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 encounter (PE), emotional connection (EC), and imaginative projection (IP) – as a framework. The study adopted a qualitative approach that explores a learning experience consisting of two interactive phases, one within a historical and cultural context, preceded by a formal educational environment, to track the transformations of the interpretive patterns. Students from the Department of Architecture are involved in this process, producing visual storytelling outputs analyzed by ‘MAXQDA Analytics Pro’. The results indicate that spatial experience enhanced spatial awareness and deepened their emotional response by transforming sensory impressions into symbolic meanings. Comparative pre-post analysis showed that after the on-site immersion, more spatial awareness (SA), atmospheric response (AR), symbolic meaning (SM), and transformative visualization (TV) became intensified, indicating more intense experience. Emotional Connection was a mediating dimension between embodied perception and imaginative reinterpretation, and transition of learning was realized through a multidimensional and not a linear process. Imagination contributed as a dynamic dimension, shifting towards context-rooted visualization. The research provides an interpretive framework that demonstrates how spatial experience can be transformed into a means of reinvigorating cultural meaning and enhancing awareness of identity. The triadic model represents an effective tool in education and training.
The research utilizes data produced by the Local Urban Management Directorate in Najaf and the imagery data from the Landsat 9 satellite, after being processed by the GIS tool. The research follows a descriptive and analytical approach; we integrated the Markov chain analysis and the cellular automation approach to predict transformations in city structure as a result of changes in land utilization. The research also aims to identify approaches to detect post-classification transformations in order to determine changes in land utilization. To predict the future land utilization in the city of Kufa, and to evaluate data accuracy, we used the Kappa Indicator to determine the potential applicability of the probability matrix that resulted from
... Show MoreThe gas material balance equation (MBE) has been widely used as a practical as well as a simple tool to estimate gas initially in place (GIIP), and the ultimate recovery (UR) factor of a gas reservoir. The classical form of the gas material balance equation is developed by considering the reservoir as a simple tank model, in which the relationship between the pressure/gas compressibility factor (p/z) and cumulative gas production (Gp) is generally appeared to be linear. This linear plot is usually extrapolated to estimate GIIP at zero pressure, and UR factor for a given abandonment pressure. While this assumption is reasonable to some extent for conventional reservoirs, this may incur
This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi
In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe
... Show MoreHM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023