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This paper aimed to analyze removed particles from stationary specimen-aluminum (Al-99.92) produced by vibratory cavitation erosion tests in distilled water and glycerol-water mixtures.

The particle morphology which include particle surface topography, size distribution, particle size parameters and particle shape parameters were examined for distilled water and glycerol-water mixtures having different viscosities.

The results showed that the variation of size parameters with viscosity was very similar to the variation of weight loss with viscosity. Both the size parameters and weight losses show a monotonic decrease in going from distilled water to glycerol-water mixtures having viscosity about 10.1 cSt, beyond which the change is very small. On the other hand, the shape parameters were much less sensitive to distinguish between the particles produced in water and glycerol-water mixtures. The mechanism of cavitation erosion is investigated in detail through observations of the removed particles. The particle surfaces topography demonstrated that the mechanism in water and glycerol-water mixtures was fatigue failure.

Cavitation often occurs in almost all machines that handle liquids, especially at high speeds, leading to irreparable damage of the components of these machines. Elucidation of such complex phenomenon demands full characterization of the erosion mechanism and controlling parameters inherent to it, so that cavitation erosion can be prevented or at least be reduced through adequate information and collection of relevant data under different operating conditions. Very few studies have been made to approach the viscosity effect upon cavitation erosion from the particle analysis standpoint. The aim of the present work is to identify the effect of liquid viscosity on the size, shape characteristics of the erosion particles and their morphological features. The prevailed mechanisms of wear and particle generation have been proposed based on the acquired information from particle analysis.

The aim of this paper is to evaluate the effect of surface treatment on slurry erosion behavior of AISI 5,117 steel using artificial neural network (ANN) technique.

The slurry erosion wear behavior of electroless nickel-phosphorus (Ni-P) coated, carburized and untreated AISI 5,117 alloy steel was investigated experimentally and theoretically using ANN technique based on error back propagation learning algorithm.

From the obtained results, it can be concluded that the proposed AAN model can be successfully used for evaluating slurry erosion behavior of the Ni-P coated, carburized and untreated AISI 5,117 steel for wide range of operating conditions and Ni-P coating and carburizing improve the slurry erosion resistance of AISI 5,117 steel; however, the coating is more efficient.

Slurry erosion is a serious problem for the performance, reliability and service life of engineering components used in many industrial applications. To improve the performance of these components, engineering surface technologies have been attracting a great deal of attention. The extent of slurry erosion is dependent on a wide range of variables. To account all variables that effect on erosion behavior, prediction of erosion behavior by soft computational technique is one of the most important requirements. ANN has the ability to tackle the problem of complex relationships among variables that cannot be accomplished by traditional analytical methods.

This paper aims to investigate the performance and working fluids screening for an ejector refrigeration cycle (ERC) activated by solar energy. Several common and new hydrofluorocarbons, hydrocarbons, hydrofluoroolefins and hydrofluoroethers are proposed as refrigerants for the ERC to determine the most appropriate one.

The ejector performance is characterized by the ejector area ratio (EAR) and entrainment ratio (ω), while the cycle performance is described by the coefficient of performance (COP). The influences of many working parameters like the evaporator, condenser and generator temperatures on the ejector and cycle performances are investigated for all candidates as well.

The results indicate that the best ejector and cycle performances are attained with the highest critical temperature dry refrigerant, i.e. R601 under all studied working conditions. From the perspective of energy efficiency and environmental issues, R601 can be considered the most appropriate working fluid amongst all candidates. However, extra attention should be considered against its flammability. The maximum COP, the corresponding ω and the necessary EAR using R601 are 0.743, 1.02 and 15.5, respectively, with 25 ºC condenser temperature and the typical values for the rest operating conditions.

Many common and new hydrofluorocarbons, hydrocarbons, hydrofluoroolefins and hydrofluoroethers are suggested as working fluids for the ERC to determine the most appropriate one. The mixing process inside the ejector constant-area section is assumed constant-pressure process.

Cationization of cotton is an effective tool that solves environmental problems by using vat dyes. In this research, the cationizing efficiency of Cibafix WFF and PrefixK are studied to replace toxic conventional redox agents with ecofriendly alternatives. It is found to be a better method when compared with the conventional and existing method of vat dyeing of cotton. Conditions, such as concentration of cationizing agents, time, temperature and pH, are optimized. Such conditions show better color strength when compared with conventional dyeing methods. The effect of cationization on color strength and fastness is observed, and show higher value in both light and dark shades. The cationized samples show comparable quality and are cheaper in cost when compared with the normal vat dyeing method. Cationization of cotton with Cibafix WFF is found to be a successfully and commercially viable process.

This study aims to adopt the Altman model in order to predict the performance of industrial companies listed on the Palestinian Stock Exchange during the period of time between 2013 and 2017.

The study sample consisted of 12 industrial companies listed on the Palestine Stock Exchange, and their financial disclosure period extended for 5 years. Multiple linear regression model was used in the analysis to determine the relationship between the independent variables and the dependent variable where the independent variables were (X₁, X₂, X₃). This study is based on one basic assumption, which is that the Altman's model cannot predict the performance of the Palestinian industrial sector.

The results of the analysis proved the negation of the zero main hypothesis. This means that Altman's model can predict the performance of the Palestinian industrial sector at the level of statistical significance (a = 0.05), as well as the existence of a statistically significant relationship between each of the independent variables (X₂, X₄, X₅) and the dependent variable (Log (Z-score)). Hence, the relationship of X₁ and X₃ with the dependent variable was not statistically significant.

This paper highlights different challenges that face the adaption of Atman's model and performance prediction in the Palestinian industrial sector. The findings of the analysis have the potential to help future researchers in examining and dealing with new challenges.

This paper presents a vital review of adopting Altman's model in the Palestinian industrial sector. A number of recommendations have been made, the most important of which is that most of the companies are located in the red zone. The Altman's model must be adapted in order to fit the Palestinian environment according to the results of statistical analysis and according to a proposed model, which is Log (Z) = −0.653 + 0.72X₂ + 0.18X₄ + 0.585X₅.