Cross-scale modelling of transpiration from stomata via the leaf boundary layer

Background and Aims Leaf transpiration is a key parameter for understanding land surface-climate interactions, plant stress and plant structure-function relationships. Transpiration takes place at the microscale level, namely via stomata that are distributed discretely over the leaf surface with a very low surface coverage (approx. 0·2-5 %). The present study aims to shed more light on the dependency of the leaf boundary-layer conductance (BLC) on stomatal surface coverage and air speed. Methods An innovative three-dimensional cross-scale modelling approach was applied to investigate convective mass transport from leaves, using computational fluid dynamics. The gap between stomatal and leaf scale was bridged by including all these scales in the same computational model (10−5-10−1 m), which implies explicitly modelling individual stomata. Key Results BLC was strongly dependent on stomatal surface coverage and air speed. Leaf BLC at low surface coverage ratios (CR), typical for stomata, was still relatively high, compared with BLC of a fully wet leaf (hypothetical CR of 100 %). Nevertheless, these conventional BLCs (CR of 100 %), as obtained from experiments or simulations on leaf models, were found to overpredict the convective exchange. In addition, small variations in stomatal CR were found to result in large variations in BLCs. Furthermore, stomata of a certain size exhibited a higher mass transfer rate at lower CRs. Conclusions The proposed cross-scale modelling approach allows us to increase our understanding of transpiration at the sub-leaf level as well as the boundary-layer microclimate in a way currently not feasible experimentally. The influence of stomatal size, aperture and surface density, and also flow-field parameters can be studied using the model, and prospects for further improvement of the model are presented. An important conclusion of the study is that existing measures of conductances (e.g. from artificial leaves) can be significantly erroneous because they do not account for microscopic stomata, but instead assume a uniform distribution of evaporation such as found for a fully-wet leaf. The model output can be used to correct or upgrade existing BLCs or to feed into higher-scale models, for example within a multiscale framewor

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection

X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400 x 400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria

Prenatally diagnosed submicroscopic familial aberrations at 18p11.32 without phenotypic effect

Background: Recent development of MLPA (Multiplex-Ligation-dependent Probe Amplification, MRC-Holland) and microarray technology allows detection of a wide range of new submicroscopic abnormalities. Publishing new cases and case reviews associated with both clinical abnormalities and a normal phenotype is of great value. Findings/results. We report on two phenotypically normal foetuses carrying a maternally-inherited interstitial submicroscopic abnormality of chromosome 18p11.32. Both abnormalities were found with the aneuploidy MLPA kit P095 during rapid aneuploidy detection, which was offered along with conventional karyotyping. Foetus 1 and its mother have a 1,7 Mb deletion and foetus 2 and its mother have a 1,9 Mb duplication. In both cases normal babies were born. We used the HumanCytoSNP-12 array of Illumina to visualize the CNVs and map the breakpoints. Conclusions: We suggest that a CNV at 18p11.32 (528,050-2,337,486) may represent a new benign euchromatic variant

Mining Statistical Relations for Better Decision Making in Healthcare Processes

An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustrate the importance of our approach in the general setting of causal model estimation