Purpose: The purpose of this study was to analyse the impact on peri-prosthetic joint infection (PJI) rate and prosthetic survival using antibiotic-loaded bone cement (ALBC) versus plain cement during total knee arthroplasty (TKA). Methods: A retrospective cohort study was conducted. The main data source was the Catalan Arthroplasty Register (RACat). TKAs with surgery date between 1 January 2011 and 31 December 2020 were analysed and followed up until 31 December 2023. The main variable of interest was the type of cement (ALBC vs. plain cement), and several endpoints (septic revision, aseptic revision, and all-cause revision) were considered. The analysed outcomes were revision rates, survival rates and risk factors' hazard ratios (HR). Results: A total of 22,781 TKAs were analysed, 13,125 (57.6%) of them with plain cement and 9656 (42.4%) with ALBC. The septic revision rate was lower in the ALBC group after 3 months of follow-up (0.52% vs. 0.78%, p value = 0.04). Prosthetic survival with respect to the aseptic revision endpoint was also higher for the ALBC group during the whole follow-up period (~158 months). Regarding risk factors for infection, ALBC showed a protective effect, HR: 0.53 (0.44, 0.63), while sex (being male) and the analysed comorbidities increased the risk. Conclusions: ALBC is associated with a reduction in both the septic revision and the aseptic revision rate after TKA, and thus with higher prosthetic survival. Level of evidence: Level III, Therapeutic, retrospective., This work was supported by the Ministerio de Ciencia e Innovación (projects PID2022-136787NB-I00 and PID2019-104156GB-I00), the European Commission H2020 program (grants 101060693, 101057693, 952026) and the Agència de Gestió d'Ajuts Universitaris i de Recerca (Award Number: 2020-18). None of the funding sources had a direct role in this work.

Norms have been widely enacted in human and agent societies to regulate individuals’ actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature’s study of the relations between norms, actions, and values, we formally define how actions and values relate through the so-called value judgment function and how norms and values relate through the so-called norm promotion function. We show that both functions provide the means to compute value alignment for a set of norms. Moreover, we detail how to cast our decision-making problem as an optimisation problem: finding the norms that maximise value alignment. We also show how to solve our problem using off-the-shelf optimisation tools. Finally, we illustrate our approach with a specific case study on the European Value Study., This work has been funded by projects VAE (TED2021-131295B-C31), VALAWAI (HE101070930), LOGISTAR (H2020-769142), AI4EU (H2020-825619), Crowd4SDG (H2020-872944), ACISUD (PID2022-136787NB-I00), COREDEM (H2020-785907), Fairtrans (PID2021-124361OBC33), AUTODEMO (SR21-00329), TAILOR (H2020-952215), assist-Decidim (23S02974-001), 2021 SGR 00313, and 2021 SGR 00754. F. Bistafa was supported by the H2020-MSCAIF- 2016 HPA4CF project. The authors have no competing interests to declare that are relevant to the content of this article. Maite Lopez-Sanchez belongs to the WAI research group (University of Barcelona) an associated unit to CSIC through the IIIA.

Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant contributions: 1) We formalize the air pollution prediction problem as a Maximum A Posteriori (MAP) estimate within the framework of a Markov Random Field and 2) we propose a message-passing algorithm, which stands out as an efficient solution that surpasses the current state of the art. The experimental procedure has been carried out using the case study of the city of Barcelona, based on a dataset extracted from the BCN Open Data portal., The work presented in this article is the result of a collaboration carried out as part of the CSIC Interdisciplinary Thematic Platform (PTI) Mobility 2030 (PTI-MOBILITY 2030). The authors gratefully acknowledge the Open Data BCN portal provided by the Barcelona city council. Bistaffa was supported by the research projects ACISUD (PID2022-136787NB-I00) and Yoma OR (OPE02570)., Peer reviewed

We adopt an emerging and prominent vision of human-centred Artificial Intelligence that requires building trustworthy intelligent systems. Such systems should be capable of dealing with the challenges of an interconnected, globalised world by handling plurality and by abiding by human values. Within this vision, pluralistic value alignment is a core problem for AI– that is, the challenge of creating AI systems that align with a set of diverse individual value systems. So far, most literature on value alignment has considered alignment to a single value system. To address this research gap, we propose a novel method for estimating and aggregating multiple individual value systems. We rely on recent results in the social choice literature and formalise the value system aggregation problem as an optimisation problem. We then cast this problem as an ℓp-regression problem. Doing so provides a principled and general theoretical framework to model and solve the aggregation problem. Our aggregation method allows us to consider a range of ethical principles, from utilitarian (maximum utility) to egalitarian (maximum fairness). We illustrate the aggregation of value systems by considering real-world data from two case studies: the Participatory Value Evaluation process and the European Values Study. Our experimental evaluation shows how different consensus value systems can be obtained depending on the ethical principle of choice, leading to practical insights for a decision-maker on how to perform value system aggregation., The authors were supported by the research projects ACISUD (PID2022-136787NB-I00), VAE (TED2021-131295B-C31), VALAWAI (HE-101070930), Fairtrans (PID2021-124361OB-C33), Yoma OR (OPE02570), TAILOR (952215), and the Hybrid Intelligence Center, a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research ., Peer reviewed

We consider the problem of forming collectives of agents inherent in application domains aligned with Sustainable Development Goals 4 and 11 (i.e., team formation and ridesharing, respectively). We propose a general solution approach based on a novel combination of an attention model and an integer linear program (ILP). In more detail, we propose an attention encoder-decoder model that transforms a collective formation instance to a weighted set packing problem, which is then solved by an ILP. Results on collective formation problems inherent in the ridesharing and team formation domains show that our approach provides comparable solutions (in terms of quality) to the ones produced by state-of-the-art approaches specific to each domain. Moreover, our solution outperforms the most recent general approach for forming collectives based on Monte Carlo tree search., The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana (through the 2014–2020 FEDER Operative Programme of Comunitat Valenciana, project IDIFEDER/2018/048) as well as the technical support provided by the Instituto de Fisica Corpuscular, IFIC (CSIC-UV). This work was supported by the “ACISUD” project (PID2022-136787NB-I00) funded by MCIN/AEI/10.13039/501100011033 and by the “YOMA Operational Research” project (OPE02570) funded by the Botnar Foundation., Peer reviewed

This paper is a follow-up of one of the most-cited articles published in the first 20 years of the existence of Physics of Life Reviews. The specific topic is “ant colony optimization”, which is a metaheuristic for solving challenging optimization problems. Due to its inspiration from natural ant colonies' shortest path-finding behavior, this optimization technique forms part of a larger field known as swarm intelligence. After a short introduction to ant colony optimization, we first provide a chronology focusing on algorithmic developments rather than applications. The main part of the paper deals with a bibliometric study of the ant colony optimization literature. Interesting trends concerning, for example, the geographic origin of publications and the change in research focus over time, can be learned from the presented graphs and numbers., 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

Many packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems., The research presented in this paper was supported by Grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Additionally, M.A. Akbay and C.B. Kalayci acknowledge support from the Technological Research Council of Turkey (TUBITAK) under Grant no. 119M236., Peer reviewed

In emergency situations, social media platforms produce a vast amount of real-time data that holds immense value, particularly in the first 72 h following a disaster event. Despite previous efforts, efficiently determining the geographical location of images related to a new disaster remains an unresolved operational challenge. Currently, the state-of-the-art approach for dealing with these first response mapping is first filtering and then submitting the images to be geolocated to a volunteer crowd, assigning the images randomly to the volunteers. In this work, we extend our previous paper (Ballester et al., 2023) to explore the potential of artificial intelligence (AI) in aiding emergency responders and disaster relief organizations in geolocating social media images from a zone recently hit by a disaster. Our contributions include building two different models in which we try to (i) be able to learn volunteers’ error profiles and (ii) intelligently assign tasks to those volunteers who exhibit higher proficiency. Moreover, we present methods that outperform random allocation of tasks, analyze the effect on the models’ performance when varying numerous parameters, and show that for a given set of tasks and volunteers, we are able to process them with a significantly lower annotation budget, that is, we are able to make fewer volunteer solicitations without losing any quality on the final consensus., This work has received funding from the European Union’s Horizon 2020 research and innovation program HumanE-AI-Net, Crowd4SDG, and K-HealthInAIR projects (N. 952026, N. 872944, and N. 101057 693, respectively) and from the grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., No

A critical aspect of simulation models used in cost-effectiveness analysis lies in accurately representing the natural history of diseases, requiring parameters such as probabilities and disease burden rates. While most of these parameters can be sourced from scientific literature, they often require calibration to align with the model’s expected outcomes. Traditional optimization methods can be time-consuming and computationally expensive, as they often rely on simplistic heuristics that may not ensure feasible solutions. In this study, we explore using Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting structural properties within the solution space. Specifically, we investigate the impact of additive kernel decomposition and a stepwise approach, which capitalizes on the sequential block structure inherent in simulation models. This approach breaks down large optimization problems into smaller ones without compromising solution quality. In some instances, parameters obtained using this methodology may exhibit less error than those derived from naive calibration techniques. We compare this approach with two state-of-the-art high-dimensional Bayesian Optimization techniques: SAASBO and BAxUS. Our findings demonstrate that Bayesian optimization significantly enhances the calibration process, resulting in faster convergence and improved solutions, particularly for larger simulation models. This improvement is most pronounced when combined with a stepwise calibration methodology., This work has been funded by the Instituto de Salud Carlos III-ISCIII (Spanish Government) through the Project PI19/01118, as well as CIBERESP CB06/02/0073 (co-funded by European Regional Development Fund, a way to build Europe), and also with the support of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia. Grants to support the activities of research groups (2021SGR1029). Jesus Cerquides’ work was supported by Grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

One of the problems with exact techniques for solving combinatorial optimization problems is that they tend to run into problems with growing problem instance size. Nevertheless, they might still be very usefully employed, even in the context of large problem instances, as a sub-ordinate method within so-called hybrid metaheuristics. “Construct, Merge, Solve and Adapt” (Cmsa) is a hybrid metaheuristic technique that allows the application of exact methods to large-scale problem instances through intelligent instance reduction. However, Cmsa does not make use of an explicit learning mechanism. In this work, an algorithm called LEARN_CMSA is presented for the application to the far from most string problem (FFMSP), which is an NP-hard combinatorial optimization problem from the field of string consensus problems. LEARN_CMSA results from hybridization between Cmsa and a population-based algorithm. By means of this hybridization, explicit learning is introduced to Cmsa. Even though the FFMSP is a well-studied problem, LEARN_CMSA achieves superior performance when compared to current state-of-the-art solvers., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. P. Pinacho-Davidson acknowledges financial support from FONDECYT through grant number 11230359. C. Blum was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists., A.R. is supported by the Special Research Fund (BOF) of Ghent University, project number 01D12519. N.A.-D. is supported by “GOA: Ex ovo omnia: from stem cell to oocyte BOF GOA2018000501”. G. A. is supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 860960. J.C. is supported by projects CI-SUSTAIN (PID2019-104156 GB-I00) and ACISUD (PID2022-136787NB-I00), both funded by the Spanish Ministry of Science and Innovation. O.B.P. is supported by Fonds Wetenschappelijk Onderzoek: 12Y5220N. D.A.-V. is supported by Fonds of COLFUTURO and the Ministerio de Ciencia Tecnología Innovación of Colombia., Peer reviewed

We propose the Multi-Constructor CMSA, a Construct, Merge, Solve and Adapt (CMSA) algorithm that employs multiple heuristic procedures, respectively solution constructors, for the Maximum Disjoint Dominating Sets Problem (MDDSP). At every iteration of the search procedure, the solution components built by the constructors are merged into a sub-instance, which is subsequently solved by an exact solver and then adapted to keep only beneficial solution components. In our CMSA the solution constructors are chosen at random according to their relative probabilities, which are adapted during the search, through a mechanism based on reinforcement learning. We test two variants of the new Multi-Constructor CMSA that employ, respectively, two and six solution constructors, on a new set of 3600 problem instances, encompassing random graphs, Watts–Strogatz networks and Barabási-Albert networks, generated through a Hammersley sampling procedure on the instance space. We compare our algorithm against six heuristics from the literature, as well as with the standard version of CMSA. Furthermore, we employ an integer linear programming (ILP) model that is able to achieve a good performance for small, sparse graphs. Overall, the experimental results show that all versions of CMSA outperform by a large margin the previous state of the art and that, among the variants of CMSA, the novel version that combines two constructors provides slightly better results than the other ones, more prominently on larger graphs., Additionally, the authors acknowledge TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215, for the support provided for this research. Furthermore, this work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Finally, the support provided by CINECA through grant ISCRA C IA4EVRP HP10CE285L is gratefully acknowledged by the authors., Peer reviewed

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

Purpose
The purpose of this study was to analyse the impact on peri-prosthetic joint infection (PJI) rate and prosthetic survival using antibiotic-loaded bone cement (ALBC) versus plain cement during total knee arthroplasty (TKA)., Methods
A retrospective cohort study was conducted. The main data source was the Catalan Arthroplasty Register (RACat). TKAs with surgery date between 1 January 2011 and 31 December 2020 were analysed and followed up until 31 December 2023. The main variable of interest was the type of cement (ALBC vs. plain cement), and several endpoints (septic revision, aseptic revision, and all-cause revision) were considered. The analysed outcomes were revision rates, survival rates and risk factors' hazard ratios (HR)., Results
A total of 22,781 TKAs were analysed, 13,125 (57.6%) of them with plain cement and 9656 (42.4%) with ALBC. The septic revision rate was lower in the ALBC group after 3 months of follow-up (0.52% vs. 0.78%, p value = 0.04). Prosthetic survival with respect to the aseptic revision endpoint was also higher for the ALBC group during the whole follow-up period (~158 months). Regarding risk factors for infection, ALBC showed a protective effect, HR: 0.53 (0.44, 0.63), while sex (being male) and the analysed comorbidities increased the risk., Conclusions
ALBC is associated with a reduction in both the septic revision and the aseptic revision rate after TKA, and thus with higher prosthetic survival., Level of Evidence
Level III, Therapeutic, retrospective., This work was supported by the Ministerio de Ciencia e Innovación (projects PID2022-136787NB-I00 and PID2019-104156GB-I00), the European Commission H2020 program (grants 101060693, 101057693, 952026) and the Agència de Gestió d'Ajuts Universitaris i de Recerca (Award Number: 2020-18). None of the funding sources had a direct role in this work., Peer reviewed

The approach of engaging students with real-world challenges to enhance collaboration and problem-solving has attracted significant interest from scholars and practitioners across diverse disciplines. Often called Challenge-Based Learning (CBL), this educational approach emphasises developing collaborative and problem-solving skills, with significant learning occurring within team settings. Prior studies highlight the influence of team composition on the efficacy of learning outcomes, pointing out that factors such as gender diversity, personality trait diversity, and a wide range of skills affect team dynamics and performance. Despite these insights, the practical organisation of these teams remains a challenge, often reliant on ad-hoc methods driven primarily by the nature of the setting at hand. Importantly, CBL is typically assessed through the final product, neglecting the impact of CBL on how the participants experience the overall process. That is, CBL is usually considered effective if the outcome is of high quality, ignoring participants' experience and participation quality. This study investigates the potential of an Artificial Intelligence team composition algorithm to improve participation quality and outcomes in collaborative CBL environments., This research was conducted under the YOMA OR project (OPE02570). Athina Georgara was supported by the Generalitat de Catalunya under grant 2019DI17, and the VALAWAI project (HE-101070930). Marc Santolini and Olga Koshagina were partly supported by the French Agence Nationale de la Recherche (ANR), under grant agreement ANR-21-CE38-0002. Jose Luis Fernandez-Marquez was supported by ALBATROSS EU project funded by the European Union under G.A. Nº 101137895. Juan A. Rodriguez-Aguilar was funded by projects ACISUD (PID2022-136787NB-I00), VAE (TED2021-131295B-C31), and VALAWAI (HE-101070930)., Peer reviewed

Since the rise of Large Language Models (LLMs) a couple of years ago,
researchers in metaheuristics (MHs) have wondered how to use their power in a
beneficial way within their algorithms. This paper introduces a novel approach
that leverages LLMs as pattern recognition tools to improve MHs. The resulting
hybrid method, tested in the context of a social network-based combinatorial
optimization problem, outperforms existing state-of-the-art approaches that
combine machine learning with MHs regarding the obtained solution quality. By
carefully designing prompts, we demonstrate that the output obtained from LLMs
can be used as problem knowledge, leading to improved results. Lastly, we
acknowledge LLMs' potential drawbacks and limitations and consider it essential
to examine them to advance this type of research further. Our method can be
reproduced using a tool available at: https://github.com/camilochs/optipattern., This work was supported by MCIN/AEI/10.13039/ 501100011033 under Grant TED2021-129319B-I00 and Grant
PID2022-136787NB-I00., Peer reviewed

This article considers the longest common square subsequence (LCSqS) problem, a variant of the longest common subsequence (LCS) problem in which solutions must be square strings. A square string can be expressed as the concatenation of a string with itself. The LCSqS problem has applications in bioinformatics, for discovering internal similarities between molecular structures. We propose a metaheuristic approach, a biased random key genetic algorithm (BRKGA) hybridized with a beam search (BS) from the literature. Our approach is based on reducing the LCSqS problem to a set of promising LCS problems. This is achieved by cutting each input string into two parts first and then evaluating such a transformed instance by solving the LCS problem for the obtained overall set of strings. The task of the BRKGA is, hereby, to find a set of good cut points for the input strings. For this purpose, the search is carefully biased by problem-specific greedy information. For each cut point vector, the resulting LCS problem is approximately solved by the existing BS approach. The proposed algorithm is evaluated against a previously proposed state-of-the-art variable neighborhood search (VNS) on random uniform instances from the literature, new nonuniform instances, and a real-world instance set consisting of DNA strings. The results underscore the importance of our work, as our novel approach outperforms former state-of-the-art with statistical significance. Particularly, they evidence the limitations of the VNS when solving nonuniform instances, for which our method shows superior performance., MCIN/AEI/10.13039/501100011033 (Grant Number: TED2021-129319B-I00 and PID2022-136787NB-I00)
Vienna Graduate School on Computational Optimization (VGSCO), Austrian Science Foundation (Grant Number: W1260-N35), Peer reviewed

Clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this paper, we propose an acceleration of the exact k-means++ algorithm using geometric information, specifically the Triangle Inequality and additional norm filters, along with a two-step sampling procedure. Our experiments demonstrate that the accelerated version outperforms the standard k-means++ version in terms of the number of visited points and distance calculations, achieving greater speedup as the number of clusters increases. The version utilizing the Triangle Inequality is particularly effective for low-dimensional data, while the additional norm-based filter enhances performance in high-dimensional instances with greater norm variance among points. Additional experiments show the behavior of our algorithms when executed concurrently across multiple jobs and examine how memory performance impacts practical speedup., This work was supported in part by the Department of Research and Universities of the Government of Catalonia by means of an European
Social Fund (ESF)-Founded Pre-Doctoral Grant of the Catalan Agency for Management of University and Research Grants (AGAUR)
under Grant 2022 FI_B 00903, in part by the Agencia Estatal de Investigación (AEI) under Grant PID-2020-112581GB-C21 (MOTION),
in part by Ministerio de Ciencia e Innovación (MCIN)/AEI/10.13039/501100011033 under Grant TED2021-129319B-I00 and Grant
PID2022-136787NB-I00, and in part by the European Commission–NextGenerationEU, through Momentum CSIC Programme: Develop
Your Digital Talent, under Project MMT24-IIIA-02., Peer reviewed

This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant applications in bioinformatics, particularly for identifying similarities and discovering mutual patterns and important motifs among DNA, RNA, and protein sequences. Building on recent advancements in solving this problem through a general search framework, this paper introduces two novel heuristic approaches designed to enhance the search process by steering it towards promising regions in the search space. The first heuristic employs a probabilistic model to evaluate partial solutions during the search process. The second heuristic is based on a neural network model trained offline using a genetic algorithm. A key aspect of this approach is extracting problem-specific features of partial solutions and the complete problem instance. An effective hybrid method, referred to as the learning beam search, is developed by combining the trained neural network model with a beam search framework. An important contribution of this paper is found in the generation of real-world instances where scientific abstracts serve as input strings, and a set of frequently occurring academic words from the literature are used as restricted patterns. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed approaches in solving the RLCS problem. Finally, an empirical explainability analysis is applied to the obtained results. In this way, key feature combinations and their respective contributions to the success or failure of the algorithms across different problem types are identified., 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” under project no. 1259084 and the project “Application of deep learning in improving the efficiency of optimization algorithms” under project no. 1259114. 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. A. Nikolikj and T. Eftimov acknowledge the support of the Slovenian Research and Innovation Agency through program grant P2-0098, project grants No. J2-4460 and No. GC-0001, BI-BA/24-25-006, and young researcher grant No. PR-12897 to AN. This work is also funded by the European Union under Grant Agreement 101187010 (HE ERA Chair AutoLearn-SI). The authors would like to acknowledge the use of the Computing Cluster Unit of the Institute of Logic and Computation at the Vienna University of Technology for this research project., Peer reviewed

For detailed results, interested parties can request information from the corresponding author. The CQ100 dataset used for the experimental evaluation is available at https://doi.org/10.17632/vw5ys9hfxw.2. The USC-SIPI dataset is available at https://sipi.usc.edu/database/database.php?volume=misc. The Kodak dataset is available at: https://r0k.us/graphics/kodak., In this paper, we propose SoftBinReduce, a data reduction method for Color Quantization. Our approach consists of four main steps: (1) identifying representatives in each channel, (2) generating three-dimensional bins, (3) distributing pixel values using a soft binning technique, and (4) repositioning the resulting bins. Additionally, we introduce an adapted Sort-Means algorithm for the Pixel Mapping phase, providing a strong initial guess. We conduct an extensive experimental evaluation primarily using the CQ100 dataset, but also the older Kodak and USC-SIPI datasets. We compare our method to two well-known data reduction techniques from the literature: pseudo-random and quasi-random sampling. The results demonstrate that our method outperforms both in terms of NMSE error versus the achieved speedup, particularly when the data is significantly reduced and with a higher number of colors in the generated palette. Moreover, an ablation study shows that all components of our method are necessary for producing high-quality results., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Guillem Rodríguez Corominas acknowledges support from the Department of Research and Universities of the Government of Catalonia by means of an ESF-founded pre-doctoral grant of the Catalan Agency for Management of University and Research Grants (AGAUR), under ref. number 2022 FI_B 00903. Maria J. Blesa acknowledges support from AEI under MICINN grant PID-2020-112581GB-C21 (MOTION). Christian Blum was supported by two grants funded by MCIN/AEI/10.13039/501100011033: TED2021-129319B-I00 and PID2022-136787NB-I00., We acknowledge administrative and technical support by the Spanish National Research Council (CSIC) and by the Universitat Politècnica de Catalunya (UPC)., Peer reviewed

Expanded Dataset for Two-Echelon Electric Vehicle Routing Problems (2E-EVRP) (Original data) (Zenodo).
2E-EVRP-Instances (Original data) (Zenodo)., This paper introduces a dataset generated for research on Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs) with additional constraints, including time windows, simultaneous pickup and delivery (SPD), and partial deliveries. The dataset is derived from established benchmark instances from the VRP and EVRP literature and further extended using methodologies from the literature. It features diverse scenarios designed to challenge and validate solution approaches proposed for two-echelon routing algorithms under various constraints. The dataset comprises a variety of instances, ranging from small (5-15 customers) to large (100 customers), and incorporates different geographical configurations, including clustered, random, and random-clustered distributions. Key modifications include integrating satellite locations, enhanced vehicle configurations concerning electric vehicle (EV) constraints, and adjusting time windows to accommodate the two-echelon structure. The dataset also supports multiple delivery scenarios, allowing for single delivery, simultaneous pickups and deliveries (SPD), and partial deliveries, enabling researchers to test the performance of their algorithms across a range of realistic constraints., 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 datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/crjimene/swarm_gpt., This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a toolchain that integrates LLMs with the NetLogo simulation platform, leveraging its Python extension to enable communication with GPT-4o via the OpenAI API. This toolchain facilitates prompt-driven behavior generation, allowing agents to respond adaptively to environmental data. For both example applications mentioned above, we employ both structured, rule-based prompts and autonomous, knowledge-driven prompts. Our work demonstrates how this toolchain enables LLMs to study self-organizing processes and induce emergent behaviors within multi-agent environments, paving the way for new approaches to exploring intelligent systems and modeling swarm intelligence inspired by natural phenomena. We provide the code, including simulation files and data at https://github.com/crjimene/swarm_gpt., The author(s) declare that financial support was received for the research and/or publication of this article. Christian Blum was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033.This research was supported by the EUTOPIA Science and Innovation Fellowship Programme and funded by the European Union Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement No 945380., Peer reviewed

Federated learning (FL) is a recent technique that emerged to handle the vast amount of training data needed in machine learning algorithms while fulfilling data owners’ privacy challenges in such scenarios. Simultaneously, the field of quantum computing (QC), using quantum properties such as entanglement and superposition to perform computation, has experienced exponential growth, theoretically proving to be more efficient in specific machine learning tasks and creating the discipline known as quantum machine learning (QML). Thus, an emerging body of knowledge has started studying the combination of these two research agendas, giving rise to the field of quantum federated learning (QFL). In this review, we systematically classify the existing literature through a novel taxonomy, identify current trends and challenges, and highlight research gaps and future directions to support the continued development of this emerging field., This work was founded partially by the Government of Catalonia Exp. Num. 2023 DI 00071 and by Strategic Platform under the development of an industrial PhD in Quantum Machine Learning. Jesus Cerquides is funded by European Union KHealthInAir, GUARDEN, and Humane-AI projects with No. 101057693, 101060693, and 952026. This work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

This paper presents a web-based instance generator for Electric Vehicle Routing Problems (EVRP) with Road Junctions and Road Types, using OpenStreetMap data. Users define an area, specify network components (depots, customers, charging stations, junctions), and customize vehicle parameters. The React-based frontend enables configuration, visualization, and queries, while the Flask backend processes road networks, classifies road types, and assigns demand and service times. A RESTful API ensures real-time instance generation. Generated instances can be downloaded as text-based datasets and interactive visualizations. The tool is open-source and contributes to the area of sustainable transportation by enabling scenario-based EVRP experimentation., This work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033 ., Peer reviewed

Sensibly planning the subjects to study during a university degree is one of the most crucial tasks that impact the future professional life of a student. Nonetheless, to the best of our knowledge, no automated solution is available for students who want to plan their desired degree path and maximize the skills required by desired or target job(s). In this paper, we consider the Degree Planning Problem (DPP), which aims at computing degree plans composed of university subjects for students during the completion of an undergraduate degree. Specifically, we aim to obtain the best set of skills matching the requirements of students’ preferred job(s). To achieve this objective, we propose a flexible and scalable approach that solves the DPP in real-time by means of a non-trivial formalization as an optimization problem that can be solved with standard solvers. Finally, we employ real data from our University’s Bachelor in Information and Communications Technology to show, through several use cases, that our approach can be a valuable decision-support tool for students and curriculum designers., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

The authors were supported by the research projects ACISUD (PID2022-136787NB-I00) and Yoma Operational Research (OPE02570)., Peer reviewed

[Data availability]
Concerning comprehensive results, individuals interested can request details from the corresponding author. The software and the problem instances are publicly available at https://github.com/ccblum/MGD_software.git., The CMSA algorithm for combinatorial optimization is a hybrid technique based on repeatedly solving sub-instances to the original problem instance. The incumbent sub-instance is extended at each iteration by the probabilistic generation of valid solutions to the original problem instance and by adding the components found in these solutions to the sub-instance. In addition, the incumbent sub-instance is reduced at each iteration by removing seemingly useless solution components. In recent years the usefulness of the CMSA algorithm has been shown by a range of applications to different combinatorial optimization problems. In this work, we provide a gentle introduction to CMSA by describing the application to the so-called minimum global domination problem as an example., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The author was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

Norms have been widely enacted in human and agent societies to regulate individuals’ actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature’s study of the relations between norms, actions, and values, we formally define how actions and values relate through the so-called value judgment function and how norms and values relate through the so-called norm promotion function. We show that both functions provide the means to compute value alignment for a set of norms. Moreover, we detail how to cast our decision-making problem as an optimisation problem: finding the norms that maximise value alignment. We also show how to solve our problem using off-the-shelf optimisation tools. Finally, we illustrate our approach with a specific case study on the European Value Study., This work has been funded by projects VAE (TED2021-131295B-C31), VALAWAI (HE-101070930), LOGISTAR (H2020-769142), AI4EU (H2020-825619), Crowd4SDG (H2020-872944), ACISUD (PID2022-136787NB-I00), COREDEM (H2020-785907), Fairtrans (PID2021-124361OB-C33), AUTODEMO (SR21-00329), TAILOR (H2020-952215), assist-Decidim (23S02974-001), 2021 SGR 00313, and 2021 SGR 00754. F. Bistaffa was supported by the H2020-MSCAIF- 2016 HPA4CF project. The authors have no competing interests to declare that are relevant to the content of this article. Maite Lopez-Sanchez belongs to the WAI research group (University of Barcelona) an associated unit to CSIC through the IIIA., Peer reviewed

Human-Aware Artificial Intelligence Special Issue., Videogame developers typically conduct user experience surveys to gather feedback from users once they have played. Nevertheless, as users may not recall all the details once finished, we propose an ethical conversational agent that respectfully conducts the survey during gameplay. To achieve this without hindering user’s engagement, we resort to reinforcement learning and an ethical embedding algorithm. Specifically, we transform the learning environment so that it guarantees that the agent learns to be respectful (i.e. aligned with the moral value of respect) while pursuing its individual objective of eliciting as much feedback information as possible. When applying this approach to a simple videogame, our comparative tests between the two agents (ethical and unethical) empirically demonstrate that endowing a survey-oriented conversational agent with this moral value of respect avoids disturbing user’s engagement while still pursuing its individual objective, which is to gather as much information as possible., The author(s) received no financial support for the research, authorship, and/or publication of this article., This research was partially supported by projects VAE TED2021-131295B-C31, funded by MCIN/AEI/10.13 039/501100011033 and NextGenerationEU/PRTR, VALAWAI (Horizon Europe #101070930), ACISUD (PID2022-136787NB-I00 funded by MICIU/AEI/10.13039/501100011033), AUTODEMO (SR21-00329) by Fundación La Caixa, Crowd4SDG (H2020-872944), COREDEM (H2020-785907) and FairTransNLP-Language (PID2021-124361OB-C33). Maite Lopez-Sanchez and Inmaculada Rodriguez belong to the WAI research group (University of Barcelona) associated unit to CSIC by IIIA. Rodriguez-Aguilar is also supported by the ‘YOMA Operational Research’ project funded by the Botnar Foundation., 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

Cheese-making is a complex process involving numerous stages, with multiple factors contributing and complex interactions occurring among the physicochemical elements involved. Understanding the process and optimizing its stages has attracted the attention of numerous investigations. In recent years, Machine Learning (ML) has established itself as one of the most advanced tools for data analysis and modeling thanks to its ability to capture complex and non-linear patterns. In the area of food science and engineering, these algorithms have started to be used as an alternative to more traditional statistical and mathematical prediction models. This paper explores the main research on ML applied to the study of cheese, from its production stages (i.e., fermentation or coagulation process) to the final product (i.e., detection of adulterations or food fraud). Particularly, we review 42 papers published between January 2014 and January 2025, with the aim of identifying common approaches. First, we present an explanation of the main concepts required to bring these approaches closer to researchers who are not experienced in applying ML. Then, we analyze the selected publications to detail the tasks of interest and the algorithms proposed to solve them. Finally, we detect gaps and opportunities to incorporate ML into future cheese research., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Daniel Pardo is funded by the AGAUR FI Joan Oró grant (2024 FI-1 00424). Jesus Cerquides and Mehmet Oguz Mulayim are 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

[Data availability statemente]
The data employed for this study were individual patient pseudo-anonymized data and were obtained under the Data Analysis Programme for Research and Innovation in Healthcare (PADRIS), managed by AQuAS, the Agency for Health Quality and Assessment of Catalonia. This data cannot be made available publicly., [Purpose]
The survival of a knee prosthesis is one of the most important indicators of the success or failure of a knee arthroplasty. An intervention that could increase prosthetic survival is the use of antibiotic-loaded bone cement (ALBC) during primary surgery, but the evidence for this is not conclusive. The question of whether such an intervention increases prosthetic survival is a causal one, and yet it has never been addressed with causal methods in the observational studies literature. This constitutes a serious limitation, as there is growing evidence that the best-suited framework for addressing causal questions with observational data is causal inference., [Methods]
In the present study, causal inference methods were employed to answer the research question of whether ALBC increases prosthetic survival. In particular, directed acyclic graphs were used for identification and causal survival forests were used to estimate the effect of interest. The rationale behind these methods is provided in the main text, and technical details are provided in Supporting Information: File S3. Data from the Catalan Arthroplasty Register were analysed., [Results]
ALBC had an effect of increasing the overall prosthetic survival by 8% after 120 months of follow-up. The intervention had a positive effect across all the subgroups of the population defined by confounding variables, but the effect was greater in men, young patients, patients with rheumatoid arthritis or obesity, and patients who smoked or abused alcohol. The chosen causal assumptions had an impact on the obtained results, empirically showing the importance of using a causal framework., [Conclusions]
ALBC increased knee prosthesis survival among patients in the Catalan public healthcare system. Causal inference methods are the most appropriate for answering causal questions about the effect of ALBC on prosthetic survival when the analysed data are observational., [Level of Evidence]
Level III., The authors thank our contributors, Olga Martínez Cruz (Catalan Arthroplasty Register [RACat], Agència de Qualitat i Avaluació Sanitàries de Catalunya [AQuAS], Barcelona, Spain), Francesc Pallisó Folch (Advisory Committee, Catalan Arthroplasty Register [RACat]; Hospital Universitari Santa Maria, Gestió de Serveis Sanitaris, Lleida, Spain) and Mireia Espallargues Carreras (Catalan Arthroplasty Register [RACat], Agència de Qualitat i Avaluació Sanitàries de Catalunya [AQuAS]; Network for Research on Chronicity, Primary Care and Health Promotion [RICAPPS], Spain) for their role in the data extraction of the RACat and their valuable comments for the manuscript. This work was supported by the Ministerio de Ciencia e Innovación (projects PID2022-136787NB-I00 and PID2019-104156GB-I00), the European Commission H2020 programme (grants 101060693, 101057693 and 952026) and the Agència de Gestió d'Ajuts Universitaris i de Recerca (Award Number: 2020-18). None of the funding sources had a direct role in this work., 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

This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases., This research project was made possible through the access granted by the Galician Supercomputing Center (CESGA) to its supercomputing infrastructure. The supercomputer FinisTerrae III and its permanent data storage system have been funded by the Spanish Ministry of Science and Innovation, the Galician Government, and the European Regional Development Fund (ERDF). Funding sources The research presented in this paper was supported by the EU-funded VALAWAI (# 101070930) project, the Spanish-funded EVASAI (# PID2024-158227NB-C31), ANNOTATE-SenSIT (# PID2024-156022OB-C33), VAE (# TED2021-131295B-C31), EMOROBCARE (# IASOMMA2024) and Rhymas (# PID2020-113594RB-100) projects. This work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. It was also funded by GUARDEN (101060693), Fairtrans (PID2021-124361OB-C33), AUTODEMO (SR21-00329), TAILOR (H2020-952215), PrepParticip2.0 ( 24S03545-001), grants 2021 SGR 00313 and 2021 SGR 00754. Maite Lopez-Sanchez belongs to the WAI research group (University of Barcelona) associated unit to CSIC by IIIA. Roxana Rădulescu was partly supported by the Research Foundation Flanders (FWO), grant number 1286223N. This research was partially supported by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program and by the FWO, grant number G062819N., Peer reviewed

In this paper, we present FastKpp, a C++ implementation of an exact accelerated k[jls-end-space/]-means(Formula presented) algorithm using two geometry-aware filters: one based on the Triangle Inequality (TIE) and another based on point norms. Both provably preserve the original D 2 sampling distribution while avoiding redundant distance computations. The library provides a header-only API, a command-line tool, and a fully reproducible capsule. Experiments show substantial reductions in distance computations and speedups that increase with k (with typical median between 2 and 8). Furthermore, FastKpp outperforms other state-of-the-art exact acceleration techniques. Results are reproducible via the public GitHub repository and Code Ocean capsule., This work was supported in part by the Department of Research and Universities of the Government of Catalonia by means of an European Social Fund (ESF)-Founded Pre-Doctoral Grant of the Catalan Agency for Management of University and Research Grants (AGAUR) under Grant [2022 FI_B 00903](https://www.sciencedirect.com/science/article/pii/S2665963826000205?via%3Dihub#GS1), in part by the Agencia Estatal de Investigación (AEI) under grants [PID-2020-112581GB-C21 (MOTION)](https://www.sciencedirect.com/science/article/pii/S2665963826000205?via%3Dihub#GS2) and [PID2022-136787NB-I00 (ACISUD)](https://www.sciencedirect.com/science/article/pii/S2665963826000205?via%3Dihub#GS2), and in part by the European Commission–NextGenerationEU, through Momentum CSIC Programme: Develop Your Digital Talent , under Project [MMT24-IIIA-02](https://www.sciencedirect.com/science/article/pii/S2665963826000205?via%3Dihub#GS3)., Peer reviewed

Norms have been widely enacted in human and agent societies to regulate individuals’ actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature’s study of the relations between norms, actions, and values, we formally define how actions and values relate through the so-called value judgment function and how norms and values relate through the so-called norm promotion function. We show that both functions provide the means to compute value alignment for a set of norms. Moreover, we detail how to cast our decision-making problem as an optimisation problem: finding the norms that maximise value alignment. We also show how to solve our problem using off-the-shelf optimisation tools. Finally, we illustrate our approach with a specific case study on the European Value Study., This work has been funded by projects VAE (TED2021-131295B-C31), VALAWAI (HE101070930), LOGISTAR (H2020-769142), AI4EU (H2020-825619), Crowd4SDG (H2020-872944), ACISUD (PID2022-136787NB-I00), COREDEM (H2020-785907), Fairtrans (PID2021-124361OBC33), AUTODEMO (SR21-00329), TAILOR (H2020-952215), assist-Decidim (23S02974-001), 2021 SGR 00313, and 2021 SGR 00754. F. Bistafa was supported by the H2020-MSCAIF- 2016 HPA4CF project. The authors have no competing interests to declare that are relevant to the content of this article. Maite Lopez-Sanchez belongs to the WAI research group (University of Barcelona) an associated unit to CSIC through the IIIA.

The purpose of this study was to analyse the impact on peri-prosthetic joint infection (PJI) rate and prosthetic survival using antibiotic-loaded bone cement (ALBC) versus plain cement during total knee arthroplasty (TKA). A retrospective cohort study was conducted. The main data source was the Catalan Arthroplasty Register (RACat). TKAs with surgery date between 1 January 2011 and 31 December 2020 were analysed and followed up until 31 December 2023. The main variable of interest was the type of cement (ALBC vs. plain cement), and several endpoints (septic revision, aseptic revision, and all-cause revision) were considered. The analysed outcomes were revision rates, survival rates and risk factors' hazard ratios (HR). A total of 22,781 TKAs were analysed, 13,125 (57.6%) of them with plain cement and 9656 (42.4%) with ALBC. The septic revision rate was lower in the ALBC group after 3 months of follow-up (0.52% vs. 0.78%, p value = 0.04). Prosthetic survival with respect to the aseptic revision endpoint was also higher for the ALBC group during the whole follow-up period (~158 months). Regarding risk factors for infection, ALBC showed a protective effect, HR: 0.53 (0.44, 0.63), while sex (being male) and the analysed comorbidities increased the risk. ALBC is associated with a reduction in both the septic revision and the aseptic revision rate after TKA, and thus with higher prosthetic survival. Level III, Therapeutic, retrospective.

Cheese-making is a complex process involving numerous stages, with multiple factors contributing and complex interactions occurring among the physicochemical elements involved. Understanding the process and optimizing its stages has attracted the attention of numerous investigations. In recent years, Machine Learning (ML) has established itself as one of the most advanced tools for data analysis and modeling thanks to its ability to capture complex and non-linear patterns. In the area of food science and engineering, these algorithms have started to be used as an alternative to more traditional statistical and mathematical prediction models. This paper explores the main research on ML applied to the study of cheese, from its production stages (i.e., fermentation or coagulation process) to the final product (i.e., detection of adulterations or food fraud). Particularly, we review 42 papers published between January 2014 and January 2025, with the aim of identifying common approaches. First, we present an explanation of the main concepts required to bring these approaches closer to researchers who are not experienced in applying ML. Then, we analyze the selected publications to detail the tasks of interest and the algorithms proposed to solve them. Finally, we detect gaps and opportunities to incorporate ML into future cheese research.

The survival of a knee prosthesis is one of the most important indicators of the success or failure of a knee arthroplasty. An intervention that could increase prosthetic survival is the use of antibiotic-loaded bone cement (ALBC) during primary surgery, but the evidence for this is not conclusive. The question of whether such an intervention increases prosthetic survival is a causal one, and yet it has never been addressed with causal methods in the observational studies literature. This constitutes a serious limitation, as there is growing evidence that the best-suited framework for addressing causal questions with observational data is causal inference. In the present study, causal inference methods were employed to answer the research question of whether ALBC increases prosthetic survival. In particular, directed acyclic graphs were used for identification and causal survival forests were used to estimate the effect of interest. The rationale behind these methods is provided in the main text, and technical details are provided in Supporting Information: File S3. Data from the Catalan Arthroplasty Register were analysed. ALBC had an effect of increasing the overall prosthetic survival by 8% after 120 months of follow-up. The intervention had a positive effect across all the subgroups of the population defined by confounding variables, but the effect was greater in men, young patients, patients with rheumatoid arthritis or obesity, and patients who smoked or abused alcohol. The chosen causal assumptions had an impact on the obtained results, empirically showing the importance of using a causal framework. ALBC increased knee prosthesis survival among patients in the Catalan public healthcare system. Causal inference methods are the most appropriate for answering causal questions about the effect of ALBC on prosthetic survival when the analysed data are observational. Level III.

Purpose: The purpose of this study was to analyse the impact on peri-prosthetic joint infection (PJI) rate and prosthetic survival using antibiotic-loaded bone cement (ALBC) versus plain cement during total knee arthroplasty (TKA). Methods: A retrospective cohort study was conducted. The main data source was the Catalan Arthroplasty Register (RACat). TKAs with surgery date between 1 January 2011 and 31 December 2020 were analysed and followed up until 31 December 2023. The main variable of interest was the type of cement (ALBC vs. plain cement), and several endpoints (septic revision, aseptic revision, and all-cause revision) were considered. The analysed outcomes were revision rates, survival rates and risk factors' hazard ratios (HR). Results: A total of 22,781 TKAs were analysed, 13,125 (57.6%) of them with plain cement and 9656 (42.4%) with ALBC. The septic revision rate was lower in the ALBC group after 3 months of follow-up (0.52% vs. 0.78%, p value = 0.04). Prosthetic survival with respect to the aseptic revision endpoint was also higher for the ALBC group during the whole follow-up period (~158 months). Regarding risk factors for infection, ALBC showed a protective effect, HR: 0.53 (0.44, 0.63), while sex (being male) and the analysed comorbidities increased the risk. Conclusions: ALBC is associated with a reduction in both the septic revision and the aseptic revision rate after TKA, and thus with higher prosthetic survival. Level of evidence: Level III, Therapeutic, retrospective., This work was supported by the Ministerio de Ciencia e Innovación (projects PID2022-136787NB-I00 and PID2019-104156GB-I00), the European Commission H2020 program (grants 101060693, 101057693, 952026) and the Agència de Gestió d'Ajuts Universitaris i de Recerca (Award Number: 2020-18). None of the funding sources had a direct role in this work.

[EN] We adopt an emerging and prominent vision of human -centred Artificial Intelligence that requires building trustworthy intelligent systems. Such systems should be capable of dealing with the challenges of an interconnected, globalised world by handling plurality and by abiding by human values. Within this vision, pluralistic value alignment is a core problem for AI- that is, the challenge of creating AI systems that align with a set of diverse individual value systems. So far, most literature on value alignment has considered alignment to a single value system. To address this research gap, we propose a novel method for estimating and aggregating multiple individual value systems. We rely on recent results in the social choice literature and formalise the value system aggregation problem as an optimisation problem. We then cast this problem as an l(p)-regression problem. Doing so provides a principled and general theoretical framework to model and solve the aggregation problem. Our aggregation method allows us to consider a range of ethical principles, from utilitarian (maximum utility) to egalitarian (maximum fairness). We illustrate the aggregation of value systems by considering real -world data from two case studies: the Participatory Value Evaluation process and the European Values Study. Our experimental evaluation shows how different consensus value systems can be obtained depending on the ethical principle of choice, leading to practical insights for a decision -maker on how to perform value system aggregation., The authors were supported by the research projects ACISUD (PID2022-136787NB-I00) , VAE (TED2021-131295B-C31) , VALAWAI (HE-101070930) , Fairtrans (PID2021-124361OB-C33) , Yoma OR (OPE02570) , TAILOR (952215) , and the Hybrid Intelligence Center, a 10-year programme funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research. Maite Lopez-Sanchez belongs to the WAI research group (University of Barcelona) , an associated unit to CSIC through the IIIA.