Abstract
The specialization of Health Human Resources (HHR) is increasingly recognized as essential for addressing evolving healthcare demands. This paper presents a comprehensive policy framework for assisting with the implementation of Clinical Nurse Specialist (CNS) roles at the national or regional level, integrating key dimensions including barriers and enablers, regulation and governance, education and training requirements, career development, workforce planning, and economic analysis. The framework was applied to the implementation of CNS roles in Portugal, resulting in the issuance of a decree-law by the government. Our findings demonstrate that the economic analysis step was critical in addressing concerns from government authorities and health system funders regarding the potential budgetary impact of CNS implementation. By providing evidence-based projections of costs and benefits, the economic analysis facilitated smoother negotiations and consensus-building among stakeholders, including nursing unions. Furthermore, the integration of workforce planning ensured the alignment of educational capacity with workforce needs, thus avoiding potential implementation bottlenecks. The application of the framework also revealed important feedback relationships between its dimensions, highlighting the interdependent nature of the implementation process. This dynamic approach, which adapts to real-time feedback and stakeholder input, underscores the necessity of a holistic and iterative strategy for successful CNS role integration. The insights gained from the Portuguese case underscore the utility of this policy framework in guiding the implementation of advanced nursing roles in diverse healthcare contexts.

Abstract
When each patient of a kidney exchange program has a preference ranking over its set of compatible donors, questions naturally arise surrounding the stability of the proposed exchanges. We extend recent work on stable exchanges by introducing and underlining the relevance of a new concept of locally stable, or L-stable, exchanges. We show that locally stable exchanges in a compatibility digraph are exactly the so-called local kernels (L-kernels) of an associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph), and we prove that finding a nonempty L-kernel in an arbitrary digraph is NP-complete. Based on these insights, we propose several integer programming formulations for computing an L-stable exchange of maximum size. We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum L-stable exchanges with the size of maximum stable exchanges. It turns out that nonempty L-stable exchanges frequently exist in digraphs which do not have any stable exchange. All the above results and observations carry over when the concept of (locally) stable exchanges is extended to the concept of (locally) strongly stable exchanges.

Abstract
This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

Abstract
To achieve lower switching losses and higher frequency capabilities in converter design, researchers worldwide have been investigating Silicon carbide (SiC) modules and MOSFETs. In power electronics, wide bandgap devices such as Silicon carbide are essential for creating more efficient, higher-density, and higher-power-rated converters. Devices like SiC and Gallium nitride (GaN) offer numerous advantages in power electronics, particularly by influencing parasitic capacitance and inductance in printed circuit boards (PCBs). A review paper on Silicon carbide converter designs using coupled inductors provides a comprehensive analysis of the advancements in SiC-based power converter technologies. Over the past decade, SiC converter designs have demonstrated both efficiency and reliability, underscoring significant improvements in performance and design methodologies over time. This review paper examines developments in Silicon carbide converter design from 2014 to 2024, with a focus on the research conducted in the past ten years. It highlights the advantages of SiC technology, techniques for constructing converters, and the impact on other components. Additionally, a bibliometric analysis of prior studies has been conducted, with a particular focus on strategies to minimize switching losses, as discussed in the reviewed articles.

Abstract
Industry 4.0 promotes a paradigm shift in the orchestration, oversight, and optimization of value chains across product and service life cycles. For instance, leveraging large-scale data from sensors and devices, coupled with Machine Learning techniques can enhance decision-making and facilitate various improvements in industrial settings, including predictive maintenance. However, ensuring data quality remains a significant challenge. Malfunctions in sensors or external factors such as electromagnetic interference have the potential to compromise data accuracy, thereby undermining confidence in related systems. Neglecting data quality not only compromises system outputs but also contributes to the proliferation of bad data, such as data duplication, inconsistencies, or inaccuracies. To consider these problems is crucial to fully explore the potential of data in Industry 4.0. This paper introduces an extensible system designed to ingest, organize, and monitor data generated by various sources, focusing on industrial settings. This system can serve as a foundation for enhancing intelligent processes and optimizing operations in smart manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Ensuring process quality in modern manufacturing is increasingly challenging due to the complexity of production processes and reliance on skilled operators, which can lead to suboptimal solutions and poor quality. To address these challenges, we introduce a novel, unsupervised, robust, nonparametric control chart for Phase II monitoring. This chart tracks the degradation of a quality characteristic using a condition index that captures mean and scale shifts without relying on assumptions, offering high flexibility and adaptability. Comparative studies with state-of-the-art nonparametric schemes demonstrate faster detection capabilities and competitive accuracy across various scenarios. We validate our approach through its application in the glass container production process, showcasing its effectiveness in monitoring multiple defective rates. Although tested on defective rates, the methodology is adaptable to any quantifiable quality characteristic.