Sketching is more than making correct drawings

Sketching in the context of a design process is not a goal in itself, but can be considered as a tool to\ud make better designs. Sketching as a design tool has several useful effects as: ordering your thoughts,\ud better understanding of difficult shapes, functioning as a communication tool, and providing an\ud iterative way of developing shapes. In our bachelor-curriculum Industrial Design Engineering we\ud developed a series of courses that addresses these effects in particular.\ud The courses are Sketching and concept drawing (SCT), Product Presentation Drawing (PPT) and\ud Applied sketching skills (TTV). This line of courses is built on three pillars:\ud - Learning to sketch; Theory, speed and control of the materials.\ud - Learning from sketching; Develop a better insight in complex 3D shapes (Figure 1).\ud - Sketching as a design tool; Communication, ordering your thoughts, iterative working.\ud As a result we see that students who have finished the courses instinctively start sketching in an\ud iterative manner, use sketching as a source of inspiration and learn that the whole process of iterative\ud sketching helps in structuring, developing and communicating the design process. In this way the\ud students become better sketchers and better designer

Kinetics of the low-temperature pyrolysis of polyethene, polypropene and polystyrene modeling, experimental determination and comparison with literature models and data

The pyrolysis kinetics of low-density polyethylene, high-density polyethylene, polypropylene, and polystyrene has been studied at temperatures below 450 C. In addition, a literature review on the low-temperature pyrolysis of these polymers has been conducted and has revealed that the scatter in the reported kinetic data is significant, which is most probably due to the use of simple first-order kinetic models to interpret the experimental data. This model type is only applicable in a small conversion range, but was used by many authors over a much wider conversion range. In this investigation the pyrolysis kinetics of the forementioned polymers and a mixture of polymers has been studied at temperatures below 450 C by performing isothermal thermogravimetric analysis (TGA) experiments. The TGA experimental data was used to determine the kinetic parameters on the basis of a simple first-order model for high conversions (70-90%) and a model developed in the present study, termed the random chain dissociation (RCD) model, for the entire conversion range. The influence of important parameters, such as molecular weight, extent of branching and -scission on the pyrolysis kinetics was studied with the RCD model. This model was also used to calculate the primary product spectrum of the pyrolysis process. The effect of the extent of branching and the initial molecular weight on the pyrolysis process was also studied experimentally. The effect of the extent of branching was found to be quite significant, but the effect of the initial molecular weight was minor. These results were found to agree quite well with the predictions obtained from the RCD model. Finally, the behavior of mixtures of the aforementioned polymers was studied and it was found that the pyrolysis kinetics of the polymers in the mixture remains unaltered in comparison with the pyrolysis kinetics of the pure polymers

Surrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems [Chapter 7]

Solution of statistical inverse problems via the frequentist or Bayesian approaches described in earlier chapters can be a computationally intensive endeavor, particularly when faced with large-scale forward models characteristic of many engineering and science applications. High computational cost arises in several ways. First, thousands or millions of forward simulations may be required to evaluate estimators of interest or to characterize a posterior distribution. In the large-scale setting, performing so many forward simulations is often computationally intractable. Second, sampling may be complicated by the large dimensionality of the input space--as when the inputs are fields represented with spatial discretizations of high dimension--and by nonlinear forward dynamics that lead to multimodal, skewed, and/or strongly correlated posteriors. In this chapter, we present an overview of surrogate and reduced order modeling methods that address these computational challenges. For illustration, we consider a Bayesian formulation of the inverse problem. Though some of the methods we review exploit prior information, they largely focus on simplifying or accelerating evaluations of a stochastic model for the data, and thus are also applicable in a frequentist context.Sandia National Laboratories (Laboratory Directed Research and Development (LDRD) program)United States. Dept. of Energy (Contract DE-AC04-94AL85000)Singapore-MIT Alliance Computational Engineering ProgrammeUnited States. Dept. of Energy (Award Number DE-FG02-08ER25858 )United States. Dept. of Energy (Award Number DESC00025217

Bayesian reconstruction of binary media with unresolved fine-scale spatial structures

We present a Bayesian technique to estimate the fine-scale properties of a binary medium from multiscale observations. The binary medium of interest consists of spatially varying proportions of low and high permeability material with an isotropic structure. Inclusions of one material within the other are far smaller than the domain sizes of interest, and thus are never explicitly resolved. We consider the problem of estimating the spatial distribution of the inclusion proportion, F(x), and a characteristic length-scale of the inclusions, δ, from sparse multiscale measurements. The observations consist of coarse-scale (of the order of the domain size) measurements of the effective permeability of the medium (i.e., static data) and tracer breakthrough times (i.e., dynamic data), which interrogate the fine scale, at a sparsely distributed set of locations. This ill-posed problem is regularized by specifying a Gaussian process model for the unknown field F(x) and expressing it as a superposition of Karhunen–Loève modes. The effect of the fine-scale structures on the coarse-scale effective permeability i.e., upscaling, is performed using a subgrid-model which includes δ as one of its parameters. A statistical inverse problem is posed to infer the weights of the Karhunen–Loève modes and δ, which is then solved using an adaptive Markov Chain Monte Carlo method. The solution yields non-parametric distributions for the objects of interest, thus providing most probable estimates and uncertainty bounds on latent structures at coarse and fine scales. The technique is tested using synthetic data. The individual contributions of the static and dynamic data to the inference are also analyzed.United States. Dept. of Energy. National Nuclear Security Administration (Contract DE-AC04_94AL85000

Human phosphodiesterase 4D7 (PDE4D7) expression is increased in TMPRSS2-ERG positive primary prostate cancer and independently adds to a reduced risk of post-surgical disease progression

background: There is an acute need to uncover biomarkers that reflect the molecular pathologies, underpinning prostate cancer progression and poor patient outcome. We have previously demonstrated that in prostate cancer cell lines PDE4D7 is downregulated in advanced cases of the disease. To investigate further the prognostic power of PDE4D7 expression during prostate cancer progression and assess how downregulation of this PDE isoform may affect disease outcome, we have examined PDE4D7 expression in physiologically relevant primary human samples. methods: About 1405 patient samples across 8 publically available qPCR, Affymetrix Exon 1.0 ST arrays and RNA sequencing data sets were screened for PDE4D7 expression. The TMPRSS2-ERG gene rearrangement status of patient samples was determined by transformation of the exon array and RNA seq expression data to robust z-scores followed by the application of a threshold >3 to define a positive TMPRSS2-ERG gene fusion event in a tumour sample. results: We demonstrate that PDE4D7 expression positively correlates with primary tumour development. We also show a positive association with the highly prostate cancer-specific gene rearrangement between TMPRSS2 and the ETS transcription factor family member ERG. In addition, we find that in primary TMPRSS2-ERG-positive tumours PDE4D7 expression is significantly positively correlated with low-grade disease and a reduced likelihood of progression after primary treatment. Conversely, PDE4D7 transcript levels become significantly decreased in castration resistant prostate cancer (CRPC). conclusions: We further characterise and add physiological relevance to PDE4D7 as a novel marker that is associated with the development and progression of prostate tumours. We propose that the assessment of PDE4D7 levels may provide a novel, independent predictor of post-surgical disease progression

Meniscus-derived matrix scafolds promote the integrative repair of meniscal defects

Meniscal tears have a poor healing capacity, and damage to the meniscus is associated with significant pain, disability, and progressive degenerative changes in the knee joint that lead to osteoarthritis. Therefore, strategies to promote meniscus repair and improve meniscus function are needed. The objective of this study was to generate porcine meniscus-derived matrix (MDM) scaffolds and test their effectiveness in promoting meniscus repair via migration of endogenous meniscus cells from the surrounding meniscus or exogenously seeded human bone marrow-derived mesenchymal stem cells (MSCs). Both endogenous meniscal cells and MSCs infiltrated the MDM scaffolds. In the absence of exogenous cells, the 8% MDM scaffolds promoted the integrative repair of an in vitro meniscal defect. Dehydrothermal crosslinking and concentration of the MDM influenced the biochemical content and shear strength of repair, demonstrating that the MDM can be tailored to promote tissue repair. These findings indicate that native meniscus cells can enhance meniscus healing if a scaffold is provided that promotes cellular infiltration and tissue growth. The high affinity of cells for the MDM and the ability to remodel the scaffold reveals the potential of MDM to integrate with native meniscal tissue to promote long-term repair without necessarily requiring exogenous cells

A novel chromatographic method allows online reanalyses in proteomic investigations and acquiring more information from biological samples

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