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
Even for the strongest human being, maintaining an elevated arm position for an extended duration represents a significant challenge, as fatigue inevitably accumulates over time. The physical strain is further intensified when the individual is engaged in repetitive tasks, particularly those involving the use of tools or heavy equipment. Such activities increase the probability of developing muscle fatigue or injuries due to overuse or improper posture. Over time, this can result in the development of chronic conditions, which may impair the individual's ability to perform tasks effectively and potentially lead to long-term physical impairment. Exoskeletons play a transformative role by reducing the perceived load on the muscles and providing mechanical support, mitigating the risk of injuries and alleviating the physical burden associated with strenuous activities. In addition to injury prevention, these devices also promise to facilitate the rehabilitation of individuals who have sustained musculoskeletal injuries. This document examines the various types of exoskeletons, investigating their design, functionality, and applications. The objective of this study is to present a comprehensive understanding of the current state of these devices, highlighting advancements in the field and evaluating their real-world impact. Furthermore, it analyzes the crucial insights obtained by other researchers, and by summarizing these findings, this work aims to contribute to the ongoing efforts to enhance exoskeleton performance and expand their accessibility across different sectors, including agriculture, healthcare, industrial work, and beyond.

Abstract

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
The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly employed to enable wireless communications, serving as communications nodes. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). We also developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate UAV energy consumption. However, MUAVE was designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that enables the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. We then use eMUAVE to evaluate the energy consumption of rotary-wing and fixed-wing UAVs in reference and random networking scenarios. The results show that rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following SUPPLY-defined trajectories.