scholarly journals IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics

2014 ◽  
Vol 30 (10) ◽  
pp. 1392-1399 ◽  
Author(s):  
Marta Rosikiewicz ◽  
Marc Robinson-Rechavi
1974 ◽  
Vol 55 (9) ◽  
pp. 554-561 ◽  
Author(s):  
Wilma M. Martens ◽  
Elizabeth Holmstrup

A new method provides a systematized approach to diagnosis and treatment and facilitates teaching, research, and monitoring for quality control


2016 ◽  
Vol 14 (1) ◽  
pp. 121-127 ◽  
Author(s):  
Sergei Bovteev ◽  
Svetlana Kanyukova ◽  
Vladimir Okrepilov ◽  
Anna Rezvaia

2018 ◽  
Vol 9 (3) ◽  
pp. 46-67
Author(s):  
Hamdy Ahmed Abdelaziz

The present article aimed at developing a research methodology to ensure quality control of eLearning field research design and production. The idea of the present research was to investigate effective and ineffective practices in eLearning field. The analysis of a sample of researches and studies (n = 200), conducted in the field of eLearning and Blended Learning in the Arab states revealed that the vast majority of eLearning researches and studies (70%) were stereotypical. Therefore, the researcher developed a list of indicators that ensure quality control of eLearning researches design. A new methodology to design and produce eLearning research is proposed. The proposed methodology contains four stages: Identify Investigate, Prototype, and Produce. Implementation of these stages required the adoption of a tetrad dialogue during the course of answering the six patterns of question: what, who, when, where, how, and why. In addition, the adoption of this new method may support producing adaptive and innovative eLearning research with high level of quality.


Author(s):  
Alexander Miropolsky ◽  
Anath Fischer

Inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its design computer model. Scan data, however, is typically very large scale (i.e. many points), unorganized, noisy and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for de-noising and reduction of scan data by Extended Geometric Filter (EGF). The proposed method is applied directly on the scanned points and is automatic, fast and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.


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