Archives of Computational Methods in Engineering最新文献

{"title":"Analytical benchmark problems and methodological framework for the assessment and comparison of multifidelity optimization methods","authors":"Laura Mainini,&nbsp;Andrea Serani,&nbsp;Hayriye Pehlivan-Solak,&nbsp;Francesco Di Fiore,&nbsp;Markus P. Rumpfkeil,&nbsp;Edmondo Minisci,&nbsp;Domenico Quagliarella,&nbsp;Sihmehmet Yildiz,&nbsp;Simone Ficini,&nbsp;Riccardo Pellegrini,&nbsp;Andrew Thelen,&nbsp;Dean Bryson,&nbsp;Melike Nikbay,&nbsp;Matteo Diez,&nbsp;Philip S. Beran","doi":"10.1007/s11831-025-10392-8","DOIUrl":"10.1007/s11831-025-10392-8","url":null,"abstract":"<div><p>As engineering systems increase in complexity and performance demands intensify, Multidisciplinary Design Optimization (MDO) methodologies are becoming essential for integrating models from multiple disciplines to optimize complex multi-physics systems. Within this context, major challenges remain in selecting appropriate disciplinary fidelity levels, and how to couple them effectively. Multifidelity methods offer a promising path forward by strategically combining information sources of varying fidelity - whether computational or experimental - to enable efficient and scalable design exploration and optimization. Despite the development of numerous multifidelity methods, their comparative performance remains difficult to assess due to the absence of standardized benchmark frameworks capable of evaluating performance across diverse optimization tasks. To address this gap, this paper introduces a comprehensive benchmarking framework that includes: (i) a suite of analytical benchmark optimization problems designed to stress-test and validate multifidelity methods; (ii) a set of assessment metrics for quantifying and comparing performance over measurable objectives; and (iii) the classification, evaluation, and comparison of several families of multifidelity optimization methods and frameworks using the proposed benchmarks to identify their respective strengths and weaknesses in real-world scenarios. The proposed benchmark problems are analytically defined functions carefully selected to capture mathematical challenges commonly encountered in real-world applications, including high dimensionality, multimodality, discontinuities, and noise. Their closed-form nature ensures computational efficiency, high reproducibility, and a clear separation of algorithmic behavior from numerical artifacts. The accompanying performance metrics support the systematic evaluation of multifidelity methods, measuring both optimization effectiveness and global approximation accuracy. By providing a rigorous, reproducible, and accessible benchmarking framework, this work aims to enable the broader community to understand, compare, and advance multifidelity optimization methods for complex problems in science and engineering.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 2","pages":"2969 - 3000"},"PeriodicalIF":12.1,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-025-10392-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147559494","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}

{"title":"A Comprehensive Survey of Heart Disease Prediction Approaches: Methods, Applications, Performance Analysis, Datasets, Research Challenges, and Future Scopes","authors":"Subhash Mondal,&nbsp;Ranjan Maity,&nbsp;Amitava Nag","doi":"10.1007/s11831-025-10438-x","DOIUrl":"10.1007/s11831-025-10438-x","url":null,"abstract":"<div><p>Heart disease remains a leading cause of global mortality, accounting for nearly 17.9 million deaths annually. Major risk factors such as hypertension, hyperglycemia, and obesity enable early identification and preventive interventions through lifestyle modifications or medical treatment. Traditional diagnostic methods, including ECGs and coronary angiography, face limitations of inefficiency, invasiveness, or high cost, which points to the importance of non-invasive, reliable, and real-time predictive approaches. Machine learning (ML) and deep learning (DL) have transformed healthcare by enabling decision support systems that analyze clinical parameters for accurate heart disease prediction. This survey provides a comprehensive review of methods for predicting heart disease reported between 2019 and 2025, covering individual classifiers, ensemble techniques, feature selection methods, and state-of-the-art DL architectures. The study evaluates model performance, highlights the role of significant features, and discusses the integration of ML or DL in early detection. The study presents a systematic analysis of publicly available datasets and benchmark studies to guide researchers in model development. Furthermore, the review emphasizes existing research challenges, including target class data imbalance, model generalizability, overfitting, feature redundancy, interpretability, and clinical applicability, while presenting potential solutions and future directions. This study highlights advances in explainable AI to improve transparency and clinician trust. While ensemble and deep learning methods outperform traditional models, challenges such as class imbalance, dataset limitations, and interpretability remain. By consolidating progress across methods, applications, and datasets, this research supports the development of precise, interpreted, and effective frameworks for cardiac disease prediction.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 5","pages":"6341 - 6385"},"PeriodicalIF":12.9,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148389072","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":"Balancing Health, Sustainability, and Culture: A Review of Linear Programming Approaches to Diet Optimization","authors":"Ousman Khan,&nbsp;Haady Jallow,&nbsp;Mohammad A. M. Abdel-Aal,&nbsp;Essam Kaoud","doi":"10.1007/s11831-025-10457-8","DOIUrl":"10.1007/s11831-025-10457-8","url":null,"abstract":"<div><p>A healthy diet is essential for combating malnutrition and managing non-communicable diseases (NCDs), yet modern dietary patterns often contribute to environmental degradation. Globalized food systems tend to promote energy-dense, nutritionally poor diets while overlooking environmental concerns such as greenhouse gas emissions (GHGEs), water usage, and land use. Linear Programming (LP) offers a promising approach to diet optimization by balancing nutritional adequacy, cost-effectiveness, sustainability, and cultural acceptability. This review investigates the application of LP in designing sustainable and culturally appropriate diets. A systematic literature search following PRISMA guidelines was conducted using ScienceDirect and Scopus, targeting peer-reviewed articles published between 2010 and 2024. A total of 39 studies were included from an initial pool of 224 results. Common objectives included minimizing cost and GHGEs while enhancing environmental sustainability and promoting plant-based diets. Several studies also aimed to maintain cultural acceptability by minimizing deviations from existing dietary patterns. In addition, a scientometric analysis was performed to examine publication trends, leading journals, influential authors, citation patterns, and geographical distribution of research. This analysis provides insights into the evolution of the field and highlights region-specific contributions to the development of sustainable diet optimization models. Overall, the findings underscore LP’s potential to support dietary strategies that integrate health, affordability, environmental sustainability, and cultural relevance.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"5435 - 5457"},"PeriodicalIF":12.1,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959239","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 Comprehensive Survey on Runge Kutta Optimizer","authors":"Krishna Gopal Dhal,&nbsp;Arunita Das,&nbsp;Totan Bharasa,&nbsp;Buddhadev Sasmal,&nbsp;Ramesh Saha","doi":"10.1007/s11831-025-10432-3","DOIUrl":"10.1007/s11831-025-10432-3","url":null,"abstract":"<div><p>The Runge Kutta Optimizer (RUN) is a mathematics-based metaheuristic algorithm (MA) designed by utilizing the principles of slope variations calculated by the Runge Kutta (RK) technique as an effective and rational search mechanism for global optimization. RUN was developed in 2021 and quickly garnered recognition in the academic field for its strong efficiency and versatility. The algorithm is faster, more accurate at convergent operations, and better at solving problems overall, making it a strong competitor to existing MAs. This study offers a thorough overview of RUN, examining the different versions and variations published in several research papers since its beginning in 2021, with 94% published in reputable peer-reviewed journals and 6% in international conference proceedings. This study addresses variants of RUN, comprising 77% of the improved version of RUN, 14% of hybridization, 3% of binary, and 3% of multi-objective variants, respectively. Moreover, the applications of RUN demonstrate its efficacy and versatility across several domains, with 45% in power and control engineering, 25% in machine learning, 21% in benchmark functions and engineering design challenges, and 5% in feature selection. The algorithm has been extensively employed in the fields of image processing and cloud computing. This paper aims to provide a comprehensive review of RUN, examining its theoretical framework, analytical methods, enhancement tactics, and practical applications across many optimization domains. Furthermore, we also evaluate the RUN’s performance in partitional clustering for digital pathology image segmentation to demonstrate its efficiency. Experimental results show that the RUN-based clustering model produces the best segmentation results compared to the other five tested MA-based clustering models as per reference and ground truth-based quality metrics. The run-based model provides over 92% segmentation accuracy with the third-lowest execution time.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"4827 - 4866"},"PeriodicalIF":12.1,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959052","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":"Linear Viscoelasticity: Mechanics, Analysis and Approximation","authors":"Michael Ortiz","doi":"10.1007/s11831-025-10426-1","DOIUrl":"10.1007/s11831-025-10426-1","url":null,"abstract":"<div>\u0000 \u0000 <p>The aim of this review is to highlight the connection between well-established <i>physical</i> and <i>mathematical</i> principles as they pertain to the theory of linear viscoelasticity. We begin by examining the physical foundations of Boltzmann and Volterra’s hereditary law formalism, and how those principles restrict the form of the hereditary law. We then turn to questions of material stability and continuous dependence on the stress history within the framework of the Lax-Milgram theorem, which we find to set forth rigorous and unequivocal conditions for the well-posedness of the linear viscoelastic problem. The outcome of this analysis is remarkable in that it gives precise meaning to fundamental physical properties such as fading memory. Finally, we turn to the question of best representation of viscoelastic materials by finite-rank hereditary operators or, equivalently, by a finite set of history or internal variables. We note that the theory of Hilbert-Schmidt operators and <i>N</i>-widths supplies the answer to the question.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4133 - 4153"},"PeriodicalIF":12.1,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665732","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 Comprehensive Overview of PSO-LSTM Approaches: Applications, Analytical Insights, and Future Opportunities","authors":"Mehdi Hosseinzadeh,&nbsp;Jawad Tanveer,&nbsp;Amir Masoud Rahmani,&nbsp;Farhad Soleimanian Gharehchopogh,&nbsp;Norfadzlia Mohd Yusof,&nbsp;Parisa Khoshvaght,&nbsp;Zhe Liu,&nbsp;Thantrira Porntaveetus,&nbsp;Sang-Woong Lee","doi":"10.1007/s11831-025-10445-y","DOIUrl":"10.1007/s11831-025-10445-y","url":null,"abstract":"<div>\u0000 \u0000 <p>The combination of particle swarm optimization algorithm with long short-term memory has led to new horizons in data analysis and solving complex problems. The particle swarm optimization has been used to solve complex and hard optimization problems since 1995 and has gained extraordinary popularity among researchers. The combination of particle swarm optimization-long short-term memory is carried out in order to simultaneously utilize the global search capability and convergence ability of the particle swarm optimization to optimize long short-term memory in modeling long-term temporal dependencies. The goal of the particle swarm optimization-long short-term memory model is to increase the prediction accuracy and reduce the error in solving complex and dynamic real-world problems. In this paper, a comprehensive and structured look at the scientific literature of particle swarm optimization-long short-term memory models between 2019 and June 2025 has been conducted. By categorizing the articles according to the publication date and place of publication, it was found that prominent publishers such as MDPI, Springer, Elsevier, and IEEE played a major role in the publication of particle swarm optimization-long short-term memory models in 2024. This paper aims to provide a clear overview of the potential applications of particle swarm optimization-long short-term memory in various domains. particle swarm optimization-long short-term memory has been widely used in engineering systems design, time series forecasting, and the oil and gas industry. This paper analyses the strengths and weaknesses, as well as the challenges of complexity in hybrid architectures, and issues related to scalability and optimization. Finally, future directions are proposed with an emphasis on performance enhancement and development of adaptable solutions for real-world problems.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"5081 - 5126"},"PeriodicalIF":12.1,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959150","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":"From Algorithms to Connectivity: A Comprehensive Review of Traffic Signal Optimization and Communication Based Cooperative Control","authors":"Viral Patel,&nbsp;Nilesh Maltare","doi":"10.1007/s11831-025-10455-w","DOIUrl":"10.1007/s11831-025-10455-w","url":null,"abstract":"<div><p>Rapid urbanization and the exponential growth of vehicular traffic have intensified congestion, travel delays, and emissions, posing serious challenges for sustainable urban mobility. To address these issues, numerous studies have explored both algorithmic and communication-based traffic management approaches. However, existing reviews often treat these domains separately, lacking a unified perspective. This paper presents a comprehensive review that bridges algorithmic optimization and communication-enabled cooperative control. The study systematically categorizes traffic signal optimization algorithms including fixed time, actuated, adaptive, fuzzy logic, genetic, reinforcement learning and game theory based methods and communication-driven strategies such as Vehicle-to-Vehicle (V2V) &amp; Vehicle-to-Infrastructure (V2I), IoV / RSU / Edge / Fog / SDN, CAV Coordination &amp; Reservation Systems, AI/ML + Communication Fusion and Safety &amp; Perception Enhancement. A PRISMA process has been followed for the selection of the papers and selected papers were analyzed to evaluate performance metrics, limitations, and research trends. The findings reveal that adaptive control and reinforcement learning, particularly deep and multi-agent RL models, dominate algorithmic research, while CAV coordination and IoV frameworks are emerging as key communication paradigms. Persistent challenges include scalability, penetration rate, real-time responsiveness, and limited real-world validation. The paper’s unique contribution lies in synthesizing these two research streams to propose a hybrid, future ready architecture that integrates local adaptive intelligence with global, communication-based coordination providing a strategic roadmap toward sustainable, intelligent traffic systems.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"5369 - 5399"},"PeriodicalIF":12.1,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959151","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":"Optimizing Wind Energy Integration: A Review of Forecasting Techniques and Emerging Trends","authors":"Jaisiva Selvaraj,&nbsp;Lakshmanan Muthuramalingam,&nbsp;Viji Karthikeyan,&nbsp;Alagar Karthick,&nbsp;Vasanthaseelan Sathiyaseelan","doi":"10.1007/s11831-025-10442-1","DOIUrl":"10.1007/s11831-025-10442-1","url":null,"abstract":"<div><p>Rapid growth in wind energy highlights the need for accurate forecasting to optimize generation and grid integration. This review analyzes current wind power prediction models, covering their methodologies, strengths, and limitations to guide researchers, engineers, and policymakers. It begins with Numerical Weather Prediction (NWP) models, which are essential yet limited by challenges in complex terrains and localized events. In response, machine learning techniques—such as artificial neural networks, support vector regression, and random forests—have gained prominence for improving forecast accuracy. Advanced methods like bootstrapping and Bayesian model averaging enhance probabilistic forecasts by quantifying uncertainty. The integration of LIDAR and satellite data has further refined wind resource assessment and forecasting accuracy. This review explores the impact of remote sensing and forecasting on decision-making for wind farm operators and grid managers, while also addressing challenges posed by climate change, extreme weather, and the evolution of smart grids. Scenario analysis and grid optimization strategies are discussed, and the review concludes by evaluating current successes and identifying future research needs—emphasizing interdisciplinary collaboration and data sharing. Notably, machine learning improved wind power prediction accuracy by 15% over traditional models. The GFS model achieved an MAE of 0.45 MW and an RMSE of 0.60 MW, demonstrating strong performance in wind energy forecasting.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 3","pages":"4261 - 4286"},"PeriodicalIF":12.1,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665631","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":"Recent Studies on Multiscale Modeling of Natural Fiber-Reinforced Composites","authors":"Gaurav Arora,&nbsp;Harshit Sharma,&nbsp;Papiya Bhowmik,&nbsp;Manoj Kumar Singh,&nbsp;Vinod Ayyappan,&nbsp;Sanjay Mavinkere Rangappa,&nbsp;Suchart Siengchin","doi":"10.1007/s11831-025-10454-x","DOIUrl":"10.1007/s11831-025-10454-x","url":null,"abstract":"<div><p>Multiscale modeling has emerged as a powerful tool for analyzing the hierarchical architecture and performance of natural fiber-reinforced composites (NFRCs) across molecular, micro, meso, and macro scales. Given the growing demand for sustainable and biodegradable materials, natural fibers are increasingly replacing synthetic reinforcements. However, challenges such as poor fiber-matrix compatibility, environmental sensitivity, and modeling complexity still hinder their broader adoption. This review systematically examines state-of-the-art multiscale modeling approaches, including finite element analysis (FEA), molecular dynamics (MD), stochastic modeling, and machine learning (ML) integration, applied to NFRCs. Recent trends such as AI-assisted surrogate modeling and uncertainty quantification are highlighted. A critical synthesis of studies reveals that, while significant progress has been made in structural simulation and durability prediction, gaps persist in modeling moisture-induced degradation, standardizing representative volume element (RVE) methods, and analyzing long-term viscoelastic behavior. This review proposes a structured classification of modeling strategies and identifies emerging pathways that combine data-driven and physics-based approaches for designing high-performance, eco-friendly NFRCs.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture><span>The alternative text for this image may have been generated using AI.</span></div></div></figure></div></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"5339 - 5367"},"PeriodicalIF":12.1,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959242","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":"In-Depth Analysis of Meta-Learning in Cancer Disease: Key Challenges and Recommendations","authors":"Shuwen Li,&nbsp;Mohsen Ghorbian,&nbsp;Mostafa Ghobaei-Arani","doi":"10.1007/s11831-025-10452-z","DOIUrl":"10.1007/s11831-025-10452-z","url":null,"abstract":"<div>\u0000 \u0000 <p>Cancer ranks among the top causes of mortality worldwide, and its complex biological processes present significant hurdles for effective detection and management. Recently, meta-learning methods have been introduced as innovative approaches to refining cancer treatment and diagnosis. With the capacity to learn from sparse data and transfer knowledge across domains, these techniques are promising for enhancing diagnostic precision and therapeutic efficacy. Nevertheless, the application of meta-learning in cancer care raises several issues. Among these challenges, we can mention data limitations, methodological complexity, and the need for advanced expertise to ensure proper deployment. This article examines the application and issues of meta-learning methods in cancer research. This survey paper seeks to explore and evaluate the most recent advances in using meta-learning to diagnose and treat common cancers, including breast, skin, lung, prostate, and hybrid cancers, and to offer a holistic perspective on the opportunities and challenges of this approach. The findings of this study show that meta-learning can significantly enhance diagnostic precision and the efficiency of cancer treatment. The accuracy of diagnosis has been enhanced by up to 32%, and treatment outcomes have improved by up to 27%. Ultimately, by analyzing the prevailing challenges and suggesting solutions, this study will assist researchers and specialists in formulating novel strategies to enhance cancer treatment and diagnosis through a deeper understanding of meta-learning applications.</p>\u0000 </div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"33 4","pages":"5261 - 5291"},"PeriodicalIF":12.1,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959042","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}