Historical cultural environments are a repository of values and symbols that pass down across generations through spatial experiences. Despite their intellectual and cultural potential, their role in fostering belonging and identity has declined; they are often viewed as silent landmarks, isolated from lived experiences. This highlights the need for an integrated model that makes spatial experience a stimulating process for reinvigorating the meaning inherent in historical contexts and reconnecting the new generation with their cultural roots. This research aims to explore how cultural meaning in historical contexts can be reactivated through spatial experience. To achieve this, the study proposes a triadic model – physical encoun

This research aimed at studying the role of calculated knowledge an its efficiency in improving the performance especially most of the organizations are living within knowledge era which concentrate on new technology investment in different fields of modern live . Under the scientific trends towards the economy of calculated knowledge which depend basically on new computer program in order to utilize the knowledge to raise the level of work performance exploiting different resources in the best way that helps the organizations to achieve their objectives because the information technology and computer programs became a means of survival and indispensible instrument within the developed world which depend on prosperity . progress

This study aims to explain the role of green target costing technique in improving the relationship with suppliers in a sample of industrial companies listed on the Iraq Stock Exchange. The descriptive analytical approach was used, where a questionnaire was designed that included a set of questions that were directed to a sample of 84 individuals including production managers, finance managers, quality managers, and purchasing managers in these companies. The study uses some statistical analyzes such as correlation analysis and regression analysis to analyze the questionnaires. The study finds a positive impact of the green target costing technique on improving the relationship with suppliers. The results indicate that the relation

Abstract. This research examines the role of artificial intelligence (AI) in improving and sustaining agricultural production in the Arab world, with a particular focus on the challenges facing Iraq in implementing this technology. It reviews several developments, such as big data, the Internet of Things (IoT), and drone technology, which improve irrigation, increase soil fertility, and reduce waste. The study highlights the diverse applications of AI in crop production and pest control, while revealing Iraq's infrastructure challenges and the need for better training for farmers, To find effective solutions, the research recommends collaboration between governments and the private sector.

This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obta

The distribution of the expanded exponentiated power function EEPF with four parameters, was presented by the exponentiated expanded method using the expanded distribution of the power function, This method is characterized by obtaining a new distribution belonging to the exponential family, as we obtained the survival rate and failure rate function for this distribution, Some mathematical properties were found, then we used the developed least squares method to estimate the parameters using the genetic algorithm, and a Monte Carlo simulation study was conducted to evaluate the performance of estimations of possibility using the Genetic algorithm GA.

Many industrial systems involve multiple criteria and objectives, and they are very complex problems in computational science, such as task scheduling. We propose bi-criteria and bi-objective scheduling problems, which are solved by two nature-inspired evolutionary algorithms, such as Simulated Annealing (SA) and Bee Algorithm (BA). This problem is characterized by scheduling a batch of tasks on multiple machines, and it is fundamental because the solution should focus on the simultaneous optimization of two conflicting objectives: the makespan minimization and the total tardiness minimization. This problem is NP-Hard, and therefore, two evolutionary methods were used to search for solutions intelligently in this huge, very complex

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri