Bayesian Inference For Exponential Distribution Based On Upper Record Range

This paper deals with Bayesian estimations of scale parameter of the exponential distribution based on upper record range (Rn). This has been done in two steps; point and interval. In the first step the quadratic, squared error and absolute error, loss functions have been considered to obtain Bayesian-point estimations. Also in the next step the shortest Bayes interval (Hight Posterior Density interval) and Bayes interval with equal tails based on upper record range have been found. Therefore, the Homotopy Perturbation Method(HPM) has been applied to obtain the limits of Hight Posterior Density intervals. Moreover, efforts have been made to meet the admissibility conditions for linear estimators based on upper record range of the form mRn+d by obtained Bayesian point estimations. So regarding the consideration of loss functions, the prior distribution between the conjunction family has been chosen to be able to produce the linear estimations from upper record range statistics. Finally, some numerical examples and simulations have been presented

Modelling and simulation for the joint maintenance-inventory optimisation of production systems

Simulation methodologies are developed to model the joint optimization of preventive maintenance and spare parts inventory for a specific industrial plant under different production configurations. First, spare parts provisioning for a single-line system is considered, with the assumption that the demand is driven by maintenance requirements. The results indicate that a periodic review policy with replenishment as frequent as inspection is cost-optimal. Second, the joint optimization model for a multi-line (parallel) system is developed. It is found that a just-in-time review policy with inspection as frequent as replenishment produces the lowest cost policy. In this latter case, an implication of the proposed methodology is that, where mathematical modelling is intractable, or the use of certain assumptions make them impractical, simulation modelling is an appropriate solution tool. Under both production settings, the long-run average cost per unit time is used as the optimality criterion for the comparison of several policies

Effect of partial replacement of dietary fish meal with soybean meal on some hematological and serum biochemical parameters of juvenile Beluga, Huso huso

This study was conducted to investigate the effects of partial replacement of dietary fish meal with soybean meal (SBM) on some blood and serum parameters of Beluga (Huso huso) juveniles. Three isonitrogenic and isoenergetic diets, as SBM1 (Soybean meal protein (SBP):Fishmeal Protein (FP) = 1:3), SBM2 (SBP:FP = 2:3) and SBM3 (SBP:FP = 1:1) were fed to triplicate groups of fish. After 8 weeks feeding on the experimental diets, blood parameters were measured. The results revealed that of partial replacement of dietary fish meal with soybean meal had no effect on, leukocyte (WBC) levels, red blood cell counts (RBC), mean corpuscular volume (MCV), mean cellular hemoglobin (MCH) or mean cell hemoglobin concentration (MCHC) (P>0.05). However, hemoglobin (Hb) concentration and hematocrit (Hct) values were significantly decreased by increasing dietary soybean meal (P<0.05). Serum glucose had significantly affected by increasing soybean meal. While total protein, phosphorus or calcium remained unaffected between groups. These results indicated that partial replacement of dietary fish meal with soybean meal could affect on some haematological and biochemical parameters in beluga which should be studied in future

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture