Documents published in Scipedia

• Carbon-Aware Last-Mile Delivery Optimization using Sparrow search algorithm and graph neural network risk assessment

  J. Xu, L. Yuan, X. Du

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 134

  
  

  Abstract

  With the advancement of global carbon neutrality strategies and explosive growth in e-commerce, urban last-mile delivery faces multiple challenges in balancing economic benefits, environmental impact, and risk management. Traditional optimization methods struggle to simultaneously address multi-objective trade-offs, network topological dependencies, and small-sample risk prediction problems. This study proposes a hybrid intelligent framework integrating an Improved Sparrow Search Algorithm (ISSA) with a Meta-Learning Graph Convolutional Network on Prototype Space (ML-GCNPS) for carbon-aware delivery optimization and risk assessment. ISSA-NSGA-III generates high-quality initial populations through Tent chaotic mapping, designs an adaptive periodic convergence factor to dynamically balance exploration and exploitation, and enhances global search capability by integrating Lévy flight with Elite Opposition-Based Learning (EOBL), achieving multi-objective collaborative optimization through embedding in the NSGA-III framework. ML-GCNPS designs a feature extraction network to extract discriminative features from multi-modal node data, explicitly models class centers through prototype space embedding to enhance small-sample generalization, constructs an adaptive Vertex-to-Edge (V2E) network to dynamically infer edge weights and capture risk propagation paths, and employs a two-layer graph convolutional architecture for sufficient information propagation. Experiments on the Kaggle Supply Chain Management dataset and Carbon Monitor risk dataset demonstrate that compared to standard SSA, ISSA-NSGA-III improves total cost, carbon emissions, and resource utilization by 14.0%, 14.2%, and 15.4%, respectively, with Pareto front quality improved by 28.5%. ML-GCNPS achieves an AUPRC of 0.850 (standard deviation 0.005) and a Macro Fl-Score of 0.850 (standard deviation 0.005) in 5-way 1-shot scenarios, reduces the False Negative Rate (FNR) to 0.080 (standard deviation 0.003), and achieves a Weighted Average Cost (WAC) of 45, significantly outperforming baseline methods such as ProtoNG and MAML-GNN (paired t-test, p less than 0.01 for AUPRC and FNR versus the second-best baseline GAT-FSL). Ablation experiments validate the necessity of the meta-learning framework, graph convolutional structure, V2E network, and prototype space embedding, while alternative design comparisons demonstrate the superiority of the technical choices. While the individual algorithmic components (Tent chaotic mapping, Lévy flight, EOBL, prototypical networks, and GCN) are established techniques, the principal contribution of this study líes in their systematic integration into a closed-loop optimization-assessment-feedback decision framework, where graph structure serves as an information bridge connecting delivery optimization with risk prediction. This study provides a teclmical solution for smart city logistics and offers theoretical basis and practica! guidance for sustainable delivery under carbon neutrality goals. All reported improvements over baseline methods are statistically significant across all evaluation metrics (paired t-test or Wilcoxon rank-sum test, p < 0.001

  Abstract
  With the advancement of global carbon neutrality strategies and explosive growth in e-commerce, urban last-mile delivery faces multiple challenges in balancing economic benefits, [...]

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• AdaptiveTrack: An Environment-Aware and Confidence-Refined Framework for Online Multi-Object Tracking

  S. Khan, P. Zhang, M. KAMAL, H. Samkari, M. Allehyani, H. Mostafa

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 133

  
  

  Abstract

  sup>Multi-object tracking (MOT) remains challenging in conditions involving occlusion, small objects, rapid motion, and crowding, wherein the accuracy of detection and the quality of association degrade simultaneously. We propose AdaptiveTrack, an online MOT framework featuring a closed-loop, confidence-aware association and recovery design: CSI-IoU adapts spatial overlap based on confidence and scale, EAMO refines similarity through density, velocity, and scale cues, and DCR updates detection confidence utilizing association context before NMS and assignment. A lightweight continuity module additionally preserves identities during missed detections. On MOT17/MOT20, AdaptiveTrack achieves HOTA 67.33 and 66.73, MOTA 82.55 and 78.30, and IDF1 83.20 and 82.57, operating at 23.5 FPS.

  Abstract
  sup>Multi-object tracking (MOT) remains challenging in conditions involving occlusion, small objects, rapid motion, and crowding, wherein the accuracy of detection and [...]

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• Hybrid AI-Quantum Computational Framework for Efficient Eddy Current Repulsion Force Simulation

  Z. Zhang, Y. Ryu, W. Li, J. Wang

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 127

  
  

  Abstract

  Eddy current repulsion forces are essential to the operation of magnetic levitation, electromagnetic braking, and wireless power transfer systems, yet their accurate simulation remains computationally intensive due to the complexity of Maxwell’s equations and the need for fine-resolution meshing in transient, high-frequency domains. This paper presents a hybrid AI-Quantum computational framework that accelerates eddy current simulation by combining adaptive meshing via Physics-Informed Neural Networks for adaptive meshing, quantum-assisted solvers (HarrowHassidim-Lloyd algorithm and Variational Quantum Eigensolver) for selected finite element subdomains, and GPU-accelerated finite element methods, and reduced-order modeling based on Proper Orthogonal Decomposition and Quantum Autoencoders. The framework selectively routes well-conditioned sparse subblocks to quantum solvers while retaining classical GPU-based methods elsewhere. Validation across three representative applications—electromagnetic braking, magnetic levitation, and wireless power transfer—demonstrates that the hybrid solver maintains accuracy within 5%–8% of full FEM results while reducing computational cost by 1.3×–2× speedup in the tested scenarios. These results confirm the feasibility of integrating AI and near-term quantum computing into electromagnetic simulation workflows and provide guidance on complexity, resource requirements, and scalability.OPEN ACCESS Received: 14/10/2025 Accepted: 22/12/2025 Published: 29/05/2026

  Abstract
  Eddy current repulsion forces are essential to the operation of magnetic levitation, electromagnetic braking, and wireless power transfer systems, yet their accurate simulation [...]

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• Computational Framework for AI-IoT Integration in Brain Tumor Detection: Numerical Modeling, Algorithmic Optimization and Real-Time Deployment

  F. Ndah Tah, M. Lawal, A. Ibrahim, S. Asir, P. Pwavodi, C. Nwekwo, S. Kaba, B. Duwa

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 132

  
  

  Abstract

  The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) presents significant computational and engineering challenges, especially in the deployment of real-time diagnostic systems. Therefore, this study proposed a numerically optimized and computationally efficient framework that combines deep learning (DL) architectures with IoMT-enabled deployment for the automated detection and classification of brain tumors using Magnetic Resonance Imaging (MRI). The methodology revolves around rigorous numerical preprocessing techniques, including normalized resizing, advanced data augmentation, and computationally efficient feature extraction via both custom and pre-trained Convolutional Neural Networks (CNNs). The key contributions of this study are tailored toward the evaluation of algorithmic performance beyond diagnostic accuracy, incorporating metrics such as model convergence, inference latency, memory footprint, and numerical stability under varied input conditions. The proposed AI-IoMT framework, known as I-BRAINDETECT, implemented as a web-based IoMT platform, demonstrates how algorithmic design and computational modeling can address the limitations of real-time medical image analysis. Performance evaluation and comparative analysis have shown that EfficientNetB0 and DenseNet121 achieved optimal performance in binary classification with 98.5 ± 0.202% accuracy, while ResNet50 excelled in multiclass classification with 95.4 ± 1.01% accuracy, both within a computationally constrained IoT environment. Validation of the trained models on an external dataset (Figshare) has shown that DenseNet121 achieved the best result with 93.99% accuracy. This work underscores the necessity of numerical robustness and algorithmic efficiency in bridging AI and IoT for scalable biomedical engineering solutions.

  Abstract
  The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) presents significant computational and engineering challenges, especially in the [...]

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• A New Green ETL Optimization Technique for Sustainable Data Warehousing Environment: A Scalable Energy Efficient Solution

  E. Gueddoudj, A. Attia, A. Almaktoom, K. Othman, A. Noorwali, E. Zafar, A. Mohamed

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (5), 131

  
  

  Abstract

  Energy consumption is an emerging concern in many fields, including information technology, particularly in data warehousing environments where Extract, Transform, Load (ETL) processes account for a significant portion of operational costs and resource utilization. Despite advances in hardware-level optimization, limited attention has been given to software-level energy optimization within ETL workflows. In reality, software is as important as hardware, and it is equally responsible for a decrease or increase in energy consumption. We argue that for modern applications in which energy efficiency is a priority, ETL processes should be optimally designed. This paper addresses this gap by proposing a Green ETL (GETL) approach designed to reduce energy consumption while maintaining high performance. The proposed method integrates transformation-level reuse through a shared transformation cache and adaptive parallel execution using Apache Spark, enabling efficient resource utilization and elimination of redundant computations. The proposed GETL removes unnecessary calculations and reduces both execution time and energy consumption, without requiring any modifications to the underlying data processing engines. To evaluate the effectiveness of the proposed GETL, experiments were conducted using the Transaction Processing Council Data Integration (TPC-DI) benchmark across multiple scale factors. The results demonstrate that the proposed approach achieves an average energy reduction of approximately 30%, with higher savings observed under large-scale workloads. In addition, GETL improves execution efficiency and reduces resource utilization compared to a traditional Spark-based ETL implementation.

  Abstract
  Energy consumption is an emerging concern in many fields, including information technology, particularly in data warehousing environments where Extract, Transform, Load (ETL) [...]

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• AI-IoMT Synergy: A Real-Time Framework for Automated Urinary Tract Infections (UTI) Detection Based on Urine Sediments

  A. Ibrahim, M. Ahmed, I. Ame, C. Nwekwo, F. Hassan, S. Asir, S. Tackie, F. Al-Turjman, J. Idoko

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 130

  
  

  Abstract

  A Urinary Tract Infection (UTI) is characterized by an infection affecting the urinary system, including the kidneys, bladder, urethra, and ureters, with clinical presentations including pyelonephritis, cystitis, and urethritis. While conventional diagnostic methods such as urinalysis and urine culture and sensitivity (C&S) remain widely used, they are limited by subjectivity, time-intensive processing, susceptibility to contamination and risks of false-positive or false-negative results. This study proposes a comprehensive deep learning (DL) and Internet of Things (IoMT) framework to automate the real-time detection and classification of UTIs using microscopic urine sediment images. The study employed 2 datasets (A and B). Dataset A, a clinically acquired dataset, comprises of 3345 images (normal, erythrocytes, fungi and pus) and Dataset B, a publicly accessible data comprises of 5377 images and 26,419 cropped microscopic images corresponding to 7 classes (casts, crystals, erythrocytes leukocytes, epithelial cells, epithelial nuclei, mycetes). A two-stage classification approach was implemented: a binary task to distinguish urine sediments from normal cases, followed by a multiclass task (clinical data and online data) to identify the specific infection type. All images underwent pre-processing, including normalization, resizing, noise removal, and augmentation to enhance feature visibility and model generalizability. The data were partitioned into training (65%), validation (25%), and test (10%) sets. Six state-of-the-art DL architectures, including ResNet50-V2, ResNet101-V2, Inception-V3, XceptionV2, Inception-ResNet-V2, and VGG19 were fine-tuned using transfer learning and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The proposed models were uploaded to a website to enable realtime detection (accessible via this link: https://uticlassification.app/). The proposed pipeline demonstrated strong performance in both classification tasks, underscoring the potential of deep learning as a reliable, rapid, and reproducible tool for automated urine sediment identification in clinical practice.

  Abstract
  A Urinary Tract Infection (UTI) is characterized by an infection affecting the urinary system, including the kidneys, bladder, urethra, and ureters, with clinical presentations [...]

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• A Multi-Strategy Mutation Differential Evolution for Mountainous Base Station Deployment

  C. Ouyang, S. Huang, Y. Zheng, C. Zhou

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 122

  
  

  Abstract

  The deployment of communication base stations establishes an efficient information transmission network; however, implementing deployment in mountainous areas with complex terrain remains amajor challenge. To address the issues of large topographical variations, dispersed villages, and low coverage efficiency, this study focuses on application issues, develops a mountainous deployment environment model that incorporates terrain elevation increments and village exclusion zones. On this basis, a Differential Evolution algorithm with Multiple Mutation Strategies (MSM-DE) is proposed to improve the balance between global exploration and local exploitation. The algorithm introduces a probabilistic multi-mutation mechanism that dynamically selects among several mutation strategies according to population diversity, and an adaptive parameter memory archive that guides the search toward promising regions.These modifications enhance both convergence speed and robustness in complex terrain optimization. Three objectives—coverage rate, village coverage satisfaction, and signal security—are combined into a weighted multi-objective function, and experiments are performed under two deployment scenarios (fixed and random village distributions).The results demonstrate that MSM-DE achieves significantly faster convergence and higher coverage performance than benchmark DE variants, validating that the proposed mutation synergy and adaptive parameter control effectively strengthen the algorithm’s optimization capability and stability in mountainous base station deployment.

  Abstract
  The deployment of communication base stations establishes an efficient information transmission network; however, implementing deployment in mountainous areas with complex [...]

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• Enhancing Few-Shot Text Classification with Parameter-Efficient Tuning of Large Language Models

  M. Zhao, Y. Sun, X. Fan, Y. Tian

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 129

  
  

  Abstract

  Traditional few-shot text classification models focus only on label prediction and cannot extract structured information such as entities or events, limiting their usefulness in real-world, semantics-driven tasks. They also rarely use external knowledge or parameter-efficient tuning, leading to shallow representations and weaker performance. To address this, this paper proposes a knowledge-aware multi-task framework that integrates few-shot classification with entity and event extraction. A single BERT encoder with IA3 adapters enables efficient tuning, while semantic triples extracted via spaCy and aligned with WordNet and ConceptNet are encoded using TransE. A BiLSTM captures sequential context and a softmax decoder performs token-level prediction. Experiments show strong results—97.97% accuracy, 98.00% precision, 97.95% recall, and 97.96% F1—surpassing state-of-the-art baselines. Ablation studies confirm the value of the knowledge-enhanced, multi-step design, demonstrating suitability for lowresource, knowledge-centric applications.

  Abstract
  Traditional few-shot text classification models focus only on label prediction and cannot extract structured information such as entities or events, limiting their usefulness [...]

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• Efficient Mean Estimation Using Repeated Measurements in PPS Sampling with Scientific Applications

  M. Azeem, S. Hussain, N. Salahuddin, F. Idrees

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. Online First, 128

  
  

  Abstract

  Research studies in many scientific disciplines need efficient estimation methods for estimating the parameters of quantitative variables, including, but not limited to, tensile strength, wind speeds, air quality, and temperature, etc. Statisticians employ a sampling design to get a random sample and estimate the population means based on the observed sample. If the units of a finite population have different selection probabilities, unequal probability methods of sample selection are used. The existing unequal probability sampling methods allocate probabilities proportional to size (PPS) to the population units by using a one-time measurement approach. Memory– type estimation methods, on the other hand, use multiple measurements and provide more precise estimates than the traditional estimators. This research study introduces a novel memory-type estimator using a PPS sampling design. Various properties of the suggested mean estimator are analyzed, and the efficiency conditions are derived. A few real-world data sets have been used from previous studies to evaluate the performance of the suggested and competing mean estimators. Our comparative study suggests that the suggested memory-based estimator performs much better than the competing estimators, which means that the suggested memory-type estimator is an appropriate estimator for application in real-life sample surveys.

  Abstract
  Research studies in many scientific disciplines need efficient estimation methods for estimating the parameters of quantitative variables, including, but not limited to, tensile [...]

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• A Novel Logarithmic Sine-G Family of Distributions with Applications to Reliability Engineering Data

  Z. Shah, D. Khan, A. Alharthi, H. Aljohani

  Rev. int. métodos numér. cálc. diseño ing. (2026). Vol. 42, (4), 126

  
  

  Abstract

  In this research paper, we make a significant attempt to present a novel statistical methodological approach for generating more versatile distributions. The proposed method is developed by incorporating the Logarithmic and Sine functions and may be referred to as a novel logarithmic sine-G (NLS-G) family of distributions. A special sub-model of the NLSG family is investigated by utilizing the Weibull model as a base member. The sub-model of the NLS-G family may be callednovel logarithmic sineWeibull (NLS-Weibull) distribution. The density and hazard functions of the NLS-Weibull distribution are graphically illustrated, showing their behavior and characteristics. Some distributional properties of the NLSG family, including quantile function, median and quartile measures, moments, and moment generating function, are derived. The Maximum Likelihood Estimation (MLE) method is employed for estimating the model parameters of the NLS-G family of distributions. A comprehensive Monte Carlo simulation study of the proposed distribution is also conducted to evaluate the practical performance of its estimators. Furthermore, the usefulness of the newly proposed NLS-Weibull distribution is illustrated by investigating four real data sets from the field of the engineering sector. The first data set represents the failure time of electronic devices. The second data set is civil engineering data and represents the breaking stress of carbon fibers. The third data set represents the strengths of 1.5 cm glass fibers. The fourth data set represents single-carbon fibers. Based on four diagnostic criteria, it is observed that the NLS-Weibull distribution may be the best choice for the considered data sets.OPEN ACCESS Received: 02/12/2025 Accepted: 12/01/2026 Published: 29/05/2026

  Abstract
  In this research paper, we make a significant attempt to present a novel statistical methodological approach for generating more versatile distributions. The proposed method [...]

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