scholarly journals Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion

2021 ◽  
Vol 1 ◽  
Author(s):  
Adéline Paris ◽  
Carl Duchesne ◽  
Éric Poulin
Keyword(s):  
Product Quality ◽  
Raw Materials ◽  
Quality Attributes ◽  
Classification Performance ◽  
Latent Structure ◽  
Quality Data ◽  
Type I ◽  
Direct Mapping ◽  
Latent Space ◽  
Selection Of

Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.

BÖHLER W403 VMR

Alloy Digest ◽  
2013 ◽  
Vol 62 (9) ◽  
Keyword(s):  
Heat Treatment ◽  
Chemical Composition ◽  
Physical Properties ◽  
Tool Steel ◽  
Raw Materials ◽  
Heat Treating ◽  
Diffusion Annealing ◽  
Selection Of

Abstract Böhler (or Boehler) W403 VMR is a tool steel with outstanding properties, based not only on a modified chemical composition, but on the selection of highly clean raw materials for melting, remelting under vacuum (VMF), optimized diffusion annealing, and a special heat treatment. This datasheet provides information on composition, physical properties, and elasticity. It also includes information on forming and heat treating. Filing Code: TS-721. Producer or source: Böhler Edelstahl GmbH.

Using International Trade Data for Evaluating the Product Specific Competitiveness and Supplied Product Quality of Countries: A Successful Example of Applied Theory

2006 ◽  
Vol 8 (2) ◽  
pp. 229 ◽  
Author(s):  
Thomas Cleff
Keyword(s):  
Product Quality ◽  
Longitudinal Section ◽  
Search Costs ◽  
Applied Theory ◽  
Present Level ◽  
Combined System ◽  
Trade Data ◽  
Simple Regression ◽  
Selection Of

This paper proposes a simple regression-based method for reducing the complexity of decisions in the international procurement process. Based on foreign trade data, the method uses indicators, which allow a product specific cross-section and longitudinal-section valuation of the international competitiveness and the supplied product quality of all potential supplier countries. The method thus provides a variety of information for procurement departments, including the present level and the dynamic of competitiveness and product quality for the potential supplier countries within every product group of the international product nomenclature (Combined System and the Harmonised System). Potential supplier countries --the companies of which have proven to be particularly competitive in the different product quality stages-- are identified. This pre-selection of countries enables the companies to limit their search for potential suppliers to the selected supplier countries. High search costs are subsequently reduced and trend prognoses can be constructed.

scholarly journals Tunable Protein Crystal Size Distribution via Continuous Slug-Flow Crystallization with Spatially Varying Temperature

CrystEngComm ◽  
2021 ◽  
Author(s):  
Nicholas Mozdzierz ◽  
Moo Sun Hong ◽  
Yongkyu Lee ◽  
Moritz Benisch ◽  
Mo Jiang ◽  
...  
Keyword(s):  
Product Quality ◽  
Size Distribution ◽  
Slug Flow ◽  
Crystal Size ◽  
Quality Attributes ◽  
Crystal Size Distribution ◽  
Protein Crystal ◽  
Manufacturing Cost ◽  
Spatially Varying

Accompanied with the growth of the biopharmaceuticals market has been an interest in developing processes with increased control of product quality attributes at low manufacturing cost, with one of the...

Exposure–Response Analysis for Selection of Optimal Dosage Regimen of Anifrolumab in Patients With Systemic Lupus Erythematosus

Rheumatology ◽  
2021 ◽  
Author(s):  
Yen Lin Chia ◽  
Linda Santiago ◽  
Bing Wang ◽  
Denison Kuruvilla ◽  
Shiliang Wang ◽  
...  
Keyword(s):  
Type I Interferon ◽  
Drug Clearance ◽  
Optimal Dosage ◽  
Type I ◽  
Efficacy And Safety ◽  
Response Relationship ◽  
Phase 3 ◽  
Optimal Dosage Regimen ◽  
Exposure Response ◽  
Selection Of

Abstract Objectives The randomized, double-blind, phase 2 b MUSE study evaluated the efficacy and safety of the type I interferon receptor antibody anifrolumab (300 mg or 1000 mg every 4 weeks) compared with placebo for 52 weeks in patients with chronic, moderate to severe SLE. Characterizing the exposure–response relationship of anifrolumab in MUSE will enable selection of its optimal dosage regimen in two phase 3 studies in patients with SLE. Methods The exposure–response relationship, pharmacokinetics (PK), and SLE Responder Index (SRI[4]) efficacy data were analysed using a population approach. A dropout hazard function was also incorporated into the SRI(4) model to describe the voluntary patient withdrawals during the 1-year treatment period. Results The population PK model found that type I IFN test–high patients, and patients with a higher body weight, had significantly greater clearance of anifrolumab. Stochastic clinical simulations demonstrated that doses <300 mg would lead to a greater-than-proportional reduction in drug exposure owing to type I interferon alpha receptor–mediated drug clearance (antigen-sink effect, more rapid drug clearance at lower concentrations) and suboptimal SRI(4) responses with wider confidence intervals. Conclusions Based on PK, efficacy, and safety considerations, anifrolumab 300 mg every 4 weeks was recommended as the optimal dosage for pivotal phase 3 studies in patients with SLE.

scholarly journals Optimized genotyping method for identification of bacterial contaminants in pharmaceutical industry

2016 ◽  
Vol 66 (2) ◽  
pp. 289-295
Author(s):  
Borche Stamatoski ◽  
Miroslava Ilievska ◽  
Hristina Babunovska ◽  
Nikola Sekulovski ◽  
Sasho Panov
Keyword(s):  
Pharmaceutical Industry ◽  
Dna Sequencing ◽  
Bacterial Contamination ◽  
Raw Materials ◽  
Pcr Primers ◽  
Rrna Gene ◽  
Bacterial Identification ◽  
Bacterial Contaminants ◽  
Microbiological Control ◽  
Selection Of

AbstractMicrobiological control is of crucial importance in the pharmaceutical industry regarding the possible bacterial contamination of the environment, water, raw materials and finished products. Molecular identification of bacterial contaminants based on DNA sequencing of the hypervariable 16SrRNA gene has been introduced recently. The aim of this study is to investigate the suitability of gene sequencing using our selection of PCR primers and conditions for rapid and accurate bacterial identification in pharmaceutical industry quality control.DNA was extracted from overnight incubated colonies from 10 bacterial ATCC strains, which are common contaminants in the pharmaceutical industry. A region of bacterial 16SrRNA gene was analyzed by bidirectional DNA sequencing. Bacterial identification based on partial sequencing of the 16SrRNA gene is the appropriate method that could be used in the pharmaceutical industry after adequate validations. We have successfully identified all tested bacteria with more than 99 % similarity to the already published sequences.

Development of Coolsmart Functional Knitted Fabrics

2012 ◽  
Vol 503-504 ◽  
pp. 498-502 ◽  
Author(s):  
Lan Qing Feng ◽  
Yan Jun Liu
Keyword(s):  
Pattern Formation ◽  
Moisture Absorption ◽  
Raw Materials ◽  
Knitted Fabric ◽  
Machine Code ◽  
Knitted Fabrics ◽  
Bacterial Function ◽  
Fabric Structures ◽  
Selection Of

Based on the main features of coolsmart fiber and the theory of knitted fabric structures， two structures of sports and leisure knitted fabrics with fast moisture absorption, description and anti-bacterial function are introduced in this article, detailing the selection of raw materials, pattern formation effect, the machine code organization and cam set out.

scholarly journals Predictive learning as a network mechanism for extracting low-dimensional latent space representations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Recanatesi ◽  
Matthew Farrell ◽  
Guillaume Lajoie ◽  
Sophie Deneve ◽  
Mattia Rigotti ◽  
...  
Keyword(s):  
Latent Variables ◽  
Latent Structure ◽  
Network Activity ◽  
Semantic Organization ◽  
Sequential Processing ◽  
Predictive Learning ◽  
Neural Representations ◽  
Latent Space ◽  
Low Dimensional ◽  
Sensory Prediction

AbstractArtificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

scholarly journals Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Go-Eun Yu ◽  
Younhee Shin ◽  
Sathiyamoorthy Subramaniyam ◽  
Sang-Ho Kang ◽  
Si-Myung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Feature Selection Method ◽  
Flower Color ◽  
Classification Performance ◽  
Snp Markers ◽  
Rna Seq ◽  
Color Classification ◽  
Dna Pcr ◽  
Selection Of

AbstractBellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

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