Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.

Abstract
Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The Small variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

Abstract
Digital transformation in entrepreneurship education is an activity that has been taking place in higher education institutions, namely, through digital access to resources, simulations, and serious games. These activities have contributed to greater student engagement and to fostering personalized learning. Despite the recognized success of these activities, entrepreneurship education is still seen as an isolated and internally implemented activity, with few synergies with other institutions and external stakeholders. This study presents a proposal for an innovative technological platform that enables entrepreneurial projects to include students from various higher education institutions helping to build businesses worldwide. The proposed approach also involves integration with investors who can invest and offer mentoring services. A prototyping methodology was employed which provides benefits in terms of rapid iteration and feedback, enabling early visualization and testing of ideas, leading to improved design, functionality, and alignment with user needs. The results of this study show that the implemented solution addresses the critical success factors (CSFs) in the implementation of a crowdsourcing platform such as usability, scalability, transparency, security, monetary compensation, and social recognition. Finally, this study is mainly relevant for higher education institutions to revolutionize their higher education processes by adopting a collaborative approach that allows them to interact with several players on a global scale.

Abstract
The energy storage batteries, employed in solar systems installed on lampposts, are usually placed in devices such as switchboards fixed at an elevation near the top of the column. However, this storage solution becomes inefficient, because it is not possible to guarantee the control of the working temperature of the batteries, due to the low thermal insulation capacity of these storage devices. In this sense, an underground compartment made of concrete, steel plate and rock wool were created, embedded in the foundation of the lamppost, with the purpose of using geothermal energy to maintain an adequate temperature inside the compartment. To verify the temperature inside the battery storage compartment, a thermal analysis was performed, where heat transfer by conduction, convection and radiation was considered. Analyses were performed in steady state, and later, transient state, considering the initial temperatures of the thermal study in the previous steady state. With a storage volume of 1m3 and the base of the compartment at a depth of 2m, it was verified that it is possible to use geothermal energy to cool or heat, depending on the season, a system through geothermal energy. Considering a typical day in July, with room temperature of 35oC, a reduction of approximately 8oC was obtained inside the storage compartment, compared to the ambient temperature.

Abstract
The article provides a comparison of Convolutional Neural Network (CNN) and Reinforcement Learning (RL) applied to the field of autonomous driving within the CARLA (CAr Learning to Act) simulator for training and evaluation. The analysis of results revealed CNNs better overall performance, as it demonstrated a more refined driving experience, shorter training durations, and a more straightforward learning curve and optimization process. However, it required data labelling. In contrast, RL relayed on an exhaustive (unsupervised) exploration of different models, ultimately selecting the model at timestep 600,000, which had the highest mean reward. Nevertheless, RL’s approach revealed its susceptibility to excessive oscillations and inconsistencies, necessitating additional optimization and tuning of hyperparameters and reward functions. This conclusion is further substantiated by a range of used performance metrics (objective and subjective), designed to assess the performance of each approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Sustainable development is crucial to ports due to the interconnection between port activities, the economy, and the environment. This study aims to explore how port digitalization initiatives played the role of promoting sustainable development. To this purpose, the author/authors adopted a mixed methods approach using as database the World Ports Sustainability Program, which features 74 port digitalization initiatives. The first step focused on a quantitative analysis of the distribution of said initiatives in terms of sustainable development goals, followed by a thematic analysis to explore their contribution. The findings indicate that more than 72% of ports addressed sustainable development goals 8, 9, 13, and 17. Digitalization initiatives in ports have mainly focused on improving their infrastructure and operational performance, enabling them to address climate change challenges. This work also recognized the role that partnerships can play in achieving this goal.