Archives of Computational Methods in Engineering最新文献

From Data to Diagnosis: A Systematic Review of EEG Databases for Epilepsy 从数据到诊断：癫痫脑电图数据库的系统回顾

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-02-26 DOI: 10.1007/s11831-026-10521-x

Sergio Cadena-Flores, Jose Hugo Barron-Zambrano, Juan Manuel Torres-Arce, Jose de Jesus Rangel-Magdaleno, Marco Aurelio Nuño-Maganda

{"title":"From Data to Diagnosis: A Systematic Review of EEG Databases for Epilepsy","authors":"Sergio Cadena-Flores, Jose Hugo Barron-Zambrano, Juan Manuel Torres-Arce, Jose de Jesus Rangel-Magdaleno, Marco Aurelio Nuño-Maganda","doi":"10.1007/s11831-026-10521-x","DOIUrl":"10.1007/s11831-026-10521-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Epilepsy remains a high-burden neurological disorder across the lifespan, with a disproportionate impact in low- and middle-income regions where specialist care and timely diagnosis are often constrained. Electroencephalography (EEG) is the most widely available, noninvasive signal source for seizure evaluation, yet the reliability of automated detection and prediction depends less on model novelty than on the quality, structure, and accessibility of the underlying data. In this systematic review, we identified 26 EEG databases explicitly used for epilepsy research, comprising eight open access resources and 18 restricted repositories that require institutional approval, ethics clearance, or data-use agreements. Following a PRISMA-inspired selection workflow, we compared data sets in terms of cohort characteristics, electrode configurations, acquisition procedures, sampling rates, recording duration, and practical access routes to make the landscape auditable and actionable. Across repositories, documentation depth, data organization, and access mechanisms vary sharply, which weakens reproducibility and complicates cross-study benchmarking even when reported performance is high under controlled or subject-dependent protocols. To keep comparisons clinically and methodologically defensible, we propose a hierarchical taxonomy that organizes databases by access regime, recording modality, population focus, and repository scope, clarifying which resources support fair benchmarking and which primarily enable clinically realistic validation, biomarker-driven analyses, prediction, or long-term implantable monitoring.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4513 - 4531"},"PeriodicalIF":12.1,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665596","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}

引用次数: 0

Review of Hybrid and Data-Efficient Methods in Medical Image Segmentation 混合数据高效医学图像分割方法综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-02-25 DOI: 10.1007/s11831-026-10514-w

Abhishek Kr. Dubey, Vinod Kumar Singh, Kanhaiya Sharma, Anjali Dubey, Zakir Ali, Aparna Singh, Sankalp Yadav

{"title":"Review of Hybrid and Data-Efficient Methods in Medical Image Segmentation","authors":"Abhishek Kr. Dubey, Vinod Kumar Singh, Kanhaiya Sharma, Anjali Dubey, Zakir Ali, Aparna Singh, Sankalp Yadav","doi":"10.1007/s11831-026-10514-w","DOIUrl":"10.1007/s11831-026-10514-w","url":null,"abstract":"<div>\u0000 \u0000 <p>Segmentation of medical images serves as the indispensable cornerstone of modern diagnostic and therapeutic frameworks, providing the precision required to delineate complex anatomical structures and pathological entities across modalities such as MRI, CT, and ultrasound. The advent of Deep Learning (DL) has fundamentally shifted this landscape, with Convolutional Neural Networks (CNNs) and Transformer architectures achieving unprecedented accuracy. However, the transition from algorithmic innovation to clinical translation is hindered by the “Medical Data Triad” of scarcity, heterogeneity, and opacity. This review presents a comprehensive taxonomic synthesis of the state-of-the-art in “Hybrid Intelligence”—defined here as the synergistic integration of localized inductive biases from CNNs with the global relational modelling of Transformers—to overcome these persistent barriers. Unlike prior surveys, this work provides a critical meta-analysis of “Data-Efficient” methods, including Self-Supervised Learning (SSL), Federated DL, and synthetic data generation via Diffusion models, specifically evaluating their efficacy in mitigating data scarcity while preserving patient privacy. We perform a rigorous cross-comparative assessment of benchmark performance (e.g. BraTS, Synapse, ACDC) against operational constraints such as computational complexity and real-time clinical feasibility. Finally, we bridge the “Technical-Clinical Divide” by mapping regulatory pathways (FDA/EMA) and identifying implementation science gaps, offering a strategic roadmap for the next generation of AI-enhanced, interpretable medical imaging systems.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4483 - 4500"},"PeriodicalIF":12.1,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665595","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}

引用次数: 0

Implicit and Explicit Treatments of Model Error in Numerical Simulation 数值模拟中模型误差的隐式和显式处理

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-02-25 DOI: 10.1007/s11831-026-10522-w

Danny Smyl

{"title":"Implicit and Explicit Treatments of Model Error in Numerical Simulation","authors":"Danny Smyl","doi":"10.1007/s11831-026-10522-w","DOIUrl":"10.1007/s11831-026-10522-w","url":null,"abstract":"<div>\u0000 \u0000 <p>Numerical simulations of physical systems exhibit discrepancies arising from unmodeled physics and idealizations, as well as numerical approximation errors stemming from discretization and solver tolerances. This article reviews techniques developed in the past several decades to approximate and account for model errors, both implicitly and explicitly. Beginning from fundamentals, we frame model error in inverse problems, data assimilation, and predictive modeling contexts. We then survey major approaches: the Bayesian approximation error framework, embedded internal error models for structural uncertainty, probabilistic numerical methods for discretization uncertainty, model discrepancy modeling in Bayesian calibration and its recent extensions, machine-learning-based discrepancy correction, multi-fidelity and hybrid modeling strategies, as well as residual-based, variational, and adjoint-driven error estimators. Throughout, we emphasize the conceptual underpinnings of implicit versus explicit error treatment and highlight how these methods improve predictive performance and uncertainty quantification in practical applications ranging from engineering design to Earth-system science. Each section provides an overview of key developments with an extensive list of references to facilitate further reading. The review is written for practitioners of large-scale computational physics and engineering simulation, emphasizing how these methods can be incorporated into PDE solvers, inverse problem workflows, and data assimilation systems.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4501 - 4512"},"PeriodicalIF":12.1,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-026-10522-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cognitive Digital Twins for Pavement Management: A Comprehensive Review 路面管理的认知数字孪生：综合综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-02-02 DOI: 10.1007/s11831-026-10503-z

Arya Daneshvar, Amir Golroo, Fereidoon Moghadas Nejad, Atena Karbalaei, Pouriya Hosseini, Mehdi Rasti

{"title":"Cognitive Digital Twins for Pavement Management: A Comprehensive Review","authors":"Arya Daneshvar, Amir Golroo, Fereidoon Moghadas Nejad, Atena Karbalaei, Pouriya Hosseini, Mehdi Rasti","doi":"10.1007/s11831-026-10503-z","DOIUrl":"10.1007/s11831-026-10503-z","url":null,"abstract":"<div><p>Cognitive Digital Twins (CDTs) offer a transformative approach to road pavement management by integrating real-time data, advanced modeling, and artificial intelligence for monitoring, maintenance, and performance prediction. This review synthesizes insights from over 100 studies and provides a comprehensive examination of the technological foundations, applications, and ecosystem required for CDT deployment in pavement systems. Unlike existing Digital Twin (DT) reviews that focus primarily on general infrastructure or isolated technical components to date, this study situates CDTs within a broader system-of-systems context; linking pavement management to smart cities, Industry 4.0/5.0 paradigms, environmental sustainability, and data-governance challenges. In addition to summarizing state-of-the-art CDT architectures and Artificial Intelligence (AI)-driven analytics, the review evaluates how CDTs interact with wider urban infrastructure ecosystems, including Internet of Things (IoT) networks, Building Information Modeling (BIM) integration, real-time sensing, and multi-level decision-making frameworks. It further highlights cross-cutting issues seldom addressed in prior reviews, such as data security, cyber-physical vulnerabilities, privacy preservation, and the environmental implications of digitalization. Key barriers are examined alongside critical research gaps such as limited long-term validation, insufficient sustainability incorporation, and the lack of holistic deployment frameworks. Future directions emphasize the need for standardized CDT architectures, advanced computational models, secure data infrastructures, and sustainability-driven performance metrics. Overall, this review positions CDTs not merely as a technical innovation but as a pivotal enabler in the transition toward resilient, sustainable, and intelligently managed smart cities.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4463 - 4482"},"PeriodicalIF":12.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665594","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}

引用次数: 0

A Systematic Review of Machine Learning Techniques for Predicting Compressive and Flexural Strength of Mortars 预测砂浆抗压和弯曲强度的机器学习技术系统综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-01-30 DOI: 10.1007/s11831-026-10507-9

Marinara Andrade do Nascimento Moura, Gisleiva Cristina dos Santos Ferreira, Armando Lopes Moreno Júnior

{"title":"A Systematic Review of Machine Learning Techniques for Predicting Compressive and Flexural Strength of Mortars","authors":"Marinara Andrade do Nascimento Moura, Gisleiva Cristina dos Santos Ferreira, Armando Lopes Moreno Júnior","doi":"10.1007/s11831-026-10507-9","DOIUrl":"10.1007/s11831-026-10507-9","url":null,"abstract":"<div>\u0000 \u0000 <p>Mortars are among the most used materials in civil construction, and their mechanical properties are directly related to several parameters, namely structural behavior, durability, and failure mechanisms. As a result, predicting these parameters is essential for preventive and corrective diagnosis of structures composed of this material. Recently, machine learning models have been applied to improve these predictions. Until recently, such predictions were based on traditional empirical procedures, which are unable to capture the variability of material parameters. Nevertheless, as this is still an emerging research topic, many limitations and gaps remain to be investigated, such as the dependence on data structure and the appropriate selection of hyperparameters. Furthermore, most studies applying machine learning to mortars focus on additions, admixtures, or durability- and performance-related aspects, rather than on assessing the efficiency and robustness of the models themselves. In this context, this systematic and critical review aims to identify how machine learning models have been applied to predict the mechanical properties of different mortars. The main input parameters used for this prediction are discussed, and the hyperparameters of several machine learning models are critically analyzed and discussed. The results indicate that tree-based ensemble models perform better than other approaches, with XGBoost showing superior predictive performance.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4441 - 4462"},"PeriodicalIF":12.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-026-10507-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Intelligent Optimization Algorithms for Unmanned Aerial Vehicle-Assisted Mobile Edge Computing 无人机辅助移动边缘计算智能优化算法综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-01-14 DOI: 10.1007/s11831-026-10496-9

Haibin Ouyang, Junlin Liu, Leisen Liang, Jinglin Wang, Steven Li, Jiewu Leng, Weiping Ding

{"title":"A Survey of Intelligent Optimization Algorithms for Unmanned Aerial Vehicle-Assisted Mobile Edge Computing","authors":"Haibin Ouyang, Junlin Liu, Leisen Liang, Jinglin Wang, Steven Li, Jiewu Leng, Weiping Ding","doi":"10.1007/s11831-026-10496-9","DOIUrl":"10.1007/s11831-026-10496-9","url":null,"abstract":"<div><p>Mobile Edge computing (MEC) can effectively improve the computing efficiency and storage capacity of edge networks, reduce delay and improve user experience. Moreover, unmanned aerial vehicle (UAV)-assisted mobile edge computing has the characteristics of strong flexibility and easy deployment, which can provide high-quality services for mobile users more efficiently. However, problems such as task allocation, UAV deployment and limited energy consumption of UAVs are the main challenges faced by current research. In recent years, researchers at home and abroad have carried out a lot of research and analysis on this. In order to facilitate the research of scholars, this paper summarizes and sorts out the literature in this field, and points out some directions that can be further studied and developed, and is committed to promoting the development of edge computing. Firstly, this paper introduces the models of UAVs assisted mobile edge computing, including the local execution model, MEC execution model, and UAV hovering model. Secondly, it focuses on the related research of UAVs assisted mobile edge computing based on intelligent optimization algorithms, and expounds the advantages and disadvantages of different types of intelligent optimization algorithms in mobile edge computing. The optimization efficiency of several commonly used intelligent algorithms is compared through specific examples. Finally, the future development trend of UAV-assisted mobile edge computing based on intelligent optimization algorithm is analyzed and prospected.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4419 - 4440"},"PeriodicalIF":12.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665680","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}

引用次数: 0

Correction: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review 修正：生物医学疾病分类混合机器学习模型的进展，使用超参数调整和特征选择方法的集成：全面回顾

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2026-01-13 DOI: 10.1007/s11831-025-10491-6

Sanjay Dhanka, Abhinav Sharma, Ankur Kumar, Surita Maini, Haswanth Vundavilli

引用次数: 0

A Comparison of CNN, RNN, and FNN Algorithms to Investigate Effective Diabetes Prediction 比较CNN、RNN和FNN算法对糖尿病的有效预测

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2025-12-02 DOI: 10.1007/s11831-025-10467-6

B. P. Pradeep Kumar

{"title":"A Comparison of CNN, RNN, and FNN Algorithms to Investigate Effective Diabetes Prediction","authors":"B. P. Pradeep Kumar","doi":"10.1007/s11831-025-10467-6","DOIUrl":"10.1007/s11831-025-10467-6","url":null,"abstract":"<div><p>Diabetes is a major global health issue that requires efficient early detection and prediction technology. Predictive analytics and healthcare are explored in this paper, with a particular emphasis on diabetes prediction. It looks at the use of machine learning techniques, primarily using deep learning neural networks as the main focus. Developing reliable and effective models is a crucial problem for diabetes prediction. To address this, the study investigates a range of deep learning neural networks, including Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN). These are the only models that can predict diabetes reliably based on many parameters such as body mass index (BMI), insulin levels, age, blood pressure, glucose levels, pregnancies, skin thickness, and diabetes pedigree function. Our extensive investigation’s findings indicated that the RNN model had the highest accuracy at 96.7%, while the FNN model placed in secondly at 95.6%. Both models demonstrated strong metrics for accuracy, recall, and F1-score, demonstrating their effectiveness in accurately forecasting the prevalence of diabetes. The accuracy rates of the CNN algorithm were 93.3% and 94.4%, respectively, showing impressive performance. The CNN model showed the fastest prediction time, at 0.107 s, whereas the FNN model showed the fastest prediction time, at 0.087 s, when we evaluated the prediction time efficiency of these models. Conversely, at 0.282 s. These findings demonstrate how well deep learning models—in particular, RNN and FNN—can predict the occurrence of diabetes. Early diagnostic and management strategies in clinical and medical settings may be significantly impacted by this.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4397 - 4417"},"PeriodicalIF":12.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665778","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}

引用次数: 0

A Comprehensive Review of Deep Learning Approaches for Automated Detection, Segmentation, and Grading of Diabetic Retinopathy 糖尿病视网膜病变自动检测、分割和分级的深度学习方法综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2025-12-02 DOI: 10.1007/s11831-025-10460-z

Varsha Bhoyar, Mitul Patel

{"title":"A Comprehensive Review of Deep Learning Approaches for Automated Detection, Segmentation, and Grading of Diabetic Retinopathy","authors":"Varsha Bhoyar, Mitul Patel","doi":"10.1007/s11831-025-10460-z","DOIUrl":"10.1007/s11831-025-10460-z","url":null,"abstract":"<div>\u0000 \u0000 <p>One of the major causes of visual impairment and blindness in the world among patients affected with diabetes is described as Diabetic Retinopathy (DR). It now needs to be combined with early diagnosis; however, manual screening is time-consuming, incomplete, and constrained by the availability of retina specialists, particularly in low-resource settings. The results of this review provide an overview analysis of the deep learning approaches of automated DR segmentation and classification on retinal fundus images, both traditional machine learning frameworks and the emerging models, modern architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), transformer-based models, attention layer, and ensemble models. This paper emphasizes the significance of preprocessing techniques, including CLAHE, green channel extraction, and image normalization, in enhancing lesion visibility and improving the quality of inputs for training the model. Nevertheless, the complexities lie in managing the imbalance problem in the dataset, differences in lesion types, the lack of generalizability, limitations of interpretation, and the high computational costs. Recent advances, including Federated Learning, Multimodal Data Fusion, lightweight models (such as EfficientNet-Lite), and Explainable AI, offer potential for improvement. The focus of future studies should be on a privacy-preserving, interpretable, and real-time DR detection system, which is studied and tested in a clinical trial.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4359 - 4380"},"PeriodicalIF":12.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665702","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}

引用次数: 0

An Elaborative Review on RHBD Techniques for Memory Structures 记忆结构的RHBD技术综述

IF 12.1 2区工程技术

Archives of Computational Methods in Engineering Pub Date : 2025-11-28 DOI: 10.1007/s11831-025-10464-9

S. Jamuna, Sourabh Konkala, Maria L. N. Dayana, Kishore K. Kumar, Hannan Ashrafi, Suraj Malagar, T. Mohammad Nisar, R. Madhura

{"title":"An Elaborative Review on RHBD Techniques for Memory Structures","authors":"S. Jamuna, Sourabh Konkala, Maria L. N. Dayana, Kishore K. Kumar, Hannan Ashrafi, Suraj Malagar, T. Mohammad Nisar, R. Madhura","doi":"10.1007/s11831-025-10464-9","DOIUrl":"10.1007/s11831-025-10464-9","url":null,"abstract":"<div>\u0000 \u0000 <p>Memory ICs are vital components in almost all computing systems, where reliability directly influences overall performance. Fault resilience is particularly critical in mission-oriented applications such as defense and space missions. The harsh radiation environment in space, dominated by high-energy particles from solar and cosmic sources, can induce single-event upsets (SEUs) and multiple-node upsets, leading to soft errors and potential system failures. This paper elaborates on the papers which have addressed the space related design challenges, various radiation-hardened-by-design (RHBD) techniques and balancing trade-offs among area, power, and delay. It also provides a comprehensive analysis of radiation effects and mitigation techniques for both volatile and non-volatile memory architectures. It integrates insights from SPICE, TCAD, and FPGA-based simulations to highlight design trade-offs and guide the development of low-power, radiation-tolerant memory systems for future space-grade applications. Recent innovations such as read-decoupled DICE SRAM cells achieve up to 72% reduction in read energy and 67% faster read delay, while transistor-level hardened designs like the RHRSE-20T SRAM demonstrate 255% improvement in read stability and 30% faster access. Hybrid MTJ-CMOS latches and emerging non-volatile memories, including ReRAM and spintronic architectures, further enhance resilience through intrinsic radiation immunity and self-recovery mechanisms.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4381 - 4396"},"PeriodicalIF":12.1,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665777","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}

引用次数: 0