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.

Abstract
Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep neural models and the complexity of the autonomous driving context motivates the study of explainability in these models that perform perception tasks. Moreover, this work explores explainable AI techniques for the object detection task in the context of autonomous driving. An extensive and detailed comparison is carried out between gradient-based and perturbation-based methods (e.g., D-RISE). Moreover, several experimental setups are used with different backbone architectures and different datasets to observe the influence of these aspects in the explanations. All the techniques explored consist of saliency methods, making their interpretation and evaluation primarily visual. Nevertheless, numerical assessment methods are also used. Overall, D-RISE and guided backpropagation obtain more localized explanations. However, D-RISE highlights more meaningful regions, providing more human-understandable explanations. To the best of our knowledge, this is the first approach to obtaining explanations focusing on the regression of the bounding box coordinates.

Abstract
The main objective of this study is to investigate a solution for the current lack of skilled workers in Europe and to optimize the utilization of expertise. For this qualitative study, 36 semi-structured interviews were conducted (with a purposive sample of financially independent (soon-to-be) retirees and employers). The thematic analysis revealed (1) on both the employer's and recruiter's side, there are many stereotypes and prejudices, as well as a lack of creativity about how to integrate these highly motivated specialists into the organization's workforce; (2) Employees, retirees and employers where asked: what could be the motivation to employ retirees, what could be the benefits, what could be the drawbacks. The results also indicate that searching for intellectual challenges and solving them with a team of co-workers is one of the main attractions for senior experts. We identified six main patterns for unretirement choices: learning and intellectual challenges, applying expertise, public perception of retirees, belonging and social connections, compensating for loss of status, and feeling appreciated. Appreciating, valuing, and channeling this drive to solve present-day problems independent of a person's chronological age should be self-evident for organizations and societies.

Abstract
Falls are one of the causes of severe hilliness among elders, and the COVID-19 pandemic increased the number of unattended cases because of the social distancing measures. This study aims to create a dataset that collects the data from a 3-axis acceleration sensor fixed on a hinged board apparatus that mimics a human fall event. The datalogging system uses off-the-shelf devices to measure, collect and store the data. The resulting dataset includes data from different angle positions and heights, corresponding to joints of the lower limbs of the human body (ankle, knee, and hip). We use the dataset with a threshold-based fall detection algorithm. The result from the Receiver Operating Characteristic curve shows a good behavior with a mean Area Under the Curve of 0.77 and allow to compute a best threshold value with False Positive Rate of 14.8% and True Positive rate of 89.1%. The optimal threshold value may vary depending on the specific population, activity patterns, and environmental conditions, which may require further customization and validation in real-world settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.