Abstract
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Abstract

Abstract
E-commerce has become an essential aspect of modern life, providing consumers globally with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. Standard LDA-based methods often lead to clusters dominated by single elements, effectively failing to manage datasets with varied cluster sizes. Our proposed Community-Based Topic Modeling with Contextual Outlier Handling (CB-TMCOH) algorithm introduces an approach to outlier detection in text data using transformer models for similarity calculations and graph-based clustering. This method efficiently separates outliers and improves clustering in large text datasets, demonstrating its utility not only in e-commerce applications but also proving effective for news and tweets datasets.

Abstract
This paper investigates the financial benefits stemming from the potential installation of battery energy storage systems behind the meter of a hybrid offshore farm including wind turbines and floating photovoltaic panels. The optimal investment and operation decisions concerning the energy storage system in the hybrid site are assessed by means of a mixed integer linear programming optimization model. The operation is also subject to technical constraints such as limitations on the connection capacity and ramping constraints imposed by the grid operator at the point of common coupling. Three design configurations for the battery system are analysed: I) offshore with the hybrid farm, II) onshore where the grid connection point is, III) both offshore and onshore. The results indicate the financial value of installing battery storage units, and other benefits deriving from this investment, as the reduction of curtailment.

Abstract
Exploring optical analogues with paraxial fluids of light has been a subject of great interest over the past years. Despite many optical analogues having been created and explored with these systems, they have some limitations that usually hinder the observation of the desired dynamics. Since these systems map the effective time onto the propagation direction, the fixed size of the nonlinear media limits the experimental effective time, and only the output state is accessible. In this work, we present a solution to overcome these problems in the form of an optical feedback loop, which consists of reconstructing the output state, by using the off-axis digital holography technique, and then re-injecting it again at the entrance of the medium through the utilization of Spatial Light Modulators. This technique enables access to intermediate states and an extension of the system effective time. Furthermore, the total control of the amplitude and phase of the beam at the input of the medium, also allows us to explore more exotic configurations that may be interesting in the context of optical analogues, that otherwise would be hard to create. To demonstrate the capabilities of the setup, we explore qualitatively some case studies, such as the dark soliton decay into vortices with the propagation of shock waves, and the collision dynamics between three flat-top states. The results presented in this work pave the way for probing new dynamics with paraxial fluids of light.

Abstract
The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.