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Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

, A novel kernel for learning a neuron model from spike train data, Advances in Neural Information Processing Systems, Vol.23, pp.595-603, 2010. [10] K.J. Friston, L. Harrison, and W. Penny, Dynamic causal modeling, NeuroImage, Vol.19, no. 4, pg.1273-1302, 2003. [11] Matteo Garofalo, Thierry Nieus, Paolo Massobrio, and Sergio Martinoia, Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks, PLoS One, Vol.4, No.8, e6482, 2009

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Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

) with correlation measures for gene expression analysis. Neurocomputing, 69(6-7):651-659, March 2006. [51] S. Saralajew and T. Villmann. Adaptive tangent metrics in generalized learning vector quantization for transformation and distortion invariant classification learning. In Proceedings of the International Joint Conference on Neural networks (IJCNN) , Vancover, pages 2672-2679. IEEE Computer Society Press, 2016. [52] S. Saralajew, D. Nebel, and T. Villmann. Adaptive Hausdorff distances and tangent distance adaptation for

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Trace Metal Concentrations in Pleurozium Schreberi and Taraxacum Officinale Along the Road No. 7

. Ultrafine particles near a major roadway in Raleigh, North Carolina: Downwind attenuation and correlation with traffic-related pollutants. Atmosph Environ. 2009;43:1229-34. DOI: 10.1016/j.atmosenv.2008.11.024. [17] Bernhardt-Römermann M, Kirchner M, Kudernatsch T, Jakobi G, Fischer A. Changed vegetation composition in coniferous forests near to motorways in Southern Germany: the effects of traffic-born pollution. Environ Pollut. 2006;143:572-81. DOI: 10.1016/j.envpol.2005.10.046. [18] Markert BA, Breure AM, Zechmeister HG. Bioindicators and Biomonitors

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Geometrical structure of surface after turning of 316L stainless steel in laser assisted conditions

Abstract

The effects of turning 316L steel in a laser assisted machining are presented in this paper. The properties of 316L stainless steel are also shown in this article. In order to show correlation between the technological parameters, microgeometry of cutting tools and geometrical structure of surface, turning of material in grade 316L supported by laser has been executed. In addition, optical examination of cutting inserts has been performed and geometrical measurements of machined surfaces have been taken. The results of researches on the effects of the technological parameters and cutting tool’s microgeometry on the geometrical structure of the 316L steel surface after turning in LAM conditions are described.

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Experimental Study of the Effects of Flow Discharge, Diameter, and Depth on Shear Stress in a Rectangular Channel with Rigid Unsubmerged Vegetation

Abstract

Shear stress is one of the most critical parameters in hydraulic and coastal engineering, which is often measured indirectly. Since there is no instrument to measure this parameter directly and given that it is usually calculated by measuring other parameters such as velocity and pressure and using some equations, shear stress measurement is often accompanied with large measurement errors. In this study, a new technique and direct measurement using physical modeling in a hydraulic knife-edge flume and load cell were employed to measure the shear stress in a rectangular channel with rigid unsubmerged vegetation with Dv= 20, 25, and 32mm in

Q=25 and 30 Lit/S and y=10, 12, 17, and 20 cm. The results indicate that the shear stress and the dimensionless τ0τ ratio decrease in a constant flow discharge with increasing the flow depth. It was also shown that the shear stress would be enhanced with an increase

in vegetation diameter due to increasing vegetation density against flow. According to dimensionless ratios of τ0τ and Dvy in the graphs and considering the trend lines with appropriate correlation coefficients, some equations were presented to calculate the shear stress in the concerned range.

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Prediction of Water Quality in Riva River Watershed

Abstract

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.

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Vibration and displacement analysis during turning of hardened steel

, G., Gupta, M.K ., Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics, Applied Sciences (Switzerland)9(5),0842, 2019. [9] Prasad, B.S., Babu, M.P., Reddy, Y.R. , Evaluation of correlation between vibration signal features and three dimensional finite element simulations to predict cutting tool wear in turning operation, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture230(2), pp. 203-214, 2016. [ 10] Tung P. D., Mathematical

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The Effect of Squeeze Pin Dimension and Operational Parameters on Material Homogenity of Aluminium High Pressure Die Cast Parts

), Optimization of Die casting process based on Taguchi approach , Materials Today, vol. 4(2A), pp.1852-1859. [7] Jahangiri, A., Marashi, S.H.P., Mohammadaliha, M, Ashofte, V., (2017) The effect of pressure and pouring temperature on the porosity, microstructure, hardness and yield stress of AA2024 aluminum alloy during the squeeze casting process , Journal of Materials Processing Technology, vol. 245, pp. 1-6. [8] Battaglia, E., Bonollo, F., Timelli, G., Fiorese, E., Kral, G., (2016), Correlation between process, microstructure and properties in high pressure die

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Effect of R, µ and T on the Fragility Curves for Two Spans Reinforced Concrete Highway Bridges

. Structural safety , 30(4): pp. 320-336. Wright, S., 1921. Correlation and causation. Journal of agricultural research, 20(7), pp. 557-585.

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A Ten-Year Biomonitoring Study of Atmospheric Deposition of Trace Elements at the Territory of the Republic of Belarus

;408(20):4569-4579. DOI: 10.1016/j.scitotenv.2010.06.016. [24] Dall’Osto M, Querol X, Amato F, Karanasiou A, Lucarelli F, Nava S et al. Hourly elemental concentrations in PM2.5 aerosols sampled simultaneously at urban background and road site. Atm Chem Phys. 2012;12(8):20135-20180. DOI: 10.5194/acpd-12-20135-2012. [25] Nickel S, Schröder W, Schmalfuss R, Saathoff M. Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe. Environ Sci Eur. 2018;30(1):53. DOI: 10.1186/s12302-018-0183-8.

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