A Portable Random Key Predistribution Scheme for Distributed Sensor Network

A distributed sensor network (DSN) can be deployed to collect information for military or civilian applications. However, due to the characteristics of DSNs such as limited power, key distribution for a distributed sensor network is complex. In this paper, a neighbor-based path key establishing method and a seed-based algorithm are put forward to improve the original random key pre-distribution scheme. The new scheme is portable because it is independent of the routing protocol. Moreover, the connectivity of the entire network also approaches 1. In particular, the new scheme can keep high connectivity by setting a small amount of redundancy in parameter values when the number of neighbors drops because of the node dormancy or death. The resilience against node capture in our scheme is not lower than that in the l-path scheme and the basic schemes when the number of hops in a path is larger than 5, and the simulation result shows that the efficiency of our scheme is also slightly higher

The Zymomonas mobilis regulator hfq contributes to tolerance against multiple lignocellulosic pretreatment inhibitors

<p>Abstract</p> <p>Background</p> <p><it>Zymomonas mobilis </it>produces near theoretical yields of ethanol and recombinant strains are candidate industrial microorganisms. To date, few studies have examined its responses to various stresses at the gene level. Hfq is a conserved bacterial member of the Sm-like family of RNA-binding proteins, coordinating a broad array of responses including multiple stress responses. In a previous study, we observed <it>Z. mobilis </it>ZM4 gene ZMO0347 showed higher expression under anaerobic, stationary phase compared to that of aerobic, stationary conditions.</p> <p>Results</p> <p>We generated a <it>Z. mobilis hfq </it>insertion mutant AcRIM0347 in an acetate tolerant strain (AcR) background and investigated its role in model lignocellulosic pretreatment inhibitors including acetate, vanillin, furfural and hydroxymethylfurfural (HMF). <it>Saccharomyces cerevisiae </it>Lsm protein (Hfq homologue) mutants and Lsm protein overexpression strains were also assayed for their inhibitor phenotypes. Our results indicated that all the pretreatment inhibitors tested in this study had a detrimental effect on both <it>Z. mobilis </it>and <it>S. cerevisiae</it>, and vanillin had the most inhibitory effect followed by furfural and then HMF for both <it>Z. mobilis </it>and <it>S. cerevisiae</it>. AcRIM0347 was more sensitive than the parental strain to the inhibitors and had an increased lag phase duration and/or slower growth depending upon the conditions. The <it>hfq </it>mutation in AcRIM0347 was complemented partially by trans-acting <it>hfq </it>gene expression. We also assayed growth phenotypes for <it>S. cerevisiae </it>Lsm protein mutant and overexpression phenotypes. Lsm1, 6, and 7 mutants showed reduced tolerance to acetate and other pretreatment inhibitors. <it>S. cerevisiae </it>Lsm protein overexpression strains showed increased acetate and HMF resistance as compared to the wild-type, while the overexpression strains showed greater inhibition under vanillin stress conditions.</p> <p>Conclusions</p> <p>We have shown the utility of the pKNOCK suicide plasmid for mutant construction in <it>Z. mobilis</it>, and constructed a Gateway compatible expression plasmid for use in <it>Z. mobilis </it>for the first time. We have also used genetics to show <it>Z. mobilis </it>Hfq and <it>S. cerevisiae </it>Lsm proteins play important roles in resisting multiple, important industrially relevant inhibitors. The conserved nature of this global regulator offers the potential to apply insights from these fundamental studies for further industrial strain development.</p

Global Analysis for Rough Solutions to the Davey-Stewartson System

The global well-posedness of rough solutions to the Cauchy problem for the Davey-Stewartson system is obtained. It reads that if the initial data is in Hs with s > 2/5, then there exists a global solution in time, and the Hs norm of the solution obeys polynomial-in-time bounds. The new ingredient in this paper is an interaction Morawetz estimate, which generates a new space-time Lt,x4 estimate for nonlinear equation with the relatively general defocusing power nonlinearity

WGCNA and molecular docking identify hub genes for cardiac aging

BackgroundCardiac aging and ageing-related cardiovascular diseases remain increase medical and social burden. Discovering the molecular mechanisms associated with cardiac aging is expected to provide new perspectives for delaying aging and related disease treatment.MethodsThe samples in GEO database were divided into older group and younger group based on age. Age-associated differentially expressed genes (DEGs) were identified by limma package. Gene modules significantly associated with age were mined using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction networks (PPI) networks were developed using genes within modules, and topological analysis on the networks was performed to identify hub genes in cardiac aging. Pearson correlation was used to analyze the association among hub genes and immune and immune-related pathways. Molecular docking of hub genes and the anti-aging drug Sirolimus was performed to explore the potential role of hub genes in treating cardiac aging.ResultsWe found a generally negative correlation between age and immunity, with a significant negative correlation between age and b_cell_receptor_signaling_pathway, fc_gamma_r_mediated_phagocytosis, chemokine signaling pathway, t-cell receptor signaling pathway, toll_like_receptor_signaling_pathway, and jak_stat_signaling_pathway, respectively. Finally, 10 cardiac aging-related hub genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were identified. 10-hub genes were closely associated with age and immune-related pathways. There was a strong binding interaction between Sirolimus-CCR2. CCR2 may be a key target for Sirolimus in the treatment of cardiac aging.ConclusionThe 10 hub genes may be potential therapeutic targets for cardiac aging, and our study provided new ideas for the treatment of cardiac aging

Fault Characteristics and Control Strategies of Multiterminal High Voltage Direct Current Transmission Based on Modular Multilevel Converter

The modular multilevel converter (MMC) is an emerging voltage source converter topology suitable for multiterminal high voltage direct current transmission based on modular multilevel converter (MMC-MTDC). This paper presents fault characteristics of MMC-MTDC including submodule fault, DC line fault, and fault ride-through of wind farm integration. Meanwhile, the corresponding protection strategies are proposed. The correctness and effectiveness of the control strategies are verified by establishing a three-terminal MMC-MTDC system under the PSCAD/EMTDC electromagnetic transient simulation environment

Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying Out-Of-Distribution (OOD) points in a LiDAR point cloud is challenging as point clouds lack semantically rich features when compared with RGB images. We revisit this problem from the perspective of selective classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any known categories but learns a point-wise abstaining penalty with a marginbased loss. Synthesizing outliers to approximate unlimited OOD samples is also critical to this idea, so we propose a strong synthesis pipeline that generates outliers originated from various factors: unrealistic object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve state-of-the-art results. Risk-coverage analysis further reveals intrinsic properties of different methods. Codes and models will be publicly available.Comment: codes is available at https://github.com/Daniellli/PAD.gi

Spatio-temporal Markov chain model for very-short-term wind power forecasting

Wind power forecasting (WPF) is crucial in helping schedule and trade wind power generation at various spatial and temporal scales. With increasing number of wind farms over a region, research focus of WPF methods has been recently moved onto exploring spatial correlation among wind farms to benefit forecasting. In this study, a spatio-temporal Markov chain model is proposed for very-short-term WPF by extending the traditional discrete-time Markov chain and incorporating off-site reference information to improve forecasting accuracy of regional wind farms. Not only are the transitions between the power output states of the target wind farm itself considered in the forecasting model, but also the transitions from the output states of reference wind farms to that of the target wind farm are introduced. The forecasting results derived from multiple spatio-temporal Markov chains regarding different reference wind farms over the same region are optimally weighted using sparse optimisation to generate forecasts of the target wind farm. The proposed method is validated by comparing with both local and spatio-temporal WPF methods, using a real-world dataset