Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)


 

Abstracts

 


ABSTRACT


Material Discriminated X-Ray CT System by Using New X-Ray Imager with Energy Discriminate Function

Toru Aoki, Takuya Nakashima, Hisashi Morii, Yoichiro Neo, Hidenori Mimura


Material discriminated X-ray CT system has been constructed by using conventional X-ray tube (white X-ray source) and photon-counting X-ray imager as an application with energy band detection. We have already reported material identify X-ray CT using K-shell edge method elsewhere. In this report the principle of material discrimination was adapted the separation of electron-density and atomic number from attenuation coefficient mapping in X-ray CT reconstructed image in two wavelength X-ray CT method using white X-ray source and energy discriminated X-ray imager by using two monochrome X-ray source method. The measurement phantom was prepared as four kinds material rods (Carbon(C), Iron(Fe), Copper(Cu), Titanium(Ti) rods of 3mm-diameter) inside an aluminum(Al) rod of 20mm-diameter. We could observed material discriminated X-ray CT reconstructed image, however, the discrimination properties were not good than two monochrome X-ray CT method. This results was could be explained because X-ray scattering, beam-hardening and so on based on white X-ray source, which could not observe in two monochrome X-ray CT method. However, since our developed CdTe imager can be detect five energy-bands at the same time, we can use multi-band analysis to decrease the least square error margin. We will be able to obtain more high separation in atomic number mapping in X-ray CT reconstructed image by using this system.

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