Ain Shams Engineering Journal最新文献

英文中文

Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103209

Jingguo Gou , Ghayas Haider Sajid , Mohanad Muayad Sabri , Mohammed El-Meligy , Khalil El Hindi , Nashwan Adnan OTHMAN

Biochar production from organic waste can reduce fossil fuel reliance and combat climate change, but current models are computationally demanding and have limited accuracy. The study creates four machine learning models using multiple linear regression, decision trees, Adaboost regressors, and bagging regressors, trained on a dataset of pyrolysis tests. The results show that the data-driven models have significantly higher predictive accuracy than existing models, with an R² of up to 0.96. The Bagging Regressor (BR) demonstrated superior efficacy compared over the MLR, AR, and DT models across all eight output parameters, with R2 values of 0.94, 0.93, 0.93, 0.94, 0.95, 0.90, 0.92, and 0.96 for Biochar Yield, Fixed Carbon, Volatile Matter, Ash, and ultimate composition parameters (C, H, O, and N), respectively. The study developed a data-driven model to predict Biochar yield and compositions, enhancing production processes and promoting sustainable farming practices.

{"title":"Optimizing biochar yield and composition prediction with ensemble machine learning models for sustainable production","authors":"Jingguo Gou , Ghayas Haider Sajid , Mohanad Muayad Sabri , Mohammed El-Meligy , Khalil El Hindi , Nashwan Adnan OTHMAN","doi":"10.1016/j.asej.2024.103209","DOIUrl":"10.1016/j.asej.2024.103209","url":null,"abstract":"<div><div>Biochar production from organic waste can reduce fossil fuel reliance and combat climate change, but current models are computationally demanding and have limited accuracy. The study creates four machine learning models using multiple linear regression, decision trees, Adaboost regressors, and bagging regressors, trained on a dataset of pyrolysis tests. The results show that the data-driven models have significantly higher predictive accuracy than existing models, with an R<sup>2</sup> of up to 0.96. The Bagging Regressor (BR) demonstrated superior efficacy compared over the MLR, AR, and DT models across all eight output parameters, with R2 values of 0.94, 0.93, 0.93, 0.94, 0.95, 0.90, 0.92, and 0.96 for Biochar Yield, Fixed Carbon, Volatile Matter, Ash, and ultimate composition parameters (C, H, O, and N), respectively. The study developed a data-driven model to predict Biochar yield and compositions, enhancing production processes and promoting sustainable farming practices.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103209"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174377","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

Adaptive, consensus-based control strategies for managing meta-populations of pests

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103191

Yasser Alrashedi , Markus Mueller , Stuart Townley

We consider management strategies for natural populations, spatially distributed in patchy landscapes. Such patchy landscapes arise naturally, due to habitat fragmentation, or by design, such as in rural farmlands. Populations disperse within these patchy landscapes resulting in meta-populations. Management of such meta-populations then involves two modes of control action – action local to each patch and coordinated control of action between patches. The challenge is two-fold: To account for uncertainty in the localised population dynamics on patches we use adaptive control approaches; To counter the effects of dispersal, we combine the localised adaptive control actions with sharing of information and actions between patches. Population dynamics on each patch are described by population projection matrices. Dispersal of populations between patches and information sharing between control actions on patches are modelled using directed graphs on the set of patches. The novelty lies in combining information sharing with output driven adaptive control. Information sharing acts to anticipate potential outbreaks and to coordinate this with the adaptive control localised to patches. We explore situations when information sharing is and is not matched with dispersal. Information sharing improves the outcomes in that the size and extent of a pest outbreak and the amount of pesticide sprayed is reduced. The results are shown to be robust to uncertainties in the demography of pests.

{"title":"Adaptive, consensus-based control strategies for managing meta-populations of pests","authors":"Yasser Alrashedi , Markus Mueller , Stuart Townley","doi":"10.1016/j.asej.2024.103191","DOIUrl":"10.1016/j.asej.2024.103191","url":null,"abstract":"<div><div>We consider management strategies for natural populations, spatially distributed in patchy landscapes. Such patchy landscapes arise naturally, due to habitat fragmentation, or by design, such as in rural farmlands. Populations disperse within these patchy landscapes resulting in meta-populations. Management of such meta-populations then involves two modes of control action – action local to each patch and coordinated control of action between patches. The challenge is two-fold: To account for uncertainty in the localised population dynamics on patches we use adaptive control approaches; To counter the effects of dispersal, we combine the localised adaptive control actions with sharing of information and actions between patches. Population dynamics on each patch are described by population projection matrices. Dispersal of populations between patches and information sharing between control actions on patches are modelled using directed graphs on the set of patches. The novelty lies in combining information sharing with output driven adaptive control. Information sharing acts to anticipate potential outbreaks and to coordinate this with the adaptive control localised to patches. We explore situations when information sharing is and is not matched with dispersal. Information sharing improves the outcomes in that the size and extent of a pest outbreak and the amount of pesticide sprayed is reduced. The results are shown to be robust to uncertainties in the demography of pests.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103191"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175376","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

Integration in CNN and FIR filters for improved computational efficiency in signal processing

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103201

A. Sridevi, A. Sathiya

This research paper explains the design process of the 8 × 8 Vedic multipliers based on the “UrdhvaTiryagbhyam” Sutra in combination with the “Nikhilam Sutra“ and the Karatsuba algorithm. To effectively generate a 16-bit product, the used architecture consists of four four-by-four Vedic modules, an 8:1 carry-save adder, and two nine-bit binary adders. The UrdhvaTiryagbhyam approach splits multiplications into pieces, the Nikhilam Sutra uses the concept of binary complements, and the Karatsuba algorithm offers improvements in large numbers of multiplications. The proposed addition microarchitecture, which consists of using a Fast Carry Switching Adder and the Kogge-Stone Adder with associated selection signals and speculative logic, improves carry propagation time. The ability of the Vedic multiplier is tested within an FIR filter and a CNN processing element, revealing significant enhancements in speed and efficiency. Importantly, the proposed multiplier based on the modification of Vedic Nikhilam yields the lowest power consumption (248.93 mW), the lowest delay (27.95 ns), and the lowest PDP (6.96 pJ), thus making it appropriate for usage in HPC related to signal processing and neural network computations. Moreover, the developed FIR filter for the CNN and the EEG signal datasets were employed to detect seizures and Alzheimer’s disease. The incorporation of the Vedic multiplier into the CNN framework reveals the application of the proposed idea in the field of biomedical signal processing with improved computational speed and accuracy. The results corroborate the multiplier’s efficiency in decreasing the computational complexity and enhancing the possibility of real-time analysis of CNN-based systems in healthcare.

{"title":"Integration in CNN and FIR filters for improved computational efficiency in signal processing","authors":"A. Sridevi, A. Sathiya","doi":"10.1016/j.asej.2024.103201","DOIUrl":"10.1016/j.asej.2024.103201","url":null,"abstract":"<div><div>This research paper explains the design process of the 8 × 8 Vedic multipliers based on the “UrdhvaTiryagbhyam” Sutra in combination with the “Nikhilam Sutra“ and the Karatsuba algorithm. To effectively generate a 16-bit product, the used architecture consists of four four-by-four Vedic modules, an 8:1 carry-save adder, and two nine-bit binary adders. The UrdhvaTiryagbhyam approach splits multiplications into pieces, the Nikhilam Sutra uses the concept of binary complements, and the Karatsuba algorithm offers improvements in large numbers of multiplications. The proposed addition microarchitecture, which consists of using a Fast Carry Switching Adder and the Kogge-Stone Adder with associated selection signals and speculative logic, improves carry propagation time. The ability of the Vedic multiplier is tested within an FIR filter and a CNN processing element, revealing significant enhancements in speed and efficiency. Importantly, the proposed multiplier based on the modification of Vedic Nikhilam yields the lowest power consumption (248.93 mW), the lowest delay (27.95 ns), and the lowest PDP (6.96 pJ), thus making it appropriate for usage in HPC related to signal processing and neural network computations. Moreover, the developed FIR filter for the CNN and the EEG signal datasets were employed to detect seizures and Alzheimer’s disease. The incorporation of the Vedic multiplier into the CNN framework reveals the application of the proposed idea in the field of biomedical signal processing with improved computational speed and accuracy. The results corroborate the multiplier’s efficiency in decreasing the computational complexity and enhancing the possibility of real-time analysis of CNN-based systems in healthcare.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103201"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175677","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

Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103206

Zhenya Qi, Yudong Feng, Shoufeng Wang, Chao Li

Hydropower plays a crucial role in electricity generation, contributing over 60% of total renewable energy output. Its ability to stabilize energy fluctuations makes it essential in green energy initiatives. Accurate prediction of hydropower production is vital, considering its dependence on various factors like weather, water storage, and electricity generation. Traditional methods struggle with the complexities involved. This study utilized Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) algorithms, both individually and in hybrid models enhanced by optimization techniques like Slime Mould Algorithm (SMA), Aquila Optimizer (AO), and Grey Wolf Optimization (GWO). XGBoost outperformed SVR in single model predictions with an R² value of 0.8632 and RMSE of 40.90, and when optimized, the hybrid XGBoost models showed superior performance, with XGBoost-SMA achieving the highest accuracy. The results revealed that the XGBoost-SMA model achieved the most desired accuracy with an R² value of 0.9713 and a root mean square error of 18.73 for the test dataset. This research highlights machine learning’s applicability in hydropower prediction and suggests hybrid models as a promising approach for better accuracy, emphasizing XGBoost’s potential in efficient hydropower forecasting to meet global electricity demands.

{"title":"Enhancing hydropower generation Predictions: A comprehensive study of XGBoost and Support Vector Regression models with advanced optimization techniques","authors":"Zhenya Qi, Yudong Feng, Shoufeng Wang, Chao Li","doi":"10.1016/j.asej.2024.103206","DOIUrl":"10.1016/j.asej.2024.103206","url":null,"abstract":"<div><div>Hydropower plays a crucial role in electricity generation, contributing over 60% of total renewable energy output. Its ability to stabilize energy fluctuations makes it essential in green energy initiatives. Accurate prediction of hydropower production is vital, considering its dependence on various factors like weather, water storage, and electricity generation. Traditional methods struggle with the complexities involved. This study utilized Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) algorithms, both individually and in hybrid models enhanced by optimization techniques like Slime Mould Algorithm (SMA), Aquila Optimizer (AO), and Grey Wolf Optimization (GWO). XGBoost outperformed SVR in single model predictions with an R<sup>2</sup> value of 0.8632 and RMSE of 40.90, and when optimized, the hybrid XGBoost models showed superior performance, with XGBoost-SMA achieving the highest accuracy. The results revealed that the XGBoost-SMA model achieved the most desired accuracy with an R<sup>2</sup> value of 0.9713 and a root mean square error of 18.73 for the test dataset. This research highlights machine learning’s applicability in hydropower prediction and suggests hybrid models as a promising approach for better accuracy, emphasizing XGBoost’s potential in efficient hydropower forecasting to meet global electricity demands.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 1","pages":"Article 103206"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175693","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

Optimized YOLOV8: An efficient underwater litter detection using deep learning

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103227

Faiza Rehman , Mariam Rehman , Maria Anjum , Afzaal Hussain

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.

{"title":"Optimized YOLOV8: An efficient underwater litter detection using deep learning","authors":"Faiza Rehman , Mariam Rehman , Maria Anjum , 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}

引用次数: 0

Diabetic retinopathy grading using curvelet CNN with optimized SSO activations and wavelet-based image enhancement

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103239

N. Mohana Suganthi, M. Arun

Background

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.

Issues

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.

Method

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.

Results

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.

{"title":"Diabetic retinopathy grading using curvelet CNN with optimized SSO activations and wavelet-based image enhancement","authors":"N. Mohana Suganthi, 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}

引用次数: 0

Elastic deformation impact on trihybrid nanofluid flow through different geometries with the combine effects of electrophoresis and thermophoresis

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103172

Munawar Abbas , A. Al-Zubaidi , Abdullah A. Faqihi , Ilyas Khan , A.F. Aljohani , Abdoalrahman S.A. Ome , Ahmed M. Gala

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

(A g),

Titanium dioxide

(T i O_{2}),

and Magnesium oxide

(M g o)

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.

{"title":"Elastic deformation impact on trihybrid nanofluid flow through different geometries with the combine effects of electrophoresis and thermophoresis","authors":"Munawar Abbas , A. Al-Zubaidi , Abdullah A. Faqihi , Ilyas Khan , A.F. Aljohani , Abdoalrahman S.A. Ome , 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 ofelectrophoretic 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}

引用次数: 0

Digital twin for advanced optimal coordination scheme of distance and Dual-Stage overcurrent relays

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103197

Feras Alasali , Naser El-Naily , Haytham Y. Mustafa , Hassen Loukil , Saad M. Saad , Abdelaziz Salah Saidi , William Holderbaum

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.

{"title":"Digital twin for advanced optimal coordination scheme of distance and Dual-Stage overcurrent relays","authors":"Feras Alasali , Naser El-Naily , Haytham Y. Mustafa , Hassen Loukil , Saad M. Saad , Abdelaziz Salah Saidi , 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}

引用次数: 0

Energy, Exergy, Entropy, Emission Factors (4E’s) and Sustainability Index analyses of thermal splintering waste paraffin Oil, di-ethyl ether − diesel blends

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103190

R. Saravanan , P. Navaneetha Krishnan , M. Rengasamy , V. Manieniyan

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.

{"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 , P. Navaneetha Krishnan , M. Rengasamy , 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}

引用次数: 0

Developing accessible routes in historic environments for wheelchair users using a smart mobile application

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal

Pub Date : 2025-01-01 DOI: 10.1016/j.asej.2024.103240

Hüseyin Özdemir

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.

引用次数: 0

首页上一页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Ain Shams Engineering Journal

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀