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.2025.12.069
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.2025.12.047
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}
Pub Date : 2025-12-26DOI: 10.1016/j.aej.2025.12.034
Shimaa.M. Meleha, Mohamed Tarek Sorour, Medhat Moustafa, Samia A. Aly
Access to clean drinking water remains a significant challenge in developing countries due to increasing demand. This study evaluates the efficiency of the Kafr El-Sheikh Water Purification Plant (KWPP). KWPP is a conventional plant without any advanced units. Hydraulic analysis revealed that the existing sedimentation tanks exceeded the allowable surface overflow rate (108 m³/m²/day) and weir loading rate (864 m³/m²/day), necessitating redesign in accordance with the Egyptian Code of Design Principles. The focus is on redesigning the plant to meet current and future water demands efficiently, with a phased expansion plan extending to 2062. Using the WatPro 4.0 water treatment process simulator, the study explores optimized chlorination strategies and advanced treatment technologies to minimize the formation of Disinfection byproducts (DBPs). The simulation examines adjustments to chlorine dosing based on influent water quality, integrating granular activated carbon (GAC) adsorption to remove organic matter, and applying ultrafiltration (UF) membrane technology to stabilize chlorine levels. The optimized approach successfully reduced the required initial chlorine dose to 2 mg/L and consistently maintained a chlorine residual of 2 mg/L, while maintaining THMs and HAA5s concentrations below 10 µg/L throughout the year. The findings highlight the importance of pre-treatment techniques in reducing DBP precursors before post-chlorination, ensuring safer drinking water.
{"title":"Performance enhancement and hydraulic optimization of water treatment processes: A case study of Kafr El-Sheikh Water Purification Plant, Egypt","authors":"Shimaa.M. Meleha, Mohamed Tarek Sorour, Medhat Moustafa, Samia A. Aly","doi":"10.1016/j.aej.2025.12.034","DOIUrl":"10.1016/j.aej.2025.12.034","url":null,"abstract":"<div><div>Access to clean drinking water remains a significant challenge in developing countries due to increasing demand. This study evaluates the efficiency of the Kafr El-Sheikh Water Purification Plant (KWPP). KWPP is a conventional plant without any advanced units. Hydraulic analysis revealed that the existing sedimentation tanks exceeded the allowable surface overflow rate (108 m³/m²/day) and weir loading rate (864 m³/m²/day), necessitating redesign in accordance with the Egyptian Code of Design Principles. The focus is on redesigning the plant to meet current and future water demands efficiently, with a phased expansion plan extending to 2062. Using the WatPro 4.0 water treatment process simulator, the study explores optimized chlorination strategies and advanced treatment technologies to minimize the formation of Disinfection byproducts (DBPs). The simulation examines adjustments to chlorine dosing based on influent water quality, integrating granular activated carbon (GAC) adsorption to remove organic matter, and applying ultrafiltration (UF) membrane technology to stabilize chlorine levels. The optimized approach successfully reduced the required initial chlorine dose to 2 mg/L and consistently maintained a chlorine residual of 2 mg/L, while maintaining THMs and HAA5s concentrations below 10 µg/L throughout the year. The findings highlight the importance of pre-treatment techniques in reducing DBP precursors before post-chlorination, ensuring safer drinking water<strong>.</strong></div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"134 ","pages":"Pages 610-620"},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837780","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.2025.12.043
Abdulla Al Mamun , Samsun Nahar Ananna , Chunhui Lu , Zhenfeng Zhang , Mithun Biswas , Shaik Baized Al Limon , Md. Asaduzzaman
This study recovers several novel accurate solutions to the 3D fractional Wazwaz-Benjamin-Bona-Mahony (WBBM) equation within the framework of numerous nonlinear physical phenomena originating from water wave mechanics. This is accomplished by transforming the WBBM equation into an ordinary differential equation (ODE). The ODE then uses two effective methods based on the conformable derivative: the Generalized Exponential Rational Function (GERF) and Modified Generalized Exponential Rational Function (MGERF) Methods. Periodic solutions, w-shaped solitons, single and multiple solitons, dark-bright, and Jacobi elliptic doubly periodic type solutions are thus explored. Applications of the GERF and MGERF approach in many scientific and technical domains assist in expanding our understanding of nonlinear systems, establishing mathematical methodologies, and supporting research solutions. The Modified Variational Iteration (MVI) method, a numerical technique, is employed to derive the approximate solutions to the WBBM problem. An analysis is conducted between this inquiry's findings and those previously acquired through different approaches. All produced wave solutions are determined to be novel in the sense of applied technique, fractionality, and unconstrained parameters. The consequences of unconstrained parameters and fractionality on the resulting solutions are explained physically and shown in a figure. When fractionality and the number of unbound parameters rise, the wave portents shift. Finally, this article dynamically demonstrates that appropriate transformation and relevant GERF and MGERF techniques are more helpful in studying the dynamics of water waves and might be used in future research to clarify other physical phenomena.
本文在水波力学的非线性物理现象框架下，对三维分数阶wazwazz - benjamin - bona - mahony （WBBM）方程恢复了几种新的精确解。这是通过将WBBM方程转换为常微分方程（ODE）来实现的。在此基础上，采用了两种有效的基于可调导数的方法：广义指数有理函数法（GERF）和修正广义指数有理函数法（MGERF）。研究了周期解、w型孤子、单孤子和多孤子、暗亮解和Jacobi椭圆双周期解。GERF和MGERF方法在许多科学和技术领域的应用有助于扩展我们对非线性系统的理解，建立数学方法，并支持研究解决方案。采用改进变分迭代（MVI）方法，推导了WBBM问题的近似解。对这次调查的结果和以前通过不同方法获得的结果进行了分析。所有产生的波解在应用技术、分数性和无约束参数方面都是新颖的。无约束参数和分数对结果解的影响在物理上得到解释，并在图中显示。当分数度和非定界参数的数量增加时，波的前兆发生移位。最后，本文动态地论证了适当的变换以及相关的GERF和MGERF技术更有助于研究水波的动力学，并可能在未来的研究中用于阐明其他物理现象。
{"title":"Impact of numerical simulation and fractionality of the 3D fractional WBBM model using GERF and MGERF methods","authors":"Abdulla Al Mamun , Samsun Nahar Ananna , Chunhui Lu , Zhenfeng Zhang , Mithun Biswas , Shaik Baized Al Limon , Md. Asaduzzaman","doi":"10.1016/j.aej.2025.12.043","DOIUrl":"10.1016/j.aej.2025.12.043","url":null,"abstract":"<div><div>This study recovers several novel accurate solutions to the 3D fractional Wazwaz-Benjamin-Bona-Mahony (WBBM) equation within the framework of numerous nonlinear physical phenomena originating from water wave mechanics. This is accomplished by transforming the WBBM equation into an ordinary differential equation (ODE). The ODE then uses two effective methods based on the conformable derivative: the Generalized Exponential Rational Function (GERF) and Modified Generalized Exponential Rational Function (MGERF) Methods. Periodic solutions, w-shaped solitons, single and multiple solitons, dark-bright, and Jacobi elliptic doubly periodic type solutions are thus explored. Applications of the GERF and MGERF approach in many scientific and technical domains assist in expanding our understanding of nonlinear systems, establishing mathematical methodologies, and supporting research solutions. The Modified Variational Iteration (MVI) method, a numerical technique, is employed to derive the approximate solutions to the WBBM problem. An analysis is conducted between this inquiry's findings and those previously acquired through different approaches. All produced wave solutions are determined to be novel in the sense of applied technique, fractionality, and unconstrained parameters. The consequences of unconstrained parameters and fractionality on the resulting solutions are explained physically and shown in a figure. When fractionality and the number of unbound parameters rise, the wave portents shift. Finally, this article dynamically demonstrates that appropriate transformation and relevant GERF and MGERF techniques are more helpful in studying the dynamics of water waves and might be used in future research to clarify other physical phenomena.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"134 ","pages":"Pages 621-643"},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837783","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 : 2025-12-24DOI: 10.1016/j.aej.2025.12.029
Jun Li , Dong Liu
Low-quality images, due to degradation issues such as noise and compression artifacts, often lead to feature extraction distortion in classification models, thereby reducing classification accuracy. This issue is particularly prominent in practical computer vision applications. To address this, this paper proposes the SR-PAN-EffNet model, which integrates a semantic-guided SRGAN restoration module with a noise-aware PANet attention module, and employs end-to-end joint training to achieve the collaborative optimization of “restoration serving classification”. Experimental results show that the model achieves Top-1 accuracy of 68.5% and 57.6% on the ImageNet-1K and CIFAR-100 low-quality datasets, respectively, improving by 4.7–6.4 percentage points over NFNet-F4, with PSNR and SSIM also leading. Ablation experiments reveal that removing core modules results in a 2.9–6.8 percentage point decrease in accuracy. Future work will focus on improving the model’s real-time performance and robustness through lightweight design, extreme degradation adaptive optimization, and category-adaptive guidance, aiming to promote its application in scenarios such as surveillance and medical imaging.
{"title":"SR-PAN-EffNet: A collaborative optimization approach for low-quality image classification and restoration","authors":"Jun Li , Dong Liu","doi":"10.1016/j.aej.2025.12.029","DOIUrl":"10.1016/j.aej.2025.12.029","url":null,"abstract":"<div><div>Low-quality images, due to degradation issues such as noise and compression artifacts, often lead to feature extraction distortion in classification models, thereby reducing classification accuracy. This issue is particularly prominent in practical computer vision applications. To address this, this paper proposes the SR-PAN-EffNet model, which integrates a semantic-guided SRGAN restoration module with a noise-aware PANet attention module, and employs end-to-end joint training to achieve the collaborative optimization of “restoration serving classification”. Experimental results show that the model achieves Top-1 accuracy of 68.5% and 57.6% on the ImageNet-1K and CIFAR-100 low-quality datasets, respectively, improving by 4.7–6.4 percentage points over NFNet-F4, with PSNR and SSIM also leading. Ablation experiments reveal that removing core modules results in a 2.9–6.8 percentage point decrease in accuracy. Future work will focus on improving the model’s real-time performance and robustness through lightweight design, extreme degradation adaptive optimization, and category-adaptive guidance, aiming to promote its application in scenarios such as surveillance and medical imaging.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"134 ","pages":"Pages 542-555"},"PeriodicalIF":6.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837445","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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