Abstract
As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery. In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality. Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles. We evaluated three different heuristics and five block sizes on four publicly available datasets. Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size.

Abstract
AbstractSeveral approaches have been proposed to obtain successful models to solve complex next-item recommendation problem in non-prohibitive computational time, such as by using heuristics, designing architectures, and applying information filtering techniques. In the current technological scenario of artificial intelligence, sequential recommender systems have been gaining attention and they are a highly demanding research area, especially using deep learning in their development. Our research focuses on an efficient and practical model for managing sequential session-based recommendations of specific products for users using the wine and movie domains as case studies. Through an innovative recommender model called X-Model4Rec – eXtensible Model for Recommendation, we explore the user's dynamic taste profile using architectures with transformer and multi-head attention mechanisms to solve the next-item recommendation problem. The performance of the proposed model is compared to that of classical and baseline recommender models on two real-world datasets of wines and movies, and the results are better for most of the evaluation metrics.

Abstract
The Eurasian grapevine (\textit{Vitis vinifera L.}) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role, as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and control activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) methods have emerged as a classification alternative to deal with the scarcity of ampelographers and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The steps of the standard DL-based classification pipeline were described for the 18 most relevant studies found in the literature, highlighting their pros and cons. Potential directions for improving this field of research were also presented.

Abstract
Considering the beauty industry's potential for further expansion and the mismatch between the attitudes of consumers and their buying behavior, brands should comprehend the factors that influence consumers' intention to purchase environmentally friendly cosmetics. As such, the present study examined what encourages consumers of environmentally friendly cosmetics to choose these products. To answer the main objective of the work, the elaborated literature review aimed at identifying the factors that influence the buying of environmentally friendly cosmetics. Thus, the following were found: environmental consciousness, certification labels, brand trust, quality expectation, lifestyle, advertising, willingness to pay the price, ethical concerns and social and financial equity, physical health considerations, and knowledge of the product. The study was conducted using exploratory research with a qualitative approach. Data was collected from eight interviews, and it was identified that factors such as environmental consciousness, lifestyle, willingness to pay the price, quality expectations, ethical concerns and social and financial equity, as well as physical health considerations and knowledge of the product are the most significant determinants in the intention to buying environmentally friendly cosmetics. One of the aims of the investigation was to distinguish between the notions of green, traditional, organic, and natural cosmetics. As a result, it was found that there is a lack of clarification of the green cosmetic concept in literature, as well as a lack of standardization of criteria used by multiple systems to define different cosmetics.

Abstract
The step response of first-order systems is vital in control systems and electronics. Understanding this behaviour is key but often challenging. This article uses Arduino with PWM to teach the step response in RC circuits, since Arduino enables real-time data acquisition and visualisation, connecting theory to practice. The research seeks to illustrate the step response of an RC circuit using Arduino, deepen knowledge of first-order systems, and offer a technique for collecting experimental data. All of this, since combining practical experiments with theoretical concepts boosts student involvement and understanding of dynamic systems. The work includes theoretical foundations, experimental procedures, and a brief discussion on the educational value of these activities.

Abstract
How we perceive and experience the world around us is inherently multisensory. Most of the Virtual Reality (VR) literature is based on the senses of sight and hearing. However, there is a lot of potential for integrating additional stimuli into Virtual Environments (VEs), especially in a training context. Identifying the relevant stimuli for obtaining a virtual experience that is perceptually equivalent to a real experience will lead users to behave the same across environments, which adds substantial value for several training areas, such as firefighters. In this article, we present an experiment aiming to assess the impact of different sensory stimuli on stress, fatigue, cybersickness, Presence and knowledge transfer of users during a firefighter training VE. The results suggested that the stimulus that significantly impacted the user's response was wearing a firefighter's uniform and combining all sensory stimuli under study: heat, weight, uniform, and mask. The results also showed that the VE did not induce cybersickness and that it was successful in the task of transferring knowledge.