Search

The virtual machine (VM) allocation problem in cloud computing has been widely studied in recent years, and many algorithms have been proposed in the literature. Most of them have been successfully applied to batch processing models such as MapReduce; however, none of them can be applied to streaming workflow well because of the following weaknesses: 1) failure to capture the characteristics of tasks in streaming workflow for the short life cycle of data streams; 2) most algorithms are based on the assumptions that the price of VMs and traffic among data centers (DCs) are static and fixed. In this paper, we propose a streaming workflow allocation algorithm that takes into consideration the characteristics of streaming work and the price diversity among geo-distributed DCs, to further achieve the goal of cost minimization for streaming big data processing. First, we construct an extended streaming workflow graph (ESWG) based on the task semantics of streaming workflow and the price diversity of geo-distributed DCs, and the streaming workflow allocation problem is formulated into mixed integer linear programming based on the ESWG. Second, we propose two heuristic algorithms to reduce the computational space based on task combination and DC combination in order to meet the strict latency requirement. Finally, our experimental results demonstrate significant performance gains with lower total cost and execution time. [ABSTRACT FROM PUBLISHER]

The long-standing Acid Growth Theory of plant cell elongation posits that auxin promotes cell elongation by stimulating cell wall acidification and thus expansin action. To date, the paucity of pertinent genetic materials has precluded thorough analysis of the importance of this concept in roots. The recent isolation of mutants of the model grass species Brachypodium distachyon with dramatically enhanced root cell elongation due to increased cellular auxin levels has allowed us to address this question. We found that the primary transcriptomic effect associated with elevated steady state auxin concentration in elongating root cells is upregulation of cell wall remodeling factors, notably expansins, while plant hormone signaling pathways maintain remarkable homeostasis. These changes are specifically accompanied by reduced cell wall arabinogalactan complexity but not by increased proton excretion. On the contrary, we observed a tendency for decreased rather than increased proton extrusion from root elongation zones with higher cellular auxin levels. Moreover, similar to Brachypodium, root cell elongation is, in general, robustly buffered against external pH fluctuation in Arabidopsis thaliana. However, forced acidification through artificial proton pump activation inhibits root cell elongation. Thus, the interplay between auxin, proton pump activation, and expansin action may be more flexible in roots than in shoots. [ABSTRACT FROM AUTHOR]

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

Bacterial cellulose (BC), an important category of polysaccharides, was investigated as a texture improver in bakery products. This study focused on the changes in the conformational and thermal properties of gluten in the wheat dough system as affected by BC. Significant reductions in the free-SH content, fluorescence intensity, and surface hydrophobicity index (H 0 ) were observed as a result of the increased BC addition. The electrophoresis profile (SDS-PAGE) and size exclusion (SE-HPLC) revealed the variation in molecular weight distribution, and the increase in the content of the 40-91 kDa molecular weight was at the expense of a decrease in the amount of the corresponding 10-40 kDa. When 0.1 % BC was added, both the α-helix and β-sheet contents increased as a result of enhanced chemical interactions, thereby contributing to the gluten matrix with higher thermal stability. Further supplementation interfered with the current ordered gluten structure, which could be supported by the lower α-helix/β-sheet content ratio and the decreased degradation temperature (T d ) of gluten with 0.2 % BC. However, the observed decrease in the ratio of β-turns to β-sheets and weight loss at 600 °C indicated that a reconstructed gluten matrix induced by extra BC addition was formed to maintain the structural stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Impact of covalent and non-covalent binding interactions on physicochemical, structural, and rheological properties of waxy rice flour were investigated using several typical types of chemicals. Dynamic rheometer was used to investigate the viscoelastic properties and resistance to deformation or shearing. Both hydrogen bonds and hydrophobic interactions were necessary for waxy rice flour to form a material with stable structure matrix. Besides this, X-ray diffraction pattern and pasting property measurements showed that the relative crystallinity and pasting viscosity values of waxy rice flour exposed to SDS and l-cysteine treatments increased whereas those of urea decreased compared with the control. Addition of acid, alkali, and NaCl might result in electrostatic forces, ion interactions, and indirectly change hydrogen bonds and hydrophobic interactions between starch and protein, which played a synthetic role on property changes of waxy rice flour., (Copyright © 2018. Published by Elsevier Ltd.)

Understanding the context-specific role of gene function is a key objective of modern biology. To this end, we generated a resource for inducible cell type-specific transactivation in Arabidopsis ( Arabidopsis thaliana ) based on the well-established combination of the chimeric GR-LhG4 transcription factor and the synthetic pOp promoter. Harnessing the flexibility of the GreenGate cloning system, we produced a comprehensive set of transgenic lines termed GR-LhG4 driver lines targeting most tissues in the Arabidopsis shoot and root with a strong focus on the indeterminate meristems. When we combined these transgenic lines with effectors under the control of the pOp promoter, we observed tight temporal and spatial control of gene expression. In particular, inducible expression in F1 plants obtained from crosses of driver and effector lines allows for rapid assessment of the cell type-specific impact of an effector with high temporal resolution. Thus, our comprehensive and flexible method is suitable for overcoming the limitations of ubiquitous genetic approaches, the outputs of which often are difficult to interpret due to the widespread existence of compensatory mechanisms and the integration of diverging effects in different cell types., (© 2018 American Society of Plant Biologists. All rights reserved.)

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The long-standing Acid Growth Theory of plant cell elongation posits that auxin promotes cell elongation by stimulating cell wall acidification and thus expansin action. To date, the paucity of pertinent genetic materials has precluded thorough analysis of the importance of this concept in roots. The recent isolation of mutants of the model grass species Brachypodium distachyon with dramatically enhanced root cell elongation due to increased cellular auxin levels has allowed us to address this question. We found that the primary transcriptomic effect associated with elevated steady state auxin concentration in elongating root cells is upregulation of cell wall remodeling factors, notably expansins, while plant hormone signaling pathways maintain remarkable homeostasis. These changes are specifically accompanied by reduced cell wall arabinogalactan complexity but not by increased proton excretion. On the contrary, we observed a tendency for decreased rather than increased proton extrusion from root elongation zones with higher cellular auxin levels. Moreover, similar to Brachypodium, root cell elongation is, in general, robustly buffered against external pH fluctuation in Arabidopsis thaliana However, forced acidification through artificial proton pump activation inhibits root cell elongation. Thus, the interplay between auxin, proton pump activation, and expansin action may be more flexible in roots than in shoots., (© 2016 American Society of Plant Biologists. All rights reserved.)