Abstract
This paper explores an innovative distributed real-time control system for a 3D-printed robotic leg. The system is constructed on a modular multi-board architecture that seamlessly integrates with ROS2 and micro-ROS, demonstrating the use of 3D printing for rapid prototyping and customized solutions. A notable feature of this robotic leg is its 360-degree rotating joint, which extends its range of motion, enabling intricate and versatile movements. Incorporating a shoulder joint further facilitates sideways mobility, augmenting its operational capabilities. A multi-board architecture is designed to ensure efficient communication, ease of component interchangeability, and robust scalability for future development. Additionally, advanced control techniques, including tuning of proportional-integral-derivative (PID) controllers, ensure responsive joint actuation tailored to the unique properties of 3D-printed materials. Experimental validation indicates low latency and stable operation, underscoring the system's effectiveness for real-time robotic applications.

Abstract
Macroeconomic forecasting is a fundamental domain for policy decisions, directly impacting the whole population of a country. The use of machine learning (ML) approaches in economics forecasting has been studied in several types of research in the academic field, aiming to improve or even replace traditional econometric approaches. However, the use of ML in forecasting is now getting closer to policy markers, which are the institutions that make policy decisions. Three relevant studies are presented and analyzed in this work; all focused on forecasting using ML of different macroeconomic variables in several economies. The studies were compared, including aspects of methodologies and results, as well as similarities and differences. In addition, several technical, legal, and philosophical questions were raised regarding the effective use of data from ML forecasting in public policies, including topics related to the standardization of the research on this topic, the explanation of the model's output, protection of trust, and ethics issues.

Abstract
Web frontends are ubiquitous, from Web pages and single-page applications to hybrid mobile apps, Web frontends play a crucial role in today's digital economy. At their core, they rely on JavaScript, whose single-threaded nature poses significant challenges to delivering smooth and responsive user experiences as complexity rises. In-browser parallelism promises responsiveness and throughput improvements, but spans several mechanisms (Web Workers, Worklets, OffscreenCanvas, WebAssembly threads) with diverse coordination models. We conducted a systematic review of primary studies on using Web Workers for parallel JavaScript in browsers, extracting design and scope choices, to collate the resources necessary for a generic ES5-compatible parallel enumeration system capable of type introspection. Such a system would enable widespread in-browser parallelization without any external plug-ins or experimental JavaScript specifications, and spare developers from managing work-splitting and result-merging.

Abstract
Some of the main challenges faced by organizations when applying Continuous Business Process Improvement are data fragmentation, limited explainability, weak governance, and the isolated use of Artificial Intelligence in Business Process Management. This study initially conducts a Systematic Literature Review on the topic of business process improvement enabled by Large Language Models or Artificial Intelligence in organizations, presenting a comprehensive analysis of prevailing research trends, conceptual frameworks, and persistent limitations, identifying seventeen recurring gaps that affect the effectiveness of integrating the capabilities of Large Language Models and other Artificial Intelligence technologies throughout the entire lifecycle of Continuous Business Process Improvement. As a result, we propose a Framework and its gap-oriented reference architecture that, through modular components, facilitates data integration, reasoning, validation, execution, and monitoring within a closed loop of continuous business process improvement. The framework is operationalized through six phases: Process Understanding, Process Diagnosis, Process Redesign, Process Validation, Process Execution Support, and Continuous Monitoring. The results suggest that designing the framework and architecture directly from the identified gaps creates a coherent foundation for AI-driven process improvement, enabling more reliable, explainable, and easily governed and managed solutions. The study improves the current state of the art by creating a cohesive framework for intelligent, scalable, lifecycle-integrated, and operationally deployable process optimization systems.

Abstract
In an era of data-driven organisational transformation, ensuring high data quality is critical to sustaining service excellence and effective decision-making. In the banking sector, where data integrity underpins trust, regulatory compliance, and operational performance, data quality is experienced not only as a technical attribute but also as a socio-organisational phenomenon shaped by user perception and governance structures. Addressing limitations in generic data quality frameworks, this study develops and analytically grounds a user-focused, context-specific data quality framework tailored to regulated banking environments. Guided by the Design Science Research methodology, the study integrates a systematic literature review with empirical insights from a structured survey of employees at a Eurozone bank. The results provide an interpretive understanding of how user perceptions of data quality influence day-to-day banking operations, revealing governance and accessibility-related patterns that affect service processes and decision-making. By explicitly positioning user perception as a central explanatory mechanism linking data systems, governance arrangements, and business processes, the proposed framework extends user-perceived data quality theory to regulated financial contexts. The study contributes to data quality and service research by demonstrating how perception-informed assessment can support continuous improvement, organisational learning, and more resilient data management practices in banking institutions.

Abstract
Gamification is increasingly used to make non-game contexts more engaging, yet integrating its elements into professional training applications often requires significant development effort. This work introduces GaToo (from “Gamification Tool”), a modular and reusable gamification plugin built for the Unity engine, designed to simplify this process and reduce the need for custom implementation. The plugin provides a structured set of components, including missions, steps, challenges and achievements, together with a base editor interface that facilitates configuration and management. To ensure broad applicability, it also includes complementary mechanisms that enable integration both in applications still under development and in existing applications, depending on the level of access to their source code. A preliminary study with 15 experienced Unity users was conducted to assess the usability of the plugin interface. The results indicated a score of 82 on the System Usability Scale, considered ’Excellent,’ supporting the ease of use of the plugin editor. By offering a flexible and extensible solution, the plugin allows developers and researchers to concentrate on the design and evaluation of gamification strategies, while minimizing the technical overhead of implementation.