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Safety Management of Water Economy. Case Study of the Water and Sewerage Company

wodociągowym” in Zarządzanie przedsiębiorstwem wodociągowym. Uwarunkowania funkcjonowania i współczesne koncepcje zarządzania . P. Chudziński, Ed. Warszawa: PWE, 2018, pp.185-202. [4] R. Brouwer, C.M. Ordens, R. Pinto, M.T. Condesso de Melo. (2018, May). „Economic valuation of groundwater protection using a groundwater quality ladder based on chemical threshold levels”. Ecological Indicators. Vol. 88, pp. 292-304. DOI: 10.1016/j.ecolind.2018.01.041. [5] D. Carstens, R. Armer. (2019, Feb.). „Spatio-temporal analysis of urban changes and surface water quality

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An Analysis and Modeling of the Dynamic Stability of the Cutting Process Against Self-Excited Vibration

dynamic equations of the system consisting of 8 equations varying with time and space are solved using the method of separation of variables with respect to time and space. The dynamic effect of the temporal term appears in these equations in the form of a frequency term. By eliminating the temporal dependence from dynamic equations governing the behavior of the system, we arrive at 8 spatial equations for positions along the beam, which can be solved by applying boundary constraints (conditions) with respect to space, as shown in Figure 4.2a . Figure 2 The

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Elastodynamical disturbances due to laser irradiation in a microstretch thermoelastic medium with microtemperatures

_{j,i},\, i, j, m=1,2,3 \end{array} $$ (9) Figure 1 Temporal profile of f ( t ) Figure 2 Profile of g ( x 1 ) Figure 3 Profile of h ( x 3 ) Figure 4 Geometry of the problem The surface of the medium is irradiated by laser heat input (following Al Qahtani and Dutta [ 20 ]): Q = I 0 f ( t ) g ( x 1 ) h ( x 3 ) , $$\begin{array}{} Q=I_{0}f\,(t)g\,(x_{1})h\,(x_{3}), \end{array} $$ (10) f ( t ) = t t 0 2 e − ( t t 0 ) , $$\begin{array}{} f\,(t)=\displaystyle \frac{t}{t_{0}^{2}}e^{-\big(\frac{{t}}{{t}_{0

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Response of thermoelastic microbeam with double porosity structure due to pulsed laser heating

^{\star}\,\bigg(\displaystyle \frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial z^{2}}\bigg) = \bigg(1+\displaystyle \tau_{0}\frac{\partial}{\partial t}\bigg)\\\displaystyle\,\left[-\beta T_{0}z\frac{\partial}{\partial t}\bigg(\frac{\partial^{2}w}{\partial x^{2}}\bigg)+\gamma_{1}T_{0}\dot{\varphi}+\gamma_{2}T_{0}\dot{\psi}+\rho C^{\star}\dot{T}-Q\right]. \end{array} $$ (13) The initial temperature distribution T ( x , z , 0) = T 0 . For t = 0, the upper surface, z = h /2, of the beam is heated uniformly by a laser pulse with non-Gaussian form temporal profile, which can

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Intelligent Performance Analysis with a Natural Language Interface

Abstract

Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

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Energy Saving Energetic Systems for Coastal Fishing Cutters

.hamworthy.com/LNGFuelGasSystems , (access 02.04.2018). Hongisto M. (2014). Impact of the emissions of international sea traffic on airborne deposition to the Baltic Sea and concentrations at the coastline, Oceanologia, Volume 56 Issue 2, pp. 349-372. IMO. (2015). Guidance for the development of a Ship Efficiency, London. Johansson L., Jalkanen J.P. and Kiukkonen J. (2017). Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution, Atmospheric Environment, vol. 167, pp 403-415. Rajewski P., Behrendt C. and Klyus O. (2013). Clean Shipping for Small

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