>.</p>
QAngaroo (MedHop + WikiHop) - Constructing Datasets for Multi-hop Reading Comprehension Across Documents
<p>Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence — effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5% on an annotated test set, compared to human performance at 85.0%, leaving ample room for improvement.</p>
Augmentation and conservation biological control of Tetranychus urticae on hops in Ohio
<p class="MsoNormal"></p> <p class="MsoNormal">The twospotted spider mite, <em>Tetranychus urticae </em>Koch<em> </em>(Acari: Tetranychidae),<em> </em>is a key pest on hops grown in the Midwestern USA, where hop production is a new industry, and little research has been done on the management of <em>T. urticae</em>.<span> </span>In 2016 and 2017, we conducted an experiment to determine the efficacy of augmentative biological control of <em>T. urticae</em> populations on the cultivar 'Cascade' at four hop yards. <span> </span>In both years, treatments compared <em>Neoseiulus fallacis</em> Garman (Acari: Phytoseiidae), released at a high rate and a low rate, and an untreated control, with eight replicates in 2016 and 17 replicates in 2017. <span> </span>Additional treatments in 2016 evaluated <em>Galendromus</em> <em>occidentalis </em>Nesbitt<em> </em>(Acari: Phytoseiidae) released at a high and a low rate. The target low rate in both years was one predator per ten <em>T. urticae</em>. The target high rate was one predator per five <em>T. urticae</em> in 2016, and one predator per two <em>T. urticae</em> in 2017. <span> </span>When weekly monitoring showed that the population reached an action threshold of one <em>T. urticae</em> per ten leaves, predatory mites were released. <span> </span>If the <em>T. urticae</em> population continued to increase, a second release was made. <span> </span>By the time of harvest, the cumulative number of mite-days for <em>T. urticae</em> did not differ significantly among treatments in either year.<span> </span>Hop yields showed a significant treatment effect in 2016, with higher yield where the high rate of <em>G. occidentalis</em> was released than in other treatments, but yields did not show any significant treatment effect in 2017.<span> </span>In 2017, we also conducted an exclusion experiment at four hop yards in Ohio, to determine the services provided by predators already present in hop yards, as well as the ability of the combination of predatory mites, <em>N. fallacis</em> and <em>Neoseiulus californicus </em><span>McGregor</span><em> </em>(Acari: Phytoseiidae), to suppress <em>T. urticae </em>by augmentative releases at three different predator to prey ratios: zero to ten, one to ten, and two to ten.<span> </span>Samples were paired; one leaf was covered with a fine mesh bag and one leaf was left uncovered, in each of 50 replicates.<span> </span>After two weeks, the average number of <em>T. urticae</em> motiles on the open leaves that received zero phytoseiids was significantly less than the starting number of ten, suggesting that ambient predation is capable of suppressing <em>T. urticae</em> populations.<span> </span>The average number of <em>T. urticae</em> motiles on the enclosed leaves that received two phytoseiids was also significantly less than the starting number of ten, while the average number of <em>T. urticae</em> motiles on the enclosed leaf that received one phytoseiid was not, showing that a ratio of one phytoseiid to five <em>T. urticae</em> is effective at reducing <em>T. urticae</em> populations.<span> </span>Our experiments showed that when <em>T. urticae </em><span>is </span>found at low to moderate densities, naturally occurring predators are able to suppress their populations in Ohio hop yards.<span> </span>Augmentation using phytoseiid mites did not have a consistent beneficial effect on yields.<span> </span>Given that naturally occurring predators are important in the suppression of <em>T. urticae</em> populations, future studies thus might concentrate on conservation biological control.</p> <p> </p>
Does Hamiltonian Replica Exchange via lambda-hopping enhance the sampling in alchemical binding free energy calculations?
<p>t-REM HREM lamba-hopping/FEP+ tests on the APA molecule with ORAC<br> (www.chim.unifi.it/orac) </p> <p>The untarred archive contains the following directories: </p> <p>t-rem -> contains input for gas-phase tests<br> st-hrem -> contains input for solvated APA with solute tempering<br> lam-hop -> contains input for solvated APA with lambda-hopping<br> bin -> scripts for REM analysis <br> lib -> APA starting conf and potential parameters for the runs </p> <p>see also README files inside each dir for further details </p>
Apothecary vessel for hop syrup
The presented majolica apothecary vessel comes from the collection of Mateusz Bronisław Grabowski (1904–1976). The jug has one, bow-shaped handle, and on the opposite side, in the upper part of the belly, there is a tubular spout in the shape of a reptile's maw. On the belly of the jug, there is a large medallion with a figure of a holy monk in a grey habit. The ingredients of this preparation included purified hop juice (Humulus lupulus L.) and fumitory juice (Fumaria officinalis L.) seasoned with sugar. This drug was supposed to clear the bile ducts ID no.: KGZ 5933 Time of creation: 2nd half of 16th century Museum: The Museum of Pharmacy at the Jagiellonian University Medical College in Kraków https://muzea.malopolska.pl/en/objects-list/970 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Hop Flower Supercritical Carbon Dioxide Extracts Coupled with Carriers with Solubilizing Properties—Antioxidant Activity and Neuroprotective Potential
<p>Article, Dataset for article</p> <p> </p> <h1>Hop Flower Supercritical Carbon Dioxide Extracts Coupled with Carriers with Solubilizing Properties—Antioxidant Activity and Neuroprotective Potential</h1> <p> </p> <h2>Abstract</h2> <div><span>Lupuli flos</span> shows many biological activities like antioxidant potential, extended by a targeted effect on selected enzymes, the expression of which is characteristic for neurodegenerative changes within the nervous system. <span>Lupuli flos</span> extracts (LFE) were prepared by supercritical carbon dioxide (scCO<sub>2</sub>) extraction with various pressure and temperature parameters. The antioxidant, chelating activity, and inhibition of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and tyrosinase by extracts were studied. The extracts containing ethanol were used as references. The most beneficial neuroprotective effects were shown by the extract obtained under 5000 PSI and 50 °C. The neuroprotective effect of active compounds is limited by poor solubility; therefore, carriers with solubilizing properties were used for scCO<sub>2</sub> extracts, combined with post-scCO<sub>2</sub> ethanol extract. Hydroxypropyl-β-cyclodextrin (HP-β-CD) in combination with magnesium aluminometasilicate (Neusilin US2) in the ratio 1:0.5 improved dissolution profiles to the greatest extent, while the apparent permeability coefficients of these compounds determined using the parallel artificial membrane permeability assay in the gastrointestinal (PAMPA GIT) model were increased the most by only HP-β-CD.</div> <div> <div> <div>Keywords: </div> <a href="https://www.mdpi.com/search?q=neuroprotection">neuroprotection</a>; <a href="https://www.mdpi.com/search?q=hop+strobile">hop strobile</a>; <a href="https://www.mdpi.com/search?q=inhibition+of+enzymes">inhibition of enzymes</a>; <a href="https://www.mdpi.com/search?q=xanthohumol">xanthohumol</a></div> </div>
Dataset for Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor
<p>This is a dataset for a paper "Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor". All details about the data are included in the readme file.</p>
Data from: Comparison between the kinematics for kangaroo rat hopping on a solid versus sand surface
<p>In their natural habitats, animals move on a variety of substrates, ranging from solid surfaces to those that yield and flow (<i>e.g.</i>, sand). These substrates impose different mechanical demands on the musculoskeletal system and may therefore elicit different locomotion patterns. The goal of this study is to compare bipedal hopping by desert kangaroo rats (<i>Dipodomys deserti)</i> on a solid versus granular substrate under speed-controlled conditions. To accomplish this goal, we developed a rotary treadmill, which is able to have different substrates or uneven surfaces. We video recorded six kangaroo rats hopping on a solid surface versus sand at the same speed (1.8 m/s) and quantified the differences in the hopping kinematics between the two substrates. W<span>e found</span> no significant differences in the hop period, hop length, or duty cycle, showing that the gross kinematics on the two substrates were similar. This similarity was surprising given that sand is a substrate that absorbs mechanical energy. Measurements of the penetration resistance of the sand showed that the combination of the sand properties, toeprint area, and kangaroo rat weight was likely the reason for the similarity.</p>
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