Abstract
In the realm of industrial software development, DevOps has emerged as the preferred approach for handling the highly iterative software production process. DevOps refers to the tight integration of development and operations activities, with Continuous Integration, Continuous Delivery, and Continuous Deployment (CI/CD) being pivotal methodologies for ensuring the iterative delivery of high-quality software. To achieve CI/CD, pipelines of activities are deployed using commercial tools. Due to the dynamic nature of these tools, CI/CD pipelines are often migrated to new versions or even new tools. Since this is mostly a manual process, it is a cumbersome and error-prone activity. To assist software engineers during this process, we propose a novel approach that leverages model-driven engineering (MDE) to support the migration of CI/CD pipelines. Our approach is inspired by the traditional reengineering horseshoe model, which abstracts existing pipeline artifacts into a comprehensive model as an intermediate representation. From these models, we can then generate semantic-equivalent pipelines for any novel CI/CD tool. Thus, our main contribution comprises a metamodel designed to represent the structure of existing CI/CD pipelines and build the foundation for MDE-based migration of CI/CD pipelines. We validated our metamodel by successfully modeling 400 existing pipelines. This evaluation demonstrated a 100% applicability rate when applied to configuration files from technologies that collectively account for over 92% of CI/CD scripts in use. Furthermore, we conducted a detailed case study demonstrating the practical applicability of our approach in real-world migration scenarios. Finally, we demonstrate that our metamodel promotes equivalence between an original pipeline and a new one generated from it in a different technology by showing through test cases that the execution traces of both pipelines are identical. © 2025 IEEE.

Abstract
Monitoring civil infrastructure is increasingly critical due to aging assets, urban expansion, and the need for early detection of structural instabilities. Interferometric Synthetic Aperture Radar (InSAR) offers high-resolution, all-weather surface deformation monitoring capabilities, which are being enhanced by recent advances in Deep Learning (DL). Despite growing interest, the existing literature lacks a comprehensive synthesis of how DL models are applied specifically to infrastructure monitoring using InSAR data. This review addresses this gap by systematically analyzing 67 peer-reviewed articles published between 2020 and February 2025. We examine the DL architectures employed, ranging from LSTMs and CNNs to Transformer-based and hybrid models, and assess their integration within various stages of the InSAR monitoring pipeline, including pre-processing, temporal analysis, segmentation, prediction, and risk classification. Our findings reveal a predominance of LSTM and CNN-based approaches, limited exploration of pre-processing tasks, and a focus on urban and linear infrastructures. We identify methodological challenges such as data sparsity, low coherence, and lack of standard benchmarks, and we highlight emerging trends including hybrid architectures, attention mechanisms, end-to-end pipelines, and data fusion with exogenous sources. The review concludes by outlining key research opportunities, such as enhancing model explainability, expanding applications to underexplored infrastructure types, and integrating DL-InSAR workflows into operational structural health monitoring systems.

Abstract
Cyber threat intelligence (CTI) has become critical in enhancing cybersecurity measures across various sectors. This systematic review aims to synthesize the current literature on the effectiveness of CTI strategies in mitigating cyber attacks, identify the most effective tools and methodologies for threat detection and prevention, and highlight the limitations of current approaches. An extensive search of academic databases was conducted following the PRISMA guidelines, including 43 relevant studies. This number reflects a rigorous selection process based on defined inclusion, exclusion, and quality criteria and is consistent with the scope of similar systematic reviews in the field of cyber threat intelligence. This review concludes that while CTI significantly improves the ability to predict and prevent cyber threats, challenges such as data standardization, privacy concerns, and trust between organizations persist. It also underscores the necessity of continuously improving CTI practices by leveraging the integration of advanced technologies and creating enhanced collaboration frameworks. These advancements are essential for developing a robust and adaptive cybersecurity posture capable of responding to an evolving threat landscape, ultimately contributing to a more secure digital environment for all sectors. Overall, the review provides practical reflections on the current state of CTI and suggests future research directions to strengthen and improve CTI's effectiveness.

Abstract
Accurately imputing missing data is critical in time series analysis. The present work compares Foundation Model Chronos against Linear Interpolation, K-Nearest Neighbor Imputer, and Gaussian Mixture Model Imputer with three types of missing data patterns: random, short sequential chunks, and a long sequential chunk. These results confirm that for random missing values, KNN and interpolation yield the highest performance, while Chronos outperforms these on sequences. Indeed, however, for longer sequences of missing values, Chronos starts suffering from cascading errors which eventually allow the simpler imputation methods to outrank it. Another test with limited quantities of training data showed different tradeoffs for the different methods. Unlike KNN and interpolation, which smooth out the gaps, Chronos generates variable synthetic data. This can be beneficial in tasks which require control or simulation. The results highlight the strengths and weaknesses of the imputers and, therefore, offer practical insights into trade-offs between computational complexities, accuracy, and suitability for time series imputation scenarios.

Abstract
This paper presents a new power chain and its control scheme that provides highly flexible low voltage ride through (LVRT) capabilities for power converters that feed the stack of Polymer Electrolyte Membrane (PEM) hydrogen electrolyzers. It introduces an intermediate power stage with a new adaptive feedforward controller, that isolates the electrolyzer stack from grid-side disturbances. An RMS model of the whole solution is developed and validated. The system was developed in MATLAB/SIMULINK and PLECS environments. Furthermore, the system was tested in DC and AC grids by subjecting it to a fault reducing the input voltage magnitude down to 0.2 pu. The system demonstrated its ability to ride through the fault whilst maintaining the power set-points and supply quality at the electrolyzer stack connection point. © 2025 Elsevier B.V., All rights reserved.

Abstract

Good sleep is crucial for human life. Research has shown that poor-quality sleep is related to several cardiovascular and metabolic disorders. Sleep disorders are well categorized, and most of them have defined diagnostic criteria, with level 1 polysomnography being the gold standard. With the increasing use of technology, specifically smartphones, in people’s everyday lives, the search for alternative ways of monitoring sleep disorders or certain sleep parameters has been gaining relevance.