International Journal of Engineering Systems Modelling and Simulation (17 papers in press)

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• Open Access

  Forms and innovative applications of fine arts factors in the design of literary and artistic products ( Free Full-text Access )

  by Xin Yu 
  Abstract: As material living standards improve, consumers now seek literary and artistic products that offer both function and visual appeal. Attention is increasingly focused on design, form, and aesthetics, making purely functional items less attractive. However, current research on image aesthetics often overlooks the role of human visual saliency and attention in beauty perception, limiting evaluation performance. To address this, the study proposes a novel method that mirrors the brain's processing of visual stimuli. It introduces two algorithms: one based on compositional edge features and another using weakly supervised learning for aesthetic classification through attention mechanisms. By combining visual saliency with aesthetic evaluation, the approach better aligns with human perception and improves assessment accuracy. This method enhances understanding of aesthetic judgment and supports the development of more appealing designs, offering a valuable framework for evaluating the visual quality of literary and artistic products in todays design-conscious market.
  Keywords: fine arts; literary design; product design; artistic products; saliency-based aesthetic analysis; visual saliency; consumer preferences; human visual perception.
  
  
• Automated recognition of power quality disturbances for internet of power quality things  
  by V. Jomole Varghese, M.P. Vidhya, B. Smitha 
  Abstract: Real-time power quality (PQ) monitoring has become most essential to evaluate the severity of voltage variations for timely protecting distributed energy systems, appliances and equipment connected with internet of things (IoT) networks. In this paper, we attempt to present a low-complexity PQ disturbance (PQD) recognition method for automatically detecting variation events, such as sags, swells, interruptions and transients according to the IEEE Std. 1159. The proposed PQD event recognition method consists of digital filtering, Hilbert transform (HT) and decision tree. The proposed PQD recognition method is evaluated using the simulated PQ signals according to the IEEE Std. 1159 and the real-time PQ signals. The proposed method had a recognition accuracy of 96-100% for detecting the voltage sag, swell, momentary interruption, transient and combined disturbances.
  Keywords: power quality; PQ; power quality disturbance; PQD; Hilbert transform; HT; internet of power quality things; IoPQT.
  DOI: 10.1504/IJESMS.2026.10076587
   
  
• Performance of single elliptical-shaped three-blade Savonius micro-wind turbine installed on car roof: simulation and experimental study  
  by Hemantchandra N. Patel, Kalpesh V. Modi, Manthan A. Modhia, Falak T. Makwana 
  Abstract: The implementation of wind energy conversion devices in automobiles to harness/harvest the energy from wind faces significant challenges, as it affects the vehicles aerodynamic characteristics and overall performance. The simulation and experimentations were conducted to study the effect of installing single elliptical-shaped three-blade Savonius micro-wind turbine (MWT) on car roof on aerodynamics and power generation. The simulations on car model were carried out at various car speeds (20 to 120 km/h) for two cases case-I: car without MWT, and case-II: car with single MWT in enclosure. The simulation results indicated that single MWT generated 2.3481 and 7.6674 W power at MWT rotation of 2,123 and 3,185 RPM (car speeds of 40 and 60 km/h). Experimental results indicated that single MWT generated 1.1562 and 5.41 W effective power at MWT rotation of 1,765 and 2,365 RPM and relative velocity of 16.36 and 23.9 m/s (car speed 59 and 86 km/h).
  Keywords: aerodynamics; drag coefficient; lift coefficient; computational fluid dynamics; CFD; wind energy; micro-wind turbine; MWT.
  DOI: 10.1504/IJESMS.2026.10077182
   
  
• Experimental analysis of soil quality using internet of LoRaWAN and predicting the soil nutrient using federated learning for next generation sustainable agriculture  
  by M. Vinodhini, C. Neeladharan 
  Abstract: Federated Learning has become an emerging technology for the analysis of the soil nutrient index (SNI). To address the existing issue, the proposed hybrid long range (LoRa) with federated learning (LFL)-based real-time soil quality management system in a specific zone, Ambur, Vellore District. The process begins with the collection of real-time soil samples from the specified zone using a LoRaWAN-enabled prototype system, ensuring continuous monitoring and data acquisition. Second, the soil samples undergo scanning electron microscopy (SEM) analysis to identify key organic indicators like biochemical oxygen demand (BOD), chemical oxygen demand (COD), phenol, chloride, and phosphate, which provide insights into soil health and contamination levels. The gathered real-time soil nutrient data is utilised to train a federated learning (FL) model, which predicts the soil nutrient index efficiently while maintaining data privacy. The proposed system integrated approach combines LoRaWAN-based monitoring with advanced AI-driven analytics, enabling effective soil health evaluation.
  Keywords: federated learning; soil nutrient index; SNI; internet of LoRaWAN prototype; sustainable agriculture; real-time soil data; machine learning classifiers; scanning electron microscopy; SEM analysis; federated learning; SNI; LoRaWAN; machine learning.
  DOI: 10.1504/IJESMS.2026.10077681
   
  
• Experimental and multi-objective optimisation study on impact strength and surface roughness in fused filament fabrication of flexible TPU component  
  by Rituparna Saha, Subhash Chandra Panja, Sankar Narayan Patra, Sunil Kumar Sharma, Sovan Sahoo, Sumit Dhar 
  Abstract: The performance of fused filament fabrication (FFF) components is strongly influenced by printing parameters, which govern deposition, interlayer bonding, and surface morphology. For flexible TPU, widely used in energy-absorbing applications, the combined effects of these parameters on impact strength and surface quality remain unclear. This study evaluates layer height, infill angle, and builds orientation using Taguchi L9 design, ANOVA, and optical microscopy, with Grey relational analysis applied for multi-objective optimisation to identify balanced settings. Results show layer height is the most influential factor, improving impact strength through better interlayer diffusion but increasing surface roughness due to stair-stepping. Build orientation affects impact strength, while infill angle has little effect. The optimal combination of 0.17 mm layer height, +-22.5 infill angle, and 0 build orientation achieves a balance between mechanical strength and surface quality. The study demonstrates the role of process parameters and layer morphology in optimising flexible TPU components.
  Keywords: fused filament fabrication; FFF; thermoplastic polyurethane; TPU; Taguchi; analysis of variance; ANOVA; single- objective optimisation; grey relational analysis; GRA.
  DOI: 10.1504/IJESMS.2026.10077850
   
  
• Enhanced image manipulation detection using lightweight MobileNet and meta graph neural networks  
  by Mahejabi Khan, Samta Gajbhiye, Rajesh Tiwari 
  Abstract: Digital manipulation of images has been made widespread by the availability of advanced editing and generative tools. While these tools make it easy to create visual content, they also pose a risk to digital authenticity, security verification, journalism credibility and forensic investigations. The increasing number of manipulation methods calls for accurate and scalable methods for detection. Many existing deep learning models such as conventional CNN, fully convolutional networks, hybrid autoencoders and CNN-LSTM frameworks still suffer from high computational cost, slower inference and limited generalisation on manipulation types. To overcome those limitations, a lightweight detection framework using MobileNet architecture is proposed and augmented with Meta Graph Neural Networks to learn relational features from each other region in the image. Depth-wise separable convolutions allow efficient feature extraction using few parameters. Using the CASIA2 dataset with 12,614 samples, the model achieved 91.67% training accuracy and 99.29% validation accuracy with 0.0220 validation loss, which is better than CNN, FCNN, CNN-AE and CNN-LSTM baselines.
  Keywords: image manipulation detection; lightweight MobileNet; meta graph neural network; Meta-GNN; digital forensics; computational efficiency.
  
  
• Interactive hydraulic analysis of side obstacles and composite structures with a comparative machine learning study for discharge prediction  
  by Rafi M. Qasim, Ammar Salman Dawood, Ahmed Sagban Khudier 
  Abstract: This study investigates hydraulics of a combined weir-gate structure and how lateral side obstacles alter the downstream flow field. Side-obstacle effects were embedded in inputs. Experiments in a rectangular flume produced 75 observations of upstream water level, gate-weir spacing, and flow cross-sectional area to relate downstream flow area and discharge to flow rate, weir and gate flow areas, discharge coefficient, and Froude and Reynolds numbers. The dataset was split into 80% training and 20% testing to evaluate eight machine-learning regressors (decision tree, random forest, bagging, AdaBoost, gradient boosting, XGBoost, KNN, and ANN) for discharge prediction. Gradient boosting performed best (test R2 = 0.9596), whereas the ANN failed (R2 = -0.30), indicating poor suitability for this problem. XGBoost and KNN achieved moderate accuracy (R2 0.75-0.78). As the first systematic comparison for composite weirs-gates with/without lateral obstacles, the work highlights MLs potential to support hydraulic design and water-resources management.
  Keywords: artificial neural network; ANN; discharge prediction; hydraulic engineering; gradient boosting; composite hydraulic structure; machine learning.
  DOI: 10.1504/IJESMS.2026.10078514
   
  
• Enhanced fault localisation in power systems leveraging phasor measurement units  
  by Minmin Su, Xiaoxia Wang 
  Abstract: This paper proposes an adaptive fault location (FL) scheme for power transmission systems, accounting for line capacitance and parameter uncertainty. Unlike traditional impedance-based methods, it utilises a distributed parameter line model (DPLM) in the phasor domain. Phasor measurement units (PMUs) provide synchronised voltage and current measurements from both ends of each line segment, enabling accurate algorithm execution. Transmission line parameters are dynamically estimated using prefault data, enhancing accuracy under uncertainty. Fault detection is achieved using wavelet transform (WT), with a detection time of 4 microseconds. The Newton-Raphson (NR) method iteratively determines the precise FL. PMU placement ensures full observability of the IEEE 24-bus system. Various fault types-including single-phase-to-ground, multi-phase faults, and resistances from 10 to 300 ohms were simulated using EMTP-RV and MATLAB. Results show the method is highly robust, with fault location errors under 0.15%, offering strong potential for real-time fault management in future smart power systems.
  Keywords: fault location; FL; distributed parameter model lines; phasor measurement units; PMUs; power system; adaptive fault detection; wavelet transform; WT; impedance-based method; Newton-Raphson algorithm; NR.
  DOI: 10.1504/IJESMS.2026.10078620
   
  
• An intelligent lifecycle framework for strengthening the resilience of critical infrastructure systems  
  by R. Rajesh Kanna, P. Praba Devi, L. Subha, T. Sindhu, K. Sackthivel, S. Namachivayam, S. Anitha 
  Abstract: Critical infrastructure networks (CINs) like power, transport, and communications networks are vital to modern society. These networks can fail catastrophically. A novel paradigm for CIN systemic resilience through intelligent lifecycle management is presented in this study. Proactive vulnerability assessment and repair across the infrastructure asset lifetime from design and building to simulated operation and decommissioning using real-time monitoring, predictive analysis, and automated reaction systems. The current work uses a synthetic dataset of 492 examples reflecting regional power grid operating regimes under varying stress levels. Dataset inputs include component condition, load distribution, and environmental conditions. The study used Pandas for data management, Scikit-learn for prediction models, and Matplotlib for charting in Python. The suggested machine learning system can forecast failures, schedule maintenance, and recommend adaptive control actions to improve global network resilience. The study shows that smart lifecycle management can extend the steady operation of fundamental infrastructure by improving disruptive resistance and resilience. In a quickly changing, uncertain global context, the study provides policymakers and infrastructure stakeholders with a solid, scalable strategy to improve critical infrastructure security and resilience.
  Keywords: systemic resilience; electricity grids; intelligent lifecycle management; predictive analysis; autonomous response; global environment; applied energy; real-time monitoring; vulnerability analysis.
  DOI: 10.1504/IJESMS.2026.10078691
   
  
• Intelligent paddy leaf disease detection and automated pesticide spraying using AI and IoT  
  by Sheeba Santhosh, G. Hari Krishnan, G. Mohandass, R. Sreelakshmi, E. Sivanandam, Ashok Kumar Srinivasan 
  Abstract: Technological innovations that enhance farmland quality and productivity have led to the development of smart farming systems with automation. One of the longstanding threats to food security is paddy leaf disease, which can significantly reduce crop yield and quality. Accurate diagnosis of these diseases has traditionally been challenging; however, recent advances in deep learning and machine vision have enabled more precise detection. In this work, a novel approach for identifying paddy leaf diseases is implemented using convolutional neural networks (CNNs). Neuron-wise and layer-wise visualisation techniques were employed to analyse the networks decision-making process. The model was trained on a publicly available paddy leaf disease image dataset, allowing it to accurately recognise the textures and patterns of lesions associated with different diseases, effectively mimicking human expert evaluation. Additionally, an Arduino-based automated pesticide spraying system was developed and integrated with the disease detection model, enabling targeted and efficient pest management.
  Keywords: deep learning; TensorFlow; Keras; convolutional neural network; CNN.
  DOI: 10.1504/IJESMS.2026.10078931
   
  
• Deep learning-driven forecasting of cloud resource utilisation using PSO-enhanced LSTM for cost-effective scaling  
  by Karthikeyan Sivanandi, C.Sathish Kumar, D. Chitra, S.Silvia Priscila, G. Rajasekaran, S. Suman Rajest 
  Abstract: Modern IT design relies on cloud computings on-demand, scalable resources and services. Cloud computing is expensive, and resource allocation controls it. Traditional resource scalability methods like threshold-based or human supervision are expensive and inefficient. Deep learning for resource forecasting and cost management is growing. In this research, a deep learning-driven algorithm predicts cloud memory and CPU utilisation. The model forecasts CPU and memory usage to enable proactive resource allocation. The suggested paradigm is implemented in Python. The experiment model was trained, validated, and tested using 2019 Google Cluster Workload Traces. The models performance was also compared to conventional models. The PSO-optimised LSTM model forecasted cloud resource use better than all others with an MSE of 0.0398 and an MAE of 0.0135. This was slightly less successful than linear regression but better than CMA-ES. The trained model can be deployed to cloud infrastructure and configured with an automated mechanism that auto-scales based on forecasts. This proactive method improves resource use and could cut costs. The model can reduce manual monitoring by integrating a feedback mechanism for retraining or fine-tuning. This article offers a cost-cutting and resource-efficient technique for cloud service providers and businesses.
  Keywords: LSTM model; cloud resource consumption; lower costs; proactive strategy; manual monitoring; cloud computing.
  DOI: 10.1504/IJESMS.2026.10078937
   
  
• Nature-inspired load balancing in cloud systems: a PSO-GA hybrid optimisation model  
  by S. Balaji, K. Krishna Prasad, S.Silvia Priscila, P.M. Praveen 
  Abstract: Computing optimisation has become an industry standard and is growing rapidly. It provides fast, affordable computing using massively virtualised data centres. Todays apps must serve millions of users with accurate text, photos, videos, and other data quickly and consistently. Cloud load balancing (LB) or resource scheduling is the most critical problem to solve due to resource heterogeneity, interdependencies, and load unpredictability. LB distributes network traffic equitably among software application resources. LB directs workloads to computing resources to boost performance. In complicated, unpredictable situations, simple LB methods fail. Metaheuristics are used for solving complex problems. Nature-inspired metaheuristic (MH) algorithms are being applied in many domains to solve difficult optimisation problems. This paper uses PSO + GA, a hybrid metaheuristic algorithm, to optimise resource scheduling for LB. Comparisons include PSO, ACO and genetic algorithms. Metaheuristic algorithm performance is measured by response time, makespan, and average resource use. Using performance factors, the CloudAnalyst simulator shows PSO + GA, PSO, ACO and GA for LB. The results suggest nature-inspired algorithms may improve load-balancing reliability and efficiency.
  Keywords: computing optimisation; load balancing; particle swarm optimisation; PSO; genetic algorithm; ant colony optimisation; ACO.
  DOI: 10.1504/IJESMS.2026.10079047
   
  
• HML-VBSP: a hybrid machine learning framework for predicting multiple vector-borne diseases simulation using soft voting strategy and hyper parameter tuning systems  
  by K. Kavitha, T. Prabhu 
  Abstract: Dengue, yellow fever, chikungunya, and Zika are vector-borne illnesses that are becoming more common as the world's population grows. Environmental variables, such as temperature and precipitation, are frequently incorporated into infectious disease models. Early warning of disease outbreaks may be provided by combining forecasting models with increasing computer capability and better AI technologies. Then, using a voting classifier with a 'hard' voting strategy, the proposed HML-VBDP model for vector-borne disease prediction is constructed by combining a random forest (RF), a support vector classifier (SVC), and a gradient boosting (GB) classifier. To optimise parameters such as the learning rate, number of estimators for GB, regularisation parameter (C), kernel coefficient (gamma) for SVC, and maximum depth and number of estimators for RF, we use RandomizedSearchCV to tune each classifier's hyperparameters before training the model. To train the HML-VBDP model, we utilise the training data. Then, to check its performance, we use the testing data. The efficacy of the model is assessed using evaluation measures including recall, accuracy, precision, F1-score, ROC curve, and confusion matrix.
  Keywords: hyper parameter tuning systems; vector-borne disease; VBD; random forest; support vector machine; SVM; gradient boosting; vector-borne diseases simulation.
  DOI: 10.1504/IJESMS.2026.10075480
   
  
• Spintronics for intelligent, low-power, and scalable electronics  
  by Payal Jangra 
  Abstract: As traditional semiconductor devices reach their limitations in terms of further scaling and integration, spintronic technologies are emerging as a viable alternative. In contrast to charge-based electronics, spintronic devices utilise the electron's spin, providing distinct benefits in terms of low leakage power, high endurance, non-volatility, and speed in read/write operations. These attributes render them extremely desirable in contrast to CMOS equivalents and well-suited for computing requirements, such as big data and the internet of things (IoT). This article offers a comprehensive overview of spintronics evolution in the last two decades, encompassing the underlying physical phenomena - such as spin-orbit driven effects like the spin hall effect, tunnelling magnetoresistance, and device-level applications, including spin valves and spin logic circuits. In addition, the article describes the present status of spintronic research and offers a vision on its future trajectory, highlighting the significance of spintronics to drive next-generation computing and memory technologies.
  Keywords: magnetic tunnel junction; MTJ; spin hall effect; SHE; spin-orbit torque; SOT; non-volatile memories; NVM; spin-transfer torque; STT; racetrack memory; RM; internet of things; IoT.
  DOI: 10.1504/IJESMS.2026.10076174
   
  
• Flow-pressure coupling characteristics analysis and leakage compensation optimisation of the pneumatic pump distributor  
  by Zhi Zhang, Qingli Qi, Bofan Chen, Guoshuai Li, Yong Sang 
  Abstract: The main feedwater isolation valve is the vital safeguard in the emergency system of nuclear power plant. As the core actuation device of this valve, the dynamic reliability of the pneumatic system directly influences the stability of control valves. This study focuses on the system failures caused by the sticking motion of the pneumatic pump distributor. A system model incorporating pneumatic loop and hydraulic actuator is established on the AMESim simulation platform. Through fault analysis, the coupling mechanism between flow and pressure is revealed. Simulation results demonstrate that the proposed dynamic pressure compensation strategy effectively suppresses pressure fluctuations, while structural optimisation of the pressure-stabilising damping orifice enhances the critical friction threshold against spool jamming. This study effectively enhances the operational reliability of the distributor under transient conditions through parametric simulation, providing a theoretical foundation for the safety design of critical active components in nuclear power systems.
  Keywords: distributor jamming; simulation analysis; AMESim; pneumatic pumps; pressure compensation.
  DOI: 10.1504/IJESMS.2026.10078994
   
  
• Computational heat transfer of MHD mixed convection in a vertical pipe: thermal non-equilibrium approach  
  by Saurabh Kapoor, Durgaprasad Nayak 
  Abstract: This study investigates fully developed mixed convective flow in a vertical porous pipe under local thermal non-equilibrium (LTNE) conditions, incorporating magnetic field and heat source effects. The non-Darcy-Brinkman-Forchheimer (NDBF) model is employed, with equations solved via the Chebyshev spectral collocation method. Validation against existing solutions shows excellent agreement. We analyse the influence of the magnetic parameter (M), heat source parameters (Q_f, Q_s), interphase heat transfer coefficient (H), and conductivity ratio (γ) on flow and thermal profiles. Results indicate that increasing M flattens the velocity profile, which exhibits inflection points at lower values. Higher Q_f induces backflow and raises both fluid and solid temperatures. Notably, increasing H enhances heat transfer from the fluid to the solid phase; consequently, fluid temperatures decrease while solid-phase temperatures rise. These findings offer valuable insights for optimising MHD flows in porous media for engineering and industrial applications.
  Keywords: porous medium; convection; magnetic field; heat source parameter; spectral collocation method; thermal non-equilibrium.
  DOI: 10.1504/IJESMS.2026.10078995
   
  
• Design of microstrip antenna using ISM band for WBAN devices  
  by S. Ashok Kumar, T. Shanmuganantham, D. Sindhanaiselvi, N. Sudhakar Reddy 
  Abstract: The advancement in body area network (BAN) was started in 1995 for the idea of combining wireless personal area network (WPAN) with wireless body area network (WBAN) which can be implemented to communicate near and around the human body. Initially, industrial, scientific, and medical (ISM) band was allocated to industries, scientific researches and medical field researches, but later, it have been allowed to use in applications such as Wi-Fi, Bluetooth, Cordless phone, etc. For the above mentioned applications, the antennas such as microstrip patch antenna, helical antenna, dielectric patch antenna, etc. have been used. Since microstrip patch antenna is compact in size and weight, they have become dominant in this field. This antenna is designed on 36 × 28 mm³ sheet of FR-4 substrate material. A wide bandwidth of 400 MHz can withstand the detuning effect caused by body posture and movement. The effect of electromagnetic radiation on free space is analysed. The radiation gain, VSWR and return loss are also measured for analysing the antenna design and making the antenna, a good component for wearable devices.
  Keywords: wireless body area network; WBAN; ISM band; microstrip patch antenna; gain; wearable devices; body area network; BAN; wireless personal area network; WPAN.
  DOI: 10.1504/IJESMS.2026.10076672