Quality control is an effective statistical tool in the field of controlling the productivity to monitor and confirm the manufactured products to the standard qualities and the certified criteria for some products and services and its main purpose is to cope with the production and industrial development in the business and competitive market. Quality control charts are used to monitor the qualitative properties of the production procedures in addition to detecting the abnormal deviations in the production procedure. The multivariate Kernel Density Estimator control charts method was used which is one of the nonparametric methods that doesn’t require any assumptions regarding the distribution o

Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted

Background: Understanding the challenges facing nurses toward providing care to patients with cerebrovascular accidents is the initial step in developing strategies to address these challenges, thereby ensuring high-quality care. Aim: The study aimed to assess the challenges experienced by nurses in delivering care to patients with CVAs in neurological wards. Results: Of the 80 questionnaire participants in the qualitative part, (MS = 0.66) reported a "moderate" rating as an overall assessment. These challenges are divided into workload (MS = 0.53) at a moderate rate, the psychological burden (MS = 0.85) at a high rate, the supporting materials (MS = 0.85) at a high rate, the sense of responsibility (MS = 0.77) at a high rate, and the role

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

  Problem solving methods and mechanisms contribute to facilitating human life by providing tools to solve simple and complex daily problems. These mechanisms have been essential tools for professional designers and design students in solving design problems.
This research dealt with one of those mechanisms, which is the (the substance-field model model), as it has been mentioning that this mechanism is characterized by the difficulty of its application, which formed the main research problem. In home gardens (the sub-problem of research), an analysis of this problem was applied and then a solution was found to address it. The researcher used the 3dsmax program to implement the proposed design.
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In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.