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
Continuous Integration and Continuous Deployment are known methodologies for software development that increase the overall quality of the development process. Several robotic software repositories make use of CI/CD tools as an aid to development. However, very few CI pipelines take advantage of using cloud computing to run simulations. Here, a CI pipeline is proposed that takes advantage of such features, applied to the development of ATOM, a ROS-based application capable of carrying out the calibration of generalized robotic systems. The proposed pipeline uses GitHub Actions as a CI/CD engine, AWS RoboMaker as a service for running simulations on the cloud and Rigel as a tool to both containerize ATOM and execute the tests. In addition, a static analysis and unit testing component is implemented with the use of Codacy. The creation of the pipeline was successful, and it was concluded that it constitutes a valuable tool for the development of ATOM and a blueprint for the creation of similar pipelines for other robotic systems.

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
Health system performance assessment (HSPA) is essential for health planning and to improve population health. One of the HSPA domains is related to effectiveness, which can be represented considering different dimensions. Composite indicators can be used to summarize complex constructs involving several indicators. One example of such efforts is the Healthcare Access and Quality Index from the Global Burden of Diseases Study, in which different causes of mortality amenable to health care are summarized in this index through principal component analysis and exploratory factor analysis. While these approaches use the variance of the indicators, marginal improvement is not considered, that is, the distance to the best practice frontier. In this study we propose an innovative benefit-of-the-doubt approach to combine frontier analysis and composite indicators, using amenable mortality estimates for 188 countries. In particular, we include flexible aggregating weighting schemes and a robust and conditional approach. The dual formulation gives information on the peers and the potential mortality rate reduction targets considering the background conditions. In absolute terms, Andorra and high-income countries are the most effective regarding healthcare access and quality, while sub-Saharan African and South Asian countries are the least effective. North African and Middle Eastern countries benefit the most when epidemiological patterns, geographical proximity, and country development status are considered.

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.