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computer Modeling in Engineering & Sciences 2025年第1期142卷 501-530页

作者： Khuram Ali Khan Saba Mubeen Ishfaq Atiqe Ur Rahman Salwa El-Morsy department of mathematics University of SargodhaSargodha40100Pakistan department of mathematics University of Management and TechnologyLahore54000Pakistan department of mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be *** concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such *** study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and *** results related to these operations are also modified for ***,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and ***,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested *** algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical ***,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

关键词： Hypersoft set Pythagorean fuzzy hypersoft set computational complexity multi-attribute decision-making optimization similarity measures uncertainty

Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied mathematics and Mechanics(English Edition) 2025年第1期46卷 193-208页

作者： K.MUHAMMAD M.SARFRAZ department of Humanities and Sciences School of Electrical Engineering and Computer Science National University of Sciences and Technology department of mathematics Quaid-i-Azam University

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates. One plate remains stationary, while the other moves downward at a squeezing velocity. The space between these plates contains a Darcy-Forchheimer porous medium. A mixture of water-based fluid with gold(Au) and silicon dioxide(Si O2) nanoparticles is formulated. In contrast to the conventional Fourier's heat flux equation, this study employs the Cattaneo-Christov heat flux equation. A uniform magnetic field is applied perpendicular to the flow direction, invoking magnetohydrodynamic(MHD) effects. Further, the model accounts for Joule heating, which is the heat generated when an electric current passes through the fluid. The problem is solved via NDSolve in MATHEMATICA. Numerical and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow, energy transport, and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management strategies. It is observed that the squeezing motion generates additional pressure gradients within the fluid, which enhances the flow rate but reduces the frictional drag. Consequently, the fluid is pushed more vigorously between the plates, increasing the flow velocity. As the fluid experiences higher flow rates due to the increased squeezing effect, it spends less time in the region between the plates. The thermal relaxation, however, abruptly changes the temperature, leading to a decrease in the temperature fluctuations.

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Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

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computer Modeling in Engineering & Sciences 2025年第1期142卷 531-553页

作者： Tmader Alballa Muhammad Ihsan Atiqe Ur Rahman Noorah Ayed Alsorayea Hamiden Abd El-Wahed Khalifa department of mathematics College of SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia 2 department of mathematics University of Management and TechnologyLahore54000Pakistan department of mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

The dramatic rise in the number of people living in cities has made many environmental and social problems *** search for a productive method for disposing of solid waste is the most notable of these *** scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated *** goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site *** characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these *** tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s *** this,an intelligent algorithm is *** steps of the suggested algorithm are explained with an example that explains *** compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their *** study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,*** uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior *** is simpler for the decisionmakers to look at each option separately because the decision is *** the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

关键词： Hypersoft expert set Sanchez’s method decision making optimization solid waste management possibility grade fuzzy parameterization

Artificial Circulation System Algorithm:A Novel Bio-Inspired Algorithm

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computer Modeling in Engineering & Sciences 2025年第1期142卷 635-663页

作者： NerminÖzcan Semih Utku Tolga Berber department of Biomedical Technologies Dokuz Eylül UniversityIzmir35390Turkey department of Biomedical Engineering Iskenderun Technical UniversityIskenderun31200Turkey department of computer Engineering Dokuz Eylül UniversityIzmir35390Turkey department of Statistics and computer Sciences Karadeniz Technical UniversityTrabzon61080Turkey

Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering appli*** present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this *** work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark *** method was subsequently examined by application to 12 CEC 2022 benchmark problems of different ***,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and ***,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing *** results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and *** outperformed 42 algorithms statistically,according to post-hoc *** also outperformed 9 algorithms *** study concludes that ACSA offers competitive solutions in comparison to popüler methods.

关键词： Bio-inspired evolutionary heuristic metaheuristic optimization

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng department of computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and computer Engineering Louisiana State UniversityBaton RougeLA 70803USA department of computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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computer Modeling in Engineering & Sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha department of Software Engineering College of computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

Improved Bound of the Fourth Hankel Determinant for a Class of Analytic Functions with Bounded Turnings Involving Cardioid Domain

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Journal of Mathematical Research with Applications 2025年第1期 39-55页

作者： Dong GUO Huo TANG Xi LUO Zongtao LI School of Mathematical Sciences Yangzhou Polytechnic College College of mathematics and computer Science Chifeng University School of mathematics Jiaying University department of mathematics Teaching Guangzhou Civil Aviation College

In the paper, a class of functions with bounded turnings involving cardioid domain,are studied in the region of the unit disc. The bounds of |a5|, |a6|, |a7| and the fourth Hankel determinant are obtained, which are m...

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Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

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computer Modeling in Engineering & Sciences 2025年第1期142卷 869-907页

作者： Santiago Bañales Raquel Dormido Natividad Duro department of computer Sciences and Automatic Control Universidad Nacional de Educación a Distancia(UNED)Madrid28040Spain Iberdrola Innovation Middle East Doha210177Qatar

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy *** study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time *** meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns *** dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series *** intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire *** the first time,a step function approach is applied to reduce time series *** attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy *** addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined *** application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the *** research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditio

关键词： Electric load clustering load profiling smart meters machine learning data mining demand flexibility demand response