Abstract
Managing multiple knowledge transfers between headquarters and subsidiaries, among subsidiaries, and also within each of these units is crucial for multinational corporations' (MNCs) survival. Therefore, this article aims to uncover the main factors influencing internal knowledge transfers in MNCs-including intra-unit knowledge transfers and transfers between units, namely, conventional, horizontal, and reverse knowledge transfers. To achieve this goal, a systematic literature review (SLR) was conducted to synthesize the content of 85 articles. From a set of 1439 papers, only 85 related to knowledge transfer and knowledge sharing were considered. Based on an inductive thematic approach, eight different research categories and 97 topics were identified. Four different internal knowledge transfers (intra knowledge transfer (IKT), horizontal knowledge transfer (HKT), conventional knowledge transfer (CKT), and reverse knowledge transfer (RKT)) are compared across eight thematic categories and 97 topics. According to the results obtained, the depth of the topics analyzed varies, as does the variety of categories, with RKT being more deeply analyzed than IKT. There is a clear dominance of vertical knowledge transfer (CKT + RHT) over HKT. The exercise of power (e.g., size, knowledge base) still dominates CKT and RKT in most of the studies analyzed, which are traditionally affected by the characteristics of MNCs, HQs and subsidiaries. The debate on HKT is affected by the classical perspectives of power-based relations (e.g., expatriates, size, knowledge base) among subsidiaries. Although important, intra-unit knowledge transfer is greatly influenced by characteristics.

Abstract
Machine Learning is a rising concept in today's society. In the past decade, ML-based systems have become part of people's daily routines, and their usage has been disseminated through diverse sectors. This evolution is supported by the exponential increase in data created worldwide. Feature Engineering is a critical process focused on transforming data into suitable inputs for Machine Learning algorithms. This work explores the Feature Engineering process by developing a baseline for its implementation. Hence, a pipeline of Feature Engineering techniques and their taxonomy is proposed, along with a set of R scripts to implement. The validity of the code is then demonstrated through its application to a real-world dataset.;MachineLearning é um conceito em crescente evolução na sociedade atual. Na última década, os sistemas baseados em ML tornaram-se parte do quotidiano da população e a sua aplicação tem vindo a disseminar-se por diversos setores. Este crescimento é suportado pelo aumento exponencial da quantidade de dados gerados a nível mundial. FeatureEngineering surge, assim, como um processo chave que permite transformar dados em inputs adequados para os algoritmos de MachineLearning. O presente trabalho pretende explorar o processo de FeatureEngineering, com vista a desenvolver uma base de suporte à sua implementação. Por conseguinte, é proposta uma pipeline de técnicas de FeatureEngineering em paralelo com a sua taxonomia, juntamente com um conjunto de scripts R, para as implementar. A validade do código é, posteriormente, demonstrada através da sua aplicação a um conjunto de dados reais.

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.