Engineering reports : open access最新文献

{"title":"Advanced Statistical Characterization and Correlation Analysis of Process Performance Indicators for Optimized Engineering Decisions","authors":"Khamiss Cheikh,&nbsp;EL Mostapha Boudi,&nbsp;Rabi Rabi,&nbsp;Hamza Mokhliss","doi":"10.1002/eng2.70614","DOIUrl":"https://doi.org/10.1002/eng2.70614","url":null,"abstract":"<p>This study develops a rigorous statistical framework for the systematic characterization and comparative evaluation of process performance indicators, with the objective of informing optimized engineering decision-making under uncertainty. System behavior is analyzed across multiple operational categories using a structured suite of descriptive and comparative statistical techniques applied to three primary indicators: performance result <i>R</i>, processing time <i>T</i>, and error margin E. The analytical methodology integrates raw observations with aggregated statistical descriptors, including arithmetic means, variances, standard deviations, medians, ranges, coefficients of variation, and Pearson correlation coefficients. This multi-level characterization enables precise assessment of expected performance, operational effort, uncertainty, and relative stability, which together define the system performance vector (<i>R</i>, <i>E</i>, <i>T</i>). The results reveal pronounced category-dependent performance profiles and demonstrate a strong performance–effort coupling between <i>R</i> and <i>T</i>, together with moderate associations involving <i>E</i>, thereby elucidating inherent trade-offs between output magnitude, efficiency, and precision. In addition to static statistical analysis, the study examines learning efficiency and convergence behavior through a comparative evaluation of quantum-inspired reinforcement learning (<i>QI-RL</i>) and classical <i>ε</i>-greedy strategies. The results indicate enhanced exploration capability and accelerated convergence in complex decision spaces. The influence of environmental uncertainty modeling is further investigated, showing that temporally correlated stochastic disturbances substantially increase performance variability relative to uncorrelated assumptions. Overall, the proposed framework provides a coherent and extensible analytical basis that integrates statistical robustness, correlation structure, adaptive learning behavior, and uncertainty sensitivity. It offers a principled foundation for performance evaluation, resource allocation, and adaptive optimization in complex engineering systems and establishes clear directions for future extensions toward dynamic modeling and data-driven control architectures.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RogueGPT: Unleashing Jailbreak Prompts on LLMs","authors":"Arpitha Shivaswaroopa,&nbsp;Vanshika Sood,&nbsp;H. L. Gururaj,&nbsp;J. Shreyas,&nbsp;Fadi Al-Turjman","doi":"10.1002/eng2.70069","DOIUrl":"https://doi.org/10.1002/eng2.70069","url":null,"abstract":"<p>Large Language Models (LLMs) have seen a remarkable surge in popularity since the latter part of 2022. These models have become vital in the lives of individuals from varying professions. While some users leverage LLMs for academic or informational purposes, others exploit them for illicit activities. Methods of exploitation include Adversarial Attacks, Instruction Tuning Attacks, Inference Attacks, and Extraction Attacks. This paper investigates a specific Instruction Tuning Attack known as jailbreaking, which manipulates LLMs with prompts to generate harmful responses to forbidden instructions. This study presents compelling evidence of how widely used LLMs, such as OpenAI's ChatGPT, Google's Gemini, Meta's LLaMa, LMSYS's Vicuna, and Alibaba Cloud's Qwen, can be manipulated to generate responses that range from mildly illegal to potentially criminal content. Jailbreak prompts were created for each LLM, encompassing a range of inquiries spanning various categories. Based on the level of response elicited, they were categorized and computed alongside the Attack-to-Success Rate (ASR). These findings highlight the effectiveness of our prompts on each LLM and their performance relative to other models. Vicuna produced the best results with ASR (0.93) and FT (0.842), followed by LLaMa with ASR (0.71) and FT (0.709), indicating their vulnerability. The category of False Information had the highest overall average, with ASR (0.864) and FT (0.96). Our conclusions were reached through a combination of human assessment and quantitative analysis, detailed in subsequent sections. Through the dissemination of this research, the aim is to encourage organizations to prioritize their security measures and raise awareness among individuals about the responsible and ethical use of LLMs, given their potential for harm.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on Explainable, Federated Multimodal AI for Heart Disease Detection Using ECG, Cardiac Imaging, and Electronic Health Records","authors":"P. Murali,&nbsp;S. Meenatchi","doi":"10.1002/eng2.70687","DOIUrl":"https://doi.org/10.1002/eng2.70687","url":null,"abstract":"<p>Heart disease remains the leading cause of morbidity and mortality worldwide, highlighting the urgent need for accurate and timely diagnostic methods. Recent advances in machine learning (ML), deep learning (DL), and federated learning (FL) have enabled automated heart disease diagnosis by leveraging diverse data modalities, including ECG signals, cardiac imaging, and electronic health records (EHR). However, the high dimensionality, multipartite nature, and variability of these datasets present challenges in model generalization, often introducing biases that reduce robustness. This study presents a systematic literature review (SLR) of heart disease prediction research published between 2015 and 2025, initially screening 550 references and narrowing to 72 studies based on strict inclusion and exclusion criteria. The review categorizes studies by evidence type and analyzes trends in predictive methods, contrasting single-modality approaches (ECG or imaging) with multimodal techniques that integrate multiple data sources. Key research gaps identified include the need for improved data fusion strategies, augmentation methods, privacy-preserving models, and explainable AI. Building on these insights, the study proposes a diagnostic framework that integrates ECG and imaging data using CNN and BiLSTM for feature extraction, enhanced with Grad-CAM and SHAP for model interpretability. The framework also incorporates diffusion models and transformer architectures to reconstruct missing data, enabling the combination of ECG, imaging, and EHR for improved predictive accuracy and robustness. Furthermore, federated learning combined with homomorphic encryption is explored to support secure, multi-institutional deployment. Overall, this review synthesizes state-of-the-art techniques, highlights critical gaps, and provides actionable strategies for developing explainable, secure, and multimodal AI-based heart disease prediction systems, advancing both diagnostic performance and clinical trust.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical and Intelligent Modeling of MHD Casson Nanofluid Heat Transfer in Fractal Porous Cavities for Energy Systems","authors":"Wajid Ullah,&nbsp;Muhammad Salim Khan,&nbsp;Zahir Shah,&nbsp;Aseel Smerat,&nbsp;Meshal Shutaywi","doi":"10.1002/eng2.70721","DOIUrl":"https://doi.org/10.1002/eng2.70721","url":null,"abstract":"<p>This study investigates the enhancement of convective heat transfer in magnetohydrodynamic (MHD) nanofluid systems containing complex internal structures. Although fractal geometries have recently attracted attention for improving thermal transport, their interaction with porous media, non-Newtonian fluid behavior, and magnetic effects remains insufficiently understood. In particular, the combined influence of fractal barriers and Casson nanofluids on flow structure and heat transfer performance has not been systematically explored. To address this gap, the present work develops a computational framework that integrates the Finite Element Method (FEM) with Artificial Neural Networks (ANN) to analyze and predict thermal behavior in porous enclosures containing fractal internal barriers. Numerical simulations are performed using COMSOL Multiphysics to examine MHD Cu–H₂O nanofluid flow under varying Rayleigh numbers, Darcy numbers, nanoparticle volume fractions, and geometric configurations. The results reveal that the geometric complexity of fractal barriers significantly modifies flow circulation, disrupts symmetry, and generates secondary vortices, leading to a 35%–48% enhancement in the local Nusselt number. Increasing the Rayleigh number intensifies buoyancy-driven convection and fluid mixing, while larger Darcy numbers improve permeability and strengthen convective transport. The application of a transverse magnetic field introduces Lorentz damping, reducing convection by up to 13% and shifting the heat transfer mechanism toward conduction-dominant regimes. To accelerate prediction and optimization, a data-driven ANN model based on Bayesian Regularization Training (BRT-ANN) is developed using the FEM simulation dataset. The trained network demonstrates excellent predictive capability with regression coefficients of <i>R</i> = 1 for training, validation, and testing datasets, rapid mean squared error convergence over 238 epochs, and very small gradient values (9.8894 × 10⁻⁸). The strong agreement between FEM and ANN predictions highlights the effectiveness of the proposed hybrid FEM–ANN framework for rapid thermal performance estimation in complex thermal systems. This integrated approach provides a reliable tool for the design and optimization of advanced heat transfer devices in aerothermal and energy engineering applications.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Smartphone and Web-Based Automated Platform for Segmenting Urinary Tract Infection Using a Deep Learning-Based Approach","authors":"Justice Williams Asare,&nbsp;Lukman Hamza,&nbsp;Pious Ackon,&nbsp;Kingsley Buabeng,&nbsp;Emmanuel Akwah Kyei,&nbsp;Martin Mabeifam Ujakpa,&nbsp;William Leslie Brown-Acquaye,&nbsp;Emmanuel Freeman","doi":"10.1002/eng2.70720","DOIUrl":"https://doi.org/10.1002/eng2.70720","url":null,"abstract":"<p>Urinary tract infections (UTIs) affect millions of people annually, with early and accurate diagnosis being essential for effective treatment. Traditional diagnostic methods such as urine culture and dipstick testing are often slow, costly, and sometimes unreliable, particularly in low-resource settings where access to advanced laboratory facilities is limited. This study presents a deep learning–powered urinary sediment analysis system designed to operate on both web and mobile platforms. At its core is a UNet++ multi-class segmentation model trained to identify various sediment types, including background, rod, red blood cells/white blood cells, yeast, miscellaneous, single epithelial cell, small epithelial cell sheet, and large epithelial cell sheet. The model was trained for 50 epochs using the Adam optimizer with a learning rate of 0.001 and evaluated using the Dice coefficient, Intersection over Union, precision, recall, and area under the curve. The background class achieved the highest accuracy (Dice coefficient = 0.9963, Intersection over Union = 0.9926, area under the curve = 0.9587), while rare categories such as yeast (Dice coefficient = 0.0092) and miscellaneous (Dice coefficient = 0.0234) were more difficult to detect due to class imbalance and visual similarity. In experimental performance tests, the system processed complex samples in about 11 s and simpler ones in 1–2 s, instantly displaying results on the web or mobile interface. This integrated approach offers a faster, more consistent alternative to traditional methods, with the potential to improve access to automated urine sediment image analysis in resource-constrained healthcare environments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Calculation Method of Line Loss of Distribution Network Based on Genetic Algorithm and Artificial Neural Network","authors":"Tianjun Chen,&nbsp;Tao Deng,&nbsp;Junyang Wu,&nbsp;Ming Wen,&nbsp;Chengming Liu","doi":"10.1002/eng2.70678","DOIUrl":"https://doi.org/10.1002/eng2.70678","url":null,"abstract":"<p>Traditional methods for calculating distribution line losses often rely on complex mathematical models and extensive operational data, leading to cumbersome processes that struggle to ensure accuracy and timeliness in practical applications. To address these limitations, this paper proposes an adaptive calculation method for distribution line losses based on a genetic algorithm (GA) and an artificial neural network (ANN). The approach integrates automated data preprocessing using MATLAB's mapminmax function for feature normalization, an improved <i>k</i>-means clustering algorithm for data grouping, and a genetic algorithm to optimize the initial weights and thresholds of a backpropagation (BP) neural network. A 16-4-1 network structure is constructed, and a time-division calculation model is introduced to enable simultaneous computation across multiple lines. Experimental results demonstrate that the proposed method significantly reduces the mean squared error and enhances the accuracy and efficiency of line loss calculation. The hybrid GA-BP model combines global search capability with nonlinear fitting, enhances data preprocessing and clustering techniques, and improves data quality and model generalization ability.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prototype of Low-Cost 3D Printer for Assistive Medical Devices Based on Alternative Materials","authors":"Mustafa F. Mahmood,&nbsp;Suhair M. Yaseen,&nbsp;Saleem Latteef Mohammed","doi":"10.1002/eng2.70740","DOIUrl":"https://doi.org/10.1002/eng2.70740","url":null,"abstract":"<p>This paper describes the design of a working and low-cost 3D printer with the explicit purpose of creating external medical aids with minimal resources in a setting-constrained area like Iraq. This work has been motivated by the limited supply of medical grade printers that are commercially available and expensive, importation restrictions, and the growing demand for personalized assistive equipment in rehabilitation and clinical care. This prototype is scientifically tested as opposed to traditional DIY documentation by using dimensional accuracy tests, repeatability, and practical construction of a medical assistive model. The printer was assembled with components that are easily found in the local Iraqi markets, which makes them easy to maintain and economical. It had a build volume of 230 × 230 × 230 mm with a measured dimensional accuracy of about 91% (≤ 0.2 mm per layer) which is acceptable in nonimplantable medical devices. The system was able to generate bespoke splints and exoskeletal components that had predictable structural functioning. The article demonstrates the possibilities of the existing fabrication technologies to help clinical, educational, and research operations in the developing world. It is also a contextualized and practical model of increasing additive manufacturing capacities in the environment where commercial solutions are hard to acquire.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning-Driven Enterprise Financial Management and Spatiotemporal Convolution Optimization Model","authors":"Qianqian Wang,&nbsp;Tong Zhang","doi":"10.1002/eng2.70730","DOIUrl":"https://doi.org/10.1002/eng2.70730","url":null,"abstract":"<p>To address sequential decision-making problems in corporate financial management involving multiple objectives, multiple constraints, and high uncertainty, this study proposes a corporate financial management framework based on the joint optimization of reinforcement learning and spatiotemporal convolution. The framework uses spatiotemporal convolution to capture cross-period dependencies and cross-entity transmission relationships in corporate financial data. It embeds structured state representations into the reinforcement learning policy. Additionally, funding cost, liquidity requirements, tail risk, and compliance thresholds are incorporated as endogenous constraints in the policy update process. This design enables end-to-end optimization and avoids the fragmented structure of the traditional “predict-then-decide” paradigm. Experimental results show that the proposed model consistently outperforms baseline methods on key indicators such as funding cost, liquidity buffer capacity, and operational efficiency. The model also demonstrates robust performance on risk indicators, including maximum drawdown, cash shortfall, and compliance trigger events. These results confirm the effectiveness and interpretability of end-to-end joint optimization in complex financial environments. The study further indicates that when decision policies capture both long-term temporal dependencies and transmission mechanisms within organizational networks, firms can achieve more balanced coordination among cost control, operational efficiency, financial robustness, and regulatory compliance. Integrating risk and compliance constraints directly into the decision process further improves the stability of financial strategies. Therefore, this study makes a meaningful contribution to research on intelligent financial decision-making and cross-departmental capital allocation in corporate finance.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Clustering Method for Fault-Tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm","authors":"Jiangtao Guo,&nbsp;Yajie Li,&nbsp;Jia Shen,&nbsp;Tao Ming,&nbsp;Yuan Cao,&nbsp;Zuosong Dai","doi":"10.1002/eng2.70690","DOIUrl":"https://doi.org/10.1002/eng2.70690","url":null,"abstract":"<p>To address the issues of lengthy encryption time, low clustering accuracy, and poor performance in existing privacy-preserving clustering methods for grid data, this paper proposes a fault-tolerant data clustering method for smart grids based on the Boneh-Goh-Nissim (BGN) homomorphic encryption algorithm. A system architecture is constructed comprising a cloud server layer, a fog node layer, a smart meter layer, and a trusted third party. Private data collected by smart meters are first denoised using robust locally weighted regression. The preprocessed data are then encrypted with the BGN algorithm. K-means clustering is applied to mine valuable data, with decryption performed at the cloud server layer. A two-layer fault-tolerant mechanism—utilizing secure channels to trusted organizations—is implemented across both the cloud servers and smart meters to ensure robust privacy-preserving clustering. Experimental results in a scenario with 100 smart meters show the proposed method requires only 10 s for encryption, significantly less than conventional methods. Clustering performance is excellent: valid and invalid data are clearly distinguished, the deviation between actual and computed cluster centers is small, and clustering accuracy reaches 89.54%. Furthermore, by integrating a Shamir (3,5) threshold secret sharing scheme and a redundancy strategy for fog node data storage, the method maintains continuous operation and data integrity despite server failures or meter data loss. The data recovery rate exceeds 98%, with less than 4% loss in clustering accuracy. These results demonstrate the method achieves efficient encryption, high clustering accuracy, and strong fault tolerance, effectively enhancing private information security in smart grids.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of PWHT and Electrical Parameters on Austenitic Phase, Sigma Phase and Corrosion in Duplex Steels S32205","authors":"Sandra Chacón-Fernández,&nbsp;José L. Meseguer-Valdenebro,&nbsp;Andrea Peña Martín","doi":"10.1002/eng2.70654","DOIUrl":"https://doi.org/10.1002/eng2.70654","url":null,"abstract":"<p>This study investigates the influence of post-weld heat treatments (PWHT) at 450, 850°C and 1020°C on phase evolution and corrosion behavior in UNS S32205 duplex stainless steel welded with GMAW. The ferrite content decreased progressively with an increase in the temperature of PWHT, accompanied by the formation of secondary austenite. Treatment at 850°C produced the highest phase fractions in sigma (<i>σ</i>), phase fractions—up to ∼5% in the fusion zone (FZ)—which corresponded to the greatest mass loss during ASTM G48 pitting corrosion testing (0.35%–0.40%). At 1020°C, <i>σ</i>-phase dissolution resulted in substantially lower mass loss (&lt; 0.1%). A significant outcome of this work is the demonstration that a 1-h PWHT at 850°C applied to the base metal reproduces the microstructural characteristics of a welded heat-affected zone (HAZ), enabling its characterization on larger and more uniform specimens. These results confirm that <i>σ</i>-phase formation is governed exclusively by PWHT temperature.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"8 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}