SIMULTECH 2026 Abstracts

Area 1 - Modeling and Simulation Methodologies
Full Papers Short Papers
Area 2 - Simulation Technologies, Tools and Platforms
Full Papers Short Papers
Area 3 - Application Domains
Full Papers Short Papers

Area 1 - Modeling and Simulation Methodologies

Full Papers
Paper Nr: 19
Title:

Broker-Based Distributed DEVS Simulation: Enabling Multi-Framework Interoperability through Message Broker Architecture
Authors:

A. Dominici, L. Capocchi, J. F. Santucci, C. Koertje and R. Kewley
Abstract: Interoperability across heterogeneous DEVS frameworks remains challenging in distributed modeling and simulation environments. Existing middleware and web-service approaches introduce overhead through synchronous protocols and centralized coordination. This paper presents a message broker-based flattened DEVS abstract simulator using Kafka for asynchronous communication. A typed Kafka-DEVS protocol maps abstract simulator operations to JSON messages, preserving DEVS semantics through strict phase ordering. The architecture embeds Client (Consumer/Producer) - Messaging Proxy and Adapter within distributed workers, enabling language-agnostic co-simulation. A local builder mechanism dynamically integrates non-native atomic models inside the simulation model. Validated on the MS4Me platform with Python, Go, and Java models, the approach demonstrates scalable multi-framework DEVS interoperability.
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Paper Nr: 22
Title:

An Empirical Evaluation of SystemC Transaction Data Storage Formats
Authors:

George Frazier and Dusti Johnson
Abstract: SystemC transaction-level modeling (TLM) is widely used for system integration, virtual platform simulation, and early software bring-up, yet storage and analysis of TLM traces remains unstandardized and largely driven by convenience or legacy tooling. This paper presents a comparative empirical evaluation of storage formats for SystemC TLM-2.0 transaction traces and, to our knowledge, the first peer-reviewed evaluation of columnar (Apache Parquet) and array-oriented (Zarr) formats in this context. We evaluate CSV, SQLite, Parquet, and Zarr across workloads ranging from deterministic microbenchmarks to full-system Linux boot, measuring write-time overhead, storage footprint, and post-simulation query performance. Results show that storage layout is a first-order determinant of footprint and query performance at scale. Parquet achieves order-of-magnitude reductions in trace size and substantial speedups for scan-heavy and temporal aggregation queries, while SQLite consistently outperforms alternatives for selective, record-oriented access. No single format dominates across all workloads; however, the findings demonstrate that modern analytical storage techniques can significantly improve SystemC transaction tracing workflows.
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Paper Nr: 23
Title:

ORCHAV: Connecting Ray-Traced Wireless Simulation and Interactive Propagation Analysis
Authors:

Tanguy Ropitault, Raied Caromi and Nada Golmie
Abstract: Modeling wireless channels with site-specific fidelity is becoming essential as digital twins and emerging 6G applications, including integrated sensing and communications (ISAC), demand propagation insights that statistical models cannot provide. Ray tracing (RT) addresses this need by computing individual propagation paths from 3D geometry and material models. Open-source engines such as Sionna RT have made this capability widely accessible. However, researchers also need tools to set up and run multi-device scenarios efficiently and to explore the resulting time-varying multi-path components (MPCs) interactively. This paper presents ORCHAV (Orchestrated Ray-Tracing / Channel Analysis & Visualization), an open-source framework that complements Sionna RT with two components. The first, a declarative scenario generator, lets researchers define complex simulations (e.g., rotating transmitters, mobile receivers, hovering drones, walking pedestrians) from concise configuration files and produces standardized per-frame ray-tracing output. The second, a high-performance visual-analytics workspace, ingests this output and provides per-path filtering, metric-driven coloring, temporal playback with coverage overlays, and live statistics. Benchmarks spanning 100 to 100 000 MPCs show that ORCHAV sustains real-time playback and interactive filtering above 60 frames per second (FPS) across the full density range. This performance enables rapid interpretation of propagation behavior for research, network-planning diagnostics, and educational demonstrations. The code and scenarios are publicly available.
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Paper Nr: 33
Title:

A Modular Architecture for Vertical Farm Modeling: System Analysis and Framework Design
Authors:

Felix Kuthe and Mohieddine Jelali
Abstract: Vertical farming enables crop production in urban environments, independent of weather and seasonal conditions. Exploiting this potential through analysis, simulation, and optimization requires comprehensive models that capture coupled energy and resource flows. Yet existing models lack structural consistency. Individual studies describe different system configurations without defining boundaries or interfaces, preventing model comparison, validation, and reuse across studies. This study presents a system architecture grounded in system dynamics principles that provides the structural foundation for simulating energy and resource flows in vertical farms. Through functional decomposition, we establish subsystem boundaries comprising invariant functional systems with defined interfaces. We identify state variables from thermodynamic conservation laws and classify them as stocks and flows, establishing their connections across subsystems. Extension procedures accommodate varying levels of modeling detail. The architecture is validated against a systematically reviewed corpus of 21 vertical farm models through a compatibility analysis evaluating coverage and interface conformance across the corpus. An influence mapping onto a representative model reveals subsystem interconnections, input dependencies, and resource consumption paths. The architecture is implemented in a Python-based simulation framework, demonstrating that the modular subsystem structure translates into an executable simulation without structural contradictions.
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Paper Nr: 36
Title:

Semantic Model Complexity for Event Graph Discrete-Event Simulation Models via Abstract State Machines
Authors:

Keng Swee Steve Yeo and Haobin Li
Abstract: Simulation runtime can vary by orders of magnitude across Discrete-Event Simulation (DES) models, yet practitioners lack a reliable way to estimate computational cost before execution. Existing complexity metrics, such as cyclomatic complexity, lines of code, Kolmogorov complexity, measure specification structure but do not capture operational semantics. A metric that accounts for what a model does during execution, not just what it is, would enable pre-simulation comparison of model designs. We propose Semantic Model Complexity (SMC) for Event Graph DES models, grounded in Nowack’s micro time complexity for Abstract State Machines. SMC assigns each event vertex a cost derived from its formal rule structure, weighted by a firing rate estimated from the cycle structure of the Event Graph, and is computed statically without simulation. On a 51-model benchmark spanning three queueing topologies, leave-one-out cross-validation shows SMC outperforms all specification-level competitors on every model (p < 0.0001), achieving predictive R2 (Q2) of 0.952 versus 0.370 for the best competitor. On an out-of-sample industrial warehouse model, SMC is the only metric whose 95% prediction interval contains the observed runtime, while specification-level metrics overpredict by 1,788–5,029%.
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Paper Nr: 43
Title:

Navigating the Use of Generative AI in Modeling and Simulation: A Case-Study-Driven Methodology for Agentic Workflows in Synthetic Data Generation for Surrogate Modeling
Authors:

Dušan Šturek and Sanja Lazarova-Molnar
Abstract: Generative Artificial Intelligence (GenAI), particularly Large Language Models with natural language interfaces, is gaining traction in Modeling and Simulation (M&S) research for its potential to simplify user interaction and support workflow automation. However, integrating GenAI into complex M&S studies poses inherent risks, including ambiguities, hallucinations, and reductions in model fidelity when automation is not adequately controlled. Surrogate Modeling (SMing) builds computationally efficient approximations of high-fidelity simulations from data. This paper investigates the use of GenAI as a middleware-based workflow component to facilitate interaction between modelers and specialized tools for SMing of continuous dynamical systems. The proposed approach is derived through a case-study-driven methodology development. The focus is on supporting automation while preserving the reliability of established SMing tools. We investigate two key challenges: enabling GenAI-assisted automation without compromising the quality of surrogate models produced by specialized SMing tools and integrating such capabilities into practical and reusable workflow for industrial applications. To this end, we develop the Surrogate Modeling Agentic Workflow (SMAW), as a case-study-driven instantiation of the proposed approach, that orchestrates SMing tools and supports synthetic data generation through structured natural language interaction.
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Paper Nr: 46
Title:

Survival Dynamics of FLOSS Communities: An Analysis via Motivation-Driven Agent-Based Modelling and Simulation
Authors:

Yuya Adachi and Toshiaki Aoki
Abstract: Free/Libre and Open Source Software (FLOSS) projects are known to have extremely low survival rates, yet the underlying mechanisms remain poorly understood due to the presence of confounding variables and the difficulty of conducting controlled experiments. This study proposes a motivation-driven agent-based simulation framework for quantifying the survival dynamics of FLOSS communities. The model defines two agent roles-Committer and Contributor-each with a continuous motivation state governed by decay and event-driven updates. It also incorporates a pull request workflow with differentiated feedback signals, a promotion mechanism, and a Poisson arrival process for new participants. Experiments using 10,000 simulation runs across four governance scenarios yield Kaplan-Meier-type survival curves and death-step statistics, addressing three research questions. The results show that (1) increasing the initial number of Committers from one to three improves the survival rate from 22.1% to 78.0%; (2) excessive influx of new Contributors leads to review queue saturation and produces non-monotonic effects; and (3) disabling the promotion mechanism reduces the survival rate to 0.5%. These findings quantitatively demonstrate that ensuring core team redundancy, appropriately managing the contributor influx rate, and establishing a promotion pipeline from Contributor to Committer are essential for the long-term survival of FLOSS projects.
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Paper Nr: 51
Title:

Microclimate-Aware Pedestrian Routing: A Shade Preference Simulation Algorithm
Authors:

Rui Filipe Antunes
Abstract: Urban environments increasingly face challenges related to heat stress and thermal discomfort, especially during warmer seasons. Traditional pedestrian simulation models often overlook microclimatic factors such as solar exposure and shade availability, which significantly influence walking behavior. This paper introduces Sol-A-Pino, a novel agent-based simulation algorithm designed to model pedestrian movement with a preference for shaded routes. By integrating real-time solar data, urban geometry, and behavioral heuristics, Sol-A-Pino enables more realistic simulations of pedestrian dynamics in thermally diverse environments. The algorithm supports urban planning efforts aimed at enhancing walkability, thermal comfort, and climate resilience in cities. Experimental results demonstrate the effectiveness of Sol-A-Pino in generating shade-sensitive pedestrian paths, offering valuable insights to design cooler and more livable urban spaces.
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Paper Nr: 59
Title:

Topspin as an Aerodynamic Stabiliser: A Physics Simulation and Probabilistic Analysis of Table Tennis Ball Flight
Authors:

Shloka Maruthi
Abstract: This paper investigates how accurately the flight of a table tennis ball can be predicted using classical physics combined with probabilistic modelling. A deterministic model based on the Watts–Ferrer Magnus lift formula, Reynolds-dependent drag, and exponential spin decay first produces ideal baseline trajectories. A Monte Carlo extension introduces Gaussian air turbulence and player execution errors to generate probability distributions of landing positions. Topspin is shown to act as a passive aerodynamic stabilisation system: it steepens the descent angle by 8.64◦, raises the maximum safe launch speed by 59% at 6,000 RPM, reduces mean squared landing displacement by 82%, and maintains an 84% shot-success rate under moderate execution error compared with 44% for a flat drive. These results provide quantitative support for coaching advice to prioritise topspin, and introduce a stochastic trajectory framework applicable to other rotating-ball sports simulations.
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Paper Nr: 60
Title:

The Quantification of Modeling Aspects of Crowd and Agent-Based Simulation
Authors:

Lukas Hofer, Irena Ruprecht and Torsten Ullrich
Abstract: The field of crowd modeling is traditionally divided into two primary approaches: rule-based methods and data-driven approaches. The present paper introduces a novel hybrid framework that bridges these methodologies. The proposed approach achieves the predictive power and accuracy of data-driven models while maintaining the transparency of rule-based systems by translating measurable trajectory features into an interpretable mathematical signature. The utilization of these signatures is instrumental in facilitating the analysis of diverse species and environmental contexts by the framework. The novel approach has the capacity to detect outliers and discern disparate behaviors. The method is designed to be data quality-sensitive; it automatically selects optimal descriptions from a library of potential rules, discarding metrics that are inconsistent with empirical data. This results in a compact, interpretable weight vector that allows for direct visual inspection and anomaly detection. This innovative approach establishes a robust foundation for the data-driven design of simulations, offering a generalized solution for characterizing complex collective behavior.
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Paper Nr: 62
Title:

Simulating Motor Neuron Diseases in Synthetic Muscles
Authors:

Cathrine Pierre Nayrouz and Andrew Hamilton-Wright
Abstract: Muscle modelling allows us to create a synthetic muscle where we could simulate muscle structure in order to better understand the way they work and react to inputs that would simply not be possible in a real person. Muscle modelling also allows us to simulate diseases and monitor their progression and behaviour with hopes of better understanding the disease, as well as how muscles change themselves and their structure during that process. It also facilitates creating tools that could improve the quality of life for many patients. Motor Neuron Diseases (MNDs) are a class of diseases where early intervention is crucial to any positive outcomes, by modelling this class of disease we can pave the way for better understanding, and early diagnosis. This paper presents a Lower Motor Neuron Disease model that corresponds to various measures to in-vivo observations described in the literature. Key findings of the experiments conducted, indicate that the order by which Motor Units (MUs) die in MNDs significantly impacts how the muscle behaves.
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Paper Nr: 64
Title:

Electric Vehicle Charging Behavior in a Microscopic Traffic Flow Simulation Using SUMO
Authors:

Eva Spachtholz, Christian Hürten, Magnus Liebherr, Dieter Schramm and Philipp Sieberg
Abstract: The transportation sector's transition towards climate neutrality depends upon a substantial expansion of the charging infrastructure, particularly in densely populated urban areas where private charging options are limited. This study represents a contribution to the STRALI research project, which is concerned with the analysis of charging behavior and charging infrastructure in two districts of the city of Essen. To achieve this objective, a microscopic traffic flow simulation was developed in SUMO. This was accomplished by combining open data sources and real-world traffic data. A dedicated algorithm was developed to model user charging behavior, distinguishing between critical charging during the trip and convenience-oriented charging at the destination. The validation of the simulation results against real-world data demonstrates a high degree of agreement with regard to the duration of occupancy and the frequency of use of the charging points. The analysis identifies existing gaps in infrastructure coverage and provides a scientifically sound basis for future site optimization. The model also enables the simulation of future electric mobility ramp-up scenarios, facilitating proactive planning for public infrastructure needs and supporting the urban mobility transition at the local level.
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Paper Nr: 65
Title:

Simulating Sudoku Solvers: A Comparative Study of Deterministic, Probabilistic and Learning-Based Approaches
Authors:

Vincent Sieso, Ludivine Lasserre and Andreï Doncescu
Abstract: While Sudoku is governed by simple rules, it represents a complex constraint-satisfaction problem ideal for simulating and evaluating the performance of intelligent agents. This study presents a simulation framework designed to evaluate the behavioral effectiveness of various automated solvers. Deterministic methods, such as backtracking and SAT solving, as well as probabilistic methods like Monte Carlo Tree Search and Deep Learning networks, were implemented and compared. Furthermore, we introduce AlphaSudoku, a hybrid simulation model that integrates probabilistic rules and strategic search. Through extensive experimentation across two thousand puzzles of varying difficulty levels, our results demonstrate that deterministic solvers maintain perfect reliability, achieving 100% success rates even on expert-level puzzles. While AlphaSudoku emerges as an effective balance between computational efficiency and solving accuracy, purely probabilistic methods exhibit significant limitations when confronted with higher-difficulty puzzles. These findings highlight the robustness of traditional symbolic methods, while also revealing the potential of hybrid and learning-based approaches in scenarios requiring adaptability and scalability.
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Paper Nr: 66
Title:

An Extended Simglucose Simulator for Type 1 Diabetes: Modeling Hormonal Counterregulation and Physical Activity
Authors:

Ludivine Lasserre, Jacques Demongeot, Cecile Fougère and Andrei Doncescu
Abstract: We present an extended version of Simglucose, the open-source Python implementation of the UVA/Padova Type 1 Diabetes (T1DM) simulator, improved with two physiologically motivated modules. The first module integrates the secretion dynamics of two anterior pituitary counterregulatory hormones: cortisol and growth hormone. Each one of them is governed by three coupled secretion components: a circadian baseline, a static glucose-threshold response and a dynamic response to falling glucose. The second module incorporates physical activity following the heart-rate-driven model of Dalla Man et al. (2009). Together, these extensions transform SimGlucose into a more comprehensive in silico simulation platform capable of reproducing the dawn phenomenon, intensity-dependent hormonal responses to hypoglycemia, and the acute glycemic perturbations induced by physical activity. These physiological phenomena are absent from the baseline simulator and are directly relevant to the evaluation of closed-loop insulin delivery algorithms. We describe the simulator architecture, propose the mathematical formulation of the hormonal dynamics and of each additional module, and illustrate the behavior and capabilities of the extended framework through representative simulation results.
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Paper Nr: 69
Title:

On the Modeling and Analysis of the Set Orienteering Problem under Uncertain Travel Costs
Authors:

Ravi Kant and Abhishek Mishra
Abstract: This paper studies the impact of edge-based uncertainty in the Set Orienteering Problem, where travel costs are modeled as random variables instead of deterministic values. In this problem, a single traveler departs from and returns to a depot and selects a sequence of sets so that the total travel cost remains within a predefined budget while maximizing the collected profit. Unlike classical SOP formulations that assume fixed edge costs, we incorporate stochastic travel times using Gamma and Normal distributions to better capture real-world variability arising in applications such as logistics and transportation. An integer linear programming formulation based on expected travel costs is proposed and solved using GAMS/CPLEX. To evaluate the effect of uncertainty, a scenario-based analysis is performed on benchmark instances derived from the Generalized Traveling Salesman Problem. The obtained solutions are evaluated under multiple stochastic scenarios to analyze their feasibility and variability in collected profit. Computational results indicate that uncertainty affects route structure and profit stability, even for small instances. In particular, solutions derived under deterministic assumptions may become infeasible when variability in travel costs is considered. These findings highlight the limitations of deterministic planning and motivate the need for incorporating uncertainty in clustered routing problems.
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Paper Nr: 72
Title:

Simulation Based Microscopic Analysis of the Impact of Emergency Standby Train Deployment on Delay Propagation
Authors:

Jun Hyeok Gil, Sang Hwa Lee and Jang Won Bae
Abstract: High-Speed railways are one of the key modes of transportation, and its demand continues to grow. To utilize limited rolling stock resources efficiently, a single trainset is typically assigned to operate multiple scheduled services. However, this operating pattern can cause delays in a specific train to be inherited by subsequent services assigned to the same trainset, resulting in cascading delays. To minimize the spread of such delays, Centralized Traffic Control (CTC) systems can deploy emergency standby trains. Accordingly, this study aims to analyze the microscopic impact of emergency standby train deployment on overall train operations and delay propagation using a simulation model based on the Discrete Event System Specification (DEVS) formalism. The simulation results show that, through microscopic analysis at the block-section level, while emergency standby train deployment effectively blocks delay inheritance for a specific trainset, it simultaneously triggers complex interactions that can either increase or decrease the delay of surrounding trains. These findings are expected to contribute to strategic decision-making in CTC systems, moving beyond single-train control to consider microscopic network interactions through predictive simulations.
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Paper Nr: 74
Title:

An Agent-Based Crowd Simulation Framework Integrating Urban Walkway Networks and Individual Pedestrian Behaviors for Congestion Analysis
Authors:

Gyu Ri Kim, Sang Hwa Lee and Jang Won Bae
Abstract: High-density crowd accidents in complex urban walkway networks represent significant public safety risks characterized by non-linear dynamics and unpredictable emergence. While existing crowd models have primarily focused on evacuation within controlled facilities, they often lack the capacity to explain how behavioral heterogeneity interacts with real-world walkway networks to generate localized congestion. This paper proposes an Agent-Based Modeling (ABM) framework specifically designed to analyze the impact of diverse pedestrian behavior compositions on network-wide congestion dynamics. The proposed framework adopts a centralized time-stepped execution structure that integrates complex movement factors, including multi-destination trip chains and density-responsive navigation. To demonstrate its practical utility, the framework is applied to a realistic urban scenario in the Itaewon district, Seoul, utilizing empirical datasets on transportation demand and network topology. The study investigates how varying the distribution of behavior profiles-such as shortest-path, route-choice, and crowd-avoid strategies-reshapes emergent crowding patterns. The results highlight the framework’s strength as a robust scenario-based analysis tool for identifying systemic risks and providing actionable insights for crowd management in dense urban environments.
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Paper Nr: 75
Title:

Virtual Continuous Testbeds with LLM-Based Test Case Generation: Toward Closed-Loop Validation of Safety-Critical Systems
Authors:

Adam Bachorek, Stefan Schwenk, Naveed Akram, Thomas Kuhn and Pablo Antonino
Abstract: This paper presents an automated approach to deriving informal test cases from Requirements Interchange Format (ReqIF)-structured system requirements based on general-purpose Large Language Models (LLMs) with integrated linkage to test evidences based on Digital Dependability Identities (DDIs). Our method enables scenario-based testing while drastically reducing effort and cost: test cases become available within hours instead of months. The automation supports formal traceability, safety argumentation, and change impact analysis. An automated conversion of derived test cases into ASAM OpenScenario (XOSC) and Open Test Sequence Exchange (OTX) formats accelerates design and implementation of platform-independent, executable test specifications while improving their reuse across development stages. A systematic review process allows domain experts to refine and extend generated specifications. We discuss testbed configuration including resource allocation, toolchain integration, and parameter initialization to satisfy test constraints and strengthen confidence in results against the original acceptance criteria. Finally, we execute the reviewed test cases in a simulated environment within a Virtual Continuous Testing (VCT) pipeline for efficient verification and validation in an X-in-the-Loop (XiL) testbed based on VCIP/FERAL. The closed-loop approach advances automated testing by combining efficiency, consistency, and scalability in test generation, which is showcased within an automotive use case.
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Paper Nr: 82
Title:

A Digital Twin–Oriented Simplified Mathematical Model of RoboSalmon for Multi-Agent Simulation
Authors:

Xu Yan and Euan McGookin
Abstract: As with other autonomous unmanned underwater vehicles, the operational endurance of Biomimetic Autonomous Underwater Vehicles (BAUVs) is constrained by their limited on board power supply. These power constraints hinder the BAUV’s ability to efficiently carry out extensive ocean sampling missions. Deploying multiple vehicles offers clear advantages in improving the efficiency of marine exploration. However, due to various interference in the underwater environment, the development of suitable coordination algorithms remains an area worth investigating. Although using digital twins in place of physical machines to develop multi-vehicle coordination algorithms is a cost-effective and space-saving approach, the feasibility of simulation runtime must be considered when modelling multi-vehicle scenarios. This study aims to explore how the computational performance of the mathematical model of a BAUV known as RoboSalmon can be improved, to achieve practical simulation times for multi-vehicle scenarios. This paper presents the simplification and update of the mathematical model for the validated BAUV RoboSalmon and analyses its computational performance. The original mathematical model is then optimised by simplifying functionality section and reducing code complexity. The results indicate that the modified model can significantly reduce simulation runtime.
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Paper Nr: 96
Title:

Topological Coverage Control in Mobile Sensor Networks Using Weighted Combinatorial Laplacian
Authors:

Shunsaku Yadokoro and Subhrajit Bhattacharya
Abstract: We represent a sensor network as a simplicial complex, which captures higher-order topological features (such as holes) in the network. We use the weighted combinatorial Laplacian operator on a simplicial complex for efficient dynamic coverage control for such a sensor network of a mobile robot team. We introduce the definition of topological coverage, which is based on the spectrum of the weighted combinatorial Laplacian. On the theoretical side, the key contribution of the paper is to investigate the spectral properties of the weighted combinatorial Laplacian operator – how the eigenvalues close to zero and the corresponding eigenvectors of them can detect almost n-dimensional holes in the given complex. On the application side, the key contribution of the paper is to use the theory of weighted combinatorial Laplacian to the development of dynamic coverage repair algorithms for a sensor network of a mobile robot team. Using the theory of relative homology, we also extend the problem of dynamic coverage repair to environments with obstacles.
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Short Papers
Paper Nr: 12
Title:

2-D Double-Density Dual-Tree Real DWT and NLM Filter for Image Denoising
Authors:

Mourad Talbi and Brahim Nasraoui
Abstract: The emergence of noise in digital images may occur throughout the stages of its acquisition, transmission, and processing. Therefore, discarding the noise from the digital image is required before further processing. This study aims to denoise noisy images using our novel image denoising technique. The latter is based on the application of non local-means (NLM) filters in the domain of the 2-D double-density dual-tree real Discrete Wavelet Transform (DWT). This technique consists firstly of applying the 2-D double-density dual tree real DWT to a noisy image (denoted as I_b) for obtaining noisy wavelet coefficients which are denoted as wb{j}{s_1 }{s_2 },1≤j≤2,〖1≤s〗_1≤2,〖1≤s〗_2≤8. Then, each of these coefficients is denoised by applying to it, the non local means filter and the obtained denoised wavelet coefficients are denoted as wd{j}{s_1 }{s_2 },1≤j≤2,〖1≤s〗_1≤2,〖1≤s〗_2≤8. The denoised image which is denoted as I_d, is finally obtained by applying the inverse of 2-D double-density dual-tree real DWT to wd{j}{s_1 }{s_2 },1≤j≤2,〖1≤s〗_1≤2,〖1≤s〗_2≤8. The performance of the image denoising technique introduced in this work, was proven by the results obtained by computing the peak signal to noise ratio (PSNR) and Structural Similarity (SSIM).
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Paper Nr: 14
Title:

Simulation-Driven Flexible Real-Time Control: A Lifelong Learning Framework for Construction Manufacturing
Authors:

Ian Flood
Abstract: This position paper proposes a simulation-driven lifelong learning framework for flexible, real-time operations control in factory-based construction manufacturing, where high customization, irregular demand, and frequent disruptions make time-critical decision-making essential yet difficult. The framework targets mesoscale control decisions (e.g., resource allocation, job sequencing, and maintenance planning) and is designed to satisfy three objectives under changing enterprise and ecosystem conditions: near-optimal production performance, real-time responsiveness, and extensibility. The proposed architecture comprises artificial neural network policy modules assembled as a module group and coordinated through a shared latent variable hub that supports interoperable information exchange and maintains system-level alignment as modules are added, removed, or adapted. Digital-twin simulation provides the environment for reinforcementlearning exploration, training, and validation, while enabling repository-based reuse and targeted updating when change is necessary. Preliminary single decision point evidence is encouraging, indicating 0.005 seconds per decision on a laptop CPU, 6.40 hours training time, and an average 18.1% reduction in per-component production idle time relative to a rule-of-thumb baseline, while also showing degradation under larger workload changes, motivating selective retraining. Future work will scale the approach to multiple decision points and quantify production performance, decision and development latency, and extensibility.
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Paper Nr: 17
Title:

Performance Optimization of Transient Pressure Acoustic Simulation by Coarse-Grain Diffusion Model
Authors:

Qian Shi, Ian Flood and Hassan Azad
Abstract: Traditional numerical solvers, such as the Finite Element Method (FEM), offer high accuracy in transient acoustic simulations but are computationally intensive and generate large, inflexible datasets. Recent data-driven approaches improve simulation efficiency but often rely on extensive precomputed data or complex architectures, including convolutional neural networks (CNNs), recurrent models (e.g., LSTMs), generative adversarial networks (GANs), and transformer-based designs. To address these limitations, we propose a coarse-grained Denoising Diffusion Probabilistic Model (DDPM) enhanced with Temporal Weighting in Space-Time Adaptive Normalization (TWiST-Norm). The model uses autoregressive generation to reconstruct sequences of 2D pressure fields from a few prior frames, significantly reducing inference time while preserving key spatiotemporal features. Compared to conventional solvers, our method achieves competitive accuracy with substantial computational savings, as validated by Sound Pressure Level Error (SPLE), a perceptually aligned metric. The proposed framework enables fast, data efficient simulations and offers a scalable alternative for modelling complex 2D acoustic environments.
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Paper Nr: 24
Title:

On the Impact of LSTM Capacity on Recurrent Reinforcement Learning for Multi-UAV Exploration
Authors:

Ali Moltajaei Farid, Jafar Roshanian and Malek Mouhoub
Abstract: Recurrent neural networks, particularly Long Short-Term Memory (LSTM) architectures, are widely used in reinforcement learning (RL) to address partial observability and temporal dependencies. While prior work has established the benefits of recurrence, the effect of LSTM capacity on learning dynamics and performance remains poorly understood. In this paper, we present a focused empirical study analyzing how the number of LSTM hidden units influences optimization stability and final policy quality in recurrent RL agents trained with Proximal Policy Optimization (PPO). In this context, we conducted a series of experiments and the results show that increasing LSTM capacity does not monotonically improve performance. Instead, intermediate hidden sizes often achieve more stable convergence and higher returns than larger models. These findings suggest that recurrent capacity should be treated as a critical design choice rather than a secondary hyperparameter. The proposed analysis is evaluated in a multi-UAV exploration setting, highlighting how recurrent capacity affects learning stability and coordination under partial observability. In particular, the experiment results across multiple UAV configurations show that LSTM with 128 hidden units achieves lower mission time and reduced variance compared to smaller and larger models.
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Paper Nr: 27
Title:

A Multi-Criteria Decision Making Framework for Selection of Open-Source Energy Systems Modelling Tools
Authors:

Thapelo Mosetlhe and Adedayo Yusuff
Abstract: The acceleration of energy transition has seen the introduction of weather-dependent generation in power supply. As they are stochastic, the classical dispatch procedure fails in deploying the generation schemes. As a result, a need for their modelling and simulation becomes paramount. To steer clear of the increasing cost of their modelling and simulation, several open-source packages have been developed. These packages are designed such that they can tackle certain types of problems with various parameters. Therefore, making the selection of a particular tool for a peculiar problem is important. This work presents a framework based on multi-criteria decision-making (MCDM) for the selection of appropriate open-source modelling and simulation tools for energy systems. Five (5) MCDM techniques are used to propose the best alternative from the ten (10) that are used and compared in this work. The results show that Calliope (A7) is the best alternative based on the formulation of the problem. However, one of the MCDM techniques (ELECTRE) suggests that ETEM (A6) could be an alternative solution.
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Paper Nr: 29
Title:

Natural Language-Guided Autonomous Agents for Counterterrorism Simulation via Deep Reinforcement Learning
Authors:

Xinmeng Li, Kai Xu, Yue Hu, Miao Zhang and Quanjun Yin
Abstract: In simulation-based counterterrorism training environments, autonomous virtual agents must interpret and execute complex natural language directives issued by instructors or team leaders. Traditional reinforcement learning methods often struggle with semantic comprehension, contextual memory, and efficient exploration in such instruction-driven tasks. To address these challenges, this paper proposes a deep reinforcement learning-based instruction execution framework that integrates a Gated Recurrent Unit (GRU) for enhanced semantic encoding, a Long Short-Term Memory (LSTM) network for spatiotemporal context retention, and an improved reward design to guide efficient exploration. The GRU-based module captures rich linguistic structures from natural language commands, while the LSTM network maintains historical perceptual and action information, enabling the agent to make more informed decisions over long horizons. Through empirical evaluation in grid-world simulation scenarios representative of navigation and task execution tasks, the proposed framework demonstrates significant improvements in instruction comprehension, spatial memory retention, and policy learning efficiency compared to conventional approaches. These results suggest that the integration of advanced sequence modeling and reinforcement learning can enhance the realism and adaptability of autonomous agents in counterterrorism simulations.
Paper Nr: 31
Title:

An Action Oriented Execution Semantics for Business Process Models: Application to Protocol Design in Hierarchical Enterprise Networks
Authors:

Mohamed Bettaz
Abstract: The objective of this paper is fourfold. First, we use rewriting logic to endow BPMN models with an execution semantics consistent with the action-oriented notation in BPMN. This contrasts fundamentally with works using state-based models. Second, we demonstrate the effectiveness of our approach by applying it to an example related to distribution layer protocols in hierarchical enterprise networks. Third, we use Maude modules to structure, execute, test and verify the rewriting logic theories capturing the semantics of our BPMN models. Maude modules are provided with an operational semantics and denotational semantics. The operational semantics allows to simulate the behaviour of BPMN processes by concurrent rewriting (sequential rewriting being a particular case of concurrent rewriting in rewriting logic). Fourth, we use the obtained results to verify the BPMN models of the specified protocols. The detection of undesirable properties is illustrated by a typical error encountered in BPMN models.
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Paper Nr: 34
Title:

DREAM: Dynamic Resource Evaluation and Assembly Model for MRO - Mathematical Formulation and Scalability Analysis
Authors:

Johanna Aigner, Florian Raddatz and Gerko Wende
Abstract: Maintenance operations in aviation are inherently dynamic and cannot be planned deterministically in advance. This uncertainty produces significant inefficiencies: resource shortages cause process delays, while excess resources generate economic waste. In this study, a state-based process-assembly and evaluation model ready for implementation for deterministic and discrete-event simulation that has previously been described conceptually is formally mathematically described. The novelty lies in the simultaneous consideration of the parts to be maintained, the resources needed as well as the available process steps: After every process step performed by available resources, the part’s state changes thereby generating a new set of feasible follow-up steps at each stage. Because resources are directly tied to the discrete steps performed, the model can only be defined in an emergent fashion: one cannot pre-define processes and then assign resources; instead, resources serve as boundary conditions, and the model must generate the optimal-whether fastest, cheapest, or otherwise-sequence of steps for planning. The scaling behaviour of this model is analyzed on a concise test set, analysing how the number of parts, the number of process steps, the parallel or serial step order, and the traversal algorithm used for generating the process states impact computational performance.
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Paper Nr: 39
Title:

A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems
Authors:

Khalil Al-Rahman Youssefi, Marija Gojković, Walter Stefanutti, Mika Auer and Melanie Schranz
Abstract: This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV) charging systems under configurable infrastructure and operational conditions. The model integrates heterogeneous EV behavior, charging column constraints, and a shared Energy Sandbox that regulates aggregate power allocation, enabling the joint study of user-centric charging dynamics and facility-level power behavior. Implemented in Python using the SimPy discrete-event framework, the approach supports scalable, event-driven simulations across varying system sizes, charger compositions, and scheduling strategies. A representative workplace charging scenario is investigated to illustrate how infrastructure configuration and coordination mechanisms influence energy delivery performance, infrastructure utilization, and aggregate load characteristics. The results highlight the context-dependence of infrastructure suitability and demonstrate how charging strategies and charger types reshape both service-level outcomes and grid-facing behavior. The proposed ABM provides a flexible and extensible simulation environment for exploring technical, operational, and grid-aware aspects of EV charging ecosystems, and for serving as a methodological basis for subsequent studies on advanced coordination strategies beyond the specific scenario analyzed in this study.
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Paper Nr: 40
Title:

Radial Distance Signal Representation of Speech Balloons for Emotion Recognition in Manga Dialogues
Authors:

Thien-Bao Nguyen, Thanh-Lam Bui, Hoai-Thuong Dang and Xuan-Nam Cao
Abstract: Emotion recognition has been an extensive research field in both computer vision and natural language processing. However, only a few works have explored emotion recognition in the comics domain. Comics provide an interesting field for the emotion recognition task. In comics, emotions are expressed through multiple channels such as dialogue texts, facial expressions, and character gestures. Among these modalities, one element of comics that has received little attention in existing works is the speech balloon. Speech balloons are a special characteristic of comics, which are also a tool for artists to describe the emotion in a character’s dialogue. In this paper, we investigate the potential of emotion recognition in comics through speech balloons and the dialogue text contained within them. First, we construct a multimodal dataset consisting of dialogue texts and images of their corresponding speech balloons, based on an automatic annotation pipeline utilizing multimodal large language models (MLLMs). We then experiment with multimodal models that combine textual and visual information. In particular, we explore a 1D representation of a speech balloon and incorporate it into the model. Experimental results show that using information about the shape of speech balloon helps the model perform better than using only text. In addition, extracting data using radial distance signal works better than using regular segmentation masks alone.
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Paper Nr: 44
Title:

Swarm Intelligence Algorithms for Robust 3D Gas Source Localization Using UAV Swarms
Authors:

Cherifi Youcef, Guiatni Mohamed and Bouzid Yasser
Abstract: The detection and localization of toxic gas sources in three-dimensional environments is a critical task for public safety during disaster scenarios and industrial accidents. Unmanned Aerial Vehicles (UAVs) equipped with gas sensors provide a safe and efficient means of monitoring contaminated areas. This paper presents a comparative study of several swarm intelligence algorithms for gas source localization (GSL) in a 3D environment using multiple UAVs. The algorithms under investigation include Adaptive Particle Swarm Optimization (APSO), Ant Colony Optimization (ACO), and Grey Wolf Optimizer (GWO). Their performance is evaluated in terms of success rate, convergence time, and mean localization error. To assess robustness under realistic conditions, the algorithms are tested under varying levels of measurement uncertainty. Simulation experiments are conducted over multiple independent runs to ensure statistical validity. The results highlight the relative strengths and weaknesses of each method. The findings provide practical guidance for selecting appropriate swarm intelligence techniques for UAV-based gas source localization in real-world scenarios.
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Paper Nr: 45
Title:

Scientific Machine Learning (SciML): A Teleological Taxonomy for Decision Support
Authors:

Johann Wiens and Christoph Reich
Abstract: The systematic selection of appropriate model architectures in Scientific Machine Learning (SciML) poses a significant challenge for engineers and scientists. Established taxonomies classify hybrid models in a primarily descriptive manner, based on the nature of the physical information source (“Drivers”) and its algorithmic integration (“Biases”). This paper demonstrates that such retrospective approaches are insufficient for practical, prescriptive model selection. To address this limitation, a novel, teleological (goal-oriented) taxonomy is proposed, introducing user intent-the epistemological and functional objective (“The Why”)-as the primary structural axis (root node). The developed decision tree categorizes problem statements into four macro-classes: Inverse Discovery, Forward Simulation, Control & Design, and Domain Adaptation, subsequently mapping established SciML architectures. An analytical comparative evaluation using established literature scenarios demonstrates how this user-centric approach efficiently and purposefully narrows the highly complex solution space to applicable architectures, outperforming purely descriptive taxonomies. The proposed framework bridges the gap between theoretical methodological development and practical application, establishing a robust foundation for future automated decision support systems (AutoML) within the SciML context.
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Paper Nr: 47
Title:

Using Lego® Serious Play® for Model Conceptualization in Discrete-Event Simulation
Authors:

Jairo R. Montoya-Torres, Esteban López-Arboleda and Eliana Lozano
Abstract: This paper investigates the use of Lego® Serious Play® (LSP) as an experiential pedagogical approach to support the teaching of conceptual modeling in discrete‑event simulation (DES). Conceptualization is widely recognized as a critical yet challenging phase of the simulation projects, particularly for novice modelers, as it requires abstraction, system thinking, and the articulation of assumptions before software implementation. The proposed educational intervention integrates LSP into a simulation course to help students collaboratively represent system structure, entities, resources, and process logic using physical models and metaphors. A case study was conducted with 3 different simulation courses with a total of 65 engineering students, and data were collected through classroom observation and a post‑activity perception survey. The results indicate high levels of perceived clarity, engagement, and collaboration, as well as a strong preference for LSP over traditional written conceptualization workshops. The findings suggest that LSP aligns well with the cognitive and social demands of conceptual modeling and with experiential learning theory, offering a valuable complement to conventional simulation instruction. The paper contributes empirical evidence and a replicable classroom design for enhancing early‑stage simulation learning.
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Paper Nr: 48
Title:

Urban Area Design Refinement via an Iterative Multi-Agent Crowd Simulation Loop
Authors:

Ryota Temma, Mamoru Yoshizoe and Hiromitsu Hattori
Abstract: Cities, where diverse people live and interact, are among the most complex artificial systems. The dynamics of people in the city emerge from the accumulation of local interactions between individuals and their surrounding environment, making it difficult to predict in advance how a planned environment will be used or what value it will produce. Consequently, a solution that appears optimal on paper can turn out to have problems once people actually live with it. In this paper, we propose an iterative, evolutionary approach to urban design using multi-agent simulation. Virtual cities are generated, crowd behaviors are simulated and evaluated, and the layout is regenerated based on the results. This loop enables bottom-up design and evaluation from the perspective of residents. By integrating approximate optimization, urban environments that improve livability can be explored efficiently. We present the system environment that implements the proposed method and illustrate it through urban design examples.
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Paper Nr: 49
Title:

Nature-Inspired Feature Weighting for Enhanced K-Means Clustering in High-Dimensional Data
Authors:

Mandana Gholami, Malek Mouhoub and Samira Sadaoui
Abstract: Feature weighting is an effective alternative to feature selection for clustering high-dimensional data sets. Instead of removing features, feature weighting preserves the full feature space and assigns a continuous importance weight to each feature, thereby reducing information loss while emphasizing informative attributes. We present a nature-inspired feature weighting framework for K-Means clustering, where each candidate solution represents a vector of feature weights in the range [0,1]. The weighted data set is then evaluated using clustering quality metrics to guide the search process. Five nature-inspired algorithms are investigated, namely Biogeography-Based Optimization (BBO), Differential Evolution (DE), a hybrid Evolution–Biogeography-Based Optimization (DE–BBO) approach, Particle Swarm Optimization (PSO), and Firefly. In this context, we conducted a set of experiments on three high-dimensional data sets: SECOM, RNA, and Parkinson’s. Clustering performance is assessed using the Davies–Bouldin Index (DBI), Silhouette score, and Calinski–Harabasz (CH) index. The results indicate that feature weighting consistently enhances clustering quality compared to the baseline K-Means algorithm and generally outperforms feature selection-based approaches. Among the evaluated methods, the hybrid DE–BBO algorithm achieves the best overall performance across all three data sets, highlighting the effectiveness of hybrid nature-inspired optimization techniques for feature weighting in clustering.
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Paper Nr: 50
Title:

Holonic Modeling of Territorial DTs for a Circular Economy
Authors:

Sidonie Boissée, Fouzia Ounnar, François Michel Lambert and Patrick Pujo
Abstract: The total annual extraction of raw materials largely exceeds the regenerative capacity of our planet. In view of this challenge, the circular economy is emerging as a reference framework to redirect past and present practices, but it still lacks operational tools to manage material flows and stocks at the territorial scale. This paper proposes the concept of the Circular Territorial Digital Twin (CTDT), a governance tool aimed at reducing the material footprint. Grounded in theories of circular supply chains and DTs, the CTDT articulates three dimensions of circular economy: intensifying product use, extending product lifespans, and optimizing material recovery. The proposed architecture comprises three layers: the physical system (objects, flows, infrastructures), the modeled system (territorial digital metabolism), and the simulation layer for testing public policy scenarios. The CTDT integrates internal data (IoT, information systems), external data (statistics), and simulated data to visualize stocks, anticipate needs, and orchestrate circular loops. In this paper, a holonic approach is proposed for this framework. This allows to transform the static territorial metabolism into a controllable system, linking micro-operational decisions with macro-level objectives for reducing the material footprint, aiming for 10 tons per person per year by 2050.
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Paper Nr: 53
Title:

Procedural Modeling as a Methodological Layer for Ray-Tracing-Based Electromagnetic Simulations
Authors:

Felipe Oliveira Ribas, Günter Vermeeren, David Plets and Wout Joseph
Abstract: Electromagnetic propagation simulation is highly sensitive to geometry, yet simulation-ready environments are often generated through ad hoc, manual, and difficult-to-reproduce workflows. This creates a methodological bottleneck, especially when environments are structurally complex, variable, or difficult to standardize. In this position paper, we argue that procedural modeling should be treated as a methodological layer within site-specific propagation simulation, where rules, parameters, and generation logic form part of the reproducible modeling artifact. By representing environments through explicit rules and reusable operations, procedural modeling supports reproducibility, controlled sensitivity analysis, scalable scene variation, and more explicit treatment of geometric assumptions. We support this perspective through related practices in vegetation-related propagation studies, site-specific ray tracing, and wireless digital twins, and through a proof-of-concept workflow based on Blender and Sionna RT.
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Paper Nr: 55
Title:

Intersection Traffic State Estimation Based on Self-Organizing Maps and Connected Vehicles
Authors:

Željko Majstorović, Martin Gregurić, Mladen Miletić, Edouard Ivanjko and Tonči Carić
Abstract: Accurate traffic state estimation at intersections is essential for intelligent traffic signal control. Existing methods struggle to handle the high-volume, real-time data streams enabled by Connected Vehicles (CVs). This paper proposes a traffic state estimation method based on the Speed Transition Matrix combined with Self-Organizing Maps, designed to scale to high-frequency, large volumes of CV data streams in real time. The method is evaluated on a simulation model of an isolated intersection. Obtained results indicate that the method is well-suited for adaptive traffic signal control in the CV environment because it enables real-time CV data utilization for traffic state estimation.
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Paper Nr: 61
Title:

Toward Verifiable Audience Digital Twins: An Agent-Based Architecture Integrating COM-B and ELM
Authors:

Yukai Zeng
Abstract: Audience-response simulation is often modelled as diffusion combined with a single opinion or sentiment update. While useful for studying aggregate dynamics, such formulations provide limited representation of persuasion route, behavioural feasibility, and the durability of change. Here, we argue for a more interpretable architecture for audience digital twins. We propose an agent-based architecture that combines the COM-B framework (Capability, Opportunity, Motivation-Behaviour) to represent behavioural feasibility with the Elaboration Likelihood Model (ELM) to represent route-dependent persuasion and differential durability of attitude change. Agents maintain explicit state variables for capability, opportunity, reflective and automatic motivation, cognitive load, attitude direction, and attitude strength. Messages are represented through theory-linked features, enabling direct scenario specification or a bounded Natural Language Processing (NLP) layer that maps text only into the model’s predefined cue variables on fixed scales without delegating cognition to opaque end-to-end updates. Exposure is modelled through social interaction and an explicit visibility proxy, keeping platform assumptions inspectable. The contribution is not a validated operational twin, but a reusable and verifiable foundation for future calibration. Specifically, this work contributes a modular architecture, a formal state-transition specification, and a verification-first workflow based on bounded-state invariants, unit tests, and sensitivity analysis. We position the architecture as a middle ground between classical opinion-dynamics models and emerging black-box social digital twins, and outline demonstration scenarios for future empirical calibration and simulation-based decision support.
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Paper Nr: 63
Title:

SketchChain-ML: A Hybrid Framework for Robust and Scalable Blockchain Anomaly Detection
Authors:

Ayush Kumar, Jay Chavan, Kedar Dhule and Dr Bhawana Rudra
Abstract: The explosive increase of blockchain platforms Mainly Ethereum has massively raised the number of transactions and brought security risks like fraud and complicated attacks. But, identifying unusual activities in such scenarios remains a challenge mainly due to the high computational cost involved in analyzing large-scale transaction datasets and inadequacy of traditional machine learning techniques. In this work, we offer SketchChain-ML - an anomaly detection system that synergizes sketch-based data summarization with machine learning.It is a hybrid approach. Heavy Hitters and HyperLogLog sketches are used to portray transaction and network patterns and an autoencoder detects abnormal behavior through the features extracted.Experimentation on Ethereum transaction data shows that the proposed method not only has a recall of about 98%, but it also reduces memory requirements by as much as 93%, and it cuts computational costs drastically. Besides, an adaptive sketch tuning mechanism makes the system maintain a high level of performance even in changing traffic scenarios, That’s why, the results validate that the solution proposed is a very effective and scalable approach for blockchain anomaly detection.
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Paper Nr: 67
Title:

An Association of OntoUML and DEMO Process Models for the Specification of Dynamic Routing Protocols: Application to the Open Shortest Protocol First
Authors:

Mohamed Bettaz
Abstract: We demonstrate that an association of OntoUML/OCL with DEMO OCD models offers an appropriate framework for modelling both the structural and dynamic behaviour of routing protocols from an ontological perspective. The practical utility of this approach is validated through its application to a partial specification of OSPF, a standard intra-autonomous system routing protocol. Subsequently, we present a mapping approach for the conceptualization and implementation of DEMO OCD models using Web Ontology Language. The implementation, serialized in Turtle, is deployed in an Apache Jena Fuseki SPARQL server, allowing the specified protocol to function as a knowledge-based system, querying and inferring new knowledge from existing data.
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Paper Nr: 68
Title:

A Diagnostic Methodology for Failure-Mode Ablation of SFM, TTC, and ORCA Pedestrian Steering Paradigms
Authors:

Palaash Gang and Arjun Veluri
Abstract: We argue that failure-mode ablations of pedestrian steering paradigms should be accompanied by minimumintervention controls that isolate each paradigm’s operative mechanism from trivial alternatives, and we instantiate this methodology on the canonical Social Force (SFM), time-to-collision (TTC), and Optimal Reciprocal Collision Avoidance (ORCA) triad. A zone-decomposed ablation across bottleneck, bidirectional, and crossing geometries (n = 25 per cell) isolates two paradigm-specific failure modes: TTC anticipation does not break symmetric arching deadlocks at narrow exits (0/25 successful evacuations for SFM and SFM+TTC at w = 0.8 m, vs. 13–14/25 for ORCA-enabled configurations), and ORCA reciprocal yielding gridlocks counterflow. A minimum-intervention control–C1+ε, the SFM-only baseline C1 with a per-step Gaussian velocity perturbation ε–locates the deadlock-resolution threshold at σ50 = 0.049 m/s (95% CI [0.044,0.053]); within n = 25, passive noise is indistinguishable from ORCA’s linear-program (LP)-based velocity selection as a deadlock-breaking mechanism, demonstrating that a headline paradigm advantage can be attributed to passive symmetry-breaking rather than to distinctive machinery. Calibration against 4 776 Forschungszentrum Jülich (FZJ) trajectories and a held-out out-of-distribution (OOD) evaluation are reported. We position the audit principle as the methodological contribution; the SFM/TTC/ORCA diagnostic as its first application.
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Paper Nr: 73
Title:

A Predictive Data-Driven Framework for Multi-Line Manufacturing Throughput Analysis
Authors:

Joana Rafaela Almeida, Raquel Paradinha, Luís Carlos Afonso, João Rafael Almeida and José Luís Oliveira
Abstract: Understanding and predicting throughput time in multi-line manufacturing environments is a core challenge in industrial simulation and production planning. This paper proposes a simulation-informed analytical framework applied to a real-world event-log dataset comprising 28,026 parts produced across 13 heterogeneous lines over 13 operating days. The framework addresses three objectives: i) characterising per-line throughput distributions, ii) quantifying the impact of equipment downtime on cycle time, and iii) forecasting shift-level production using pre-shift features. Downtime is significantly associated with increased cycle times (p = 0.020), although correlation patterns vary across lines. Change-point detection (PELT) identifies intra-shift disruptions in 11.2% of shifts, typically occurring in the second half, suggesting cumulative degradation effects. A consistent time-of-day effect is observed across most lines. For prediction, global models outperform per-line approaches due to data sparsity. Under a rolling-window protocol, Ridge Regression achieves R2 = 0.570 (MAE = 36.3 parts/shift). Feature importance analysis indicates that recent production history dominates predictive performance.
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Paper Nr: 84
Title:

Energy-Aware Persistent Storage Optimization for Mobile Embedded Edge IoT Platforms
Authors:

Luis Miguel Samaniego Campoverde, Mauro Tropea and Floriano De Rango
Abstract: This paper presents the design and evaluation of a heterogeneous mobile edge node based on the STM32F103VET6 microcontroller, optimized for efficient data collection in Smart City vehicular environments. The challenge of high CPU overhead, energy consumption and latency caused by frequent write operations to mass storage via SPI is addressed through a temporary storage management architecture employing batching and double-buffering techniques with non-blocking DMA control. The system was evaluated using BLE, 802.15.4, and WiFi communication protocols under intermittent connectivity scenarios. Experimental results show that by using an optimal buffer size, the edge node reduces SPI bus usage for reading and writing data to the SD card from 38.0% to 3.1%. This significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption by 35.3%. Additionally, a 100% data recovery rate was verified during network intermittency. This research contributes to remote monitoring in smart cities using low-cost, low-power VANET networks, providing an efficient edge computing solution under strict storage and energy constraints.
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Paper Nr: 18
Title:

Atmospheric Effects of Car-Free Days: A Data Mining and Digital Twin Approach
Authors:

Juan Manuel Castrillón Alvarez, Omar Danilo Castrillón Gomez and Sofia Castrillón Alvarez
Abstract: The objective of this research is to determine the influence of Car-Free Days on air quality in the city of Manizales, Colombia. To this end, a database was constructed using Python-based data acquisition and preprocessing scripts, enabling automated extraction, filtering, and structuring of environmental and meteorological variables, which integrates environmental and meteorological variables associated with air quality corresponding to the 2023–2025 period. The database allows for the comparison of the behavior of these variables between days with vehicular circulation and car-free days. This comparison with data mining techniques, applied using the WEKA platform, in order to identify patterns and discriminant variables between both scenarios. As a result of the classification process, complete separation between clusters was observed under the applied configuration, indicating that the following variables could be grouped into two fundamental classes associated with days where cars were allowed and with car-free days: PM2.5 and PM10 particulate matter, tropospheric ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, environmental temperature, relative humidity and surface pressure. Finally, the classification model obtained was simulated using the Jaamsim software by means of stochastic simulation, allowing to dynamically represent the behavior of the system and validate its results.
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Paper Nr: 21
Title:

Environmental Analysis of License-Plate-Based Driving Restrictions on Air Quality Using a Digital Twin
Authors:

Juan Manuel Castrillón Alvarez, Omar Danilo Castrillón Gomez and Sofia Castrillón Alvarez
Abstract: The objective of this research is to determine the influence of the license-plate-based vehicle restriction measure known as “pico y placa”, which limits the amount of vehicles that can circulate in the city on any given day. To this end, a Python-based data acquisition process was implemented to construct a database of air-quality-related variables for the city of Manizales, Colombia, covering the years 2023 and 2025; years with and without the “pico y placa” restriction in effect, respectively. Through the use of statistical techniques, a year-on-year comparison was made considering representative variables of vehicular pollution, classified as direct and indirect impact variables: fine particulate matter (PM₂.₅), tropospheric ozone (O₃), nitrogen dioxide (NO₂) and carbon monoxide (CO). The results show increases of 13.2% in PM₂.₅, 15.9% in O₃, 35.3% in NO₂ and 30.6% in CO between 2023 and 2025, indicating an increase in pollutant levels that may be associated with changes in vehicular traffic conditions. Finally, the behavior of these variables is simulated using a stochastic simulation model developed in JaamSim, which allows dynamically representing the impact of vehicle restriction on air quality and constitutes a support tool for the evaluation of urban mobility public policies.
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Paper Nr: 37
Title:

Comparative Study of Intelligent Optimization Algorithms for Small Hydropower Plants
Authors:

André Mampuya Nzita, Guyh Dituba Ngoma and Clément N'zau Umba-di-Mbudi
Abstract: The global transition toward sustainable energy systems has increased interest in small hydropower plants because of their low environmental impact and their ability to provide decentralized renewable electricity. However, the performance of these systems remains strongly dependent on hydraulic efficiency, flow regulation, and energy management strategies. In this context, optimization methods used in pumping and hydraulic distribution systems may also improve hydropower operations. This study reviews and compares results reported in the literature concerning the application of metaheuristic algorithms to hydraulic and hydropower systems. The objective is to critically analyze previous studies and identify the most efficient optimization approaches under hydraulic and energy constraints. The methodology is based on a comparative literature review of published scientific studies, focusing on algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Ant Colony Optimization (ACO), NSGA-II, MOPSO, and hybrid optimization methods. The analysis considers convergence speed, energy efficiency, hydraulic performance, robustness, and operational stability. The reviewed studies show that hybrid and multiobjective methods generally provide better optimization results. PSO ensures rapid convergence, whereas NSGA-II is more suitable for multiobjective problems. Further experimental validation and standardized benchmarking are recommended to develop reliable optimization frameworks for small hydropower plants.
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Paper Nr: 41
Title:

Integrating Dual-Behaviour Components into the Refinement-Based Workflow Modeling Approach
Authors:

Yevheniia Yehorova and Marina Waldén
Abstract: This paper proposes an extension of the refinement-driven role-based workflow model to support the modeling of system components as process participants. The approach is based on role-based workflow modeling, where process behaviour emerges from the interaction of human and system roles. In modern sociotechnical processes, the system can be either an autonomous participant or a resource used by humans, depending on the context. This distinction is important for explicitly modeling such systems, as component behaviour may affect workflow execution. This paper introduces a modeling approach that distinguishes between activation condition and architectural interpretation, and considers system components whose interpretation is determined by their mode of participation in a specific operation. Thus, the same component may be represented either as a resource or as an autonomous system role, while only its behavioural and architectural interpretation changes. Modeling rules and interpretation scenarios for system components in refined workflow layers are introduced. A decision support system (DSS) integrated into a healthcare workflow is used as an illustrative example of a dual-behaviour component. The proposed approach combines activation conditions, architectural interpretation, and modeling rules into a unified modeling framework for representing system components in role-based workflow models, improving the clarity and consistency of workflow representations.
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Paper Nr: 52
Title:

Simulation of Technological Knowledge Recovery in a Post-Apocalyptic Scenario
Authors:

Enida Baljić, Almir Mulalić, Nedim Badžak, Nina Bijedić and Sanja Kapetanović
Abstract: This paper presents the simulation model “Simulation of Technological Knowledge Recovery in a Post-Apocalyptic Scenario”, designed to explore the key question of how widely technological knowledge must be distributed to survive a cataclysmic event. The model is implemented in the NetLogo environment using an agent-based approach to simulate a complex system of education and knowledge distribution related to electrical grid infrastructure. The study focuses on critical factors affecting technological sustainability and applies the Knowledge Survival Formula (ZP) to evaluate system resilience. The formula incorporates four main components: knowledge distribution, center availability, critical mass of experts, and internal resources. The simulation results offer insight into strategies for preserving technological knowledge under crisis conditions and show that greater decentralization of knowledge significantly improves resilience and long-term survival.
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Paper Nr: 57
Title:

Physics-Based Simulation as a Critical Methodology for Advanced Air Mobility Emergency Logistics: A Position Paper
Authors:

Kuldeep S. Rawat, Chandra B. Asthana and Aron C. Bechiom
Abstract: This position paper argues that current approaches to Advanced Air Mobility evaluation are insufficient for capturing mission-level and system-level performance, particularly in high-stakes logistics and disaster response scenarios. We propose that a physics-based simulation framework integrating vehicle design parameters with operational deployment models is essential for demonstrating the reliability of hybrid-configuration electric vertical take-off and landing systems. By shifting away from empirical heuristics toward first-principles modeling, we demonstrate how vehicle-level design decisions directly influence system-level outcomes such as delivery efficiency and response time. Four illustrative results substantiate this position: (1) a sensitivity tornado chart demonstrating that wind speed is 2.7 times more influential on mission range than battery energy density; (2) a N-2 contingency analysis establishing that five vehicles constitute the minimum resilient fleet; (3) Service Level Agreement compliance curves showing a 28 percentage-point degradation in critical delivery performance under 15 m/s wind; and (4) an Energy-Latency Pareto frontier revealing design trade-off surfaces inaccessible without physics. Collectively, these results demonstrate that non-physics-based approaches produce qualitatively incorrect policy recommendations in emergency logistics contexts where failures directly affect survival.
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Paper Nr: 58
Title:

MOSIMA: An Agent-Based and Systemic Approach to Model and Simulate Complex Systems
Authors:

Jean-Daniel Kant, Maël Franceschetti, Tristan Bersoux and Cédric Herpson
Abstract: Modelling complex systems such as national economies or energy systems requires capturing the interactions of heterogeneous entities across multiple scales. We argue that agent-based modelling (ABM) is particularly well suited to implement a systemic approach, in which macro-level consistency, micro-level heterogeneity, and feedback dynamics are jointly represented. We propose MOSIMA (Multi-agent Oriented Simulation for Impact and Multi-level Analysis), a structured methodology that guides the construction, validation, and progressive refinement of systemic ABMs. MOSIMA starts from a macro-level model in which each subsystem is represented by a single agent, validates it against empirical data, and then selectively disaggregates subsystems into full multi-agent sub-models as the research question demands. We illustrate the approach through a national economy model, with a detailed energy sub-system.
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Paper Nr: 77
Title:

Stochastic Consensus Simulator for Organisational Structures: Architecture and Research Instrumentation
Authors:

Johannes S. Vorster and Louise Leenen
Abstract: This paper presents a stochastic consensus simulator designed as a reusable research instrument for studying consensus formation in complex organisations, group structures, and communication topologies. The simulator combines a graph-theoretical model of agents, artefacts, topics, and organisational access with a layered architecture for stochastic execution, replicated experiment orchestration, operational dashboarding, and statistical analysis. This structure supports large experimental sweeps, progress monitoring, early detection of validity problems, and the conversion of completed stochastic histories into comparable evidence. The paper describes the simulator architecture and demonstrates its research value through selected results on group consensus formation problems.
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Paper Nr: 81
Title:

Consensus Formation in Hierarchical Project Teams
Authors:

Johannes S. Vorster and Louise Leenen
Abstract: Project delivery depends on reaching sufficient consensus across requirements, designs, plans, and implementation decisions, yet this consensus work is often hidden inside meetings, reporting lines, informal communication, and written artefacts. This paper uses a stochastic multi-agent consensus simulator to study consensus formation in simple hierarchical organisations. We vary the Watts-Strogatz rewiring parameter β, which introduces informal cross-team communication into an otherwise regular hierarchy, and examine the resulting effects on consensus time, effort, meeting efficiency, team size, and artefact use. The results show that modest rewiring can sharply reduce both time and effort, indicating that a small number of cross-team networking can remove much of the hierarchical bottleneck. Larger teams increase total effort, but elapsed time does not necessarily grow at the same rate when cross-team networks allow more parallel consensus work. The simulations also show a late meeting-efficiency regime: early progress is rapid because consensus resolution resolves many topics, while the final stage is dominated by sparse one-topic disagreements, taking much longer to resolve
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Paper Nr: 85
Title:

Agentic Workflow for the Modelling of Daily Activity Schedules of Individual Behaviour for the Generation of Utility Consumption at Urban Scale
Authors:

Vladimir Estivill-Castro, Manuel Portela and Toni Rubio Serrano
Abstract: We propose a simulator designed to generate realistic Activities of Daily Living (ADLs) to model urban-scale utility consumption (water, electricity, and gas). We utilize a Multi-Agent Based Simulation (MABS) implemented via the Repast Symphony toolkit. The core innovation is an Agentic AI architecture using Large Language Models (LLMs) to generate 24-hour activity schedules. This workflow employs Generator-Evaluator pairs where agents propose schedules that are then verified against personal profiles and duration guidelines. The schedules are influenced by the integration of several data that provide realism. Real-time weather data (rain, solar radiation, temperature) from Barcelona’s XEMA network at 30-minute intervals provides climatic conditions. Geo-referenced data of dwelling square footage, building age, and census-based household compositions are incorporated from Barcelona’s Open Data site. Several datasets in the literature, including by Time-Use (TU) surveys provide activity labels and examples of schedules. Datasets about appliance diversity and models of consumption incorporate information to integrate many models of items deriving consumption. A immediate application is a personal comparison tool that enables residents to contrast themselves against similar households without needing to identify themselves (ensuring privacy).
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Paper Nr: 86
Title:

A Neuro-Symbolic Strategy to Support the Model-Driven Design of Systems Engineering Experiments
Authors:

Diego Ferreira, Rakshit Mittal, Lucas Lima and Hans Vangheluwe
Abstract: The design and specification of experiments in Model-Based Systems Engineering is challenging: state-of-the-art tools are deemed either precise, but too cumbersome or too imprecise due to natural-language descriptions that lack formal semantics. This is compounded by the high complexity of systems, especially in safety-critical domains. Large Language Models (LLMs) offer a promising avenue for automating the elicitation step, but their probabilistic nature precludes unmediated use: hallucinations cannot be allowed to propagate into formal artifacts. We propose a neuro-symbolic framework combining LLM-driven elicitation constrained by a rule-based reasoner fed by an ontology-compliant knowledge graph. A deterministic orchestrator drives an elicitation loop where the symbolic engine poses context-sensitive questions, the LLM proposes candidate answers, and every candidate is validated against formal domain constraints before acceptance. We present a proof-of-concept implementing the proposed framework and an empirical evaluation across three case studies using four state-of-the-art LLMs. Results indicate that the framework reliably prevents hallucinations from propagating into formal specifications.
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Area 2 - Simulation Technologies, Tools and Platforms

Full Papers
Paper Nr: 15
Title:

Democratizing AI Social Simulation: A No-Code Web Interface for the Concordia Framework
Authors:

Ng S. T. Chong
Abstract: Generative agent simulations offer transformative potential for computational social science, yet the requirement for sophisticated programming expertise limits their adoption to technical specialists, excluding many expert domains. This paper presents an open-source, web-based platform that democratizes access to the Concordia framework through an intuitive interface, allowing researchers to configure, execute, and analyze multi-agent simulations without writing code. Key technical contributions include a reliability layer for sustaining long-running simulations with automatic checkpointing, mid-simulation cancellation, and timeout protection to mitigate LLM API instability, and a provider-agnostic adapter supporting both commercial and locally hosted models. The platform streamlines research workflows through 38 scenario templates spanning 8 game master types, census-based agent generation from demographic distributions, batch execution with parameter sweeps, structured data export, and automated LLM-powered analysis. We evaluate the system across diverse scenarios and discuss trade-offs between this interface and the native Concordia Python framework. By eliminating programming barriers while preserving advanced simulation capabilities, this tool serves as both an accessible entry point for non-technical researchers and a rapid prototyping environment for experts.
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Paper Nr: 76
Title:

CellScript: A DSML for Cell-DEVS Modelling and Simulation
Authors:

Tom van Loon, Sascha Hammes, Philipp Zech and Hans Vangheluwe
Abstract: Cell-DEVS combines the spatial grid of cellular automata with the discrete-event timing of DEVS and is well-suited for modeling systems that evolve over both space and time. However, existing tools either require low-level C++or Python programming or lack compile-time validation and modern IDE support, which limits adoption outside specialist communities. We present CellScript, a domain-specific modeling language for Cell-DEVS implemented with the Xtext language workbench. CellScript provides a declarative, rule-based syntax aligned with Cell-DEVS concepts, an Eclipse-based IDE with code completion and static validation, and a model-to-text code generator targeting PythonPDEVS. We accompany the language with a four-step modeling methodology that guides users from spatial structure definition to executable simulation. Evaluation on four scenarios from the literature shows a consistent 10× reduction in the code a modeler must write, relative to the executable PythonPDEVS code that CellScript generates automatically, with code generation time remaining constant regardless of grid size.
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Paper Nr: 80
Title:

Can Swarms Be Trusted? Showcasing Swarm Intelligence and Privacy Preservation through AR
Authors:

Marija Gojković, Horia Rares-Vulcu, Kseniia Harshina and Melanie Schranz
Abstract: Swarm intelligence provides a robust approach for decentralized coordination in nowadays systems, yet its algorithmic principles, like local decision-making, role differentiation, and emergent global behavior are often difficult to convey to individuals without prior experience in swarm-based control. This creates practical barriers when deploying swarm-enabled solutions in domains such as shared electric vehicle charging, energy management, or mobility systems, where engineers, operators, and stakeholders must reliably understand how decentralized processes produce system-level outcomes. To address this challenge, we developed an Augmented Reality (AR) game that operationalizes a swarm model inspired by the Artificial Bee Colony algorithm and exposes key algorithmic elements, including information propagation, neighborhood interactions, and collective resource allocation-Swarm AR. The system also illustrates how decentralization can reduce data concentration, which may support privacy advantages under certain assumptions about information flow and system design, without requiring explicit protection mechanisms. A shared electric vehicle charging scenario serves as a use case to demonstrate load balancing and the necessity of distributed coordination. We evaluate the tool through a mixed-method user study using pre/post quantitative measures and qualitative analysis. Results indicate modest improvements in participants’ understanding of swarm coordination logic, decentralized decision processes, and emergent behavior relevant for infrastructure control. These findings suggest that AR-based interactive visualization can serve as an effective technical aid for communicating, validating, and reasoning about the operational characteristics of self-organizing systems, supporting informed engineering design and deployment of decentralized, privacy-aware coordination strategies.
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Short Papers
Paper Nr: 16
Title:

Critical Air Quality Variables: A Data Mining Approach
Authors:

Sofia Castrillón Alvarez, Omar Danilo Castrillón Gomez and Juan Manuel Castrillón Alvarez
Abstract: Air quality monitoring is widely implemented worldwide; however, most existing approaches focus on measuring pollutant concentrations rather than identifying the minimal set of variables that most strongly influence air quality dynamics. This limitation increases system complexity and operational costs. Therefore, this study aims to identify the most influential environmental variables affecting air quality, represented by fine particulate matter (PM₂.₅) levels and classified into three states: Good, Moderate, and Poor. To do this, a database from the Science Data Bank was used, which includes: a) atmospheric pollutants (carbon monoxide (CO), sulfur dioxide (SO₂), nitric oxide (NO), nitrogen oxides (NOₓ) and tropospheric ozone (O₃)) and b) meteorological conditions (air temperature (TA), relative humidity (RH), atmospheric pressure (PA), dew point (DP), net radiation (NR), solar radiation (SR), direction (DIR) and average wind speed (VEL)). Using the J48 algorithm on the WEKA platform, complemented with a machine learning model developed in Python, a selection and prediction process of the dependent variable was carried out. The results show, with an accuracy of 94%, that the most influential variables in air quality are: a) pollutants: CO, Nox/TA, NOx, O₃ and b) meteorological: Air temperature (TA). These findings simplify environmental modeling and provide a replicable methodological basis for predictive air quality systems in different urban contexts.
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Paper Nr: 28
Title:

Real-Time Simulation of Complex Suspension Kinematics and Frame Torsion for Heavy-Duty All-Terrain Vehicles
Authors:

Mahadev Manohar Krishna Kanth Pilla, Jannis Egelhof and Karsten Berns
Abstract: Autonomous machines and high-fidelity digital models rely on accurate proprioceptive simulation for decision-making and visual realism in challenging environments like off-road terrain. Traditional vehicle models often lack the fidelity required to simulate complex structural behaviors such as the extreme axle articulation and chassis torsion characteristic of specialized off-road vehicles. This work addresses these limitations by developing a C++ component (RRLabVMC) in Unreal Engine that bridges the gap between mechanical simulation and procedural animation. The system computes real-time axle movement, utilizing a soft-body rig to simulate realistic torsional responses. Beyond improving sensor perception for autonomous systems, this approach offers a robust framework for realistic procedural animation in interactive media and game development. The model was validated against real-world data collected using IMU sensors on a physical Unimog traversing a test track in Otigheim, Germany ¨ . The results demonstrate that the simulation successfully captures complex mechanical articulation, providing a reliable and visually authentic tool for both autonomous system training and physically-driven vehicle animation in virtual environments.
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Area 3 - Application Domains

Full Papers
Paper Nr: 25
Title:

Learning-Guided Symbolic Solver Selection for Dynamic Multi-UAV Missions in Simulation
Authors:

Muhyun Byun, Seok-Joo Doo and Eunae Lee
Abstract: Multi-unmanned Aerial Vehicle (multi-UAV) missions require task assignment strategies that adapt to dynamic urgency, threats, and resource constraints. Fixed heuristics lack flexibility, while end-to-end learned policies often omit explicit safety and verification mechanisms. This paper proposes a learning-guided neuro-symbolic orchestration framework in which reinforcement learning operates at a meta level to select among heterogeneous symbolic solvers within a closed simulation loop. Candidate assignments are regulated through a dual-layer verification mechanism comprising a feasibility filter and an episode-level simulator-based evaluator. The meta-decision layer is trained using a Double Deep Q-Network (Double DQN) with rewards reflecting completion, expiration, battery consumption, and threat exposure. Experiments across diverse scenarios demonstrate the effectiveness of adaptive solver selection and improved safety–efficiency trade-offs compared to fixed baselines.
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Paper Nr: 79
Title:

Epicenter Retrieval from Synthetic Sparse Acoustic Seismograms: A Machine Learning Benchmark on the Marmousi Field
Authors:

Tribel Pascal and Gianluca Bontempi
Abstract: Determining the epicenter of a wave propagation, using a minimal number of measurement sources, is a fundamental inverse problem in wave propagation study, especially when the number of available measurement devices is lower than three. Traditionally, the inverse problem-solving approaches rely on the triangulation method, on the orthogonal Primary and Secondary waves composing an elastic wave, which break down when using an acoustic wave and fewer than three measurements. We study the capacity of Machine Learning methods to compute the epicenter of acoustic waves using one and two raw seismograms in heterogeneous propagation fields. We evaluate seven Machine Learning architectures for this regression problem, ranging from Ridge Regression to Temporal Convolutional Neural Networks and Extra Trees, on a controlled synthetic benchmark generated with the PyAWD library, in the Marmousi field. We show that TCNNs and Extra Trees substantially outperform simpler baselines, and we provide spatial error heatmaps that reveal the field regions that are most challenging for each model. We further study three data budgeting questions: the sensitivity of model performance to training set size, to the number of interrogators (stations), and to input noise.
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Paper Nr: 89
Title:

Simulating Crowding Scenarios in a World Heritage Tourism Destination
Authors:

Duarte Sampaio de Almeida, Fernando Brito Abreu and Inês Boavida-Portugal
Abstract: Pena Park, located within the Cultural Landscape of Sintra, a UNESCO World Heritage Site in Portugal, is one of the country’s most visited destinations, facing increasing challenges associated with tourism pressure, including visitor congestion and reduced visitor experience quality. The park’s complex topography, dense vegetation, and interconnected circulation network make visitor management particularly demanding, requiring computational tools capable of supporting sustainable heritage management strategies. To address these challenges, this study proposes a spatially explicit GPU-enhanced agent-based model to simulate pedestrian dynamics based on an adapted Social Force Model with group behavior dynamics, describing heterogeneous visitor behaviors, crowd formation, and route selection processes under varying tourism scenarios. The model integrates spatial data derived from OpenStreetMap and is implemented using FLAME GPU 2 to leverage GPU parallelization for large-scale and computationally efficient, faster-than-real-time simulations, facilitating iterative scenario testing and creating opportunities for future integration with real-time monitoring systems and digital twin applications. The proposed solution aims to support heritage managers and planners by identifying congestion hotspots, evaluating carrying capacity, and assessing alternative visitor management strategies. A proof-of-concept scenario successfully demonstrated the plausibility and applicability of the proposed model. Performance and stochasticity analyses showed satisfactory computational efficiency and model robustness. Results suggest that GPU-enhanced agent-based modeling constitutes a promising approach for sustainable tourism management in protected cultural and natural landscapes and may be transferable to similar heritage destinations worldwide.
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Paper Nr: 98
Title:

Wireless Sensor Network Using Acoustic Communications for Monitoring Oil Spill Expansion in the Sea
Authors:

Mohammed Ibrahim Alquzayzi and Jaime Lloret
Abstract: Subsurface marine oil spills pose an escalating threat to oceanic ecosystems and coastal economies, yet conventional monitoring technologies —including satellite optical imaging and aerial surveillance—remain fundamentally incapable of detecting hydrocarbon plumes confined within the water column. This paper addresses that critical detection gap by presenting a simulation-based design, implementation, and evaluation of an Underwater Wireless Sensor Network (UWSN) that relies exclusively on acoustic communications for realtime subsurface oil spill detection and geospatial localization. The proposed architecture comprises 21 nodes deployed across a 1000 m × 1000 m × 500 m operational volume: a passive leak source at 490 m depth, a horizontal benthic sensor array of 15 nodes at 430–460 m depth, four vertical relay nodes forming a multi-hop routing chain, and a surface ship acting as the data sink. A 50 kbps acoustic channel model with a 15% fading error rate is adopted, with Optimized Link State Routing (OLSR) as the proactive routing protocol. Each sensor applies a concentration-based adaptive data-rate scheme, transmitting at rates inversely proportional to its distance from the leak source. Network-level simulation is conducted in NS-3 v3.41, with performance metrics extracted from Flow Monitor XML reports and analyzed via Python post-processing scripts. Results demonstrate that the UWSN successfully localizes an off-center leak at X = 320 m with a spatial error of only 47.5 m, achieves a mean Packet Delivery Ratio (PDR) of 68.2%, sustains end-to-end delays well within emergency response thresholds, and estimates the spill boundary at a radius of 254 m using a weighted-centroid algorithm.
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Short Papers
Paper Nr: 42
Title:

Modeling and Unified Active Disturbance Rejection Control of a Tilt-Rotor Convertible UAV
Authors:

Zakaria Zerdia, Oualid Araar, Mohamed Zakaria Mimouni and Moussaab Lebid
Abstract: This paper investigates unified nonlinear control strategies for tilt-rotor convertible unmanned aerial vehicles operating across hover, transition, and cruise flight regimes. The proposed proportional-derivative active disturbance rejection control (PD-ADRC) framework with extended state observer (ESO) treats model uncertainties, inter-channel coupling, and external disturbances as total disturbance. Simulation results demonstrate superior trajectory tracking performance compared to sliding mode control (SMC), achieving smaller errors without chattering across the full flight envelope.
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Paper Nr: 54
Title:

Modelling Irradiance Distribution and LVREA in a Photocatalytic Slurry Bubble Column in OpenFOAM with Multiphase P1 Approach
Authors:

Danya Maree and Kyle Abrahams
Abstract: Photocatalytic slurry bubble columns are attractive for applications like water treatment, hydrogen production, and CO2 conversion, but performance depends on coupled hydrodynamics, radiation, and light absorption. Using a novel multiphase P1 model in OpenFOAM, this study simulates irradiance and energy absorption onto TiO2 under varying gas flow, lamp spectrum (UV-A/B), and catalyst loading. The model reproduces linear intensity scaling, strong UV-B enhancement, and increased light redistribution at higher gas holdup. Below 5% holdup, higher loading creates steep irradiance gradients. Above 5%, bubble-induced scattering enhances uniformity, allowing LVREA to rise nearly proportionally with loading and intensity - a trend which intensifies at 30% holdup. Therefore, gas holdup is a key design variable for optimising photocatalytic reactor performance.
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Paper Nr: 70
Title:

Digital Twins as an Operational Layer for Short-Term Rental Property Management
Authors:

Renato Marques Chainho, Stephane Madeira, Oksana Erm, Simão Campos, Pedro Ivo Marques and Felipe Ribas
Abstract: Short-Term rental property management is operationally demanding, involving distributed units, frequent guest turnover, time-sensitive interventions, and coordination across multiple functional areas. In practice, these activities are often supported by partially integrated platforms, limiting real-time visibility and coordinated decision-making. In this position paper, we argue that digital twins are especially relevant to this context when understood as process-oriented operational layers rather than merely as visual replicas of buildings. In this view, a digital twin combines an as-is representation of current portfolio state with a what-if simulation layer for evaluating alternative interventions and their likely consequences. To make this perspective concrete, we present a review-centered digital-twin workflow deployed in a short-term rental property-management organization, in which booking-level risk is updated from historical reviews, operational signals, and guest communication, and candidate interventions are simulated before execution. We also clarify the operational value of the what-if layer, discuss validation through historical back-testing and prospective monitoring, and position short-term rental property management as an underexplored application area for process-oriented digital twins.
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Paper Nr: 71
Title:

A Governance and Architectural Framework for Agentic AI-Driven Gamified Security Awareness
Authors:

Luís Filipe Gomes, Luís Miguel Batista, António Deus, José Luis Oliveira and João Rafael Almeida
Abstract: The cybersecurity landscape has evolved from being predominantly technical to becoming a complex socio-technical challenge, where the human element stands as a major attack vector. Traditional Security Education, Training, and Awareness programs often fail due to generalized approaches that neglect individual learner variability, the psychological costs of compliance, and the accelerating volatility of the threat landscape driven by Generative Artificial Intelligence. This work follows a Design Science Research approach, focusing on artifact design and theoretical integration for the proposal of an innovative learning system. By synthesizing topics such as the theory of Nonlinear Dynamic Motivation and the Social Engineering Attack Framework, we present a systematic multi-agent assisted architectural model for cybersecurity awareness training. We acknowledge the important role of Diegetic Connectivity in enhancing training engagement, as it seamlessly embeds learning within a realistic narrative context. As such, we propose leveraging organizational intranet data to derive more convincing and contextually grounded scenarios while reflecting on inherent data privacy concerns.
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Paper Nr: 78
Title:

Shock after Shock: A System Dynamics Model of Aerogel Diffusion under Uncertainty
Authors:

Mehdi Mounsif and Roxane Rossetto
Abstract: Energy-related shocks can reshape technology diffusion by simultaneously disrupting production conditions and increasing the salience of energy vulnerability. Whether such shocks suppress adoption or instead trigger positive momentum depends largely on their interaction with policy response, institutional frictions, and initial deployment conditions. This configuration can symbolize the state of aerogel insulation market with an emerging high-performance material whose diffusion remains constrained by high cost, scale-up challenges, and market bottlenecks. In this work, we propose a compact System Dynamics model to explore how these dynamics could translate. The model centers on feedbacks between deployment, learning-by-doing, competitiveness, and market expansion, while also accounting for bottlenecks and lock-in mechanisms that can delay or suppress scale-up. Building on these endogenous diffusion dynamics, recurrent energy shocks are introduced as stylized pulses with dual effects: they temporarily increase production costs while also raising salience and potentially triggering mitigation efforts or policy support. We examine how shock frequency, policy strength, lock-in intensity, and uncertainty in key parameters shape diffusion trajectories across scenarios and Monte Carlo realizations. Results show that shocks have countervailing effects and that positive outcomes for the industry and scale-out occurs only in a subset of cases, most clearly when repeated shocks are accompanied by strong and timely policy support and low lock-in conditions.
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Paper Nr: 94
Title:

Using the Harmonic Bias-Reduced Estimator for Reliable and Secure Gateway Selection in LoRaWAN Downlink Communication
Authors:

Samira Abboud and Ihab A. Agha
Abstract: In LoRaWAN, multiple gateways may receive the same uplink transmission, and the Network Server must select a single gateway as the most appropriate candidate for tasks such as downlink transmission, Adaptive Data Rate support, load balancing, and security-aware decision-making. Traditional scoring approaches based on the arithmetic mean may not sufficiently penalize weak or unreliable gateways, potentially leading to suboptimal gateway selection decisions. In this paper, we propose a harmonic aggregation method, referred to as HBRE, for gateway scoring. The proposed method emphasizes weak metric values during aggregation, resulting in a more conservative evaluation of gateway quality. We study its main algebraic properties, show its suitability for aggregating heterogeneous LoRaWAN metrics, and validate its effectiveness through simulations. Results show that HBRE provides more robust and security-aware gateway selection compared to conventional approaches.
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Paper Nr: 95
Title:

From Wireless Sensor Networks to Territorial Cyber-Physical Systems: An Interoperability Framework for IoT-Driven Digital Twins
Authors:

Donat Fortini, Thierry Antoine-Santoni and Paul-Antoine Bisgambiglia
Abstract: This paper explores the transition from wireless sensor networks (WSN) to territorial digital twins (TDT) in the context of smart and resilient territories. We argue that WSN infrastructures, while essential for environmental monitoring, represent a specific architectural lock-in that requires explicit interoperability engineering to evolve into TDTs. We propose a structured roadmap for this transition, organized around three sequential steps: (1) human and organizational objective framing; (2) WSN repurposing through syntactic, semantic, and functional interoperability; and (3) the activation of visualization, simulation, optimization, and reciprocal action capabilities. The ‘Lià’ project in southern Corsica serves as a case study to illustrate these challenges and the proposed framework. Our findings highlight that digital maturity cannot be inferred from software accumulation alone; rather, it depends on effective data coupling and decision coupling for a clearly defined collective purpose.
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