Abstract
This tutorial delves into the transformative power of asking effective questions in engineering information systems. We explore how crafting well-defined questions in both the problem space (what issue are we addressing?) and the solution space (how will we approach it?) is paramount for success. The session will unveil the intricate relationship between these questions - how the what shapes the how and vice versa. We move beyond the fear of asking naive questions, demonstrating how these can spark innovation and reveal hidden assumptions. By the end, attendees will have a powerful and easy-to-use technique that removes the fear from questions.

Abstract
Traditionally, proportional-integral (PI) control has ensured the successful application of automatic generation control (AGC). Two design features of AGC-PI are the following: (1) it is merely a reactive system which does not take full advantage of existing knowledge about the system and (2) the control signal sent to all units is divided proportionally to their participation in the AGC. These two features ensure simplicity and, thus, reliability for the regular functioning of the power system. However, when the power system is recovering from a loss of generation, such features can become shortcomings. This paper proposes a model predictive control (MPC) to improve performance of AGC in such a scenario. The contrast with the traditional approach is as follows: instead of using merely two system measures which are also the control objectives (frequency and interconnection flow), the proposed controller relies on an internal model that takes advantage of further known variables of the power system, especifically the ramping capabilities of participating units. While still respecting the participation factors, it is shown that the proposed model allows to exhaust earlier the availability of faster units, such as some demand response, as the one to be provided by hydrogen electrolysers, and thus reestablishes the frequency and interconnection flows in a faster way than typical AGC-PI.

Abstract
Energy consumption has increased exponentially due to population growth leading to an increasing impact on natural resources. Green hydrogen (H-2) offers a safer alternative to fossil fuels, making it a promising alternative for sustainable energy consumption. However, due to H-2's flammability it is crucial to monitor its concentrations in the environment. Optical sensors have been developed to monitor H-2 concentrations in harsh environments with high sensitivity and remote measurement. In this work, a numerical study and experimental validation of an optical fiber sensor based on Surface Plasmon Resonance (SPR) for H-2 detection are presented. This sensor is composed of a multi-mode fiber with a SPR structure of a metal/dielectric/Pd, where the Pd acts as a sensitive layer. The plasmonic active materials studied are Ag and Au, while TiO2 and SiO2 are used as dielectrics, finding that the metal materials have more impact on the SPR band definition, while the dielectric layers have an impact on the band spectral position. The optimized configuration with 25nm/60nm/3nm of Au/TiO2/Pd was experimentally developed, obtaining a wavelength shift of 19nm for 2kPa of H-2, validating the numerical results, and confirming the possibility of using this type of system for H-2 detection.

Abstract
In manufacturing chatter is an unwanted phenomenon that can lead to product quality reduction and tool wear. Real time chatter detection is key to preventing these issues and improving overall machining efficiency. In this paper we propose an unsupervised chatter detection method using autoencoders (AE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm that uses internal signals of Computer Numerical Control (CNC) machines. The proposed method starts by using an AE to extract features from raw internal signals collected from CNC machines. This step reduces the dimensionality of the data and captures the underlying patterns of chatter. Then the extracted features are fed into DBSCAN clustering algorithm which is a density based algorithm that groups similar data points and identifies outliers. We tested the proposed method with real world data collected from various CNC machines. The results show that our unsupervised chatter detection method has high accuracy, precision and recall, can detect chatter and distinguish it from normal machining. Also the method is robust to noise and can adapt to dynamic machining conditions. In summary our work presents an unsupervised chatter detection method using AE and DBSCAN clustering that uses internal signals of CNC machines. This method is a reliable and efficient solution for real time chatter detection so manufacturers can improve product quality, optimize machining process and reduce tool wear during machining.

Abstract
Virtual reality (VR) for training helps minimize risks and costs by allowing more frequent and varied use of experiential training experiences, leading to active and improved learning. However, creating VR training experiences is costly and time-consuming, requiring software development experts. Additionally, current authoring tools are desktop-oriented, which detaches the process of creating the immersive experience from experiencing it in a situated context. This paper presents the development of an immersive authoring tool designed to create immersive virtual environments that can be used to train operatives. The authoring tool can record and replay animations of each action the user performed that can later be used to instruct other users how the task should be performed. Participants were divided into two groups, and the proposed authoring tool was evaluated using usability, satisfaction, presence and cybersickness. Between groups, Independent T-tests revealed that there were no significant differences between expert and non-expert groups in any of the studied variables. Also, the results showed that the authoring tool had high usability and satisfaction, average presence, and low probability of cybersickness symptoms.

Abstract
The speed and scale of technological change are raising concerns about the extent to which new technologies will radically transform workplaces. Competition for the best talent is being intensified, and talent management requires new approaches and innovative strategies for developing talent based on corporate culture and its unique properties. By implementing and adopting technology in Human Resources Management (HRM), organizations create a digital employee lifecycle that spans from the initial Hiring Process to encompassing areas such as Performance Management, Learning and Development until the Offboarding, shaping a Talent Management journey. Despite the implementation of technologies being a continuous practice observed in numerous organizations, there are still challenges. The HRM technological market has become massive, and concerns arise about adopting these technologies' costs, practicality, and purpose. Because of that, designing strategies for implementing technologies in HRM, specifically in talent management, is hard to overview. In this context, this document aims to present the necessity and significance in developing a framework that aggregates the implementation process of technologies in talent management supported by Design Science Research (DSR). The holistic perspective of the forthcoming framework consolidates insights into business challenges and their correlation with technology selection, technological capabilities, implementation procedures, as well as anticipated metrics and their impact.