Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.032
Ahmed M. Alghamdi , Adel Bahaddad , Khalid Almarhabi , Asmaa A. Al-Zobidi
Hajj and Umrah services annually attract millions of pilgrims to Saudi Arabia, making their efficient management crucial to achieving Vision 2030’s objectives. This paper explores the use of artificial intelligence (AI) and machine learning to predict and optimize key performance indicators for these services. We propose an AI-driven framework that processes vast datasets to enhance decision making, improve service provision, and optimize the pilgrimage experience. Our results demonstrate significant improvements in KPI prediction accuracy, supporting Saudi Arabia’s efforts to advance the quality of Hajj and Umrah services while aligning with Vision 2030’s goals.
{"title":"Predictive analysis of Hajj and Umrah performance using key performance indicators (KPIs) and machine learning (ML)","authors":"Ahmed M. Alghamdi , Adel Bahaddad , Khalid Almarhabi , Asmaa A. Al-Zobidi","doi":"10.1016/j.aej.2025.12.032","DOIUrl":"10.1016/j.aej.2025.12.032","url":null,"abstract":"<div><div>Hajj and Umrah services annually attract millions of pilgrims to Saudi Arabia, making their efficient management crucial to achieving Vision 2030’s objectives. This paper explores the use of artificial intelligence (AI) and machine learning to predict and optimize key performance indicators for these services. We propose an AI-driven framework that processes vast datasets to enhance decision making, improve service provision, and optimize the pilgrimage experience. Our results demonstrate significant improvements in KPI prediction accuracy, supporting Saudi Arabia’s efforts to advance the quality of Hajj and Umrah services while aligning with Vision 2030’s goals.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 400-406"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.069
Wei Liu, Dexian Li
Mountain flood disasters in rugged terrains pose significant challenges due to rapid onset, complex spatiotemporal dynamics, and data scarcity, where traditional hydrological models and pairwise graph neural networks struggle to capture multi-scale dependencies and uncertainty in propagation patterns. This study proposes the Adaptive Spatiotemporal Uncertainty-Guided Neural Network (ASTUNN), a hybrid framework that synergistically combines Bidirectional Gated Recurrent Units (BiGRU) for temporal modeling, Spherical Manifold Graph Learning (SMGL) for non-Euclidean spatial analysis, Fractional-Order Dynamic Attention (FODA) for long-memory patterns, Stochastic Variational Inference (SVI) for uncertainty quantification, and Adaptive Feature Synthesis (AFS) for multi-scale fusion. Key innovations include: (1) hyperedge-aware spatiotemporal message passing with fractional-order attention to model higher-order interactions and long-range dependencies in river networks and terrain gradients; and (2) stochastic variational uncertainty estimation to provide calibrated probabilistic forecasts and prevention capability rankings. These contributions overcome limitations of static graphs and deterministic models under rapid environmental changes. Validated on multi-source hydrological datasets from seven high-risk mountainous regions in southwest China, ASTUNN achieves an AUC-ROC of 0.947, MAE of 0.103 for prevention capability rankings, and ECE of 0.029, outperforming state-of-the-art baselines by 15–25 % while reducing false alarms by 18 % and enabling early warnings up to 48 h ahead.
{"title":"ASTUNN: An enhanced spatiotemporal uncertainty guided neural network for flood management in mountainous areas","authors":"Wei Liu, Dexian Li","doi":"10.1016/j.aej.2025.12.069","DOIUrl":"10.1016/j.aej.2025.12.069","url":null,"abstract":"<div><div>Mountain flood disasters in rugged terrains pose significant challenges due to rapid onset, complex spatiotemporal dynamics, and data scarcity, where traditional hydrological models and pairwise graph neural networks struggle to capture multi-scale dependencies and uncertainty in propagation patterns. This study proposes the Adaptive Spatiotemporal Uncertainty-Guided Neural Network (ASTUNN), a hybrid framework that synergistically combines Bidirectional Gated Recurrent Units (BiGRU) for temporal modeling, Spherical Manifold Graph Learning (SMGL) for non-Euclidean spatial analysis, Fractional-Order Dynamic Attention (FODA) for long-memory patterns, Stochastic Variational Inference (SVI) for uncertainty quantification, and Adaptive Feature Synthesis (AFS) for multi-scale fusion. Key innovations include: (1) hyperedge-aware spatiotemporal message passing with fractional-order attention to model higher-order interactions and long-range dependencies in river networks and terrain gradients; and (2) stochastic variational uncertainty estimation to provide calibrated probabilistic forecasts and prevention capability rankings. These contributions overcome limitations of static graphs and deterministic models under rapid environmental changes. Validated on multi-source hydrological datasets from seven high-risk mountainous regions in southwest China, ASTUNN achieves an AUC-ROC of 0.947, MAE of 0.103 for prevention capability rankings, and ECE of 0.029, outperforming state-of-the-art baselines by 15–25 % while reducing false alarms by 18 % and enabling early warnings up to 48 h ahead.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"136 ","pages":"Pages 140-156"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2026.01.005
Yang Xia , Yu Wang , Peng Yu
Online learning platforms’ intelligent feedback mechanisms suffer from static strategies and inadequate content adaptability, failing to meet learners’ dynamic needs. This paper proposes the Dynamic Reinforcement Feedback Network (DRF-Net), integrating Dynamic State Perception, PPO decision-making, and LLaMA 3 generation modules. Experiments on the KDD Cup and OpenEdX datasets show that DRF-Net achieves a learning effect improvement rate of 0.350.05 (34.6% higher than traditional models) and a cumulative reward of 56.84.2 (33.6% higher than single reinforcement learning models). Ablation experiments confirm the necessity of each module — removing the Dynamic State Perception module reduces the learning effect improvement rate by 22.9%. Future work will expand datasets, optimize adaptability to extreme states, and promote the model’s application in real scenarios.
{"title":"A dynamic reinforcement feedback network-based intelligent feedback mechanism in online learning platforms","authors":"Yang Xia , Yu Wang , Peng Yu","doi":"10.1016/j.aej.2026.01.005","DOIUrl":"10.1016/j.aej.2026.01.005","url":null,"abstract":"<div><div>Online learning platforms’ intelligent feedback mechanisms suffer from static strategies and inadequate content adaptability, failing to meet learners’ dynamic needs. This paper proposes the Dynamic Reinforcement Feedback Network (DRF-Net), integrating Dynamic State Perception, PPO decision-making, and LLaMA 3 generation modules. Experiments on the KDD Cup and OpenEdX datasets show that DRF-Net achieves a learning effect improvement rate of 0.35<span><math><mo>±</mo></math></span>0.05 (34.6% higher than traditional models) and a cumulative reward of 56.8<span><math><mo>±</mo></math></span>4.2 (33.6% higher than single reinforcement learning models). Ablation experiments confirm the necessity of each module — removing the Dynamic State Perception module reduces the learning effect improvement rate by 22.9%. Future work will expand datasets, optimize adaptability to extreme states, and promote the model’s application in real scenarios.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"136 ","pages":"Pages 30-44"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.026
Xiaodan Li , Yue Zhou , Fengchun Gao , Di Cheng , Wushan Li , Kaijian Xia , Hongsheng Yin
Precise prediction of postpartum hemorrhage (PPH) is of great significance for early identification of high-risk pregnant women, optimizing medical resource allocation, and reducing maternal mortality. However, existing PPH prediction methods suffer from limitations such as coarse prediction granularity, and single-stage prediction processes, leading to insufficient prediction accuracy. This has made prediction methods based on hybrid network architectures an important research direction in current PPH studies. This paper proposes a Multi-Granularity Hybrid Network Model (MGHNM) for PPH prediction, which integrates advanced methods such as ensemble learning, convolutional neural networks (CNN), and variational autoencoders (VAE). By leveraging multi-level feature extraction, the model effectively suppresses interference from secondary information, thus significantly enhancing prediction accuracy. The MGHNM model introduces a learnable control switch mechanism to achieve dynamic feature selection, significantly enhancing the model's discriminative ability. By organically combining the CatBoost classifier, CNN feature extractor, VAE representation learning module, and Vision Transformer (ViT), the hybrid network prediction model achieves a significant improvement in prediction accuracy for the three-level classification task of PPH severity (mild, moderate, and severe). The experimental data in this paper is derived from a PPH dataset constructed from the electronic medical record (EMR) system of the Maternal and Child Health Hospital in Jinan, Shandong Province, China. Three experiments were designed: First, the hyperparameters of the prediction model were optimized and analyzed. Second, a multi-model comparative experiment was conducted. Finally, an ablation study was performed. The experimental results demonstrate the significant superiority of the proposed MGHNM model for PPH prediction. It achieves an overall mean accuracy of 89.50 % with a standard deviation of 0.0045 %, substantially outperforming both the baseline and state-of-the-art (SOTA) methods.
{"title":"MGHNM: A multi-granularity based on hybrid network model for postpartum hemorrhage prediction","authors":"Xiaodan Li , Yue Zhou , Fengchun Gao , Di Cheng , Wushan Li , Kaijian Xia , Hongsheng Yin","doi":"10.1016/j.aej.2025.12.026","DOIUrl":"10.1016/j.aej.2025.12.026","url":null,"abstract":"<div><div>Precise prediction of postpartum hemorrhage (PPH) is of great significance for early identification of high-risk pregnant women, optimizing medical resource allocation, and reducing maternal mortality. However, existing PPH prediction methods suffer from limitations such as coarse prediction granularity, and single-stage prediction processes, leading to insufficient prediction accuracy. This has made prediction methods based on hybrid network architectures an important research direction in current PPH studies. This paper proposes a Multi-Granularity Hybrid Network Model (MGHNM) for PPH prediction, which integrates advanced methods such as ensemble learning, convolutional neural networks (CNN), and variational autoencoders (VAE). By leveraging multi-level feature extraction, the model effectively suppresses interference from secondary information, thus significantly enhancing prediction accuracy. The MGHNM model introduces a learnable control switch mechanism to achieve dynamic feature selection, significantly enhancing the model's discriminative ability. By organically combining the CatBoost classifier, CNN feature extractor, VAE representation learning module, and Vision Transformer (ViT), the hybrid network prediction model achieves a significant improvement in prediction accuracy for the three-level classification task of PPH severity (mild, moderate, and severe). The experimental data in this paper is derived from a PPH dataset constructed from the electronic medical record (EMR) system of the Maternal and Child Health Hospital in Jinan, Shandong Province, China. Three experiments were designed: First, the hyperparameters of the prediction model were optimized and analyzed. Second, a multi-model comparative experiment was conducted. Finally, an ablation study was performed. The experimental results demonstrate the significant superiority of the proposed MGHNM model for PPH prediction. It achieves an overall mean accuracy of 89.50 % with a standard deviation of 0.0045 %, substantially outperforming both the baseline and state-of-the-art (SOTA) methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 371-383"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.036
Feryal Abdullah Aladsani , Ali Muhib
In this paper, we focus on finding new oscillation criteria for third-order differential equations. We used a variety of analytical techniques and combined them with new relationships to address some of the problems that have hindered previous studies. As a result, and by using comparability principles, we were able to obtain results that improve and extend some of the previous results published in the literature. We provide some examples to illustrate the effectiveness of the obtained results.
{"title":"New oscillation results for nonlinear delay differential equations of third-order in the canonical case","authors":"Feryal Abdullah Aladsani , Ali Muhib","doi":"10.1016/j.aej.2025.12.036","DOIUrl":"10.1016/j.aej.2025.12.036","url":null,"abstract":"<div><div>In this paper, we focus on finding new oscillation criteria for third-order differential equations. We used a variety of analytical techniques and combined them with new relationships to address some of the problems that have hindered previous studies. As a result, and by using comparability principles, we were able to obtain results that improve and extend some of the previous results published in the literature. We provide some examples to illustrate the effectiveness of the obtained results.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 138-143"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.037
P. Antony Prince , Sekar Elango , L. Govindarao , Bundit Unyong
This article presents numerical techniques for solving two-parameter singularly perturbed differential equations, which include a Fredholm integral term. Such problems arise in shell structures interacting with two-parameter elastic foundations. The proposed approach employs a developed exponentially fitted operator for the spatial component, the composite trapezoidal rule for the integral component on a uniform grid, and the backward Euler method for the temporal component to approximate the solution. The method achieves a first-order convergence rate when , and a second-order rate when in the spatial direction and first-order convergence in the temporal direction. Numerical findings are presented to demonstrate the theoretical framework of the proposed technique.
{"title":"Numerical techniques for two-parameter elastic foundation using integro-partial differential equations","authors":"P. Antony Prince , Sekar Elango , L. Govindarao , Bundit Unyong","doi":"10.1016/j.aej.2025.12.037","DOIUrl":"10.1016/j.aej.2025.12.037","url":null,"abstract":"<div><div>This article presents numerical techniques for solving two-parameter singularly perturbed differential equations, which include a Fredholm integral term. Such problems arise in shell structures interacting with two-parameter elastic foundations. The proposed approach employs a developed exponentially fitted operator for the spatial component, the composite trapezoidal rule for the integral component on a uniform grid, and the backward Euler method for the temporal component to approximate the solution. The method achieves a first-order convergence rate when <span><math><mrow><mi>ϵ</mi><mo>≪</mo><msup><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>, and a second-order rate when <span><math><mrow><msup><mrow><mi>μ</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>≪</mo><mi>ϵ</mi></mrow></math></span> in the spatial direction and first-order convergence in the temporal direction. Numerical findings are presented to demonstrate the theoretical framework of the proposed technique.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 100-113"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.047
Richen Huang , Wenhua Zhou , Li Li , Jiyang Ye , Xiaolong Chen , Shuhua Peng
Deep supervised hashing, learning compact binary codes under label supervision through deep neural networks, is mainstream for large-scale image retrieval. However, existing methods still face two limitations. First, existing methods typically adopt a category-agnostic optimization mechanism for intra-category compactness, which neglects the varying intra-category diversity caused by category granularity and thereby limits model generalization. Moreover, these methods assign equal importance to all extracted features when feeding them into the hash layer, neglecting the incorporated irrelevant background information during feature extraction, reducing hash codes discriminative power. To address these, we propose a novel Granularity-guided Saturation Proxy Hashing (GSPH) framework. First, we introduce a Granularity-guided Saturation Proxy (GSP) loss that employs category-specific Hamming balls to achieve an optimization saturation mechanism: samples within their Hamming balls are deemed saturated and thus terminated from optimization, while those outside continue to be optimized toward their proxy. Additionally, GSP establishes a negative boundary with fixed margin outside each category’s Hamming ball, effectively ensuring inter-category separability. Second, we develop a Self-adaptive Feature Importance (SFI) module that employs gating mechanism to regulate feature importance during feature extraction, ensuring more discriminative representations. Extensive experiments on four benchmark datasets demonstrate that our method consistently outperforms existing methods.
{"title":"GSPH: Granularity-guided Saturation Proxy Hashing with Self-adaptive Feature Importance for image retrieval","authors":"Richen Huang , Wenhua Zhou , Li Li , Jiyang Ye , Xiaolong Chen , Shuhua Peng","doi":"10.1016/j.aej.2025.12.047","DOIUrl":"10.1016/j.aej.2025.12.047","url":null,"abstract":"<div><div>Deep supervised hashing, learning compact binary codes under label supervision through deep neural networks, is mainstream for large-scale image retrieval. However, existing methods still face two limitations. First, existing methods typically adopt a category-agnostic optimization mechanism for intra-category compactness, which neglects the varying intra-category diversity caused by category granularity and thereby limits model generalization. Moreover, these methods assign equal importance to all extracted features when feeding them into the hash layer, neglecting the incorporated irrelevant background information during feature extraction, reducing hash codes discriminative power. To address these, we propose a novel Granularity-guided Saturation Proxy Hashing (GSPH) framework. First, we introduce a Granularity-guided Saturation Proxy (GSP) loss that employs category-specific Hamming balls to achieve an optimization saturation mechanism: samples within their Hamming balls are deemed saturated and thus terminated from optimization, while those outside continue to be optimized toward their proxy. Additionally, GSP establishes a negative boundary with fixed margin outside each category’s Hamming ball, effectively ensuring inter-category separability. Second, we develop a Self-adaptive Feature Importance (SFI) module that employs gating mechanism to regulate feature importance during feature extraction, ensuring more discriminative representations. Extensive experiments on four benchmark datasets demonstrate that our method consistently outperforms existing methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 144-159"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.062
Mamatov Abrorjon , Aziza Nurumova , Mohammed Alharthi , Eman Ghareeb Rezk , Zaid Bassfar , Marwa M. Alzubaidi
This paper investigates a nonlinear thermo-chemical diffusion-reaction system characterized by coupled parabolic equations that govern the spatio-temporal evolution of temperature and concentration-dependent processes. The model incorporates nonlinear diffusion coefficients, cross-coupling terms, and nonlinear source functions, which collectively describe the complex interplay between heat and mass transfer in reactive media. A robust numerical framework is developed based on an Alternating Direction Implicit (ADI) scheme of the Peaceman-Rachford type, allowing for efficient and stable time integration of the nonlinear system. The implementation ensures high computational efficiency and improved numerical stability, particularly for stiff reaction terms and strongly coupled diffusion dynamics. Comprehensive numerical experiments are conducted to validate the accuracy and stability of the proposed scheme. Error convergence analysis confirms the expected second-order spatial and first-order temporal accuracy, while the time-step sensitivity and stability tests demonstrate the robustness of the algorithm under various discretization parameters. Boundary layer behavior is also examined to capture localized gradients and nonlinear interaction patterns. The obtained results reveal that the proposed computational framework accurately reproduces the characteristic thermo-chemical diffusion phenomena and maintains stability even under extreme parameter regimes. The study provides a reliable numerical tool for analyzing multi-scale diffusion-reaction systems relevant to chemical engineering, materials processing, and thermal energy storage applications.
{"title":"Mathematical model analysis and solution properties of nonlinear filtration processes in multidimensional domains","authors":"Mamatov Abrorjon , Aziza Nurumova , Mohammed Alharthi , Eman Ghareeb Rezk , Zaid Bassfar , Marwa M. Alzubaidi","doi":"10.1016/j.aej.2025.12.062","DOIUrl":"10.1016/j.aej.2025.12.062","url":null,"abstract":"<div><div>This paper investigates a nonlinear thermo-chemical diffusion-reaction system characterized by coupled parabolic equations that govern the spatio-temporal evolution of temperature and concentration-dependent processes. The model incorporates nonlinear diffusion coefficients, cross-coupling terms, and nonlinear source functions, which collectively describe the complex interplay between heat and mass transfer in reactive media. A robust numerical framework is developed based on an Alternating Direction Implicit (ADI) scheme of the Peaceman-Rachford type, allowing for efficient and stable time integration of the nonlinear system. The implementation ensures high computational efficiency and improved numerical stability, particularly for stiff reaction terms and strongly coupled diffusion dynamics. Comprehensive numerical experiments are conducted to validate the accuracy and stability of the proposed scheme. Error convergence analysis confirms the expected second-order spatial and first-order temporal accuracy, while the time-step sensitivity and stability tests demonstrate the robustness of the algorithm under various discretization parameters. Boundary layer behavior is also examined to capture localized gradients and nonlinear interaction patterns. The obtained results reveal that the proposed computational framework accurately reproduces the characteristic thermo-chemical diffusion phenomena and maintains stability even under extreme parameter regimes. The study provides a reliable numerical tool for analyzing multi-scale diffusion-reaction systems relevant to chemical engineering, materials processing, and thermal energy storage applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"136 ","pages":"Pages 73-88"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.048
Mobeen Ur Rehman , Zeeshan Abbas , Muhammad Fahad Nasir , Irfan Hussain
The quality of underwater imagery is critical to the success of marine exploration, ecological monitoring, and autonomous underwater operations, where visual data often serve as the primary sensory modality. However, underwater image acquisition is fundamentally constrained by the physics of light propagation in water leading to color distortions, turbidity, scattering-induced haze, and loss of structural detail. Despite significant advancements in underwater image enhancement (UIE), the field of underwater image quality assessment (UIQA) remains underexplored, particularly in no-reference (NR) settings where pristine images are unavailable. Existing NR UIQA methods are either overly reliant on handcrafted features or exhibit limited generalizability across diverse underwater domains. In this paper, we introduce PUIQA, a physically grounded, multi-domain multi-scale descriptor framework for robust no-reference underwater image quality prediction. Our approach systematically fuses features derived from physical imaging priors (e.g., non-uniform illumination, veiling light gradients), perceptual features (e.g., local entropy, edge energy, contrast), and frequency-domain signatures (e.g., DCT-based structural degradation). To further model scale-variant degradations, we extend these descriptors across Gaussian and resolution-based multiscale domains. The extracted features are combined into a high-dimensional representation and regressed via a support vector regression (SVR) pipeline optimized for perceptual fidelity. To validate the generalizability and robustness of PUIQA, we conduct extensive experiments on two diverse and publicly available underwater image datasets: UID2021, and UIEB. PUIQA achieves SROCC of 0.726/0.768 and PLCC of 0.754/0.773 on UWIQA and UID2021, outperforming existing NR-IQA metrics, demonstrating strong cross-dataset transferability and effectiveness in handling both real and synthetic underwater distortions. This work presents a substantial step toward establishing a principled, generalizable foundation for blind UIQA in practical underwater imaging systems. The full implementation of PUIQA is publicly available at: https://github.com/Rehman1995/PUIQA.
{"title":"A multiscale physics-informed framework for robust no-reference underwater image quality evaluation","authors":"Mobeen Ur Rehman , Zeeshan Abbas , Muhammad Fahad Nasir , Irfan Hussain","doi":"10.1016/j.aej.2025.12.048","DOIUrl":"10.1016/j.aej.2025.12.048","url":null,"abstract":"<div><div>The quality of underwater imagery is critical to the success of marine exploration, ecological monitoring, and autonomous underwater operations, where visual data often serve as the primary sensory modality. However, underwater image acquisition is fundamentally constrained by the physics of light propagation in water leading to color distortions, turbidity, scattering-induced haze, and loss of structural detail. Despite significant advancements in underwater image enhancement (UIE), the field of underwater image quality assessment (UIQA) remains underexplored, particularly in no-reference (NR) settings where pristine images are unavailable. Existing NR UIQA methods are either overly reliant on handcrafted features or exhibit limited generalizability across diverse underwater domains. In this paper, we introduce PUIQA, a physically grounded, multi-domain multi-scale descriptor framework for robust no-reference underwater image quality prediction. Our approach systematically fuses features derived from physical imaging priors (e.g., non-uniform illumination, veiling light gradients), perceptual features (e.g., local entropy, edge energy, contrast), and frequency-domain signatures (e.g., DCT-based structural degradation). To further model scale-variant degradations, we extend these descriptors across Gaussian and resolution-based multiscale domains. The extracted features are combined into a high-dimensional representation and regressed via a support vector regression (SVR) pipeline optimized for perceptual fidelity. To validate the generalizability and robustness of PUIQA, we conduct extensive experiments on two diverse and publicly available underwater image datasets: UID2021, and UIEB. PUIQA achieves SROCC of 0.726/0.768 and PLCC of 0.754/0.773 on UWIQA and UID2021, outperforming existing NR-IQA metrics, demonstrating strong cross-dataset transferability and effectiveness in handling both real and synthetic underwater distortions. This work presents a substantial step toward establishing a principled, generalizable foundation for blind UIQA in practical underwater imaging systems. The full implementation of PUIQA is publicly available at: <span><span>https://github.com/Rehman1995/PUIQA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 114-125"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1016/j.aej.2025.12.044
Nazmul Sharif, M.S. Alam, Helal Uddin Molla
A newly modified version of the homotopy perturbation method (MHPM) is developed to obtain accurate periodic solutions for strongly nonlinear oscillators, including the fractal Duffing oscillator with arbitrary initial conditions and nonlinear oscillators in microelectromechanical systems. This modification builds on He’s homotopy perturbation method by presenting time scaling and an improved treatment of the power series expansion for the frequency. The key feature of this method is the systematic truncation of the infinite series at each approximation order before applying it to the next-order differential equation, ensuring improved convergence and accuracy. The proposed method is validated for a wide range of initial amplitudes, demonstrating an excellent agreement between the approximate and exact solutions. Notably, even the first-order approximate frequency provides remarkable precision for both small and large oscillation amplitudes. The frequency–amplitude relationship is also derived using He’s frequency formulation. Comparisons with other analytical and numerical methods confirm that MHPM is not only computationally efficient but also provides highly accurate and rapidly converging solutions, making it a powerful tool for analyzing complex nonlinear oscillatory systems. These results suggest that the MHPM can be effectively applied to the study and design of MEMS devices and other complex engineering systems involving nonlinear vibrations.
{"title":"A new modified homotopy perturbation method for strongly nonlinear oscillators","authors":"Nazmul Sharif, M.S. Alam, Helal Uddin Molla","doi":"10.1016/j.aej.2025.12.044","DOIUrl":"10.1016/j.aej.2025.12.044","url":null,"abstract":"<div><div>A newly modified version of the homotopy perturbation method (MHPM) is developed to obtain accurate periodic solutions for strongly nonlinear oscillators, including the fractal Duffing oscillator with arbitrary initial conditions and nonlinear oscillators in microelectromechanical systems. This modification builds on He’s homotopy perturbation method by presenting time scaling and an improved treatment of the power series expansion for the frequency. The key feature of this method is the systematic truncation of the infinite series at each approximation order before applying it to the next-order differential equation, ensuring improved convergence and accuracy. The proposed method is validated for a wide range of initial amplitudes, demonstrating an excellent agreement between the approximate and exact solutions. Notably, even the first-order approximate frequency provides remarkable precision for both small and large oscillation amplitudes. The frequency–amplitude relationship is also derived using He’s frequency formulation. Comparisons with other analytical and numerical methods confirm that MHPM is not only computationally efficient but also provides highly accurate and rapidly converging solutions, making it a powerful tool for analyzing complex nonlinear oscillatory systems. These results suggest that the MHPM can be effectively applied to the study and design of MEMS devices and other complex engineering systems involving nonlinear vibrations.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"134 ","pages":"Pages 596-609"},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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