Nurse scheduling problem is one of combinatorial optimization problems and it is one of NP-Hard problems which is difficult to be solved as optimal solution. In this paper, we had created an proposed algorithm which it is hybrid simulated annealing algorithm to solve nurse scheduling problem, developed the simulated annealing algorithm and Genetic algorithm. We can note that the proposed algorithm (Hybrid simulated Annealing Algorithm(GS-h)) is the best method among other methods which it is used in this paper because it satisfied minimum average of the total cost and maximum number of Solved , Best and Optimal problems. So we can note that the ratios of the optimal solution are 77% for the proposed algorithm(GS-h), 28.75% for Simulated annealing algorithm (SA), 35.7% for Improved Simulated annealing (SA*), 26.25% for Genetic algorithm ( GA) and 45.6% for Improved Genetic algorithm for all problems (2000 problems).
. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a
... Show MoreIn recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
... Show MoreObjectives: To find out the effectiveness of education program application on nurses-midwives' knowledge toward prevention and management of postpartum hemorrhage in delivery room and some socio demographic characteristics Methodology: a quasi -experimental "test-retest"design has carried throughout the present study with the application of a pre –test and post- test for nurses-midwives' knowledge toward postpartum hemorrhage. The study was conducted in six hospitals in Baghdad: Fatima Al – Zahra for Maternity and Pediatric, Al -Elwia maternity, Baghdad Teaching, AL-Imamine Al - Kadhimin Teaching, Al-Karckh maternity and Al-Yarmouk Teaching hospital for the period from 27th May
... Show MoreObjectives of the study: To assess nurses knowledge regarding oxytocin administration during labor and
delivery in maternity hospitals, and to find out the relationship between nurses knowledge and studied
variables (age, level of education, work times (shift) experience year, training course in nursing field).
Methodology: Descriptive analytic study was conducted on non-probability sample (convenient) of (70) nurses
to assess nurse’s knowledge related to oxytocin administration. The study is conducted at Al- kut Hospital for
Gynecology Obstetrics and Pediatrics and Al- Zahraa Teaching Hospital during periods 5th February to 24th
April 2013, A questionnaire was used as a tool of data collection to fulfill with objecti
We studied the effect of certain environmental conditions for removing heavy metal elements from contaminated aqueous solutions (Cd, Cu, Pb, Fe, Zn, Ni, Cr) using the bacterium Bacillus subtilis to appoint the optimal conditions for removal ,The best optimum temperature range for two isolate was 30-35○C while the hydrogen number for the maximum mineral removal range was 6-7. The best primary mineral removal was 100 mg/L, while the maximum removal for all minerals was obtained after 6 hrs of Cu element time and the maximum removal efficiency was obtained after 24 hrs of Cu element. The results have proved that the best aeration for maximum removal was obtained at rotation speed of 150 rpm/minute. Inoculums of 5ml/100ml which contained 1
... Show MoreMixture experiments are response variables based on the proportions of component for this mixture. In our research we will compare the scheffʼe model with the kronecker model for the mixture experiments, especially when the experimental area is restricted.
Because of the experience of the mixture of high correlation problem and the problem of multicollinearity between the explanatory variables, which has an effect on the calculation of the Fisher information matrix of the regression model.
to estimate the parameters of the mixture model, we used the (generalized inverse ) And the Stepwise Regression procedure
... Show MoreThe presence of heavy metals in the environment is major concern due to their toxicity. In the present study a strong acid cation exchange resin, Amberlite IR 120 was used for the removal of lead, zinc and copper from simulated wastewater. The optimum conditions were determined in a batch system of concentration 100 mg/L, pH range between 1 and 8, contact time between 5 and 120 minutes, and amount of adsorbent was from 0.05 to 0.45 g/100 ml. A constant stirring speed, 180 rpm, was chosen during all of the experiments. The optimum conditions were found to be pH of 4 for copper and lead and pH 6 for zinc, contact time of 60 min and 0.35 g of adsorbent. Three different temperatures (25, 40 and 60°C) were selected to investigate the effect
... Show MoreThe corrosion behavior of Titanium in a simulated saliva solution was improved by Nanotubular Oxide via electrochemical anodizing treatment using three electrodes cell potentiostat at 37°C. The anodization treatment was achieved in a non-aqueous electrolyte with the following composition: 200mL ethylene glycol containing 0.6g NH4F and 10 ml of deionized water and using different applied directed voltage at 10°C and constant time of anodizing (15 min.). The anodized titanium layer was examined using SEM, and AFM technique.
The results showed that increasing applied voltage resulted in formation titanium oxide nanotubes with higher corrosion resistance