Showing papers on "Node (networking) published in 1969"

Copper binding and cholinesterase activity around the node of rnvier

Abstract: In their letter (.1. Histochein. Gytochein. 17: 66. 1969), 1)rs. Langley and Landon confirm that the node of Ranvier in teased nerve fibers l)ilids copper and report that such cripriphilia can be detected eveir when the copper solution is buffered as a citrate complex at p11 6, as in Karnovsky and Roots’ (J. Histochem. Cytochem. 12: 219. 1964) cholinesterase medium. This aspect of their results conflicts with those recently published by us (Adams, Bayliss and Grant. Histochem. J. 1: 68. 1968), but Langley and Landoit mainly relied on potassium ferrocyanide in 0.07 X HC1 (pH 1.9) to detect copper bound at the node. On repeating their modified technique we have confirmed that acidified ferrocyanide does reveal that copper is bound to the iiode at pH 6 but, unlike them, we have still beeii completely titiable to detect bound copper with our ferrocyanide reagent buffered with 0.15 M phosphate to pH 6.0 (Adams et a!., 1968). Hubeanic acid (Holczinger. Acta Histochem. (.Jena) 8: 167. 1959) at pH 1.9 or pH 6.0 does not detect copper specifically bound at the itode at pH 6; in fact, the nerve fiber is just diffusely stained light green. The results discussed in the preceding paragraph refer to nodal copper binding after treating teased rat sciatic nerve fibers with buffered copper solutioiis for 1 hr, the tissue having been fixed in

Research and Implementation of the Localization Algorithm Based on RSSI Technology

Abstract: The efficiency of sensor localization is a key issue in the Internet of things. There are possibilities for improvements in existing sensor localization systems, such as design complexity, cost. To address these issues, a location algorithm based on Distributed Range-Free Localization (DRFL) is proposed. In our positioning system can be composed by some single species node which can send and receive radio frequency signal, so it reduces the hardware requirements for the system. With this algorithm, system designed by us computes the anchor node weights with the RSSI values of anchor nodes and unknown nodes. Finally, the system acquires the data of unknown node localization with the weights and data of the anchor nodes. In this paper, a prototype was implemented to evaluate the algorithm. The results of experiments were shown that our system was stable. The algorithm, which had a little loss in sensor localization accuracy, had an effect on reducing the account of anchor nodes in the system

Incremental Subspace Data-mining Algorithm Based on Data-flow Density of Complex Network

Abstract: In order to improve the accuracy of data-mining in large-scale complex networks, an incremental subspace data-mining algorithm based on data-flow density of complex network is proposed in this paper. In order to accommodate this goal, a latent variable model is first introduced and incorporated into Data-flow density model so that the network is divided into different communities and the defect of traditional data-mining can be explained. The undirected traversing loop is used to determine their corresponding communities for data-flow. The incremental subspace data-mining algorithm is also used to calculate the correlation between community network and data-flow and the correlation coefficient between data-flow node and time so as to accurately determine the destination of the object data-flow. In the case of a larger number of nodes and communities, data analysis and simulation experiment results demonstrate that the incremental subspace data-mining algorithm still has a higher mining accuracy