In the face of ever-increasing complexity and global uncertainty, today’s industries are under immense pressure to develop adaptive and resilient production models. The ability to effectively manage and respond to disruptions has become not only a competitive advantage but an operational necessity. Digital Twins enables the creation of a virtual, real-time model of physical assets and processes. Through continuous data collection, a Digital Twins accurately mirrors the state and behaviour of machines, workflows, and supply chains, allowing companies to monitor, analyse, and predict operational conditions as they evolve. This research centres on a case study within the fashion industry, exploring the practical application of Digital Twins to manage and mitigate disruptions in mixed-model production environments. This work contributes to the growing body of knowledge on Digital Twin technology, emphasizing its potential as a tool for immediate operational resilience.
This paper presents a simulation model to estimate the instantaneous power consumption of a mobile robot by taking into account both its mechanical and computational components. The simulation model is adaptable to be tuned based on the level of accuracy needed for estimating the power consumption for the robot and the simulation time penalty. This makes a multi-fidelity power estimation tool for the robot with the capability to run-time changing the fidelity according to environmental conditions and internal computational capabilities. Such multi-fidelity energy prediction is suitable for run-time predictive decision making in a wide range of usages such as training process in model-based Reinforcement Learning (RL) as well as decision making in Model Predictive Control (MPC). The experimental results show that the simulation accurately estimates energy consumption at different fidelity levels. Higher-fidelity models closely match real-world measurements, while lower-fidelity models trade some accuracy for faster predictions. Therefore, higher estimation precision comes at the cost of increased computation.
As global energy demand rises, the search for renewable and sustainable power sources has become a primary ob- jective for researchers and industry leaders. The goal is to develop resource-efficient and carbon-neutral technolo- gies that bridge the current energy landscape and a more sustainable future. In this context, offshore power hubs have emerged as a viable solution due to their robustness and potential for repurposing decommissioned petroleum production vessels. This study presents a mathematical model solved by a simulated annealing meta-heuristics to optimise the ge- ometry of a semi-submersible platform, enabling it to accommodate a gas turbine power plant on its main deck. Results demonstrate that the heuristic approach effectively handles this design’s complex and highly non-linear na- ture within an acceptable computation time and with feasi- ble optimal solutions. Additionally, findings suggest that repurposing a decommissioned platform, rather than an FPSO, significantly enhances power generation capacity due to its larger deck area and increased weight capacity.
This paper explores the application of Neural Ordinary Differential Equations (Neural ODEs) for modeling and simulating complex dynamic systems. We begin with the classical 1D harmonic oscillator to show how Neural ODEs can learn unknown system parameters and initial conditions directly from data, achieving accurate and robust performance under various external forces. Extending our investigation to the chaotic Lorenz system, we demonstrate the model’s capacity to capture highly nonlinear and sensitive dynamics. The results highlight the ability of Neural ODEs to approximate continuous trajectories adapt to long-term dependencies inherent in chaotic systems. Our experiments show that Neural ODEs can accurately model various dynamic systems, handle chaotic behavior, and adapt to external forces. These findings highlight the potential of Neural ODEs for improving the robustness and efficiency of hybrid machine learning models in scientific applications.
This paper investigates an extended problem of the Multi- Task Simultaneous Supervision with Dual-Resource Con- strained (MTSSDRC), observed in a service provider spe- cializing in industrial computed tomography-based quality control. Unlike previous studies that consider a job with an automated work unit, this paper schedules each job com- posed of an automated work unit and a manual work unit. Additionally, working shifts of technicians are incorpo- rated. To address this scheduling problem, an optimization model is developed to minimize total job tardiness. The Genetic Algorithm (GA) and the Particle Swarm Opti- mization (PSO) are employed to solve the optimization model, and their performance is evaluated through a series of numerical experiments.
In this study, we demonstrate that static estimates are inadequate for characterizing the use-phase performance of IoT-enabled systems. We present a case study based on a simulation that evaluates the thermal behavior of a room over one year using real 2023 outdoor temperature data from Konstanz (Germany). The simulation integrates a conventional heating source with a smart thermostat that adjusts the heating based on occupancy and time of day preferences. By varying key parameters such as wall thickness and temperature setpoints, we reveal significant variations in predicted energy savings. The savings distributions are irregular, often fat-tailed or multi-modal. This shows the complexity of the use phase and the need for dynamic modeling when assessing the environmental impact of such systems.
This paper aimed to identify existing approaches of automated risk management procedures for companies in their supply chains and to present an approach for automated risk analysis based on textual news using artificial intelligence. Methodologically, a structured literature review was carried out in the databases "Web of Science" and "Scopus", in which the relevant results were categorized by the aim of the presented approaches on the basis of the records’ abstracts. Four approaches were categorized as “Textual risk evaluation using AI/LLM” and were presented in process flow diagrams. Including learnings from these approaches, a new framework for an automated risk analysis was developed. Further research efforts are recommended for optimization in the area of process models and prompt engineering for automated risk analysis.
The self-similar nature of Web traffic has been widely recognized, but factors influencing its degree remain an open research question. Our paper investigates relationships between Web traffic self-similarity and various features of HTTP traffic at the input of an e-commerce Web server. Using real server log data, self-similarity was quantified through the Hurst parameter, which was estimated with the use of various methods. Web traffic burstiness was analyzed, and correlations between the average Hurst parameter and selected Web traffic features were examined. Results confirm that the analyzed Web traffic exhibits self-similarity across different time scales. The degree of burstiness and traffic intensity varies significantly throughout the day. In particular, the nighttime traffic is less intensive and not bursty whereas the traffic observed during afternoon and evening hours is characterized by increased user activity and burstiness. Results show a weak negative correlation between self-similarity and both burstiness parameters and request arrival rates, suggesting that high traffic intensity and burstiness do not necessarily correspond to increased self-similarity. In contrast, there is a weak positive correlation between self-similarity and the proportion of Web bot requests, particularly for marketing bots, while search engine bots exhibited no significant relationship with self-similarity. The findings suggest that some bot activity may contribute to Web traffic self-similarity.
In our article, we model the fundamental dilemma of corporate liquidity management—the stochastic nature of revenues and expenditures—using the board game called "Hotel," which provides a complex yet realistic representation of these dynamics. The objective of the paper is multifaceted: on the one hand, we seek to develop an experience-based teaching methodology suitable for today’s university students to build fundamental financial intuitions. On the other hand, the steps of constructing and programming the model are integrated into the curricula of several university courses. The applied serious game is not aimless; the reader/student experiences that during decision-making, it is crucial to consider not only the expected cash flows but also the entire distribution behind them. Even within a fully controlled and transparent framework, it is necessary to account for extreme outcomes, which we address using Monte Carlo Simulation (MCS) in the article. By the end of the paper, the stochastic nature underlying the decision criteria, often presented as simple calculated values, becomes apparent.
Industrial companies are increasingly required and willing to adopt advanced algorithms, particularly due to significant advancements in artificial intelligence. However, the effective utilization of modern algorithms necessitates a comprehensive digital transformation within a company. This transformation encompasses various dimensions, including business models, connectivity, retrofitting of non-digital plants, and the integration of data from multiple sources and use cases. In this context, digital twins serve as a central integrating element, enabling the fusion of physical-world data to support analytics, planning, control, and business model development. Once an industrial company commits to digital transformation, the critical question arises: how can a digital twin be created, and what are the necessary fields of action? The challenge lies in the fact that this depends on both the specific data objects required for company-specific use cases and the company's existing level of digitalization. Hence, the objective of this paper is to develop an action model for digital transformation based on digital twins. The methodology employed is a multiple-case study analysis of ten cases. The resulting model provides a systematic framework for practitioners and researchers to strategically implement digital twins.
Traditional surgical training methods often lack the immersive and interactive elements necessary for optimal skill acquisition. Virtual reality (VR) technology has emerged as a transformative tool in surgical education, offering realistic simulations that enhance technical proficiency, psychomotor skills, and cognitive planning. This paper presents a novel collaborative cross-platform VR framework that supports multi-user interaction, enabling trainees and instructors to engage in a shared virtual environment. Structured around a modular digital hospital, the system facilitates real-time training sessions, interactive knowledge sharing, and procedural simulations with integrated feedback mechanisms. A user study demonstrated a significant reduction in errors, improved precision, and faster task completion, underscoring the system’s effectiveness in enhancing surgical training efficiency. Participants rated usability highly, highlighting the system’s intuitive design, engagement, and potential for democratizing surgical education.
Open software and repositories able to support teaching and research in maritime design and engineering are gaining importance lately, specially under the current hype of AI and LLMs. Our discussion starts thus with arguments towards open and collaborative actions in maritime engineering. Later, a short summary of relevant free software and open libraries is presented. The leap that large language models gave in AI from the last couple of years is thus discussed in light of publicly available data, emphasizing the importance of training the right model and the model right. The article closes with a call for public funded research to be made available open, as to instigate collaboration.
This paper presents a system for recognizing static gestures of Polish Sign Language (PJM) in Extended Reality (XR) using hand tracking. Developed in Unity3D with the XR Hands package and OpenXR, it employs a heuristic rule-based approach for real-time recognition without external machine learning models. The system was tested on Meta Quest 3 and validated on Meta Quest 2, demonstrating high accuracy under varying conditions. Results confirm the feasibility of XR-based sign language recognition, with potential applications in accessibility and education.
This paper presents a new framework for upper-air vertical profile data processing. The framework can produce 201 indices useful in rawinsonde analysis, weather forecasting and reanalysis climatology evaluation. The evaluation proved that the framework deliver products in reasonable runtime maintaining agreement of results with the testing sample.
We consider a two-dimensional, two-component dispersed random composite consisting of non-overlapping, identical circular disks embedded in a uniform host. The homogenization theory imposes the condition of a strictly stationary distribution of inclusions. The aRVE theory addresses the constructive question of homogenization. The Eisenstein summation method is used to analyze the convergence of the series that arise during the homogenization process. These series are defined for a random set of inclusion centers. Theoretical investigation of their convergence is necessary for analytical derivation and computational simulations of the effective constants. It is demonstrated that the coefficients of the power concentration series of the effective conductivity tensor are expressed in terms of conditionally convergent series. Various methods of summation formally yield different results. Their proper interpretation is discussed.
Urbanization has drastically increased sealed surfaces, leading to higher stormwater runoff and subsequent associated risks such as flooding and water quality degradation. Nature-based solutions such as bioretention cells are a sustainable approach to manage stormwaters. The present work studies the effects of soil layer porosity on water detention efficiency of a bioretention cell, using computational fluid dynamics (CFD). Three porosities (0.34, 0.40, and 0.43) and multiple soil layering configurations were analyzed under two inflow rates (1 l/s and 1.5 l/s). Results reveal that higher porosity layers reduce bypass runoff compared to lower porosity layers. Configurations with porosity increasing from bottom to top in order from 0.34, 0.40 and 0.43 achieved the lowest runoff, reducing it by up to 22.14\% for 1 l/s. Configurations with low-porosity layers at the top produced the highest runoff, emphasizing the importance of layer arrangement.
The article concerns the use of modern technologies related to information processing and retrieval, as well as modeling and simulation of the operation of water distribution system (WDS). The analyzed problem involves counteracting situations (or minimizing their duration) in which WDS are exposed to loss of quality parameters of distributed water (i.e., failure of water pipes) as defined by relevant laws and regulations issued by state authorities. In the event of a pipe failure, segment the network based on the location of the valves. Based on it, it is possible to determine the valve IDs necessary to isolate the damaged component. However, current methods of segmentation and determining the valves to close a specific segment do not fully utilize all available information. Based on the hydraulic simulation (flow analysis) and topographical knowledge of the analyzed WDS, the proposed method allows dividing a segment (defined by the location of the valves) into smaller fragments (subsegments) thus enabling the isolation of the damaged water supply pipe for repair operations, as well as reducing the total repair time by reducing the number of valves that need to be opened and closed. The method was tested on a model of a real distribution system from the Miejskie Przedsiebiorstwo Energetyki Cieplnej, Wodociagow i Kanalizacji. Sp. z o.o. in Sroda Wielkopolska in Poland.
This paper builds upon and expands the research method presented in Ligocki (2024)(*) at the previous ECMS conference, offering an update on the current state of the study. It explores the challenges encountered by the author in selecting appropriate mathematical methods for image analysis and justifies these decisions through graphical visualizations. The article outlines the stages of development that led to the final choice of methods and algorithms. Compared to the previous article, the current results show greater promise, offering enhanced potential for high-quality analyses. In the earlier work, certain inaccuracies were identified, which were addressed in subsequent discussions and consultations after the ECMS 2024 presentation. The paper introduces a novel quantitative criterion for detecting collective behavior in bacteria, based on a robust computational theory known as aRVE, which leverages structural sums. Application of this criterion to both simulated and experimental data on Bacillus subtilis reveals significant differences, underscoring the presence of collective motion in the bacteria. (*) Application of structural sums to study collective behavior of bacteria. European Council for Modelling and Simulation, 594:304– 409.
This paper presents a simulation-based methodology to evaluate the performance of a privacy-compliant edge–blockchain architecture for smart city environments. The proposed model combines edge computing with a private, permissioned blockchain to ensure low-latency processing, secure data management, and verifiable transactions. Using a discrete-event simulation framework, we analyze the behavior of the system under realistic workloads and time-varying traffic conditions. The model captures edge operations, including preprocessing and cryptographic tasks, as well as blockchain validation using Proof of Stake consensus. Several experiments explore saturation thresholds, resource utilization, and latency dynamics, under both synthetic and realistic traffic profiles. Results reveal how architectural bottlenecks shift depending on resource allocation and input rate, and demonstrate the importance of balanced dimensioning between edge and blockchain layers.
CAR-T cell therapy presents significant logistical and cost challenges due to its complex manufacturing and supply chain requirements. This study applies a simheuristic approach that integrates metaheuristic optimization with stochastic simulation to address these uncertainties. The proposed framework optimizes transportation, manufacturing scheduling, and patient prioritization, ensuring cost efficiency while maintaining timely delivery. Experimental results demonstrate that Simulated Annealing (SA) outperforms traditional scheduling heuristics, particularly as problem complexity increases. This research highlights the potential of simulation-driven optimization for improving decision-making in personalized medicine logistics.
