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Gastroprotective effects of methanol extract of Eremomastax speciosa leaf harvested in Southern part of Nigeria in rat

. Pradeepkumar B, Bhavyamadhuri CP, Padmanabhareddy Y, Veerabhadrappa K V., Narayana G, Haranath C, et al. Evaluation of antiulcer activity of Peltophorum pterocarpum. J Clin Diagnostic Res 2017; 11(6): FF01-FF03. 11. Amang PA, Tan P V, Patamaken SA, Mefe MN. Cytoprotective and antioxidant effects of the methanol extract of Eremomastax speciosa in rats. African J Tradit Complement Altern Med 2014; 11(1): 165-171. 12. André Perfusion A, Tan P V, Ernestine N, Barthélemy N. Antisecretory action of the extract of the aerial parts of Eremomastax

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Modelling Of Rock Joints Interface Under Cyclic Loading

different normal loads ( σ 1 < σ 2 < σ 3 , ρ ini = 2.3 . 10 3 kg/m 3 , θ max = 25° ) depending on the tangential displacement u t : a) variations of shear stress, b) variations of dilatancy Figure 11 Shape of primary asperities depending on g 0 parameter Figure 12 Simulation results for different normal loads ( σ 1 < σ 2 < σ 3 , ρ ini = 2.3 . 10 3 kg/m 3 , θ max = 25° , asperities ) depending on the tangential displacement u t : a) variations of shear stress, b) variations of dilatancy, c) asperity shape assumed for calculations

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Effect of randomly distributed polypropylene fiber reinforcement on the shear behavior of sandy soil

.A. (2003). Study on shear strength of sands reinforced with randomly distributed discrete fibers, Geotextiles and Geomembranes, 21 (2), 103–110. 10.1016/S0266-1144(03)00003-7 Yetimoglu T. Salbas O.A. 2003 Study on shear strength of sands reinforced with randomly distributed discrete fibers Geotextiles and Geomembranes 21 2 103 – 110 [42] Zornberg, G. (2002). Discrete framework for limit equilibrium analysis of fiber reinforced soil. Géotechnique, 52(8), 593–604 10.1680/geot.2002.52.8.593 Zornberg G. 2002 Discrete framework for limit

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Effective Friction Angle Of Deltaic Soils In The Vistula Marshlands

93 31 44 https://doi.org/10.1016/j.enggeo.2007.03.009 [5] Coutinho, R.Q., Lacerda, W.A., 1989. Strength characteristics of Juturnaiba organic clays. Presented at the 12th International conference on Soil Mechanics and Foundation Engineering, Balkema, Rio de Janeiro, pp. 1731–1734. Coutinho R.Q. Lacerda W.A. 1989 Strength characteristics of Juturnaiba organic clays Presented at the 12th International conference on Soil Mechanics and Foundation Engineering Balkema Rio de Janeiro 1731 1734 [6] Dan, G., Sultan, N., Savoye, B. 2007. The

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Penetrative convection due to absorption of radiation in a magnetic nanofluid saturated porous layer

occur with the choice of new boundary conditions. Using these more realistic boundary conditions, Yadav et al. [ 4 ] analyzed the thermal instability of rotating nanofluids. The authors mentioned that the model selected in their study is more realistic physically than those used in the previous studies (i.e., models with non-zero nanoparticle flux at boundaries). Different aspects of natural convection in porous media have been examined thoroughly by many researchers (see, e.g., Ref. [ 5 –9]). An experimental investigation of the onset of convection in a stably

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An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

analyzed, each containing 30 min of annotated ECG recordings of continuous ECG. The ECG recordings in this database contain the normal clinical recordings, complex ventricular, junctional, and supraventricular arrhythmias [ 25 ]. These records were sampled at 360 Hz and band pass filtered at 0.1 – 100 Hz [ 25 ]. In this paper, our method was evaluated by five classes of beats including: non-ectopic beats (N), fusion beats (F), supraventricularectopic beats (S), ventricular ectopic beats (V), and unknown beats (U). The summarization of the five classes of ECG beat samples

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New Exact Solutions for Generalized (3+1) Shallow Water-Like (SWL) Equation

720 728 [25] Bulut, H., Yel, G. and Baskonus, H.M. 2016. An Application Of Improved Bernoulli Sub-Equation Function Method To The Nonlinear Time-Fractional Burgers Equation, Turkish Journal of Mathematics and Computer Science, 5, 1-17. Bulut H. Yel G. Baskonus H.M. 2016 An Application Of Improved Bernoulli Sub-Equation Function Method To The Nonlinear Time-Fractional Burgers Equation Turkish Journal of Mathematics and Computer Science 5 1 17 [26] Dusunceli, F. 2018. "Solutions for the Drinfeld-Sokolov Equation Using an IBSEFM Method" MSU Journal Of Science. 6

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Analysis of crack propagation in a “pull-out” test

out the anchor for any material and for any length of anchoring. The HILTI HDA-P M20x250/100 anchor was adopted for pull-out tests. with anchoring length of 25 cm will be used ( “European Technical Assessment…” ), the picture of this anchor is shown in Fig. 1 . Figure 1 Pre-set undercut Hilti HDA-P anchor. To mount this anchor, it is placed in a prepared hole in the anchored surface. Then, a drill is attached to the anchor, and while drilling, the anchor undercuts itself with deflecting elements. Scheme of mounting the anchor is shown in Fig. 2

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Experimental and statistical analysis of blast-induced ground vibrations (BIGV) prediction in Senegal’s quarry

1318 6.86 9.27 2.54 9.27 Conveyor belt 458 140 653 2.16 6.35 2.41 6.35 6 P1 (Upper exploitation level) 82 140 139 48.26 39.62 25.4 48.26 Garage station 624 140 2022 2.03 3 1.71 3 Lake Ddoudj 300 140 2020 5.33 8.76 5.9 8.76 Macodo 455 140 1318 6.1 8.54 3.43 8.54 Conveyor belt 436 50 653 5.34 13.97 3.87 13.97 7 P1 (Upper exploitation level) 150 50 139 13 13.84 8.25 13.84 Garage station 597 50 2022 4.76 7

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Possibilities in the application of machine learning on bioimpedance time-series

presenting and analyzing bioimpedance data. The advantage in machine learning methods is the possibility of learning generalizable predictive patterns in combining variables in a non-linear fashion, possibly increasing the predictive performance compared to simpler models. In addition, machine learning can be used to perform automatic feature extraction, useful when there is a lot of variables (e.g. different immittance parameters over many frequencies) and the important ones are not known. In some cases, such as clinical monitoring, the prediction performance is

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