Ain Shams Engineering Journal最新文献

{"title":"Optimized YOLOV8: An efficient underwater litter detection using deep learning","authors":"Faiza Rehman ,&nbsp;Mariam Rehman ,&nbsp;Maria Anjum ,&nbsp;Afzaal Hussain","doi":"10.1016/j.asej.2024.103227","DOIUrl":"10.1016/j.asej.2024.103227","url":null,"abstract":"<div><div>Underwater litter has been a major issue in preserving the marine ecosystem. Human waste is deposited into lakes, rivers, and seas which leads to polluted water. The underwater litter harms aquatic life and pollutes water bodies and ecosystems. Therefore, there is a need for effective and efficient methods for detecting underwater litter. An improved YOLOV8s model is proposed for the detection of underwater litter. The fine-tuning of all the YOLOV8 variants was performed to choose the best model i.e. YOLOV8s. OFAT technique examines how various configurations affect the performance of the optimized YOLOV8s model. YOLOV8s was used to optimize and tune the hyperparameters of the model. Additionally, two hyperparameter tuning techniques were compared, and the results demonstrated that the OFAT is the superior optimization approach. Additionally, the research compares the underwater litter detection results of the optimized model and the pre-trained model of YOLOV8s. The “UW_Garbage_Debris_Dataset,” dataset comprises of 15 different classes of underwater litter which were used to train the dataset for the proposed research. From experimental results, the optimized YOLOV8s model showed an outstanding precision of 98.8 %. In comparison with other optimizers, learning rates, batch sizes, and epoch sizes, the optimized YOLOV8s performed better at 64 batch size in terms of effectiveness and efficiency. ICRA19 and UW_Garbage_Debris_Dataset were the two datasets used in the proposed study to test the proposed optimized YOLOV8s model. In terms of effectiveness and efficiency, the UW_Garbage_Debris_Dataset gave better results in comparison with the studies of literature. Furthermore, the research synthesis was conducted which shows the overall model’s performance is outstanding. Future research should try various optimizers, batch sizes, and learning rates, as well as other hyperparameters tuning techniques.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103227"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143173943","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":"Diabetic retinopathy grading using curvelet CNN with optimized SSO activations and wavelet-based image enhancement","authors":"N. Mohana Suganthi,&nbsp;M. Arun","doi":"10.1016/j.asej.2024.103239","DOIUrl":"10.1016/j.asej.2024.103239","url":null,"abstract":"<div><h3>Background</h3><div>Diabetic retinopathy (DR) is a significant risk of blindness among diabetic patients, necessitating early and accurate detection. Existing methods often fall short in identifying key markers like hard exudates (HE), leading to challenges in assessing disease severity.</div></div><div><h3>Issues</h3><div>Diabetes patients need to be diagnosed early for diabetic retinopathy (DR) to reduce the risk of blindness. Many conventional methods fail to detect hard run-in retinopathy images used to determine diabetes severity.</div></div><div><h3>Method</h3><div>In this paper, a novel Curvelet convolutional neural networks (CCNN) framework has been proposed to detect DR. Initially, the input retinal fundus images (RFI) are denoised using Wavelet Integrated Retinex (WIR) Algorithm to reduce the noise artifacts. After that, Curvelet convolutional neural networks (CCNN) are utilized to categorize the image as normal and abnormal. Furthermore, the Salp Swarm Optimization (SSO) algorithm is employed to enhance the classification performance of CCNN.</div></div><div><h3>Results</h3><div>The proposed method achieves a remarkable 99.46 % accuracy, significantly surpassing the performance of leading CNN-based models. The Proposed Curvelet CNN approach enhances the overall accuracy by 2.17 %, 7.42 %, and 20.46 % better than DenseNet 121, Triple-DRNet, and EfficientNetB4 respectively<strong>.</strong></div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103239"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174847","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":"Elastic deformation impact on trihybrid nanofluid flow through different geometries with the combine effects of electrophoresis and thermophoresis","authors":"Munawar Abbas ,&nbsp;A. Al-Zubaidi ,&nbsp;Abdullah A. Faqihi ,&nbsp;Ilyas Khan ,&nbsp;A.F. Aljohani ,&nbsp;Abdoalrahman S.A. Ome ,&nbsp;Ahmed M. Gala","doi":"10.1016/j.asej.2024.103172","DOIUrl":"10.1016/j.asej.2024.103172","url":null,"abstract":"<div><div>This study inspected the impacts of thermophoresis and electrophoresis on the rate of aerosol particle deposition through cone, plate, wedge geometries in a Marangoni convective flow. The proposed mathematical model becomes more innovative by taking into account the effects of elastic deformation, variable thermal conductivity and mixed convection. It uses a trihybrid nanofluid composed of water-based fluid, Silver <span><math><mrow><mo>(</mo><mi>A</mi><mi>g</mi><mo>)</mo><mo>,</mo></mrow></math></span> Titanium dioxide <span><math><mrow><mo>(</mo><mi>T</mi><mi>i</mi><msub><mi>O</mi><mn>2</mn></msub><mo>)</mo><mo>,</mo></mrow></math></span> and Magnesium oxide <span><math><mrow><mo>(</mo><mi>M</mi><mi>g</mi><mi>o</mi><mo>)</mo></mrow></math></span> nanoparticles. One important area of use is in the design and improvement of trihybrid nanofluid-based materials with specialized thermal, electrical, and mechanical properties. Improvements in heat transmission in microfluidic and nanofluidic devices are critical for chemical reactions, electronics cooling, and biological applications. Improved materials processing methods, precise drug administration mechanisms, and more effective cooling systems can all result from an understanding of the interactions between fluid flow, elastic deformation, and nanoparticle dynamics under the impact of electrical and temperature gradients. The equations corresponding to the suggested PDEs (particle differential equations) are converted into ODEs<!--> <!-->(ordinary differential equations) by choosing suitable similarity transformation. The semi-analytical technique HAM (Homotopy analysis method) is executed to drive the solution of the proposed problem. When the values of the elastic deformation parameter increase, the thermal and velocity profiles decline. With higher values of<!--> <!-->electrophoretic parameter, the concentration profile becomes augmented.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103172"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175681","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":"Digital twin for advanced optimal coordination scheme of distance and Dual-Stage overcurrent relays","authors":"Feras Alasali ,&nbsp;Naser El-Naily ,&nbsp;Haytham Y. Mustafa ,&nbsp;Hassen Loukil ,&nbsp;Saad M. Saad ,&nbsp;Abdelaziz Salah Saidi ,&nbsp;William Holderbaum","doi":"10.1016/j.asej.2024.103197","DOIUrl":"10.1016/j.asej.2024.103197","url":null,"abstract":"<div><div>Integrating Distributed Generators (DGs), particularly renewable energy sources such as wind systems, into traditional power network presents significant protection coordination challenges. This study introduces a new optimal coordination scheme for distance and double-Stage Overcurrent (OCR) characteristics relays, utilizing digital twin technology. The proposed dual-stage of OCR protection to enhance the efficacy of the coordination between distance relays and OCRs. By employing advanced digital twin models and Hardware-in-the-Loop (HIL) testing, the proposed scheme aims to enhance fault management and relay coordination for microgrids. The scheme’s effectiveness is evaluated using a reference power network (CIGRE radial and mesh network) with and without wind systems under various fault types and locations. The study demonstrates substantial improvements over traditional and modern dynamic distance relays coordination approaches, including a reduction in maximum tripping time from 0.85 s to 0.19 s with the dual-stage scheme. Comparative analysis of digital simulation and physical twin relays further validates the accuracy and robustness of the proposed scheme.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103197"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175692","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":"Energy, Exergy, Entropy, Emission Factors (4E’s) and Sustainability Index analyses of thermal splintering waste paraffin Oil, di-ethyl ether − diesel blends","authors":"R. Saravanan ,&nbsp;P. Navaneetha Krishnan ,&nbsp;M. Rengasamy ,&nbsp;V. Manieniyan","doi":"10.1016/j.asej.2024.103190","DOIUrl":"10.1016/j.asej.2024.103190","url":null,"abstract":"<div><div>The study involves using thermal cracking techniques and enhancing fuel quality by incorporating di-ethyl ether, referred to as blended stock solution (BSS). In this work, an investigation was conducted on the Entropy, Energy, Emission Factors, Sustainability Index (SI) analyses, Exergy of BSS-diesel blends and the correlation between BSS-diesel blends and pure diesel. A single cylinder diesel engine running at speed of 1500 rpm was used for this experiment. The engine was tested with various blend ratios, including BSS20, BSS40, BSS60, BSS100, and pure diesel. The findings indicate that BSS60 has the best levels of exergy efficiency and energy, with a value of 30.35 % and 28.50 % respectively, surpassing other blends and pure diesel. The research findings suggest that BSS60 exhibited the lowest level of entropy formation compared to other fuel blends and pure diesel. The BSS60 exhibits the highest level of sustainability index. Emission factors of carbon monoxide (CO), demonstrate lower emission index (EI) and specific emissions (SE) as compared to both pure diesel and its blends. The emissions of NOx have shown a notable increase of 3.37 % in both the EI and SE when compared to pure diesel. The conclusions suggest the BSS60 exhibits superior performance and is suitable for use in direct-injection diesel engines.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103190"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175694","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":"Developing accessible routes in historic environments for wheelchair users using a smart mobile application","authors":"Hüseyin Özdemir","doi":"10.1016/j.asej.2024.103240","DOIUrl":"10.1016/j.asej.2024.103240","url":null,"abstract":"<div><div>In cities with historical heritage, wheelchair users cannot benefit from urban spaces due to the lack of design in the physical environment. In this study, Konya city centre (Turkey), which has a historical richness for the solution of this problem, is analysed according to Turkish Standard (TS9111), The Americans with Disabilities Act (ADA) and the United Nations (UN) standards for wheelchair users. Based on the data obtained, problems where accessibility cannot be provided are identified. These problems were solved by developing an “accessible route” mobile application through “Flutter software developed by Google”. With the ease provided by the developed mobile application, wheelchair users can be easily integrated into the historical environment. The mobile application developed within the scope of this study can also be created by incorporating a series of alternatives, such as different language options, various locations and eating and drinking places and can be expanded in other research.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103240"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174846","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":"FTNet-HiLa: An adaptive multimodal histopathological image categorization network","authors":"Shuo Yin ,&nbsp;Dong Zhang ,&nbsp;YongKang Zhang ,&nbsp;Xing Zhao ,&nbsp;XuYing Zhao","doi":"10.1016/j.asej.2024.103211","DOIUrl":"10.1016/j.asej.2024.103211","url":null,"abstract":"<div><div>The integration of artificial intelligence in medical imaging has witnessed a surge in neural network applications for pathological image classification, with Vision Transformers (ViTs) emerging as highly accurate models in general visual recognition tasks. Addressing the challenge of limited pathological image data, this study introduces HiLa, a novel training framework for ViTs. The framework leverages pre-training on non-medical data, followed by an adaptive fine-tuning process using medical data to bridge the data gap. Furthermore, to fuse the text and image modalities, we propose the Co-attention Fusion Block (CaFB) module, enabling the development of a Multi-scale full Transformer-based network (FTNet). Extensive experiments on diverse datasets demonstrate the efficacy of the HiLa framework, achieving 99.77% accuracy on the BreakHis dataset for 8-class classification. Combined with FTNet-HiLa, it sets a new benchmark in benign-malignant classification. These findings highlight the improved performance of ViT models with HiLa and its scalability to multimodal applications.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103211"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175413","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":"Single-stage modified Vienna rectifier SEPIC AC-DC LED driver","authors":"Kenan Gürçam ,&nbsp;M. Nuri Almalı","doi":"10.1016/j.asej.2024.103200","DOIUrl":"10.1016/j.asej.2024.103200","url":null,"abstract":"<div><div>This paper presents an innovative approach by combining a modified first-stage Vienna rectifier with a single-stage SEPIC converter. The proposed Vienna rectifier structure significantly reduced the switch stress. This allows the use of switches with low voltage values and reduced resistances. The use of a non-electrolytic capacitor in the output stage contributed to extending the life of the LED driver. These features enhance the efficiency of the proposed driver. The recommended LED driver complies with the mandatory standards specified in IEC61000-3–2 for Class C loads. To verify the effectiveness of this proposed system, a setup of an 18 W LED driver operating was presented and simulated. This research aims to address power factor, switch stress, and harmonic distortion issues commonly associated with LED driver circuits, contributing to more efficient and compatible LED lighting systems.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103200"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175676","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":"Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images","authors":"Mary Charles Sheeba ,&nbsp;Christopher Seldev Christopher","doi":"10.1016/j.asej.2024.103188","DOIUrl":"10.1016/j.asej.2024.103188","url":null,"abstract":"<div><div>This research introduces the Adaptive Deep Residual Network (AdResNet), a deep convolutional neural network designed for effective image denoising in computer vision applications. Configured with the Adaptive White Shark Optimizer (AWSO), AdResNet removes noise while preserving key visual features. The model is tested on multiple noise types (Gaussian, Salt-and-Pepper, Poisson, and mixed noise) at various intensity levels, demonstrating versatility. Evaluations across medical, natural, and satellite images ensure its robustness for real-world applications. AdResNet achieves superior denoising results, with low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and high Structural Similarity Index Measure (SSIM). For example, the model recorded average metrics of MSE 13.61, PSNR 48.81 dB, and SSIM 0.96 on medical images, highlighting its efficacy. These results confirm AdResNet’s suitability for applications requiring high image quality, such as medical and satellite imaging.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103188"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174616","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":"Porosity and Joule heating effects in chemically reactive peristaltically driven motion of Alumina-Ethylene glycol-Water nanofluid","authors":"S. Farooq ,&nbsp;S. Ravikumar ,&nbsp;S. Khalid ,&nbsp;Hala H. Abd El-Gawad ,&nbsp;Khalid Abdulkhaliq M. Alharbi","doi":"10.1016/j.asej.2024.103181","DOIUrl":"10.1016/j.asej.2024.103181","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Due to the immense use of biological materials in the medicine industry, its mathematical modelling has gained incredible attention from scientists and researchers. To understand the flows of biological fluids due to contraction and relaxation in various biological and industrial areas, the knowledge of peristaltic mechanism is very important. In addition, due to nanoparticles immense industrial and engineering (i.e., nuclear, chemical, mechanical and medical) processes likely during heat exchange in heat exchangers, in boiling and cooling of devices, micro-electromechanical devices, in cooling of automobile engine, in arrays of laser diode, in various medicines and surgeries performed through laser technology attain much attention by the researchers. Keeping in mind all the above stated discussion the prime aim here is to examine the biological materials flow formulation in the presence of nano particles because of their noticeable involvement in various biomechanics fields. For example, in gastrointestinal tract the chyme motion and significantly in various surgeries to overcome blood flow via handling the magnetic field intensity.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methodology&lt;/h3&gt;&lt;div&gt;The main purpose here is to analyse the peristaltic mechanism of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;A&lt;/mi&gt;&lt;msub&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/msub&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;mo&gt;-&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mi&gt;O&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; nano fluid model through asymmetrical movement of asymmetric channel waves under the consideration of magnetohydrodynamic, porous media, Joule heating, energy dissipation, Brownian and thermophoretic motions and chemical reaction. To simplify the modelled system of equations, non-dimensionless variables, approximations of low Reynolds number and large wave length are employed. Then the exact solution of simplified momentum equation is examined, where the energy and concentration equations are solved numerically using shooting and RK-Felburgh algorithms with step-size is taken &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;0.1&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; because of non-linearity and coupling. Using the obtained solution flow quantities are plotted against the parameters of interest. Finally, the discussion section provides a detailed illustration of the physical behavior of flow quantities.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Significant Findings&lt;/h3&gt;&lt;div&gt;The physical discussion section described that decline in velocity and enhancement in temperature versus a larger nano particle volume fraction parameter, it happens because when nano particles are submerged into ordinary liquids, which denser the material appropriately and thus velocity is declined and the temperature is enhanced because of high resistance in the flow field. Due to porosity effects velocity becomes lower at the centre of the channel. Brownian and thermophoretic motions enhance the fluid","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103181"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175411","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}