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Title of the Paper: Information Process in Vestibular System
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Authors: Alexandrov V. V, Alexandrova T. B, Vega R, Castillo Quiroz G,
Angeles Vazquez A, Reyes Romero M and Soto E
Abstract: A mathematical model of the output generation of the vestibular mechanoreceptor is presented. The model consider the series of events in the vestibular end organs that lead to the activation of the afferent neurons in the vestibular nerve and the generation of the afferent impulses. We have considered five compartments: mechano-electrical transduction, adaptation of transduction, hair cell ionic currents, synaptic transmission, and afferent neuron discharge. The numerical parameters of the model were obtained from experiments that were done in the inner ear of the rat. The results of the numerical analysis of the model showed that the mathematical modelling of the output from the vestibular mechanoreceptor may be used to construct an encoder system for artificial sensors (e.g.: vibrational gyroscope) contributing to the development of a reliable vestibular prosthesis prototype. This model can be used as the basis for the mathematical modelling of the vestibular sensors.
Keywords: Mechanical stimulus, hair cell, primary neuron, vestibular mechanoreceptor, afferent impulses
Title of the Paper: Histo-Anatomical Strategies of Chenopodiaceae Halophytes: Adaptive, Ecological snd Evolutionary Implications
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Authors: Marius-Nicuşor Grigore, Constantin Toma
Abstract: The authors of the present study have investigated six halophyte species from Chenopodiaceae
family. These species have been harvested from different saline habitats. Some interesting and intriguing
features such as successive cambia phenomenon, succulence, salt hairs, tracheids, fleshy tissues and peculiar
foliar anatomy structures have been noticed. These aspects are discussed in relation to their ecological, adaptive
and evolutionary significance.
Keywords: adaptation, lignin, C4 plants, Kranz anatomy, photosynthesis, strategy, evolution
Title of the Paper: Detector Generation Algorithm Based on Online GA for Anomaly Detection
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Authors: Dongyong Yang, Jinyin Chen, Matsumoto Naofumi
Abstract: Detector plays an important role in intrusion detection system in artificial immune system, which makes detector generation algorithm especially significant. Traditional NSA cannot satisfy current network demands because the affinity limit r is difficult to fix in prior. A novel online GA-based algorithm is come up with self-adaptive mutation probability, in which affinity limit r is self-adaptive. Compared with GA-based detector maturation algorithm, detectors in online GA-based algorithm evolve online during the detection process which realizes self-organization and online learning to be adaptive to dynamic network. Finally simulation results testify that TP (true positive) value and FP (false positive) value of online GA-based algorithm is much better than NSA, GA-based and IGA-based algorithms without significant algorithm complexity increase.
Keywords: Detector generation algorithm, GA, Online GA, Self-adaptive, Intrusion detection
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