Maintenance of hospital buildings and its management are regarded as an important subject which needs attention because hospital buildings are service institutions which are very important to a society, requiring the search for the best procedure to develop maintenance in hospitals. The research is aimed to determine an equation to estimate the annual maintenance cost for public hospital. To achieve this aim, Al-Sader City Hospital maintenance system in Al-Najaf province has been studied with its main elements through survey of data, records and reports relating to maintenance during the years of the study 2008-2014 and to identify the strengths, weaknesses, opportunities and threat points in the current system through Swat analysis, which represents " analyzing the internal factors represented by strengths and weaknesses points and the external factors represented by opportunities and threats," [41]. On the basis of that, an equation has been obtained to estimate the cost of annual maintenance. To achieve this aim, the following issues should be taken into consideration:
Studying the actual work program of the maintenance department in Al-Sader Hospital,
Identifying the procedures used in maintenance and ways they are implemented,
Studying the maintenance records and reports,
Holding interviews with the manager of maintenance and its staff to integrate the information and
Integrating the analysis of maintenance cost by using statistical analysis system (ASA 2012).
To assess the validity of the model for the annual maintenance cost, the predicted values of the . equation are plotted against the actual measured (observed) values. Based on the validation data set, the coefficient of determination (R) was found to be equal to (82.5%), therefore it can be concluded that the developed equation showed moderate agreement with the actual measurements. Finally, it can be concluded from the study that, the developed equation should be pplied in hospital to overcome the problems and weak points of the current system and to estimate the annual maintenance cost for hospitals based on the scientific method.
In many scientific fields, Bayesian models are commonly used in recent research. This research presents a new Bayesian model for estimating parameters and forecasting using the Gibbs sampler algorithm. Posterior distributions are generated using the inverse gamma distribution and the multivariate normal distribution as prior distributions. The new method was used to investigate and summaries Bayesian statistics' posterior distribution. The theory and derivation of the posterior distribution are explained in detail in this paper. The proposed approach is applied to three simulation datasets of 100, 300, and 500 sample sizes. Also, the procedure was extended to the real dataset called the rock intensity dataset. The actual dataset is collecte
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The deficiency of potable water resources and energy supply is emerging as a significant and concerning obstacle to sustainable development. Solar and waste heat-powered humidification dehumidification (HDH) desalination systems become essential due to the severe impacts of global warming and water shortages. This problem highlights the need to apply boosted water desalination solutions. Desalination is a capital-intensive process that demands considerable energy, predominantly sourced from fossil fuels worldwide, posing a significant carbon footprint risk. HDH is a very efficient desalination method suitable for remote areas with moderate freshwater requirements for domestic and agricultural usage. Several operational and maintenance conce
... Show MoreNumerous integral and local electron density’s topological parameters of significant metal-metal and metal-ligand bonding interactions in a trinuclear tetrahydrido cluster [(Cp* Ir) (Cp Ru)2 (μ3-H) (μ-H)3]1 (Cp = η5 -C5Me5), (Cp* = η5 -C5Me4Et) were calculated and interpreted by using the quantum theory of atoms in molecules (QTAIM). The properties of bond critical points such as the delocalization indices δ (A, B), the electron density ρ(r), the local kinetic energy density G(r), the Laplacian of the electron density ∇2ρ(r), the local energy density
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