How to profoundly change the beliefs of the polytheists during the age of revelation from the denial of the Prophet (pbuh) to the certainty of his Message and the acceptance of his full guardianship is a question worthy of attention. The order of verses about prophecy based on the order of revelation is the reason to lead a transformative Process and a special Quranic method in institutionalizing the belief of prophecy in the lives of Arabs. In the first stage, the Holy Quran in the first Makkī (Arabic: المکّیّ, suras revealed in Mecca) chapters has prepared the community from the motivational side to accept the truth of the Prophet’s (pbuh) prophecy and briefly stating the key issues surrounding it. The Holy Book of Quran at the time of the emergence of oppositions and doubts (the second stage) destroyed the arguments of the polytheists and explained the prophecy and proved its authenticity from a cognitive perspective in the second stage by presenting clear arguments. and the Holy Book of Quran in the third stage after Madanī (Arabic: المدنیّ, suras revealed in Medina) period has undertaken measures in order to fulfill the goals of the Massage by explaining the true position of the Prophet (pbuh) to deepen and operationalize the Prophetic belief through explaining practical duties for the Prophet (pbuh) and behavior based on the acceptance of Walaya (Arabic: ولایة, meaning “guardianship” or “governance”).

In Statistical quality control a very widely used measure is average run length (ARL) which may be worked out by different methods like integral equation, approximations, and Monte Carlo simulations. The ARL measure and the other related measures are of major significance in every type of production Process. An omission in its computation (and hence its related measures such as extra quadratic loss (EQL)) may cause a loss. This necessitates great care in the choice of its evaluation method. This article will deal with this issue using some approximation methods and the Monte Carlo simulations. The discrepancies among the results will be examined to highlight the deficiencies of using approximation methods in quality control techniques.

Many Process-aware organizations need to monitor the execution of their Business Processes (BP). Changes in BP execution can be reported as events while real-time detection of event patterns from such events can help the Monitoring of model-execution conformance or business activities. Complex Event Processing (CEP) techniques can detect event patterns that are specified as CEP rules. Given the high rate of events and numerous number of complex rules, existing CEP-based solutions are not scalable. We present a novel scalable Event Driven Process Monitoring Mechanism (EDBPM) using distributed CEP. Events are partitioned by Process instance identifier and the events of each partition is dispatched to a compute node. As such, the Processing load of BP Monitoring is distributed adaptively to compute nodes in a load balanced manner. Using a prototyped implementation of EDBPM we show that EDBPM scales well horizontally, i. e. increases in throughput are nearly linear when the number of compute nodes increases. Compared to CPU and memory balancing in a general purpose distributed CEP-based solution, EDBPM keeps the CPU load doubly balanced and does balance the memory too, which is lacking in similar solutions

Today, with the growth of technology, Monitoring Processes by the use of video and satellite sensors have been more expanded, due to their rich and valuable information. Recently, some researchers have used sequential images for defect detection because a single image is not sufficient for Process Monitoring. In this paper, by adding the time dimension to the image-based Process Monitoring problem, we detect Process changes (such as the changes in the size, location, speed, color, etc.). The temporal correlation between the images and the high dimensionality of the data make this a complex problem. To address this, using the sequential images, a statistical approach with RIDGE regression and a Q control chart is proposed to monitor the Process. This method can be applied to color and gray images. To validate the proposed method, it was applied to a real case study and was compared to the best methods in literature. The obtained results showed that it was more effective in finding the changes.