Planar hexagonal meshing for architecture

published_or_final_versio

Autonomous deployment for load balancing k-surface coverage in sensor networks

published_or_final_versio

Leucine-rich repeat kinase 2 mutations and Parkinson’s disease: three questions

Mutations in the gene encoding LRRK2 (leucine-rich repeat kinase 2) were first identified in 2004 and have since been shown to be the single most common cause of inherited Parkinson’s disease. The protein is a large GTP-regulated serine/threonine kinase that additionally contains several protein–protein interaction domains. In the present review, we discuss three important, but unresolved, questions concerning LRRK2. We first ask: what is the normal function of LRRK2? Related to this, we discuss the evidence of LRRK2 activity as a GTPase and as a kinase and the available data on protein–protein interactions. Next we raise the question of how mutations affect LRRK2 function, focusing on some slightly controversial results related to the kinase activity of the protein in a variety of in vitro systems. Finally, we discuss what the possible mechanisms are for LRRK2-mediated neurotoxicity, in the context of known activities of the protein

Ketamine abuse and apoptosis in the cortex in monkeys and mice

International Journal of Neuropsychopharmacology, 2008, v. 11, suppl. 1, p. 236-237, abstract no. P-06.11published_or_final_versionThe 26th CINP Congress, Munich, Germany, 13-17 July 2008

LAACAD: Load bAlancing k-area coverage through autonomous deployment in wireless sensor networks

Session 6B: Coverage & LocalizationAlthough the problem of k-area coverage has been intensively investigated for dense wireless sensor networks (WSNs), how to arrive at a k-coverage sensor deployment that optimizes certain objectives in relatively sparse WSNs still faces both theoretical and practical difficulties. In this paper, we present a practical algorithm LAACAD (Load bAlancing k-Area Coverage through Autonomous Deployment) to move sensor nodes toward k-area coverage, aiming at minimizing the maximum sensing range required by the nodes. LAACAD enables purely autonomous node deployment as it only entails localized computations. We prove the convergence of the algorithm, as well as the (local) optimality of the output. We also show that our optimization objective is closely related to other frequently considered objectives. Therefore, our practical algorithm design also contributes to the theoretical understanding of the k-area coverage problem. Finally, we use extensive simulation results both to confirm our theoretical claims and to demonstrate the efficacy of LAACAD. © 2012 IEEE.postprin

Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets

Effect of isospin dependent cross-section on fragment production in the collision of charge asymmetric nuclei

To understand the role of isospin effects on fragmentation due to the collisions of charge asymmetric nuclei, we have performed a complete systematical study using isospin dependent quantum molecular dynamics model. Here simulations have been carried out for $^{124}X_{n}+ ^{124}X_{n}$, where n varies from 47 to 59 and for $^{40}Y_{m}+ ^{40}Y_{m}$, where m varies from 14 to 23. Our study shows that isospin dependent cross-section shows its influence on fragmentation in the collision of neutron rich nuclei

Galactic and Extragalactic Samples of Supernova Remnants: How They Are Identified and What They Tell Us

Supernova remnants (SNRs) arise from the interaction between the ejecta of a supernova (SN) explosion and the surrounding circumstellar and interstellar medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However, to understand SNRs as a whole, large samples of SNRs must be assembled and studied. Here, we describe the radio, optical, and X-ray techniques which have been used to identify and characterize almost 300 Galactic SNRs and more than 1200 extragalactic SNRs. We then discuss which types of SNRs are being found and which are not. We examine the degree to which the luminosity functions, surface-brightness distributions and multi-wavelength comparisons of the samples can be interpreted to determine the class properties of SNRs and describe efforts to establish the type of SN explosion associated with a SNR. We conclude that in order to better understand the class properties of SNRs, it is more important to study (and obtain additional data on) the SNRs in galaxies with extant samples at multiple wavelength bands than it is to obtain samples of SNRs in other galaxiesComment: Final 2016 draft of a chapter in "Handbook of Supernovae" edited by Athem W. Alsabti and Paul Murdin. Final version available at https://doi.org/10.1007/978-3-319-20794-0_90-