This paper presents the application of a three-factor linear model for the analysis and optimization of the main cutting resistance during the machining of PTFE (Polytetrafluoroethylene) material, with nominal dimensions of ∅50 × 500 mm. PTFE is known for its unique mechanical and thermal properties, which pose challenges in machining processes. Key machining parameters—specifically cutting depth, spindle speed, and cutting speed—were initially identified and then collectively examined as machining parameters through regression analysis to determine their interactions and impact on cutting resistance. The aim of this research was to optimize these machining parameters through mathematical modeling to reduce cutting resistance, extend tool life, and enhance productivity. The results demonstrated that proper optimization of the machining parameters can significantly reduce tool wear, lower costs, and improve machining efficiency.
{"title":"Optimization of the main cutting force in the machining of polytetrafluoroethylene (PTFE)","authors":"Slavica Prvulovic , Predrag Mosorinski , Ljubisa Josimovic , Jasna Tolmac , Branislava Radisic , Uros Sarenac","doi":"10.1016/j.asej.2025.103927","DOIUrl":"10.1016/j.asej.2025.103927","url":null,"abstract":"<div><div>This paper presents the application of a three-factor linear model for the analysis and optimization of the main cutting resistance during the machining of PTFE (Polytetrafluoroethylene) material, with nominal dimensions of ∅50 × 500 mm. PTFE is known for its unique mechanical and thermal properties, which pose challenges in machining processes. Key machining parameters—specifically cutting depth, spindle speed, and cutting speed—were initially identified and then collectively examined as machining parameters through regression analysis to determine their interactions and impact on cutting resistance. The aim of this research was to optimize these machining parameters through mathematical modeling to reduce cutting resistance, extend tool life, and enhance productivity. The results demonstrated that proper optimization of the machining parameters can significantly reduce tool wear, lower costs, and improve machining efficiency.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103927"},"PeriodicalIF":5.9,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736128","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}
Pub Date : 2025-12-12DOI: 10.1016/j.asej.2025.103888
Asif Raza , Zi-Hong Jiang , Yi-Die Ye , Muhammad Punhal Sahto , Ahmed Lotfy Haridy , Said I. Abouzeid , Ghalib Raza , Jibran Hussain
Techno-economic analysis of off-grid hybrid AC-DC microgrids (HMGs) in desert areas has primarily focused on meeting the load demands in residential, household, domestic, and agricultural applications, while the healthcare sector has been comparatively less considered. These analyses contribute to the development of effective financial techniques. This paper presents a techno-economic design for an HMG that combines AC and DC elements such as diesel generators (DG), photovoltaic (PV), wind turbines (WT), batteries (BAT), and power converters (Conv) to satisfy the power demand of a rural health clinic with a daily load of 110 kWh, situated in the desert area of Nubian in Aswan, Egypt. The optimization of HMG is performed through HOMER Pro based on hourly wind speed, solar irradiance, and clinic load data to evaluate the cost of electricity, carbon emissions, loss of power supply probability, and renewable fraction. The results are compared across four different HMG combinations: PV/WT/BAT/Conv, DG/PV/BAT/Conv, DG/PV/WT/BAT/Conv, and WT/DG. The simulation outcomes indicate that the system incorporating the WT/DG/PV/BAT/Conv provides the most efficient techno-economic solution for meeting the clinic’s power demand. The optimal configuration includes 4 kW of DG, 10 kW of WT, 9 kW of PV, 36 batteries, and 16 kW of power converters. This system achieves the lowest 51.76 k$ of net present cost and 0.107 $/kWh of cost of electricity, 3051 kg/year of CO2 emissions, and a significant renewable contribution of 90.7 %. Furthermore, the sensitivity assessment verifies that the system costs are greatly affected by factors including solar radiation, wind speed, discount rate, and diesel cost.
{"title":"Techno-economic feasibility analysis of a hybrid off grid AC-DC microgrid to support a health clinic","authors":"Asif Raza , Zi-Hong Jiang , Yi-Die Ye , Muhammad Punhal Sahto , Ahmed Lotfy Haridy , Said I. Abouzeid , Ghalib Raza , Jibran Hussain","doi":"10.1016/j.asej.2025.103888","DOIUrl":"10.1016/j.asej.2025.103888","url":null,"abstract":"<div><div>Techno-economic analysis of off-grid hybrid AC-DC microgrids (HMGs) in desert areas has primarily focused on meeting the load demands in residential, household, domestic, and agricultural applications, while the healthcare sector has been comparatively less considered. These analyses contribute to the development of effective financial techniques. This paper presents a techno-economic design for an HMG that combines AC and DC elements such as diesel generators (DG), photovoltaic (PV), wind turbines (WT), batteries (BAT), and power converters (Conv) to satisfy the power demand of a rural health clinic with a daily load of 110 kWh, situated in the desert area of Nubian in Aswan, Egypt. The optimization of HMG is performed through HOMER Pro based on hourly wind speed, solar irradiance, and clinic load data to evaluate the cost of electricity, carbon emissions, loss of power supply probability, and renewable fraction. The results are compared across four different HMG combinations: PV/WT/BAT/Conv, DG/PV/BAT/Conv, DG/PV/WT/BAT/Conv, and WT/DG. The simulation outcomes indicate that the system incorporating the WT/DG/PV/BAT/Conv provides the most efficient techno-economic solution for meeting the clinic’s power demand. The optimal configuration includes 4 kW of DG, 10 kW of WT, 9 kW of PV, 36 batteries, and 16 kW of power converters. This system achieves the lowest 51.76 k$ of net present cost and 0.107 $/kWh of cost of electricity, 3051 kg/year of CO<sub>2</sub> emissions, and a significant renewable contribution of 90.7 %. Furthermore, the sensitivity assessment verifies that the system costs are greatly affected by factors including solar radiation, wind speed, discount rate, and diesel cost.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103888"},"PeriodicalIF":5.9,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736132","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}
Pub Date : 2025-12-12DOI: 10.1016/j.asej.2025.103904
Harish M.S , Lokesh S , Sakthivel P , Akshaya B
Recent Intrusion Detection (ID) networks face difficulties in handling the enlarging volume of network traffic and adapting to emerging cyber threats. Handling data traffic and addressing data imbalance are key requirements for identifying recent cyber threats. This paper proposes a novel hybrid ID system designed to mitigate data imbalance issues. The proposed methodology uses advanced deep learning techniques and optimized characteristic fusion models, making it suitable for high-traffic environments. This research conducts a comprehensive experimental study on five standard ID datasets, focusing on network traffic and system behavior data, which are crucial for detecting potential intrusions. In the deep feature extraction phase, multiple features are considered, including statistical information, T-SNE features, and high-level deep learning features. T-SNE features capture similarities between data points, helping preserve the most important features. For feature fusion, optimal weights are identified using the proposed RCMPA. The fused feature set, created from these optimized weights, improves that a more appropriate and discriminative characteristics are utilized for training. A system then employs a “Multi-scale Dilated Deep Hybrid Network with Attention Mechanism” (MDDHN-AM) for intrusion diagnosis. This developed model integrates TCNN and RNN to detain both temporal and spatial dependencies. TCNN processes sequential information to identify temporal patterns, while RNN captures the dynamic nature of network traffic. The attention mechanism prioritizes the most significant features, enabling more accurate intrusion detection. At last, the presentation of MDDHN-AM was compared to traditional and state-of-the-art intrusion detection methods across multiple metrics. The developed model achieved 96.43% detection accuracy and 97.38% precision, illustrating its efficiency in handling diverse digital attacks and data imbalance. An improved performance over traditional methods highlights its potential as a robust solution for secure communication and protection against evolving digital attacks.
{"title":"Hybrid deep learning model for network intrusion detection using optimal feature fusion","authors":"Harish M.S , Lokesh S , Sakthivel P , Akshaya B","doi":"10.1016/j.asej.2025.103904","DOIUrl":"10.1016/j.asej.2025.103904","url":null,"abstract":"<div><div>Recent Intrusion Detection (ID) networks face difficulties in handling the enlarging volume of network traffic and adapting to emerging cyber threats. Handling data traffic and addressing data imbalance are key requirements for identifying recent cyber threats. This paper proposes a novel hybrid ID system designed to mitigate data imbalance issues. The proposed methodology uses advanced deep learning techniques and optimized characteristic fusion models, making it suitable for high-traffic environments. This research conducts a comprehensive experimental study on five standard ID datasets, focusing on network traffic and system behavior data, which are crucial for detecting potential intrusions. In the deep feature extraction phase, multiple features are considered, including statistical information, T-SNE features, and high-level deep learning features. T-SNE features capture similarities between data points, helping preserve the most important features. For feature fusion, optimal weights are identified using the proposed RCMPA. The fused feature set, created from these optimized weights, improves that a more appropriate and discriminative characteristics are utilized for training. A system then employs a “Multi-scale Dilated Deep Hybrid Network with Attention Mechanism” (MDDHN-AM) for intrusion diagnosis. This developed model integrates TCNN and RNN to detain both temporal and spatial dependencies. TCNN processes sequential information to identify temporal patterns, while RNN captures the dynamic nature of network traffic. The attention mechanism prioritizes the most significant features, enabling more accurate intrusion detection. At last, the presentation of MDDHN-AM was compared to traditional and state-of-the-art intrusion detection methods across multiple metrics. The developed model achieved 96.43% detection accuracy and 97.38% precision, illustrating its efficiency in handling diverse digital attacks and data imbalance. An improved performance over traditional methods highlights its potential as a robust solution for secure communication and protection against evolving digital attacks.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103904"},"PeriodicalIF":5.9,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736133","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}
Pub Date : 2025-12-12DOI: 10.1016/j.asej.2025.103900
Hong Cheng , Zihan Hao , Xiangruike Li , Shupeng Huang
The waste material recycling generates environmental and economic benefits to industry and society, incentivizing companies to invest in recycling convenience service for recycling volume expansion. However, as multiple stakeholders and influencing factors are involved in recycling operations, it is difficult for recycling model selection and convenience service investment. To address it, this study models three recycling models using game theory based on practices: (1) settled model, (2) self-built model, and (3) dual-channel model. The equilibrium solutions of three models are solved, demonstrating that customers’ preference for the platform significantly influences recyclers’ investment strategies in convenience services. By integrating consumers’ preference for platform and recycling convenience service investments and by considering both positive and negative impacts of the recycling activities on the environment for three models, this study advances scholarship in waste recovery systems. The findings can support the design of economical and sustainable recycling policies for practitioners.
{"title":"Selection of recycling model and investment strategy from economic and environmental perspectives","authors":"Hong Cheng , Zihan Hao , Xiangruike Li , Shupeng Huang","doi":"10.1016/j.asej.2025.103900","DOIUrl":"10.1016/j.asej.2025.103900","url":null,"abstract":"<div><div>The waste material recycling generates environmental and economic benefits to industry and society, incentivizing<!--> <!-->companies to invest in recycling convenience service for recycling volume expansion. However, as multiple stakeholders and influencing factors are involved in recycling operations, it is difficult for recycling model selection and convenience service investment. To address it, this study models three recycling models using game theory based on practices: (1) settled model, (2) self-built model, and (3) dual-channel model. The equilibrium solutions of three models are solved, demonstrating that customers’ preference for the platform significantly influences recyclers’ investment strategies in convenience services. By integrating consumers’ preference for platform and recycling convenience service investments and by considering both positive and negative impacts of the recycling activities on the environment for three models, this study advances scholarship in waste recovery systems. The findings can support the design of economical and sustainable recycling policies for practitioners.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103900"},"PeriodicalIF":5.9,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736130","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}
Pub Date : 2025-12-11DOI: 10.1016/j.asej.2025.103891
Ahmet Yilmaz, İlya Kuş
This study addresses the challenge of hyperparameter selection, a key factor affecting convolutional neural networks (CNNs) performance in biomedical image classification. A genetic algorithm (GA) is employed to optimize activation function, padding, number of filters, kernel size, dropout rate, pooling size, and batch size. The optimized CNN is trained on brain Magnetic Resonance Imaging (MRI) images of Multiple Sclerosis (MS) and validated on Alzheimer’s MRI and COVID-19 chest X-ray datasets. Results show substantial improvements across all datasets. On MS, the proposed model achieves up to 37.6 % F1-score and 33.7 % accuracy gains compared to other models. On Alzheimer’s, improvements reach 32.8 % in F1-score and 32.5 % in accuracy. For COVID-19, gains are smaller but consistent, ranging from 0.8 % to 12.1 %. Overall, the GA-optimized CNN consistently outperforms widely used architectures such as Xception, InceptionV3, VGG16, VGG19, AlexNet, ResNet50, and GoogleNet, demonstrating both enhanced classification performance and strong generalizability across biomedical imaging tasks.
{"title":"General CNN model for biomedical image classification via genetic algorithm-based hyperparameter optimization","authors":"Ahmet Yilmaz, İlya Kuş","doi":"10.1016/j.asej.2025.103891","DOIUrl":"10.1016/j.asej.2025.103891","url":null,"abstract":"<div><div>This study addresses the challenge of hyperparameter selection, a key factor affecting convolutional neural networks (CNNs) performance in biomedical image classification. A genetic algorithm (GA) is employed to optimize activation function, padding, number of filters, kernel size, dropout rate, pooling size, and batch size. The optimized CNN is trained on brain Magnetic Resonance Imaging (MRI) images of Multiple Sclerosis (MS) and validated on Alzheimer’s MRI and COVID-19 chest X-ray datasets. Results show substantial improvements across all datasets. On MS, the proposed model achieves up to 37.6 % F1-score and 33.7 % accuracy gains compared to other models. On Alzheimer’s, improvements reach 32.8 % in F1-score and 32.5 % in accuracy. For COVID-19, gains are smaller but consistent, ranging from 0.8 % to 12.1 %. Overall, the GA-optimized CNN consistently outperforms widely used architectures such as Xception, InceptionV3, VGG16, VGG19, AlexNet, ResNet50, and GoogleNet, demonstrating both enhanced classification performance and strong generalizability across biomedical imaging tasks.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103891"},"PeriodicalIF":5.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736131","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}
Pub Date : 2025-12-11DOI: 10.1016/j.asej.2025.103920
Chee Lok Yong , An Qi Tan , Fengyi Zhang , Hwei Voon Lee , Kim Hung Mo
The incorporation of industrial by-product such as ground granulated blast furnace slag (GGBS) and ladle furnace slag (LFS) in cement-based materials has gained attention as a sustainable approach to reduce carbon emissions, as well as transforming them into valuable resources. However, their effectiveness in early-strength development and performance under accelerated carbonation remain underexplored. This study investigates the influence of GGBS and LFS on the early-age mechanical properties and carbonation behaviour of blended cement mortars exposed to accelerated CO2 curing (ACC). Mortar samples with up to 50 % replacement of cement by GGBS or LFS were prepared and subjected to both standard curing and ACC. Compressive strength tests are conducted to evaluate hardened performances. The hydration and carbonation behaviour of the samples are analysed through X-ray diffraction, thermogravimetric analysis, and microstructural characterisation. Results indicate that GGBS can replace cement up to 50 % while maintaining early strength through C-S-H formation. In contrast, LFS is effective only up to 10 %, as excess replacement leads to weaker C-A-H formation. Under ACC, GGBS-blended cement undergoes greater carbonation, while LFS-blended cement shows lower carbonation potential due to pre-existing CaCO3 and stable C-A-H phases. These findings demonstrate the potential of ACC to improve performance in GGBS-based green cement, whereas its benefit in LFS systems is limited by its inherent mineralogical stability. This highlights the importance of tailoring ACC parameters to slag composition is crucial for maximising performance and supporting the transition toward more sustainable and low-carbon construction technologies.
{"title":"Sustainable utilization of industrial furnace slags as CO2-reactive materials for construction","authors":"Chee Lok Yong , An Qi Tan , Fengyi Zhang , Hwei Voon Lee , Kim Hung Mo","doi":"10.1016/j.asej.2025.103920","DOIUrl":"10.1016/j.asej.2025.103920","url":null,"abstract":"<div><div>The incorporation of industrial by-product such as ground granulated blast furnace slag (GGBS) and ladle furnace slag (LFS) in cement-based materials has gained attention as a sustainable approach to reduce carbon emissions, as well as transforming them into valuable resources. However, their effectiveness in early-strength development and performance under accelerated carbonation remain underexplored. This study investigates the influence of GGBS and LFS on the early-age mechanical properties and carbonation behaviour of blended cement mortars exposed to accelerated CO<sub>2</sub> curing (ACC). Mortar samples with up to 50 % replacement of cement by GGBS or LFS were prepared and subjected to both standard curing and ACC. Compressive strength tests are conducted to evaluate hardened performances. The hydration and carbonation behaviour of the samples are analysed through X-ray diffraction, thermogravimetric analysis, and microstructural characterisation. Results indicate that GGBS can replace cement up to 50 % while maintaining early strength through C-S-H formation. In contrast, LFS is effective only up to 10 %, as excess replacement leads to weaker C-A-H formation. Under ACC, GGBS-blended cement undergoes greater carbonation, while LFS-blended cement shows lower carbonation potential due to pre-existing CaCO<sub>3</sub> and stable C-A-H phases. These findings demonstrate the potential of ACC to improve performance in GGBS-based green cement, whereas its benefit in LFS systems is limited by its inherent mineralogical stability. This highlights the importance of tailoring ACC parameters to slag composition is crucial for maximising performance and supporting the transition toward more sustainable and low-carbon construction technologies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103920"},"PeriodicalIF":5.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736129","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}
Pub Date : 2025-12-11DOI: 10.1016/j.asej.2025.103896
Betul Mete , Emirhan Mustafa Anık , Sinan Nacar , Adem Bayram , Murat Kankal
This study evaluates the comparative performance of different neural network models in predicting dissolved oxygen (DO) concentrations in the Clackamas River, USA. It examines the effects of site characteristics, water flow and quality parameters, and data distribution characteristics on these predictions. The study comprehensively compares the Kolmogorov–Arnold networks (KANs) method, applied for the first time in this study, with the multilayer perceptron (MLP), bidirectional long short-term memory (Bi-LSTM), and bidirectional gated recurrent unit (Bi-GRU) methods. Eight models were created using daily mean water temperature (T), discharge (Q), pH, specific conductance (SC), and DO data from two different monitoring site for the 2019–2021 period, and the models were evaluated using four performance metrics. Uncertainty (prediction interval) and significance (paired t-test) analyses were also applied to evaluate the prediction success of the methods from a different perspective than performance metrics. Furthermore, the relationship between input features and DO concentration was examined using the LOWESS curves with SHAP values. The results revealed that the KANs and MLP methods exhibited higher accuracy than Bi-LSTM and Bi-GRU. The KANs method provides a significant advantage in high prediction success and interpretability due to its ability to generate symbolic equations. Furthermore, it was determined that the distribution characteristics of the input variables affected the performance of MLP, Bi-LSTM, and Bi-GRU more than KANs. Logarithmic transformation improved the model success in non-normally distributed data. This study fills an essential gap in literature by applying the KANs method to water quality modeling for the first time. The results show that the KANs method offers an explainable, reliable, and low-data alternative, and therefore can be an effective tool for DO prediction and water quality management in conditions where data deficiencies are experienced.
{"title":"Predicting dissolved oxygen concentration using different neural network models","authors":"Betul Mete , Emirhan Mustafa Anık , Sinan Nacar , Adem Bayram , Murat Kankal","doi":"10.1016/j.asej.2025.103896","DOIUrl":"10.1016/j.asej.2025.103896","url":null,"abstract":"<div><div>This study evaluates the comparative performance of different neural network models in predicting dissolved oxygen (DO) concentrations in the Clackamas River, USA. It examines the effects of site characteristics, water flow and quality parameters, and data distribution characteristics on these predictions. The study comprehensively compares the Kolmogorov–Arnold networks (KANs) method, applied for the first time in this study, with the multilayer perceptron (MLP), bidirectional long short-term memory (Bi-LSTM), and bidirectional gated recurrent unit (Bi-GRU) methods. Eight models were created using daily mean water temperature (T), discharge (Q), pH, specific conductance (SC), and DO data from two different monitoring site for the 2019–2021 period, and the models were evaluated using four performance metrics. Uncertainty (prediction interval) and significance (paired <em>t</em>-test) analyses were also applied to evaluate the prediction success of the methods from a different perspective than performance metrics. Furthermore, the relationship between input features and DO concentration was examined using the LOWESS curves with SHAP values. The results revealed that the KANs and MLP methods exhibited higher accuracy than Bi-LSTM and Bi-GRU. The KANs method provides a significant advantage in high prediction success and interpretability due to its ability to generate symbolic equations. Furthermore, it was determined that the distribution characteristics of the input variables affected the performance of MLP, Bi-LSTM, and Bi-GRU more than KANs. Logarithmic transformation improved the model success in non-normally distributed data. This study fills an essential gap in literature by applying the KANs method to water quality modeling for the first time. The results show that the KANs method offers an explainable, reliable, and low-data alternative, and therefore can be an effective tool for DO prediction and water quality management in conditions where data deficiencies are experienced.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103896"},"PeriodicalIF":5.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736127","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}
Pub Date : 2025-12-10DOI: 10.1016/j.asej.2025.103887
Anas Alsuhaibani , Tallha Akram , Adeel Akram
Plant diseases pose a significant risk to global nutrition and can have a severe impact on small-scale farmers who rely on their crops for survival. Early and accurate detection of plant diseases is essential, yet traditional identification methods are often time-intensive and prone to human error. The development of Computer-Aided Diagnostic (CAD) systems facilitates the early detection of plant diseases for both farmers and experts. These sets of intelligent systems utilize machine learning and computer vision-based techniques to identify and categorize leaf diseases accurately. Such automated approaches not only save time and reduce labor costs but also minimize crop losses by optimizing the yield. This article presents a comprehensive framework for leaf disease classification of three main crops, beginning with image acquisition, proceeding to feature extraction and selection, and concluding with classification. The existence of redundant and irrelevant feature information leads to the problem of “curse of dimensionality”. To address this challenge, a bio-inspired optimization approach, known as the Entropy-Controlled Generalized Learning Equilibrium Optimizer (E-CGLEO), is proposed. Unlike the standard GLEO, we used the entropy-based technique to select more diverse features. The conventional GLEO had various constraints that are effectively addressed by our proposed approach: (1) minimal diversity, (2) selection of redundant feature information, and (3) selection based on structural contribution, leading to overfitting. The proposed feature selection framework successfully addresses the identified problems by modifying the objective function and equilibrium condition, while also updating velocity and position, thereby enhancing performance in terms of accuracy, precision, sensitivity, and F1-score.
{"title":"LeafDeSNet: A MultiClass plant leaf diseases classification model with entropy-controlled GLEO for feature selection","authors":"Anas Alsuhaibani , Tallha Akram , Adeel Akram","doi":"10.1016/j.asej.2025.103887","DOIUrl":"10.1016/j.asej.2025.103887","url":null,"abstract":"<div><div>Plant diseases pose a significant risk to global nutrition and can have a severe impact on small-scale farmers who rely on their crops for survival. Early and accurate detection of plant diseases is essential, yet traditional identification methods are often time-intensive and prone to human error. The development of Computer-Aided Diagnostic (CAD) systems facilitates the early detection of plant diseases for both farmers and experts. These sets of intelligent systems utilize machine learning and computer vision-based techniques to identify and categorize leaf diseases accurately. Such automated approaches not only save time and reduce labor costs but also minimize crop losses by optimizing the yield. This article presents a comprehensive framework for leaf disease classification of three main crops, beginning with image acquisition, proceeding to feature extraction and selection, and concluding with classification. The existence of redundant and irrelevant feature information leads to the problem of “<em>curse of dimensionality</em>”. To address this challenge, a bio-inspired optimization approach, known as the Entropy-Controlled Generalized Learning Equilibrium Optimizer (E-CGLEO), is proposed. Unlike the standard GLEO, we used the entropy-based technique to select more diverse features. The conventional GLEO had various constraints that are effectively addressed by our proposed approach: (1) minimal diversity, (2) selection of redundant feature information, and (3) selection based on structural contribution, leading to overfitting. The proposed feature selection framework successfully addresses the identified problems by modifying the objective function and equilibrium condition, while also updating velocity and position, thereby enhancing performance in terms of accuracy, precision, sensitivity, and F1-score.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103887"},"PeriodicalIF":5.9,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736125","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}
Pub Date : 2025-12-09DOI: 10.1016/j.asej.2025.103919
Dalia Amer Ali , Amir Ahmed Elgamal , Rania Rushdy Moussa
This study investigated the efficiency of thermal carbon chitosan (TCCS) sorbent for CO2 capture from vehicle exhaust emissions within a designed adsorption system. TCCS was synthesized and meticulously characterized using a series of analytical techniques, including Brunauer-Emmett-Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Energy Dispersive X-ray Spectroscopy (EDX), and Differential Scanning Calorimetry (DSC). The TCCS adsorbent showed high thermal stability and a heating value (HHV) of 23.5 MJ/kg. Adsorption isotherm study demonstrated that the maximum capacity of CO2 adsorption is 0.084 kg.CO2/kg.TCCS, as well as confirmation of the exothermic nature of the process with an enthalpy change (ΔH) of −26.42 kJ/mol. Kinetics study indicated that the adsorption mechanism was physical in nature, characterized by an activation energy (ED) of 4.27 kJ/mol, which is lower than the threshold of 8 kJ/mol. The experimental breakthrough curve revealed a breakpoint time (tb) of 1280 s, a saturation time (ts) of 2300 s and illustrated that about 70 % of the adsorption bed (Hb) was used during the CO2 adsorption process. To further validate the experimental results, a Computational Fluid Dynamics (CFD) simulation was conducted, revealing a strong correlation with the experimental data. The low error values between the experimental and CFD predicted results underscore the reliability of the TCCS-based adsorption system for effective CO2 capture. This research contributes valuable insight into the potential of TCCS as a sustainable adsorbent for mitigating CO2 emissions from automotive sources.
{"title":"Advancing sustainable CO2 mitigation: Experimental and computational analysis of thermal carbon chitosan sorbent for automotive exhaust capture","authors":"Dalia Amer Ali , Amir Ahmed Elgamal , Rania Rushdy Moussa","doi":"10.1016/j.asej.2025.103919","DOIUrl":"10.1016/j.asej.2025.103919","url":null,"abstract":"<div><div>This study investigated the efficiency of thermal carbon chitosan (TCCS) sorbent for CO<sub>2</sub> capture from vehicle exhaust emissions within a designed adsorption system. TCCS was synthesized and meticulously characterized using a series of analytical techniques, including Brunauer-Emmett-Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Energy Dispersive X-ray Spectroscopy (EDX), and Differential Scanning Calorimetry (DSC). The TCCS adsorbent showed high thermal stability and a heating value (HHV) of 23.5 MJ/kg. Adsorption isotherm study demonstrated that the maximum capacity of CO<sub>2</sub> adsorption is 0.084 kg.CO<sub>2</sub>/kg.TCCS, as well as confirmation of the exothermic nature of the process with an enthalpy change (ΔH) of −26.42 kJ/mol. Kinetics study indicated that the adsorption mechanism was physical in nature, characterized by an activation energy (E<sub>D</sub>) of 4.27 kJ/mol, which is lower than the threshold of 8 kJ/mol. The experimental breakthrough curve revealed a breakpoint time (t<sub>b</sub>) of 1280 s, a saturation time (t<sub>s</sub>) of 2300 s and illustrated that about 70 % of the adsorption bed (H<sub>b</sub>) was used during the CO<sub>2</sub> adsorption process. To further validate the experimental results, a Computational Fluid Dynamics (CFD) simulation was conducted, revealing a strong correlation with the experimental data. The low error values between the experimental and CFD predicted results underscore the reliability of the TCCS-based adsorption system for effective CO<sub>2</sub> capture. This research contributes valuable insight into the potential of TCCS as a sustainable adsorbent for mitigating CO<sub>2</sub> emissions from automotive sources.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103919"},"PeriodicalIF":5.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736126","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}
Pub Date : 2025-12-05DOI: 10.1016/j.asej.2025.103881
Bashar Al-Haj Moh’d , Amin Al-Habaibeh , Mohammad Al Takrouri
<div><div>Photovoltaic (PV) water heating systems are one of the water heating technologies that have been attracting significant attention in recent years. Although from the thermodynamics point of view, solar thermal systems such as vacuum tubes, are more efficient in heating water for the same area exposed to the sun, but this is not the only factor influence the utilisation of solar energy. The main reason for the interest in using PV solar systems for water heating and thermal storage is their lower cost, higher reliability, reduced maintenance, water savings and minimising complexity of retrofitting. In this work, a novel PV water heating system is proposed where the near maximum power point tracking (Near-MPPT) is performed using different rated heating elements via a simple control circuit without the need for a relatively expensive DC-DC converter. The selection of the heating elements equivalent resistance is made based on the photovoltaic array size and the solar irradiation data. An optimisation process is considered in the design to reduce cost and maximise efficiency. A novel generic methodology is developed to select the values of the hating elements for optimum performance. This has been achieved by obtaining the maximum number of possible variations in resistance configurations from two selected heating elements while sustaining high efficiency and DC voltage switching capability. The control circuit is designed by utilising electromechanical and semiconductor switching devices. A control algorithm is created to track the maximum power point via the manipulation of the switching circuit using PV power measurements. The proposed system is compared to a PV system equipped with a DC-DC converter and a PV system directly connected to fixed heating elements. The system is experimentally tested to evaluate the idea including the payback period, performance and costings. Further, simulation is conducted and validated for conditions in Jordan as well as Egypt and the UK. The results have shown that proposed system provides a lower cost and reasonable efficiency when compared to the conventional system. The results of the proposed solar technology system in Jordan, for example, have shown a small reduction in the yearly produced energy of only 3.8 % when compared to a conversional system equipped with MPPT DC-DC converter system and an increase in efficiency of about 11.5 % in comparison to a fixed load system. However, the cost saving is expected to be 44.7 % when the proposed simple switching system is compared to a standard DC-DC converter; with water savings in winter of 83.7 % in comparison to a solar thermal system. The payback period of the system for Jordan, Egypt and the UK is found approximately 2.75 years, 6.5 years and 3 years respectively. In comparison to the conventional MPPT DC-DC converter, the proposed novel system Near-MPPT is expected to provide a much lower cost system (between 24.5 % and 44.7 %) to encourage the adap
{"title":"An innovative low-cost and water-saving direct photovoltaic water heating system using simplified near maximum power point tracking","authors":"Bashar Al-Haj Moh’d , Amin Al-Habaibeh , Mohammad Al Takrouri","doi":"10.1016/j.asej.2025.103881","DOIUrl":"10.1016/j.asej.2025.103881","url":null,"abstract":"<div><div>Photovoltaic (PV) water heating systems are one of the water heating technologies that have been attracting significant attention in recent years. Although from the thermodynamics point of view, solar thermal systems such as vacuum tubes, are more efficient in heating water for the same area exposed to the sun, but this is not the only factor influence the utilisation of solar energy. The main reason for the interest in using PV solar systems for water heating and thermal storage is their lower cost, higher reliability, reduced maintenance, water savings and minimising complexity of retrofitting. In this work, a novel PV water heating system is proposed where the near maximum power point tracking (Near-MPPT) is performed using different rated heating elements via a simple control circuit without the need for a relatively expensive DC-DC converter. The selection of the heating elements equivalent resistance is made based on the photovoltaic array size and the solar irradiation data. An optimisation process is considered in the design to reduce cost and maximise efficiency. A novel generic methodology is developed to select the values of the hating elements for optimum performance. This has been achieved by obtaining the maximum number of possible variations in resistance configurations from two selected heating elements while sustaining high efficiency and DC voltage switching capability. The control circuit is designed by utilising electromechanical and semiconductor switching devices. A control algorithm is created to track the maximum power point via the manipulation of the switching circuit using PV power measurements. The proposed system is compared to a PV system equipped with a DC-DC converter and a PV system directly connected to fixed heating elements. The system is experimentally tested to evaluate the idea including the payback period, performance and costings. Further, simulation is conducted and validated for conditions in Jordan as well as Egypt and the UK. The results have shown that proposed system provides a lower cost and reasonable efficiency when compared to the conventional system. The results of the proposed solar technology system in Jordan, for example, have shown a small reduction in the yearly produced energy of only 3.8 % when compared to a conversional system equipped with MPPT DC-DC converter system and an increase in efficiency of about 11.5 % in comparison to a fixed load system. However, the cost saving is expected to be 44.7 % when the proposed simple switching system is compared to a standard DC-DC converter; with water savings in winter of 83.7 % in comparison to a solar thermal system. The payback period of the system for Jordan, Egypt and the UK is found approximately 2.75 years, 6.5 years and 3 years respectively. In comparison to the conventional MPPT DC-DC converter, the proposed novel system Near-MPPT is expected to provide a much lower cost system (between 24.5 % and 44.7 %) to encourage the adap","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 1","pages":"Article 103881"},"PeriodicalIF":5.9,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684334","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}
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