We propose a novel strategy to optimize the test suite required for testing both hardware and software in a production line. Here, the strategy is based on two processes: Quality Signing Process and Quality Verification Process, respectively. Unlike earlier work, the proposed strategy is based on integration of black box and white box techniques in order to derive an optimum test suite during the Quality Signing Process. In this case, the generated optimal test suite significantly improves the Quality Verification Process. Considering both processes, the novelty of the proposed strategy is the fact that the optimization and reduction of test suite is performed by selecting only mutant killing test cases from cumulating t-way test cases. As such, the proposed strategy can potentially enhance the quality of product with minimal cost in terms of overall resource usage and time execution. As a case study, this paper describes the step-by-step application of the strategy for testing a 4-bit Magnitude Comparator Integrated Circuits in a production line. Comparatively, our result demonstrates that the proposed strategy outperforms the traditional block partitioning strategy with the mutant score of 100% to 90%, respectively, with the same number of test cases.
unacceptable social behaviors, particularly withdrawal behavior that appears in children with autism represent a major problem hindering the process of communication with those around them and therefore the process of mergence with them be difficult.
The withdrawal causes a real affect deficit for children with autism limits the possibility of development of their intellectual and mental growth due to their solitude and the weakness of their focus in the acquisition of pedagogical skills and lack the necessary social skills to maintain the relations of friendship and enjoyment of them.
withdrawal children fail to participate
... Show MoreGas-lift technique plays an important role in sustaining oil production, especially from a mature field when the reservoirs’ natural energy becomes insufficient. However, optimally allocation of the gas injection rate in a large field through its gas-lift network system towards maximization of oil production rate is a challenging task. The conventional gas-lift optimization problems may become inefficient and incapable of modelling the gas-lift optimization in a large network system with problems associated with multi-objective, multi-constrained, and limited gas injection rate. The key objective of this study is to assess the feasibility of utilizing the Genetic Algorithm (GA) technique to optimize t
The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreCodes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
... Show MoreType 2 diabetes mellitus is a progressive and chronic disease manifested by β-cell dysfunction and improved insulin resistance. Higher levels of urokinase-type plasminogen activator receptors have been found to predict morbidity and mortality among diabetic patients with cardiac disease.
This study aims to explore the role of serum urokinase-type plasminogen activator receptor levels as a prognostic marker among type 2 diabetic Iraqi patients.
Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference
... Show MoreA new colorimetric-flow injection method has been developed and validated for the detection of Cefotaxime sodium in pharmaceutical formulations. This method stands out for its rapid and sensitive nature. The formation of a brown-colored complex between Cefotaxime sodium and the Biuret reagent in a highly alkaline environment serves as the basis for the detection. The intensity of this colored complex is measured using a custom-built Continuous Flow Injection Analyzer, enabling accurate quantification of Cefotaxime sodium. Optimization studies of the chemical and physical parameters such as dilution of Biuret reagent, effect of the medium basicity, flow rate, sample loop and others have been investigated. The calibration gra
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