TE2024 Poster videos

Title: Transdisciplinary Engineering Competencies for Brazilian Automotive Maintenance Workers: Personality Assessment Approach for Industry 5.0

Authors: Eduardo dos Santos Monteiro1, Jorge Muniz Jr.1, Rosa Isabel Rodrigues2, Marcelo Augusto Santos Amaral11: UNESP-Universidade Estadual Paulista, Brazil; 2: ISG-Instituto Superior de Gestão, Portugal

Abstract: Automotive companies have begun implementing Industry 4.0 technologies and are facing various issues related to unclear economic benefits and digital investments, cybersecurity, and a shortage of professionals with digital skills, especially in maintenance activities. These professionals are highly sought after by automotive companies, which are challenged to hire new teams for training and to internally identify individuals with the skills to be retrained. This study focuses on this theme and aims to investigate whether Personality and Skills are good predictors of performance in Industry 4.0. The sample consists of 70 maintenance professionals from two automotive sector companies in Brazil (an automaker and a major Tier 1 parts supplier). The study was operationalized using a quantitative methodology with a convenience sample. Data were collected through a Short Skills Inventory, developed based on the Great Eight model and the Big Five Personality Inventory. The results demonstrated that Conscientiousness and Openness to Experience are the personality dimensions that best characterize the participants. It was also found that, concerning skills, the highest values belong to Adaptation and Coping, as well as Analysis and Interpretation. Personality and skills are known in the literature as strong predictors of performance in various contexts, but the combination of both for Industry 4.0 has never been tested. This research represents another step to help fill this gap. The transdisciplinary nature of the study provides added value to human resources, as it contributes to increasing the robustness of the recruitment and selection process and ensures a better person-function fit.

Title: Socio-ethical challenges of integrating augmented reality into transdisciplinary engineering programs

Authors: Abdullah Ezzeldin, Nada Ayman, Omar Tawfik, Farah Ragheb, Mariam Makramalla. NewGiza University, Egypt

Abstract: In our attempts to re-envision our identities in a communal sense that extends beyond the narrow view of engineering as a purely technical enterprise, this study explores ethical challenges pertaining to the social responsibility of engineering students that interact with augmented reality (AR) as part of their instructional approach. Utilizing the ethical engineering practices as an underpinning theoretical framework, our aim is to consider the impact of a transdisciplinary integration of AR in engineering curricula on the socio-emotional development of environmentally responsible engineers. The study builds on a technical review of literature on big data engineering curriculum integration and a social review of literature on citizenship and responsible pedagogical development of change agents for sustainability. We ask questions about the different learners that would benefit from this integrated socio-technical approach and how to best reach them. To unpack this challenge, we utilize an innovative AR design to build a protected learning ecosystem that integrates different engineering disciplines at varying levels of integration. We intend to conduct a comparative qualitative investigation between two focus groups, each comprising of five undergraduate engineering students of mixed genders. Each group will be subjected to a different level of discipline integration in the aforementioned ecosystem. Findings will be reported in a follow up study. This study is relevant beyond our local context, particularly in view of our growing need to interconnect disciplines and empower responsible learners to become change agents.

Title: Transdisciplinary Practices amongst Engineering and Creative Teams

Authors: Anisha Vyas, Bryan Moser. System Design and Management, Massachusetts Institute of Technology, United States of America

Abstract: In the dynamic landscape of the entertainment industry, the emergence of transdisciplinary engineering teams is increasingly prevalent. This unique amalgamation is indispensable for achieving success in an environment where the ultimate goal is not just driven by programmatic goals but by the realization of a creative vision. This paper contends that, within the entertainment industry, the success of projects is intricately linked to the transdisciplinary nature of the teams involved. The fusion of engineering disciplines and a focus on creative objectives is paramount in delivering safe and enjoyable experiences for guests and entertainers alike. As the entertainment industry holds immense sway over societal dynamics, the impact of these transdisciplinary teams extends beyond project success. By emphasizing the significance of effective team interfaces on these transdisciplinary teams, which facilitate communication and information sharing, the research aims to quantify their value beyond traditional project management metrics. The assessment of the ideal skills required for effective team interfaces within transdisciplinary teams is a focal point of this study. Ultimately, by identifying factors contributing to successful team interfaces, the study aims to support the hypothesis that interface managers within complex transdisciplinary teams are more effective in driving project success than traditional project managers. This exploration contributes to improving team performance and project outcomes and underscores the transformative potential of transdisciplinary engineering in shaping the landscape of the themed entertainment industry as a powerful tool for social change

Title: Human Decision Making Assisted by Artificial Intelligence: Electricity Demand Forecasting in Japan 

Author: Yichuan ZHANG, Kazuo HIEKATA The University of Tokyo, Japan; 7211395887@edu.k.u-tokyo.ac.jp 

Abstract: Transdisciplinary engineering bridges the gap between diverse fields of expertise, fostering innovative solutions to complex problems. This study investigates the synergy of human expertise and artificial intelligence (AI) in the field of electricity prediction. The accurate forecasting of electricity demand is a critical component of sustainable engineering practice, particularly in energy-intensive economies like Japan. This research tackles the pressing challenge of utilizing algorithms for future electricity demand forecasting by AI-driven predictions. This research explores the efficacy of a transdisciplinary human-AI collaborative approach in enhancing the precision and reliability of electricity demand forecasts, considering the shortcomings of algorithmic predictions in dealing with sparse datasets and unforeseen events. By revisiting the actual past cases Tokyo electricity demand prediction and providing participants test subjects with foundational industry knowledge and an interactive data analysis interface, the research collects comprehensive data throughout the exercise from both technical and non-technical individuals in the forecasting task in the user experiments. The findings indicate that human-AI collaboration can significantly refine forecast accuracy under certain conditions. A notable enhancement is observed when AI predictions are constrained by data limitations and unexpected events. Additionally, individuals with strong technical backgrounds excel in augmenting AI forecasts, although the risk of human-induced biases and over-adjustments presents a challenge. Confirming the benefits of a human-AI collaborative model, we identify potential strategies for AI to better support human decision-making in energy engineering. 

Title: Leveraging artificial intelligence for early cancer detection in resource-constrained healthcare settings: Lessons from Indian MedTech

Author: Pallavi Joshi. Open University, United Kingdom pallavi.joshi@open.ac.uk

Abstract: About 70 per cent of cancer cases in India are diagnosed at an advanced stage, rendering the treatment ineffective or unaffordable. The lack of timely access to suitable early detection technologies is one of its primary reasons. Previous research highlights that misaligned health and industrial policies in India have resulted in a mismatch between local health needs and relevant industrial innovations. In recent years, the Indian Medical Technology (MedTech) sector has seen a surge in artificial intelligence (AI)-driven innovations addressing challenges in early cancer detection in resource-constrained healthcare systems. This paper examines three case studies of early cancer detection MedTech innovations for breast, cervical, and oral cancers by Indian start-ups. Employing a novel Inclusive Health Innovation framework and 75 years of policy evolution, along with online stakeholder interviews in India, this paper identifies and analyses factors, actors, networks, and knowledge and technology driving these inclusive innovation efforts. The paper reveals that strong interactions in science, technology, and innovation (STI), industrial, and healthcare policies have fostered a MedTech ecosystem, enhancing technological capabilities to develop early cancer detection solutions suited to the Indian healthcare system. While demand-side policies and healthcare delivery are cautiously adapting to this technological change, they signify pocket wins in increasing the availability of locally relevant cancer screening solutions. The findings of this paper provide theoretical, empirical, and policy insights for low-resource healthcare settings utilising emerging technologies for enhanced healthcare access.

Title: Using the TRIZ method to design a tool for depanelization and diagnostics of PCB panels

Authors: Mateusz Łyczek1, Wojciech Skarka2. 1: AIUT Sp. z o.o.; 2: Faculty of Mechanical Engineering Silesian University of Technology

Abstract: Panels and PCBs are some of the most important components of the modern world. Production processes related to them, such as diagnostics or depanelization, use tools whose design does not allow them to be combined into an object with a similar and coherent structure. Therefore, one of the goals of this article is to use the "TRIZ" method (theory of solving inventive problems) to design a tool that will combine PCB depanelization methods and their diagnostics. Another goal of this publication is to compare traditional ways of searching for articles and conference papers to searching for publications using language models such as ChatGPT. The searches will be about the "TRIZ" method. The process of designing the tool using the "TRIZ" method is to help obtain an optimal solution that will meet a number of requirements for PCB diagnostic and depanelization tools. On the other hand, the comparison of publication search methods is aimed at indicating differences in the process of preparing a literature review on a given topic and determining trends in this topic. Both aspects of the publication should make a significant contribution to the development of the electronics industry. On the other hand, the very aspect of comparing review methods should have an impact on the creation of future literature reviews.