Computational Fluid Dynamics of Catalytic Reactors

Today, the challenge in chemical and material synthesis is not only the development of new catalysts and supports to synthesize a desired product, but also the understanding of the interaction of the catalyst with the surrounding flow field. Computational Fluid Dynamics or CFD is the analysis of fluid flow, heat and mass transfer and chemical reactions by means of computer-based numerical simulations. CFD has matured into a powerful tool with a wide range of applications in industry and academia. From a reaction engineering perspective, main advantages are reduction of time and costs for reactor design and optimization, and the ability to study systems where experiments can hardly be performed, e.g., hazardous conditions or beyond normal operation limits. However, the simulation results will always remain a reflection of the uncertainty in the underlying models and physicochemical parameters so that in general a careful experimental validation is required. This chapter introduces the application of CFD simulations in heterogeneous catalysis. Catalytic reactors can be classified by the geometrical design of the catalyst material (e.g. monoliths, particles, pellets, washcoats). Approaches for modeling and numerical simulation of the various catalyst types are presented. Focus is put on the principal concepts for coupling the physical and chemical processes on different levels of details, and on illustrative applications. Models for surface reaction kinetics and turbulence are described and an overview on available numerical methods and computational tools is provided

A novel probabilistic risk analysis to determine the vulnerability of ecosystems to extreme climatic events

We present a simple method of probabilistic risk analysis for ecosystems. The only requirements are time series — modelled or measured — of environment and ecosystem variables. Risk is defined as the product of hazard probability and ecosystem vulnerability. Vulnerability is the expected difference in ecosystem performance between years with and without hazardous conditions. We show an application to drought risk for net primary productivity of coniferous forests across Europe, for both recent and future climatic conditions

Effect of Static Compressive Strain, Anisotropy, and Tissue Region on the Diffusion of Glucose in Meniscus Fibrocartilage

Osteoarthritis (OA) is a significant socio-economic concern, affecting millions of individuals each year. Degeneration of the meniscus of the knee is often associated with OA, yet the relationship between the two is not well understood. As a nearly avascular tissue, the meniscus must rely on diffusive transport for nutritional supply to cells. Therefore, quantifying structure–function relations for transport properties in meniscus fibrocartilage is an important task. The purpose of the present study was to determine how mechanical loading, tissue anisotropy, and tissue region affect glucose diffusion in meniscus fibrocartilage. A one-dimensional (1D) diffusion experiment was used to measure the diffusion coefficient of glucose in porcine meniscus tissues. Results show that glucose diffusion is strain-dependent, decreasing significantly with increased levels of compression. It was also determined that glucose diffusion in meniscus tissues is anisotropic, with the diffusion coefficient in the circumferential direction being significantly higher than that in the axial direction. Finally, the effect of tissue region was not statistically significant, comparing axial diffusion in the central and horn regions of the tissue. This study is important for better understanding the transport and nutrition-related mechanisms of meniscal degeneration and related OA in the knee

A 330 bp region of the spinach nitrite reductase gene promoter directs nitrate-inducible tissue-specific expression in transgenic tobacco.

Nitrite reductase is an enzyme in the nitrate assimilatory pathway whose expression is induced upon the addition of nitrate. Furthermore, it is known to be located in chloroplasts in leaves and plastids in roots. A 3.1 kb 5′ upstream region of the spinach nitrite reductase (NiR) gene promoter was shown previously to confer nitrate inducibility on the β-glucuronidase (GUS) reporter gene expression in both the leaves and the roots of transgenic tobacco plants. In the present study, this 3.1 kb promoter fragment as well as a series of promoter deletion constructs, fused to a GUS gene, were utilized to delineate the region of NiR promoter involved in nitrate regulation of NiR expression by studying the cellular localization of NiR-GUS expression as well as its regulation by nitrate. In plants carrying the longest promoter fragment (-3100 from the transcription start site) and promoter sequences progressively deleted to -330 bp, the expression of GUS was markedly increased in the presence of nitrate, and this expression was found to occur in mesophyll cells in leaves and in the vascular tissues of stem and roots. When nitrate was added to NiR-GUS plants grown in the absence of nitrate, significant levels of GUS activity could be seen in the roots after 2 h and in the leaves after 6 h. Furthers 5′ deletion of the promoter to -200 bp abolished the nitrate induction of GUS expression, indicating that the 130 bp region of the nitrite reductase promoter located between -330 and -200 is required for full nitrate-inducible tissue-specific expression

Isolation of the spinach nitrite reductase gene promoter which confers nitrate inducibility on GUS gene expression in transgenic tobacco.

Nitrite reductase is the second enzyme in the nitrate assimilatory pathway. The transcription of this gene is regulated by nitrate as well as a variety of other environmental and developmental factors. Genomic clones containing the entire nitrite reductase gene have been isolated from a spinach genomic library and sequenced. The sequence is identical in the transcribed region to a previously isolated spinach NiR cDNA clone (Back et al., 1988) except for the presence of three introns. The analysis of the genomic clones and DNA blot hybridization demonstrates that there is a single NiR gene per haploid genome in spinach. This is in contrast to what has been found for other plant species. The transcription initiation site has been determined by S1 mapping and the 5′ upstream region has been used to regulate the GUS reporter gene in transgenic tobacco plants. This gene was found to be regulated by the addition of nitrate in the transgenic plants

Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Background: Deep learning convolutional neural networks (CNN) May facilitate melanoma detection, but data comparing a CNN\u2019s diagnostic performance to larger groups of dermatologists are lacking. Methods: Google\u2019s Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists\u2019 diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN\u2019s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (6standard deviation) sensitivity and specificity for lesion classification of 86.6% (69.3%) and 71.3% (611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (69.6%, P \ubc 0.19) and specificity to 75.7% (611.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN\u2019s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians\u2019 experience, they May benefit from assistance by a CNN\u2019s image classification