Abstract
The Machine Learning Data Market (MLDM), which relies on multi-agent systems, necessitates robust negotiation strategies to ensure efficient and fair transactions. The Contract Net Protocol (CNP), a well-established negotiation strategy within Multi-Agent Systems (MAS), offers a promising solution. This paper explores the integration of CNP into MLDM, proposing the CNP-MLDM model to facilitate data exchanges. Characterized by its task announcement and bidding process, CNP enhances negotiation efficiency in MLDM. This paper describes CNP tailored for MLDM, detailing the proposed protocol following experimental results. © 2022 Copyright for this paper by its authors.

Abstract
The medical imaging field contends with limited data for training deep learning (DL) models. Our study evaluated traditional data augmentation (DA) and Generative Adversarial Networks (GANs) in enhancing DL models for identifying stomach precancerous lesions. Classic DA consistently outperformed GAN-based methods with ResNet50 (0.94 vs 0.93 accuracy) and ViT (0.85 vs 0.84 accuracy) models achieving higher accuracy and other performance metrics with DA compared to GANs. Despite this, GAN augmentation showed significant improvements when compared to train with the original dataset, highlighting its role in diversifying datasets and aiding generalization across different medical imaging datasets. Combining both augmentation techniques can enhance model robustness and generalisation capabilities in DL applications for medical diagnostics, leveraging DA's consistency and GANs' diversity. © 2025 Elsevier B.V.. All rights reserved.

Abstract
Welding is a challenging, risky, and time-consuming profession. Recently, there has been a documented shortage of trained welders, and as a result, the market is pushing for an increase in the rate at which new professionals are trained. To address this growing demand, training institutions are exploring alternative methods to train future professionals. The emergence of virtual reality technologies has led to initiatives to explore their potential for welding training. Multiple studies have suggested that virtual reality training delivers comparable, or even superior, results when compared to more conventional approaches, with shorter training times and reduced costs in consumables. This paper conducts a comprehensive review of the current state of the field of welding simulators. This involves exploring the different types of welding simulators available and evaluating their effectiveness and efficiency in meeting the learning objectives of welding training. The aim is to identify gaps in the literature, suggest future research directions, and promote the development of more effective and efficient welding simulators in the future. The research also seeks to develop a categorical system for evaluating and comparing welding simulators. This system will enable a more systematic and objective analysis of the features and characteristics of each simulator, identifying the essential characteristics that should be included in each level of classification.

Abstract
The evolution of urban morphology and urban mobility reveals a complex and multidimensional relation that has been historically linked to the evolution of technology and its influence on people's behaviour, desires, and needs. The increasing level of digitalization of human interactions in both social and work environments has created a new paradigm for urban mobility. Alongside, sustainability concerns are also accelerating the design of new policies for improving citizens' quality of life in urban areas. To address this new paradigm, municipalities need to consider new methodologies encompassing the different dimensions of the urban environment. This can be achieved if key stakeholders participate in co-creating and co-designing new solutions for urban mobility. In this paper we propose a multidisciplinary approach to these problems, supported by service-dominant logic concepts. The approach was used to design the CoDUMIS framework that brings together four dimensions of urban areas (social, urban, technological, and organizational). The application of the framework to four distinct cases, in Portuguese municipalities, resulted in a set of guidelines that help municipalities to improve their services and policies in a participatory environment, involving all the stakeholders.

Abstract
Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes’ movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract
This study aims to improve the accuracy of wind power generation forecasting by selecting the potential locations for weather stations, which serve as crucial data sources for wind predictions. The proposed method is based on using Shapley values. First, they are assigned to stations that are already available in the region based on their contribution to forecasting error. Second, the values are interpolated to cover the area of interest. We test the hypothesis that taking weather measurements in areas with negative Shapley values leads to a decrease in the error of forecasting the volume of wind power generation. We estimate the method's impact on forecasting error by using long short-term memory neural network and linear regression with quadratic penalization. The results of this proof-of-concept study indicate that it is possible to improve the short-term wind power forecasts using additional weather observations in the selected regions. The future research should be dedicated to the expansion of the case study area to other locations, including offshore power plants.