International Journal of Engineering Systems Modelling and Simulation (21 papers in press)

Regular Issues

• Machine learning-driven innovations for energy efficiency engineering systems empower greener technologies  
  by R. Regin, K. Selvamani, S. Kanimozhi, Pallavi Ahire, Swakantik Mishra, Sukhwinder Sharma, Sushma Rani 
  Abstract: The research investigates the role of high-energy electronics as a key player in the strength efficiency and sustainability sector. In addition, we look at recent developments in power electronics, including advanced semiconductor materials and novel topologies with machine learning-enhanced control strategies to bring technological innovations towards climate-smart technology. Our process combines a complete literature research and architectural analysis to illuminate innovative power electronics through machine learning and data-driven optimisation. Where the consequences of this study not only show substantial enhancements in power performance and sustainability, but also strengthen the case for embedding advanced energy electronics across myriad programs perfectly aligned with eco-green tech. The discussion extends to how our results may influence the integration of renewable electricity, industrial strategies, and environmental sustainability through transformational system learning-driven innovations. This paper outlines a scenario where green technology meets machine learning to usher in a new era of energy efficiency for a greener planet, highlighting power electronics’ immense potential and future direction. Current constraints are noted as side comments.
  Keywords: sustainable power systems; machine learning optimisation; advanced power electronics; renewable energy integration; energy efficiency solutions; green technology innovations; smart grid technologies; eco-friendly semiconductor materials.
  DOI: 10.1504/IJESMS.2025.10068738
   
  
• Modelling and optimisation of structural parameters of main landing gear during touchdown and taxing  
  by Mantesh Basappa Khot, Abhijit Prekash, R. Gopalakrishna, Karan Nanda, Hriday Ghosh 
  Abstract: Runway irregularities induce vibrations in the fuselage of aircraft during take-off, taxiing, and landing, leading to fatigue stresses in the airframe. These vibrations impacts passenger comfort and affect the functioning of instruments. To reduce fuselage vibrations in the Fokker-70 aircraft, an optimisation of parameters is conducted, aiming to lower the peak of the frequency response at resonant conditions and minimise the time difference between the fuselage and tire stabilisation after touchdown. This prevents airframe failure due to excessive vibration at resonance. MATLAB s Nelder-Mead simplex algorithm is used for optimisation. Additionally, a PID controller is implemented in the landing gear model to further mitigate vibrations. The controller s effectiveness is tested using a runway model with various bumps, adhering to Boeing s runway roughness criteria. Results show the controller smoothens fuselage response to runway excitations, reducing vibration and enhancing the airframe s fatigue life.
  Keywords: complex modal analysis; Nelder-Mead simplex method; optimisation; Oleo pneumatic shock absorber; PID controller.
  
  
• Exploring algorithmic solutions and network modelling to address optimisation challenges in IoT environments  
  by Rashmi Prava Das, Debendra Muduli, Ashish Kr. Luhach 
  Abstract: Internet of things (IoT) has a transformative technology, reshaping the landscape of connectivity and information exchange. It represents an intricate network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity, enabling them to collect and exchange data effortlessly. The paper focuses on measuring the efficacy of optimisation algorithms, namely the hybrid simulated annealing-local search algorithm (SA-LSA), genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA), in addressing multi-objective optimisation challenges and complex function minimisation scenarios. It aims to provide a comprehensive understanding of selecting appropriate algorithms for diverse optimisation challenges, considering factors such as solution space complexity, exploration-exploitation trade-off preferences, and convergence speed. The potential of this work lies in contributing valuable insights into the performances of optimisation algorithms, specifically in navigating trade-offs and converging towards optimal solutions. This work conducts a comparative analysis of algorithms, evaluating the overall performance to provide insights into their strengths and weaknesses, facilitating the selection of optimisation approaches for specific applications spanning multi-objective scenarios and complex function minimisation tasks.
  Keywords: energy consumption modelling; particle swarm optimisation; PSO; hybrid simulated annealing-local search algorithm; SA-LSA; genetic algorithm; GA; multi-objective optimisation.
  
  
• Optimal magnetorheological damper for two-wheeled vehicle using analytical method  
  by Keshav Manjeet, Dhawade Eashan, C. Sujatha 
  Abstract: Magnetorheological (MR) fluid technology has gained significant development in effectively isolating undesirable vibrations by use of MR fluids acting as a dissipating medium in a damper. The current work aims at optimally designing an MR damper for a commercial 110 cc two-wheeled vehicle; this damper could replace the conventional passive damper. The geometric dimension of the valve is designed in such a way that it should match the performance of the passive damper in field-off state and includes dimensional constraints from the geometry of the vehicle. After the dimensions of the damper are decided on, valve geometry is optimised in the MATLAB environment for multiple objective functions using an analytical method of solving a magnetic circuit. Further, the optimised MR damper is implemented in a 5-DOF half-car vibration model developed and the response is then compared with that of the vehicle model with conventional passive dampers at both ends.
  Keywords: MR damper; two-wheeled vehicle model; passive damper; optimisation; analytical magnetostatic model; 5-DOF vibrating model.
  
  
• A novel keyframe extraction technique systems using deep reinforcement learning simulation  
  by M. Dhanushree, R. Priya, P. Aruna, R. Bhavani 
  Abstract: Smartphones and wearables make video capture easy, increasing video generation. The abundance of videos led to the development of a video summarisation study. The most important moments of a longer input video are selected to minimise its length while maintaining context. Video summary using keyframe extraction outputs key frames of key events. Its uses include anomaly detection, efficient video storage, indexing, and retrieval. Extracting semantically meaningful frames is difficult, and a big research gap exists. The keyframe extraction using deep reinforcement learning (KE_DRL) approach extracts representative and distinct semantically relevant keyframes. Frame-level and video-level characteristics are extracted. Frame-level characteristics are extracted using modified ResNet50 and I3Dnet. They are aggregated to generate a feature vector and global average pooled to get video-level features. attention-based video summariser network (AVSumnet) uses semantic video attributes as input. It is trained via reinforcement learning. A new summariser network training reward mechanism is proposed. Experimental findings show that the KE_DRL method creates better video keyframes than existing methods.
  Keywords: keyframe extraction; video summarisation; bi-directional gated recurrent unit; BDGRU; deep reinforcement learning; attention mechanism; squeeze and excitation block; policy gradient; reward function; representativeness.
  
  
• Research on steady speed control strategy of direct-drive pump-controlled hydraulic motor based on improved extended state observer  
  by Guoshuai Li, Yong Sang, Qingli Qi 
  Abstract: This paper introduces the direct-drive volumetric control (DDVC), focusing on the direct-drive pump-controlled hydraulic motor electro-hydraulic system as the primary subject of research. Aiming to address the problem of parameter uncertainties and unknown nonlinear disturbances in the system. A robust backstepping controller based on the extended state observer (ESO) is designed to improve the systems dynamic performance and robustness. In the controller, the ESO is used to estimate the total uncertainties in the system. Then, the robust backstepping controller is designed to enhance both the dynamic and robust performance. Moreover, Lyapunov theory is utilised to rigorously demonstrate the stability of both the ESO and the closed-loop control system. Finally, the results indicate the designed controller has excellent robustness and dynamic performance.
  Keywords: direct drive; electro-hydraulic system; nonlinear disturbances; robust control; extended state observer.
  
  
• Impact analysis and control of EV charging on grid connected to optimal structure hybrid wind-solar PV system  
  by Abdul Zeeshan, Swapnil Srivastava 
  Abstract: A detailed review of research work has been done for wind-PV hybrid generation system (WPVHGS) to identify suitable plant structure. There is scope of optimisation of WPVHGS when feeding to grid at unbalanced voltage conditions. In addition to this, optimal sizing of battery energy storage (BES) and supercapacitor (SC) is required for the same. To support the review, simulations are carried out on MATLAB Simulink for comparative analysis of impact on grid due to EV charging, where a centralised charger is charging battery equivalent to 10 EVs. Using simulations, severity of grid current imbalance is observed, when battery charger is connected to 575-V bus. When doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) is introduced then currents and voltages are balanced but severe third harmonics of current is introduced. By replacing proportional-integral (PI) controller with adaptive neuro-fuzzy inference system (ANFIS), its observed that voltage and current total harmonic distortion (THD) gets mitigated.
  Keywords: hybrid; wind; solar PV; battery; supercapacitor; doubly-fed induction generator; DFIG; electric vehicle; battery energy storage; BES; adaptive neuro-fuzzy inference system; ANFIS; total harmonic distortion; THD; wind energy conversion system; WECS.
  
  
• H controller synthesis for multiple time-varying delays systems with application to double diabetes mellitus  
  by S. Syafiie, F. Tadeo 
  Abstract: Many physical, biological, chemical, electrical, and industrial systems exhibit time-varying delays in their inner dynamics, caused by aftereffects or dead-time phenomena. As the expectation is that the mathematical models of these systems behave like the real process, the techniques to develop control systems should consider these multiple delays. In this context, this paper aims to synthesise a memory-less controller satisfying H performance. More precisely, the controller gain is selected to guarantee closed-loop stability in the presence of delays, by using a Lyapunov-Krasovskii functional (LKF) and a reciprocally convex approach to upper bound integration functions. The closed-loop stability condition is presented as linear matrix inequalities (LMI), solving the stability to extract the optimal controller gain after minimisation of the H performance. The approach is illustrated numerically for a double diabetes mellitus (DDM) system. It is shown that the proposed controller synthesis is simple and the controller gain is able to drive the blood glucose concentration to the desired level upon periodic glucose intakes.
  Keywords: time-delay systems; multiple delays; H control; glycemic regulation; double diabetes mellitus.
  
  
• Earthquake prediction using deep learning based-recurrent neural network technique  
  by J. Sahaya Ruben, M. Adams Joe, M. Anand, M.Prem Anand 
  Abstract: This article explores the application of deep learning techniques to enhance earthquake prediction and detection, addressing the critical need for improved disaster preparedness and risk mitigation. With natural disasters like earthquakes causing widespread harm to people, and ecosystems, the study focuses on leveraging machine learning methodologies, including recurrent neural networks and reinforcement learning, to develop an innovative earthquake prediction framework. The proposed approach begins with feature extraction using CNN to capture essential seismic data patterns. Subsequently, the RNN model is trained to analyse time series seismic data, allowing for the prediction of earthquake events with enhanced precision. In addition, Q-learning is integrated into the process to make informed decisions based on the current state, potentially reducing false alarms and improving overall prediction accuracy. The promising results and potential impact of this research underscore the importance of ongoing efforts to harness technology for more effective earthquake prediction and mitigation strategies.
  Keywords: convolutional neural network; CNN; recurrent neural network; RNN; Q-learning; deep learning.
  
  
• Analysis of grid-tied inverter based distributed generation with focus on control of active power and voltage compensation  
  by Siddharthsingh K. Chauhan, Vaidehi D. Sathwara, P.N. Tekwani 
  Abstract: Nowadays, renewable energy sources (RES), especially photovoltaic (PV) systems, are popularly used as distributed generators. This is due to availability of high-power grid-tied inverters (GTIs) used for interconnecting PV systems with grid. The PV based distributed generation (DG), conventionally used for exchange of active power with grid, further can be controlled for added functionalities of reactive power exchange, voltage compensation, and harmonic mitigation. This multi-functional operation is achieved by effectively controlling the grid-tied inverter of DG. Such multi-functional control of grid-tied inverter is analysed in this paper. For control of multi-functional grid-tied inverter (MFGTI), comprehensive power quality evaluation (CPQE) index technique is used. In this paper, effective control of the space vector modulation (SVM)-based hysteresis controlled CPQE-based GTI is presented through simulation studies. Effective performance of the proposed GTI for active power exchange as well as voltage compensation during fault condition is presented.
  Keywords: comprehensive power quality evaluation; CPQE; distributed generation; multi-functional grid-tied inverter; MFGTI; voltage compensation; renewable energy sources; RES; grid-tied inverters; GTIs; space vector modulation; SVM.
  
  
• Cultivating resilience in wheat agriculture: a cutting-edge approach to disease management through high-precision wheat leaf segmentation and cross-dataset analysis  
  by Sai Ram Paidipati, Sathvik Pothuneedi, Vijaya Nagendra Gandham, Lovish Jain, Sandeep Kumar, Arpit Jain 
  Abstract: Efficient detection of diseases in wheat plants is essential for boosting agricultural productivity and ensuring food security. This paper introduces a computer vision-based approach using region of interest (ROI) and bounding box (BB) techniques to automate the identification and localization of disease symptoms on wheat leaves. By employing datasets like LWDCD2020 and Wheat Leaf Dataset, the study demonstrates a robust method for image segmentation, achieving superior accuracy. The proposed system integrates pre-processing, feature extraction, and segmentation to detect diseased areas effectively. Experimental results show the approach delivers 99.78% accuracy and a 99.87% dice coefficient on the LWDCD-2020 dataset, while achieving 99.33% accuracy on the Wheat Leaf dataset. The results confirm the superiority of the method against geometric attacks and other state-of-the-art techniques, ensuring high precision and efficiency in disease detection.
  Keywords: wheat plant diseases; computer vision; image segmentation; region of interest; ROI; bounding box; BB; LWDCD2020 dataset; agricultural productivity; food security; precision agriculture; sustainable agriculture.
  
  
• Excitation effects of arc grounding fault on PT ferroresonance of distribution networks connected to photovoltaic in high-altitude areas  
  by Jinpeng Yuan, Jingwen Sun, ZiBin Li, Wande Lin, Zile Wang, Jianwu Li 
  Abstract: Power distribution network systems in high-altitude areas with low air pressure are prone to arc grounding fault, and abundant solar resources lead to the access of numerous distributed new energy sources, which further increases the risk of potential transformer (PT) ferroresonance excited by arc grounding fault. This study designed a device simulating the arcing characteristics in high altitude environment, established a Mayr arc model for simulating arc grounding fault, analysed the fault characteristics of PT ferroresonance overvoltage caused by arc grounding, metallic grounding and accessing to new energy in high altitude areas, and compared the effects of connecting damping resistor at the open delta of PT secondary winding and connecting single-phase PT at the neutral of primary winding to suppress ferroresonance under different conditions, which may provide a reference for suppressing ferroresonance caused by new energy access in high altitude areas.
  Keywords: ferroresonance; overvoltage; potential transformer; arc grounding fault.
  DOI: 10.1504/IJESMS.2025.10071011
   
  
• Spectrum sharing using deep learning: multi-agent reinforcement learning  
  by B.V. Santhosh Krishna, A. Bharathidhasan, N. Ashokkumar, K. Periyar Selvam 
  Abstract: The number of people using cell phones and the requirement for the radio band has increased over the last few years. The fast rise of 5G networks for wireless communication and wireless communication has met this need. There is reason to believe that the issue of improper use of the wireless spectrum could be resolved with the progress of cognitive radio and its spectrum-sensing technology. Deep learning technology is known for being able to learn and change amazingly quickly. The purpose of this research is to provide a brief summary of the approach used in cognitive radio spectrum-sensing technology and deep learning technology. The first part of this study talks about the common spectrum-sensing methods to give a big picture of the benefits of deep learning-based spectrum-sensing algorithms. We find that our method can increase the accuracy of previous work and conventional learning strategies by as much as 83%.
  Keywords: cognitive radio; spectrum sensing; wireless communication; cooperative spectrum sensing.
  DOI: 10.1504/IJESMS.2025.10071409
   
  
• Comparative analysis of marine debris simulation using ensemble learning with XGBoost and deep convolutional neural networks  
  by S. Belina V.J. Sara, Gnaneswari Gnanaguru, S.Silvia Priscila 
  Abstract: Marine ecosystems, wildlife, and human activities are seriously threatened by marine garbage. Deep learning-based systems for categorisation can automate classifying distinct types of marine debris from photos or video recordings, allowing for more effective and precise monitoring and assessment of debris levels in different maritime circumstances. DL is a useful tool that can help with environmental conservation efforts by categorising marine waste. To improve classification accuracy, sensitivity, and specificity for different types of marine debris, we investigate the use of ensemble learning approaches in this work and used for execution in Python. We compare three distinct implementations of the powerful gradient boosting method XGBoost with innovative deep convolutional neural networks: XGBoost with Adam and GoogleNet optimiser, XGBoost with VGG19 and Adam optimiser, and XGBoost with ResNet and Adam optimiser. The XGBoost algorithm and feature extraction from three different pre-trained CNN architectures, GoogLeNet, VGG19, and ResNet, are used in this study to examine the effectiveness of classifying maritime debris. We highlight the outstanding results obtained by combining ResNet + Adam with XGBoost, a reliable and effective method for classifying maritime trash and producing an accuracy of 91%, specificity of 0.88, and sensitivity of 0.91, respectively.
  Keywords: marine debris simulation; XGBoost ensemble; convolution neural network; CNN; Adam optimiser; image classification; environmental monitoring; optimisation techniques; sensitivity enhancement; deep learning; DL.
  DOI: 10.1504/IJESMS.2025.10071659
   
  
• Improving cooling load prediction in residential buildings with multi-layer perceptron models  
  by Yang Wu, Lanlan You 
  Abstract: Today, building energy efficiency is prioritised since it affects operational costs. Buildings take a lot of energy to maintain pleasant temperatures. Combining this researchs cooling load (CL) forecasting method may optimise building energy use. MLPs forecast household cooling demands. MLP models and regressions generally have linear input-output relationships. This research uses two innovative optimisers, cheetah optimiser (CHO) and adaptive opposition slime mould algorithm, to improve MLP model performance. The data used to train the approaches describes each samples unique traits. These methods will be tested on a simulated dataset using CLs as neural network output variables and building technical attributes as input factors. During the process testing phase, the MLCO (2) (MLP+CHO in layer 2) gets the lowest RMSE value of 0.672 and the greatest R2 value of 0.995. The results demonstrate that the proposed hybrid models MLCO and MLAO significantly outperform the standalone MLP and conventional optimisation methods, achieving a minimum error rate. These findings confirm the proposed models superior predictive accuracy and reliability, underscoring their potential for practical application in enhancing energy efficiency in residential buildings.
  Keywords: cooling load; multi-layer perceptron; MLP; cheetah optimiser; CHO; adaptive opposition slime mould algorithm; artificial intelligence; support vector machines; SVM; artificial neural networks; ANNs.
  DOI: 10.1504/IJESMS.2025.10071781
   
  
• A versatile biomedical device employed for diverse applications in minimally invasive surgical procedures  
  by Md. Abdul Raheem Junaidi 
  Abstract: The research revolutionises to introduce a new design of an instrument that combines the functionality of Maryland forceps with that of a suction irrigation device. Currently, the above two operations have to be carried out sequentially, which adds to the amount of time and effort spent by the surgeon. Integrating both these features within the same device can ensure that both processes can take place simultaneously or one after the other as many times as required, without unnecessary removal of the device from the incision. Thus the article has modelled the instrument which can potentially benefit in all other various laparoscopic procedures.
  Keywords: laparoscopic instruments; irrigation; suction; forceps; mechanism; multi-functional.
  DOI: 10.1504/IJESMS.2024.10064909
   
  
• Multi-period planning of fish breeding chains and investigation of its efficiency under demand uncertainty  
  by Sajad Moradi 
  Abstract: This article studies an issue in the fish farming industry, aiming to find the best multi-period plan for managing various chains, including spawning, breeding, harvesting, and selling trout over a given time horizon. It provides a new mixed integer linear programming model that efficiently finds the optimum solution. In the proposed model, some intermediate stages of the breeding chains that do not affect key decisions are ignored, thereby reducing the size and complexity of the proposed model without compromising the optimality of the answers. When weekly demands are considered uncertain data, by simulating weekly demand, it is shown that using the first-in, first-out policy during the selling season, the schedule provided by the deterministic model, in which average value is considered for the weekly demand, will still be effective relatively. By analysing the obtained results, some approaches are suggested to improve the proposed program.
  Keywords: fishing industry; mathematical modelling; demands uncertainty; sales management; simulation.
  DOI: 10.1504/IJESMS.2024.10066291
   
  
• An optimised AES algorithm and its FPGA implementation for secure information  
  by G. Mallikharjuna Rao, K. Deergha Rao 
  Abstract: Security algorithms play a crucial role across various communication networks, encompassing both wired and wireless infrastructures. As technology rapidly evolves, particularly in the realm of 5G communications, the demand for more robust security measures is becoming increasingly prominent. Research to date has focused on the AES 128-bit encryption standard, with its implementation being extensively tested, synthesised, and applied to different FPGA platforms such as Spartan, Virtex, and Kintex. Nevertheless, existing studies fall short in providing an AES algorithm optimised for minimising power consumption, reducing latency, and conserving space, all of which are critical for effective security. This study introduces an enhanced AES algorithm tailored for FPGA implementation, specifically designed to meet the stringent criteria of reduced latency, decreased power usage, and lower spatial requirements for the purposes of simulation and synthesis, using Xilinx-ISE v14.7 tool.
  Keywords: advanced encryption standard; AES; FPGA; information security; 5G communication; IoT.
  DOI: 10.1504/IJESMS.2025.10071154
   
  
• Character classification enhancement through hybrid feature fusion in challenging scripts systems  
  by Sobia Habib, Manoj Kumar Shukla, Rajiv Kapoor 
  Abstract: One of the most intriguing research problems is to achieve high accuracy in character recognition of degraded scripts, which is essential for applications such as document digitisation, language translation, and text-to-speech systems. We aim to recognise two degraded scripts of Devanagari and Urdu languages, which have unique difficulties, mainly due to the presence of broken and merged dots. Traditional character recognition techniques, including template matching and feature-based methods, have been widely used but need to be more efficient to handle the complexities of Urdu and Devanagari scripts. We propose classifying damaged scripts using zone-based power curve fitting and a pre-trained VGG19 model that trains on script degradation patterns. Using 6,250 printed examples with distortions from damaged Devanagari and Urdu manuscripts, we fine-tune the VGG19 model. It helps the proposed model understand these characters' intricate features and minimises overfitting. Our changes improve accuracy and strengthen the script damage detection system. Our results show that the VGG19 architecture works well across most feature extraction strategies, with accuracy scores ranging from 89.26% to 93.42%, while combining the power curve fitting methodology with VGG19 improves classification accuracy to 97.42%.
  Keywords: power curve fitting; VGG19; challenging scripts systems; broken characters; merged dots characters; deep learning features.
  DOI: 10.1504/IJESMS.2025.10071150
   
  
• Quantum vs. classical methods in information security, computing and machine learning domains: an empirical study  
  by Kriti Srivastava, Akshit Gabhane, Ankit Ladva, Pushkar Waykole, S. Suman Rajest 
  Abstract: The rise in big data has necessitated the development of new computing technologies that can process large volumes of data in a faster and more efficient manner. In recent years, quantum computing has emerged as a promising candidate for this purpose due to its ability to work with high dimensionality data and its potential for solving complex problems that classical computers struggle with. This research work conducts a comparative analysis of quantum computing and classical computing in the fields of information security, computing, and machine learning, which are all critical fields in the modern world. The study uses a variety of methods, including theoretical analysis, simulation, and experimental implementation to demonstrate the benefits of quantum computing. This study serves as a basis for future research in the field of quantum computing and its applications, which could lead to significant advancements in various areas of science and technology.
  Keywords: comparative analysis; Grover's algorithm; Shor's algorithm; quantum computing; quantum SVM; quantum CNN; information security.
  DOI: 10.1504/IJESMS.2025.10071149
   
  
• Transforming electrical simulation and management with smart grid technologies  
  by K. Chitra, S. Silvia Priscila, Edwin Shalom Soji, R. Rajpriya, B. Gayathri, A. Chitra 
  Abstract: Electrical simulation and management are essential for ensuring reliable, efficient, and sustainable power supply to various consumers. However, the traditional power grid faces many challenges, such as ageing infrastructure, increasing demand, integration of renewable energy sources, power quality issues, and cyber-attacks. Smart grid technologies offer a promising solution to overcome these challenges and transform the electrical distribution and management system. Intelligent grid systems encompass sophisticated sensing technology, advanced metering devices, communications infrastructure, control mechanisms, data interpretation tools, and automation components. These elements facilitate two-way information and electrical power exchange between those responsible for grid management and end-users. This article reviews intelligent grid technologies' impact on electrical distribution and operational management. A case study of a mid-sized urban region informs the article's organised approach to creating and deploying an intelligent grid network. The findings show that intelligent grid technologies improve electrical distribution system dependability, operational efficiency, environmental sustainability, and security. This article explores the problems and opportunities of intelligent grid systems and provides guidance for future research and technology.
  Keywords: smart grid; electrical simulation; power management; smart meter and microgrid; systems' reliability; operational efficiency; environmental sustainability; grid management.
  DOI: 10.1504/IJESMS.2025.10071151