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
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run.

Abstract
Ambient gamma dose rate represents the integrated near-surface gamma radiation field resulting from contributions of terrestrial radionuclides and radon progeny, secondary cosmic radiation, and atmospheric radiation sources. Continuous monitoring of ambient gamma dose rate constitutes a fundamental component of radiological early-warning systems, as it provides a direct operational proxy for external radiation exposure to population. Time series of ambient gamma dose rate exhibit variability over a wide range of temporal scales, including short-term anomalies driven by meteorological processes, geophysical conditions, or anthropogenic influences. Accurate characterisation of these anomalies, and robust discrimination between natural drivers - such as soil–atmosphere exchange processes, boundary-layer dynamics, and hydrometeorological forcing - and potential anthropogenic contributions, is essential for enhancing early-warning capabilities and improving the detection of anomalous radioactive releases. A key challenge in this context is the scarcity of high-resolution, high-quality collocated meteorological observations required to support such analyses.This study presents a detailed characterization of anomalies in ambient gamma dose rate using comprehensive meteorological information and high-resolution (1-min) gamma dose-rate measurements from the Eastern North Atlantic (ENA) observatory, part of the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program. Through the joint analysis of gamma radiation and a broad set of meteorological parameters - including precipitation, eddy covariance fluxes, aerosol properties, and lidar derived atmospheric structure - we identify and classify distinct types of short-term gamma radiation anomalies. These include precipitation-induced enhancements, quasi-daily anomalies associated with stable nocturnal boundary-layer conditions and near-surface radon accumulation, and anomalies linked to long-range transported dust events. This AI-ready, supervised dataset enables detailed investigation and modelling of ambient gamma dose-rate variability in the Azores and provides a transferable framework for training machine-learning algorithms to automatically classify gamma radiation anomalies at monitoring sites lacking comprehensive meteorological instrumentation. The present study is part of project NuClim (Nuclear observations to improve Climate research and GHG emission estimates). Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515). The NuClim field campaign at the Eastern North Atlantic, Graciosa Island ARM Observatory is supported by the U.S. Department of Energy (DOE), Office of Science, through the ARM Program.

Abstract
Regional sea-level change in the semi-enclosed Baltic Sea is strongly influenced by atmospheric forcing and wind-driven redistribution of water masses, leading to significant spatial variability in absolute sea level trends across the different sub-basins. This study focusses on absolute sea level trends in the Baltic Sea using satellite gridded sea level anomalies (0.0625º) from the European Seas Gridded L4 product provided by the E.U. Copernicus Marine Service (https://doi.org/10.48670/moi-00141). The daily time series (from January 1993 to the end of December 2023) are first deseasoned by removing the average annual cycle at each point. Then robust linear trends are estimated at each grid point by computing median slopes. In contrast to ordinary least-squares slopes characterising linear trends in the mean, these median slopes are calculated by minimising the mean absolute deviation of a linear trend model from the observations instead of the mean quadratic deviation, which makes them more robust to outliers and sensitive to the typical tendency of changes rather than to large deviations. Uncertainty is computed assuming non-independence by the Huber sandwich robust estimator for the covariance matrix.The derived median slopes are in general higher than ordinary linear trends in the mean, except in the northern and easternmost areas of the Baltic. In the Bay of Bothnia ordinary linear trends and median trends are very similar, while in the eastern end of the Gulf of Finland median trends are similar or even slightly lower than ordinary linear trends. In the remaining areas, median trends are significantly larger than ordinary linear trends, the largest difference occurring in the Bothnian Sea. Coastal areas exhibit trends that differ from those in the adjacent basins. In the Gulf of Finland, median trends are higher than ordinary linear trends along the Finnish coast, whereas along the Roslagen coast (northern Stockholm Archipelago) the two slope estimates are in good agreement. Along the southern coastline of the Bothnian Sea, median sea-level trends reach the highest values, exceeding 6 mm/year.The present study is financed within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021 - 2026, within project NewSpacePortugal, with reference 11.

Abstract
Investigating the complex coupling between the lithosphere, atmosphere, and ionosphere (LAI) requires a fundamental understanding of the physical forces governing tectonic processes and their electromagnetic manifestations. While various pre-seismic signals have been successfully identified, a persistent gap remains between the empirical observation of these phenomena and the fundamental physical laws that describe nature across all scales, from the subatomic realm to cosmic expansion. Exploring these interrelations presents significant challenges due to divergent scientific languages, specialized expertise, and unique terminologies across fields. The recently approved COST Action CA24101 "Testing Fundamental Physics with Seismology" (FuSe) aims to bridge this gap by exploring how seismic phenomena and earthquake precursors can serve as a "multi-messenger" window into fundamental interactions.At the heart of FuSe is the belief that imprints of non-standard physics, such as scalar fields or "fifth forces”, may be embedded within seismic and geomagnetic data. Conversely, theoretical insights from fundamental physics can refine our understanding of Earth’s interior by improving models of density and thermodynamic parameters like elasticity and bulk modulus. This refined modeling is essential for accurately interpreting the electromagnetic and gravitational perturbations that occur within the complex Earth-atmosphere-space system.To ensure these breakthroughs translate into practical advancements, FuSe focuses on several strategic pillars:-   Building a common language: developing a shared methodology to equip the next generation of scientists with cross-disciplinary skills.-   Interfacing communities: creating dynamic research groups that unite scientists from particle physics, gravity, planetary science, and seismology.-   Cross-disciplinary data integration: consolidating seismic data from the Earth and Moon with particle physics and geomagnetic data into AI-ready, FAIR-compliant streams.-   SME collaboration: partnering with small and medium-sized enterprises (SME) to advance sensor networks, AI algorithms, and real-time natural catastrophe alert systems.In this presentation, we outline the roadmap of the FuSe Action. We invite researchers with a background in electromagnetic precursors and LAI coupling to join this collaborative environment, where the synergy between geosciences and fundamental physics promises to drive innovative breakthroughs and unlock new paradigms in our comprehension of the Earth and the Universe.This abstract is based upon work from COST Action CA24101, Testing Fundamental Physics with Seismology (FuSe), supported by COST (European Cooperation in Science and Technology).