Abstract
Motivation and engagement play a crucial role in student success in a course. Students may lose interest or underestimate courses that tackle non-core learning outcomes to their specific curriculum or program. Gamification, using game elements (e.g., rewards, challenges) in non-game contexts, is one way to motivate and engage students. Some educational courses use project-based learning, where students tackle problems, overcome obstacles, and gain knowledge. Quest-based games are designed as systems of challenges that players must complete to advance and win the game. They were linked with education by applying specific game mechanics to a computing course unit. This paper case studies the application of a quest-based gamification approach in a mandatory software engineering course to boost engagement among higher education students. Results were collected through observational methods and surveying the students, indicating a tendency for higher grades in course years implementing gamification while maintaining satisfactory levels of motivation and engagement. © 2023 International Conference on Higher Education Advances. All rights reserved.

Abstract
We consider a nondurable good monopolist that collects data on its customers in order to profile them and subsequently practice price discrimination on returning cus-tomers. The monopolist's price discrimination scheme is leaky in the sense that an endogenous fraction of consumers choose to incur a privacy cost to conceal their identity when they return in the following periods. We characterize the Markov perfect equili-brium of the game under two alternative customer profiling regimes: full information acquisition (FIA) and purchase history information (PHI). In both cases, we find that, contrary to what could be expected, the monopolist's aggregate profit is not monotoni-cally increasing in the level of the privacy cost, but a U-shaped function of it, leading to ambiguous profit effects: a reduction in privacy costs increases the fraction of customers who choose to be anonymous (detrimental profit effect), but it also softens the firm's introductory price, reducing the pace at which prices targeted to new customers fall over time (positive profit effect). When comparing results under FIA and PHI, we find that market expansion is faster, and more customers conceal their identity under FIA than under PHI. Equilibrium profits are also higher in the FIA case. Although equili-brium profits are U-shaped functions of the privacy cost in both profiling regimes, they tend to be globally decreasing with the privacy cost under PHI and globally increasing under FIA.

Abstract
Industrial companies live in a context of dynamic technological innovation, in which new technologies are adopted with a high impact internally and externally, leveraging their competitive advantages. A usual situation is managers deciding to adopt technologies, often without realising the impacts on the company but mainly supported by a strategic vision and the pursuit of differentiation factors. This article aims to present the results of a literature review on the impacts of Industry 4.0 technologies adoption in sustainability dimensions by industrial companies. These impacts were presented according to the three dimensions of sustainability: economic, environmental and social. The results of this study can be used by practitioners and researchers for an overview of the I4.0 technologies adoption by manufacturing companies and their impacts on sustainability dimensions, summarising the knowledge concerning this topic. © 2023 IEEE.

Abstract
Censored data are frequently found in diverse fields including environmental monitoring, medicine, economics and social sciences. Censoring occurs when observations are available only for a restricted range, e.g., due to a detection limit. Ignoring censoring produces biased estimates and unreliable statistical inference. The aim of this work is to contribute to the modelling of time series of counts under censoring using convolution closed infinitely divisible (CCID) models. The emphasis is on estimation and inference problems, using Bayesian approaches with Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms.

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

Abstract
Water vapor sorption is a powerful tool for the analysis of cement paste, one of the most used substances by mankind. The monitoring of cementitious materials is fundamental for the improvement of infrastructure resilience, which has a deep impact on the economy, the environment, and on society. In this work, a multimode fiber was embedded in cement paste for real-time monitoring of cement paste water vapor sorption. Changes in the reflected light intensity due to the build-up of water in the cement paste's pores were exploited for this purpose. The sample was 7-day moist cured, and the relative humidity was controlled between 8.9% and 97.6%. Reflected light intensity was converted into a specific surface area of cement paste (133 m(2)/g) and thickness of water through the Brunauer-Emmett-Teller (BET) method and into a pore size distribution through the Barret-Joyner-Halenda (BJH) method. The results achieved through reflected light intensity agree with those found in the literature, validating the usage of this setup for the monitoring of water vapor sorption, breaking away from standard gravimetric measurements.