The granular matter is the second most handled material by man after water and is thus ubiquitous in daily life and industry only after water. Since the eighteenth century, mechanical and chemical engineers have been striving to manage the many difficulties of grain handling, most of which are related to flow problems. Many continuum models for dense granular flow have been proposed. Herein, we investigated Mohr-Coulomb failure analysis as it has been the cornerstone of stress distribution studies in industrial applications for decades. These datasets gather over 120 granular materials from several industrial sectors, as varied as cement and flour, including raw materials, food, pharmaceuticals, and cosmetics. A phenomenological law derived from the yield locus and governed exclusively by one dimensionless number from adhesive interactions has been found using Principal Component Analysis (PCA). Surprisingly, and in contrast to the common perception, flow in the quasi-static regime is actually independent of the friction, the packing fraction and any other grains/bulk intrinsic properties. The simplicity and accuracy of the model are remarkable in light of the complex constitutive properties of granular matter. Manuscript under revision in Advanced Powder Technology dataGM: Description of the samples used to construct all datasets. Some of the materials are submitted to confidential agreements. In these cases, some or no information has been provided. dataset 1: Principal component Analysis (PCA) data, allowing to generate figures 3 and 4. dataset 2: Principal component Analysis (PCA) data, allowing to generate figure 5 publication. dataset 3: Principal component Analysis (PCA) data, allowing to generate figure 6 publication.

Granular Urea Market Outlook

The global granular urea market size was valued at approximately USD 40 billion in 2023 and is projected to reach USD 60 billion by 2032, growing at a compound annual growth rate (CAGR) of 4.5% during the forecast period. The growth of this market is driven by a combination of factors including the increasing demand for efficient fertilizer solutions, advancements in agricultural technologies, and the need for sustainable farming practices.

One of the primary growth factors for the granular urea market is the rising global population, which is driving the demand for increased food production. As the world population is expected to reach 9.7 billion by 2050, there is a significant pressure on agricultural systems to enhance productivity. Granular urea, being a highly effective nitrogen fertilizer, plays a crucial role in meeting this demand by improving crop yields. Additionally, the growing awareness about the efficient use of fertilizers to minimize environmental impact is propelling the adoption of granular urea in modern farming practices.

Another critical driver for market growth is the increasing adoption of precision agriculture. Precision agriculture involves the use of advanced technologies such as GPS, IoT, and data analytics to optimize farming practices and resource use. Granular urea, due to its controlled-release properties, is highly suitable for precision farming applications. This not only ensures the efficient use of fertilizers but also reduces wastage, thereby leading to cost savings for farmers. The integration of precision agriculture with granular urea is expected to significantly contribute to the market's expansion during the forecast period.

The industrial sector's demand for granular urea is also contributing to market growth. Granular urea is used in various industrial applications, including the production of resins, adhesives, and pharmaceuticals. The chemical industry, in particular, utilizes urea for manufacturing melamine and urea-formaldehyde resins, which are essential components in the production of laminates, adhesives, and molded objects. The increasing demand for these industrial products is driving the consumption of granular urea, thereby boosting market growth.

Regionally, Asia Pacific holds the largest share in the granular urea market, accounting for more than 40% of the global market in 2023. The region's dominance is attributed to the extensive agricultural activities in countries such as China, India, and Indonesia. Additionally, the presence of a large rural population engaged in farming, coupled with government initiatives to promote sustainable agricultural practices, is further supporting market growth in the region. North America and Europe are also significant markets, driven by advanced farming technologies and high awareness about efficient fertilizer use.

Sulfur Coated Urea is emerging as a significant innovation in the fertilizer industry, particularly within the granular urea market. This product is designed to enhance the efficiency of nitrogen release, making it an ideal solution for farmers seeking to optimize nutrient management. By coating urea granules with sulfur, the product achieves a controlled-release mechanism that aligns with sustainable agricultural practices. This not only minimizes nitrogen loss through leaching and volatilization but also provides a steady supply of nutrients to crops over an extended period. As a result, sulfur coated urea is gaining traction among farmers who are increasingly focused on improving crop yields while reducing environmental impact. The adoption of this technology is expected to contribute to the growth of the granular urea market, particularly in regions where sustainable farming is prioritized.

Product Type Analysis

In the product type segment, standard granular urea holds a significant market share due to its widespread use and cost-effectiveness. Standard granular urea is a high-concentration nitrogen fertilizer that is easy to apply and is suitable for a wide range of crops. Its granule form ensures even distribution in the soil, enhancing nitrogen absorption by plants. This type of urea is extensively used in both small-scale and large-scale farming operations, making it a staple in the agricultural sector.

Coated granular urea, on the other hand, is gaining traction due to its controlled-release properties. This type of urea is coated with materia

Temporal and spatial distribution of niclosamide in the water column and sediment were evaluated after the application of granular Bayluscide in six lentic Sea Lamprey (Petromyzon marinus) larval assessment plots. Water and sediment were collected 0.25, 1, 3, 5, and 7 hours after application and were analyzed for niclosamide, the active ingredient in granular Bayluscide. Water samples were collected from five heights in the water column (1 cm, 13 cm, 26 cm, ½ water column, and water surface) at five locations inside and four locations 10 m outside of each assessment plot. Sediment was collected from 18 locations within each plot. Niclosamide water concentrations inside and outside of the plots did not vary by depth but did vary between plots and by time. Niclosamide water concentrations also varied by sampler _location outside of the plots. Following granular Bayluscide applications the mean niclosamide concentration in water for all levels, within the plots, decreased from 0.12 mg∙L-1 (SD = 0.12 mg∙L-1) at 15 minutes to 0.061 mg∙L-1 (SD = 0.040 mg∙L-1) at hour 1. The mean niclosamide concentration in the top 4 cm of sediment was 2.9 mg∙kg-1 (SD = 2.4 mg∙kg-1) 15 minutes after application and was 1.3 mg∙kg-1 (SD = 1.8 mg∙kg-1) at hour 7. Concentrations in the sediment ranged from 0.000 to 30.730 mg∙kg-1 and varied between the six plots. Niclosamide concentrations measured in sediment samples were more than 1 order of magnitude greater than in the water and varied spatially by over 4 orders of magnitude. The datasets included are as follows: Niclosamide Sediment Concentrations Dataset (NicSed) Niclosamide Water Column Concentrations Dataset (NicWaterColumn) Niclosamide Water Concentrations Outside Plot Dataset (NicWaterOutside) Plot pH, depth and temperature dataset (PlotData) Plot Sediment pH before and after treatment (SedpHPlotTrt) Plot Sediment Temp before and after treatment (SedTempPlotTrt)

Abstract Objective: To review the imaging features of granular cell tumors of the breast (on mammography, ultrasound, and magnetic resonance imaging), establishing a pathological correlation, in order to familiarize radiologists with this entity and make them aware of the differential diagnoses, other than malignancy, of lesions with spiculated margins. Materials and Methods: We reviewed the medical records (from a clinical-pathology database and picture archiving and communication system) of five patients with a pathologically confirmed diagnosis of granular cell tumor of the breast, treated at the Portuguese Oncology Institute of Lisbon, in the city of Lisbon, Portugal, between January 2012 and December 2018. Results: All five tumors exhibited imaging features highly suggestive of malignancy (BI-RADS 5 lesions), namely spiculated margins, significant depth, and posterior acoustic shadowing (on ultrasound). One tumor showed a kinetic curve indicative of washout on magnetic resonance imaging, two were adherent to the pectoralis muscle, and one was accompanied by skin retraction. Pathology provided the definitive diagnosis in all cases. Conclusion: Granular cell tumors of the breast pose a diagnostic challenge because they can present with clinical and imaging features mimicking malignancy, and the diagnosis is therefore provided by pathology. Radiologists should be familiarized with this entity, so they can be aware of the fact that breast lesions with spiculated margins can be indicative of diagnoses other than malignancy.

The dataset presented here corresponds to data collected in an experimental campaign on dry granular flows, in which one experiment was repeated 31 times. The experimental campaign was performed in a 1.5 m length facility sloping at 20 degrees, where a volume of granular material was released from an upstream gate and trapped through a vertical obstruction in the downstream area of the channel, simulating slit dam conditions.

The experiments carried out presented the following characteristics: a) uni-sized polystyrene particles (d=1.8mm); b) 3 litres volume of particles and c) the obstruction used had a double distance from the chute lateral walls of twice the diameter of the particles. Images from the experiments were collected by means of one high-speed camera located at the downstream part of the channel with a target frame rate of 300 frames per second and an exposure time of 200µs.

The collected data was processed and filtered by means of Matlab algorithms aiming the assembly of a along-chute (u) and wall-normal (w) velocity ensemble database for the total time evaluated of 437 frames.

The "Wikipedia Category Granularity (WikiGrain)" data consists of three files that contain information about articles of the English-language version of Wikipedia (https://en.wikipedia.org).

The data has been generated from the database dump dated 20 October 2016 provided by the Wikimedia foundation licensed under the GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 3.0 License.

WikiGrain provides information on all 5,006,601 Wikipedia articles (that is, pages in Namespace 0 that are not redirects) that are assigned to at least one category.

The WikiGrain Data is analyzed in the paper

Jürgen Lerner and Alessandro Lomi: Knowledge categorization affects popularity and quality of Wikipedia articles. PLoS ONE, 13(1):e0190674, 2018.

===============================================================
Individual files (tables in comma-separated-values-format):

---------------------------------------------------------------
* article_info.csv contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "granularity"
(decimal) The granularity of an article A is defined to be the average (mean) granularity of the categories of A, where the granularity of a category C is the shortest path distance in the parent-child subcategory network from the root category (Category:Articles) to C. Higher granularity values indicate articles whose topics are less general, narrower, more specific.

- "is.FA"
(boolean) True ('1') if the article is a featured article; false ('0') else.

- "is.FA.or.GA"
(boolean) True ('1') if the article is a featured article or a good article; false ('0') else.

- "is.top.importance"
(boolean) True ('1') if the article is listed as a top importance article by at least one WikiProject; false ('0') else.

- "number.of.revisions"
(integer) Number of times a new version of the article has been uploaded.

---------------------------------------------------------------
* article_to_tlc.csv
is a list of links from articles to the closest top-level categories (TLC) they are contained in. We say that an article A is a member of a TLC C if A is in a category that is a descendant of C and the distance from C to A (measured by the number of parent-child category links) is minimal over all TLC. An article can thus be member of several TLC.
The file contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "id.of.tlc"
(integer) Unique identifier for TLC in which the article is contained; identical with the page_id in the Wikipedia database.

- "title.of.tlc"
(string) Title of the TLC in which the article is contained.

---------------------------------------------------------------
* article_info_normalized.csv
contains more variables associated with articles than article_info.csv. All variables, except "id" and "is.FA" are normalized to standard deviation equal to one. Variables whose name has prefix "log1p." have been transformed by the mapping x --> log(1+x) to make distributions that are skewed to the right 'more normal'.
The file contains the following variables:

- "id"
Article id.

- "is.FA"
Boolean indicator for whether the article is featured.

- "log1p.length"
Length measured by the number of bytes.

- "age"
Age measured by the time since the first edit.

- "log1p.number.of.edits"
Number of times a new version of the article has been uploaded.

- "log1p.number.of.reverts"
Number of times a revision has been reverted to a previous one.

- "log1p.number.of.contributors"
Number of unique contributors to the article.

- "number.of.characters.per.word"
Average number of characters per word (one component of 'reading complexity').

- "number.of.words.per.sentence"
Average number of words per sentence (second component of 'reading complexity').

- "number.of.level.1.sections"
Number of first level sections in the article.

- "number.of.level.2.sections"
Number of second level sections in the article.

- "number.of.categories"
Number of categories the article is in.

- "log1p.average.size.of.categories"
Average size of the categories the article is in.

- "log1p.number.of.intra.wiki.links"
Number of links to pages in the English-language version of Wikipedia.

- "log1p.number.of.external.references"
Number of external references given in the article.

- "log1p.number.of.images"
Number of images in the article.

- "log1p.number.of.templates"
Number of templates that the article uses.

- "log1p.number.of.inter.language.links"
Number of links to articles in different language edition of Wikipedia.

- "granularity"
As in article_info.csv (but normalized to standard deviation one).

Erythritol Granular Market Outlook

The global erythritol granular market size was valued at USD 260 million in 2023 and is projected to reach USD 570 million by 2032, growing at a compound annual growth rate (CAGR) of 9.2% during the forecast period. This market growth is largely driven by the increasing consumer shift towards low-calorie and natural sweeteners due to rising health consciousness and the global prevalence of diabetes and obesity.

One of the significant growth factors in the erythritol granular market is the rising consumer awareness regarding the adverse effects of excessive sugar consumption. With a growing number of people being diagnosed with diabetes and obesity, there has been a consequential shift towards healthier dietary options. Erythritol, being a low-calorie, natural sugar substitute, has become a preferred choice among health-conscious consumers. This trend is further bolstered by various health organizations advocating for reduced sugar intake, thus driving the demand for erythritol granular products.

Another critical growth driver is the increasing adoption of erythritol in the food and beverage industry. Manufacturers are continuously innovating to introduce products that cater to the taste preferences of consumers while ensuring health benefits. The versatility of erythritol, which can be used in a range of products from baked goods to beverages, has made it a popular ingredient in the industry. Additionally, the growing trend of clean label products, which emphasize natural ingredients, has further fueled the market demand for erythritol granular.

The pharmaceutical and personal care sectors are also contributing to the market growth. Erythritol is being increasingly used in pharmaceutical formulations and personal care products due to its non-cariogenic properties and its ability to enhance the taste without adding calories. The rise in demand for functional foods and nutraceuticals, which often incorporate erythritol for its health benefits, is another factor propelling the market forward.

From a regional perspective, North America has been leading the erythritol granular market, driven by high consumer awareness and significant demand from the food and beverage sector. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This surge can be attributed to the rising disposable incomes, increasing health awareness, and the rapid expansion of the food processing industry in countries like China and India. The growing influence of Western dietary habits in these regions is also playing a crucial role in boosting the market for erythritol granular.

Product Type Analysis

In the erythritol granular market, the product type segment is divided into Organic Erythritol Granular and Conventional Erythritol Granular. The demand for organic erythritol granular has been witnessing a significant rise owing to the growing consumer inclination towards organic and natural products. This trend is particularly prevalent among health-conscious consumers who are willing to pay a premium for organic products. The organic segment is expected to grow at a higher CAGR compared to the conventional segment, driven by the increasing availability and consumer acceptance of organic products across various regions.

The conventional erythritol granular segment, however, continues to dominate the market in terms of volume. Conventional erythritol granular is widely used due to its affordability and widespread availability. It is a popular choice among manufacturers in the food and beverage industry due to its cost-effectiveness and similar functional properties to organic erythritol. The conventional segment is likely to maintain its dominance owing to its extensive application in various industrial processes and household uses.

Organic erythritol granular is increasingly favored in premium and specialty food products. As consumers become more health-conscious and environmentally aware, products with organic certification are gaining traction. The organic segment also benefits from the growing trend of clean label products, where consumers seek transparency about the ingredients used in their food and beverages. This has led to an increased presence of organic erythritol granular in retail stores, especially in developed regions like North America and Europe.

On the other hand, the conventional segment benefits from its established supply chain and lower production costs. This makes it a preferred choice for bulk buyers, includi

This dataset contains the information recorded for approximately 50,000 hopper flows under varying conditions. Each file represents a single flow event, from beginning of flow to final clog forming. The files are matlab structures, containing the positions, radii, frame number, tracked particle ID's and velocities for every grain in the camera field of view throughout the flow. Other values, such as the position of the outlet and the total mass ejected during the flow are also included., Data are the tracked positions of grains throughout individual hopper flows (beginning of flow to final clog). A camera records images of grains near the outlet at 130 frames per second, which was then analyzed using matlab code. The grain centers were located and species (size) identified; this information was then fed into a tracking algorithm to uniquely identify grains throughout flow. From these unique identifiers, velocity and ejected mass information was calculated. , , # Data from: Cornerstones are the key stones: Using interpretable machine learning to probe the clogging process in 2D granular hoppers

Grain positions during hopper flow for Autohopper

Data are the grain positions, velocities, sizes, and IDs throughout the flow from the breaking of a clog to a new stable clog forming in a granular hopper. Grains are a tridisperse mixture of discs made from anti-static Ultra High Molecular Weight Polyethylene. The hopper is two vertical sheets of plexiglass with enough space between them to admit a single layer of discs with minimal displacement. Images of the grains during flow were recorded using a camera, and grain locations were tracked using custom MATLAB code.

Description of the data and file structure

The data are separated into three folders reflecting different experimental conditions. The folder FixedParticle.zip contains 5551 flows in which a fixed grain was inserted near the outlet, systematically affecting the flow. SingleOutletNo...,

Granular sludge is an efficient and compact biofilm process for wastewater treatment. However, the ecological factors involved in microbial community assembly during the granular biofilm formation are poorly understood, and little is known about the reproducibility of the process. Here, three replicate bioreactors were used to investigate microbial succession during the formation of granular biofilms. We identified three successional phases. During the initial phase, the successional turnover was high and α-diversity decreased as a result of the selection of taxa adapted to grow on acetate and form aggregates. Despite these dynamic changes, the microbial communities in the replicate reactors were similar. The second successional phase occurred when the settling time was rapidly decreased to selectively retain granules in the reactors. The influence of stochasticity on succession increased and new niches were created as granules emerged, resulting in temporarily increased α-diversity. The third successional phase occurred when the settling time was kept stable and granules dominated the biomass. Turnover was low, and selection resulted in the same abundant taxa in the reactors, but drift, which mostly affected low-abundant community members, caused the community in one reactor to diverge from the other two. Even so, performance was stable and similar between reactors.

Granular materials like cereal, pharmaceuticals, sand and concrete commonly organize such that grains segregate according to size rather than uniformly mixing. For example, in a jar of nuts, the largest ones are commonly found at the top. Here, we use computer simulations to explore how grain shape controls this phenomenon in industrial and natural settings. We find that even small differences in shape can substantially change the amount and style of segregation, with different effects depending on whether the system is wet or dry. This study demonstrates the importance of grain shape in different systems ranging from food and medicine production to geophysical hazards and processes such as landslides, river erosion, and debris flows on Earth and other celestial bodies. This dataset contains examples in how to perform the simulations and the results shown in the manuscript How particle shape affects granular segregation in industrial and geophysical flows.

The bulk dynamics of dense granular materials arise through a combination of particle-scale and mesoscale effects. Theoretical and numerical studies have shown that collective effects are created by particle-scale anisotropic structures such as grain connectivity, force transmission, and frictional mobilization, all of which influence bulk properties like bulk friction and the stress tensor through the Stress-Force-Fabric (SFF) relationship. To date, establishing the relevance of these effects to laboratory systems has remained elusive due to the challenge of measuring both normal and frictional contact forces at the particle scale. In this study, we perform experiments on a sheared photoelastic granular system in a quasi-2D annular cell. During these experiments, we measure particle locations, contacts, and normal and frictional force vectors during loading. We reconstruct the angular distributions of the contact and force vectors, and extract the corresponding emergent anisotropies fo..., , # Particle scale anisotropy controls bulk properties in sheared granular materials

Dataset DOI: 10.5061/dryad.m905qfvds

Description of the data and file structure

This repository contains four folders corresponding to the data (at different stages of processing) of a full rotation of the shear on an annular shear cell.

Files and variables

File: rawdata.zip

Description:Â The data in this folder contains 3 files named as centers_tracked.txt, Adjacency_list.txt, and annulusgeometry.txt

centers_tracked.txt: This file contains the particle position information and radius.

The columns are: [frame, particleID, x, y, r, edge]

frame:Â image number corresponding to a strain step

particleID: individual id of one particle that should persist from frame to frame

x: x position of particle, in pixels

y:Â y position of particle, in pixels

r*:*Â radius of particle, in pixels

edge*:*Â boolean edge flag, 0 not near an edge, -1 near the inner bou...,

This dataset includes raw images from granular and two-phase experiments, as well as force data from each individual experiment. More information about methods and materials can be accessed in the README.Two-phase systems, where one phase is solid and the other fluid, are widespread in nature. Examples include reservoir rocks holding vital fluids like water or petroleum, slurries of partially crystallized magmas, the semi-brittle middle crust, and fluids migrating along faults filled with fault gouge. Previous studies of two-phase systems have shown that the weak phase plays an important role on deformation localization and dynamics. Here, we investigate the influence of a weak phase on force distribution in a granular media during simple shear. We use photoelastic polyurethane discs as the granular or strong phase and a linear-viscous silicone as the weak phase. The photoelastic property of the discs allows for direct observation and measurement of force magnitude and distribution. We compare the two-phase experiments to granular experiments without silicone. The addition and percentage of the weak phase has a strong impact on the force distribution and the overall force chain orientations.

The dataset consists of simulated events of electron-positron pairs (e+e−) with flat transverse momentum pT distribution pT ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The original dataset (CMS-internal).

This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this TWiki (CMS-internal).

A description of each variable is below.

    Variable
    Description
    Type




    run
    Run number
    int


    event
    Event number
    int


    lumi
    Luminosity section
    int


    gen_n
    Number of primary generated particles
    int


    gen_PUNumInt
    Number of pileup interactions
    int


    gen_TrueNumInt
    Number of true interactions
    float


    vtx_x
    Simulated primary vertex x position in cm
    float


    vtx_y
    Simulated primary vertex y position in cm
    float


    vtx_z
    Simulated primary vertex z position in cm
    float


    gen_eta
    Primary generated particle pseudorapidity η
    vector


    gen_phi
    Primary generated particle azimuthal angle ϕ
    vector


    gen_pt
    Primary generated particle transverse momentum pT in GeV
    vector


    gen_energy
    Primary generated particle energy in GeV
    vector


    gen_charge
    Initial generated particle charge
    vector


    gen_pdgid
    Primary generated particle PDG ID
    vector


    gen_status
    Primary generated particle generator status
    vector


    gen_daughters
    Primary generated particle daughters (empty)
    vector>


    genpart_eta
    Primary and secondary generated particle pseudorapidity η
    vector


    genpart_phi
    Primary and secondary generated particle azimuthal angle ϕ
    vector


    genpart_pt
    Primary and secondary generated particle transverse momentum pT in GeV
    vector


    genpart_energy
    Primary and secondary generated particle energy in GeV
    vector


    genpart_dvx
    Primary and secondary generated particle decay vertex x position in cm
    vector


    genpart_dvy
    Primary and secondary generated particle decay vertex y position in cm
    vector


    genpart_dvz
    Primary and secondary generated particle decay vertex z position in cm
    vector


    genpart_ovy
    Primary and secondary generated particle original vertex y position in cm
    vector


    genpart_ovz
    Primary and secondary generated particle original vertex z position in cm
    vector


    genpart_mother
    Primary and secondary generated particle parent particle index (-1 indicates no parent)
    vector


    genpart_exphi
    Primary and secondary generated particle azimuthal angle ϕ extrapolated to the corresponding HGCAL coordinate
    vector


    genpart_exeta
    Primary and secondary generated particle pseudorapidity η extrapolated to the corresponding HGCAL coordinate
    vector


    genpart_exx
    Primary and secondary generated particle decay vertex x extrapolated to the corresponding HGCAL coordinate
    vector


    genpart_exy
    Primary and secondary generated particle decay vertex y extrapolated to the corresponding HGCAL coordinate
    vector


    genpart_fbrem
    Primary and secondary generated particle decay vertex z extrapolated to the corresponding HGCAL coordinate
    vector


    genpart_pid
    Primary and secondary generated particle PDG ID
    vector


    genpart_gen
    Index of associated primary generated particle
    vector


    genpart_reachedEE
    Primary and secondary generated particle flag: 2 indicates that the particle reached the HGCAL, 1 indicates the particle reached the barrel calorimeter, and 0 indicates other cases
    vector


    genpart_fromBeamPipe
    Deprecated variable, always true
    vector


    genpart_posx
    Primary and secondary generated particle position x coordinate in cm
    vector>


    genpart_posy
    Primary and secondary generated particle position y coordinate in cm
    vector>


    genpart_posz
    Primary and secondary generated particle position z coordinate in cm
    vector>


    ts_n
    Number of trigger sums
    int


    ts_id
    Trigger sum ID
    vector


    ts_subdet
    Trigger sum subdetector
    vector


    ts_zside
    Trigger sum endcap (plus or minus endcap)
    vector


    ts_layer
    Trigger sum layer ID
    vector


    ts_wafer
    Trigger sum wafer ID
    vector


    ts_wafertype
    Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 μm thick silicon, 1 indicates coarse divisions of wafer with 200 μm thick silicon, and 2 indicates coarse divisions of wafer with 300 μm thick silicon
    vector


    ts_data
    Trigger sum ADC value
    vector


    ts_pt
    Trigger sum transverse momentum in GeV
    vector


    ts_mipPt
    Trigger sum energy in units of transverse MIP
    vector


    ts_energy
    Trigger sum energy in GeV
    vector


    ts_eta
    Trigger sum pseudorapidity η
    vector


    ts_phi
    Trigger sum azimuthal angle ϕ
    vector


    ts_x
    Trigger sum x position in cm
    vector


    ts_y
    Trigger sum y position in cm
    vector


    ts_z
    Trigger sum z position in cm
    vector


    tc_n
    Number of trigger cells
    int


    tc_id
    Trigger cell unique ID
    vector


    tc_subdet
    Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)
    vector


    tc_zside
    Trigger cell endcap (plus or minus endcap)
    vector


    tc_layer
    Trigger cell layer number
    vector


    tc_waferu
    Trigger cell wafer u coordinate; u-axis points along  − x-axis
    vector


    tc_waferv
    Trigger cell wafer v coordinate; v-axis points at 60 degrees with respect to x-axis
    vector


    tc_wafertype
    Trigger cell wafer type: 0 indicates fine divisions of wafer with 120 μm thick silicon, 1 indicates coarse divisions of wafer with 200 μm thick silicon, and 2 indicates coarse divisions of wafer with 300 μm thick silicon)



    tc_cellu
    Trigger cell u coordinate within wafer; u-axis points along  − x-axis
    vector


    tc_cellv
    Trigger cell v coordinate within wafer; v-axis points at 60 degrees with respect to x-axis
    vector


    tc_data
    Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding
    vector


    tc_uncompressedCharge
    Trigger cell ADC data at full precision before compression
    vector


    tc_compressedCharge
    Trigger cell ADC data compressed into 7-bit encoding
    vector


    tc_pt
    Trigger cell transverse momentum pT in GeV
    vector


    tc_mipPt
    Trigger cell energy in units of transverse MIPs
    vector


    tc_energy
    Trigger cell energy in GeV
    vector


    tc_simenergy
    Trigger cell energy from simulated particles in GeV
    vector


    tc_eta
    Trigger cell pseudorapidity η
    vector


    tc_phi
    Trigger cell azimuthal angle ϕ
    vector


    tc_x
    Trigger cell x position in cm
    vector


    tc_y
    Trigger cell y position in cm
    vector


    tc_z
    Trigger cell z position in cm
    vector


    tc_cluster_id
    ID of the 2D cluster in which the trigger cell is clustered
    vector


    tc_multicluster_id
    ID of the 3D cluster in which the trigger cell is clustered
    vector


    tc_multicluster_pt
    Transverse momentum pT in GeV of the 3D cluster in which the trigger cell is clustered
    vector

The dataset presented here contains the results of mechanical testing of two granular materials (quartz sand and glass micro beads) that are commonly used in analogue tectonic experiments. The data were acquired using a ring-shear tester RST-01.pc [Schulze, 1994]. Tests were performed at different normal loads ranging from 125 Pa to 4000 Pa and with eight to ten repetitions per normal load and material. The parameters measured are: rotation velocity, shear stress, normal load and sample dilation, all as a function of time. A detailed analysis and interpretation of the data can be found in the main article of [Ritter et al., 2016].The data were measured in the ring-shear tester RST-01.pc [Schulze, 1994, see below] at GFZ Potsdam’s analogue laboratory for tectonic modelling. All samples have been prepared and measured by the same person. Preparation was by sifting from a constant height of 30 cm into the shear cell. Tests were performed at different normal loads ranging from 125 Pa to 4000 Pa and with eight to ten repetitions per normal load and material. For normal loads below 500 Pa, the samples were pre-loaded by shortly increasing the normal load to 500 Pa and then resetting it to the desired value prior to the onset of deformation. This pre-loading was carried out for technical reasons. Preliminary tests at a normal load of 300 Pa have shown that this does not affect the strength.The data are presented as shear curves in tab-separated text files. The file names consist of (in this order) material, normal load and a running number. Each file contains one shear curve and consists of a header describing the individual measurements followed by a table with one column per parameter (read more in the dataset description pdf).References:Schulze, D. (1994) Entwicklung und Anwendung eines neuartigen Ringschergerätes, Aufbereitungstechnik, 35(10), 524–535.

The objective of this study was to document the vertical avoidance behavior of the tadpole madtom, as a surrogate to the northern madtom, in response to granular Bayluscide® when applied to control or assess larval sea lamprey populations. The data set consists of one hour recordings of the avoidance behavior of tadpole madtoms after exposure to granular Bayluscide. Each trial (replicated 15 times) consisted of three treated (Bayluscide granules) and three control (washed sand only) clear Plexiglas vertical columns (107 cm in height, 30.5 cm in diameter) with a single madtom per column. Video recordings were analyzed for vertical avoidance at 30 second intervals after addition of Bayluscide granules/washed sand over a period of one hour. Vertical migration of the normally bottom dwelling madtoms to greater than 15 centimeters off the bottom of the column was considered avoidance.

Transforming Growth Factor Beta-induced (TGFBI)-related dystrophies constitute the most common heritable forms of corneal dystrophy worldwide. However, other than the underlying genotypes of these conditions, a limited knowledge exists of the exact pathomechanisms of these disorders. This study expands on our previous research investigating dystrophic stromal aggregates, with the aim of better elucidating the pathomechanism of 2 conditions arising from the most common TGFBI mutations: granular corneal dystrophy (GCD1; R555W), and lattice corneal dystrophy (LCD1; R124C). GCD1 and LCD1 patient corneas were stained with H&E and Congo red to visualise stromal non-amyloid and amyloid deposits, respectively. Laser capture microdissection was used to isolate aggregates and extracted protein was analyzed by mass spectrometry. Proteins were identified and their approximate abundances were determined. Spectra of TGFBIp peptides were also recorded and quantified. In total, 3 proteins were found within GCD1 aggregates that were absent in the healthy control corneal tissue. In comparison an additional 18 and 24 proteins within stromal LCD1 and Bowman’s LCD1 deposits, respectively, were identified. Variances surrounding the endogenous cleavage sites of TGFBIp were also noted. An increase in the number of residues experiencing cleavage was observed in both GCD1 aggregates and LCD1 deposits. The study reveals previously unknown differences 1 between the protein composition of GCD1 and LCD1 aggregates, and confirms the presence of the HtrA1 protease in LCD1-amyloid aggregates. In addition, we find mutation specific differences in the processingof mutant TGFBIp species, which may contribute to the variable phenotypes noted in TGFBI-related dystrophies.

Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.

The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.

S1_data_granular flow_velocity provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.

S2_data_granular flow_stress provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.

S3_data_granular flow_flow depth provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs.

S4_data_granular flow_deposit provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.

S5_data_granular flow_density gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.

The videos of the granular flows under different experimental conditions during their propagation are provided in 'S6_video_granular flow.zip' to show the granular flow behavior and its evolution. S6_video_granular flow includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow .

S7_codes_data analysis provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth.

The utilization of fluidized solids techniques in a shale retort, which has a heat transfer surface separating a retorting zone and a combustion zone, offers one possible way to reduce the size of the two zone retort for a given throughput rate. A large heat transfer surface for separating the two zones can be formed conveniently by making a retort similar in style to a conventional vertical shell and tube heat exchanger, with the fluidized shale being retorted in one zone and the spent shale being burned in the other. Fluidization in multiple parallel tubes such as would be formed by such a unit is inherently nonuniform due to the falling pressure drop-flow characteristics of a fluidized bed with increasing fluidization gas velocity.

Lactate is a chemical produced by the body as cells consume energy - in times of stress more lactate is produced. In the past, we thought that lactate was just a waste product, but more recently we have learned that lactate has an important role to play in the body.

People suffering from certain severe illnesses may have a high ‘lactate’ level in their blood. This is particularly common in the following:

Severe infections which the body cannot properly control (sepsis)

People who have sustained severe injuries (traumatic injury)

People who are critically unwell with other illnesses (needing treatment in an intensive care unit)

Some patients will develop a high lactate level when they are in hospital. Doctors recognise that this indicates the patient is becoming more unwell, but it is often challenging to know exactly what is causing the lactate level to be raised.

Raised lactate level has been associated with worse outcome in other syndromes, including major trauma and undifferentiated critical illness; however healthy individuals may generate very high lactate levels during strenuous exercise from which they recover without any harm. It is unclear whether lactate in itself is harmful to patients. This dataset provides unique insight into the potential role of lactate as not only a biomarker but a therapeutic target in acute illness.

PIONEER geography The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”.

Scope: Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (BMI, temperature and weight), Sample analysis results (blood sodium level, lactate, haemoglobin, oxygen saturations, and others) drug administered and all outcomes.

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.