1,427 research outputs found
DayJet-third task order
Issued as final reportDayjet Corporatio
Estimates of Potential Evapotranspiration Over The State of Qatar
Several methods for the estimation of potential evapotranspiration are reported in the literature covering a wide variation in the complexity of calculation and nature of climatic data required. Five of the more commonly used methods (Blaney-Criddle, Thomthwaite, Pan Evaporation, Radiation and Penman) are used to estimate mean monthly potential evapotranspiration values using data from three agro-hydro-meteorological stations sited in the north, central and south-western areas of Qatar. The results are compared and analyzed. Effect of micro-climatic conditions that varies between desert and oasis type environments was found pronounced
Quadratic set covering problem
Issued as Annual report, and Final report, Project no. E-24-61
Necessary optimality criteria in mathematical programming in the presence of differentiability
AbstractWe consider the problem of minimizing a function over a region defined by an arbitrary set, equality constraints, and constraints of the inequality type defined via a convex cone. Under some moderate convexity assumptions on the arbitrary set we develop Optimality criteria of the minimum principle type which generalize the Fritz John Optimality conditions. As a consequence of this result necessary Optimality criteria of the saddle point type drop out. Here convexity requirements on the functions are relaxed to convexity at the point under investigation. We then present the weakest possible constraint qualification which insures positivity of the lagrangian multiplier corresponding to the objective function
An algorithm for linearly constrained nonlinear programming problems
AbstractIn this paper an algorithm for solving a linearly constrained nonlinear programming problem is developed. Given a feasible point, a correction vector is computed by solving a least distance programming problem over a polyhedral cone defined in terms of the gradients of the βalmostβ binding constraints. Mukai's approximate scheme for computing the step size is generalized to handle the constraints. This scheme provides an estimate for the step size based on a quadratic approximation of the function. This estimate is used in conjunction with Armijo line search to calculate a new point. It is shown that each accumulation point is a Kuhn-Tucker point to a slight perturbation of the original problem. Furthermore, under suitable second order optimality conditions, it is shown that eventually only one trial is needed to compute the step size
An Integer Linear Programming approach to minimize the cost of the refurbishment of a façade to improve the energy efficiency of a building
[EN] Buildings account 40% of the EU's total energy consumption. Therefore, they represent a key potential source of energy savings to fight, among others, against climate change. Furthermore, around 54% of the buildings in Spain date back before 1980, when no thermal regulation was available. The refurbishment of a façade of an old building is usually the most effective way to improve its energy efficiency, by adding layers to the external envelope in order to reduce its thermal transmittance.
This paper deals with the problem of minimizing costs for the thermal refurbishment of a façade with thickness and thermal ransmittance bounds and with an intervention both on the opaque part (wall) and the transparent part (windows). Among thousands, even millions of combinations of materials and thicknesses for the different layers to be added to the opaque part, types of frame, and combinations of glasses and air chambers for the transparent
part, the aim is to choose the one that minimizes the cost without violating any restriction imposed to the thermal refurbishment, in particular the current energy efficiency regulations in the zone.
To optimally solve this problem, it will be modelled as an Integer Linear Programming problem with binary variables. The case study will be Building 1B of the School for Building Engineering of the Polytechnic University of Valencia, Spain. It was built in the late 1960s and has had a very inefficient energy consumption record. The optimal solution will be found among more than 6
million feasible solutions.Salandin, A.; Soler FernΓ‘ndez, D.; Bevivino, M. (2020). An Integer Linear Programming approach to minimize the cost of the refurbishment of a faΓ§ade to improve the energy efficiency of a building. Mathematical Methods in the Applied Sciences. 43(14):8067-8088. https://doi.org/10.1002/mma.6029S806780884314Nearly zeroβenergy buildingshttps://ec.europa.eu/energy/en/topics/energyβefficiency/buildings/nearlyβzeroβenergyβbuildings(accessed 27.12.2018).Building stock characteristicshttps://ec.europa.eu/energy/en/euβbuildingsβfactsheetsβtopicsβtree/buildingβstockβcharacteristics(accessed 27.12.2018).BoletΓn Especial Censo2011Parque edificatorio Publicaciones del Ministerio de Fomento http://www.fomento.gob.es/MFOM.CP.Web/handlers/pdfhandler.ashx?idpub=BAW021(accessed 27.12.2018).Boosting Building Renovation.What Potential and Value for Europe? Study for the ITRE Committee 2016http://www.europarl.europa.eu/RegData/etudes/STUD/2016/587326/IPOL_STU(2016)587326_EN.pdf(accessed 27.12.2018).Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency (Text with EEA relevance).https://eurβlex.europa.eu/legalβcontent/EN/TXT/?uri=celex:32018L0844(accessed 27.12.2018).How to Refurbish All Buildings by 2050 Final ReportJune 2012https://www.eui.eu/projects/think/documents/thinktopic/thinktopic72012.pdf(accessed 27.12.2018).2020 climate & energy package.https://ec.europa.eu/clima/policies/strategies/2020_en(accessed 27.12.2018).2030 climate & energy framework.https://ec.europa.eu/clima/policies/strategies/2030_en(accessed 27.12.2018).2050 lowβcarbon economyhttps://ec.europa.eu/clima/policies/strategies/2050_en(accessed 27.12.2018).Lidberg, T., Gustafsson, M., Myhren, J. A., Olofsson, T., & Γdlund (former Trygg), L. (2018). Environmental impact of energy refurbishment of buildings within different district heating systems. Applied Energy, 227, 231-238. doi:10.1016/j.apenergy.2017.07.022MickaitytΔ, A., Zavadskas, E. K., Kaklauskas, A., & TupΔnaitΔ, L. (2008). THE CONCEPT MODEL OF SUSTAINABLE BUILDINGS REFURBISHMENT. International Journal of Strategic Property Management, 12(1), 53-68. doi:10.3846/1648-715x.2008.12.53-68Passer, A., Ouellet-Plamondon, C., Kenneally, P., John, V., & Habert, G. (2016). The impact of future scenarios on building refurbishment strategies towards plus energy buildings. Energy and Buildings, 124, 153-163. doi:10.1016/j.enbuild.2016.04.008Energy efficiency in buildings.https://www.buildingtechnologies.siemens.com/bt/global/en/buildingβknowledge/pages/energyβefficiency.aspx(accessed 27.12.2018).Baglivo, C., & Congedo, P. M. (2015). Design method of high performance precast external walls for warm climate by multi-objective optimization analysis. Energy, 90, 1645-1661. doi:10.1016/j.energy.2015.06.132Baglivo, C., Congedo, P. M., DβAgostino, D., & ZacΓ , I. (2015). Cost-optimal analysis and technical comparison between standard and high efficient mono-residential buildings in a warm climate. Energy, 83, 560-575. doi:10.1016/j.energy.2015.02.062Corgnati, S. P., Fabrizio, E., Filippi, M., & Monetti, V. (2013). Reference buildings for cost optimal analysis: Method of definition and application. Applied Energy, 102, 983-993. doi:10.1016/j.apenergy.2012.06.001Uβvalues in Europe.https://www.eurima.org/uβvaluesβinβeurope(accessed 27.12.2018).CTE.CΓ³digo TΓ©cnico de la EdificaciΓ³n (Spanish Technical Building Act). Documento BΓ‘sico de Ahorro de EnergΓa (Basic Document for Energy Saving). Version of 2013 with comments of 2016.http://www.codigotecnico.org/images/stories/pdf/ahorroEnergia/DccHE.pdf(accessed 27.12.2018).Sherali, H. D., & Driscoll, P. J. (2000). Evolution and state-of-the-art in integer programming. Journal of Computational and Applied Mathematics, 124(1-2), 319-340. doi:10.1016/s0377-0427(00)00431-3Kurnitski, J., Saari, A., Kalamees, T., Vuolle, M., NiemelΓ€, J., & Tark, T. (2013). Cost optimal and nearly zero energy performance requirements for buildings in Estonia. Estonian Journal of Engineering, 19(3), 183. doi:10.3176/eng.2013.3.02Congedo, P. M., Baglivo, C., DβAgostino, D., & ZacΓ , I. (2015). Cost-optimal design for nearly zero energy office buildings located in warm climates. Energy, 91, 967-982. doi:10.1016/j.energy.2015.08.078Sambou, V., Lartigue, B., Monchoux, F., & Adj, M. (2009). Thermal optimization of multilayered walls using genetic algorithms. Energy and Buildings, 41(10), 1031-1036. doi:10.1016/j.enbuild.2009.05.007Di Perna, C., Stazi, F., Casalena, A. U., & DβOrazio, M. (2011). Influence of the internal inertia of the building envelope on summertime comfort in buildings with high internal heat loads. Energy and Buildings, 43(1), 200-206. doi:10.1016/j.enbuild.2010.09.007Privitera, G., Day, A. R., Dhesi, G., & Long, D. (2011). Optimising the installation costs of renewable energy technologies in buildings: A Linear Programming approach. Energy and Buildings, 43(4), 838-843. doi:10.1016/j.enbuild.2010.12.003Ashouri, A., Fux, S. S., Benz, M. J., & Guzzella, L. (2013). Optimal design and operation of building services using mixed-integer linear programming techniques. Energy, 59, 365-376. doi:10.1016/j.energy.2013.06.053Lindberg, K. B., Doorman, G., Fischer, D., KorpΓ₯s, M., Γ
nestad, A., & Sartori, I. (2016). Methodology for optimal energy system design of Zero Energy Buildings using mixed-integer linear programming. Energy and Buildings, 127, 194-205. doi:10.1016/j.enbuild.2016.05.039Ogunjuyigbe, A. S. O., Ayodele, T. R., & Oladimeji, O. E. (2016). Management of loads in residential buildings installed with PV system under intermittent solar irradiation using mixed integer linear programming. Energy and Buildings, 130, 253-271. doi:10.1016/j.enbuild.2016.08.042Soler, D., Salandin, A., & MicΓ³, J. C. (2018). Lowest thermal transmittance of an external wall under budget, material and thickness restrictions: An integer linear programming approach. Energy and Buildings, 158, 222-233. doi:10.1016/j.enbuild.2017.09.078Salandin, A., & Soler, D. (2018). Computing the minimum construction cost of a buildingβs external wall taking into account its energy efficiency. Journal of Computational and Applied Mathematics, 338, 199-211. doi:10.1016/j.cam.2018.02.003Generador de Precios de Elementos de la ConstrucciΓ³n CYPE Ingenieros S.A. EspaΓ±a 2017http://www.generadordeprecios.info(accessed 27.12.2018).Wolfram Mathematica http://www.wolfram.com/mathematica(accessed 27.12.2018)
A model predictive control approach to the periodic implementation of the solutions of the optimal dynamic resource allocation problem
This paper proposes a model predictive control (MPC) approach to the periodic implementation of the optimal solutions of a class of resource allocation problems in which the allocation requirements and conditions repeat periodically over time. This special class of resource allocation problems includes many practical energy optimization problems such as load scheduling and generation dispatch. The convergence and robustness of the MPC algorithm is proved by invoking results from convex optimization. To illustrate the practical applications of the MPC algorithm, the energy optimization of a water pumping system is studied
Adrenocortical status in infants and children with sepsis and septic shock
AbstractBackgroundThe benefit from corticosteroids remains controversial in sepsis and septic shock and the presence of adrenal insufficiency (AI) has been proposed to justify steroid use.AimTo determine adrenal state and its relation with outcome in critical children admitted with sepsis to PICU of Cairo University, Children Hospital.MethodsThirty cases with sepsis and septic shock were studied. Cortisol levels (CL) were estimated at baseline and after high-dose short ACTH stimulation in those patients and in 30 matched controls. Absolute AI was defined as basal CL<7ΞΌg/dl and peak CL<18ΞΌg/dl. Relative AI was diagnosed if cortisol increment after stimulation is <9ΞΌg/dl.ResultsOverall mortality of cases was 50%. The mean CL at baseline in cases was higher than that of controls (51.39ΞΌg/dl vs. 12.83ΞΌg/dl, p=0.000). The mean CL 60min after ACTH stimulation was higher than that of controls (73.38ΞΌg/dl vs. 32.80ΞΌg/dl, p=0.000). The median of %rise in cases was lower than that of controls (45.3% vs. 151.7%). There was a positive correlation between basal and post-stimulation cortisol with number of system failure, inotropic support duration, mechanical ventilation days, and CO2 level in blood. There was a negative correlation between basal and post stimulation cortisol with blood pH and HCO3.ConclusionRAI is common with severe sepsis/septic shock. It is associated with more inotropic support and has higher mortality. Studies are warranted to determine whether corticosteroid therapy has a survival benefit in children with RAI and catecholamine resistant septic shock
Evaluation of Treated Wastewater Quality Changes through the Vadose Zone
Due to water challenges in arid and semi-arid regions including water scarcity and increasing demands, wastewater reuse in irrigation is becoming more widely practiced. This paper presents a case study for Sadat City, Egypt, to assess the impacts of using treated wastewater (TWW) in irrigation on soil and evaluating the natural attenuation of the TWW in the vadose zone. A field and laboratory program was conducted to identify the hydraulic properties of the soil and the contaminant concentration in water and soil. Water flow and solute transport are simulated in the vadose zone using HYDRUS 1D for five soil profiles in the study area through 50 years from 1992 to 2042. Six contaminants of concern were selected to simulate (Mg, Cl, Fe, NH3, NO3 and Fecal Coliform to study the bio-clogging effect on the soil). Six irrigation scenarios were selected to simulate flow and transport according to thewastewater treatment (primary, secondary, oxidation pond, tertiary treated wastewater, tertiary for double field water duty and irrigation with two year rotation(primary treated wastewater and groundwater)). The results show the concentration of contaminants of concern which will reach to groundwater aquifer after the purification and soil leaching.The results indicate that the concentrations of contaminants of concern were affected sensitively by the initial concentration of soil columns. Keywords: Wastewater, groundwater, vadose zone, HYDRUS 1
ΠΠ°Π½ΠΎΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠΎΠΊΠ°. ΠΠΈΠ½ΠΈ-ΠΎΠ±Π·ΠΎΡ
In the past two decades, nano-science is widely used in different applications and the increased interest in the utilization of nanoparticles in food processing is clear. Such applications include processing, packaging, development of functional food, safety, foodborne pathogens detection, and shelf-life extension. In this article, the essential facts and the latest uses of nano-science in fruit and vegetable juices were described. The green synthesis of nanoparticles with antioxidant, antibacterial and antifungal characteristics is of great interest in food preservation. These nanoparticles such as metals, oxidized metals and its bioactivity in juice were reviewed. The current procedures to prepare nanojuice including nanofiltration and the most recent nanomilling were presented. Beside the preparation, special emphasis has also been given to the chemical as well as the biological (microbial and enzymatic) quality of the produced nanojuice. The role of nanotechnology in the development of the smart and the active food packaging systems for the improvement of food shelf- life and quality was also discussed. Since the physical and chemical characteristics of nanoparticles are completely different from those of macro-size. Therefore, special and urgent attention by responsible authorities should be given and effective policies should be applied for food products to ensure product quality, customer health and safety as well as the environmental protection.Π ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π΄Π²Π° Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΡ Π½Π°Π½ΠΎΠ½Π°ΡΠΊΠ° ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΎΠ±Π»Π°ΡΡΡΡ
ΠΈ ΠΎΡΠ΅Π²ΠΈΠ΄Π΅Π½ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΊ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π°Π½ΠΎΡΠ°ΡΡΠΈΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΏΠΈΡΠ΅Π²ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ². Π’Π°ΠΊΠΈΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π²ΠΊΠ»ΡΡΠ°ΡΡ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΡ, ΡΠΏΠ°ΠΊΠΎΠ²ΠΊΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ², Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ ΠΏΠΈΡΠ΅Π²ΡΡ
ΠΏΠ°ΡΠΎΠ³Π΅Π½ΠΎΠ² ΠΈ ΠΏΡΠΎΠ΄Π»Π΅Π½ΠΈΠ΅ ΡΡΠΎΠΊΠΎΠ² Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΎΠΏΠΈΡΠ°Π½Ρ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΠ΅ ΡΠ°ΠΊΡΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π½Π°Π½ΠΎΠ½Π°ΡΠΊΠΈ Π΄Π»Ρ ΡΡΡΠΊΡΠΎΠ²ΡΡ
ΠΈ ΠΎΠ²ΠΎΡΠ½ΡΡ
ΡΠΎΠΊΠΎΠ². ΠΠΎΠ»ΡΡΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ Π²ΡΠ·ΡΠ²Π°Π΅Ρ Π·Π΅Π»Π΅Π½ΡΠΉ ΡΠΈΠ½ΡΠ΅Π· Π½Π°Π½ΠΎΡΠ°ΡΡΠΈΡ Ρ Π°Π½ΡΠΈΠΎΠΊΡΠΈΠ΄Π°Π½ΡΠ½ΡΠΌΠΈ, Π°Π½ΡΠΈΠ±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΈ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ³ΡΠΈΠ±ΠΊΠΎΠ²ΡΠΌΠΈ ΡΠ²ΠΎΠΉΡΡΠ²Π°ΠΌΠΈ Π΄Π»Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΡΠΎΠΊΠΎΠ² Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ ΠΏΠΈΡΠ΅Π²ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ². Π‘Π΄Π΅Π»Π°Π½ ΠΎΠ±Π·ΠΎΡ Π½Π°Π½ΠΎΡΠ°ΡΡΠΈΡ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΌΠ΅ΡΠ°Π»Π»Ρ, Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Π²ΠΈΠ΄ΠΎΠ² ΠΈΡ
ΠΎΠΊΡΠΈΠ΄ΠΎΠ² ΠΈ ΠΎΠΊΠΈΡΠ»ΠΎΠ², ΠΈ ΠΈΡ
Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠ°Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π² ΡΠΎΠΊΠ΅. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° Π½Π°Π½ΠΎ-ΡΠΎΠΊΠΎΠ², Π²ΠΊΠ»ΡΡΠ°Ρ Π½Π°Π½ΠΎ-ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΡ ΠΈ ΡΠ°ΠΌΠΎΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ Π½Π°Π½ΠΎ-ΠΈΠ·ΠΌΠ΅Π»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠΌΠΈΠΌΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π°, ΠΎΡΠΎΠ±ΡΠΉ Π°ΠΊΡΠ΅Π½Ρ Π² ΠΎΠ±Π·ΠΎΡΠ΅ ΡΠ΄Π΅Π»Π°Π½ Π½Π° Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
, Π° ΡΠ°ΠΊΠΆΠ΅ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
(ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅ΡΠΌΠ΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΡ
) ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°Ρ
ΠΏΡΠΎΠΈΠ·Π²Π΅Π΄Π΅Π½Π½ΡΡ
Π½Π°Π½ΠΎ-ΡΠΎΠΊΠΎΠ². Π’Π°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½Π° ΡΠΎΠ»Ρ Π½Π°Π½ΠΎ-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΡΠΈΡΡΠ΅ΠΌ Β«ΡΠ°Π·ΡΠΌΠ½ΠΎΠΉΒ» ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΡΠΏΠ°ΠΊΠΎΠ²ΠΊΠΈ Π΄Π»Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΡΠΎΠΊΠΎΠ² Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ ΠΈ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΏΠΈΡΠ΅Π²ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ². ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ Π½Π°Π½ΠΎΡΠ°ΡΡΠΈΡ ΠΏΠΎΠ»Π½ΠΎΡΡΡΡ ΠΎΡΠ»ΠΈΡΠ°ΡΡΡΡ ΠΎΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ°ΡΡΠΈΡ ΠΌΠ°ΠΊΡΠΎΡΠ°Π·ΠΌΠ΅ΡΠ°. Π‘Π΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠΌ ΠΏΠΈΡΠ΅Π²ΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠΈ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π½ΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄ΠΎΠ»ΠΆΠ½ΠΎ ΡΠ΄Π΅Π»ΡΡΡΡΡ ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΅Π΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π΄Π»Ρ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΡ Π΄ΠΎΠ²Π΅ΡΠΈΡ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»Π΅ΠΉ. ΠΡΠΈ ΡΡΠΎΠΌ ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΡΡΡΠΈΠΌΠΈ ΠΈ ΡΠ΅Π³ΡΠ»ΠΈΡΡΡΡΠΈΠΌΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠΌΠΈ Π΄ΠΎΠ»ΠΆΠ½Π° ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½Π°Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ° ΠΏΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΏΠΈΡΠ΅Π²ΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠΈ, ΡΠΎΡ
ΡΠ°Π½Π½ΠΎΡΡΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ ΠΏΠΎΡΡΠ΅Π±ΠΈΡΠ΅Π»Π΅ΠΉ ΠΈ Π·Π°ΡΠΈΡΡ ΠΎΠΊΡΡΠΆΠ°ΡΡΠ΅ΠΉ ΡΡΠ΅Π΄Ρ
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