24th European Conference, EvoCOP 2024 Held as Part of EvoStar 2024 Aberystwyth, UK, April 3–5, 2024, Proceedings, Thomas Stützle, Markus Wagner, editors, In this work we apply machine learning to better guide a biased random key genetic algorithm (Brkga) for the longest common square subsequence (LCSqS) problem. The problem is a variant of the well-known longest common subsequence (LCS) problem in which valid solutions are square strings. A string is square if it can be expressed as the concatenation of a string with itself. The original Brkga is based on a reduction of the LCSqS problem to the LCS problem by cutting each input string into two parts. Our work consists in enhancing the search process of Brkga for good cut points by using a machine learning approach, which is trained to produce promising cut points for the input strings of a problem instance. In this study, we show the benefits of this approach by comparing the enhanced Brkga with the original Brkga, using two benchmark sets from the literature. We show that the results of the enhanced Brkga significantly improve over the original results, especially when tackling instances with non-uniformly generated input strings., Jaume Reixach and Christian Blum are supported by grants TED2021-129319B-I00
and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. G¨unter
R. Raidl is supported by the Vienna Graduate School on Computational Optimization
(VGSCO), Austrian Science Foundation, project no. W1260-N35. Marko Djukanovi´c
is supported by the project entitled “Development of artificial intelligence models and
algorithms for solving difficult combinatorial optimization problems” funded by the
Ministry of Scientific and Technological Development and the Higher Education of the
Republic of Srpska., Peer reviewed

XVI, 192, 15 b/w illustrations, 43 illustrations in colour., This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver., During the time of writing this book, the author was supported as principal investigator of the following three grants funded by MCIN/AEI/10.13039/501100011033: PID2019-104156GB-I00, PID2022-136787NB-I00, and TED2021-129319B-I00., Peer reviewed

En Computational Science – ICCS 2024, 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V.- Conference proceedings., This paper deals with the restricted longest common subsequence (RLCS) problem, an extension of the well-studied longest common subsequence problem involving two sets of strings: the input strings and the restricted strings. This problem has applications in bioinformatics, particularly in identifying similarities and discovering mutual patterns and motifs among DNA, RNA, and protein molecules. We introduce a general search framework to tackle the RLCS problem. Based on this, we present an exact best-first search algorithm and a meta-heuristic Beam Search algorithm. To evaluate the effectiveness of these algorithms, we compare them with two exact algorithms and two approximate algorithms from the literature along with a greedy approach. Our experimental results show the superior performance of our proposed approaches. In particular, our exact approach outperforms the other exact methods in terms of significantly shorter computation times, often reaching an order of magnitude compared to the second-best approach. Moreover, it successfully solves all problem instances, which was not the case with the other approaches. In addition, Beam Search provides close-to-optimal solutions with remarkably short computation times., The research of M. Djukanović is partially supported by the Ministry for Scientific and Technological Development and Higher Education of the Republic of Srpska, B &H in the course of the bilateral research project between B &H and Slovenia entitled “Theoretical and computational aspects of some graph problems with the application to graph network information spreading” and the COST Action ROAR-NET under no. CA22137. A. Kartelj was supported by grant 451-03-47/2023-01/200104 funded by the Ministry of Science Technological Development and Innovations of the Republic of Serbia. J. Reixach and C. Blum are supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033, Peer reviewed

This volume constitutes the refereed proceedings of Metaheuristics on 15th International Conference, MIC 2024, held in Lorient, France, during June 4–7, 2024.- The 36 full papers presented together with 34 short papers were carefully reviewed and selected from 127 submissions. The conference focuses on artificial intelligence, combinatorial optimization, computer science, graph theory, evolutionary algorithms, genetic algorithms, simulated annealing, optimization, optimization problems., This work leverages reinforcement learning for designing a new variant of Construct, Merge, Solve and Adapt (CMSA), a rather new hybrid metaheuristic for combinatorial optimization. We demonstrate a twofold improvement over the standard CMSA. Firstly, the new variant simplifies CMSA by eliminating the need for a greedy function to probabilistically generate solutions. Additionally, it performs better, as we demonstrate in the context of the Minimum Dominating Set (MDS) problem., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024.- Proceedings, Part VI., The Maximum Boolean Satisfiability Problem (also known as the Max-SAT problem) is the problem of determining the maximum number of disjunctive clauses that can be satisfied (i.e., made true) by an assignment of truth values to the formula’s variables. This is a generalization of the well-known Boolean Satisfiability Problem (also known as the SAT problem), the first problem that was proven to be NP-complete. With the proliferation of quantum computing, a current approach to tackle this optimization problem is Quantum Annealing (QA). In this work, we compare several gadgets that translate 3-SAT problems into Quadratic Unconstrained Binary Optimization (QUBO) problems to be able to solve them in a quantum annealer. We show the performance superiority of the not-yet-considered gadgets in comparison to state-of-the-art approaches when solving random instances in D-Wave’s quantum annealer., This work was founded partially by the Government of Catalonia Exp. Num. 2023 DI 00071, by Enzyme Advising Group under the development of an industrial PhD on Quantum Machine Learning, by CSIC’s JAE Intro JAEICU_23_00782 and by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Jesus Cerquides is funded by European Union KHealthInAir, GUARDEN, and Humane-AI projects with No. 101057693, 101060693, and 952026., Peer reviewed

11th Multidisciplinary International Social Networks Conference Bali Indonesia August 21 - 23, 2024, research-article., Social media can serve as a valuable source of timely and valuable information about the impacts on people and infrastructure during a disaster. However, due to the lack of geographical data in most social media posts, this information is often underutilized by first responders. This paper proposes and analyses an approach that
combines text from social media posts with textual information extracted from images to improve the geolocation of social media data during a given disaster. The implementation incorporates ChatGPT
in location prediction. We use real-world dataset from Twitter that represent four different events, including floods and earthquake. The experimental results demonstrate that our proposal improves the location prediction’s quantity and precision. We expect that our findings help policymakers consider the application of the proposed methodology in disaster response., This work was funded by the Indonesia Endowment Fund For Education (LPDP) and CUI University of Geneva, Switzerland, and partially supported by the European Union’s Horizon 2020 research and innovation programme K-HealthInAIR project with ID 101057693 and by the grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

GECCO '24: Genetic and Evolutionary Computation Conference, July 14 - 18, 2024, VIC, Melbourne, Australia., The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field., C. Chacón and C. Blum were supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/ 501100011033., Peer reviewed

This volume constitutes the refereed proceedings of Metaheuristics on 15th International Conference, MIC 2024, held in Lorient, France, during June 4–7, 2024., Ponencia de conferencia., In the realm of optimization, where intricate landscapes conceal possibly hidden pathways to high-quality solutions, STNWeb serves as a beacon of clarity. This novel web-based visualization platform empowers researchers to delve into the intricate interplay between algorithms and optimization problems, uncovering the factors that influence algorithm performance across diverse problem domains, be they discrete/combinatorial or continuous. By leveraging the inherent power of visual data representation, STNWeb transcends traditional analytical methods, providing a robust foundation for dissecting algorithm behavior and pinpointing the mechanisms that elevate one algorithm above another. This visually-driven approach fosters a deeper understanding of algorithmic strengths and weaknesses, ultimately strengthening the discourse surrounding algorithm selection and refinement for complex optimization tasks., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

15th International Conference, MIC 2024, Lorient, France, June 4–7, 2024, Proceedings, Part II., Search Trajectory Networks (STNs) are visualizations of directed graphs designed to analyze the behavior of stochastic optimization algorithms such as metaheuristics. Their purpose is to provide researchers with a tool that allows them to gain a deeper understanding of the behavior exhibited by multiple algorithms when applied to a specific instance of an optimization problem. In this short paper, we present two examples of our work in which STN graphics have helped us to discover interesting and useful algorithm/problem characteristics., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

Included in the following conference series: International Conference on Information Technology, Adaptive Intelligence: Select Proceedings of InCITe 2024, Volume 1, Conference proceedings., In the field of approximate algorithms for combinatorial optimization, researchers frequently assess their optimization algorithms by comparing them to integer linear programming solvers like CPLEX and Gurobi, whenever feasible. Predictably, the general trend indicates that soft computing techniques such as evolutionary algorithms and tabu search tend to surpass integer linear programming solvers once the difficulty of the considered problem instances exceeds a certain threshold. In this paper, we advocate for adopting (or additionally using) a recent hybrid technique known as CMSA for evaluating newly developed optimization algorithms. CMSA employs an iterative approach, applying integer linear programming solvers to opportunely defined sub-instances of the addressed problem instances. Remarkably, this technique tends to outperform black-box integer linear programming solvers, especially in the context of larger and more challenging problem instances. We substantiate our argument by presenting results related to the bin packing problem with conflicts., The author was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025).
Organized by the European Association for Artificial Intelligence (EurAI) and the Italian Association for Artificial Intelligence (AIxIA). Hosted by the University of Bologna., This publication presents the proceedings of ECAI-2025, the 28th European Conference on Artificial Intelligence, held from 25 to 30 October 2025 in Bologna, Italy. Originally organized as a biennial event, ECAI is now held annually, and continuing a long-standing tradition, this edition of ECAI was held alongside the 14th Conference on Prestigious Applications of Intelligent Systems (PAIS). Since its inception in 1974, ECAI has become the premier venue for presenting AI research in Europe, where researchers and practitioners gather to discuss the latest trends and challenges and showcase innovative applications and advances in AI technology. A record total of 3,266 abstract submissions were received for this year’s main track, 2,667 of which were followed by full papers (acceptance rate 23%), together with 81 submissions to the demo track (acceptance rate 33%). Topics cover the full breadth of AI, with many papers exploring interdisciplinary themes. PAIS, an important forum for the sharing of insights into the applicability, development, and deployment of intelligent systems, received 82 submissions (acceptance rate 45%). As well as the papers presented at ECAI and PAIS, this volume also includes eight invited papers on Frontiers in AI corresponding to short invited talks highlighting important new results, techniques, and trends, and the abstracts of 15 recently published papers not previously included in an archival proceedings.

This volume of proceedings will be of interest to all those working in the field of Artificial Intelligence., Conference paper., This paper introduces a hybrid algorithm that integrates the Construct, Merge, Solve and Adapt (CMSA) metaheuristic with Column Generation (CG) to solve the Variable-Sized Bin Packing Problem (VSBPP). The VSBPP, which requires packing items of varying sizes into bins of different types and costs, is a core combinatorial optimization problem with applications in logistics and resource allocation. Our approach builds upon a previously proposed CMSA-based framework by incorporating a column generation procedure to strengthen the linear programming relaxation and generate improved patterns. In each iteration, heuristic solution construction and merging phases produce a pool of feasible packing patterns. These are then used in a linear programming master problem, from which dual values are extracted and used to solve a knapsack pricing subproblem. This identifies new patterns (columns) with negative reduced cost to be added to the model. Finally, an integer programming phase refines the solution using a reduced subinstance. The algorithm is evaluated on benchmark instances and compared with both the previously published standalone CMSA approach and a standalone column generation method. Experimental results show that the combined CMSA-COLGEN approach consistently outperforms both baselines in terms of solution quality and robustness, particularly on larger and more complex instances., This work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by the project Cátedra ENIA UAB-Cruïlla (TSI-100929-2023-2)., Peer reviewed

GECCO '25: Genetic and Evolutionary Computation Conference NH Malaga Hotel Malaga Spain July 14 - 18, 2025, The Minimum Global Domination (MGD) problem is a challenging NP-hard variant of the classical Minimum Dominating Set (MDS) problem, which has numerous practical applications. Given an undirected graph, a global dominating set is a set of vertices that dominates all vertices both in the given graph and in its complement graph. In this work, we propose a Population-Based Iterated Greedy (PBIG) algorithm to effectively address the MGD problem. The algorithm employs a semi-greedy solution reconstruction strategy and a redundancy removal mechanism to enhance efficiency and solution quality. We benchmark PBIG against current state-of-the-art approaches, including the CMSA metaheuristic and the CPLEX solver, across 1440 problem instances. Experimental results demonstrate that PBIG outperforms existing methods in solution quality while significantly reducing computational time, establishing it as a powerful and efficient algorithm for the MGD problem., C. Blum was supported by grant PID2022-136787NB-I00 funded byMCIN/AEI/10.13039/ 501100011033., Peer reviewed

This paper presents a concise bibliometric review of metaheuristics, a class of high-level, problem-independent algorithmic frameworks designed for heuristic optimization. Since their rise in the late twentieth century, driven largely by advances in computational power, metaheuristics have become increasingly popular in solving complex optimization problems. In this review, we examine the evolution of the metaheuristics literature, highlighting key trends, developments, and the growing impact of this field over time., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/ AEI/10.13039/501100011033., Peer reviewed

28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025).
Organized by the European Association for Artificial Intelligence (EurAI) and the Italian Association for Artificial Intelligence (AIxIA). Hosted by the University of Bologna., This publication presents the proceedings of ECAI-2025, the 28th European Conference on Artificial Intelligence, held from 25 to 30 October 2025 in Bologna, Italy. Originally organized as a biennial event, ECAI is now held annually, and continuing a long-standing tradition, this edition of ECAI was held alongside the 14th Conference on Prestigious Applications of Intelligent Systems (PAIS). Since its inception in 1974, ECAI has become the premier venue for presenting AI research in Europe, where researchers and practitioners gather to discuss the latest trends and challenges and showcase innovative applications and advances in AI technology. A record total of 3,266 abstract submissions were received for this year’s main track, 2,667 of which were followed by full papers (acceptance rate 23%), together with 81 submissions to the demo track (acceptance rate 33%). Topics cover the full breadth of AI, with many papers exploring interdisciplinary themes. PAIS, an important forum for the sharing of insights into the applicability, development, and deployment of intelligent systems, received 82 submissions (acceptance rate 45%). As well as the papers presented at ECAI and PAIS, this volume also includes eight invited papers on Frontiers in AI corresponding to short invited talks highlighting important new results, techniques, and trends, and the abstracts of 15 recently published papers not previously included in an archival proceedings.

This volume of proceedings will be of interest to all those working in the field of Artificial Intelligence., Conference paper., This work presents a new variant of construct, merge, solve, and adapt (CMSA), a hybrid metaheuristic for combinatorial optimization. This variant, denoted as DL-CMSA, implements an online deep learning mechanism, which allows it to learn how to construct solutions in its 'construct' step. Compared to an existing variant of CMSA with a learning mechanism, the so-called RL-CMSA, the deep learning component of the presented variant allows for a higher degree of detail in the learning process, as the partial solution under construction is taken into account for deciding which solution component to add next. An experimental evaluation is performed on the classical Maximum Independent Set (MIS) problem. The results show how the proposed deep learning mechanism is beneficial, yielding statistically superior results., The authors are supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033, Peer reviewed

Conference Paper, Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS 2025), which took place on September 14-17, 2025, in Kraków, Poland., The integration of Large Language Models (LLMs) into optimization has created a powerful synergy, opening exciting research opportunities. This paper investigates how LLMs can enhance existing optimization algorithms. Using their pre-trained knowledge, we demonstrate their ability to propose innovative heuristic variations based on a semantic understanding of the algorithm's components. To evaluate this, we applied a nontrivial optimization algorithm, Construct, Merge, Solve & Adapt (CMSA)---a hybrid metaheuristic for combinatorial optimization problems that incorporates a heuristic in the solution construction phase. Our results show that an alternative heuristic proposed by GPT-4o outperforms the expert-designed heuristic of CMSA, with the performance gap widening on larger and denser graphs., C. Chacón Sartori and C. Blum were supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Moreover, thanks to FreePik, from where we extracted the icons used in
Figures 1 and 3., Peer reviewed

In this chapter, we describe the Construct, Merge, Solve & Adapt (CMSA) algorithm, a metaheuristic framework designed to address hard combinatorial optimization problems. CMSA combines the probabilistic construction of solutions within an adaptive, iterative process with solution merging and exact optimization techniques. The algorithm probabilistically constructs solutions through heuristic methods, which are then merged into a reduced subproblem. This subproblem is solved using exact optimization approaches, mostly integer programming. Based on the feedback from this solving phase, the algorithm adapts its construction and merging strategies in subsequent iterations, progressively finding solutions of improving quality over time. CMSA’s hybrid nature allows it to balance between the speed of heuristic construction and the accuracy of exact methods, making it particularly effective for large-scale problems. After a general description of standard CMSA, we outline a recent self-adaptive variant and a variant that uses reinforcement learning to improve the search process. All three algorithm variants are applied to the classical maximum independent set problem. Moreover, an experimental evaluation is provided to show their comparative behavior., The authors were supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

21st IFIP WG 12.5 International Conference, AIAI 2025, Limassol, Cyprus, June 26–29, 2025, Proceedings, Part I, Conference proceedings.
IFIP (International Federation for Information Processing)., In this paper, we improve a recently introduced accelerated k-means++ algorithm by proposing a dynamic method for calculating bounds during its execution. This approach produces high-quality bounds without requiring a full iteration over the dataset, using the Triangle Inequality and the norm of the points for efficient computation. We propose three evaluation metrics to assess the quality of the calculated bounds and analyze their behavior across a diverse dataset. Results show that this dynamic method outperforms the traditional approach of initializing bounds at the end of the process, before the execution of the
-means algorithm., The research presented in this paper was supported by grant 2022 FI_B 00903 funded by the AGAUR; grant PID-2020-112581GB-C21 (MOTION) funded by the AEI; grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033; and project MMT24-IIIA-02 funded by Red.es, Peer reviewed

28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025).
Organized by the European Association for Artificial Intelligence (EurAI) and the Italian Association for Artificial Intelligence (AIxIA). Hosted by the University of Bologna., This publication presents the proceedings of ECAI-2025, the 28th European Conference on Artificial Intelligence, held from 25 to 30 October 2025 in Bologna, Italy. Originally organized as a biennial event, ECAI is now held annually, and continuing a long-standing tradition, this edition of ECAI was held alongside the 14th Conference on Prestigious Applications of Intelligent Systems (PAIS). Since its inception in 1974, ECAI has become the premier venue for presenting AI research in Europe, where researchers and practitioners gather to discuss the latest trends and challenges and showcase innovative applications and advances in AI technology. A record total of 3,266 abstract submissions were received for this year’s main track, 2,667 of which were followed by full papers (acceptance rate 23%), together with 81 submissions to the demo track (acceptance rate 33%). Topics cover the full breadth of AI, with many papers exploring interdisciplinary themes. PAIS, an important forum for the sharing of insights into the applicability, development, and deployment of intelligent systems, received 82 submissions (acceptance rate 45%). As well as the papers presented at ECAI and PAIS, this volume also includes eight invited papers on Frontiers in AI corresponding to short invited talks highlighting important new results, techniques, and trends, and the abstracts of 15 recently published papers not previously included in an archival proceedings.

This volume of proceedings will be of interest to all those working in the field of Artificial Intelligence., Conference paper., This paper introduces the Approximate Ethical Embedding Process, an algorithm for automating the design of ethical environments for learning agents. Our algorithm helps build environments wherein multiple agents learn policies that align with an ethical (moral) value while simultaneously pursuing their individual objectives. Therefore, we contribute to endowing environment designers with algorithmic tools for building ethical environments. We demonstrate the ethical design process for two different settings of an environment where agents have to adhere to beneficence to promote the collective survival of the population. Our experiments show that our approximate embedding process successfully generates environments that incentivise the learning of value-aligned policies., The research presented in this paper was supported by the EU-funded
VALAWAI (# 101070930) project, and the Spanish-funded VAE
(# TED2021-131295B-C31), EMOROBCARE (# IASOMMA2024),
and ACISUD (PID2022-136787NB-I00) projects., Peer reviewed

Proceedings of the 27th International Conference of the Catalan Association for Artificial Intelligence. The International Conference of the Catalan Association for Artificial Intelligence (CCIA) is a well-established annual event that brings together researchers in Artificial Intelligence from across the Catalan-speaking territories (southern France, Catalonia, Valencia, the Balearic Islands, and Alghero in Italy) as well as participants from around the world., In cheese production, the ripening phase is crucial to developing the final product’s sensory characteristics. However, non-invasive and real-time monitoring remains a challenge. To address this, computer vision techniques offer a promising opportunity for more precise, automated, and continuous tracking of the ripening process. This work proposes a deep-learning-based methodology to classify cheese wheels by ripening day, providing valuable insights for quality control and process standardization, which are critically needed in the cheese industry. Using digital images, we assess the feasibility of a Machine Learning system to estimate ripening stages visually. As a proof of concept, we employ pre-trained Convolutional Neural Networks to extract visual descriptors for classification. Unlike previous studies relying solely on embeddings from pre-trained models, this work fine-tunes these models to adapt the visual representation to the specific domain of cheese ripening, allowing a comparative analysis of both approaches., Daniel Pardo is funded by the AGAUR FI Joan Oro grant (2024 FI-1 00424). Mehmet ´
Oguz Mulayim is funded by European Union KHealthInAir and GUARDEN projects
with No. 101057693 and 101060693. This work was supported by grant PID2022-
136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Manuel Castillo is
funded by pHotoYOGURT (ACC 2023 EXP SIA001 40 0000092; DACC, GENCAT)
and OPTICmizing (2023 INNOV 00027; AGAUR, GENCAT) projects., Peer reviewed