alexandria engineering journal最新文献

{"title":"One-parameter lie scaling for tri-hybrid maxwell nanofluid in electro-magnetohydrodynamics field with bio-convection and blowing effects","authors":"Musharafa Saleem , Imran Siddique , Bushra Shakoor , Taha Radwan , Dilsora Abduvalieva","doi":"10.1016/j.aej.2026.04.052","DOIUrl":"10.1016/j.aej.2026.04.052","url":null,"abstract":"<div><div>The growing demand for enhanced heat transfer efficiency in industrial applications has driven significant research into advanced nano-fluids, with tri-hybrid formulations representing the latest frontier in thermal management technology. While conventional fluids exhibit limited thermal conductivity that restricts their cooling capabilities, the incorporation of multiple nanoparticle types offers unprecedented opportunities for performance optimization. Despite extensive research on mono and hybrid nano-fluids, the complex interactions within tri-hybrid systems under combined electromagnetic fields and bio-convective conditions remain inadequately understood, particularly for non-Newtonian Maxwell fluids exhibiting both viscous and elastic properties. The current research introduces a mathematical model aimed at examining the flow of a non-Newtonian electro-magneto-hydrodynamic (EMHD) tri-hybrid Maxwell nano-fluid (THMNF) across a stretching porous surface. This model takes into account various factors, porous medium, including heat transport rate influenced by blowing, radiation, heat source and sink effects, convective boundary conditions, and the presence of microorganisms causing bio-convection. One-parameter Lie group scaling analysis is employed to identify the symmetries and establish similarity transformations that are essential for transforming partial differential equations (PDEs) into coupled ordinary differential equations (ODEs), which are then solved numerically by the bvp4c method in MATLAB. An inclusive review is performed on essential factors, which encompass the Biot coefficient, bio-convection Rayleigh coefficient and non-linear convection factor. This study examines the effects of these factors on different flow distributions, including velocity, thermal, and microorganisms, thereby enhancing the depth and practical significance of the results. The validity and robustness of the findings are established through a comparative analysis of the numerical results reported in the existing literature. Results demonstrate that tri-hybrid nano-fluid achieves superior energy transfer capabilities compared to mono and hybrid nanofluids as <span><math><mrow><mn>12.2</mn><mo>%</mo><mspace></mspace><mo>(</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>3</mn></mrow></msub><mspace></mspace><mo>=</mo><mspace></mspace><mn>0.02</mn><mo>)</mo></mrow></math></span>, <span><math><mrow><mn>25.4</mn><mo>%</mo><mspace></mspace><mo>(</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><msub><mrow><mi>Φ</mi></mrow><mrow><mn>3</mn></mrow></msub><mspace></mspace><mo>=</mo><mspace></mspace><mn>0.04</mn><mo>)</mo></mrow></math></span>, and <span><math><mrow><mn>41.3</mn><mo>%</mo><mspace></mspace><mo>(</mo><msub><mrow><mi>Φ</mi","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 410-429"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147861320","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}

{"title":"RSCO-Net: A refined single candidate optimizer for multimodal colorectal cancer postoperative recurrence prediction","authors":"Zhijian Wei ,&nbsp;Kun Ye ,&nbsp;Yuyang Hou ,&nbsp;Yongxiang Li ,&nbsp;Lei Meng ,&nbsp;Aman Xu","doi":"10.1016/j.aej.2026.04.044","DOIUrl":"10.1016/j.aej.2026.04.044","url":null,"abstract":"<div><div>Postoperative recurrence remains a critical challenge in the clinical management of colorectal cancer (CRC), affecting approximately 30%–50% of patients who undergo curative-intent surgery. Precise risk stratification is essential for personalized treatment and improved prognosis, yet traditional markers often fall short in predictive accuracy. This study introduces <strong>RSCO-Net</strong>, an integrated multimodal framework that leverages a Refined Single Candidate Optimizer (RSCO) for enhanced CRC recurrence prediction. Our approach uniquely combines 512-dimensional visual features from whole-slide images (WSIs) via attention-based multiple-instance learning (MIL) with 512-dimensional semantic embeddings from pathology reports via transformer-based language models. To navigate the high-dimensional, non-convex optimization landscape of the fused 1024-dimensional feature space, we propose the RSCO metaheuristic. RSCO employs a two-phase search strategy that balances global exploration with local exploitation, coupled with an escape mechanism to avoid stagnation. Experimental evaluations on the TCGA-COAD/READ dataset demonstrate that RSCO-Net achieves a state-of-the-art AUROC of 0.839 and an AUPRC of 0.731, significantly outperforming benchmark population-based optimizers and unimodal baselines. Ablation studies confirm that RSCO provides a 2.1% gain over simple candidate optimization, while multimodal fusion contributes a 9.1% improvement over WSI-only models. Furthermore, we provide clinical interpretability through SHAP-based feature importance and attention-driven WSI visualization. Our findings establish RSCO-Net as a robust and computationally efficient tool for precision oncology, advancing the frontier of multimodal medical AI for CRC prognosis.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 377-388"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147861319","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}

{"title":"A multi-scale feature enhancement and context-aware convolutional network for small object detection in remote sensing images","authors":"Laomo Zhang ,&nbsp;Ying Ma ,&nbsp;Guowei Li ,&nbsp;Tianrui Li ,&nbsp;Wendi Yang","doi":"10.1016/j.aej.2026.04.043","DOIUrl":"10.1016/j.aej.2026.04.043","url":null,"abstract":"<div><div>In remote sensing imagery, detecting extremely small objects is inherently challenging due to severe scale imbalance, sparse pixel representation, and complex background interference. In high-resolution aerial scenes, targets often occupy only a few pixels, which weakens feature responses and leads to unstable optimization. Although multi-scale detection architectures partially alleviate this issue, they often lack mechanisms for structural enhancement and scale-aware supervision. To address these challenges, CEMF-Net is proposed, a unified detection framework that integrates frequency-guided multi-scale modeling, context-selective feature modulation, and scale-consistent label assignment. By enhancing high-frequency structural cues and incorporating scale alignment into the supervision process, the proposed framework improves feature representation and localization stability for tiny objects in complex aerial environments. Extensive experiments on AI-TOD, DOTA-v1.5, and VisDrone demonstrate consistent performance gains across diverse benchmarks. On AI-TOD, CEMF-Net achieves 67.3% [email protected] and 54.6% <span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mi>s</mi><mi>m</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></msub></mrow></math></span>, highlighting its effectiveness for detecting extremely small objects. These results demonstrate the effectiveness of CEMF-Net as a unified framework for remote sensing small object detection, with potential value for practical applications such as UAV traffic monitoring, maritime surveillance, and emergency response.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"144 ","pages":"Pages 129-142"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147804835","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}

{"title":"A transfer-federated learning framework integrating toxicological risk modeling for heterogeneous UAV, UGV, and IoT air-quality monitoring","authors":"Montaser N.A. Ramadan ,&nbsp;Mohammed A.H. Ali ,&nbsp;Nik Nazri Nik Ghazali ,&nbsp;Hadi Jaber ,&nbsp;Azzam Abu Rayash ,&nbsp;Mohammed Ghazal ,&nbsp;Mohammad Alkhedher","doi":"10.1016/j.aej.2026.04.057","DOIUrl":"10.1016/j.aej.2026.04.057","url":null,"abstract":"<div><div>Existing air quality monitoring systems rely on static sensors, failing to capture personal exposure dynamics and hindering secure cross-platform data sharing. To address these limitations, this study introduces a novel Transfer-Federated Learning (TFL) framework for collaborative learning among heterogeneous devices—an unmanned ground vehicle (UGV), an unmanned aerial vehicle (UAV), and fixed IoT stations—while preserving data privacy. The TFL approach uniquely adapts a pretrained model to clients with different sensor configurations through layer-wise transfer and FedAvg aggregation. Lightweight models (Tiny-TCN, TFT-lite) predict both pollutant concentrations (PM₂.₅, PM₁₀, CO₂, CH₂O, VOCs) and mechanistic toxicological risks (inhalation dose and hazard quotient). Field tests across three industrial sites demonstrated high cross-platform accuracy (global R² = 0.984) and real-time inference (&lt;2 s), reducing personal exposure estimation error by over 40% compared to static fixed-sensor baselines. The core contribution lies in the system-level integration of transfer learning and federated learning across heterogeneous platforms, with toxicological metrics used to translate predictions into actionable occupational risk indicators. This work establishes a new paradigm for privacy-preserving, health-aware monitoring in industrial environments.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 295-312"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147860840","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}

{"title":"A novel graph-driven diffusion-transformer based dynamic emotion network for multimodal emotion recognition","authors":"Sathiyamoorthi Arthanari,&nbsp;Yeon-Kug Moon","doi":"10.1016/j.aej.2026.04.060","DOIUrl":"10.1016/j.aej.2026.04.060","url":null,"abstract":"<div><div>Multimodal emotion recognition in conversational contexts has attracted increasing attention due to its ability to analyze human emotions by jointly modeling visual, textual, and audio cues in dynamic interactions. However, existing methods struggle to capture hierarchical relationships among emotional cues, are highly sensitive to noisy or missing data, and often fail to model fine-grained emotional transitions over time. These limitations hinder the interpretation of subtle emotional variations and lead to inconsistent predictions in real-world scenarios. To address these issues, we propose GDiffTransNet, a Graph-Driven Diffusion-Transformer integrated Dynamic Emotion Network. The framework introduces a Hierarchical Graph Fusion Network (HGFN) to capture inter- and intra-modal relationships, enabling fine-grained emotional dependency modeling. A Gated Cross-Modal Transformer (GCMT) is employed to dynamically regulate information flow through gated cross-attention, allowing effective feature integration across modalities. To improve robustness under incomplete or corrupted inputs, a diffusion module is designed to reconstruct missing or noisy modalities in a context-aware manner. Additionally, a Dynamic Emotion Network (DEN) models temporal and contextual variations, enhancing the recognition of evolving emotions. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach, achieving 83.12%/82.52% on IEMOCAP, 72.94%/72.01% on MELD, and 74.8%/49.7% on CMU-MOSEI in terms of W-Acc/W-F1.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 344-362"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147861321","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}

{"title":"GDBERT-score: Semantic Graph-Enhanced DeBERTa for automated essay scoring in higher education","authors":"Tiantian Mi ,&nbsp;Tianjiao Yu","doi":"10.1016/j.aej.2026.04.019","DOIUrl":"10.1016/j.aej.2026.04.019","url":null,"abstract":"<div><div>Automated essay scoring (AES) systems typically rely on either sequential text models for contextual semantics or graph-based approaches for discourse structure, but rarely integrate both effectively. We present GDBERT-Score, a hybrid architecture that combines DeBERTa’s disentangled attention with graph convolutional networks (GCN) for holistic essay scoring. Each essay is represented as a sentence-level semantic graph, where nodes are DeBERTa mean-pooled sentence embeddings and edges connect sentence pairs whose cosine similarity exceeds a learned threshold (<span><math><mrow><mi>τ</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>4</mn></mrow></math></span>). GCN-derived structural embeddings are concatenated with DeBERTa’s document-level contextual embedding and fused through a progressive reduction network (<span><math><mrow><mn>896</mn><mo>→</mo><mn>512</mn><mo>→</mo><mn>256</mn><mo>→</mo><mn>128</mn></mrow></math></span>) to produce a final holistic score. Evaluated on the Kaggle Automated Essay Scoring 2.0 benchmark via six-fold cross-validation, GDBERT-Score achieves QWK <span><math><mo>=</mo></math></span> 0.7777 <span><math><mo>±</mo></math></span> 0.0017 (mean <span><math><mo>±</mo></math></span> std across 5 random seeds), significantly outperforming both the DeBERTa-only baseline (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>0001</mn></mrow></math></span>) and the TF–IDF graph variant (<span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>0010</mn></mrow></math></span>). Ablation experiments reveal that node feature quality, rather than graph topology, is the primary determinant of GCN effectiveness in automated essay scoring: replacing surface-level node features with DeBERTa sentence embeddings yields significant improvements over both the DeBERTa-only baseline (<span><math><mi>Δ</mi></math></span>QWK <span><math><mo>=</mo></math></span> +0.0294, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>0001</mn></mrow></math></span>) and the graph-augmented variant (<span><math><mi>Δ</mi></math></span>QWK <span><math><mo>=</mo></math></span> +0.0239, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>0010</mn></mrow></math></span>), confirmed across five random seeds (QWK <span><math><mo>=</mo></math></span> 0.7777 <span><math><mo>±</mo></math></span> 0.0017).</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 154-163"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147861318","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}

{"title":"Corrigendum to “SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection” [Alex. Eng. J. 126 (2025), 90–104]","authors":"Baotong Wang ,&nbsp;Chenxing Xia ,&nbsp;Xiuju Gao ,&nbsp;Bin Ge ,&nbsp;Kuan-Ching Li ,&nbsp;Xianjin Fang ,&nbsp;Yan Zhang ,&nbsp;Yuan Yang","doi":"10.1016/j.aej.2026.04.013","DOIUrl":"10.1016/j.aej.2026.04.013","url":null,"abstract":"","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"143 ","pages":"Page 237"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147799118","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}

{"title":"Mathematically structured quantum time-series analytics with dual-stream convolutional recurrent network for advanced smart grid fault detection","authors":"Iyad Katib ,&nbsp;Mahmoud Ragab ,&nbsp;Sanaa A. Sharaf ,&nbsp;Wafi Bedewi ,&nbsp;Emad Albassam ,&nbsp;Abdullah A. Al-Ghamdi","doi":"10.1016/j.aej.2026.04.033","DOIUrl":"10.1016/j.aej.2026.04.033","url":null,"abstract":"<div><div>Deep learning models are used to identify fault type, recognize fault location, and find out faulty portions, learns emerging failures and their reasons, and predict the fault patterns. Quantum machine learning connects the gap between theoretical developments in quantum computing and applications in machine learning. In general, it shows the combination of significant ML techniques in a quantum module. This article proposes a Quantum Time-Series Analytics Framework for Advanced Smart Grid Fault Detection. This work aims to develop an intelligent system capable of precisely detecting and classifying diverse types of faults in smart grid systems. The raw data are initially pre-processed to enhance data quality and ensure reliable model training. A quantum approximate optimization algorithm-based feature selection algorithm is applied to identify the best subset of features. The selected features are then passed to a dual-stream convolutional recurrent network for accurate smart grid fault detection. The learning performance and convergence stability of the proposed architecture are enhanced by employing the AdaBelief optimizer. The experimental result analysis of the QTSA-ASGFD technique was conducted using a smart grid monitoring dataset, and the results were evaluated under various aspects. The simulation analysis demonstrated the superiority of the QTSA-ASGFD technique over recent state-of-the-art methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 85-96"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147806678","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}

{"title":"A multifunctional fluorescent probe FTU for simultaneous detection of Hg²⁺and F⁻: Insights from theoretical studies","authors":"Yu-ling Liu ,&nbsp;Yue Deng ,&nbsp;Jian Feng ,&nbsp;Yong-jin Peng ,&nbsp;Liang Fu","doi":"10.1016/j.aej.2026.04.047","DOIUrl":"10.1016/j.aej.2026.04.047","url":null,"abstract":"<div><div>This study reported the development of a novel fluorescent probe, FTU, capable of selectively detecting Hg²⁺and F⁻ions in aqueous solutions. The probe exhibited distinct spectral responses to both ions, characterized by red shifts in absorption wavelengths and changes in fluorescence intensity. Quantum chemical calculations revealed that FTU existed in two low-energy conformations (FTU-A and FTU-B) with different reactivity toward Hg²⁺and F⁻. Specifically, F⁻forms hydrogen-bonded complexes (FTU-FA and FTU-FB) with varying binding energies (IGMH isosurface volumes: 1.2 ų vs 0.8 ų), where FTU-FA retained strong fluorescence due to local excitation, while FTU-FB underwent charge transfer, leading to weak fluorescence. In contrast, Hg²⁺bound to two FTU molecules (FTU-HgA and FTU-HgB), forming stable complexes via van der Waals interactions (IGMH volumes: 11.6 ų vs. 3.8 ų), both of which suppress fluorescence through charge transfer. In addition, the conformations FTU-FC formed by F⁻ abstracting the H atom of the N–H bond to eliminate HF and FTU-HgC formed by Hg²⁺ binding with the S atom to eliminate HgS were also calculated in this work. Meanwhile the simulated UV-Vis spectra are quantitatively consistent with experimental results, validating the reliability of theoretical models. The theoretical predictions aligned well with experimental observations, providing a mechanistic understanding of the probe’s dual-ion detection capability. This work highlighted the potential of rationally designing multifunctional probes by leveraging conformational diversity and electronic structure.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 77-84"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147806679","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}

{"title":"The role of austenitic butter layers in modifying impact behavior of hardfaced high-strength steels","authors":"Ákos Meilinger,&nbsp;Gábor Terdik","doi":"10.1016/j.aej.2026.04.053","DOIUrl":"10.1016/j.aej.2026.04.053","url":null,"abstract":"<div><div>Hardfacing is commonly used to improve surface wear resistance; however, it may reduce substrate toughness due to microstructural changes in the heat-affected zone. To mitigate this effect, an austenitic stainless steel butter layer is often applied between the high-strength steel substrate and the hardfacing layer. In this study, the influence of the butter layer and heat input on impact behavior was investigated for S690QL, S960QL, and S1100QL steels using instrumented Charpy impact testing. Microstructural characterization was performed by optical and scanning electron microscopy, supported by hardness mapping on cross-sections. The presence of the austenitic butter layer significantly reduced the absorbed impact energy for S690QL and S960QL, while increasing it for S1100QL. In general, increasing heat input resulted in lower impact energy and higher peak impact force. The best performance was obtained for S960QL processed with low heat input, showing a 132% increase in impact energy, whereas S1100QL exhibited the lowest impact energy under the same condition; however, at the highest heat input, the impact energy of S1100QL increased by 113%. These findings are particularly relevant for demolition shears, where components are typically subjected to dynamic loading, and highlight the importance of optimizing substrate - butter layer combinations and hardfacing parameters.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"145 ","pages":"Pages 29-43"},"PeriodicalIF":6.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147806758","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}