Magnesium Alleviates Adverse Effects of Lead on Growth, Photosynthesis, and Ultrastructural Alterations of Torreya grandis Seedlings

Magnesium (Mg2+) has been shown to reduce the physiological and biochemical stress in plants caused by heavy metals. To date our understanding of how Mg2+ ameliorates the adverse effects of heavy metals in plants is scarce. The potential effect of Mg2+ on lead (Pb2+) toxicity in plants has not yet been studied. This study was designed to clarify the mechanism of Mg2+-induced alleviation of lead (Pb2+) toxicity. Torreya grandis (T. grandis) seedlings were grown in substrate contaminated with 0, 700 and 1400 mg Pb2+ per kg-1 and with or without the addition of 1040 mg kg-1 Mg2+. Growth parameters, concentrations of Pb2+ and Mg2+ in the plants’ shoots and roots, photosynthetic pigment, gas exchange parameters, the maximum quantum efficiency (Fv/Fm), root oxidative activity, ultrastructure of chloroplasts and root growth were determined to analyze the effect of different Pb2+ concentrations in the seedlings as well as the potential ameliorating effect of Mg2+ on the Pb2+ induced toxicity. The growth of T. grandis seedlings cultivated in soils treated with 1400 mg kg-1 Pb2+ was significantly reduced compared with that of plants cultivated in soils treated with 0 or 700 mg kg-1 Pb2+. The addition of 1040 mg kg-1 Mg2+ improved the growth of the Pb2+-stressed seedlings, which was accompanied by increased chlorophyll content, the net photosynthetic rate and Fv/Fm, and enhanced chloroplasts development. In addition, the application of Mg2+ induced plants to accumulate five times higher concentrations of Pb2+ in the roots and to absorb and translocate four times higher concentrations of Mg2+ to the shoots than those without Mg2+ application. Furthermore, Mg2+ addition increased root growth and oxidative activity, and protected the root ultrastructure. To the best of our knowledge, our study is the first report on the mechanism of Mg2+-induced alleviation of Pb2+ toxicity. The gener¬ated results may have important implications for understanding the physiological interactions between heavy metals and plants, and for successful management of T. grandis plantations grown on soils contaminated with Pb2+

Predicting Roughness Effects on Velocity and Temperature in Turbulent Flow - A Data-Driven Approach

Accurately predicting the impact of arbitrary rough surfaces on turbulent fluid flow is crucial for industrial applications and for enhancing the precision of RANS simulations. Thermohydraulic turbulent roughness properties, specifically the augmentation in velocity $\Delta U^+$ and temperature $\Delta \Theta^+$, describe this effect, but their prediction for a rough surface currently requires a detailed simulation. Recently, the application of neural networks for these types of predictions have been developed to circumvent the costly simulations. $93$ high fidelity direct numerical simulations of a fully-developed turbulent channel flow at $Re_{\tau} \approx 800$ with artificially generated realistic rough surfaces provide a sufficient database for training, validation, and testing of a neural network architecture with strong predictive capabilities for $\Delta U^+$. For better explainability of the \u27black-box\u27 model, a genetic programming technique, namely symbolic regression, is applied to translate the data-driven model into an understandable, simple expressions using well-known statistical parameters. Nevertheless, the prediction of the temperature augmentation using an analog procedure remains insufficient, particularly since the dependencies on the Prandtl number $Pr$ have not yet fully been investigated. As supercomputing facilities are transitioning towards heterogeneous cluster architectures and GPU accelerated computation showed massive speed-up in other domains, using these new capabilities to overcome the data limitation motivated the use of a GPU accelerated DNS-solver. Using this GPU-code for rough surface channel flow with non-conjugate heat transfer is tested, and the applicability for data generation on the Tier 2 Cluster HoreKa will be additionally discussed in the conference presentation

Global metabolic responses of the lenok (Brachymystax lenok) to thermal stress

High temperature is a powerful stressor for fish living in natural and artificial environments, especially for cold water species. Understanding the impact of thermal stress on physiological processes of fish is crucial for better cultivation and fisheries management. However, the metabolic mechanism of cold water fish to thermal stress is still not completely clear. In this study, a NMR-based metabonomic strategy in combination with high throughput RNA-Seq was employed to investigate global metabolic changes of plasma and liver in a typical cold water fish species lenok (Brachymystax lenok) subjected to a sub-lethal high temperature. Our results showed that thermal stress caused multiple dynamic metabolic alterations of the lenok with prolonged stress, including repression of energy metabolism, shifts in lipid metabolism, alterations in amino acid metabolism, changes in choline and nucleotide metabolisms. Specifically, thermal stress induced an activation of glutamate metabolism, indicating that glutamate could be an important biomarker associated with thermal stress. Evidence from Hsp 70 gene expression, blood biochemistry and histology confirmed that high temperature exposure had negative effects on health of the lenok. These findings imply that thermal stress has a severe adverse effect on fish health and demonstrate that the integrated analyses combining NMR-based metabonomics and transcriptome strategy is a powerful approach to enhance our understanding of metabolic mechanisms of fish to thermal stress.</p

Study on Temperature Force Control Mechanism of CRTSⅡ Slab Track: Control Conditions of Temperature Cracking

Diseases such as track slab arching and joint concrete crushing of China Railway Track System (CRTS)II slab track were caused by huge temperature force, which seriously threatens driving safety of trains. In this study, a longitudinal weak connection scheme of CRTSII slab track was proposed to adjust the temperature force in track slab and reduce diseases of longitudinal continuous track slab. This paper focuses on the cracking characteristics of the longitudinal heterogeneous concrete composite structure. The equation which was originally developed to calculate crack width and structure stress under temperature loads, was put forward to consider deformation difference of different elastic modulus. The influence law of various parameters was analyzed. The reinforcement stress and crack width of CRTSII slab track after longitudinal connection weakening were calculated, and the reasonable limit value of tensile force of connection reinforcement and the minimum value of bond resistance of reinforcement in joint position were obtained. The result shows that, in order to reduce the bond resistance between the joint material and the reinforcement, the elastic modulus of the elastic material should be less than 5000 MPa; in order to ensure that the reinforcement does not produce large stress, the elastic modulus of the joint should be greater than 1000 MPa

DNS-based thermohydraulic assessment of artificial roughness surrogates

Engineering-related surfaces are commonly rough to different extent. In contrast to smooth surfaces, turbulent flows over rough surfaces exhibit enhanced heat and momentum transfer in the near-wall region due to surface undulation. A long-standing research question in this field is how to predict the roughness effect based solely on its topographical properties. In this regard, a large body of research devoted to characterizing roughness topographies has demonstrated that the usage of reduced statistical properties like skewness or effective slope of the surface remains insufficient to recover the overall roughness effect. Having this in mind, we discuss the potential of characterizing the roughness effect on the temperature field based on the roughness height probability density function (PDF) and power spectrum (PS) in the present contribution. Hereby, different types of realistic roughness from various engineering applications are considered. A mathematical roughness reproduction method is utilized to generate artificial rough surfaces based on the realistic PDF and PS. The artificially reproduced surfaces are subsequently compared with their original surfaces in terms of the thermal properties. For this purpose, direct numerical simulations (DNS) of flow over the roughness are carried out in a fully developed turbulent channel flow at friction Reynolds number Reτ = 500 - 2000 to cover different rough regimes. Successful reproduction of the flow statistics by the artificial roughness surrogates indicates the feasibility of the current roughness characterization/reproduction method. Less than 4% discrepancies in the global flow statistics are achieved. However, the present roughness reproduction framework is shown not applicable for the roughness with strong surface anisotropy

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.Comment: 7 pages, 4 figure

A comparison of hydrodynamic and thermal properties of artificially generated against realistic rough surfaces

The mathematical roughness generation approaches enjoy outstanding flexibility in delivering desired roughness geometries to perform systematic research. However, whether an mathematically (artificially) generated roughness can be considered an adequate surrogate of a realistic surface in terms of its influence on the flow remains nonetheless an open question. Motivated by this, the present study discusses the possibility of reproducing flow properties over realistic roughness with artificial roughness. To this end, six types of artificial rough surfaces are generated through imitation of the realistic height probability density function (PDF) and the roughness power spectrum (PS) preserving the stochastic nature of the roughness structure. The flow properties of the artificial surfaces are assessed using direct numerical simulations (DNS) in a fully-developed turbulent channel flow at Re_ = 500−2000. An excellent match in terms of global flow properties, mean velocity and temperature profiles, Reynolds stresses as well as equivalent sand grain sizes is found compared to their original counterpart with exception of a strongly anisotropic sample (surface anisotropy ratio $≈ 1.7$). Additionally, some artificial surfaces are generated by matching only the PS, and it was shown that only at adequately low effective slopes this can lead to similar flow properties. Overall, the results suggest that artificial roughness generated using the employed method by mimicking realistic PDF and PS can be applied as a full-fledged surrogate for realistic roughness under the premise of surface isotropy

An active learning approach for the prediction of hydrodynamic roughness properties

Realistic surfaces of flow-related equipment are often hydraulically rough due to wear or fouling. Predicting the skin friction exerted by such rough surfaces is a challenging task since the topography of these surfaces is inherently irregular and complex. Recent developments in data-driven methods and increasing affordability of high-fidelity direct numerical simulations (DNS) have created new possibilities for estimation of drag on irregular rough surfaces. In the present work we aim to demonstrate a viable approach to efficiently train a predictive model for the estimation of drag for irregular roughness based on its height probability density function (PDF) and the surface height power spectrum (PS). An active learning (AL) framework is employed to efficiently navigate the construction of a training database. Training data is generated by conducting direct numerical simulations of a flow over artificially generated rough surfaces in minimal channels in order to minimize the computational effort. An ensemble neural network (ENN) model is trained based on the database. The ENN model shows promising potential in predicting the skin friction as well as estimating the epistemic (model) uncertainty. Furthermore, the model – trained on artificial surfaces – is tested on five realistic surface scans, showing that a maximum error of 8.7% between the predicted roughness function ∆U+ and the DNS results is achieved. Overall, the AL framework shows a great potential as a basis for future research towards a universal predictive tool for any arbitrary roughness