Publicaciones

@article{DelaVarga2025,

title = {Learning-based state estimation in distribution systems with limited real-time measurements},

author = {J.G. De la Varga and S. Pineda and J.M. Morales and A. Porras},

doi = {10.1016/j.epsr.2024.111268},

issn = {0378-7796},

year  = {2025},

date = {2025-04-00},

journal = {Electric Power Systems Research},

volume = {241},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pérez-Ramos2025,

title = {Synthesis of (C^N, S^S)-cyclometallated phosphonodithiolate gold(III) complexes},

author = {Paula Pérez-Ramos and David Elorriaga and Daniel García-Vivó and Salomé López and Raquel G. Soengas and Humberto Rodríguez-Solla},

doi = {10.1016/j.poly.2024.117363},

issn = {0277-5387},

year  = {2025},

date = {2025-02-00},

journal = {Polyhedron},

volume = {267},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guerra2025,

title = {Synthesis and protonation reactions of nitrosyl complexes [M2Cp(μ-κ:η5-C5H4)(μ-PR2)(CO)(NO)2] to give κ:η-NO-bridged derivatives (M = Mo, W; R = Cy,  Bu)},

author = {Ana M. Guerra and Adrián Toyos and M. Angeles Alvarez and Daniel García-Vivó and Miguel A. Ruiz},

doi = {10.1016/j.jorganchem.2024.123475},

issn = {0022-328X},

year  = {2025},

date = {2025-02-00},

journal = {Journal of Organometallic Chemistry},

volume = {1025},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paz-López2025,

title = {Pre-operative DNA methylation marks as predictors of weight loss outcomes after sleeve gastrectomy},

author = {Guillermo Paz-López and Teresa M. Linares-Pineda and Andrés González-Jiménez and Raquel Sancho-Marín and Luis Ocaña-Wilhelmi and Francisco J. Tinahones and Sonsoles Morcillo and Carolina Gutiérrez-Repiso},

doi = {10.1016/j.molmet.2024.102087},

issn = {2212-8778},

year  = {2025},

date = {2025-02-00},

journal = {Molecular Metabolism},

volume = {92},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ginés2024,

title = {Magnesium and Calcium Bisguanidinates: Catalytic Activity in the Hydroboration of Unsaturated Molecules},

author = {Carlos Ginés and Blanca Parra‐Cadenas and Rafael Fernández‐Galán and Daniel García‐Vivó and David Elorriaga and Alberto Ramos and Fernando Carrillo‐Hermosilla},

doi = {10.1002/adsc.202400843},

issn = {1615-4169},

year  = {2025},

date = {2025-01-07},

journal = {Adv Synth Catal},

volume = {367},

number = {1},

publisher = {Wiley},

abstract = {AbstractThe role of ligands in catalytically active metal complexes can be crucial. Herein, we present a study on the synthesis and catalytic behavior of alkaline earth bisguanidinato complexes. The reactivity of trisubstituted guanidines, specifically (iPrHN)2CNR (R=Ph, 2‐Ph2PC6H4, 2‐MeSC6H4), towards magnesium and calcium alkyls or amides was explored. All the compounds were found to be very active in the hydroboration of acetophenone with pinacolborane. Notably, a calcium‐based complex was especially effective in the hydroboration of carbonyl compounds, demonstrating significant chemoselectivity against other reactive functional groups. This complex was also particularly effective in the double hydroboration of nitriles and moderately active in the hydroborative dearomatization of pyridine. Our experimental and DFT studies allow us to propose a mechanism in which the participation of the guanidinato ligands, along with the metal, is key to the activation of both the unsaturated species and borane.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ani15030431,

title = {miR-215 Modulates Ubiquitination to Impair Inflammasome Activation and Autophagy During Salmonella Typhimurium Infection in Porcine Intestinal Cells},

author = {Carmen Entrenas-García and José M. Suárez-Cárdenas and Raúl Fernández-Rodríguez and Rocío Bautista and M. Gonzalo Claros and Juan J. Garrido and Sara Zaldívar-López},

url = {https://www.mdpi.com/2076-2615/15/3/431},

doi = {10.3390/ani15030431},

issn = {2076-2615},

year  = {2025},

date = {2025-01-01},

journal = {Animals},

volume = {15},

number = {3},

abstract = {The host response to S. Typhimurium infection can be post-transcriptionally regulated by miRNAs. In this study, we investigated the role of miR-215 using both in vivo porcine infection models and in vitro intestinal epithelial cell lines. Several miRNAs were found to be dysregulated in the porcine ileum during infection with wild-type and SPI2-defective mutant strains of S. Typhimurium, with some changes being SPI2-dependent. Notably, miR-215 was significantly downregulated during infection. To explore its functional role, gain-of-function experiments were performed by transfecting porcine intestinal epithelial cells (IPEC-J2) with a miR-215-5p mimic, followed by label-free quantitative (LFQ) proteomic analysis. This analysis identified 157 proteins, of which 35 were downregulated in response to miR-215 overexpression, suggesting they are potential targets of this miRNA. Among these, E2 small ubiquitin-like modifier (SUMO)-conjugating enzyme UBC9 and E3 ubiquitin-ligase HUWE1 were identified as key targets, both of which are upregulated during S. Typhimurium infection. The miR-215-mediated downregulation of these proteins resulted in a significant decrease in overall ubiquitination, a process crucial for regulating inflammasome activation and autophagy. Consistently, inflammasome markers caspase 1 (CASP1) and apoptosis-associated speck-like protein containing a CARD (ASC), as well as autophagy markers microtubule-associated protein 1A/1B-light chain 3 (LC3B) and Ras-related protein Rab-11 (RAB11A), showed decreased expression in miR-215 mimic-transfected and infected IPEC-J2 cells. To further validate these findings, human intestinal epithelial cells (HT29) were used as a complementary model, providing additional insights into conserved immune pathways and extending the observations made in the porcine system. Overall, our findings demonstrate that miR-215 plays a significant role in modulating host inflammasome activation and autophagy by targeting proteins involved in ubiquitination during S. Typhimurium infection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Molina-Cabello2025,

title = {Toward Questionnaire Complexity Reduction by Decreasing the Questions},

author = {Miguel A. Molina-Cabello and José Serrano-Angulo and Jesús Benito-Picazo and Karl Thurnhofer-Hemsi},

doi = {10.3390/app15020841},

issn = {2076-3417},

year  = {2025},

date = {2025-01-00},

journal = {Applied Sciences},

volume = {15},

number = {2},

publisher = {MDPI AG},

abstract = {Data analysis can unearth important insights like patterns, trends, and deductions. In education, it can be utilized to tailor teaching methods to suit student traits or devise new activities to foster different skills or reinforce existing ones, for example. Understanding the particular context and past experiences can aid in this endeavor. Surveys and questionnaires yield a wealth of data. Yet, the sheer volume of questions can lead to challenges in data management for teachers and a decline in student interest due to the time-consuming nature of fulfilling these tasks. This work presents a methodology designed to decrease the number of questions in questionnaires. This method can be applied to general questionnaires that consist of closed-ended questions with a set number of response choices, where each question can have a varying number of options compared to the other questions in the form. This methodology has been adapted into a newly developed software tool for examining learning styles based on a specific learning styles questionnaire: the Honey–Alonso Learning Styles Questionnaire (CHAEA). This software is available to the public and integrated with Moodle, arguably the most extensively used learning management system globally. To evaluate the effectiveness of the proposed method, it has been used across various subjects in Computer Sciences Engineering degrees over different academic years. The outcomes from this case study validate the appropriateness of the technique. Consequently, these findings could establish patterns that could assist in devising more suitable learning methodologies for students.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Muñoz2025,

title = {PaFESD: Patterns Augmented by Features Epileptic Seizure Detection},

author = {Felipe Muñoz and Rafael Asenjo Plaza and Angeles Navarro},

doi = {10.1109/tbme.2024.3441090},

issn = {1558-2531},

year  = {2025},

date = {2025-01-00},

journal = {IEEE Trans. Biomed. Eng.},

volume = {72},

number = {1},

pages = {137--151},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alvarez2024,

title = {N O bond cleavage and N2 activation reactions of the Nitrosyl-Bridged complex [Mo2Cp2(µ-P Bu2)(µ-NO)(NO)2]},

author = {M. Angeles Alvarez and M. Esther García and Daniel García-Vivó and Ana M. Guerra and Miguel A. Ruiz},

doi = {10.1016/j.jorganchem.2024.123375},

issn = {0022-328X},

year  = {2024},

date = {2024-12-00},

journal = {Journal of Organometallic Chemistry},

volume = {1022},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jiménez‐Partinen2024,

title = {CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography},

author = {Ariadna Jiménez‐Partinen and Miguel A. Molina‐Cabello and Karl Thurnhofer‐Hemsi and Esteban J. Palomo and Jorge Rodríguez‐Capitán and Ana I. Molina‐Ramos and Manuel Jiménez‐Navarro},

doi = {10.1111/exsy.13708},

issn = {1468-0394},

year  = {2024},

date = {2024-12-00},

journal = {Expert Systems},

volume = {41},

number = {12},

publisher = {Wiley},

abstract = {AbstractCoronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well‐developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high‐quality open‐access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease‐related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer‐aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning‐based methods and giving the scientific community a starting point to improve CAD detection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Molina-Cabello2024,

title = {Grades and Learning Styles: New Software for Analyzing Correlations in a Longitudinal Study Across Different Branches of Knowledge and Years},

author = {Miguel A. Molina-Cabello and José Serrano-Angulo and Jesús Benito-Picazo and Karl Thurnhofer-Hemsi},

doi = {10.3390/app142411941},

issn = {2076-3417},

year  = {2024},

date = {2024-12-00},

journal = {Applied Sciences},

volume = {14},

number = {24},

publisher = {MDPI AG},

abstract = {Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, has been developed in this work. A comprehensive case study involving one thousand students across various subjects and degrees from several branches of knowledge in different years has been conducted using this tool. The analysis reveals low participation in the survey, with only 6 out of 19 courses having a participation rate that surpasses 50%), where respondents generally achieved higher grades compared to non-participants (on a grade scale from 0 to 10, participants scored on average one point higher than those who did not complete the questionnaire), suggesting that they were more motivated. Higher-level courses have higher participation rates, but overall participation has declined over the years. Despite slight differences in learning styles between courses, the general pattern remains similar. The correlation between learning styles and grades, analyzed through linear regression, shows that this model performs better when both learning styles and grade marks are distributed by score rather than by level.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khan2024,

title = {Natural Language Processing Tools and Workflows for Improving Research Processes},

author = {Noel Khan and David Elizondo and Lipika Deka and Miguel A. Molina-Cabello},

doi = {10.3390/app142411731},

issn = {2076-3417},

year  = {2024},

date = {2024-12-00},

journal = {Applied Sciences},

volume = {14},

number = {24},

publisher = {MDPI AG},

abstract = {The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pino-Carmona2024,

title = {Intercomparison of WRF-chem aerosol schemes during a dry Saharan dust outbreak in Southern Iberian Peninsula},

author = {Miguel Pino-Carmona and José A. Ruiz-Arias and Sol Fernández-Carvelo and Juan A. Bravo-Aranda and Lucas Alados-Arboledas},

doi = {10.1016/j.atmosenv.2024.120872},

issn = {1352-2310},

year  = {2024},

date = {2024-12-00},

journal = {Atmospheric Environment},

volume = {339},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Martorell-Marugán2024,

title = {Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics},

author = {Jordi Martorell-Marugán and Raúl López-Domínguez and Juan Antonio Villatoro-García and Daniel Toro-Domínguez and Marco Chierici and Giuseppe Jurman and Pedro Carmona-Sáez},

doi = {10.1093/bib/bbae673},

issn = {1477-4054},

year  = {2024},

date = {2024-11-22},

volume = {26},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons motivated us to develop singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. We used singleDeep to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus, Alzheimer’s disease and coronavirus disease 2019. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, singleDeep outperformed traditional machine learning methods and alternative single-cell approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Atencia-Jiménez2024,

title = {Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images},

author = {Ignacio Atencia-Jiménez and Adayabalam S. Balajee and Miguel J. Ruiz-Gómez and Francisco Sendra-Portero and Alegría Montoro and Miguel A. Molina-Cabello},

doi = {10.3390/app142210440},

issn = {2076-3417},

year  = {2024},

date = {2024-11-00},

journal = {Applied Sciences},

volume = {14},

number = {22},

publisher = {MDPI AG},

abstract = {The Dicentric Chromosome Assay (DCA) is widely used in biological dosimetry, where the number of dicentric chromosomes induced by ionizing radiation (IR) exposure is quantified to estimate the absorbed radiation dose an individual has received. Dicentric chromosome scoring is a laborious and time-consuming process which is performed manually in most cytogenetic biodosimetry laboratories. Further, dicentric chromosome scoring constitutes a bottleneck when several hundreds of samples need to be analyzed for dose estimation in the aftermath of large-scale radiological/nuclear incident(s). Recently, much interest has focused on automating dicentric chromosome scoring using Artificial Intelligence (AI) tools to reduce analysis time and improve the accuracy of dicentric chromosome detection. Our study aims to detect dicentric chromosomes in metaphase plate images using an ensemble of artificial neural network detectors suitable for datasets that present a low number of samples (in this work, only 50 images). In our approach, the input image is first processed by several operators, each producing a transformed image. Then, each transformed image is transferred to a specific detector trained with a training set processed by the same operator that transformed the image. Following this, the detectors provide their predictions about the detected chromosomes. Finally, all predictions are combined using a consensus function. Regarding the operators used, images were binarized separately applying Otsu and Spline techniques, while morphological opening and closing filters with different sizes were used to eliminate noise, isolate specific components, and enhance the structures of interest (chromosomes) within the image. Consensus-based decisions are typically more precise than those made by individual networks, as the consensus method can rectify certain misclassifications, assuming that individual network results are correct. The results indicate that our methodology worked satisfactorily in detecting a majority of chromosomes, with remarkable classification performance even with the low number of training samples utilized. AI-based dicentric chromosome detection will be beneficial for a rapid triage by improving the detection of dicentric chromosomes and thereby the dose prediction accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Essarroukh2024,

title = {Convective Heat Transfer in Uniformly Accelerated and Decelerated Turbulent Pipe Flows},

author = {Ismael Essarroukh and José M. López},

doi = {10.3390/math12223560},

issn = {2227-7390},

year  = {2024},

date = {2024-11-00},

journal = {Mathematics},

volume = {12},

number = {22},

publisher = {MDPI AG},

abstract = {This study presents a detailed investigation of the temporal evolution of the Nusselt number (Nu) in uniformly accelerated and decelerated turbulent pipe flows under a constant heat flux using direct numerical simulations. The influence of different acceleration and deceleration rates on heat transfer is systematically studied, addressing a gap in the previous research. The simulations confirm several key experimental findings, including the presence of three distinct phases in the Nusselt number temporal response—delay, recovery, and quasi-steady phases—as well as the characteristics of thermal structures in unsteady pipe flow. In accelerated flows, the delay in the turbulence response to changes in velocity results in reduced heat transfer, with average Nu values up to 48% lower than those for steady-flow conditions at the same mean Reynolds number. Conversely, decelerated flows exhibit enhanced heat transfer, with average Nu exceeding steady values by up to 42% due to the onset of secondary instabilities that amplify turbulence. To characterize the Nu response across the full range of acceleration and deceleration rates, a new model based on a hyperbolic tangent function is proposed, which provides a more accurate description of the heat transfer response than previous models. The results suggest the potential to design unsteady periodic cycles, combining slow acceleration and rapid deceleration, to enhance heat transfer compared to steady flows.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Díaz-Francés2024,

title = {Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks},

author = {José Ángel Díaz-Francés and José David Fernández-Rodríguez and Karl Thurnhofer-Hemsi and Ezequiel López-Rubio},

doi = {10.1142/s0129065724500576},

issn = {1793-6462},

year  = {2024},

date = {2024-11-00},

journal = {Int. J. Neur. Syst.},

volume = {34},

number = {11},

publisher = {World Scientific Pub Co Pte Ltd},

abstract = { Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by [Formula: see text] and [Formula: see text]. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to [Formula: see text] for the binary image segmentation approach. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Torres-Salomón2024,

title = {Exploring the Prognostic Impact of Non-Obstructive Coronary Artery Lesions through Machine Learning},

author = {Pablo Torres-Salomón and Jorge Rodríguez-Capitán and Miguel A. Molina-Cabello and Karl Thurnhofer-Hemsi and Francesco Costa and Pedro L. Sánchez-Fernández and Mario Antonio Muñoz-Muñoz and Ada del Mar Carmona-Segovia and Miguel Romero-Cuevas and Francisco Javier Pavón-Morón and Manuel Jiménez-Navarro},

doi = {10.3390/app14199079},

issn = {2076-3417},

year  = {2024},

date = {2024-10-00},

journal = {Applied Sciences},

volume = {14},

number = {19},

publisher = {MDPI AG},

abstract = {The prognostic impact of non-obstructive coronary artery disease (CAD) remains controversial. Therefore, the objective of this study is to assess the long-term prognostic significance of non-obstructive CAD using machine learning models. We designed a multicenter retrospective, longitudinal, and observational study that included 3265 patients classified into three groups: 1426 patients with lesions < 20%, 643 patients with non-obstructive CAD (lesions 20–50%), and 1196 patients with obstructive CAD (lesions > 70%). A composite cardiovascular event (acute myocardial infarction, stroke, hospitalization due to heart failure, or cardiovascular-related death) was assessed after a mean follow-up of 43 months. To achieve this, various machine learning models were constructed. The model with the highest accuracy was selected to perform a Shapley Additive Explanations (SHAP) analysis, revealing the contribution of different variables in predicting an event. The SHAP analysis suggested that the percentage of coronary lesion was the most significant predictor of cardiovascular events. None of the models demonstrated adequate capability in predicting the event, showing only a good predictive ability for the absence of an endpoint. In conclusions, this study demonstrates how machine learning techniques may facilitate the development of high-performing models for predicting long-term cardiovascular events in patients undergoing coronary angiography.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Domínguez2024,

title = {The Cooperative Maximal Covering Location Problem with ordered partial attractions},

author = {Concepción Domínguez and Ricardo Gázquez and Juan Miguel Morales and Salvador Pineda},

doi = {10.1016/j.cor.2024.106782},

issn = {0305-0548},

year  = {2024},

date = {2024-10-00},

journal = {Computers & Operations Research},

volume = {170},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Paredes2024,

title = {Revenue stacking of BESSs in wholesale and aFRR markets with delivery guarantees},

author = {Ángel Paredes and José A. Aguado},

doi = {10.1016/j.epsr.2024.110633},

issn = {0378-7796},

year  = {2024},

date = {2024-09-00},

journal = {Electric Power Systems Research},

volume = {234},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}