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opencc-by-4.0Apr 2024View details →

Long-lived magnetization in an atomic spin chain tuned to a diabolic point

This is the OpenData Folder for the paper under the same name.

Рис. 7–9. Coelorinchus idiolepis sp. nov. (7 — гоΛотип, 8 — паратип) и C. anisacanthus, гоΛотип, 81,5 мм HL (9), гоΛова, виΔ сбоку. Обозначения: oo — Δиаметр гΛаза, po — посторбитаΛьная ΔΛина. Масштаб: 7 — 20 мм; 8, 9 — 10 мм Figs. 7–9. Coelorinchus idiolepis sp. nov. (7 — holotype, 8 — paratype) and C. anisacanthus, holotype, 81.5 mm HL (9), head, lateral view. Symbols: oo — diameter of eye, po — postorbital length. Scale bars: 7 — 20 mm; 8, 9 — 10 mm in Coelorinchus From The Hawaiian-Emperor Seamount Chain (The Pacific Ocean) (Teleostei, Gadiformes, Macrouridae)

Рис. 7–9. Coelorinchus idiolepis sp. nov. (7 — гоΛотип, 8 — паратип) и C. anisacanthus, гоΛотип, 81,5 мм HL (9), гоΛова, виΔ сбоку. Обозначения: oo — Δиаметр гΛаза, po — посторбитаΛьная ΔΛина. Масштаб: 7 — 20 мм; 8, 9 — 10 мм Figs. 7–9. Coelorinchus idiolepis sp. nov. (7 — holotype, 8 — paratype) and C. anisacanthus, holotype, 81.5 mm HL (9), head, lateral view. Symbols: oo — diameter of eye, po — postorbital length. Scale bars: 7 — 20 mm; 8, 9 — 10 mm

Рис. 10–20. Coelorinchus idiolepis sp. nov., изоΛированные чешуи (10–17 — гоΛотип, 18–20 — паратип): 10, 11 — преΔорсаΛьная обΛасть; 12, 13, 18 — истмус; 14–16 — бока теΛа в интерΔорсаΛьном промежутке; 17 — абΔомен; 19, 20 — бока теΛа наΔ основанием груΔного пΛавника. Масштаб: 10–17 — 2,5 мм (Λинейка общая); 18–20 — 1,5 мм (Λинейка общая) in Coelorinchus From The Hawaiian-Emperor Seamount Chain (The Pacific Ocean) (Teleostei, Gadiformes, Macrouridae)

Рис. 10–20. Coelorinchus idiolepis sp. nov., изоΛированные чешуи (10–17 — гоΛотип, 18–20 — паратип): 10, 11 — преΔорсаΛьная обΛасть; 12, 13, 18 — истмус; 14–16 — бока теΛа в интерΔорсаΛьном промежутке; 17 — абΔомен; 19, 20 — бока теΛа наΔ основанием груΔного пΛавника. Масштаб: 10–17 — 2,5 мм (Λинейка общая); 18–20 — 1,5 мм (Λинейка общая)

opencc-by-4.0Jul 2024View details →

Individual allotype responses to HEK-293T-based cell lines ex-pressing single MHC class I chain-related gene B alleles

Figure S1. Individual allotype responses between 64 sera collected from kidney transplant patients and 5 single MICB allele-expressing cell lines established in HLA class I, MICA, and MICB-null HEK-293T cells. HLA class I, MICA, and MICB genes were removed using CRISPR/Cas9 in previous studies [20, 21]. Some of the 64 sera showed responses to single MICA allele-expressing cell lines [21]. However, none of the 64 sera showed individual allotype responses in this study.

opencc-by-4.0Jul 2024View details →

Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"

<div> <div> <div> This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation. </div> </div> </div>

opencc-by-4.0Aug 2015View details →

Figure 9. Experimental Page Rank dependency on Markov Chain length with balanced distribution-Study of a Random Navigation on the Web Using Software Simulation

This paper explored different implementation choices for analyzing the most important parameters about a web. Many researchers explored the use of new search engines for studying the evolution of the web (Ntoulas, Cho and Olston, 2004). Another important research is realized about the Link Structure Graph (LSG). The LSG captures a complete hyperlink structure from the web and models link associations reflected in the page layout (Rodrigues, Milic-Frayling and Fortuna, 2007). For further works ideas like extrapolation methods for accelerating page rank calculation can be developed (Kamvar et al., 2003).

opencc-by-4.0Aug 2015View details →

Figure 7. Experimental Page Rank dependency on Markov Chain length-Study of a Random Navigation on the Web Using Software Simulation

The next diagram proves that the values for Experimental Page Rank depend on the length of the Markov Chain, while Algorithmic Page Rank remains constant.

opencc-by-4.0Aug 2015View details →

Figure 1. Markov Chain Model&Figure 2. Transition matrix-Study of a Random Navigation on the Web Using Software Simulation

For a good simulation it is very important to find methods for navigating through the web (Levene and Wheeldon, 2004). John Kemeny and Laurie Snell have proposed the use of Markov models for web simulations (Kemeny and Snell, 1960). Cadez et al. (2000) used Markov models for classifying the sessions into different categories for browsers. Some other proposed techniques choose to combine different order Markov models for obtaining low state complexity and improving accuracy, as Deshpande and Karypis (2004). Dongshan and Junyi (2002) used for predicting the access providing good scalability and high coverage a hybrid-order tree-like Markov model. As an alternative to the Markov model Pitkow proposed a longest subsequence model (Pitkow and Pirolli, 1999), also for predicting the next page accessed by the user Sarukkai chose Markov models (Sarukkai, 2000). Transitions are simulated using the Markov Chain nodes, Google matrix and an arbitrary initial probability distribution. Examples can be seen in Figure 1 and Figure 2.

opencc-by-4.0Apr 2018View details →

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 7. Drawing the time chain for Females primary stage students after the first difference

We use the Unit Radix Dickey-Fuller Test to assert the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.47242, Statistic Test = 1.22797, P-Value = 0.9445 We notice from the values above P-Value =0.9445 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is noticed that the stability of the time series has been accomplished. See Figure 7.

opencc-by-4.0Feb 2019View details →

Example chain for pippi

This is the example MultiNest chain from <a href="https://inspirehep.net/record/831563">arXiv:0909.3300</a>, used in the <a href="http://github.com/patscott/pippi">pippi</a> example, and referred to in the pippi <a href="https://inspirehep.net/record/1117819">paper</a> and documentation.

opencc-by-4.0Jun 2019View details →

Updated MCMC chains and subsamples for Hector calibration paper

These csvs contain MCMC chains and sampled subsets for calibration of the Hector simple climate model (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>, DOI:10.5194/gmd-8-939-2015). This series of five calibrations with different observational constraints and free parameters is described in Vega-Westhoff et al. (2019, DOI:10.1029/2018EF001082; see their table 1). The calibrations use a version of Hector that includes the BRICK sea-level module (<a href="https://github.com/scrim-network/BRICK">https://github.com/scrim-network/BRICK</a>, DOI:10.5194/gmd-10-2741-2017). Hector with BRICK is available on my fork of the Hector model (https://github.com/bvegawe/hector/tree/dev_slr). The calibration process is also adapted from BRICK. The code used to produce these chains can be found at https://github.com/bvegawe/hector_probabilistic, DOI:10.5281/zenodo.3236411. These five sets of MCMC chains were produced using hector_calib_driver.R. Inputs used to create each calibration are specified below:  T.csv:  Rscript hector_calib_driver.R -f *output folder* -n 200000 --forcing TRUE --np 10 --model_set onlyT_model --obs_set onlyT_obs TOHC.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set doeclim_model --obs_set doeclim_obs TOHCGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set noTE_model --obs_set noTE_obs TTE.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set onlyTE_model --obs_set doeclimTE_obs TTEGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set all_model --obs_set noOcheat_obs   V1.1 - Updated with new calibrations. Previously only included two calibration files, <a href="https://zenodo.org/api/files/5f877c29-3990-4d12-92f0-1a7c6f7d271c/wSLR_calib.csv?versionId=b9c5ea10-6f52-4b91-b30a-be68cb58fba6">wSLR_calib.csv</a>, which was a 'full' model calibration, including both ocean heat and thermosteric sea level historical constraints, and woSLR_calib.csv, which included only ocean heat and temperature constraints. After review, we redid the calibration experiment, including additional combinations of constraints, and removing the 'full' calibration, which treated the ocean heat and thermosteric sea level constraints as independent.

Fig. 3 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 3. Phylogenetic tree of seven Trypanosome species and subsequent genotypes constructed with sequences of the amplicons generated by the HRMqPCR primers.

Fig. 4 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 4. Amplification plots, melt curves and standard curves of T. copemani, T. vegrandis G7 and T. noyesi G8 prepared from a plasmid containing trypanosome species.

Fig. 5. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 5. A-D: Derivative melt curves showing mock mixed infections generated from plasmid clones containing the following DNA: (A) T. noyesi G8 and T. copemani; (B) T. vegrandis G7 and T. copemani; (C) T. vegrandis G7 and T. noyesi G8; (D) T. vegrandis G7, T. noyesi G8 and T. copemani.

Fig. 1 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 1. Multiple sequence alignment of a portion of the 18S rDNA of seven Trypanosome species and subsequent genotypes used to design the HRM-qPCR assays.

Fig. 2 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain

Fig. 2. Percentage of Chironomidae larvae consumed by Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 in low, intermediate and high habitat complexities in the presence and in the absence of the piscivore Hoplerythrinus unitaeniatus (Spix & Agassiz 1829).

Fig. 4 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain

Fig. 4. Correlation between the percentages of Chironomidae larvae consumed by Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 and the percentage of individuals of M. forestii consumed by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) in the all levels of habitat complexity.

Fig. 5 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain

Fig. 5. Conceptual scheme describing prey consumption by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) and Moenkhausi forestii Benine, Mariguela & C. de Oliveira, 2009 reported in this experimental study. Bold solid arrows represent trophic interactions and thin solid arrows represent direct effects. Dashed arrow represents indirect effects. Habitat complexity, represented by macrophytes density, affects prey capture strategy of H. unitaeniatus and indirectly affects invertivore foraging.

Fig. 1 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain

Fig. 1. The experimental design. The (A) axis represents habitat complexity [(a) low, (b) intermediate and (c) high] and the (B) axis represents the piscivore [(d) absence and (e) presence]. Numbers in the aquaria represent the number of replicates for each treatment combination.

Fig. 3 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain

Fig. 3. Percentage of Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 individuals consumed by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) in low, intermediate and high habitat complexities.