Abstract
This review discusses the integration of intelligent technologies into customer interactions in organizations and highlights the benefits of using artificial intelligence systems based on a multimodal approach. Multimodal learning in marketing is explored, focusing on understanding trends and preferences by analyzing behavior patterns expressed in different modalities. The study suggests that research in multimodality is scarce but reveals that it is as a promising field for overcoming decision-making complexity and developing innovative marketing strategies. The article introduces a methodology for accurately representing multimodal elements and discusses the theoretical foundations and practical impact of multimodal learning. It also examines the use of embeddings, fusion techniques, and explores model performance evaluation. The review acknowledges the limitations of current multimodal approaches in marketing and encourages more guidelines for future research. Overall, this work emphasizes the importance of integrating intelligent technology in marketing to personalize customer experiences and improve decision-making processes.

Abstract
The increase in the use of mobile phones in recent years has raised significant concerns regarding road safety, more specifically motorcycle accidents. Driver distraction caused by mobile phone use is a topic of great importance, given the negative impact on traffic safety and the occurrence of accidents. In this way, the design of optimized and strategically planned interfaces for electric motorcycle displays can play a crucial role in driver safety, minimizing distractions and, consequently, the risk of accidents. This paper proposes a systematic review of the literature on improving electric motorcycle displays in the context of mobile applications, exploring the best way for the display to become the only digital tool needed while driving, without causing distraction to the driver. To achieve this result, recent studies from the scientific literature were analyzed, highlighting the importance of a clean layout containing only relevant information to the visual elements. The interface structure should be easy to understand and recognize, with feedback always available so as not to overwhelm the user experience. Through the review, we studied how to improve the design of motorcycle displays and reduce the use of mobile phones while driving. The results were essential for us to understand that the usability of the interface is crucial for consistency in its structure, as well as the vocabulary, which must be coherent and in familiar language.

Abstract
With the recent surge in the electric vehicle market, there is a pressing demand for solutions and platforms to enhance vehicle lifecycle management. This is particularly pertinent for motorcycles, which are widely used in urban environments (e.g., for food delivery services) and require frequent maintenance. The present study proposes the research and development of a platform, along with mobile and web applications, focusing on optimizing the lifecycle of electric motorcycles. Central to this project is the implementation of Product Lifecycle Management (PLM) to simplify the planning of technical maintenance and the recording and access to technical events and information in the most transparent and non-intrusive way for all involved parties. This project aims to establish innovative and effective communication between owners, manufacturers, and service partners, ensuring the longevity and reliability of motorcycles.

Abstract
Manual text annotation is a complex and time-consuming task. However, recent advancements demonstrate that such a task can be accelerated with automated pre-annotation. In this paper, we present a methodology to improve the efficiency of manual text annotation by leveraging LLMs for text pre-annotation. For this purpose, we train a BERT model for a token classification task and integrate it into the INCEpTION annotation tool to generate span-level suggestions for human annotators. To assess the usefulness of our approach, we conducted an experiment where an experienced linguist annotated plain text both with and without our model’s pre-annotations. Our results show that the model-assisted approach reduces annotation time by nearly 23%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper presents a novel Nonlinear Model Predictive Controller (NMPC) architecture for trajectory tracking of omnidirectional robots. The key innovation lies in the method of handling constraints on maximum velocity and acceleration outside of the optimization process, significantly reducing computation time. The controller uses a simplified process model to predict the robot’s state evolution, enabling real-time cost function minimization through gradient descent methods. The cost function penalizes position and orientation errors as well as control effort variation. Experimental results compare the performance of the proposed controller with a generic Proportional-Derivative (PD) controller and a NMPC with integrated optimization constraints. The findings reveal that the proposed controller achieves higher precision than the PD controller and similar precision to the NMPC with integrated constraints, but with substantially lower computational effort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Pet owners are increasingly becoming conscious of their pet's necessities and are paying more attention to their overall wellness. The well-being of their pets is intricately linked to their own emotional and physical well-being. Some veterinary system solutions are emerging to provide proactive healthcare options for pets. One such solution offers the continuous monitoring of a pet's activity through accelerometer tracking devices. Based on data collected by this application, in this paper, we study different time aggregation and three unsupervised machine learning techniques to identify anomalies in pet behaviour data. Specifically, three algorithms, Isolation Forest, Local Outlier Factor, and K-Nearest Neighbour, with various thresholds to differentiate between normal and abnormal events. Results conducted on ten pets (five cats and five dogs) show that the most effective approach is to use daily data divided into periods. Moreover, the Local Outlier Factor is the best algorithm for detecting anomalies when prioritizing the identification of true positives. However, it also produces a high false positive ratio.