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b-PEI25-decorated [CeLn]3/4+-doped maghemite (γ-Fe2O3) nanoparticles were prepared for siRNA-mediated gene silencing using coordination chemistry as an inorganic way of functionalization.

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Summary We define the concept of information quality ‘InfoQ’ as the potential of a data set to achieve a specific (scientific or practical) goal by using a given empirical analysis method. InfoQ is different from data quality and analysis quality, but is dependent on these components and on the relationship between them. We survey statistical methods for increasing InfoQ at the study design and post‐data‐collection stages, and we consider them relatively to what we define as InfoQ. We propose eight dimensions that help to assess InfoQ: data resolution, data structure, data integration, temporal relevance, generalizability, chronology of data and goal, construct operationalization and communication. We demonstrate the concept of InfoQ, its components (what it is) and assessment (how it is achieved) through three case‐studies in on‐line auctions research. We suggest that formalizing the concept of InfoQ can help to increase the value of statistical analysis, and data mining both methodologically and practically, thus contributing to a general theory of applied statistics.

et. al (2004). In this paper we apply Bayesian Networks to the analysis of Customer Satisfaction Surveys and demonstrate the potential of the approach. Bayesian Networks offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers.

The Shiryayev± Roberts control chart has been proposed as a powerful competitor of the Shewhart control chart and the CUSUM procedure, on theoretical grounds. W e demonstrate here the application of a Shiryayev± Roberts control chart to a non-hom ogeneous Poisson process. W e show that, from a data-analytic point of view, the Shiryayev± Roberts surveillance scheme has several advantages over classical C USUM charts. A case study of power failure times in a computer centre is used to illustrate our main points.

The move towards advanced manufacturing and Industry 4.0 is fed by increased demand for speeding up innovation, increasing flexibility, improving maintenance, and becoming more customized while saving on the total cost of operations. This is accompanied by increased dependence on virtual product and process development, data‐driven processes, and product knowledge. Other characteristics, affecting modern design, include big data intelligence in product, process, and maintenance. New technologies that empower data analytics include added manufacturing, flexible manufacturing, robotics, sensor technology, smart value/supply chains, and industrial information backbones. This paper is about surrogate models, also called digital twins, that provide an important complementary capacity to physical assets. Digital twins capture past, present, and predicted behavior of physical assets. Digital twin models are updated periodically to represent the current state of physical assets. This distinguishes digital twins from conventional simulations in that sensor data can continuously feed them. The type of curated information on the state of physical asset's history depends on how digital twins are used. For example, if a digital twin is used for fault classification, the history captured is operational data from equipment in healthy and faulty states. We provide here a review of digital twins, with an emphasis on Industry 4.0 applications.

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