Rankings for Australian managed funds: Contrariness and performance index failure

2009 ◽  
Vol 10 (3) ◽  
pp. 138-157 ◽  
Author(s):  
Mike Dempsey
Keyword(s):  
Performance Index ◽  
And Performance ◽  
Managed Funds

Growth and performance index for agricultural productivity

2020 ◽  
Vol 57 (6) ◽  
pp. 915-922
Author(s):  
Poonam Kataria ◽  
Baldev Singh Dhillon
Keyword(s):  
Performance Index ◽  
Agricultural Productivity ◽  
And Performance

scholarly journals PENERAPAN METODE KONSEP NILAI HASIL (EARNED VALUE CONCEPT) PADA PROYEK PENINGKATAN JALAN ASPAL DI LOKASI F4 (SUNGAI SIRIH)

2020 ◽  
Vol 6 (2) ◽  
pp. 109
Author(s):  
Surya Adinata ◽  
Akbar Alfa
Keyword(s):  
Performance Index ◽  
Cost Control ◽  
Project Work ◽  
Volume Data ◽  
Earned Value ◽  
Planning Time ◽  
Project Construction ◽  
S Curve ◽  
And Performance ◽  
Value Concept

The cost control is a very important aspect in project management. Poor cost control often results in project construction costs that differ from planned costs. The applying of the Earned Value Concept method for analytical calculations requires the following data: Implementation time along with the S curve; Volume data of each work item; Percentage of work weighted; Standard daily reports, weekly reports, and monthly project reports; Actual project financial data and SPI. The stage of data analysis for each job are: determining of BCWS (Budgete Cost Of Work Schedule); BCWP (Budgeted Cost of Work Performed; ACWP (Actual Cost of Work performed) Analysis of variance and performance index from the Earned Value Concept method. It consists of: Cost Variance (CV) and Schedule Variance (SV), Cost Performance Index (CPI) and Schedule Performance Index (SPI). The results of the analysis of the applied of the Earned Value Concept method of PT. Riau Maju Cemerlang obtained a gross cost gain of around Rp. 173,591,516.52 or 5.00% of the total construction price without VAT value. The initial planning time for project work began on 26 July and the initial planning time ended on 17 December (145 HK) turned out to be 27 December (155 HK). The applied of the Result Value Concept method to the project can find out the remaining value of the project costs for the remaining work and how much the total cost has been used for the project.

scholarly journals Precipitação e temperatura do ar simuladas pelo modelo ETA/CPTEC - HADCM3 para o estado do Rio de Janeiro

2020 ◽  
Vol 13 (5) ◽  
pp. 2037
Author(s):  
FERNANDA DA SILVA PINHEIRO ◽  
EDNALDO OLIVEIRA DOS SANTOS ◽  
GUSTAVO BASTOS LYRA ◽  
GILBERTO FERNANDO FISCH ◽  
HENDERSON SILVA WANDERLEY
Keyword(s):  
Air Temperature ◽  
Performance Index ◽  
Rio De Janeiro ◽  
Climate Model ◽  
Coefficient Of Determination ◽  
Grid Points ◽  
And Performance ◽  
Hadcm3 Model ◽  
Climatic Series ◽  
Janeiro State

O trabalho avaliou simulações de precipitação e temperatura do ar do modelo Eta-CPTEC para o Rio de Janeiro de 1961-1990. Nas simulações, considerou-se resolução espacial de 40 km para uma grade que compreendeu a América do Sul, com o Eta-CPTEC inicializado com o modelo HadCM3. As séries climáticas observadas das variáveis estudadas foram obtidas de estações meteorológicas do INMET distribuídas no estado do Rio de Janeiro. Estas séries foram comparadas com aquelas extraídas dos pontos de grade do modelo mais próximas das estações. Nas avaliações considerou-se o coeficiente de determinação (r²) da regressão linear simples entre dados observados e simulados; o índice de concordância de Willmott (d), o índice de desempenho (c) e a Raiz do Quadrado Médio do Erro (RQME) e seus componentes sistemático (RQMEs) e não sistemático (RQMEu). As simulações de precipitação apresentaram r² menores do que 0,32, o que indicou baixa precisão, enquanto que a exatidão (d) foi superior a 0,50, com exceção de Bangu (0,16). A baixa precisão comprometeu o desempenho (c) das simulações, com 0,07 <= c <= 0,42, classificados entre “péssimo” e “ruim”. A RQME variou entre 76,2 e 133,4 mm, que correspondeu a um erro de 78,1 e 115,5% em relação à precipitação média. As simulações de temperatura do ar mostraram desempenho melhor do que a precipitação, com maior precisão (0,39 <= r² <= 0,53), exatidão (0,50 <= d <= 0,79) e desempenho (0,36 <= c <= 0,52). A RQME ficou entre 1,9 e 5,7oC, representando 9 e 26% da média da temperatura do ar. Na maior parte das estações, o RQMEs se sobressaiu em relação ao RQMEu, indicando que as simulações podem ser corrigidas usando técnicas estatísticas. Precipitation and air temperature numerical simulations through ETA/CPTEC - HADCM3 model in Rio de Janeiro A B S T R A C TThe present study evaluated the precipitation and air temperature simulations of the Eta-CPTEC model for Rio de Janeiro state in 1961-1990. In the simulations, a spatial resolution of 40 km was considered for a grid that comprised South America, with Eta-CPTEC initialized with HadCM3 model. The observed climatic series of the studied variables were obtained from INMET meteorological stations distributed at Rio de Janeiro state. These series were compared with those extracted from the grid points of the model near to the stations. The coefficient of determination (r²) of the simple linear regression between observed and simulated, Willmott's index of agreement (d), performance index (c), Root Mean Square Error (RQME) and their systematic (RQMEs) and unsystematic (RQMEu) components were considered in the evaluations. The precipitation simulations showed r² less than 0.32, which indicated low precision, while the accuracy (Willmott's d) was greater than 0.50, except for Bangu station. The low precision compromised the performance (index “c”) of the simulations, with 0,07 £ c £ 0,42, classified as "terrible" and "bad". The RQME varied between 76.2 and 133.4 mm, which corresponded error of 78.1 and 115.5% in relation to mean precipitation. The simulations of air temperature showed better performance than precipitation, with greater precision (0.39 £ r² £  0.53), accuracy (0.50 £ d £ 0.79) and performance (0.36 £ c £ 0.52). The RQME was between 1.9 and 5.7oC, which represented respectively 9 and 26% for average of air temperature. In most stations, RQMEs were higher than the RQMEu, which indicated that simulators can be fitted using statistical techniques.Keywords: climate model, meteorological dataset, downscaling

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