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Structural and Semantic Enrichment of Models for the Interactive Discovery of Clinical Processes

Structural and Semantic Enrichment of Models for the Interac...

International Workshops which were held in conjunction with the 6th International Conference on Process Mining, ICPM 2024

作者： Bayo-Montón, Jose Luis Martínez-Salvador, Begoña Fernández-Llatas, Carlos Marcos, Mar Department of Computer Engineering and Science Universitat Jaume I Castelló Spain SABIEN-ITACA Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9783031822247

Process Mining (PM) is a relatively new field which provides techniques to analyze business processes in different areas. In the field of Medicine, PM seeks to infer clinical processes from the data routinely collected during healthcare activities. In most frameworks, workflows are used to represent the results obtained by PM techniques. A problem with these workflows is that their structure is complex and not always easy to understand and hence to exploit by the clinician. A different problem, related to Clinical Practice Guidelines (CPGs), is that their development is mostly manual. We posit that workflows inferred by PM techniques could be improved and enriched by providing interactive tools to support their structuring and semantic annotation by clinicians. We also postulate that these improved and enriched models can be used to facilitate the development of CPGs. In this paper we describe our approach to the interactive discovery of clinical processes as well as an implementation to support it within the PMApp PM tool. © The Author(s) 2025.

关键词： Clinical Process Models Interactive Process Discovery Model Enrichment

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Evaluation of the Applicability of Heuristic Optimization and Reinforcement Learning for Pavement Maintenance Planning 2nd

Evaluation of the Applicability of Heuristic Optimization an...

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2nd decision science Alliance International Summer Conference, DSA ISC 2024

作者： Molinero-Pérez, Noelia Sanz-Benlloch, Amalia Montalbán-Domingo, Laura Pellicer, Eugenio García-Segura, Tatiana School of Civil Engineering Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031782404

Efficient road maintenance is crucial for the socioeconomic development of a country. However, traditional pavement analysis systems have limitations in their adaptability to the current reality, which hinders the effective planning of pavement rehabilitation operations. In response to this need, this article presents two techniques to improve decision making in the optimization of road infrastructure solutions: heuristic optimization and reinforcement learning. Both techniques present weaknesses and strengths when applied to road network maintenance management. Heuristic optimization offers robust solutions, but its effectiveness is compromised in uncertainty scenarios. In contrast, reinforcement learning stands out for its ability to adapt to changing and uncertain environments, offering more dynamic and efficient planning, but has limitations in including constraints. This study underscores the importance of adopting innovative and adaptive approaches to road maintenance planning to ensure the long-term sustainability and efficiency of road infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Heuristic Optimization Pavement Maintenance Planning Reinforcement Learning

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Smart Strategies for Project Scheduling: An Adaptive BR-deH Approach 2nd

Smart Strategies for Project Scheduling: An Adaptive BR-DEH...

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2nd decision science Alliance International Summer Conference, DSA ISC 2024

作者： Martin, Xabier A. Panadero, Javier Juan, Angel A. Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

In recent years, management of multiple complex projects has become increasingly common among large-scale companies, posing significant challenges for company managers. These projects are often identified by numerous tasks that have precedence relations and share a set of limited resources. The main goal of project scheduling is to minimize the total time required to complete these projects. However, this requires careful consideration of resource allocation, careful scheduling choices, and exact sequencing to maximize efficiency while managing the complex interactions between task dependencies. A novel discrete-event heuristic is presented to solve this project scheduling problem, which is later extended into a probabilistic algorithm using biased-randomization techniques. In addition, an adaptive mechanism was introduced to tune the parameters of the algorithm for optimal scheduling in different problem instances. Computational results demonstrate the effectiveness of our approach, finding high-quality solutions for difficult problem sets in short computational times. This intuitive and efficient approach can empower company managers to efficiently improve operational efficiency, reduce project costs, and ultimately increase their business’s success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Scheduling algorithms

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Energy Management of Electric Vehicles: AI-Driven Strategies for Smart Grid-Connected Charging Hubs 2nd

Energy Management of Electric Vehicles: AI-Driven Strategie...

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2nd decision science Alliance International Summer Conference, DSA ISC 2024

作者： Peyman, Mohammad Martin, Xabier A. Panadero, Javier Research Center on Production Management and Engineering Universitat Politècnica de València Plaza Ferrandiz-Salvador Alcoy03801 Spain Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Carrer de les Sitges Bellaterra08193 Spain

ISBN: (纸本)9783031782404

This paper presents an innovative approach to improving electric vehicle (EV) routing in smart cities by combining heuristics and discrete-event simulation, specifically addressing the team orienteering problem. Initially, a biased-randomized heuristic approach is used to discover the most efficient EV routes, but this does not take into account the limitation imposed by the interconnected nature of the power grid. While these routes appear efficient when assessed independently, a more comprehensive evaluation is performed using a simulation model that includes the complex network of interconnected charging stations. This crucial second phase evaluates the practicality of the heuristic solutions in real-world scenarios by investigating how node interconnection affects route efficiency and charging station service. The simulation results provide concrete insights into optimizing EV mobility, enabling route adaptability to the dynamic urban landscape. This approach efficiently bridges the gap between theoretical optimization and actual implementation, emphasizing the need for simulation in verifying and refining routing strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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A Simulation-Optimisation Tool for Routing Drones in Realistic Conditions 16th

A Simulation-Optimisation Tool for Routing Drones in Realis...

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16th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Martin, Xabier A. Keenan, Peter Panadero, Javier McGarraghy, Sean Juan, Angel A. Research Center on Production Management and Engineering Universitat Politècnica de València Alcoy Spain Centre for Business Analytics School of Business University College Dublin Belfield Ireland Department of Computer Architecture and Operating Systems Universitat Autònoma de Barcelona Bellaterra Spain

ISBN: (纸本)9783031873447

The use of drones for routing and monitoring tasks has grown significantly, with applications such as traffic surveillance and road inspections gaining prominence . These real-world scenarios often involve unpredictable factors like fluctuating service times, which add complexity to traditional routing problems. This paper introduces a simulation-optimisation framework for routing drones under realistic conditions . To efficiently solve this problem, we propose a simheuristic approach that integrates a biased-randomised iterated local search metaheuristic with Monte Carlo simulation. Our computational experiments validate the efficiency, robustness, and speed of the proposed method, providing high-quality solutions to routing challenges in uncertain environments. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Heuristic methods

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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome

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BMC Medical Informatics and decision Making 2025年第1期25卷 1-22页

作者： Pons-Suñer, Pedro Signol, François Alvarez, Noemi Sargas, Claudia Dorado, Sara Ortí, Jose Vicente Gil delgado Sanchis, Juan A. Llop, Marta Arnal, Laura Llobet, Rafael Perez-Cortes, Juan-Carlos Ayala, Rosa Barragán, Eva ITI Universitat Politècnica de València Valencia Spain Hospital Universitario 12 de Octubre Imas12 Departament of Medicine Complutense University Madrid Spain Instituto de Investigación Sanitaria La Fe Valencia Spain Altum Sequencing s.l. Computer Science and Engineering Department Carlos III University Madrid Spain

Background and objective: This study has two main objectives. First, to evaluate a feature selection methodology based on SEQENS, an algorithm for identifying relevant variables. Second, to validate machine learning models that predict the risk of complications in patients with acute myeloid leukemia (AML) using data available at diagnosis. Predictions are made at three time points: 90 days, six months, and one year post-diagnosis. These objectives represent fundamental steps toward the development of a tool to assist clinicians in therapeutic decision-making and provide insights into the risk factors associated with AML complications. Methods: A dataset of 568 patients, including demographic, clinical, genetic (VAF), and cytogenetic information, was created by combining data from Hospital 12 de Octubre (Madrid, Spain) and Instituto de Investigación Sanitaria La Fe (valencia, Spain). Feature selection based on an enhanced version of SEQENS was conducted for each time point, followed by the comparison of four classifiers (XGBoost, Multi-Layer Perceptron, Logistic Regression and decision Tree) to assess the impact of feature selection on model performance. Results: SEQENS identified different relevant features for each prediction horizon, with Age, TP53, − 7/7Q, and EZH2 consistently relevant across all time points. The models were evaluated using 5-fold cross-validation, XGBoost achieve the highest average ROC-AUC scores of 0.81, 0.84, and 0.82 for 90-day, 6-month, and 1-year predictions, respectively. Generally, performance remained stable or improved after applying SEQENS-based feature selection. Evaluation on an external test set of 54 patients yielded ROC-AUC scores of 0.72 (90-day), 0.75 (6-month), and 0.68 (1-year). Conclusions: The models achieved performance levels that suggest they could serve as therapeutic decision support tools at different times after diagnosis. The selected variables align with the European LeukemiaNet (ELN) 2022 risk classification, an

关键词： Acute myeloid leukemia Complications risk factors Cytogenetics Machine learning Mortality Patient evolution Recurrence Therapy outcome Variant Allele Frequency

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Study on the Failure Mechanism of deep Foundation Pit of High-Rise Building: Comprehensive Test and Microstructure Coupling

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Buildings 2025年第8期15卷

作者： Zhou, Zhiwu Yepes-Bellver, Lorena Alcalá, Julián Yepes, Víctor Hunan Provincial Key Laboratory of Intelligent Protection and Utilization Technology in Masonry Artifacts Hunan University of Science and Engineering Yongzhou425006 China Mechanics of Continuous Media nd Theory of Structures Department Universitat Politènica de València València46022 Spain Universitat Politècnica de València València46022 Spain

Under the adverse geological conditions of silty soft soil in coastal, lakeside, and river areas of countries worldwide, safety and quality during deep foundation pit construction are research challenges that researchers must overcome. Through 3D simulation, micro-finite element coupling modeling and construction site monitoring tests, this paper comprehensively analyzes the formation mechanism and causes of deep foundation pit foundation quality defects and diseases under the most unfavorable environment and multi-factor interference and puts forward scientific treatment suggestions. The research process accurately applies multidisciplinary coupling model research such as computer science, instrument science and technology, and material mechanics to solve the impact of multi-factor and uncertain environments on construction. The final research results provide sufficient theoretical and physical cases for improving the safety and stability of deep foundation pits under soft foundation conditions and provide rich practical specifications for the testing, monitoring and construction of similar projects;it provides a strong guarantee for the global deep foundation pit monitoring and early warning system under soft foundation conditions. © 2025 by the authors.

关键词： Fracture mechanics

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Support planning to the implementation of school of engineering in computer science - An oriented approach to teacher training

Support planning to the implementation of School of Engineer...

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Promotion and Innovation with New Technologies in engineering Education (FINTDI)

作者： Esther Sanabria Codesal Rosa Maria Alcover Angeles Calduch Jose Antonio Ontalba Jose Luis Poza Natividad Prieto School of Engineering in Computer Science Universitat Politècnica de València Valencia Spain

Main objective of Action Plan for European Convergence (PACE) that universitat politecnica de valencia (UPV) has been developing since academic year 2005-2006 has been to facilitate the process of adaptation of new courses of bachelor's degree to the European Higher Education Area (EEES). To carry out this ambitious project, centers have identified a set of coordinated actions by teams of teachers interested in teaching innovation. One of the activities promoted by school of engineering in computer science (ETSINF) of UPV is a training plan for teachers. The main objective is to provide teachers with tools that facilitate the design of new courses of Bachelor's degree in computer engineering under the current regulations, with particular emphasis on two aspects: the use of active methodologies and the evaluation criteria. This article describes the training activities that have taken place in the ETSINF during academic year 2009-2010 and the results of opinion poll for teachers about the appropriateness of these actions and their impact on the process to adapt their courses.

关键词： Ice Europe Portals Software Hardware

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deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Afraji, Doaa Mohsin Abd Ali Lloret, Jaime Peñalver, Lourdes Department of Computer Engineering Universitat Politècnica de València Valencia Spain Department of Computer Science College of Education Mustansiriyah University Baghdad Iraq Integrated Management Coastal Zones Research Institute Universitat Politècnica de València Valencia Spain

The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment. © 2025

关键词： Cybersecurity Dataset limitations DDoS attacks deep learning Explainable AI Internet of things Machine learning algorithms

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A biased-randomised iterated local search for the team orienteering arc routing problem allowing different origin and destination

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Journal of Heuristics 2025年第2期31卷 1-27页

In this paper, we consider the hybridisation of the team orienteering problem and the arc routing problem, the so-called team orienteering arc routing problem (TOARP). This problem has recently raised interest among researchers and practitioners as it can model new routing problems involving unmanned aerial vehicles or other types of electric vehicles. In the TOARP, a fixed fleet of vehicles, initially located at a depot, has to collect as much reward as possible from visiting a set of arcs, while they must visit a set of required arcs. At the same time, they must return to the depot on or before a given deadline (which can be time-based or distance-based). In this paper, we explore an extension of the TOARP in which the origin depot and the destination depot may be different nodes in a network. To solve this version of the problem, we propose a novel biased-randomised iterated local search algorithm. Computational results show the capability, efficiency and robustness of our approach, which provides competitive solutions to the TOARP in short computing times and outperforms some of the best-known solution approaches in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Local search (optimization)

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