Pub Date : 2026-01-01DOI: 10.1016/j.aej.2025.12.058
Jijia Zhang , Ruipu Wang , Fei Gan , Hong Wang , Haoyu Zhou , Quan Zhao , Xinhai Yuan , Licheng Wu
Traditional methods typically assume homogeneous soil properties in soil-like slopes. However, in reality, parameters such as permeability and shear strength exhibit rotational spatial variability due to geological processes such as weathering and sedimentation. Neglecting this variability can lead to discrepancies in slope stability assessments. Therefore, this study employs stochastic field theory and fluid-solid coupling numerical simulations to develop an evaluation framework that accounts for the general rotational spatial variability of soil parameters. The impact of spatial variation structure and related parameters on the rainfall stability and destruction characteristics of soil like slopes was analyzed in detail. The key findings are as follows: The impact of rotational spatial variability on the stability of soil-like slopes is significant. When the direction of the weathered soil layer intersects with the slope surface at a small angle, the slope is most prone to instability. Further research reveals that when evaluating the stability of soil-like slopes, the influence of cohesion, internal friction angle, and weathering layer inclination should be mainly considered, while the fluctuation range can be ignored.These findings provide new insights for engineering design.
{"title":"Influence of rotational spatial variability in geotechnical parameters on the rainfall-induced stability of soil-like slopes","authors":"Jijia Zhang , Ruipu Wang , Fei Gan , Hong Wang , Haoyu Zhou , Quan Zhao , Xinhai Yuan , Licheng Wu","doi":"10.1016/j.aej.2025.12.058","DOIUrl":"10.1016/j.aej.2025.12.058","url":null,"abstract":"<div><div>Traditional methods typically assume homogeneous soil properties in soil-like slopes. However, in reality, parameters such as permeability and shear strength exhibit rotational spatial variability due to geological processes such as weathering and sedimentation. Neglecting this variability can lead to discrepancies in slope stability assessments. Therefore, this study employs stochastic field theory and fluid-solid coupling numerical simulations to develop an evaluation framework that accounts for the general rotational spatial variability of soil parameters. The impact of spatial variation structure and related parameters on the rainfall stability and destruction characteristics of soil like slopes was analyzed in detail. The key findings are as follows: The impact of rotational spatial variability on the stability of soil-like slopes is significant. When the direction of the weathered soil layer intersects with the slope surface at a small angle, the slope is most prone to instability. Further research reveals that when evaluating the stability of soil-like slopes, the influence of cohesion, internal friction angle, and weathering layer inclination should be mainly considered, while the fluctuation range can be ignored.These findings provide new insights for engineering design.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 243-261"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923678","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.2025.12.042
Tasawar Abbas , Taseer Muhammad , Nabil El Kadhi , Aaqib Majeed
This paper gives an extensive numerical study of the two-dimensional, steady magnetohydrodynamic (MHD) heat transmission and flow properties of a Williamson hybrid nanofluid (HNF) over a stretching sheet. The hybrid nanofluid consists of water as the base fluid, comprising a suspension of magnesium oxide (MgO) and silver (Ag) nanoparticles, in order to enhance the conductivity of heat and flow behavior. The model incorporates the impacts of viscous dissipation, an externally applied magnetic field, Joule heating, and convective boundary condition at the surface. The non-Newtonian performance is characteristized through the Williamson fluid model, and the thermophysical aspects of the hybrid nanofluid are assessed by established correlations. The governing partial differential equations (PDEs), which are derived from mass conservation, momentum conservation, and energy conservation laws, are simplified into a system of nonlinear ordinary differential equations (ODEs) through similarity transformations. Similarity transformation are utilized to convert model equations PDEs to nonlinear ODEs. Such ODEs were solved numerically through hybrid approach (Spectral collection scheme along Legender Wavelets (SCSLW)) combined with shooting technique. Key parameters like the volume of nanoparticles, Biot, Eckert and Weissenberg number, and the influential magnetic parameter significantly impact on thermal and velocity fields. Important physical parameters like skin friction and Nusselt number have been taken to examine shear stress and heat transfer. The consequences of non-Newtonian behavior and Lorentz forces play a stunning role in the thermal energy characteristics of Ag-Mgo hybrid nanoparticles, thus enhancing the transfer of heat
{"title":"Analysis of Ag-MgO /water Williamson hybrid nanofluid flow with viscous dissipation over a radially stretching surface under magnetic field and heat source effects","authors":"Tasawar Abbas , Taseer Muhammad , Nabil El Kadhi , Aaqib Majeed","doi":"10.1016/j.aej.2025.12.042","DOIUrl":"10.1016/j.aej.2025.12.042","url":null,"abstract":"<div><div>This paper gives an extensive numerical study of the two-dimensional, steady magnetohydrodynamic (MHD) heat transmission and flow properties of a Williamson hybrid nanofluid (HNF) over a stretching sheet. The hybrid nanofluid consists of water as the base fluid, comprising a suspension of magnesium oxide (MgO) and silver (Ag) nanoparticles, in order to enhance the conductivity of heat and flow behavior. The model incorporates the impacts of viscous dissipation, an externally applied magnetic field, Joule heating, and convective boundary condition at the surface. The non-Newtonian performance is characteristized through the Williamson fluid model, and the thermophysical aspects of the hybrid nanofluid are assessed by established correlations. The governing partial differential equations (PDEs), which are derived from mass conservation, momentum conservation, and energy conservation laws, are simplified into a system of nonlinear ordinary differential equations (ODEs) through similarity transformations. Similarity transformation are utilized to convert model equations PDEs to nonlinear ODEs. Such ODEs were solved numerically through hybrid approach (Spectral collection scheme along Legender Wavelets (SCSLW)) combined with shooting technique. Key parameters like the volume of nanoparticles, Biot, Eckert and Weissenberg number, and the influential magnetic parameter significantly impact on thermal and velocity fields. Important physical parameters like skin friction and Nusselt number have been taken to examine shear stress and heat transfer. The consequences of non-Newtonian behavior and Lorentz forces play a stunning role in the thermal energy characteristics of Ag-Mgo hybrid nanoparticles, thus enhancing the transfer of heat</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 318-326"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923679","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.2026.01.004
Taedeuk Kim , Sang Woo Kim
The satellite industry is shifting from large monolithic satellites to small-satellite constellations, resulting in increased requirements for high reliability and mission longevity. The attitude and orbit control system (AOCS), a key subsystem, employs control moment gyroscopes (CMGs) driven by permanent magnet synchronous motors (PMSMs) for continuous high-speed operation. This study proposes a fault-tolerant (FT) outer-rotor spoke-type PMSM featuring a single-layer fractional-slot concentrated winding (FSCW) and a ferrite-based magnetic notch. The single-layer FSCW enhances electromagnetic phase decoupling through the introduction of uncoiled teeth. The magnetic notch functions as a flux barrier that reduces inter-phase mutual inductance while preserving air-gap flux density. An ordinary Kriging surrogate model is employed as a computationally efficient alternative to the finite element method (FEM), and multi-objective optimization is conducted using the Non-dominated Sorting Genetic Algorithm 2 (NSGA-2). The optimized design achieves reductions of 63.20 % in cogging torque, 46.66 % in torque ripple, 28.37 % in mutual inductance, and 39.89 % in flux linkage deviation. All reported performance improvement is expressed as a relative value with respect to the conventional model. The results confirm the effectiveness of the proposed motor in enhancing the electromagnetic fault tolerance of CMG systems.
{"title":"Optimal design of fault-tolerant outer-rotor spoke-type PMSM with single-layer FSCW and magnetic notches using Kriging and NSGA-2 for satellite CMGs","authors":"Taedeuk Kim , Sang Woo Kim","doi":"10.1016/j.aej.2026.01.004","DOIUrl":"10.1016/j.aej.2026.01.004","url":null,"abstract":"<div><div>The satellite industry is shifting from large monolithic satellites to small-satellite constellations, resulting in increased requirements for high reliability and mission longevity. The attitude and orbit control system (AOCS), a key subsystem, employs control moment gyroscopes (CMGs) driven by permanent magnet synchronous motors (PMSMs) for continuous high-speed operation. This study proposes a fault-tolerant (FT) outer-rotor spoke-type PMSM featuring a single-layer fractional-slot concentrated winding (FSCW) and a ferrite-based magnetic notch. The single-layer FSCW enhances electromagnetic phase decoupling through the introduction of uncoiled teeth. The magnetic notch functions as a flux barrier that reduces inter-phase mutual inductance while preserving air-gap flux density. An ordinary Kriging surrogate model is employed as a computationally efficient alternative to the finite element method (FEM), and multi-objective optimization is conducted using the Non-dominated Sorting Genetic Algorithm 2 (NSGA-2). The optimized design achieves reductions of 63.20 % in cogging torque, 46.66 % in torque ripple, 28.37 % in mutual inductance, and 39.89 % in flux linkage deviation. All reported performance improvement is expressed as a relative value with respect to the conventional model. The results confirm the effectiveness of the proposed motor in enhancing the electromagnetic fault tolerance of CMG systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 421-432"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923614","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.2025.12.035
Amjad Hussain , Meerub Qureshi , Adil Jhangeer , Muhammad Zeeshan
The Konopelchenko–Dubrovsky (KD) model, which predicts the propagation of nonlinear waves in various physical media, including fluids in elastic tubes, dusty plasmas, and highly nonlinear optical systems, is investigated in this study using a powerful analytical method known as the Jacobi elliptic function (JEF) method. Numerous accurate wave solutions, including various stable wave forms and pulse shapes and different kinds of trigonometric and hyperbolic wave forms, are produced by this method. We present graphical representations of the dynamical behavior of the governing equation using various tools like phase portraits, time series and sensitivity analysis, Poincare maps, power spectra, and analysis of the system’s energy and stability. The qualitative analysis of the system in terms of its Hamiltonian structure makes it possible to distinguish bistable double-well and stable single-well potential energy landscapes, which are shown to correspond directly to the formation of kink solitons and periodic wave solutions, respectively. This paper provides a more physical background to the bifurcations of solutions and the changeovers between various wave forms. In addition to strengthening our knowledge of nonlinear wave propagation, the study offers a flexible framework for investigating additional nonlinear evolution equations in applied scientific and engineering settings.
{"title":"Soliton dynamics and qualitative analysis of the (2+1)-dimensional Konopelchenko–Dubrovsky system","authors":"Amjad Hussain , Meerub Qureshi , Adil Jhangeer , Muhammad Zeeshan","doi":"10.1016/j.aej.2025.12.035","DOIUrl":"10.1016/j.aej.2025.12.035","url":null,"abstract":"<div><div>The Konopelchenko–Dubrovsky (KD) model, which predicts the propagation of nonlinear waves in various physical media, including fluids in elastic tubes, dusty plasmas, and highly nonlinear optical systems, is investigated in this study using a powerful analytical method known as the Jacobi elliptic function (JEF) method. Numerous accurate wave solutions, including various stable wave forms and pulse shapes and different kinds of trigonometric and hyperbolic wave forms, are produced by this method. We present graphical representations of the dynamical behavior of the governing equation using various tools like phase portraits, time series and sensitivity analysis, Poincare maps, power spectra, and analysis of the system’s energy and stability. The qualitative analysis of the system in terms of its Hamiltonian structure makes it possible to distinguish bistable double-well and stable single-well potential energy landscapes, which are shown to correspond directly to the formation of kink solitons and periodic wave solutions, respectively. This paper provides a more physical background to the bifurcations of solutions and the changeovers between various wave forms. In addition to strengthening our knowledge of nonlinear wave propagation, the study offers a flexible framework for investigating additional nonlinear evolution equations in applied scientific and engineering settings.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 179-193"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923615","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.2025.12.061
Rubin Fan, Jicheng Dai, Fazhi He
The 3D mesh segmentation is pivotal in diverse fields such as industrial design, intelligent manufacturing, and other technological domains. Many 3D modeling operations are contingent on efficient and precise mesh segmentation. However, the inherent complexity and irregularity of 3D mesh data render manual segmentation exceedingly costly and labor-intensive. Prevailing deep learning-based methods continue to face challenges, including suboptimal utilization of raw data, inefficiencies, and limited overall accuracy.
To address these challenges, this paper introduces FFMeshSeg, an innovative 3D mesh segmentation model based on cross-element attention and feature fusion. The model incorporates three distinct feature aggregation branches, each designed to extract topological element features from grid vertices, edges, and faces. Subsequently, a cross-element feature fusion method grounded in a cross-attention mechanism is proposed. Additionally, a cross-element adjacency mask matrix is constructed to facilitate sparse attention feature fusion within local neighborhoods, resulting in more precise boundary segmentation.
Experimental results demonstrate that the per-face segmentation accuracy of the FFMeshSeg in the COSEG dataset compared to SOTA models improves by at least 1% in the Vases and Chairs subsets. Furthermore, while maintaining a relative low amount of parameters, the computational efficiency has increased by over 50% in specific dataset compared to the classical methods.
{"title":"FFMeshSeg: A 3D feature fusion mesh segmentation model based on cross element attention","authors":"Rubin Fan, Jicheng Dai, Fazhi He","doi":"10.1016/j.aej.2025.12.061","DOIUrl":"10.1016/j.aej.2025.12.061","url":null,"abstract":"<div><div>The 3D mesh segmentation is pivotal in diverse fields such as industrial design, intelligent manufacturing, and other technological domains. Many 3D modeling operations are contingent on efficient and precise mesh segmentation. However, the inherent complexity and irregularity of 3D mesh data render manual segmentation exceedingly costly and labor-intensive. Prevailing deep learning-based methods continue to face challenges, including suboptimal utilization of raw data, inefficiencies, and limited overall accuracy.</div><div>To address these challenges, this paper introduces FFMeshSeg, an innovative 3D mesh segmentation model based on cross-element attention and feature fusion. The model incorporates three distinct feature aggregation branches, each designed to extract topological element features from grid vertices, edges, and faces. Subsequently, a cross-element feature fusion method grounded in a cross-attention mechanism is proposed. Additionally, a cross-element adjacency mask matrix is constructed to facilitate sparse attention feature fusion within local neighborhoods, resulting in more precise boundary segmentation.</div><div>Experimental results demonstrate that the per-face segmentation accuracy of the FFMeshSeg in the COSEG dataset compared to SOTA models improves by at least 1% in the Vases and Chairs subsets. Furthermore, while maintaining a relative low amount of parameters, the computational efficiency has increased by over 50% in specific dataset compared to the classical methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 342-352"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923611","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.2025.12.049
Yongho Ko, Jhury Kevin Lastre, Hoseok Kwon, Ilsun You
The Remote SIM Provisioning (RSP) protocol, standardized by the Global System for Mobile Communications Association (GSMA), facilitates the secure download of Subscriber Identity Module (SIM) profiles onto device equipment and is widely recognized under the term embedded-SIM (eSIM). By enabling greater physical flexibility, RSP replaces traditional Universal SIM (USIM) cards and supports automated profile provisioning in Machine-to-Machine (M2M) scenarios. Through these benefits, the technology is increasingly regarded as a key enabler in emerging private 5G network environments. Since the SIM profile includes credentials required for authentication, ensuring the security, performance, and effectiveness of the RSP protocol is of paramount importance, particularly as M2M deployments must balance cryptographic robustness with computational efficiency. While Ahmed et al. have conducted formal verification of the Consumer RSP variant, their studies were confined to foundational analyses and did not consider performance-related aspects. Moreover, to the best of our knowledge, no formal security analysis or performance evaluation has been conducted on the M2M RSP protocol, despite its growing relevance in industrial environments. In this paper, we present the first integrated analysis of the M2M RSP protocol that combines formal security verification and implementation-based performance evaluation. Using ProVerif under the Dolev–Yao model, we uncover critical vulnerabilities, most notably the absence of Perfect Forward Secrecy (PFS), and show how reliance on intermediaries such as the SM-SR can enable man-in-the-middle attacks. Complementary performance modeling demonstrates predictable memory scaling but volatile CPU utilization during cryptographic operations, highlighting the tight coupling between security weaknesses and computational inefficiency. Based on these findings, we introduce concrete enhancements, including (i) replacing TLS 1.2 with TLS 1.3 to reduce handshake latency, (ii) migrating from symmetric SCP03t to asymmetric SCP11b for stronger key establishment, and (iii) incorporating hybrid post-quantum key agreement schemes to ensure long-term resilience. These proposals directly address the identified vulnerabilities while providing pathways to improved scalability and efficiency in future large-scale M2M deployments.
{"title":"Revisiting the M2M remote SIM provisioning protocol: A comprehensive security and performance analysis","authors":"Yongho Ko, Jhury Kevin Lastre, Hoseok Kwon, Ilsun You","doi":"10.1016/j.aej.2025.12.049","DOIUrl":"10.1016/j.aej.2025.12.049","url":null,"abstract":"<div><div>The Remote SIM Provisioning (RSP) protocol, standardized by the Global System for Mobile Communications Association (GSMA), facilitates the secure download of Subscriber Identity Module (SIM) profiles onto device equipment and is widely recognized under the term embedded-SIM (eSIM). By enabling greater physical flexibility, RSP replaces traditional Universal SIM (USIM) cards and supports automated profile provisioning in Machine-to-Machine (M2M) scenarios. Through these benefits, the technology is increasingly regarded as a key enabler in emerging private 5G network environments. Since the SIM profile includes credentials required for authentication, ensuring the security, performance, and effectiveness of the RSP protocol is of paramount importance, particularly as M2M deployments must balance cryptographic robustness with computational efficiency. While Ahmed et al. have conducted formal verification of the Consumer RSP variant, their studies were confined to foundational analyses and did not consider performance-related aspects. Moreover, to the best of our knowledge, no formal security analysis or performance evaluation has been conducted on the M2M RSP protocol, despite its growing relevance in industrial environments. In this paper, we present the first integrated analysis of the M2M RSP protocol that combines formal security verification and implementation-based performance evaluation. Using ProVerif under the Dolev–Yao model, we uncover critical vulnerabilities, most notably the absence of Perfect Forward Secrecy (PFS), and show how reliance on intermediaries such as the SM-SR can enable man-in-the-middle attacks. Complementary performance modeling demonstrates predictable memory scaling but volatile CPU utilization during cryptographic operations, highlighting the tight coupling between security weaknesses and computational inefficiency. Based on these findings, we introduce concrete enhancements, including (i) replacing TLS 1.2 with TLS 1.3 to reduce handshake latency, (ii) migrating from symmetric SCP03t to asymmetric SCP11b for stronger key establishment, and (iii) incorporating hybrid post-quantum key agreement schemes to ensure long-term resilience. These proposals directly address the identified vulnerabilities while providing pathways to improved scalability and efficiency in future large-scale M2M deployments.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 1-19"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897858","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.2025.12.028
Yanxi Shen , Anran Li
Emotion recognition and feedback play a critical role in enhancing user engagement and emotional learning experiences. However, existing models often struggle to accurately recognize complex emotions and provide adaptive feedback, particularly in real-time scenarios. This paper proposes EmoMamba, a deep learning model that integrates Bi-Mamba for bidirectional temporal feature extraction, GAT for emotional association reasoning, and DQN for dynamic feedback optimization. The model aims to address challenges in emotion recognition and feedback accuracy, focusing on real-time emotional state adaptation. Experiments on the RAVDESS and TESS datasets demonstrate that EmoMamba outperforms traditional methods and state-of-the-art models in terms of accuracy (94.2% on RAVDESS, 93.8% on TESS) and Macro-F1 (93.1% on RAVDESS, 92.4% on TESS). Additionally, it excels in feedback effectiveness, achieving high user satisfaction and emotion improvement degree (EID of 0.87 on RAVDESS and 0.85 on TESS). The proposed model provides a robust solution for dynamic emotional interaction, offering significant potential for applications in personalized learning and emotion-aware systems.
{"title":"EmoMamba: Real-time emotional state recognition and adaptive feedback generation in auditory learning environments","authors":"Yanxi Shen , Anran Li","doi":"10.1016/j.aej.2025.12.028","DOIUrl":"10.1016/j.aej.2025.12.028","url":null,"abstract":"<div><div>Emotion recognition and feedback play a critical role in enhancing user engagement and emotional learning experiences. However, existing models often struggle to accurately recognize complex emotions and provide adaptive feedback, particularly in real-time scenarios. This paper proposes EmoMamba, a deep learning model that integrates Bi-Mamba for bidirectional temporal feature extraction, GAT for emotional association reasoning, and DQN for dynamic feedback optimization. The model aims to address challenges in emotion recognition and feedback accuracy, focusing on real-time emotional state adaptation. Experiments on the RAVDESS and TESS datasets demonstrate that EmoMamba outperforms traditional methods and state-of-the-art models in terms of accuracy (94.2% on RAVDESS, 93.8% on TESS) and Macro-F1 (93.1% on RAVDESS, 92.4% on TESS). Additionally, it excels in feedback effectiveness, achieving high user satisfaction and emotion improvement degree (EID of 0.87 on RAVDESS and 0.85 on TESS). The proposed model provides a robust solution for dynamic emotional interaction, offering significant potential for applications in personalized learning and emotion-aware systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 84-99"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897850","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.2026.01.007
Alaa A. Ismail, Mohamed Rabah, Abdelmomen Mahgoub
The rapid global increase in wind turbine (WT) penetration necessitates compliance with grid codes for effective frequency control and stability. This study focuses on the integration of a phase-locked loop (PLL) tuning method with a step-wise inertia control to support frequency regulation in Permanent Manet Synchronous Generator (PMSG) WT systems. The proposed approach addresses the challenge of providing inertia support through power electronics in type-3 WT systems, which lack inherent inertia due to their decoupling from the grid. By modeling the dynamic inertia of the PMSG WT system and optimizing the PLL control, the study aims to enhance grid frequency response during disturbances. The research involves simulation and experimental validations to show the effectiveness of the coordinated control strategy, focusing on performance metrics such as the minimum grid frequency and the rate of change of frequency (ROCOF). The findings highlight the importance of PLL bandwidth selection in balancing inertia emulation and frequency tracking accuracy, proposing guidelines for optimal PLL design to improve WT system inertia and overall grid stability.
{"title":"Phase-locked loop tuning with step-wise inertia control for frequency support of PMSG wind turbine systems","authors":"Alaa A. Ismail, Mohamed Rabah, Abdelmomen Mahgoub","doi":"10.1016/j.aej.2026.01.007","DOIUrl":"10.1016/j.aej.2026.01.007","url":null,"abstract":"<div><div>The rapid global increase in wind turbine (WT) penetration necessitates compliance with grid codes for effective frequency control and stability. This study focuses on the integration of a phase-locked loop (PLL) tuning method with a step-wise inertia control to support frequency regulation in Permanent Manet Synchronous Generator (PMSG) WT systems. The proposed approach addresses the challenge of providing inertia support through power electronics in type-3 WT systems, which lack inherent inertia due to their decoupling from the grid. By modeling the dynamic inertia of the PMSG WT system and optimizing the PLL control, the study aims to enhance grid frequency response during disturbances. The research involves simulation and experimental validations to show the effectiveness of the coordinated control strategy, focusing on performance metrics such as the minimum grid frequency <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>n</mi><mi>a</mi><mi>d</mi><mi>i</mi><mi>r</mi></mrow></msub></math></span> and the rate of change of frequency (ROCOF). The findings highlight the importance of PLL bandwidth selection in balancing inertia emulation and frequency tracking accuracy, proposing guidelines for optimal PLL design to improve WT system inertia and overall grid stability.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"136 ","pages":"Pages 1-15"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915136","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.2025.12.060
Tangwang Sun, Yan Xiong, Jiajia Hu
To address the issue that the passive increase in network order resulting from the rise of the Pi-Sigma (PS) modules for the ridge polynomial neural network (RPNN) adversely affects network performance, we propose a homogeneous ridge polynomial neural network (HRPNN) with a symmetrical structure. Furthermore, based on its characteristic of a two-layer weight structure, an asynchronous gradient method with the PS module as the gradient update unit is presented to enhance the network performance. The convergence theorem of this method is strictly proven, theoretically confirming the feasibility. The experimental results of the UCI datasets also verify the effectiveness and practicability of the proposed network structure and the training method. The Homogeneous ridge polynomial neural network significantly outperforms the ridge polynomial neural network in classification tasks, especially on the Wbc dataset, where classification accuracy is approximately 36.30% higher, demonstrating higher accuracy and stability. In function fitting tasks, the mean squared error of HRPNN using the PS module asynchronous gradient (PSMAG) method was reduced by 31.38% and 41.10% compared to the other two algorithms, respectively. These comparison results show that our network with the novel training method has excellent classification and approximation capabilities.
{"title":"Homogeneous ridge polynomial neural network training by PS module asynchronous gradient method","authors":"Tangwang Sun, Yan Xiong, Jiajia Hu","doi":"10.1016/j.aej.2025.12.060","DOIUrl":"10.1016/j.aej.2025.12.060","url":null,"abstract":"<div><div>To address the issue that the passive increase in network order resulting from the rise of the Pi-Sigma (PS) modules for the ridge polynomial neural network (RPNN) adversely affects network performance, we propose a homogeneous ridge polynomial neural network (HRPNN) with a symmetrical structure. Furthermore, based on its characteristic of a two-layer weight structure, an asynchronous gradient method with the PS module as the gradient update unit is presented to enhance the network performance. The convergence theorem of this method is strictly proven, theoretically confirming the feasibility. The experimental results of the UCI datasets also verify the effectiveness and practicability of the proposed network structure and the training method. The Homogeneous ridge polynomial neural network significantly outperforms the ridge polynomial neural network in classification tasks, especially on the Wbc dataset, where classification accuracy is approximately 36.30% higher, demonstrating higher accuracy and stability. In function fitting tasks, the mean squared error of HRPNN using the PS module asynchronous gradient (PSMAG) method was reduced by 31.38% and 41.10% compared to the other two algorithms, respectively. These comparison results show that our network with the novel training method has excellent classification and approximation capabilities.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"136 ","pages":"Pages 16-29"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915137","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}
The prediction of exchange rates remains a critical and challenging task in international finance, given their sensitivity to macroeconomic, energy, and geopolitical factors. This paper focuses on forecasting the EUR/USD exchange rate using daily data between January 2024 and June 2025, with particular attention to the influence of oil prices and the Geopolitical Risk (GPR) index. Unlike most studies that rely on static machine learning (ML) models, we propose a temporal feature engineering framework that transforms Support Vector Regression (SVR), Random Forest (RF), and k-Nearest Neighbors (KNN) into time-aware predictors. Specifically, lagged variables over a seven-day horizon are incorporated, yielding 21 explanatory features. To further enhance model interpretability and efficiency, feature importance is computed via RF, allowing us to identify and retain the most relevant predictors. The proposed methodology is evaluated by comparing six models: SVR, RF, and KNN trained with the complete feature set versus their counterparts trained only on the selected variables. Results demonstrate that embedding temporal dependencies significantly improves forecasting performance and that variable selection provides both accuracy gains and economic insights. These findings highlight the pivotal role of oil price dynamics, geopolitical uncertainty, and short-term memory in driving EUR/USD fluctuations. Overall, our study offers a practical and interpretable ML-based framework for exchange rate forecasting, bridging the gap between econometric intuition and modern data-driven approaches.
{"title":"Transitioning from static to temporal machine learning models: Forecasting the EUR/USD exchange rate utilizing lagged factors and feature significance","authors":"Turke Althobaiti , Manjula Pattnaik , Yousef Asiri , Razaz Houssien Felimban , Ali Algarni , Zaid Bassfar","doi":"10.1016/j.aej.2025.12.051","DOIUrl":"10.1016/j.aej.2025.12.051","url":null,"abstract":"<div><div>The prediction of exchange rates remains a critical and challenging task in international finance, given their sensitivity to macroeconomic, energy, and geopolitical factors. This paper focuses on forecasting the EUR/USD exchange rate using daily data between January 2024 and June 2025, with particular attention to the influence of oil prices and the Geopolitical Risk (GPR) index. Unlike most studies that rely on static machine learning (ML) models, we propose a temporal feature engineering framework that transforms Support Vector Regression (SVR), Random Forest (RF), and k-Nearest Neighbors (KNN) into time-aware predictors. Specifically, lagged variables over a seven-day horizon are incorporated, yielding 21 explanatory features. To further enhance model interpretability and efficiency, feature importance is computed via RF, allowing us to identify and retain the most relevant predictors. The proposed methodology is evaluated by comparing six models: SVR, RF, and KNN trained with the complete feature set versus their counterparts trained only on the selected variables. Results demonstrate that embedding temporal dependencies significantly improves forecasting performance and that variable selection provides both accuracy gains and economic insights. These findings highlight the pivotal role of oil price dynamics, geopolitical uncertainty, and short-term memory in driving EUR/USD fluctuations. Overall, our study offers a practical and interpretable ML-based framework for exchange rate forecasting, bridging the gap between econometric intuition and modern data-driven approaches.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"135 ","pages":"Pages 168-178"},"PeriodicalIF":6.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923620","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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