Sensor Node Easy Moving Monitoring Region Location Algorithm in Internet of Things

Because of the influence from geographical location, weather and other kinds of circumstances in monitored areas, the shift of the node location and non-uniform distribution, this paper proposed an improved DV-Hop location algorithm. First of all, the package structure by changing the anchor nodes to reduce the number of hops data acquisition phase node data storage; introducing weights to the average hop distance calculation phase the original average hop distance calculation method was improved, and between the node and anchor node distance calculated on the basis of reference anchor nodes are different; then, iterative refinement of node localization stage through the use of multilateral measurement method and Taylor series. Finally, simulation experiment of this method, and compared with the existing methods, the results prove that the method in this paper can greatly reduce positioning errors without adding hardware equipment and network traffic, improve the positioning accuracy, a better solution to the problem of node localization networking monitoring area

Fulfilling information needs of online patients using domain knowledge in online health communities

Background: Online health communities (OHCs) experience difficulties in utilizing patient-reported posts to fulfill the information needs of online patients concerning healthrelated issues. Objectives: We aim to propose a comprehensive method that leverages medical domain knowledge to extract useful information from posts to fulfill information needs of online patients. Methods: A knowledge representation framework based on authoritative knowledge sources in the medical field for the OHC is proposed. On the basis of the framework, a health-related information extraction process for analyzing the posts in the OHC is proposed. Then, knowledge support rate (KSR) and effective information rate (EIR) are introduced as metrics to evaluate changes in knowledge extracted from the knowledge sources in terms of fulfilling the information needs of patients in the OHC. Results: On the basis of a dataset with 372,343 posts in an OHC, experimental results indicate that our method effectively extracts relevant knowledge for online patients. Moreover, KSR and EIR are feasible metrics of changes in knowledge in terms of fulfilling the information needs. Conclusions: The OHCs effectively fulfill the information needs of patients by utilizing authoritative domain knowledge in the medical field. Knowledge-based services for online patients facilitate an intelligent OHC in the future

CD103 Expression Is Required for Destruction of Pancreatic Islet Allografts by CD8+ T Cells

The mechanisms by which CD8 effector populations interact with epithelial layers is a poorly defined aspect of adaptive immunity. Recognition that CD8 effectors have the capacity to express CD103, an integrin directed to the epithelial cell-specific ligand E-cadherin, potentially provides insight into such interactions. To assess the role of CD103 in promoting CD8-mediated destruction of epithelial layers, we herein examined the capacity of mice with targeted disruption of CD103 to reject pancreatic islet allografts. Wild-type hosts uniformly rejected islet allografts, concomitant with the appearance of CD8+CD103+ effectors at the graft site. In contrast, the majority of islet allografts transplanted into CD103−/− hosts survived indefinitely. Transfer of wild-type CD8 cells into CD103−/− hosts elicited prompt rejection of long-surviving islet allografts, whereas CD103−/− CD8 cells were completely ineffectual, demonstrating that the defect resides at the level of the CD8 cell. CD8 cells in CD103−/− hosts exhibited normal effector responses to donor alloantigens in vitro and trafficked normally to the graft site, but strikingly failed to infiltrate the islet allograft itself. These data establish a causal relationship between CD8+CD103+ effectors and destruction of graft epithelial elements and suggest that CD103 critically functions to promote intragraft migration of CD8 effectors into epithelial compartments

Endothelial ether lipids link the vasculature to blood pressure, behavior, and neurodegeneration

Vascular disease contributes to neurodegeneration, which is associated with decreased blood pressure in older humans. Plasmalogens, ether phospholipids produced by peroxisomes, are decreased in Alzheimer's disease, Parkinson's disease, and other neurodegenerative disorders. However, the mechanistic links between ether phospholipids, blood pressure, and neurodegeneration are not fully understood. Here, we show that endothelium-derived ether phospholipids affect blood pressure, behavior, and neurodegeneration in mice. In young adult mice, inducible endothelial-specific disruption of PexRAP, a peroxisomal enzyme required for ether lipid synthesis, unexpectedly decreased circulating plasmalogens. PexRAP endothelial knockout (PEKO) mice responded normally to hindlimb ischemia but had lower blood pressure and increased plasma renin activity. In PEKO as compared with control mice, tyrosine hydroxylase was decreased in the locus coeruleus, which maintains blood pressure and arousal. PEKO mice moved less, slept more, and had impaired attention to and recall of environmental events as well as mild spatial memory deficits. In PEKO hippocampus, gliosis was increased, and a plasmalogen associated with memory was decreased. Despite lower blood pressure, PEKO mice had generally normal homotopic functional connectivity by optical neuroimaging of the cerebral cortex. Decreased glycogen synthase kinase-3 phosphorylation, a marker of neurodegeneration, was detected in PEKO cerebral cortex. In a co-culture system, PexRAP knockdown in brain endothelial cells decreased glycogen synthase kinase-3 phosphorylation in co-cultured astrocytes that was rescued by incubation with the ether lipid alkylglycerol. Taken together, our findings suggest that endothelium-derived ether lipids mediate several biological processes and may also confer neuroprotection in mice

H∞ filtering for non-linear systems with stochastic sensor saturations and Markov time delays: The asymptotic stability in probability

This study is concerned with the filtering problem for a class of non-linear systems with stochastic sensor saturations and Markovian measurement transmission delays, where the asymptotic stability in probability is considered. The sensors are subject to random saturations characterised by a Bernoulli distributed sequence. The transmission time delays are governed by a discrete-time Markov chain with finite states. In the presence of the non-linearities, stochastic sensor saturations and Markovian time delays, sufficient conditions are established to guarantee that the filtering process is asymptotically stable in probability without disturbances and also satisfies the H∞ criterion with respect to non-zero exogenous disturbances under the zero-initial condition. Moreover, it is illustrated that the results can be specialised to linear filters. Two simulation examples are presented to show the effectiveness of the proposed algorithms.This work was supported by National Natural Science Foundation of China under Grants 61490701, 61290324, 61273156, 61473163, and 61210012, and Research Fund for the Taishan Scholar Project of Shandong Province of China, and Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Finance and Economics of China under Grant JSEB201301, and Tsinghua University Initiative Scientific Research Program

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Research Application of Support Vector Machine in Fault Diagnosis of Certain Type Engine

For the engine fault diagnosis in real problems, the number of samples available are limited, and the progress of research on the theory of the most limited to assume that the data samples, so that the network training data examples, in engineering applications has been slow, in this paper, the application of support vector machine in fault diagnosis of engine, the segmentation of the training sample set, in order to achieve the optimal analysis of the machine, the reasoning ability best. First introduced the two classification method of support vector machine and multi classification method based on two classification methods of the study, and applied to the fault diagnosis of engine, and then the simulation test for this method, and compared with the existing methods, the results show the effectiveness of the classification method, the results of the analysis also can use the tree diagram or table form, simple and intuitive; but also can save the contribution to some extent in time