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Reliable Localization for Wireless Sensor Networks in Complex Environments
Xiaolei Liu,Yongji Ren,Xuguang Xin,Liping Zhang,Jixiang Chen 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.10
Although localization has been widely studied for Wireless Sensor Networks (WSNs), the complex environments and the large network scale pose severe challenges and make it necessary to develop new reliable localization algorithms. In this paper, we propose a novel Multi-Hop Localization Algorithm for large-scale WSNs in complex environments. This work is based on the consideration that the localization process would encounter several kinds of adverse factors with different nature at the same time (e.g. anisotropic network characteristics, ranging uncertainty, link quality of multihop paths, etc.), which lead to obvious degradation of localization performance. Unlike most of the existing schemes, we transform the localization problem in complex environments into a hybrid constraint satisfaction problem (CSP) which is composed of three different kinds of constraints, i.e. spatial constraint, network situation constraint, and confidence constraint. Set-membership approach and interval analysis method have been utilized to deal with the CSP and determine the positions of sensor nodes. Simulation results show that our scheme is an effective and efficient approach to localization in large-scale WSNs.
Wanhong Peng,Qingyu Tan,Minglan Yu,Ping Wang,Tingting Wang,Jixiang Yuan,Dongmei Liu,Dechao Chen,Chaohua Huang,Youguo Tan,Kezhi Liu,Bo Xiang,Xuemei Liang 대한신경정신의학회 2021 PSYCHIATRY INVESTIGATION Vol.18 No.5
Objective Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs).Methods Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways.Results Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10<sup>-16</sup> and 1.09×10<sup>-13</sup>, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data.Conclusion It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.