Effects of Saline and Alkaline Stresses on Growth and Physiological Changes in Oat (Avena sativa L.) Seedlings

Two neutral salts (NaCl and Na2SO4) and alkaline salts (NaHCO3 and Na2CO3) were both mixed in 2:1 ratio, and the effects of saline and alkaline stresses on growth and physiological changes in oat seedlings were explored. The result showed that biomass, water content and chlorophyll content decreased while cell membrane permeability significantly increased under alkaline stress. Saline stress did not have an obvious effect on pH value in tissue fluids of shoot and root, but alkaline stress increased pH value in the root tissue fluid. The contents of Na+, Na+/K+, SO42- increased more, and K+, NO3-, H2PO4- decreased more under alkaline stress, the Cl- content increased obviously under saline stress but had little change under alkaline stress. The increments of proline and organic acid were both greater under alkaline stress, but organic acid content kept the same level under saline stress. Alkaline stress caused more harmful effects on growth and physiological changes in oat seedlings especially broke the pH stability in the root tissue fluid. Physiological adaptive mechanisms of oat seedlings under saline stress and alkaline stress were different, which mainly took the way of accumulating organic acid under alkali stress but accumulating Cl- under saline stress

Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Analytical behaviour of concrete-encased CFST box stub columns under axial compression

[EN] Concrete-encased CFST (concrete-filled steel tube) members have been widely used in high-rise buildings and bridge structures. In this paper, the axial performance of a typical concrete-encased CFST box member with inner CFST and outer reinforced concrete (RC) is investigated. A finite element analysis (FEA) model is established to analyze the compressive behavior of the composite member. The material nonlinearity and the interaction between concrete and steel tube are considered. A good agreement is achieved between the measured and predicted results in terms of the failure mode and the load-deformation relation. The verified FEA model is then used to conduct the full range analysis on the load versus deformation relations. The loading distributions of different components inclouding concrete, steel tube and longitudinal bar during four stages are discussed. Typical failure modes, internal force distribution, stress development and the contact stress between concrete and steel tube are also presented. The parametric study on the compressive behavior is conducted to investigate the effects of various parameters, e.g. the strength of concrete and steel, longitudinal bar ratio and stirrup space on the sectional capacity and the ductility of the concrete-encased CSFT box member.Chen, J.; Han, L.; Wang, F.; Mu, T. (2018). Analytical behaviour of concrete-encased CFST box stub columns under axial compression. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 401-408. https://doi.org/10.4995/ASCCS2018.2018.6966OCS40140

Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation

When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated with the new patients (query) features and then decoded to get the style-adapted segmentations. The model is independent of practice styles and anatomical structures. It meta-learns with simulated style differences and does not need to be exposed to any real clinical stylized structures during training. Once trained on the simulated data, it can be deployed for clinical use to adapt to new practice styles and new anatomical structures without further training. To show the proof of concept, we tested the Prior-guided DDL network on six different practice style variations for three different anatomical structures. Pre-trained segmentation models were adapted from post-operative clinical target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and from rectum segmentation to segment Rectumsuperior and Rectumposterior. The mode performance was quantified with Dice Similarity Coefficient (DSC). With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice style

Buffer Management and Hybrid Probability Choice Routing for Packet Delivery in Opportunistic Networks

Due to the features of long connection delays, frequent network partitions, and topology unsteadiness, the design of opportunistic networks faces the challenge of how to effectively deliver data based only on occasional encountering of nodes, where the conventional routing schemes do not work properly. This paper proposes a hybrid probability choice routing protocol with buffer management for opportunistic networks. A delivery probability function is set up based on continuous encounter duration time, which is used for selecting a better node to relay packets. By combining the buffer management utility and the delivery probability, a total utility is used to decide whether the packet should be kept in the buffer or be directly transmitted to the encountering node. Simulation results show that the proposed routing outperforms the existing one in terms of the delivery rate and the average delay