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5,946 results for “stocks”
Dataset used in article "A 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry"
<p>The dataset presented is part of the one used in the article "A 2-dimensional guillotine cutting stock problem with variable-sized stock for the honeycomb cardboard industry" by P. Terán-Viadero, A. Alonso-Ayuso and F. Javier Martín-Campo, published in International Journal of Production Research (2023), doi: <a href="https://doi.org/10.1080/00207543.2023.2279129">10.1080/00207543.2023.2279129</a>. </p><p>In the paper mentioned above, two mathematical optimisation models are proposed for the Cutting Stock Problem in the honeycomb cardboard sector. This problem appears in a Spanish company and the models proposed have been tested with real orders received by the company, achieving a reduction of up to 50% in the leftover generated. </p><p>The dataset presented here includes six of the twenty cases used in the paper (the rest cannot be presented for confidentiality reasons). For each case, the characteristics of the order and the solution obtained by the two models are provided for the different scenarios analysed in the paper.</p><p> </p><p>*Version 1.1 contains the same data but renamed according to the instances name in the final version of the article.<br>*Version 1.2 adds the PDF with the accepted version of the article publised in International Journal of Production Research (2023), doi: <a href="https://doi.org/10.1080/00207543.2023.2279129">10.1080/00207543.2023.2279129</a>. </p>
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
Fig. 2. Haplotype network calculated from the E in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 2. Haplotype network calculated from the E. andrei COI haplotypes found in the South African earthworm groups investigated. The size of the circles is proportional to the number of earthworms sharing the same haplotype. The numbers on the branches indicate the positions of mutations on the COI sequences, mv1 represents a median vector (intermediate haplotypes, not found in this study).
Fig. 1 in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 1. Neighbour-joining tree based on the K2P method. Bootstrap support obtained for specific nodes are reported. Genbank accession numbers or BOLD process IDs are provided in brackets for the sequences downloaded from either Genbank or BOLD. Allolobophoridella eiseni and Microscolex phosphoreus were included as outgroups. Asterisk indicates dubious E. andrei sequences from BOLD.
Collection of German Stock Exchange Rules
<p><span>The STOCK EXCHANGE RULES collection contains rules and regulations from German securities exchanges starting in 1879, but with no claim to completeness. It builds on an initial collection by Andreas Fleckner (Humboldt-Universität zu Berlin) who kindly agreed to contribute it. The authors approached exchanges as well as some of the exchange supervisory authorities (‘Börsenaufsichtsbehörden’) and economic archives (‘Wirtschaftsarchive’) in the German states (‘Länder’). In some instances, the respective institutions were visited and documents were collected on site. Other rules and regulations were obtained from the law journal ‘Wertpapier-Mitteilungen’ by sifting through the tables of contents of the yearly volumes from 1985 to 2000 and from websites, notably the Internet Archive’s Wayback Machine. </span></p>
FIGURE 6 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 6 Quadratic discriminant function analysis of the (a) spawning energetics, (b) otolith element:Ca ratios and (c) both techniques combined in female Ethmalosa fimbriata sampled at Joal (Senegalese southern coast), Djifer (Saloum River mouth) and Foundiougne (Saloum River middle reaches) from February to October 2014. Ellipsoids encompass c. 50% of a sampling station's data points
FIGURE 4 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 4 Relationships between Ethmalosa fimbriata oocyte dry mass and (a) lipid content, and (b) protein content from fish in southern Senegalese coastal waters. Specimens were sampled at Joal (Senegalese southern coast), Djifer (Saloum River mouth) and Foundiougne (Saloum River middle reaches) from February to October 2014
FIGURE 2 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 2 Ground otolith of a female Ethmalosa fimbriata specimen (total length, LT = 23.1 cm) embedded in epoxy resin. The specimen was sampled at Foundiougne (Sine Saloum, Senegal) in May 2014. The ablation path can be seen along the edge of the otolith's rostrum
FIGURE 1 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 1 The Senegalese southern coast and the Sine Saloum Estuary, including sampling sites (): Joal, Senegalese southern coast; Djifer, Saloum River mouth; Foundiougne, Saloum River middle reaches
FIGURE 3 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 3 Total length–frequency distribution of Ethmalosa fimbriata females with hydrated oocytes. Sampling took place at Joal (Senegalese southern coast), Djifer (Saloum River mouth) and Foundiougne (Saloum River middle reaches) from February to October 2014
FIGURE 7 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 7 Mean (a) Ba:Ca and (b) Sr:Ca ratios of female Ethmalosa fimbriata otoliths in relationship with surface water temperature. Individuals were sampled at Joal (Senegalese southern coast), Djifer (Saloum River mouth) and Foundiougne (Saloum River middle reaches) from February to October 2014
FIGURE 5 in Spawning energetics and otolith microchemistry provide insights into the stock structure of bonga shad Ethmalosa fimbriata
FIGURE 5 (a) Monthly mean (SD; n, sample size) oocyte energy content and (b) boxplots (, median; ⊺, 5th and 95th percentiles;, outliers) of spawning batch energy content of female Ethmalosa fimbriata sampled at Joal (Senegalese southern coast), Djifer (Saloum River mouth) and Foundiougne (Saloum River middle reaches) from February to October 2014. Different lowercase letters indicate significant differences (P <0.05). Movfree, Ovary-free body mass
PRA1_best_stocks
<pre>This dataset shows the ranking of the stocks with the most appearances in investment funds that have beaten the Nasdaq during the last 10 years: these stocks would be the best right now according to the best mutual fund managers. The information has been obtained with web scraping from the Morningstar and Yahoo Finance websites.</pre>
Fig. 3. Pennatulicola piscatorius n in A New Species of Pennatulicola Humes and Stock (Copepoda: Cyclopoida: Rhynchomolgidae) Associated with a Pennatulacean from Tokyo Bay, Japan
Fig. 3. Pennatulicola piscatorius n. sp. Female, paratype: A, leg 4 (arrowhead indicates inner terminal process that is usually absent); B, exopod of leg 5; C, genital aperture. Male, paratype: D, habitus, dorsal; E, urosome, ventral; F, right caudal ramus, ventral; G, maxilliped; H, exopod of leg 5. Scale bars: A–C, E, H, 0.05 mm; D, 0.1 mm; F, G, 0.02 mm.
Fig. 2. Pennatulicola piscatorius n in A New Species of Pennatulicola Humes and Stock (Copepoda: Cyclopoida: Rhynchomolgidae) Associated with a Pennatulacean from Tokyo Bay, Japan
Fig. 2. Pennatulicola piscatorius n. sp., female, paratype. A, mandible; B, paragnath; C, maxillule; D, maxilla (Roman numerals I–III indicate setae I–III); E, maxilliped; F, leg 1; G, leg 2; H, leg 3. Scale bars: A–E, 0.02 mm; F–H, 0.05 mm.
Data for Nicola Chinook Ricker stock-recruit model with environmental covariates
<ol> <li>Climate change and human activities are transforming river flows globally, with potentially large consequences for freshwater life. To help inform watershed and flow management, there is a need for empirical studies linking flows and fish productivity.</li> <li>We tested the effects of river conditions and other factors on 22 years of Chinook salmon productivity in a watershed in British Columbia, Canada.</li> <li>Freshwater conditions during adult salmon migration and spawning, as well as during juvenile rearing, explained a large amount of variation in productivity.</li> <li>August river flows while salmon fry reared had the strongest effect on productivity – our model predicted that cohorts that experience 50% below average flow in the August of rearing have 21% lower productivity.</li> <li>These contemporary relationships are set within long-term changes in climate, land use, and hydrology. Over the last century, average August river discharge decreased by 26%, air temperatures warmed, and water withdrawals increased. 17% of the watershed was logged in the last 20 years. </li> <li>Our results suggest that, in order to remain stable, this Chinook salmon population being assessed for legal protection requires substantially higher August flow than previously recommended. Changing flow regimes – driven by watershed impacts and climate change – can threaten imperiled fish populations.</li> </ol>
Fig. 2 in Abundance And Summer Distribution Of A Local Stock Of Black Sea Bottlenose Dolphins, Tursiops Truncatus (Cetacea, Delphinidae), In Coastal Waters Near Sudak (Ukraine, Crimea)
Fig. 2. Sightings of bottlenose dolphins near Sudak in 2011–2012. Sightings are indicated by circles of different size, depending on the group size category; sightings during the line transect survey (LTS) on August 4, 2012, are marked as filled circles, and other sightings (non LTS) are marked as empty circles. The LTS transects are shown as a zigzag line, and the LTS area is bordered by a contour line.
Cultivation of the seaweed Ulva spp. with effluent from a shrimp biofloc rearing system: different species and stocking density
<p>This work evaluated the use of effluent from a marine shrimp biofloc rearing system to cultivate the green seaweed <em>Ulva</em>. First, the growth of two <em>Ulva </em>species, <em>U. ohnoi</em> and <em>U. fasciata,</em> was evaluated. Second, the best-performing species was cultivated under two different stocking densities (2 g L<sup>-1</sup> and 4 g L<sup>-1</sup>) to evaluate both growth and nutrient uptake rates, considering total ammonia nitrogen, nitrate, and orthophosphate. In both cases, environmental variables were monitored, and the cultivation medium, consisting of 25% biofloc water and 75% seawater, was exchanged weekly. <em>U. ohnoi</em> grew significantly better, considering all variables evaluated (<em>p</em><0.05). The smaller stocking density produced a higher specific growth rate (<em>p</em><0.05). Yield, however, was unaffected (<em>p</em>≥0.05). No significant differences in the nutrient uptake rates were observed (<em>p</em>≥0.05). Overall, this work highlights the importance of species selection for seaweed destined for aquaculture. Additionally, it also optimizes the cultivation of seaweeds, specifically <em>U. ohnoi</em>, using effluent from biofloc systems.</p>
Data from: Sex-specific life history affected by stocking in juvenile brown trout
<p>Salmonids are a socioeconomically and ecologically important group of fish that are often managed by stocking. Little is known about potential sex-specific effects of stocking, but recent studies found that the sexes differ in their stress tolerances already at late embryonic stage, i.e., before hatchery-born larvae are released into the wild and long before morphological gonad formation. It has also been speculated that sex-specific life histories can affect juvenile growth and mortality, and that a resulting sex-biassed demography can reduce population growth. Here we test whether juvenile brown trout (Salmo trutta) show sex-specific life histories and whether such sex effects differ in hatchery- and wild-born fish. We modified a genetic sexing protocol to reduce false assignment rates and used it to study the timing of sex differentiation in a laboratory setting, and in a large-scale field experiment to study growth and mortality of hatchery and wild-born fish in different environments. We found no sex-specific mortality in any of the environments we studied. However, females started sex differentiation earlier than males, and while growth rates were similar in the laboratory, they differed significantly in the field depending on location and origin of fish. Overall, hatchery-born males grew larger than hatchery-born females while wild-born fish showed the reverse pattern. Whether males or females grew larger was location-specific. We conclude that juvenile brown trout show sex-specific growth that is affected by stocking and by other environmental factors that remain to be identified.</p>
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
<p>This data is used for Stock Market Prediction. </p>
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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.