Abstract
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.

Abstract
This paper aims to study battery response under two operation strategies to analyze the annual cycles and operation costs (revenues) via sensitivity analysis. A battery model that considers performance parameters (AC-AC RTE, DOD, and C-rates) for different technologies is approached to identify how these parameters influence battery behavior and revenue. Strategies refer to (A) energy arbitrage, EA, and (B) EA and the provision of tertiary reserve. Simulations conducted for real data from Portuguese electricity and regulation markets showed regardless of the strategy used, the annual cycles and revenue are dominated by the performance parameters, instead of price volatility. In addition, for batteries with higher C-rates, as the AC-AC RTE is reduced up to 80%, the annual cycles and revenues are significantly reduced to 50% and 45% respectively, regarding its ideal model (100% AC-AC RTE). For lower C-rates, the annual cycles and revenues are slightly reduced with AC-AC RTE reductions. Specifically, strategy B revealed that annual cycles and revenue could also be influenced by the capacity requirements and the control area where batteries are providing services. © 2024 IEEE.

Abstract
Energy Communities (ECS) and Self- consumption structures are receiving significant attention in Europe due to their potential contribution to a sustainable energy transition and the decarbonization process of the energy system. They are considered a powerful instrument to involve end-consumers in active participation in the energy system by becoming self-producers of renewable electricity and increasing their awareness of their potential contribution by adapting their energy behavior to the global or local power system needs. An EC can also contribute to alleviating energy poverty, which occurs when low incomes and poorly efficient buildings and appliances place a high proportion of energy costs on households. The main driver would be the reduction in energy costs obtained if some members agree to share their surplus electricity at a lower price with vulnerable members. Similarly, a renewable EC (REC) can facilitate access to energy assets by sharing the investments among the community members and exploiting existing complementarities. For example, vulnerable members could share their roofs with others to install solar panels in exchange for low-cost electricity. RECs can also help vulnerable members by reducing the barriers to accessing subsidies for building efficiency investments thanks to collective community initiatives, easing information dissemination and helping with bureaucratic processes.

Abstract
The scandalous fraud of the Theranos company and every step taken by its founder, Elizabeth Holmes, is well known in the media. In order to learn more about this topic and identify areas in which the company's management has fallen short, a variety of articles, newsletters, and papers were examined. The whole story of this enterprise was told by carefully putting in context what happened to Holmes in the workplace and in her personal life. In college, when she met Balwani, a romantic partner and important member of Theranos' management, the Stanford dropout began working on her company idea. Both were convicted of offenses connected to this type of business management. Five essential questions, as outlined by Lafley & Martin (2023), encapsulate the decision cascade that should be incorporated into any business plan. This essay attempts to explain Theranos' decision-making process in light of these considerations, complementing it with the strategic view defined by Porter's five forces. The facade of success that made it possible for investment to keep coming in while the technology did not make any progress was analysed too, divided into two parts: red flags and Elizabeth's crafted discourse about the company. Through this article, it is easy to conclude that Theranos' mismanagement was responsible for its collapse. With all that said, the following questions remain: Is there a bright future after a huge mistake? Perhaps a second attempt to help people with no ulterior motives? What will happen next? What can be expected?. © 2024 Academic Conferences and Publishing International Limited. All rights reserved.

Abstract
This work-in-progress aims to analyze perspectives of secondary and higher education students regarding the feasibility of integrating immersive Virtual Reality (VR) into the classroom. The harvesting of students' opinions was conducted through oral and written questionnaires after a Virtual Reality Environment activity held during two sessions of an event and other in an undergraduate class. The answers enable the understanding of challenges they faced during the activity, identifying elements that contributed to participants' immersion, assessment of perceived realism, and individuals' opinions on the integration of VR in the classroom. Conclusions regarding the applicability of VR from the perspective of secondary and higher education students can be drawn.

Abstract
Medical imaging classification improves patient prognoses by providing information on disease assessment, staging, and treatment response. The high demand for medical imaging acquisition requires the development of effective classification methodologies, occupying deep learning technologies, the pool position for this task. However, the major drawback of such techniques relies on their black-box nature which has delayed their use in real-world scenarios. Interpretability methodologies have emerged as a solution for this problem due to their capacity to translate black-box models into clinical understandable information. The most promising interpretability methodologies are concept-based techniques that can understand the predictions of a deep neural network through user-specified concepts. Concept activation regions and concept activation vectors are concept-based implementations that provide global explanations for the prediction of neural networks. The explanations provided allow the identification of the relationships that the network learned and can be used to identify possible errors during training. In this work, concept activation vectors and concept activation regions are used to identify flaws in neural network training and how this weakness can be mitigated in a human-in-the-loop process automatically improving the performance and trustworthiness of the classifier. To reach such a goal, three phases have been defined: training baseline classifiers, applying the concept-based interpretability, and implementing a human-in-the-loop approach to improve classifier performance. Four medical imaging datasets of different modalities are included in this study to prove the generality of the proposed method. The results identified concepts in each dataset that presented flaws in the classifier training and consequently, the human-in-the-loop approach validated by a team of 2 clinicians team achieved a statistically significant improvement.