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
This chapter provides an overview of the development Phases involved in transforming a technology originated in research into a medical product for commercialization. It first describes the four main Phases, from the emergence of the need for the product to its post-marketing obligations. It is intended to help the interested reader understand the stages, documents, guidelines, and regulations that a medical device must go through in order to be marketed. Special highlight is given to the necessary topics that must be addressed in order for the device to be certified. Every product that goes to market must be certified by some regulatory body in order to ensure that it will not cause any negative impact on its users. Further, for medical devices, these requirements are heightened, as they may come in contact with the user, potentially causing a direct risk to them. Thus, reading this chapter will provide the reader with an understanding of these Phases within the industrial environment as well as the aspects that must be taken into account before placing a medical device on the market. © Springer Nature Switzerland AG 2026.

Abstract