124 research outputs found

    Learning Curves and p-charts for a preliminary estimation of asymptotic performances of a manufacturing process

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    This paper presents a method for a preliminary estimation of asymptotic performances of a manufacturing process based on the knowledge of its learning curve estimated during the setting up of p-chart. The main novelties of the method are the possibility of estimating the asymptotic variability of a process and providing a simple approach for evaluating the period of revision of process control limits. An application of the method to a real example taken from the literature is also provided

    Why do we need a theory and metrics of technology upgrading?

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    This paper discusses why we need theory and metrics of technology upgrading. It critically reviews the existing approaches to technology upgrading and motivates build-up of theoretically relevant but empirically grounded middle level conceptual and statistical framework which could illuminate a type of challenges relevant for economies at different income levels. It conceptualizes technology upgrading as three dimensional processes composed of intensity and different types of technology upgrading through various types of innovation and technology activities; broadening of technology upgrading through different forms of technology and knowledge diversification, and interaction with global economy through knowledge import, adoption and exchange. We consider this to be necessary first step towards theory and metrics of technology upgrading and generation of more relevant composite indicator of technology upgrading

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    Framework of Construction Innovation: A Review of Diffusion of Sustainable Innovation in the Building Sector

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    Opinion

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