Urban design plays an important role in architectural education and its subdivisions. When architects are looking at cities, they not only recognize three dimensionality and geographical appearances of spaces but also see the necessity of incorporating artistic, cultural, social, environmental and psychological elements alongside. Urban design is currently studied as a fundamental discipline with the use of developing technology and influential changes in educational practices, pedagogies and methods. In this study, the artistic aesthetic principles of Hermann Maertens towards monuments and public squares are used to analyze theoretical planning of urban spaces together with technological transformations in the practice with an educational software program called Space Syntax "depth map" as a case-study to redefine the multidisciplinary perspectives of urban design; artistic, cultural, social, psychological as well as accessibility and usability perspectives. The analysis puts forth the necessity of new multidisciplinary approaches in urban design practices and education.

This study evaluates the factors forming Modern Art by examining the Barbizon school's approach in art education, in comparison to the academic understanding of art. The invention of photography steered the visual art schools towards a new conceptualization of nature. The awareness that Impressionist and Post-impressionist movements achieved in the depiction of nature came to define modern art. Modern art finds character with the learning outcomes of new art education and approaches that brake away from the traditional Renaissance depiction of nature. Cubist movement which forms a foundation for Modern art transformed the images of nature through a conceptual perception that experiences visual reality as a mental stage. The impact of Cubist geometric and multi-perspective portrayal of nature will be examined in Modern Art painting samples. The artistic performance of Jackson Pollock will be associated with the development of Modern Art to provide a fresh perspective on art-nature relationship.

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In the past, like many companies, we tried to automate the data collection of handheld meters and other devices using Optical Character Recognition (OCR) technology, only to learn OCR technology has its limitations; we discovered any change in position, lighting, angle and even glare would throw off the OCR, resulting in bad numbers. So we changed direction and switched to Artificial Intelligence (AI) with Deep Learning algorithms. Our goal was to implement a “learn as you go,” AI-assisted solution that will learn to read a handheld meter as good, or better than, a human. The continual learning/training would train the AI to read measurements at any angle, in most lighting conditions. The trained AI would even be smart enough to understand scaled values based on the prefixes. Over time, with a much larger data set, the AI would be able to read just about any new display.