Disc springs. Quality specifications. Dimensions

2016 ◽  
Keyword(s):  
Quality Specifications

scholarly journals Spanish society of laboratory medicine external quality assurance programmes: evolution of the analytical performance of clinical laboratories over 30 years and comparison with other programmes

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Carmen Perich ◽  
Carmen Ricós ◽  
Fernando Marqués ◽  
Joana Minchinela ◽  
Angel Salas ◽  
...  
Keyword(s):  
Quality Assurance ◽  
State Of The Art ◽  
Laboratory Medicine ◽  
Analytical Performance ◽  
External Quality Assurance ◽  
Biological Variability ◽  
External Quality ◽  
Spanish Society ◽  
Clinical Laboratories ◽  
Quality Specifications

AbstractThe purpose of this study is to understand the evolution of the analytical performance of the laboratories participating in the Spanish society of laboratory medicine (SEQCML) external quality assurance (EQA) programmes during its 30 years of operation and to compare it with the performance of other EQA programmes to establish whether the results are similar. The results obtained during this period are evaluated by applying the biological variability (BV) and state of the art-derived quality specifications. In addition, the results are compared with those obtained by other EQA programme organisations. It is noted that the laboratories participating in the EQA–SEQCML programmes have improved their performance over 30 years of experience and that the specifications derived from biological variation are achievable. It is difficult to compare EQA programmes, due to lack of accessibility and the differences in the design of these programmes (control materials, calculations used and analytical specifications established). The data from this study show that for some biological magnitudes the results obtained by the programmes are not yet harmonised, although efforts are being made to achieve this. Organisers of EQA programmes should also join the harmonisation effort by providing information on their results to enable comparison.

Quality specifications: self pleasure for clinical laboratories or added value for patient management?

2007 ◽  
Vol 45 (4) ◽  
Author(s):  
Mario Plebani
Keyword(s):  
Decision Making ◽  
Patient Management ◽  
Clinical Decision Making ◽  
Laboratory Data ◽  
Clinical Decision ◽  
Added Value ◽  
Internal Quality ◽  
Laboratory Services ◽  
Management Procedures ◽  
Quality Specifications

AbstractAnalytical quality specifications play a key role in assuring and continuously improving high-quality laboratory services. However, I believe, that there are two “missing links” in the effective management of quality specifications in the delivery of laboratory services. The first is the evidence that pre-analytical variation and related problems are not taken into great consideration by laboratory professionals. The second missing link is the communication of quality specifications to clinicians and other possible stakeholders. If quality specifications represent “the level of performance required to facilitate clinical decision-making”, they cannot be used only for internal quality management procedures but must be communicated to facilitate clinical reasoning, decision-making and patient management. A consensus should be achieved in the scientific community on these issues to assure better utilization of laboratory data and, ultimately, improved clinical outcomes.Clin Chem Lab Med 2007;45:462–6.

A Hierarchical Case-Based Reasoning System for Quality Design

2010 ◽  
Vol 20-23 ◽  
pp. 1015-1020
Author(s):  
Cai Yan Liu ◽  
You Fa Sun
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Case Based Reasoning ◽  
Retrieval Accuracy ◽  
Reasoning System ◽  
Quality Specifications ◽  
Computation Formula ◽  
Quality Design ◽  
Similarity Computation ◽  
Case Based

Quality design means designing quality specifications and processing specifications of products with low cost and high efficiency. This paper presents a hierarchical case-based reasoning approach for quality design. The structure and expression of case-base, the hierarchical case retrieval algorithms and similarity computation formula between cases are all studied. Such a hierarchical case-based reasoning method will greatly improve the retrieval accuracy and efficiency.

Sadash soft white spring wheat

2009 ◽  
Vol 89 (6) ◽  
pp. 1099-1106 ◽  
Author(s):  
R S Sadasivaiah ◽  
R J Graf ◽  
H S Randhawa ◽  
B L Beres ◽  
S M Perkovic ◽  
...  
Keyword(s):  
Triticum Aestivum ◽  
Grain Yield ◽  
Spring Wheat ◽  
Triticum Aestivum L ◽  
Quality Analysis ◽  
Head Blight ◽  
Cultivar Description ◽  
Quality Specifications ◽  
Soft White Spring Wheat ◽  
End Use

Sadash is a soft white spring wheat (Triticum aestivum L.) that meets the end-use quality specifications of the Canada Western Soft White Spring class. Sadash is well-adapted to the wheat-growing regions of southern Alberta and southern Saskatchewan. Based on data from the Western Soft White Spring Wheat Cooperative Registration Test from 2003 to 2005, Sadash exhibited high grain yield, mid-season maturity, semi-dwarf stature with very strong straw, and good resistance to shattering. Sadash expressed resistance to the prevalent races of stem rust and powdery mildew, intermediate resistance to loose smut, moderate susceptibility to leaf rust and common bunt, and susceptibility to Fusarium head blight. Based on end-use quality analysis performed at the Grain Research Laboratory of the Canadian Grain Commission, Sadash had improved test weight over the check cultivars AC Reed and AC Phil and similar milling and baking performance.Key words: Triticum aestivum L., cultivar description, wheat (soft white spring), grain yield, quality, disease resistance

scholarly journals 4. Analytical Quality Specifications

1994 ◽  
Vol 99 (3) ◽  
pp. 231-236
Author(s):  
Elizabeth M.S. Gowans ◽  
Per Hyltoft Petersen ◽  
Ole Blaabjerg
Keyword(s):  
Analytical Quality ◽  
Quality Specifications

Quality specifications and performance indicators for road bridges in Europe

2018 ◽  
pp. 1822-1831
Author(s):  
Alfred Strauss ◽  
Sérgio Fernandes ◽  
Joan R. Casas ◽  
Lisa Mold ◽  
José C. Matos
Keyword(s):  
Performance Indicators ◽  
Quality Specifications ◽  
And Performance

An overview of COST Action TU 1406, Quality Specifications for Roadway Bridges (BridgeSpec)

2016 ◽  
pp. 1511-1517 ◽  
Author(s):  
J. Campos e Matos ◽  
J. Amado ◽  
S. Fernandes ◽  
N. Galvão
Keyword(s):  
Cost Action ◽  
Quality Specifications

scholarly journals Harmonizing National Topographic Data Using an Automated Quality Validation Process

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Nils Mesterton ◽  
Mari Isomäki ◽  
Antti Jakobsson ◽  
Joonas Jokela
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Quality Evaluation ◽  
Validation Process ◽  
Topographic Data ◽  
Spatial Data Quality ◽  
General Rules ◽  
Land Survey ◽  
Quality Specifications ◽  
Quality Process

<p><strong>Abstract.</strong> The Finnish National Topographic Database (NTDB) is currently developed by the National Land Survey of Finland (NLS) together with municipalities and other governmental agencies. It will be a harmonized database for topographic data in Finland provided by municipalities, the NLS and other agencies. The NTDB has been divided into several themes, of which the buildings theme was the focus in the first stage of development. Data collection for the NTDB is performed by different municipalities and governmental organizations. Having many supplying organizations can lead to inconsistencies in spatial data. Without a robust quality process this could lead to a chaos. Fortunately data quality can be controlled with an automated data quality evaluation process. Reaching a better degree of harmonization across the database is one of the main goals of NTDB in the future, besides reducing the amount of overlapping work and making national topographic data more accessible to all potential users.</p><p>The aim of the NTDB spatial data management system architecture is to have a modular architecture. Therefore, the Data Quality Module named as QualityGuard can also be utilized in the National Geospatial Platform which will be a key component in the future Spatial Data Infrastructure of Finland. The National Geospatial Platform will include the NTDB data themes but also addresses, detailed plans and other land use information. FME was chosen as the implementation platform of the QualityGuard because it is robust and highly adaptable, allowing development of even the most complicated ETL workflows and spatial applications. This approach allows effortless communication with different applications via various types of interfaces, thus efficiently enabling the modularity requirement in all stages of development and integration.</p><p>The QualityGuard works in two modes: a) as a part of the import process to NTDB, and b) independently. Users can validate their data using the independent QualityGuard to find possible errors in their data and fix them. Once validated and the data is fixed, data producers can import their data using the import option. The users receive a data quality report containing statistics and a quality error dataset regarding their imported data, which can be inspected in any GIS software, e.g. overlaid on original features. Geographical locations of quality errors are displayed as points. Each error finding produces a row in the error dataset, containing information about the type and cause of the error as short descriptions.</p><p>Data quality evaluation is based on validating the conformance against data product specifications specified as quality rules. Three different ISO 19157 quality elements are utilized: format consistency, domain consistency and topological consistency. The quality rules have been defined in a co-operation with specialists in the field and the technical developing team. The definition work is based on the concept developed in the ESDIN project, quality specifications of INSPIRE, national topographic database quality specifications, national and international quality recommendations and standards, quality rules developed in European Location Framework (ELF) project and interviews of experts from National Land Survey of Finland and municipalities. In fact the NLS was one of the first agencies in the world who published a quality model for the digital topographic data in 1995.</p><p>Quality rules are currently documented in spreadsheet documents representing each theme. Each quality rule has been defined using RuleSpeak, a structured notation for expressing business rules. RuleSpeak provides a consistent structure for each definition. The rules are divided in general rules and feature-specific rules. General rules are relevant for all feature types of a specific theme, although exceptions can be defined.</p><p>A nation-wide, centralized automated spatial data quality process is one of the key elements in an effort towards achieving better harmonization of the NTDB. In principle, the greater aim is to achieve compliance with the auditing process described in ISO 19158. This process is meant to ensure that the supplying organizations are capable of delivering data of expected quality. However, implementing a nation-wide process is rather challenging because municipalities and other organizations might not have the capability or resources to repair the quality issues identified by the QualityGuard. Inconsistent data quality is not desirable, and data quality requirements will be less strict at first phases of implementation. Some of the issues will be automatically repaired by the software once the process has been established, but the organizations will still receive a notification about data quality issues in any conflicting features.</p><p>The Finnish NTDB is in a continuous state of development and currently effort is made towards reaching automation, improved data quality and less overlapping work in co-operation with municipalities and other data producers. The QualityGuard has enabled an automated spatial data quality validation process for incoming data and it is currently being evaluated in practice. The results have already been well received by the users. Automating data quality validation is no longer a work of fiction. As indicated earlier we believe this will be a common practice with all SDI datasets in Finland.</p></p>

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