Optimization is essentially the art, science and mathematics of choosing the best among a given set of finite or infinite alternatives. Though currently optimization is an interdisciplinary subject cutting through the boundaries of mathematics, economics, engineering, natural sciences, and many other fields of human Endeavour it had its root in antiquity. In modern day language the problem mathematically is as follows - Among all closed curves of a given length find the one that closes maximum area. This is called the Isoperimetric problem. This problem is now mentioned in a regular fashion in any course in the Calculus of Variations. However, most problems of antiquity came from geometry and since there were no general methods to solve such problems, each one of them was solved by very different approaches.
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This study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a
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Journal of Theoretical and Applied Information Technology is a peer-reviewed electronic research papers & review papers journal with aim of promoting and publishing original high quality research dealing with theoretical and scientific aspects in all disciplines of IT (Informaiton Technology
Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
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The aim of this study is to estimate the parameters and reliability function for kumaraswamy distribution of this two positive parameter (a,b > 0), which is a continuous probability that has many characterstics with the beta distribution with extra advantages.
The shape of the function for this distribution and the most important characterstics are explained and estimated the two parameter (a,b) and the reliability function for this distribution by using the maximum likelihood method (MLE) and Bayes methods. simulation experiments are conducts to explain the behaviour of the estimation methods for different sizes depending on the mean squared error criterion the results show that the Bayes is bet
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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga
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