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Steel

Steel: A hard, strong alloy of iron and carbon, used extensively in construction and manufacturing.
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Most cited protocols related to «Steel»

Simulating Ancestral Character Reconstruction Challenges

Cited 62 times

In this study, we basically followed the simulation procedure used in Gascuel and Steel (2014) (link). We generated pure-birth trees with n = 1,000 tips. To obtain a broad range of ACR difficulties, we used 16 values of the speciation/evolutionary rate ratio (ω) ranging from 0.2 to 8.0, which correspond to an average number of state changes per branch of 2.5 and 0.0625, respectively (Steel and Mooers 2010 ). With a high number of state changes per branch (e.g., 2.5) ACR is very difficult, especially for the tree root, whereas with a low number of changes (e.g., 0.0625) ACR becomes easy as all tips and nodes tend to have the same state value. For each value of ω, 50 trees were generated, and for each tree we simulated the evolution of 50 unique characters with 4 states, and 50 with 20 states. To ease the implementation, reproducibility and interpretation of the results, we used DNA and protein models, although the method and software are intended for unique characters. We generated 4-state data sets using Seq-Gen v1.3.2 (Rambaut and Grass 1997 ) and the HKY model (Hasegawa et al. 1985 (link)) with equilibrium frequencies of A, C, G, and T being equal to 0.2, 0.1, 0.3, and 0.4, respectively, and a transition/transversion ratio κ of 8.0. These relatively extreme values were chosen to challenge ACR when using the F81 model implemented in PastML. Likewise, we generated 20-state data sets using Seq-Gen and the JTT model (Jones et al. 1992 ) with its default amino-acid equilibrium frequencies. We thusly obtained 16 (ω values) × 50 (1,000-tip trees) × 50 (number of characters) × 2 (4-state/20-state) data sets to assess the accuracy of ACR methods. During the simulation procedure with Seq-Gen, we recorded the ancestral state of the character seen at each internal node, including the root. Thus, the “true” ancestral scenario was known. All these data sets are available from https://pastml.pasteur.fr/.

Ishikawa S.A., Zhukova A., Iwasaki W, & Gascuel O. (2019). A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios. Molecular Biology and Evolution, 36(9), 2069-2085.

Publication 2019

Amino Acids Biological Evolution Character Childbirth Plant Roots Poaceae Proteins Steel Trees Vision

Rapid Pepsin Digestion and Cooled UPLC-MS

Cited 47 times

Unlabeled proteins, highly deuterated peptides and cytochrome c were analyzed using the UPLC system and conventional HPLC. In both LC-systems, labeled samples (50 µLs) were injected at a flow rate of 100 µL/min into a 2.1 mm × 50 mm stainless steel column that was packed with pepsin immobilized on POROS-20AL beads [prepared as described in8 (link), 9 (link)]. Under these conditions, the digestion time was approximately 30 seconds.
In the HPLC experiments, a Shimadzu HPLC (LC-10ADvp) system was used. Peptic peptides eluting from the online pepsin digestion step were trapped and desalted on a 1 mm × 8 mm C-18 peptide trap (Michrom Biosciences) and desalted for 3 min. The trap was placed inline with the analytical column, a Zorbax C-18, 3.5 µm 300 Å, 1.0 mm × 50 mm column (Agilent Technologies), and eluted into the mass spectrometer with a gradient of 15 to 30% acetonitrile in 6 min at a flow rate of 40 µL/min. HPLC mobile phases contained 0.05 % trifluoroacetic acid. The C-18 peptide trap and analytical column, as well as the injection and switching valves were placed in an ice-bath to maintain the required 0 °C. The mobile phases were kept in a separate ice-bath and then flowed through pre-cooling stainless steel loops (located before the gradient mixing tee) in the main ice-bath to ensure that they were cool prior to meeting deuterated sample. The pepsin column was held above the ice bath at approximately 15 °C9 (link).
In the UPLC experiments, peptic peptides from online pepsin digestion were trapped and desalted on a VanGuard Pre-Column (2.1 mm × 5 mm, ACQUITY UPLC BEH C18, 1.7 µm) for 3 min. The trap was placed in-line with an ACQUITY UPLC BEH C18 1.7 µm 1.0 × 100 mm column (Waters Corp.) and eluted into the mass spectrometer with a 8–40 % gradient of acetonitrile over 6 min at a flow rate of 40 µL/min. The volume of the system from the mixer to the head of the analytical column was ~ 30 µL which includes ~ 8 µL volume of the trap column in line. All mobile phases for the UPLC system contained 0.1 % formic acid.
Mass spectral analyses were carried out on a Waters LCT classic or QToF Premier. The LCT was used for initial validation of the cooled UPLC module chromatography and not for any analyses of deuterium incorporation. LCT classic instrument settings were: 3.2kV cone and 40 V capillary voltages. The LCT source and desolvation temperatures were 150 and 175 °C, respectively with a desolvation gas flow of 1024 L/hour and a cone gas flow of 99 L/hour. LCT mass spectra were acquired using a 0.50 sec scan time and 0.1 sec interscan delay time. QTof instrument settings were: 3.5kV cone and 40 V capillary voltages. The QTof source and desolvation temperatures were 80 and 175 °C, respectively with a desolvation gas flow of 600 L/hour. QTof mass spectra were acquired using a 0.450 sec scan time and 0.050 sec interscan time. All QTof data were collected in ESI (+) and V mode. Deuteration levels were calculated by subtracting the centroid of the isotopic distribution for peptide ions of undeuterated sample from the centroid of the isotopic distribution for peptide ions from the deuterium labeled sample. Deuterium levels were not corrected for back-exchange and are therefore reported as relative 1 (link).

Wales T.E., Fadgen K.E., Gerhardt G.C, & Engen J.R. (2008). High-speed and high-resolution UPLC separation at zero degrees Celsius. Analytical chemistry, 80(17), 6815-6820.

Publication 2008

acetonitrile ARID1A protein, human Bath C-Peptide Capillaries Chromatography Cytochromes c Deuterium Digestion formic acid Head High-Performance Liquid Chromatographies Ions Isotopes Mass Spectrometry Neoplasm Metastasis Pepsin A Peptides Proteins Radionuclide Imaging Retinal Cone Stainless Steel Steel Thrombin Receptor Activating Peptides Trifluoroacetic Acid

Exploring Phylogenetic Tree Landscapes

Cited 37 times

treespace generalizes an approach used by Amenta and Klingner (Amenta & Klingner, 2002) and later by Hillis et al. (2005), implemented as the treesetviz module for mesquite (Maddison & Maddison, 2003). This method used the Robinson–Foulds metric (Robinson & Foulds, 1979, 1981) to visualize relationships between labelled trees with identical tips in a Euclidean space. Here, we generalize this approach to any tree metric, and add the use of multiple clustering approaches to formally identify “tree islands”.
The core idea underlying tree space exploration is to map variability in tree topology or branch length onto a low‐dimensional, Euclidean space, which can then be used for visualizing relationships between the phylogenies and, potentially, to define clusters of similar trees (Figure 1). First, pairwise distances between all pairs of trees in the sample are computed (Figure 1a,b). Typically, measures of distances between trees rely on mapping each phylogeny to a vector of labelled numbers corresponding to pairwise comparisons of tips or internal nodes and then computing the Euclidean distance between the resulting vectors (Figure S1). treespace implements an extensive selection of distances relying on this principle (Kendall & Colijn, 2015; Pavoine et al., 2008; Robinson & Foulds, 1979, 1981; Steel & Penny, 1993; Williams & Clifford, 1971), as well as the BHV metric (Billera, Holmes, & Vogtmann, 2001), which directly computes distances between trees without intermediate feature extraction (Table 1).
Once pairwise distances between trees are computed, they are decomposed into a low‐dimensional space using metric multidimensional scaling (MDS), also known as principal coordinate analysis (PCoA, Gower, 1966; Dray & Dufour, 2007; Legendre & Legendre, 2012). This method finds independent (uncorrelated) synthetic variables, the “principal components” (PCs), which represent as well as possible the original distances inside a lower‐dimensional space (Figure 1c). By inspecting the proportion of the total distances between trees represented by specific axes (the “eigenvalues” of the different PCs), one can assess the number of relevant PCs to examine and, ideally, separate structured phylogenetic variation from random noise (Legendre & Legendre, 2012). Importantly, MDS can only be applied to Euclidean distances (Legendre & Legendre, 2012). In the case of non‐Euclidean tree distances (Billera et al., 2001; Robinson & Foulds, 1981), we use Cailliez's transformation (Cailliez, 1983) to render these distances Euclidean before MDS.
Exploring tree spaces using MDS allows the main features of a given phylogenetic landscape to be explored and evaluated. In particular, the resulting typology may exhibit discrete clusters of related trees (the “phylogenetic islands”), indicating that several distinct phylogenies may actually be supported by the data (Figure 1c). To identify such clusters formally, we implemented various hierarchical clustering methods based on the projected distances, including the single linkage, complete linkage, Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and Ward's method (Legendre & Legendre, 2012).
This approach allows the user to seek representative trees for each cluster separately (Figure 1d). A method for selecting such representative trees is given in Kendall and Colijn (2015) and implemented in treespace as the function “medTree.” This function identifies the geometric median tree(s), which are the tree(s) closest to the mean of the Kendall–Colijn tree vectors for a given cluster. Such trees serve as alternatives to other summary tree approaches such as the consensus tree (Felsenstein, 1985) or the maximum clade credibility (MCC) tree (Drummond & Rambaut, 2007; Ronquist & Huelsenbeck, 2003), with the key advantage that they correspond to specific trees in the sample, thus avoiding implausible negative branch lengths (Heled & Bouckaert, 2013). However, given a collection of trees in a cluster, any summary approach such as MCC could be used.
All the functionalities described above are implemented in treespace as standard R functions, fully documented in a vignette tutorial, as well as in a user‐friendly web interface for interactive data analysis. This interface can be started locally (i.e. without Internet connection) from R using a simple instruction (treespaceServer()) and, therefore, demands virtually no knowledge of the R language. Alternatively, we also provide an online instance of the application at http://shiny.imperial-stats-experimental.co.uk/users/mlkendal/treespace

Jombart T., Kendall M., Almagro‐Garcia J, & Colijn C. (2017). treespace: Statistical exploration of landscapes of phylogenetic trees. Molecular Ecology Resources, 17(6), 1385-1392.

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Publication 2017

Cloning Vectors Epistropheus Prosopis Steel Trees

Fly Locomotion Tracking Platform

Cited 37 times

The walking arena used in most of our experiments consisted of a temperature-controlled 24.5 cm diameter platform surrounded by a static backlit visual pattern (Fig. 1). Flies were maintained in the arena by a thermal barrier around the outside edge of the walking platform and by clipping the wings as described above. The thermal barrier consisted of a rope heater wrapped around a galvanized steel band insulated from the platform by a layer of neoprene. Although some flies would occasionally hop over the arena’s edge, most would avoid walking off the platform due to the heat barrier. Above the arena were mounted infrared-LEDs and a 1280×1024 pixel camera sensitive in the near-infrared. Images were recorded at 20 fps by a computer using the Motmot Python camera interface package31 . For more details see the Supplementary Note. Although our software was developed in conjunction with this set up, it is adaptable to other arrangements with similar characteristics (Supplementary Videos 6–7).

Branson K., Robie A., Bender J., Perona P, & Dickinson M. (2009). High-throughput Ethomics in Large Groups of Drosophila. Nature methods, 6(6), 451-457.

Publication 2009

Diptera Neoprene Python Steel

Expiratory Droplet Measurement Setup

Cited 35 times

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Publication 2008

Exhaling Eye Fingers Head Humidity Light Oral Cavity Radiation Reflex Safety Steel Voluntary Workers

Most recents protocols related to «Steel»

Novel Fluoropolymer Synthesis and Characterization

Not available on PMC !

EXAMPLE 1

In an AISI 316 steel vertical autoclave, equipped with baffles and a stirrer working at 570 rpm, 3.5 liter of demineralized water were introduced. The temperature was then brought to reaction temperature of 80° C. and the selected amount of 34% w/w aqueous solution of cyclic surfactant of formula (VI) as defined above, with X_a=NH₄, was added. VDF and ethane were introduced to the selected pressure variation reported in Table 1. A gaseous mixture of TFE-VDF in the molar nominal ratio reported in Table 1 was subsequently added via a compressor until reaching a pressure of 20 bar. Then, the selected amount of a 3% by weight water solution of sodium persulfate (NaPS) as initiator was fed. The polymerization pressure was maintained constant by feeding the above mentioned TFE-VDF while adding the PPVE monomer at regular intervals until reaching the total amount indicated in the table 1.

When 1000 g of the mixture were fed, the reactor was cooled at room temperature, the latex was discharged, frozen for 48 hours and, once unfrozen, the coagulated polymer was washed with demineralized water and dried at 160° C. for 24 hours.

The composition of the obtained polymer F-1, as measured by NMR, was Polymer (F-1)(693/99): TFE (69.6% mol)—VDF (27.3% mol)—PPVE (2.1% mol), having melting point T_m=218° C. and MFI=5 g/10′.

The procedure of example 1 was repeated, by introducing the amount of ingredients indicated in the third column of Table 1.

The composition of the obtained polymer P-1, as measured by NMR, was Polymer (C-1)(693/67): TFE (71% mol)—VDF (28.5% mol)—PPVE (0.5% mol), having melting point T_m=249° C. and MFI=5 g/10′.

EXAMPLE 2

The procedure of example 1 was repeated, by introducing the amount of ingredients indicated in the second column of Table 1.

The composition of the obtained polymer F-2, as measured by NMR, was Polymer (F-1)(693/100): TFE (68% mol)—VDF (29.8% mol)—PPVE (2.2% mol), having melting point T_m=219° C. and MFI=1.5 g/10′.

In an AISI 316 steel horizontal reactor, equipped with a stirrer working at 42 rpm, 56 liter of demineralized water were introduced. The temperature was then brought to reaction temperature of 65° C. and the selected amount of 40% w/w aqueous solution of cyclic surfactant of formula (VI) as defined above, with X₁=NH₄, was added. VDF and ethane were introduced to the selected pressure variation reported in Table 1.

A gaseous mixture of TFE-VDF in the molar nominal ratio reported in Table 1 was subsequently added via a compressor until reaching a pressure of 20 bar.

Then, the selected amount of a 0.25% by weight water solution of sodium persulfate (NaPS) as initiator was fed. The polymerization pressure was maintained constant by feeding the above mentioned TFE-VDF while adding the PPVE monomer at regular intervals until reaching the total amount indicated in the table 1.

When 16000 g of the mixture were fed, the reactor was cooled at room temperature, the latex was discharged, frozen for 48 hours and, once unfrozen, the coagulated polymer was washed with demineralized water and dried at 160° C. for 24 hours. The composition of the obtained polymer C-2, as measured by NMR, was Polymer (C-2)(SA1100): TFE (70.4% mol)—VDF (29.2% mol)—PPVE (0.4% mol), having melting point T_m=232° C. and MFI=8 g/10′.

EXAMPLE 3

The procedure of Comparative Example 2 was repeated, by introducing the following changes:

- demineralized water introduced into the reactor: 66 litres;
- polymerization temperature of 80° C.
- polymerization pressure: 12 abs bar
- Initiator solution concentration of 6% by weight
- MVE introduced in the amount indicated in table 1
- Overall amount of monomers mixture fed in the reactor: 10 000 g, with molar ratio TFE/VDF as indicated in Table 1.

All the amount of ingredients are indicated in the fifth column of Table 1.

The composition of the obtained polymer (C-3), as measured by NMR, was Polymer (C-3)(693/22): TFE (72.1% mol)—VDF (26% mol)—PMVE (1.9% mol), having melting point T_m=226° C. and MFI=8 g/10′.

TABLE 1

(F-1)(F-2)(C-1)(C-2)(C-3)

Surfactant solution [g]505050740800

Surfactant [g/l]4.854.854.855.284.12

Initiator solution [ml]1001001002500600

Initiator [g/kg]3.03.03.00.396.0

VDF [bar]1.81.801.81.8

TFE/VDF mixture 70/3070/3070/3070/3069/30¹

[molar ratio]

FPVE [g]122122316600²

Ethane [bar]0.60.30.2520.1

¹gaseous mixture containing 1% moles of perfluoromethylvinylether (FMVE);

²initial partial pressure of FMVE 0.35 bar.

The results regarding polymers (F-1), (F-2) of the invention, and comparative (C-1), (C-2) and (C-3) are set forth in Table 2 here below

TABLE 2

693/99693/100693/67SA1100693/14

(F-1)(F-2)(C-1)(C-2)(C-3)

Elongation at5777392904035

break [%, 200° C.]

Tensile modulus425374484594500

[MPa, 23° C.]

Tensile yield stress11.611.414.015.512.5

[MPa, 23° C.]

Tensile modulus29385676—

[MPa, 170° C.]

Tensile modulus1210484723

[MPa, 200° C.]

SHI [MPa, 23° C.]3.65.11.91.61.7

ESR as yieldingNoNoYieldingYieldingYielding

[time, 23° C.]YieldingYieldingafter 1after 1after 1

minminmin

In particular, the polymer (F) of the present invention as notably represented by the polymers (F-1), (F-2), surprisingly exhibits a higher elongation at break at 200° C. as compared to the polymers (C-1) and (C-2) of the prior art.

Also, the polymer (F) of the present invention as notably represented by the polymers (F-1), (F-2), despite its lower tensile modulus, which remains nevertheless in a range perfectly acceptable for various fields of use, surprisingly exhibits a higher strain hardening rate by plastic deformation as compared to the polymers (C-1) and (C-2) of the prior art.

Finally, the polymer (F) of the present invention as notably represented by the polymers (F-1) and (F-2) surprisingly exhibits higher environmental stress resistance when immersed in fuels as compared to the polymers (C-1) and (C-2) of the prior art.

Yet, comparison of polymer (F) according to the present invention with performances of polymer (C-3) comprising perfluoromethylvinylether (FMVE) as modifying monomer shows the criticality of selecting perfluoropropylvinylether: indeed, FMVE is shown producing at similar monomer amounts, copolymer possessing too high stiffness, and hence low elongation at break, unsuitable for being used e.g. in O&G applications.

US11859033B2. Melt-processible fluoropolymer (2024-01-02). SOLVAY SPECIALTY POLYMERS ITALY S.P.A. [IT]. Inventors: Mattia Bassi [IT], Alessio Marrani [IT], Valeriy Kapelyushko [IT].

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Patent 2024

Ethane Fluorocarbon Polymers Freezing G-800 Gases Latex Molar N-(4-aminophenethyl)spiroperidol Nevus Partial Pressure Polymerization Polymers Pressure Sclerosis sodium persulfate Steel Surface-Active Agents

Characterizing Ester Blend Friction and Wear

Not available on PMC !

Example 3

Reciprocating tests were used to characterize both friction and wear behavior of the ester blends at 25° C. and 40° C. under boundary lubrication. As mentioned prior, each ester was blended at a concentration of 1% by weight. Neat oil served as the control. The testing device is a custom ball-on-flat microtribometer as seen in FIG. 3 and was operated in conjunction with a temperature-controlled stage. In brief, precise normal loading of a probe onto the sample substrate is performed via software controlled linear stages. The sample substrate is forced to slide against the probe and subsequent lateral or frictional forces are measured. The temperature-controlled stage consists of an aluminum block that contains an oil reservoir. The block's temperature is monitored and controlled via an adhesive thermocouple connected to a PID controller. In addition, the oil temperature is monitored with a thermistor. Prior to testing, an equal volume filled the reservoir and the oil temperature was equilibrated. The oil level sits just above the substrate surface so there is a constant supply of oil into the contact zone.

Reciprocating tests were carried out using a SiC-steel interface: a 4 mm diameter silicon carbide ball on an AISI 8620 steel substrate. The ceramic was chosen for its superior hardness relative to the substrate in order to isolate the majority of the wear to the substrate and preserve the probes geometry. In this way, a consistent contact pressure can be maintained. A constant normal load of 3.4 N (maximum Hertzian pressure of 1.5 GPa) was applied as the substrate was translated at a rate of 10 mm/s over a 8 mm stroke length for 4500 cycles. The load was chosen after initial tests with the PEs at 1.0 GPa were not sufficient to generate measureable wear scars (wear depths were on the same order as the surface roughness). The substrate was isotropically polished to a finish of 0.043 μm Ra determined from a scan area of 1.41 mm×1.88 mm using a Zygo optical profilometer. Based on EHL theory, the roughness, load, and viscosity parameters placed this study well within the boundary lubrication regime as the estimated λ ratio was much less than one.

After test completion, the substrate and probes were wiped with isopropyl alcohol before undergoing SEM and EDS analysis. In addition, the substrate wear scars were scanned using the Zygo optical profilometer. Nine to eleven unique scan areas were gathered to capture the entire length of each scar. All topographic and force data was then imported into MATLAB where the average wear depth and coefficient of friction was calculated. Three replicate tests were completed for each treatment.

US11866671B2. Base oil or lubricant additive (2024-01-09). Iowa State University Research Foundation, Inc. [US]. Inventors: George A. Kraus [US], Kyle Podolak [US], Derek Lee White [US], Sriram Sundararajan [US].

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Patent 2024

Aluminum Cardiac Arrest Cerebrovascular Accident Cicatrix DNA Replication Esters Friction Isopropyl Alcohol Lubrication Medical Devices Oil Reservoirs Pressure Radionuclide Imaging Steel Viscosity Vision

Stearic Acid Metal Powder Deposition

Not available on PMC !

Example 7

Stearic acid was mixed with copper (5 g SA:50 g Copper) or steel (15 g SA:100 g Steel), heated and deposited onto a surface to build up objects (FIG. 16).

The employed copper (SPHERICAL, APS 10 MICRON) had an average particle size around 10 micrometer.

The employed steel was a type 316-L (Mesh 325). Thus, the particles have a size equal to or below 44 mikrometer.

In sum, mixing metal powders with stearic acid enable heated deposition and subsequent solidification of the SA/metal mixture.

US11877900B2. Process for 3D printing (2024-01-23). Ossiform ApS [DK]. Inventors: Morten Østergaard Andersen [DK], Martin Bonde Jensen [DK], Casper Slots [DK].

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Patent 2024

Copper Metals Powder stearic acid Steel

Hydrochloric Acid Pickling and Ultrafiltration

Not available on PMC !

Example 1

The recirculation tank 3 contains 60 m³of hydrochloric acid at 15% having a silicon content of roughly 59 mg·L⁻¹. The acid pickles different steel grades, e.g.: interstitial steel, medium carbon, HSLA and dual phase steels. The used pickling acid is sent by pumps to the ultrafiltration device at a flow of 17 m³·h⁻¹. The ultrafiltration device 2 is made of 68 m²of ceramic membrane area having a pore size of 7 nm (10 kDa Molecular Weight). A flow of 14 m³·h⁻¹of filtered flow containing 38 mg·L⁻¹of silicon is fed back inside the bath while a flow of 3 m³·h⁻¹of unfiltered flow containing 157 mg·L⁻¹.

Flow rateSi concentrationColloidal and suspended

[m³· h⁻¹][mg · L⁻¹]matter > 7 nm

Entering flow1759100

Filtered flow14380

Unfiltered flow3157100

US11879174B2. Method for pickling steel sheets (2024-01-23). ArcelorMittal [LU]. Inventors: Andrea Naves Arnaldos [ES], Elena Piedra Fernandez [ES], Vanesa Menendez Delmiro [ES], Salome Lopez Gonzalez [ES].

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Patent 2024

Acids AT2G25170 protein, Arabidopsis Bath Carbon Hydrochloric acid Medical Devices Silicon Steel Tissue, Membrane Ultrafiltration

Domestic Abuse Narratives and Homelessness

Repeated experiences of domestic abuse were apparent in the biographies of almost all women though it was not always perceived as such. Relationships were often idealised in the first few months then quickly descended into abuse:

You think you find the right person, you think they’re so nice and everything’s perfect for the first 6 to 12 months and then after 12 months it just goes pfffft. Like woah. And by the time that’s happened you’re just too far involved. And then you end up the one that’s out on the street (Rosa).

One of the most harmful aspects of domestic abuse is detachment from social networks, thus further deepening exclusion. Here, Sally describes being isolated her from family and friends and eventually her children: Nobody knew what was going on. So I eventually left, and unknown to me … I was made out to be the bad person, like a complete weirdo (Sally).
Several women described long term physical and mental health impact resulting from injuries caused by their partner. Dee was using heroin to manage chronic pain caused by physical injuries as well as trauma from abuse: “I was married once. And I’d never do it again. He was a woman batterer. Steel plate in my head. He was so violent” (Dee).
Other women described how their partner provided resources but also perpetuated further trauma:

he used to say “you’ve got nobody. You’ll never go hungry if you stay with me...” And it’s just hard like. I struggle every day. So it’s like I’m either, it’s easier for food, I’d get lifts if I needed to go to places or I’m not being with that person and struggle. Erm, but not arguing and not fighting. It’s just hard (Sienna).

Michelle describes how her relationship commands a lot of her attention and energy, with expressions of affection interspersed with mental turmoil and uncertainty:

Me partner who lives with me, [name], he’s really well known here. He got kicked out of a hostel a while ago and that’s how I met him... he’s playing us [me] along saying he loves me and wants to be with me, and it’s ripping me to bits, my head’s battered. … he doesn’t have a good word for us. Constantly puts us down. I don’t know. But he walked away a couple of month ago when he got paid, spent £750 left me with not a penny and went away for a week and come back when he had nothing. I knew then, he didn’t love me. No-one who loved someone would do that to them. You know. I couldn’t see the lad on the streets, I just couldn’t (Michelle).

Amongst the women who had exited homelessness, many chose to live alone: “I mean I just don’t intend getting into a relationship to discover how to have one. I’m done. I’ve had enough bad ones. I’ve loved, and I’ve been loved back a couple of times. But it hurts even harder when they’re the ones that try to kill you” (Tracy).
Most of the women who had successfully exited homelessness actively avoided situations where they might meet a new partner and expressed no desire for intimate relationships. This perhaps relates to not only their overwhelmingly bad experiences of relationships, but provides context to their perception of relationships primarily driven by necessity to obtain shelter, protection and resources.

McGrath J., Crossley S., Lhussier M, & Forster N. (2023). Social capital and women’s narratives of homelessness and multiple exclusion in northern England. International Journal for Equity in Health, 22, 41.

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Publication 2023

Attention Child Chronic Pain Drug Abuse ErbB Receptors Food Friend Head Heroin Hunger Injuries Mental Health Physical Examination Rosa Steel Woman Wounds Wounds and Injuries

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JMP Pro 14 is a statistical discovery software designed for data analysis and visualization. It provides a range of tools for exploring, modeling, and interpreting data. JMP Pro 14 supports various data types and offers features for regression analysis, clustering, and predictive modeling.

Trizol reagent by Thermo Fisher Scientific

Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand

TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.

Jmp pro by SAS Institute

Sourced in United States, Japan, Germany, Canada, Austria

JMP Pro is an advanced data analysis software developed by SAS Institute. It provides sophisticated statistical and visual analysis tools to assist users in exploring, modeling, and interpreting complex data. JMP Pro offers a range of analytical capabilities, including multivariate techniques, predictive modeling, and interactive visualizations, enabling users to gain deeper insights from their data.

What are the different types or variations of steel?

Steel comes in a variety of types and compositions, each with its own unique properties and applications. Some common steel variations include carbon steel, stainless steel, tool steel, and alloy steel. Each type is engineered to meet specific performance and durability requirements, such as corrosion resistance, hardness, or strength. Understanding the differences between these steel types is crucial when selecting the right material for your project or research needs.

How can PubCompare.ai help optimize my steel research?

PubCompare.ai is a powerful tool that can streamline and enhance your steel research in several ways. First, it allows you to efficiently screen protocol literature from a wide range of sources, including publications, pre-prints, and patents. This can save you time and effort in locating the most relevant information. Second, the platform's AI-driven analysis can help you identify critical insights that might be missed in manual reviews, such as key differences in the effectiveness of various steel protocols. This can enable you to choose the most reproducible and accurate approaches for your specific research goals. By leveraging PubCompare.ai, you can experience the future of reproducible steel research today.

What are some common applications of steel?

Steel is an incredibly versatile material with a wide range of applications. It is extensively used in construction for building frameworks, bridges, and infrastructure. In manufacturing, steel is a crucial component in the production of automobiles, machinery, appliances, and a variety of other industrial products. Steel is also used in the production of cutting tools, surgical instruments, and other specialized equipment due to its durability and strength. The unique properties of different steel types make it an indispensable material across many industries and applications.

What are some challenges in using steel effectively?

While steel is a remarkably strong and durable material, there are some challenges that researchers and professionals may encounter when working with it. One common issue is corrosion, which can compromise the structural integrity of steel components over time. Proper coatings, finishes, and maintenance are often required to mitigate corrosion. Another challenge is the complexity of steel manufacturing and processing, which can impact the final properties and performance of the material. Researchers may need to carefully consider factors like heat treatment, alloying, and manufacturing techniques to ensure the steel meets their specific requirements. Fortunately, PubCompare.ai can help you navigate these challenges by providing access to a wealth of information and insights on the most effective steel protocols and best practices.

How can PubCompare.ai assist in comparing steel protocols and products?

PubCompare.ai's AI-driven platform is designed to help researchers compare steel protocols and products with unprecedented ease and accuracy. The tool can analyze a wide range of literature, including publications, pre-prints, and patents, to identify key differences in the effectiveness, reproducibility, and suitability of various steel protocols. This allows you to make informed decisions about the best approaches for your specific research needs. Additionally, the platform's AI-powered comparisons can highlight the unique features and performance characteristics of different steel products, enabling you to select the most appropriate materials for your projects. By leveraging PubCompare.ai, you can streamline your steel research and experiance the future of reproducible science.

More about "Steel"

Steel is a versatile and essential material that has transformed industries and shaped the modern world.
This ferrous alloy, composed primarily of iron and carbon, is renowned for its exceptional strength, durability, and corrosion resistance.
From the towering skyscrapers that dot the skyline to the intricate machinery that powers our economy, steel is the backbone of countless engineering marvels.
Discover the power of cutting-edge tools like PubCompare.ai, the leading AI-driven platform that optimizes steel research.
This revolutionary tool enables researchers and professionals to effortlessly locate protocols from literature, pre-prints, and patents, while leveraging AI-driven comparisons to identify the best protocols and products.
Streamline your steel research and experience the future of reproducible research today.
Steel's versatility extends beyond its structural applications.
It is also a crucial component in the manufacturing of a wide range of products, from automobiles and household appliances to surgical instruments and TissueLyser II equipment.
The TissueLyser, a popular tool for homogenizing tissue samples, relies on the strength and precision of steel components to ensure reliable and efficient sample preparation.
As the industry continues to evolve, advancements in steel technology, such as those powered by SAS 9.4 and JMP software, have enabled researchers and engineers to push the boundaries of what's possible.
From developing new alloys with enhanced properties to optimizing manufacturing processes, these cutting-edge tools and software solutions are transforming the way we think about steel.
In addition to its practical applications, steel also plays a crucial role in the development of cutting-edge research tools like the TissueLyser LT and TRIzol reagent.
These innovative products, leveraging the durability and precision of steel, are revolutionizing the way scientists conduct their experiments, leading to groundbreaking discoveries and advancements in fields ranging from materials science to biotechnology.
As the demand for steel continues to grow, the industry's reliance on tools like JMP Pro 14 and SAS version 9.4 will only intensify.
These powerful software solutions enable researchers and engineers to analyze data, optimize processes, and make informed decisions that drive innovation and progress in the steel industry.
Whether you're a materials scientist, an engineer, or simply someone fascinated by the remarkable capabilities of steel, exploring the insights and resources provided by platforms like PubCompare.ai and leveraging the power of cutting-edge tools and software can help you stay at the forefront of this dynamic and ever-evolving field.
Experiance the future of steel research and innovation today.