• Journal of Internet Technology
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• v.21 no.1
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• pp.249-264
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• 2020

Cellular (C) setups facilitate the connectivity amongst the devices with better provisioning of services to its users. Vehicular networks are one of the representative setups that aim at expanding their functionalities by using the available cellular systems like Long Term Evolution (LTE)-based Evolved Universal Terrestrial Radio Access Network (E-UTRAN) as well as the upcoming Fifth Generation (5G)-based functional architecture. The vehicular networks include Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Pedestrian (V2P) and Vehicle to Network (V2N), all of which are referred to as Vehicle to Everything (V2X). 5G has dominated the vehicular network and most of the upcoming research is motivated towards the fully functional utilization of 5G-V2X. Despite that, credential management and edge-initiated security are yet to be resolved under 5G-V2X. To further understand the issue, this paper presents security management as a principle of sustainability and key-management. The performance tradeoff is evaluated with the key-updates required to maintain a secure connection between the vehicles and the 5G-terminals. The proposed approach aims at the utilization of high-speed mmWave-based backhaul for enhancing the security operations between the core and the sub-divided functions at the edge of the network through a dual security management framework. The evaluations are conducted using numerical simulations, which help to understand the impact on the sustainability of connections as well as identification of the fail-safe points for secure and fast operations. Furthermore, the evaluations help to follow the multiple tradeoffs of security and performance based on the metrics like mandatory key updates, the range of operations and the probability of connectivity.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.109-109
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• 2017

The core components of ABA-dependent gene expression signaling have been identified in Arabidopsis and rice. This signaling pathway consists of four major components; group A OsbZIPs, SAPKs, subclass A OsPP2Cs and OsPYL/RCARs in rice. These might be able to make thousands of combinations through interaction networks resulting in diverse signaling responses. We tried to characterize those gene functions using transient gene expression for rice protoplasts (TGERP) because it is instantaneous and convenient system. Firstly, in order to monitor the ABA signaling output, we developed reporter system named pRab16A-fLUC which consists of Rab16A promoter of rice and luciferase gene. It responses more rapidly and sensitively to ABA than pABRC3-fLUC that consists of ABRC3 of HVA1 promoter in TGERP. We screened the reporter responses for over-expression of each signaling components from group A OsbZIPs to OsPYL/RCARs with or without ABA in TGERP. OsbZIP46 induced reporter most strongly among OsbZIPs tested in the presence of ABA. SAPKs could activate the OsbZIP46 even in the ABA independence. Subclass A OsPP2C6 and -8 almost completely inhibited the OsbZIP46 activity in the different degree through the SAPK9. Lastly, OsPYL/RCAR2 and -5 rescued the OsbZIP46 activity in the presence of SAPK9 and OsPP2C6 dependent on ABA concentration and expression level. By using TGERP, we could characterize successfully the effects of ABA dependent gene expression signaling components in rice. In conclusion, TGERP represents very useful technology to study systemic functional genomics in rice or other monocots.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.15-22
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• 2006

An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

• Environmental Engineering Research
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• v.21 no.4
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• Journal of Plant Biotechnology
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• v.2 no.2
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• pp.61-71
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• 2000

By screening a cDNA library of auxin-treated mung bean (Vigna radiata L.) hypocotyls, we have isolated two full-length cDNA clones, pVR-ACS6 and pVR-ACS7, for 1-aminocyclopropane-1-carboxylate (ACC) synthase, the rate-limiting enzyme in the ethylene biosynthetic pathway. While PVR-ACS6 corresponds to the previously identified PCR fragment pMBA1, pVR-ACS7 is a new cDNA clone. A comparison of deduced amino acid sequences among auxin-induced ACC synthases reveal that these enzymes share a high degree of homology (65-75%) to VR-ACS6 and VR-ACS7 polypeptides, but only about 50% to VR-ACS1 polypeptide. ACS6 and ACS7 are specifically induced by auxin, while ACS1 is induced by cycloheximide, and to lesser extent by excision and auxin treatment. Results from nuclear run-on transcription assay and RNA gel blot studies revealed that all three genes were transcriptionally active displaying unique patterns of induction by IAA and various hormones in etiolated hypocotyls. Particularly, 24-epibrassinolide (BR), an active brassinosteroid, specifically enhanced the expression of VR-ACS7 by distinct temporal induction mechanism compared to that of IAA. In addition, BR synergistically increased the IAA-induced VR-ACS6 and VR-ACS7 transcript levels, while it effectively abolished both the IAA- and kinetin-induced accumulation of VR-ACS1 mRNA. In light-grown plants, VR-ACS1 was induced by IAA in roots, whereas W-ACS6 in epicotyls. IAA- and BR-treatments were not able to increase the VR-ACS7 transcript in the light-grown tissues. These results indicate that the expression of ACC synthase multigene family is regulated by complex hormonal and developmental networks in a gene- and tissue-specific manner in mung bean plants. The VR-ACS7 gene was isolated, and chimeric fusion between the 2.4 kb 5'-upstream region and the $\beta$-glucuronidase (GUS) reporter gene was constructed and introduced into Nicotiana tobacum. Analysis of transgenic tobacco plants revealed the VR-ACS7 promoter-driven GUS activity at a highly localized region of the hypocotyl-root junction of control seedlings, while a marked induction of GUS activity was detected only in the hypocotyl region of the IAA-treated transgenic seedlings where rapid cell elongation occurs. Although there was a modest synergistic effect of BR on the IAA-induced GUS activity, BR alone failed to increase the GUS activity, suggesting that induction of VR-ACS7 occurs via separate signaling pathways in response to IAA and BR.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of Acupuncture Research
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• v.37 no.2
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• pp.128-135
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• 2020

Background: Classical acupuncture is being used in the treatment of rheumatoid arthritis (RA). To explore the biological response to acupuncture, a network-based analysis was performed on gene expression data collected from an animal model of RA treated with acupuncture. Methods: Gene expression data were obtained from published microarray studies on blood samples from rats with collagen induced arthritis (CIA) and non-CIA rats, both treated with manual acupuncture. The weighted gene co-expression network analysis was performed to identify gene clusters expressed in association with acupuncture treatment time and RA status. Gene ontology and pathway analyses were applied for functional annotation and network visualization. Results: A cluster of 347 genes were identified that differentially downregulated expression in association with acupuncture treatment over time; specifically in rats with CIA with module-RA correlation at 1 hour after acupuncture (-0.27; p < 0.001) and at 34 days after acupuncture (-0.33; p < 0.001). Functional annotation showed highly significant enrichment of porphyrin-containing compound biosynthetic processes (p < 0.001). The network-based analysis also identified a module of 140 genes differentially expressed between CIA and non-CIA in rats (p < 0.001). This cluster of genes was enriched for antigen processing and presentation of exogenous peptide antigen (p < 0.001). Other functional gene clusters previously reported in earlier studies were also observed. Conclusion: The identified gene expression networks and their hub-genes could help with the understanding of mechanisms involved in the pathogenesis of RA, as well understanding the effects of acupuncture treatment of RA.

• Journal of Microbiology and Biotechnology
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• v.17 no.3
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• pp.496-510
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• 2007

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-a-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an integrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 $(2^3)$ fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

• Analytical Science and Technology
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• v.18 no.5
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• pp.386-390
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• 2005

The zinc(II) complex, $Zn(NCS)_2(C_3H_4N_2S)_2$, I, has been synthesized and characterized by single crystal X-ray diffraction, thermal analysis and infrared spectroscopy. The complex I crystallizes in the triclinic system, $P\bar{1}$ space group with a = 7.587(1), b = 8.815(1), $c=12.432(2){\AA}$, ${\alpha}=75.584(8)$, ${\beta}=83.533(9)$, ${\gamma}=68.686(8)^{\circ}$, $V=750.0(2){\AA}^3$, Z = 2, $R_1=0.036$ and ${\omega}R_2=0.101$. The central Zn(II) atom has a tetrahedral coordination geometry, with the heterocyclic nitrogen atoms of 2-aminothiazole ligands and the nitrogen atoms of isothiocyanate ligands. The crystal structure is stabilized by one-dimensional networks of the intermolecular $N-H{\cdots}S$ hydrogen bonds between the amino group of 2-aminothiazole ligands and the sulfur atom of isothiocyanate ligands. Based on the results of thermal analysis, the thermal decomposition reaction of complex I was analyzed to have three distinctive stages such as the loss of 2-aminothiazole, the decomposition of isothiocyanate and the formation of metal oxide.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.387-396
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• 2004

One of the main problems associated with MANETs is that mobility and the associated route discovery and maintenance procedures of reactive routing protocols cause severe interruptions on real-time video streams. Some of these interruptions are too large to be concealed using any sort of video technology, resulting in communications breaks unpleasant for the final end user. We present a solution for enhanced video transmission that increases route stability by using an improved route discovery process based on the DSR routing protocol, along with traffic splitting algorithms and a preventive route discovery mechanism. We also present some video adaptative mechanisms that improve the overall performance of multipath routing in terms of video data replication and video packet splitting strategies. Combining our proposals, we achieve up to 97% less interruptions on communication with high mobility and over 1.2 dB of improvements in terms of video distortion.