Abstract
This paper focuses on the modeling, control, and simulation of an over-actuated hexacopter tilt-rotor (HTR). This configuration implies that two of the six actuators are independently tilted using servomotors, which provide high maneuverability and reliability. This approach is predicted to maintain zero pitch throughout the trajectory and is expected to improve the aircraft's steering accuracy. This arrangement is particularly beneficial for precision agriculture (PA) applications where accurate monitoring and management of crops are critical. The enhanced maneuverability allows for precise navigation in complex vineyard environments, enabling the unmanned aerial vehicle (UAV) to perform tasks such as aerial imaging and crop health monitoring. The employed control architecture consists of cascaded proportional (P)-proportional, integral and derivative (PID) controllers using the successive loop closure (SLC) method on the five controlled degrees of freedom (DoFs). Simulated results using Gazebo demonstrate that the HTR achieves stability and maneuverability throughout the flight path, significantly improving precision agriculture practices. Furthermore, a comparison of the HTR with a traditional hexacopter validates the proposed approach.

Abstract
We present Caos: a programming framework for computer-aided design of structural operational semantics for formal models. This framework includes a set of Scala libraries and a workflow to produce visual and interactive diagrams that animate and provide insights over the structure and the semantics of a given abstract model with operational rules. Caos follows an approach where theoretical foundations and a practical tool are built together, as an alternative to foundations-first design (tool justifies theory) or tool-first design (foundations justify practice). The advantage of Caos is that the tool-under-development can immediately be used to automatically run numerous and sizeable examples in order to identify subtle mistakes, unexpected outcomes, and unforeseen limitations in the foundations-under-development, as early as possible. More concretely, Caos supports the quick creation of interactive websites that help the end-users better understand a new language, structure, or analysis. End-users can be research colleagues trying to understand a companion paper or students learning about a new simple language or operational semantics. We include a list of open-source projects with a web frontend supported by Caos that are used both in research and teaching contexts.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
This chapter aims to analyze and map the behaviors and strategies employed by organizations recognized for their innovation, with the goal of developing a comprehensive innovation management framework. This framework is designed to merge innovation practices with elements of traditional management, creating a hybrid model to support companies, universities, and research institutes in fostering innovation. Rooted in an understanding of human evolution, the framework will reflect changes in needs, skills, and behaviors over time, enabling institutions to adapt their innovation strategies to align with societal and individual development. Adopting an interdisciplinary approach, it will integrate concepts from innovation, organizational management, and the human sciences to establish a structure that supports sustainable innovation while addressing contemporary challenges. © 2025, IGI Global Scientific Publishing.

Abstract
Climate change has led to the need to transition to clean technologies, which depend on an number of critical metals. These metals, such as nickel, lithium, and manganese, are essential for developing batteries. However, the scarcity of these elements and the risks of disruptions to their supply chain have increased interest in exploiting resources on the deep seabed, particularly polymetallic nodules. As the identification of these nodules must be efficient to minimize disturbance to the marine ecosystem, deep learning techniques have emerged as a potential solution. Traditional deep learning methods are based on the use of convolutional layers to extract features, while recent architectures, such as transformer-based architectures, use self-attention mechanisms to obtain global context. This paper evaluates the performance of representative models from both categories across three tasks: detection, object segmentation, and semantic segmentation. The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. Furthermore, recent versions of You Only Look Once (YOLO) have obtained competitive results in terms of mean average precision.

Abstract
The evolution of transport technologies, marked by integrating connectivity and automation, has led to innovative approaches such as truck platooning. This concept involves linking multiple trucks through automated driving and vehicle-to-vehicle communication, promising to revolutionize the freight industry by enhancing efficiency and reducing operational costs. This systematic review explores the current state of truck platooning testing literature, focusing on simulator and on-road tests. The objective is to identify key scenarios and requirements for successfully developing and implementing the truck platooning concept. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines, we searched the Web of Science and Scopus databases, leading to the inclusion of thirty pertinent articles encompassing simulation-based, on-road, and mixed-environment experiments. In addition to the type of testing environment, these articles were assorted into three groups corresponding to their main thematic scope, human-centered, technology-centered, and energy efficiency studies, each providing unique insights into core themes for the development of truck platooning. The results reveal a commonly preferred platoon formation consisting of three trucks maintaining a constant speed of 80 km/h and a stable distance of 10 m between them. Simulator-based studies have predominantly concentrated on human factors, examining driver behavior and interaction within the platooning framework. In contrast, on-road trials have yielded tangible data, offering a more technology-driven perspective and contributing practical insights to the field. While the literature on truck platooning has grown considerably, this review recognizes some limitations in the existing literature and suggests paths for future research. Overall, this systematic review provides valuable insights to the ongoing development of robust and effective truck platooning systems.