The dataset is a Soil Corrosivity Map for the U.K. based on the BGS DIGMapGB-PLUS Map. The creation of this dataset involves scoring the Soil Parent Material types for five different attributes that contribute towards the corrosion of underground assets. These are (i) high or low soil pH, (ii) general soil moisture, (iii) the likelihood that soil saturated and undergo periods of anaerobic conditions, (iv) the presence of sulphides and sulphates and (v) the resistivity of the soil parent material. The scoring of each of these parameters was undertaken based on the Cast Iron Pipe Association (CIPA) (now the Ductile Iron Pipe Research Association, DIPRA) rating system. By combining the scores of each parameter a GIS layer has been created that identifies those areas that may provide a corrosive environment to underground cast iron assets. The final map has been classified into three categories signifying: 'GROUND CONDITIONS BENEATH TOPSOIL ARE UNLIKELY TO CAUSE CORROSION OF IRON', 'GROUND CONDITIONS BENEATH TOPSOIL MAY CAUSE CORROSION TO IRON', 'GROUND CONDITIONS BENEATH TOPSOIL ARE LIKELY TO CAUSE CORROSION TO IRON'. The dataset is designed to aid engineers and planners in the management of and maintenance of underground ferrous assets.

Automated Corrosion Mapping Market Outlook

The automated corrosion mapping market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 4.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 16.2% during the forecast period. This market growth is driven largely by the increasing demand for efficient and reliable corrosion detection solutions across various industries.

One of the primary growth factors for the automated corrosion mapping market is the rising awareness about the economic and safety impacts of corrosion. Corrosion can lead to significant financial losses due to the deterioration of infrastructure and machinery, particularly in industries such as oil and gas, power generation, and transportation. As a result, there is a heightened demand for advanced technologies that can detect and map corrosion accurately, thereby enabling timely maintenance and reducing overall costs.

Technological advancements in the field of non-destructive testing (NDT) have also significantly contributed to the market growth. Innovations in ultrasonic testing, eddy current testing, and magnetic flux leakage have enhanced the accuracy and efficiency of corrosion mapping. These technologies have become more sophisticated, enabling detailed imaging and analysis, which helps in the early detection of potential failure points. The integration of artificial intelligence and machine learning with these technologies further enhances their predictive capabilities, making them indispensable tools for maintenance and safety protocols.

Furthermore, the increasing regulatory requirements and safety standards across various industries are also driving the adoption of automated corrosion mapping solutions. Governments and regulatory bodies worldwide are imposing stringent safety norms to prevent accidents and ensure the longevity of critical infrastructure. Compliance with these regulations necessitates regular inspection and maintenance, thereby boosting the demand for advanced corrosion mapping technologies. Additionally, the growing emphasis on environmental sustainability is encouraging industries to adopt methods that minimize material wastage and extend the life of assets.

Regionally, North America currently dominates the automated corrosion mapping market, thanks to its well-established industrial base and stringent regulatory framework. However, significant growth is also anticipated in the Asia Pacific region, driven by rapid industrialization and infrastructure development. Countries like China and India are investing heavily in upgrading their industrial and infrastructural facilities, which is expected to drive the demand for corrosion mapping technologies. Europe remains a steady market, with ongoing advancements in the automotive and aerospace sectors contributing to the demand.

Technology Analysis

In the realm of automated corrosion mapping, ultrasonic testing stands out as a predominant technology. Ultrasonic testing uses high-frequency sound waves to detect and characterize corrosion, offering a non-invasive method that can provide detailed images of the internal structure of materials. This technology is widely used in industries where precision and reliability are paramount, such as aerospace and power generation. Recent advancements have led to the development of phased array ultrasonic testing, which allows for the inspection of complex geometries and provides a higher resolution of corrosion mapping, further driving its adoption.

Eddy current testing (ECT) is another crucial technology in the automated corrosion mapping market. ECT operates on the principle of electromagnetic induction to detect surface and near-surface corrosion. This technology is particularly effective in inspecting conductive materials and is extensively used in the aerospace industry for the inspection of aircraft structures and components. The non-contact nature of ECT makes it a preferred choice for applications where surface preparation is challenging or time-consuming. The integration of advanced data analytics with ECT systems has enhanced their diagnostic capabilities, making them more accurate and efficient.

Magnetic flux leakage (MFL) technology is also gaining traction in the market, particularly for the inspection of ferromagnetic materials such as steel pipelines and storage tanks. MFL involves the use of magnetic fields to detect and map corrosion, providing a rapid and reliable method for assessing the integrity of large structures. This technology is highly valued in the oil and gas industry, wh

Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations.Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry.The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection.Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992) and SEV based on visual estimation of the percent flag leaf area affected by the pathogen including associated chlorosis and necrosis (0-100%).DNA was extracted from seedlings, and genotyping-by-sequencing was conducted as described previously (Guttieri, 2020) on a subset of 189 lines (187 RILS and 2 parents) of which 23 RILs were F6-derived and 164 RILs were F9-derived. Single nucleotide polymorphisms (SNPs) were identified in parallel using reference-based calling in the TASSEL pipeline (Bradbury et al., 2007) using both the IWGSC v2.1 reference genome (Zhu et al., 2021) and the Jagger reference sequence (Wheat Genomes Project (http://www.10wheatgenomes.com/10-wheat-genomes-project-and-the-wheat-initiative/). The TASSEL pipeline was executed with the following parameters: minimum read count = 1, minimum quality score = 0, minimum locus coverage = 0.19, and minimum minor allele frequency = 0.005, minimum heterozygous proportion = 0, and removal of minor SNP states. The resulting SNP datasets from each reference sequence were filtered in TASSEL by taxa (RILs) and sites (SNPs). The RILs were filtered to include those RILs for which at least 20% sites were present. The sites were filtered to include sites for which > 60% of RILs were called, minor allele frequency (MAF) > 0.25, maximum allele frequency < 0.75, maximum heterozygous proportion = 0.25, and removal of minor SNP states. The ABH plugin in TASSEL was applied to this reduced dataset to identify parental genotypes.Resources in this dataset:Resource Title: Multilocation Stripe Rust Data File Name: MultiLocRawData_Yr.xslxResource Title: OvOv_CS_TasselSNPCalls File Name: KSM17-OvOv-parentsmerge1.hmp.txt Resource Description: Output of TASSEL GBS SNP calling pipeline using Chinese Spring v2 refseq. Starting point for map construction pipeline.Resource Title: OvOv GBS SNP Calls Jagger RefSeq File Name: KSM17-OvOv-Jaggerpmerge1.hmp.txt Resource Description: TASSEL output from reference-based SNP calling using the Jagger reference sequenceResource Title: QTL-Associated KASP Markers with IT and SEV BLUPs File Name: KASP_Data_IT_SEV.xlsx Resource Description: Multilocation best linear unbiased predictors (BLUPs) for stripe rust infection type and severity of recombinant inbred lines. KASP assay results for QTL-associated SNPs, coded Overley = 2, Overland = 0, Het = 1, Missing = "."

The automatic detection of the degree of surface corrosion on metal structures is of significant importance for assessing structural damage and safety. To effectively identify the corrosion status on the surface of coastal metal facilities, this study proposed a CBG-YOLOv5s model for metal surface corrosion detection, based on the YOLOv5s model. Firstly, we integrated the Convolutional Block Attention Module (CBAM) into the C3 module and developed the C3CBAM module. This module effectively enhanced the channel and spatial attention capabilities of the feature map, thereby improving the feature representation. Second, we introduced a multi-scale feature fusion concept in the feature fusion part of the model and added a small target detection layer to improve small target detection. Finally, we designed a lighter C3Ghost module, which reduced the number of parameters and the computational load of the model, thereby improving the running speed of the model. In addition, to verify the effectiveness of our method, we constructed a dataset containing 6000 typical images of metal surface corrosion and conducted extensive experiments on this dataset. The results showed that compared to the YOLOv5s model and several other commonly used object detection models, our method achieved superior performance in terms of detection accuracy and speed.

"Risk of corrosion" pertains to potential soil-induced electrochemical or chemical action that corrodes or weakens uncoated steel. The rate of corrosion of uncoated steel is related to such factors as soil moisture, particle-size distribution, acidity, and electrical conductivity of the soil. Special site examination and design may be needed if the combination of factors results in a severe hazard of corrosion. The steel in installations that intersect soil boundaries or soil layers is more susceptible to corrosion than the steel in installations that are entirely within one kind of soil or within one soil layer.

The risk of corrosion is expressed as "low," "moderate," or "high."

SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey. The most common use of these data is communication of soil conditions to contractors working in the park. Additional uses of these data include analysis by park partners and researchers of the physical and chemical properties of soils, including their effect and influence on the management of natural habitats, ecosystem health, and natural resource inventory.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a 7.5 minute quadrangle format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is required. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Record relational database, which gives the proportionate extent of the component soils and their properties.

These data represent a specific interpretation of the SSURGO soils data produced by the NRCS, using the NRCS Soil Data Viewer version 6.0. Building site development interpretations are designed to be used as tools for evaluating soil suitability and identifying soil limitations for various construction purposes. As part of the interpretation process, the rating applies to each soil in its described condition and does not consider present land use. Example interpretations can include corrosion of concrete and steel, shallow excavations, dwellings with and without basements, small commercial buildings, local roads and streets, and lawns and landscaping.

This is a hybrid data product produced using NRCS SSURGO soils data. These data should not be considered SSURGO-compliant, as data used in this product is the result of merging data from several separate SSURGO databases. The NRCS does not endorse or support this hybrid product.These data are authoritative data published by the National Park Service. Search for additional authoritative park GIS and Map data within this system by performing a keyword search of "Great Smoky Mountains National Park". These data can also be accessed through the National Park Service Integrated Resource Management Applications Portal using Reference Codes 2219228 and 2198041

Pogo model files used in PRSA paper by AAE Zimmermann, P Huthwaite, B Pavlakovic, 2020, 'High-resolution thickness maps of corrosion using SH1 guided wave tomography'.

These models utilise frames to excite various transducer types:

Directional rectangular SH transducer array on surface

pogoSolve3d modelname --startFrame 1 --stopFrame 120

SH0 point source transducer array

pogoSolve3d modelname --startFrame 121 --stopFrame 240

SH1 point source transducer array

pogoSolve3d modelname --startFrame 241 --stopFrame 360

SH2 point source transducer array

pogoSolve3d modelname --startFrame 361 --stopFrame 480

SH point source transducer array on surface

pogoSolve3d modelname --startFrame 481 --stopFrame 600

Concrete, metals and other materials gradually deteriorate through chemical reactions with their environment. This process, known as corrosion, occurs slowly in some soils and more quickly in others. When in contact with the soil, concrete can deteriorate due to a chemical reaction between a base (concrete) and a weak acid (wet soil). This layer classifies soils with low, moderate, and high rates of corrosion of concrete based on the soil texture, organic content, pH, and chemical composition. For more information on the classification system, see the National Soil Survey Handbook Part 618.80 Guides for Estimating Risk of Corrosion Potential for Uncoated Steel. Dataset SummaryPhenomenon Mapped: Corrosion of concreteGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: Three classes - low, medium, and highCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for concrete corrosion is derived from the gSSURGO component table field Corrosion Concrete (corcon). The value in this layer is the dominant condition found within the map unit. What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selectingAddthenBrowse Living Atlas Layers. A window will open. Type "concrete corrosion" in the search box and browse to the layer. Select the layer then clickAdd to Map.In ArcGIS Pro, open a map and selectAdd Datafrom the Map Tab. SelectDataat the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then selectLiving Atlas. Type "concrete corrosion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. The ArcGIS Living Atlas of the World provides an easy way to explore many otherbeautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

​Metals and other materials gradually deteriorate through chemical reactions with their environment. This process, known as corrosion, occurs slowly in some soils and more quickly in other. When in contact with soil, the iron in steel is dissolved and the steel weakened through chemical processes that convert iron into its ions. This layer classifies soils with low, moderate, and high rates of corrosion of uncoated steel based on drainage class, acidity, and electrical conductivity of the soil. For more information on the classification system, see the National Soil Survey Handbook.Low corrosion riskModerate corrosion riskHigh corrosion risk Dataset SummaryPhenomenon Mapped: Corrosion of steelGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m soils produced by the Natural Resources Conservation Service (NRCS). The value for steel corrosion is derived from the gSSURGO component table field Corrosion Steel (corsteel). The value in this layer is the dominant condition found within the map unit. What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "steel corrosion" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "steel corrosion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

31454 Global exporters importers export import shipment records of Corrosion prevention with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

These pictures are the raw AFM topography and Volta potental map at different depths from the surface of the material treated at 0.2 MPa. These Excel tables are the residual stress, average Volta potential, and potential distribution (slightly processed) at different depths from the surface of the material treated at 0.2 MPa.

Wheat Rust Images For Diseases Map

532 Global exporters importers export import shipment records of Vci corrosion protect with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

10 Global import shipment records of Corrosion Chambers with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

73 Global import shipment records of Corrosion Inhibitor with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, and QYR-APR-4D.2) for YR APR and four QTLs (QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, and QLR-APR-5A.2) for LR APR were identified. In silico analysis revealed that the key putative candidate genes such as Cytochrome P450, Protein kinase-like domain superfamily, Zinc-binding ribosomal protein, SANT/Myb domain, WRKY transcription factor, Nucleotide-sugar transporter, and NAC domain superfamily were in the QTL regions and involved in the regulation of host response towards the pathogen infection. The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS). Methods Phenotypic dataset Seedling resistance in greenhouse The P. striiformis races were differentiated in 2020 using a set of 12 wheat lines developed in the Avocet wheat background and on nine supplemental wheat differentials using a method developed by Johnson et al. 1972. Determination of the type of plant reaction was carried out twice within 14–20 days after infection according to the Gassner and Straib accounting scale (Gassner and Straib, 1932). At the same time, reactions of 0, 1, and 2 points were assigned to the resistant type R (Resistant), and 3 and 4 points were assigned to the susceptible type S (Susceptible). The P. triticina races were also differentiated during 2020 using 20 near-isogenic lines (NILs) developed in Thatcher background each carrying one of the LR-resistant genes. (Kolmer et al., 2014; Schachtel et al., 2012; Kolmer and Ordonez, 2007). The virulence of the phenotypes was determined on these 20 differential lines and encoded with 0 and 1 for avirulence and virulence, respectively (Kolmer and Ordonez, 2007; Long and Kolmer, 1989). The Virulence Analysis Tools (Schachtel et al., 2012) was used for the nomenclature of P. triticina races. The type of response to leaf rust was determined twice within 14-20 days after infection according to the scale of Mains and Jackson (1926). Reactions of 0, 1, and 2 points were assigned to the resistant type R (Resistant), and 3 and 4 points were assigned to the susceptible type S (Susceptible). The seedlings of the RIL population from Almaly × Avocet S cross along with the parents were inoculated with two races of P. striiformis i.e., 108E187 (Pst_1) and 110E191 (Pst_2) and two races of P. triticina i.e., MLTTH and TLTTR to determine the race-specific resistance. Similarly, the RIL population from Almaly × Anza cross along with parents were inoculated with two races of P. striiformis i.e., 108E187 (Pst_1) and 101E191 (Pst_3) and four races of P. triticina i.e., THTTQ, TCTTR, TCPTQ, and THTTR. The plants were infected with spores at 3-leaf stage and humid chamber was created for 24 hours. The seedling infection type of RIL was scored using the same approach as for rases differentiation. Phenotyping for Adult Plant Resistance in Field The field phenotyping for YR and LR APR was done during 2018 and 2019 for both the populations and an additional year during 2020 for LR APR for the Anza population at Kazakh Research Institute of Agriculture and Crop Production (KazNIIZiR), Almalybak. Pathogen racial mixtures from the local population were used to inoculate the mapping populations. The method of Roelfs et al. (1992) was followed for spore sampling, storage, and propagation. The pathogen was propagated in a greenhouse on the susceptible wheat variety, Morocco. The experimental wheat material was inoculated with a mixture of spores and talc in the ratio of 1:100 by spraying with an aqueous suspension of spores with 0.001% Tween-80 at a stem elongation stages (Z21-32). After infection, the plots were wrapped with plastic cover for 16-18 hours to create high humidity. After the manifestation of diseases on susceptible control varieties, an assessment (2–3 times) of rust resistance was carried out. Leaf and yellow rust resistance of wheat accessions was evaluated using the modified Cobb scale (Peterson et al., 1948; McIntosh et al., 1995). The scoring was based both on disease severity (proportion of leaf area infected) and on the plant response to infection (reaction type). Plant responses were recorded as resistant (R), moderately resistant (MR), moderately susceptible (MS), and susceptible (S) reactions. Genotypic dataset The genomic DNA was extracted from parents and each RIL from both populations following the modified CTAB (cetyltrimethylammonium bromide) method (Dreisigacker et al., 2012). The DArTseq technology was used for genotyping of both the RILs in Genetic Analysis and Service for Agriculture (SAGA) lab based in Mexico (Edet et al., 2018). Briefly, the sequencing of mapping populations was carried out at 192-plexing on Illumina HiSeq2500 with 1 × 77-bp reads. Allele calls for SNPs were generated through proprietary analytical pipeline developed by DArT P/L (Sansaloni et al., 2011). Further, genetic locations of the SNPs were identified by using 100K consensus map given by SAGA (Sansaloni et al. unpublished).
The markers were filtered and removed the monomorphic markers, markers with >30% missing data, high heterozygosity percentage (>30%), low allele frequency (<5%) using MS Excel. The BIN functionality in IciMapping 4.2 QTL software was used to remove redundant markers. A filtered set of 1293 and 1127 high-quality SNPs were finally used for QTL analysis in Anza and Avocet populations.

Wellington City Corrosion Zone as determined by NZS 3604:2011.

Corrosion Zones relate to the severity of exposure to wind-driven salt, with B being low risk, C medium risk and D high risk (Category A is not applicable in New Zealand). The Wellington City territory authority area is located in zone C aside from the coastal zone. For this reason the coastal zone is the only corrosion zone included in this layer as its shape is determined by the shape of the coast and is needed for an easy reference for any particular property.

The Wellington City Corrosion Zones layer was originally created from a 500m buffer of MHWS (mean high water springs). MHWS is calcuated at 0.855m elevation above 0 using the 1m DEM.

For further information on Corrosion Zones you can visit:

www.branz.co.nz

This item has been created to be used in WCC's Open Data Portal.

This national digital GIS product produced by the British Geological Survey indicates the susceptibility of corroded underground ferrous (iron) assets (e.g. pipes) to failure, as a result of ground instability. It is largely derived from the digital geological map and expert knowledge. The GIS dataset contains eight fields. The first field is a summary map that gives an overview of where corroded ferrous assets may fail. The other seven fields indicate the properties of the ground with respect to corrosivity and hazards associated with soluble rocks, landslides, compressible ground, collapsible ground, swelling clays and running sands. The data is useful to asset managers in water companies, local authorities and utility companies who would like to understand where underground ferrous assets are susceptible to failure as a result of ground conditions.

The global anti-corrosion packaging market, currently valued at $499 million in 2025, is projected to experience steady growth, driven by a Compound Annual Growth Rate (CAGR) of 3.2% from 2025 to 2033. This growth is fueled by several key factors. The increasing demand for packaged goods across diverse sectors like electronics, automotive, and consumer goods necessitates robust protection against corrosion, particularly during transportation and storage. Advancements in packaging materials, including the development of biodegradable and sustainable options, are further stimulating market expansion. The rising adoption of sophisticated packaging technologies, such as vacuum sealing and modified atmosphere packaging (MAP), enhances product lifespan and minimizes corrosion, thereby boosting market demand. Furthermore, stringent regulations concerning product safety and environmental concerns are driving the adoption of effective anti-corrosion packaging solutions. The market segmentation, encompassing various applications (Electrical & Electronics, Automotive, Consumer Goods, Industrial Goods) and types of packaging (Bag, Foil, Film, Paper), reflects the broad applicability of anti-corrosion packaging across numerous industries. The significant presence of established players like Nefab, Smurfit Kappa Group, and others, indicates a mature market with established supply chains. However, potential challenges remain, including fluctuating raw material prices and the need for continuous innovation to meet evolving industry needs. The regional distribution of the market likely reflects established manufacturing hubs and consumer markets. North America and Europe are anticipated to hold significant market shares due to their advanced manufacturing sectors and stringent regulatory frameworks. The Asia-Pacific region, particularly China and India, is poised for substantial growth owing to their rapidly expanding industrial sectors and increasing consumer spending. The competitive landscape is marked by a mix of large multinational corporations and specialized packaging providers, leading to ongoing innovation and competition in terms of pricing, material choices, and technological advancements. Future growth will depend on continued technological innovation, addressing sustainability concerns, and catering to the specific needs of different industries. Further research into specific regional trends and the impact of emerging materials will provide a more granular understanding of future market dynamics.

We used three approaches to map the yellow rust resistance gene Yr7 and identify associated SNPs in wheat. First, we used a traditional QTL mapping approach using a double haploid (DH) population and mapped Yr7 to a low-recombination region of chromosome 2B. To fine map the QTL, we then used an association mapping panel. Both populations were SNP array genotyped allowing alignment of QTL and genome-wide association scans based on common segregating SNPs. Analysis of the association panel spanning the QTL interval, narrowed the interval down to a single haplotype block. Finally, we used mapping-by-sequencing of resistant and susceptible DH bulks to identify a candidate gene in the interval showing high homology to a previously suggested Yr7 candidate and to populate the Yr7 interval with a higher density of polymorphisms. We highlight the power of combining mapping-by-sequencing, delivering a complete list of gene-based segregating polymorphisms in the interval with the high recombination, low LD precision of the association mapping panel. Our mapping-by-sequencing methodology is applicable to any trait and our results validate the approach in wheat, where with a near complete reference genome sequence, we are able to define a small interval containing the causative gene.

23 Global export shipment records of Corrosion Paper with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.