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pmartR : Quality Control and Statistics for Mass Spectrometry-Based Biological Data
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Surface photovoltage method for the quality control of silicon epitaxial layers on sapphire
The surface photovoltage method is used to study “silicon-on-sapphire” epitaxial layers with a thickness of 0.3–0.6 μm, which are used to fabricate p-channel MOS (metal—oxide-semiconductor) transistors with improved radiation hardness. It is shown that the manner in which the photoconductivity of the epitaxial layer decays after the end of a light pulse generated by a light-emitting diode (wavelength ∼400 nm) strongly depends on the density of structural defects in the bulk of the structure. This enables control over how a “silicon-on-sapphire” structure is formed to provide the manufacturing of MOS structures with optimal operating characteristics.
Energy Auditor and Quality Control Inspector Certification Updates
This presentation discusses recent and future Home Energy Professional credential and resource updates at the National Renewable Energy Laboratory in support to the U.S Department of Energy's Weatherization Assistance Program (WAP). It will provide an in-depth overview of planned improvements to the EA and QCI certification schemes.
Integrated System of Thermal/Dimensional Analysis for Quality Control of Metallic Melt and Ductile Iron Casting Solidification
The main objective of the present work is to introduce a specific experimental instrument and technique for simultaneously evaluating cooling curves and expansion or contraction of cast metals during solidification. Contraction/expansion analysis illustrates the solidification parameters progression, according to the molten cast iron characteristics, which are dependent on the melting procedure and applied metallurgical treatments, mold media rigidity and thermal behavior [heat transfer parameters]. The first part of the paper summarizes the performance of this two-mold device. Its function is illustrated by representative shrinkage tendency results in ductile cast iron as affected by mold rigidity (green sand and furan resin sand molds) and inoculant type (FeSi-based alloys), published in part previously. The second part of the paper illustrates an application of this equipment adapted for commercial foundry use. It conducts thermal analysis and volume change measurements in a single ceramic cup so that mold media as well as solidification conditions are constants, with cast iron quality as the variable. Experiments compared gray and ductile cast iron solidification patterns. Gray iron castings are characterized by higher undercooling at the beginning and at the end of solidification and lower graphitic expansion. Typically, ductile cast iron exhibits higher graphitic, initial expansion, conducive for shrinkage formation in soft molds.
Loss of protein synthesis quality control in host-restricted organisms
Significance This case study reveals that parasitic and symbiotic lifestyles affect the structure of essential molecular machineries of a living cell. We provide evidence that intracellular parasitism and endosymbiosis cause degeneration of the editing domains in aminoacyl-tRNA synthetases, a defect that is known to cause inaccurate translation of the genetic code. This finding suggests that most intracellular pathogens, including causative agents of human disease, have an unanticipated proteome diversity caused by inaccurate translation of the genetic code. Our finding may change current approaches to the study of proteomes of intracellular parasites, parasite–host interactions, and parasites’ sensitivity to drugs, which cause errors in transcription, translation, and protein folding.
Quality control of concrete at the stage of designing its composition and technology
The results of tests on samples of foam concrete with a hardening accelerator are presented. As the setting and hardening accelerators the following chemical additives were used: Universal-P-2 and Asilin 12. All additives were added into the insulating foam concrete mix of brand D 400 in the amount of 0.5% to 1% of cement weight. By using of additives in foam concrete technology – hardening accelerators Asilin 12 and Universal P2 in the amount of 0.5 % - and 1.0% by weight of cement foam concrete structure formation is accelerated and increases strength by 60%. For the industrial preparation of foam concrete mix technological regulations are worked out, in which it is recommended to use additives – hardening accelerators Asilin 12 in the amount of 0.5% and Universal P2 - 1% of cement weight.
SU-E-T-153: Establish a Comprehensive Patient-Specific Plan QA Database for Instituitional Quality Control Program
Purpose: Since the publication of TG-119, several new treatment techniques requiring new QA devices have been implemented in the clinic. To monitor and analyze our institutional QA performance, we have created a comprehensive QA database using Research Electronic Data Capture (REDCap). The database will also assist us in creating IMRT QA Analysis practice guidelines. Methods: Since Oct. 2013, 336 patient plan QA results were entered into the database. Plan parameters such as plan ID, treatment site, technique, energy, optimization constraints, modulation factor, leaf speed, leaf opening.etc were automatically extracted from the Varian Eclipse database to allow us to refine our evaluation and analysis method. Specific QA device, LINAC-related information, and measurement and analysis results were manually entered by the QA team. IMRT plans were measured using MapCHECK2 while RapidArc plans were measured using ArcCHECK. Distance-To-Agreement 3%/3mm without global maximum normalization was used. Results: The data indicates that different treatment techniques might benefit from a different site-specific action level(AL) depending on the complexity of the plan and optimization parameter used (e.g., breast IMRT QA= 97.8% and pelvis IMRT QA=93.1%). Different QA devices may also benefit from a different AL (MapCHECK2 = 94.1% while ArcCHECK = 83.0%). The relationship between the parameters and passing rate suggests that the complexity of each plan, characterized by leaf travel, leaf opening and modulation factor, affect the passing rate significantly. The database is reviewed regularly, and any abnormal point of the QA result or a trend of lower QA passing rate on a specific LINAC is further investigated. Conclusion: Establishing a comprehensive QA database provides an overview of the quality assurance program. It not only helps in answering the question “what is a reasonable and achievable standard for each institution”, but also saves time in monitoring and investigating the daily machine and QA performance.
Experience with the Quality Control of Radioactive Waste Packages for the German Repository Konrad - 16493
The German repository Konrad for radioactive waste with negligible heat generation is designed to accept 303,000 m{sup 3} of waste packages in total. Based on the Konrad licensing conditions, any treatment and conditioning of radioactive waste should be planned and carried out in accordance with the waste acceptance requirements of the repository Konrad in order to produce waste packages suitable for disposal. The qualification of the waste treatment processes and the resulting products are achieved by interaction of different parties involved. On behalf of the Federal Office for Radiation Protection - the competent German authority for disposal of radioactive waste - TUV NORD EnSys Hannover GmbH and Co. KG acts as one of the independent expert organizations for more than twenty years in the field of qualification of waste treatment campaigns and review of waste packages documentations. The permitted amount of nuclide activity in the radioactive waste packages depends on the characteristics of the radioactive waste products. Projecting a suitable strategy for loading the waste containers contributes to determine in advance, what requirements the radioactive waste products have to fulfill as well as what properties of the waste products need to be verified and documented during conditioning or treatment process. On the basis of various examples, it is being demonstrated how appropriate advance process planning can ensure that radioactive waste packages are generated to comply with the waste acceptance requirements. In consequence, technical measures due to requalification can be avoided even in case of unpredictable difficulties. (authors)
Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product
Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.
Data Quality Control for Vessel Mounted Acoustic Doppler Current Profiler. Application for the Western Mediterranean Sea
A systematic Data Quality Checking Protocol for vessel Mounted Acoustic Doppler Current Profiler observations is proposed. Previous-to-acquisition conditions are considered along with simultaneous ones.
The space shuttle advanced solid rocket motor: Quality control and testing
The Congressional committees that authorize the activities of NASA requested that the National Research Council (NRC) review the testing and quality assurance programs for the Advanced Solid Rocket Motor (ASRM) program. The proposed ASRM design incorporates numerous features that are significant departures from the Redesigned Solid Rocket Motor (RSRM). The NRC review concentrated mainly on these features. Primary among these are the steel case material, welding rather than pinning of case factory joints, a bolted field joint designed to close upon firing the rocket, continuous mixing and casting of the solid propellant in place of the current batch processes, use of asbestos-free insulation, and a lightweight nozzle. The committee's assessment of these and other features of the ASRM are presented in terms of their potential impact on flight safety.
Information quality-control model
Model serves as graphic tool for estimating complete product objectives from limited input information, and is applied to cost estimations, product-quality evaluations, and effectiveness measurements for manpower resources allocation. Six product quality levels are defined.
Practical quality control tools for curves and surfaces
Curves (geometry) and surfaces created by Computer Aided Geometric Design systems in the engineering environment must satisfy two basic quality criteria: the geometric shape must have the desired engineering properties; and the objects must be parameterized in a way which does not cause computational difficulty for geometric processing and engineering analysis. Interactive techniques are described which are in use at Boeing to evaluate the quality of aircraft geometry prior to Computational Fluid Dynamic analysis, including newly developed methods for examining surface parameterization and its effects.
Predictive Techniques for Spacecraft Cabin Air Quality Control
As assembly of the International Space Station (ISS) proceeds, predictive techniques are used to determine the best approach for handling a variety of cabin air quality challenges. These techniques use equipment offgassing data collected from each ISS module before flight to characterize the trace chemical contaminant load. Combined with crew metabolic loads, these data serve as input to a predictive model for assessing the capability of the onboard atmosphere revitalization systems to handle the overall trace contaminant load as station assembly progresses. The techniques for predicting in-flight air quality are summarized along with results from early ISS mission analyses. Results from groundbased analyses of in-flight air quality samples are compared to the predictions to demonstrate the technique's relative conservatism.
Users guide for ERB 7 MAT (including the first year quality control)
In the first section of this report background information for the use of the ERB-7 Master Archival Tapes (MAT) is provided. The second section gives details regarding the scientific validity and quality of the MAT. The MAT data analyzed covers the period from November 16, 1978 to October 31, 1979.
Machine Learning for Joint Quality Control
The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.
Quality control of EUVE databases
The publicly accessible databases for the Extreme Ultraviolet Explorer include: the EUVE Archive mailserver; the CEA ftp site; the EUVE Guest Observer Mailserver; and the Astronomical Data System node. The EUVE Performance Assurance team is responsible for verifying that these public EUVE databases are working properly, and that the public availability of EUVE data contained therein does not infringe any data rights which may have been assigned. In this poster, we describe the Quality Assurance (QA) procedures we have developed from the approach of QA as a service organization, thus reflecting the overall EUVE philosophy of Quality Assurance integrated into normal operating procedures, rather than imposed as an external, post facto, control mechanism.