Engineering reports : open access最新文献

{"title":"RFID-Based Enhanced Resource Optimization for 5G/6G Network Applications","authors":"Stella N. Arinze,&nbsp;Augustine O. Nwajana","doi":"10.1002/eng2.70218","DOIUrl":"https://doi.org/10.1002/eng2.70218","url":null,"abstract":"<p>In the rapidly evolving landscape of 5G/6G networks, efficient resource optimization is critical to addressing the escalating demands for high-speed, low-latency, and energy-efficient communication. This study explores the integration of Radio Frequency Identification (RFID) technology as a novel approach to enhance resource management in 5G networks. The motivation behind this research lies in overcoming persistent challenges such as spectrum congestion, high latency, and inefficient load balancing, which impede the performance of traditional resource allocation methods. To achieve this, RFID tags were embedded in critical network components, including user devices, base stations, and Internet of Things (IoT) nodes, enabling the collection of real-time data on device status, location, and resource utilization. RFID readers strategically placed across the network continuously captured this data, which was processed by a centralized controller using a custom-designed optimization algorithm. This algorithm dynamically managed key network resources, including spectrum allocation, load balancing, and energy consumption, ensuring efficient operation under varying network conditions. Simulations were conducted to evaluate the performance of the RFID-based model against traditional 4G dynamic resource allocation techniques. The results demonstrated substantial improvements in key performance metrics. The proposed system achieved a 25% increase in spectrum utilization, a 30% reduction in average latency, a 15% boost in network throughput, and a 20% decrease in overall energy consumption. These gains highlight the effectiveness of the RFID-based optimization model in meeting the stringent performance requirements of 5G networks, particularly in high-density deployments. This study provides a scalable, cost-effective solution for optimizing resource management in 5G and lays the groundwork for future advancements in 6G networks. By leveraging real-time data and intelligent resource allocation, the proposed model addresses critical challenges in modern communication systems, ensuring enhanced network efficiency, reliability, and sustainability.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171963","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":"Advanced Thermal Optimization of Solar Stills Using Encapsulated Phase Change Materials and Graphene Oxide Nanoparticles for Enhanced Energy Efficiency","authors":"T. Sathish,&nbsp;R. Saravanan,&nbsp;Jayant Giri,&nbsp;Ahmad O. Hourani,&nbsp;Nidhal Becheikh,&nbsp;Boutheyna Belhaj Bettaieb,&nbsp;A. Johnson Santhosh,&nbsp;A. Anderson","doi":"10.1002/eng2.70186","DOIUrl":"https://doi.org/10.1002/eng2.70186","url":null,"abstract":"<p>Solar desalination is a widely employed system that harnesses solar energy to produce drinking water. The key advantages of solar stills include that the generation of freshwater is a renewable energy-based, eco-friendly, and cost-effective process. This sustainable approach addresses the critical need for clean water while minimizing ecological impact and offering economic benefits. In the solar still system, the configuration of the absorber plays a crucial role, as an ineffective absorber can lead to lower thermal performance and reduced water productivity. This investigation focuses on an absorber design that incorporates a tube container containing Phase Change Material (PCM) of paraffin wax. The encapsulation of PCM within the still enhances heat transfer and provides heat energy, especially during radiation fluctuations. Moreover, the thermal properties of the PCM were improved by introducing graphene oxide nanoparticles dispersed within. Three different concentrations of graphene oxide (0.3 wt%, 0.6 wt%, and 0.9 wt%) were investigated. It was explored that paraffin with 0.9 wt% graphene oxide nanoparticle demonstrates superior thermal performance compared to paraffin alone. Significantly, at a concentration of 0.9 wt%, the paraffin/graphene oxide nanoparticles showed increased water productivity, temperature, and still thermal efficiency by around 33.9%, 41.2%, and 68.7%, respectively.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171584","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":"Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases","authors":"Abdul Karim,&nbsp;Touqeer Ahmed Jumani,&nbsp;Muhammad Amir Raza,&nbsp;Shadi Khan Baloch,&nbsp;Nouman Qadeer Soomro,&nbsp;Muhammad Masud,&nbsp;Muhammad Salman Saeed,&nbsp;Mouaaz Nahas,&nbsp;Aamir Ali Patoli","doi":"10.1002/eng2.70206","DOIUrl":"https://doi.org/10.1002/eng2.70206","url":null,"abstract":"<p>This research proposed a new hybrid system for the detection of diseases in Pepper, Tomatoes, and Potatoes vegetable crops. Vegetable plant disease poses a significant threat to both the quality and quantity of crop yields, leading to enormous economic losses in the agricultural industry. Early detection and prompt measures are essential for the effective control of plant diseases. Extreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN) algorithm were employed to detect diseases precisely. CNN derived intricate patterns from images, whereas the XGBoost algorithm is employed for precise categorization. The model is built for Pepper, Tomatoes, and Potatoes diseases and also for plant type classification using the dataset which was captured by mobile phone camera from Kotri and Unerpur, Sindh, Pakistan with accuracy of 93%, 92%, 99%, and 98%, respectively. Detection of new diseases which were not detected earlier is another primary aim of this research. For the detection of the unknown kind of diseases in plants, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was utilized for clustering the similar captured images and for disease classification; then CNN XGBoost was applied on the newly developed dataset. This study employed a new automated hybrid framework that combines the strengths of CNN and XGBoost to identify familiar and unfamiliar diseases in vegetable crops.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171585","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":"Vision-Language Models for Automated Chest X-ray Interpretation: Leveraging ViT and GPT-2","authors":"Md. Rakibul Islam,&nbsp;Md. Zahid Hossain,&nbsp;Mustofa Ahmed,&nbsp;Most. Sharmin Sultana Samu","doi":"10.1002/eng2.70220","DOIUrl":"https://doi.org/10.1002/eng2.70220","url":null,"abstract":"<p>Radiology plays a pivotal role in modern medicine due to its non-invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time-consuming and prone to errors. It creates a significant bottleneck in clinical workflows. Despite advancements in AI-generated radiology reports, challenges remain in achieving detailed and accurate report generation. In this study, we have evaluated different combinations of multimodal models that integrate Computer Vision and Natural Language Processing to generate comprehensive radiology reports. We employed a pretrained Vision Transformer (ViT-B16) and a SWIN Transformer as the image encoders. The BART and GPT-2 models serve as the textual decoders. We used Chest X-ray images and reports from the IU-Xray dataset to evaluate the usability of the SWIN Transformer-BART, SWIN Transformer-GPT-2, ViT-B16-BART, and ViT-B16-GPT-2 models for report generation. We aimed to find the best combination among the models. The SWIN-BART model performs as the best-performing model among the four models, achieving remarkable results in almost all the evaluation metrics like ROUGE, BLEU, and BERTScore.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171586","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 of Modeling and Control Techniques for Unmanned Aerial Vehicles","authors":"Elisabeth Andarge Gedefaw,&nbsp;Nardos Belay Abera,&nbsp;Chala Merga Abdissa","doi":"10.1002/eng2.70215","DOIUrl":"https://doi.org/10.1002/eng2.70215","url":null,"abstract":"<p>Unmanned Aerial Vehicles (UAVs), particularly quadcopters, have found growing applications across diverse sectors such as surveillance, precision agriculture, and transport. However, their nonlinear dynamics, underactuated systems, and sensitivity to disturbances present persistent challenges in achieving robust and autonomous control. This review systematically examines advancements in UAV modeling and control techniques over the past five years. The study evaluates key modeling frameworks, Newton–Euler, Newton–Quaternion, and Geometry-Based Stochastic Models (GBSM), and analyzes a spectrum of control strategies, including observer-based, sliding mode, H-infinity, model predictive, and neural network-based controllers. Through a comparative assessment of their robustness, computational efficiency, and adaptability, the manuscript identifies critical limitations in handling uncertainties, scalability in UAV systems, and energy constraints. The findings highlight that hybrid control strategies incorporating adaptive mechanisms, learning-based algorithms, and quaternion-based modeling offer significant potential for enhancing autonomy and control. Therefore, this review provides a foundational roadmap for researchers and practitioners aiming to develop intelligent, efficient, and scalable UAV control systems capable of thriving in dynamic operational environments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171592","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":"Advanced Poly-Fiber Hybrid-Nanocomposites: Fabrication and Strengthening With Silicon Carbide Integration","authors":"Solairaju Jothi Arunachalam,&nbsp;Rathinasamy Saravanan,&nbsp;T. Sathish,&nbsp;Rebwar Nasir Dara,&nbsp;Mustafa Abdullah,&nbsp;Eman Ramadan Elsharkawy,&nbsp;Ankur Bahl,&nbsp;A. Johnson Santhosh","doi":"10.1002/eng2.70217","DOIUrl":"https://doi.org/10.1002/eng2.70217","url":null,"abstract":"<p>This study examines the mechanical and thermal properties of materials made from jute, glass, and kenaf fibers reinforced with various weight percentages of silicon carbide (SiC). The composites were manufactured with different SiC loadings, and their tensile strength, flexural strength, fracture toughness, moisture absorption, and thermal stability were evaluated. Tensile and flexural examinations were conducted to assess the structural integrity of the laminate under stress, revealing that the incorporation of 3% SiC led to a 27% improvement in tensile strength and an 18% increase in flexural strength, indicating enhanced load-bearing capacity and flexibility. Microhardness and fracture toughness were also measured to determine resistance to crack propagation; results showed a 28% rise in microhardness and a 33% enhancement in fracture toughness with 3% SiC, signifying improved durability for structural applications. A moisture absorption study was carried out to evaluate the hydrophobic properties of the composites, which are crucial for long-term performance in humid environments. The analysis demonstrated a significant reduction in water uptake with 3 wt% SiC, improving the composite's performance by minimizing water-induced degradation. Thermogravimetric analysis (TGA) was employed to assess thermal stability and decomposition behavior, with findings indicating improved thermal stability with increasing SiC percentage. Overall, the integration of 3% SiC significantly enhanced mechanical strength, crack resistance, and moisture resistance, making the composite more suitable for demanding structural and environmental conditions.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171587","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":"Combining Generative Adversarial Networks (GANs) With Gaussian Noise for Anomaly Detection in Internet of Things (IoT) Traffic","authors":"Roya Morshedi,&nbsp;S. Mojtaba Matinkhah","doi":"10.1002/eng2.70205","DOIUrl":"https://doi.org/10.1002/eng2.70205","url":null,"abstract":"<p>This study presents an innovative approach for anomaly detection in <i>Internet of Things (IoT)</i> network traffic based on <i>Generative Adversarial Networks (GANs)</i>. To evaluate the model's performance, the CICIDS2017 dataset, which includes various attack types and normal network traffic, was used. The preprocessing process involved feature scaling, the addition of Gaussian noise to enhance model generalization, and the extraction of the Hurst self-similarity parameter to analyze the dynamic behavior of the data. The proposed model consists of a generator that produces pseudo-real data and a discriminator capable of distinguishing between real and fake data. This structure enables the identification of anomaly patterns in IoT traffic data. <i>Performance evaluation demonstrated that the proposed method achieved an accuracy of 99.88%, a recall of 99.88%, in anomaly detection, significantly outperforming traditional detection methods.</i> The main innovation of this research lies in the combination of GAN with the calculation of the Hurst parameter and the addition of noise to the input data, improving the model's ability to detect <i>complex attacks, including low-frequency and zero-day attacks.</i> The results indicate that this model offers superior performance in learning attack patterns, enhancing detection accuracy, and <i>reducing false positives.</i> This approach can serve as a powerful tool in Intrusion Detection Systems for the security of IoT networks.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171964","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 Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling","authors":"Reza Lotfi,&nbsp;Soheila Sadeghi,&nbsp;Sadia Samar Ali,&nbsp;Fatemeh Ramyar,&nbsp;Ehsan Ghafourian,&nbsp;Ebrahim Farbod","doi":"10.1002/eng2.70161","DOIUrl":"https://doi.org/10.1002/eng2.70161","url":null,"abstract":"<p>This study proposes a novel Robust, Resilient, and Risk-Based approach in Machine Learning (3RML) that emphasizes the application of project scheduling for the first time. A robust stochastic LASSO regression model is proposed to predict project duration. This model seeks to enhance a traditional LASSO regression by minimizing the expected value and the Weighted Value at Risk (WVaR) of the Mean Absolute Deviation (MAD) while penalizing the regression coefficients. The 3R requirements, which prioritize robustness, resilience, and risk aversion, are integrated into the mathematical model to ensure flexibility and disaster consideration. A comparative analysis was carried out between the square root, logarithm, and mixed linear/square root models and the baseline model. The Robust, Resilience MAD with Risk-Averse (RRMADR) and <i>R</i>-squared values were computed. The square root regression model demonstrated a 36% enhancement compared with the primary model. The conservatism coefficient affects risk levels, where a 5% increase results in a 2% decrease in the RRMADR. Varying confidence levels influence the model. The penalty coefficient in the lasso regression affects RRMADR and <i>R</i>-squared. The resiliency coefficient impacts both the RRMADR and <i>R</i>-squared. Probability scenarios influence RRMADR but do not affect <i>R</i>-squared. The type of probability density influences the RRMADR but does not impact <i>R</i>-squared.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171953","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":"Artificial Polymer Lightweight Aggregate Concrete With Coal Fly Ash for Biomedical Infrastructure: Mechanical, Physical, and Microstructural Investigation","authors":"Ani Firda,&nbsp;Rosmalinda Permatasari,&nbsp;Hendrik Jimmyanto,&nbsp;Muhammad Imam Ammarullah","doi":"10.1002/eng2.70196","DOIUrl":"https://doi.org/10.1002/eng2.70196","url":null,"abstract":"<p>Aggregates constitute ~60%–80% of concrete volume and play a crucial role in determining its mechanical and durability properties. In the context of sustainable construction, artificial aggregates derived from industrial by-products are gaining prominence as environmentally responsible alternatives to natural aggregates. This study presents the development and performance evaluation of a novel lightweight concrete incorporating artificial polymer lightweight aggregate synthesized from coal fly ash (CFA), epoxy resin, and a hardener in varying CFA-to-resin ratios (70:30, 74:26, and 80:20 by weight). The proposed mix design aims to address the increasing demand for lightweight, durable, and sustainable materials suitable for biomedical infrastructure applications, which require enhanced thermal insulation, fire resistance, and seismic performance. Concrete mixtures were designed to achieve target compressive strengths of 17.5, 20, and 30 MPa, with both lightweight (BR series) and normal weight (BN series) concrete formulations evaluated. Results demonstrated that the incorporation of polymer lightweight aggregates reduced the bulk density of concrete by up to 15.36%, while meeting or exceeding the required compressive strength thresholds for BR_17.5 and BR_20 mixtures. Although the BR_30 mix did not meet the target strength, polymer lightweight aggregate-based concrete exhibited significantly improved flexural strength (up to 60.57% higher than conventional mixes) and enhanced chemical durability when exposed to acidic and saline environments. However, its resistance to elevated temperatures was lower compared to that of conventional concrete. The findings suggest that polymer lightweight aggregate concrete offers a promising sustainable material solution for biomedical infrastructure and other applications demanding lightweight, durable, and thermally efficient construction materials. The utilization of industrial waste in polymer lightweight aggregate production not only contributes to environmental conservation but also advances the development of next-generation building materials aligned with circular economy principles.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171518","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":"Implementing Supply Chain Management 4.0: Potential Driving Forces and Strategies From an Empirical Study of Pharmaceutical Industries","authors":"Ismail W. R. Taifa,&nbsp;Johnson Subby Nzowa","doi":"10.1002/eng2.70190","DOIUrl":"https://doi.org/10.1002/eng2.70190","url":null,"abstract":"<p>This study investigates the potential driving forces and strategies for implementing supply chain management 4.0 (SCM 4.0) in pharmaceutical manufacturing industries (PMIs). Pertinent data were collected from 111 related companies using a mixed-methods research approach. The study used IBM SPSS and AMOS version 21 for exploratory and confirmatory factor analysis, respectively. The driving forces include regulatory and compliance, market, technological, and economic drivers, while research, development, and innovation emerged as the first-ranked strategy. With the manufacturing landscape in Tanzania transitioning towards digital transformation, implementing SCM 4.0 is essential. Digital transformation in PMIs can improve supply chain performance by enabling predictive analytics, real-time tracking, and better resource optimisation. Incorporating digital technologies like the Internet of Things, artificial intelligence, blockchain technology, and big data analytics is crucial for PMIs to maintain competitiveness and resilience in a globalized market. The digital transformation can boost efficiency, precision, and regulatory compliance while mitigating SCM risks. The transformation in deploying advanced robotics and automating the production systems within the PMIs can assist in streamlining the manufacturing workflows, diminishing human errors, and ultimately increasing the PMI outputs. Likewise, collaboration between PMIs, academia, research institutions, and government agencies is essential for knowledge sharing and addressing common PMIs' challenges. PMIs should be customer-focused and use SCM 4.0 technologies to improve competitiveness and satisfy changing customer demands. Likewise, to develop new technology and business models, it is essential to support innovation and entrepreneurship through funding programs, incubators, and hubs.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171483","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}