Pub Date : 2024-11-08DOI: 10.1016/j.aej.2024.10.068
Mohammed Zidan , Mohamed N. El-Qersh , Mahmoud Abdel-Aty , Montasir Qasymeh , Hichem Eleuch
Distinguishing unknown non-orthogonal qubits is an essential requirement for addressing various challenges in quantum computation, including quantum machine learning, quantum communications, and quantum technologies. For instance, while quantum teleportation enables the transfer of unknown individual qubits between distant parties, an algorithm is necessary to define the associated state of a teleported qubit at the receiving end. In this paper, we propose a novel quantum algorithm designed to effectively determine the state of a given unknown qubit and distinguish a subset of non-orthogonal qubits. The proposed algorithm can efficiently identify the state of an unknown qubit in the form using the operator. By estimating the angle through the measurement of entanglement degree, the proposed algorithm can identify the state of an unknown qubit. Experimental validation of the proposed algorithm is conducted using the IBM quantum computer simulator chip ibmqx2. Furthermore, a t-test is conducted to compare the proposed algorithm with the direct measurement approach. The results indicate a significant difference between the two methods, demonstrating the superior performance of the proposed algorithm.
要应对量子计算（包括量子机器学习、量子通信和量子技术）中的各种挑战，区分未知的非正交量子比特是一项基本要求。例如，虽然量子远距传态可以在远距离双方之间传输未知的单个量子比特，但需要一种算法来定义接收端远距传态量子比特的相关状态。在本文中，我们提出了一种新型量子算法，旨在有效确定给定未知量子比特的状态，并区分非正交量子比特子集。所提出的算法可以利用 Mz 算子有效地识别 cosθ2|0〉+sinθ2|1〉 形式的未知量子比特状态。通过测量纠缠度来估计角度θ，所提出的算法可以识别未知量子比特的状态。利用 IBM 量子计算机模拟芯片 ibmqx2 对所提算法进行了实验验证。此外，还进行了 t 检验，以比较提出的算法和直接测量方法。结果表明，两种方法之间存在显著差异，证明了所提算法的优越性能。
{"title":"A quantum entanglement-based algorithm for discriminating non-orthogonal qubits","authors":"Mohammed Zidan , Mohamed N. El-Qersh , Mahmoud Abdel-Aty , Montasir Qasymeh , Hichem Eleuch","doi":"10.1016/j.aej.2024.10.068","DOIUrl":"10.1016/j.aej.2024.10.068","url":null,"abstract":"<div><div>Distinguishing unknown non-orthogonal qubits is an essential requirement for addressing various challenges in quantum computation, including quantum machine learning, quantum communications, and quantum technologies. For instance, while quantum teleportation enables the transfer of unknown individual qubits between distant parties, an algorithm is necessary to define the associated state of a teleported qubit at the receiving end. In this paper, we propose a novel quantum algorithm designed to effectively determine the state of a given unknown qubit and distinguish a subset of non-orthogonal qubits. The proposed algorithm can efficiently identify the state of an unknown qubit in the form <span><math><mrow><mo>cos</mo><mfenced><mrow><mfrac><mrow><mi>θ</mi></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></mfenced><mrow><mo>|</mo><mn>0</mn><mo>〉</mo></mrow><mo>+</mo><mo>sin</mo><mfenced><mrow><mfrac><mrow><mi>θ</mi></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></mfenced><mrow><mo>|</mo><mn>1</mn><mo>〉</mo></mrow></mrow></math></span> using the <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>z</mi></mrow></msub></math></span> operator. By estimating the angle <span><math><mi>θ</mi></math></span> through the measurement of entanglement degree, the proposed algorithm can identify the state of an unknown qubit. Experimental validation of the proposed algorithm is conducted using the IBM quantum computer simulator chip ibmqx2. Furthermore, a t-test is conducted to compare the proposed algorithm with the direct measurement approach. The results indicate a significant difference between the two methods, demonstrating the superior performance of the proposed algorithm.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 339-348"},"PeriodicalIF":6.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663559","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}
Pub Date : 2024-11-08DOI: 10.1016/j.aej.2024.09.097
Mehmet Rizelioğlu
This study presents a current and extensive bibliometric analysis of pavement deterioration detection, monitoring, and assessment using various sensors alongside machine learning and deep learning algorithms. The impact of electronic sensors, machine learning, and deep learning on road pavement evaluation and monitoring within the transportation sector is highlighted. Conducting a bibliometric analysis covering research until March 1, 2024, 639 publications from 71 countries were examined. Productive countries, journals, institutions, and authors were analyzed and ranked. A standard research score and cumulative output score were calculated to normalize differences in the data. The findings reveal a significant recent increase in studies in this area. The most productive countries, journals, institutions, and authors are China, Transportation Research Record, Southeast University China, and Golroo Amir, respectively. This study serves as a valuable resource for both academic and industry researchers, offering insights into road pavement monitoring and guiding future research. In addition, accelerometer and GPS were the most used sensors, ANN and CNN were the most preferred algorithms, and cracks and potholes were the most studied topics. This study has the potential to be a good map for both academic and industrial researchers for monitoring the state of road pavements and a good guide.
{"title":"An extensive bibliometric analysis of pavement deterioration detection using sensors and machine learning: Trends, innovations, and future directions","authors":"Mehmet Rizelioğlu","doi":"10.1016/j.aej.2024.09.097","DOIUrl":"10.1016/j.aej.2024.09.097","url":null,"abstract":"<div><div>This study presents a current and extensive bibliometric analysis of pavement deterioration detection, monitoring, and assessment using various sensors alongside machine learning and deep learning algorithms. The impact of electronic sensors, machine learning, and deep learning on road pavement evaluation and monitoring within the transportation sector is highlighted. Conducting a bibliometric analysis covering research until March 1, 2024, 639 publications from 71 countries were examined. Productive countries, journals, institutions, and authors were analyzed and ranked. A standard research score and cumulative output score were calculated to normalize differences in the data. The findings reveal a significant recent increase in studies in this area. The most productive countries, journals, institutions, and authors are China, Transportation Research Record, Southeast University China, and Golroo Amir, respectively. This study serves as a valuable resource for both academic and industry researchers, offering insights into road pavement monitoring and guiding future research. In addition, accelerometer and GPS were the most used sensors, ANN and CNN were the most preferred algorithms, and cracks and potholes were the most studied topics. This study has the potential to be a good map for both academic and industrial researchers for monitoring the state of road pavements and a good guide.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 349-366"},"PeriodicalIF":6.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663560","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}
Pub Date : 2024-11-08DOI: 10.1016/j.aej.2024.11.005
Lili Zhang , Kang Yang , Ke Zhang , Wei Wei , Jing Li , Hongxin Tan
Fixed-section detection methods, with radar and video as representatives, frequently encounter incomplete detection data at controlled intersections because of high construction costs and insufficient maintenance. This results in ineffective signal control strategies. On the other hand, mobile detection methods, represented by floating cars, can perceive both macro and micro spatial-temporal characteristics of traffic flow. However, their current low penetration rate limits their ability to provide sufficient data support for signal control at intersections.To address this issue, this paper proposes an innovative method to obtain more accurate flow rates for each phase at an intersection through simulation approximation of calibrated parameters. This method utilizes the Webster delay theory to quantitatively describe the relationship between phase flow and vehicle delay, allowing for the inverse estimation of flow rates. These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. Comprehensive experimental results demonstrate that the proposed method effectively improves the accuracy of inverse flow data estimation. This enables the effective utilization of low-penetration-rate floating car data (FCD) in signal control at urban intersections. By leveraging this innovative approach, signal control systems can make more informed decisions, leading to smoother traffic flow and improved traffic management in urban areas.
{"title":"Estimating traffic flow at urban intersections using low occupancy floating vehicle data","authors":"Lili Zhang , Kang Yang , Ke Zhang , Wei Wei , Jing Li , Hongxin Tan","doi":"10.1016/j.aej.2024.11.005","DOIUrl":"10.1016/j.aej.2024.11.005","url":null,"abstract":"<div><div>Fixed-section detection methods, with radar and video as representatives, frequently encounter incomplete detection data at controlled intersections because of high construction costs and insufficient maintenance. This results in ineffective signal control strategies. On the other hand, mobile detection methods, represented by floating cars, can perceive both macro and micro spatial-temporal characteristics of traffic flow. However, their current low penetration rate limits their ability to provide sufficient data support for signal control at intersections.To address this issue, this paper proposes an innovative method to obtain more accurate flow rates for each phase at an intersection through simulation approximation of calibrated parameters. This method utilizes the Webster delay theory to quantitatively describe the relationship between phase flow and vehicle delay, allowing for the inverse estimation of flow rates. These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. Comprehensive experimental results demonstrate that the proposed method effectively improves the accuracy of inverse flow data estimation. This enables the effective utilization of low-penetration-rate floating car data (FCD) in signal control at urban intersections. By leveraging this innovative approach, signal control systems can make more informed decisions, leading to smoother traffic flow and improved traffic management in urban areas.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 374-383"},"PeriodicalIF":6.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663537","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}
Pub Date : 2024-11-08DOI: 10.1016/j.aej.2024.10.101
C. Rajathi, P. Rukmani
The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.
{"title":"Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers","authors":"C. Rajathi, P. Rukmani","doi":"10.1016/j.aej.2024.10.101","DOIUrl":"10.1016/j.aej.2024.10.101","url":null,"abstract":"<div><div>The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 384-396"},"PeriodicalIF":6.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663538","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}
Pub Date : 2024-11-08DOI: 10.1016/j.aej.2024.10.123
Awatif J. Alqarni , Essam M. Elsaid , Mohamed R. Eid , Mohamed S. Abdel-wahed
Heat features of a ternary nanofluid are examined in a heated flowing condition among a curvy conduit and endoscope that undergo peristalsis motion and sinusoidal variabilities. Three nanoparticles, copper, silver, and aluminum oxide were dispersed in blood as a basic fluid to study their potential effects on the flow and temperatures of the mixed fluid under thermal radiation and magnetic resonance, as well as the system's entropy optimization. The authors explored this system to understand flowing and heat diffusion in peristaltic conduits and as a medical application that may offer a future perspective for all researchers. Continuity and energy equations in their partial differential form related to the Maxwell equation due to the influence of radial magnetic force determined issue modeling based on basic regulating equations. This system was simplified by assuming a long wavelength and translated to ODEs using similarity. Closed-form solutions in magnetic fields were calculated using Mathematica software. Comparing results to earlier studies proved validity. Figures and tables showed how the issue factors affected pumping, temperature, pressure gradient, and heat transfer rate. The most significant findings record that the boluses density climbs considerably and the pressure gradient grows up as the magnetic resonance and gab ratio increases.
{"title":"Enhancement of blood flow containing tri-nanoparticles between bent peristaltic conduit and endoscope via thermal radiation and magnetic resonance","authors":"Awatif J. Alqarni , Essam M. Elsaid , Mohamed R. Eid , Mohamed S. Abdel-wahed","doi":"10.1016/j.aej.2024.10.123","DOIUrl":"10.1016/j.aej.2024.10.123","url":null,"abstract":"<div><div>Heat features of a ternary nanofluid are examined in a heated flowing condition among a curvy conduit and endoscope that undergo peristalsis motion and sinusoidal variabilities. Three nanoparticles, copper, silver, and aluminum oxide were dispersed in blood as a basic fluid to study their potential effects on the flow and temperatures of the mixed fluid under thermal radiation and magnetic resonance, as well as the system's entropy optimization. The authors explored this system to understand flowing and heat diffusion in peristaltic conduits and as a medical application that may offer a future perspective for all researchers. Continuity and energy equations in their partial differential form related to the Maxwell equation due to the influence of radial magnetic force determined issue modeling based on basic regulating equations. This system was simplified by assuming a long wavelength and translated to ODEs using similarity. Closed-form solutions in magnetic fields were calculated using Mathematica software. Comparing results to earlier studies proved validity. Figures and tables showed how the issue factors affected pumping, temperature, pressure gradient, and heat transfer rate. The most significant findings record that the boluses density climbs considerably and the pressure gradient grows up as the magnetic resonance and gab ratio increases.</div></div><div><h3>Data Availability</h3><div>Manuscript has no associated data.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 397-410"},"PeriodicalIF":6.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663539","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}
Pub Date : 2024-11-07DOI: 10.1016/j.aej.2024.10.104
M.H. Heydari , D. Baleanu , M. Bayramu
In this paper, a new type of piecewise fractional derivative (PFD) is introduced. The ordinary and distributed-order fractional derivatives in the Caputo sense are used to define this type of PFD. A new version of nonlinear reaction–diffusion equations with variable coefficients is defined using this type of PFD. The orthonormal piecewise second kind Chebyshev functions (CFs), as a new family of basic functions, are generated. An explicit formula is extracted for PFD of these piecewise functions. A hybrid method based on the orthonormal piecewise second kind CFs and orthonormal second kind Chebyshev polynomials is proposed to solve the aforementioned problem. The established approach transforms solving the expressed problem into solving an algebraic system of equations. To illustrate the accuracy of the developed method, some numerical examples are considered.
{"title":"Piecewise second kind Chebyshev functions for a class of piecewise fractional nonlinear reaction–diffusion equations with variable coefficients","authors":"M.H. Heydari , D. Baleanu , M. Bayramu","doi":"10.1016/j.aej.2024.10.104","DOIUrl":"10.1016/j.aej.2024.10.104","url":null,"abstract":"<div><div>In this paper, a new type of piecewise fractional derivative (PFD) is introduced. The ordinary and distributed-order fractional derivatives in the Caputo sense are used to define this type of PFD. A new version of nonlinear reaction–diffusion equations with variable coefficients is defined using this type of PFD. The orthonormal piecewise second kind Chebyshev functions (CFs), as a new family of basic functions, are generated. An explicit formula is extracted for PFD of these piecewise functions. A hybrid method based on the orthonormal piecewise second kind CFs and orthonormal second kind Chebyshev polynomials is proposed to solve the aforementioned problem. The established approach transforms solving the expressed problem into solving an algebraic system of equations. To illustrate the accuracy of the developed method, some numerical examples are considered.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 319-326"},"PeriodicalIF":6.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663589","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}
Pub Date : 2024-11-07DOI: 10.1016/j.aej.2024.10.119
Nurhana Mohamad , Shuguang Li , Umair Khan , Anuar Ishak , Ali Elrashidi , Mohammed Zakarya
Investigating the effects of waste discharge on nanofluids using a non-Newtonian fluid model is vital for enhancing heat and mass transfer performance in engineering systems, such as cooling systems in power plants, oil, and gas drilling operations, and wastewater treatment facilities, while simultaneously mitigating the environmental impact of pollutant diffusion in these industrial processes. Therefore, this study examines the effects of porous medium, thermal radiation, magnetic effect, and external pollutants in a water-based ternary hybrid nanofluid flow within the context of the Reiner-Philippoff fluid model. The suitable similarity transformations are utilized to transform the partial differential equations (PDEs) into ordinary differential equations (ODEs). The resulting set of ODEs are solved numerically to find the solutions using the function bvp4c available in MATLAB software. The ternary hybrid nanofluid (Ag-Cu-TiO2) significantly enhances heat and mass transfer rates by about 42.72 % and 2.53 % compared to water (H2O) at around 4.36 % and 0.60 % relative to the hybrid nanofluid (Ag-TiO2), respectively. In pollutant-free conditions, the heat and mass transfer of ternary hybrid nanofluid (Ag-Cu-TiO2) progresses up to 0.34 % and 0.26 %, respectively, compared to H2O. Meanwhile, for hybrid nanofluid (Ag-TiO2), it develops by about 0.24 % and 0.31 %, respectively. This indicates that the impact of the external pollutants significantly delays mass transfer but increases the concentration field and destabilizes the flow near the shrinking sheet. Trio slip parameters reduce shear stress, heat, and mass transfer rates, while the mixed convection parameter enhances the skin friction coefficient in the assisting flow and diminishes it in the opposing flow. The magnetic parameter enlarges shear stress with the help of the Lorentz force but thermal radiation increases the heat transfer rate while reducing surface drag. Additionally, nanoparticle volume fractions and the porous medium elevate shear stress and heat transfer rate. This research provides insights into optimizing nanofluids in pollutant-laden environments, with potential applications in industrial processes involving heat exchangers and pollution control.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
{"title":"Role of stability analysis and waste discharge concentration of ternary hybrid nanofluid in a non-Newtonian model with slip boundary conditions","authors":"Nurhana Mohamad , Shuguang Li , Umair Khan , Anuar Ishak , Ali Elrashidi , Mohammed Zakarya","doi":"10.1016/j.aej.2024.10.119","DOIUrl":"10.1016/j.aej.2024.10.119","url":null,"abstract":"<div><div>Investigating the effects of waste discharge on nanofluids using a non-Newtonian fluid model is vital for enhancing heat and mass transfer performance in engineering systems, such as cooling systems in power plants, oil, and gas drilling operations, and wastewater treatment facilities, while simultaneously mitigating the environmental impact of pollutant diffusion in these industrial processes. Therefore, this study examines the effects of porous medium, thermal radiation, magnetic effect, and external pollutants in a water-based ternary hybrid nanofluid flow within the context of the Reiner-Philippoff fluid model. The suitable similarity transformations are utilized to transform the partial differential equations (PDEs) into ordinary differential equations (ODEs). The resulting set of ODEs are solved numerically to find the solutions using the function bvp4c available in MATLAB software. The ternary hybrid nanofluid (Ag-Cu-TiO<sub>2</sub>) significantly enhances heat and mass transfer rates by about 42.72 % and 2.53 % compared to water (H<sub>2</sub>O) at around 4.36 % and 0.60 % relative to the hybrid nanofluid (Ag-TiO<sub>2</sub>), respectively. In pollutant-free conditions, the heat and mass transfer of ternary hybrid nanofluid (Ag-Cu-TiO2) progresses up to 0.34 % and 0.26 %, respectively, compared to H<sub>2</sub>O. Meanwhile, for hybrid nanofluid (Ag-TiO2), it develops by about 0.24 % and 0.31 %, respectively. This indicates that the impact of the external pollutants significantly delays mass transfer but increases the concentration field and destabilizes the flow near the shrinking sheet. Trio slip parameters reduce shear stress, heat, and mass transfer rates, while the mixed convection parameter enhances the skin friction coefficient in the assisting flow and diminishes it in the opposing flow. The magnetic parameter enlarges shear stress with the help of the Lorentz force but thermal radiation increases the heat transfer rate while reducing surface drag. Additionally, nanoparticle volume fractions and the porous medium elevate shear stress and heat transfer rate. This research provides insights into optimizing nanofluids in pollutant-laden environments, with potential applications in industrial processes involving heat exchangers and pollution control.</div></div><div><h3>Data availability</h3><div>The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 327-338"},"PeriodicalIF":6.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663556","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}
Pub Date : 2024-11-06DOI: 10.1016/j.aej.2024.11.002
Zhiyi Liu , Deqing Gan , Haikuan Sun , Zhenlin Xue , Youzhi Zhang
Cemented paste backfill (CPB) is easy to withstand the coupling effect of the mining operation equipment's crushing, water immersion, forming all kinds of intrinsic or extrinsic defects affecting its load-bearing capacity. In this paper, the initial immersion age and immersion time were used as variables, the damage and uniaxial compression characteristics of CPB under the coupling effect of water-static load were explored. Results show that the damage of water-immersed CPB under static load are mainly affected by water lubrication and pore water pressure and it improves the plastic deformation of CPB and weakens the energy storage capacity. When the initial immersion age was 3d, the effect is more significant. Water immersion increases the rate of damage with strain before peak strain and decreases the rate of damage with strain after peak strain. The strength of CPB varies from 0.3 MPa to 0.8 MPa at the same initial immersion age. The damage constitutive model of CPB under water-static load coupling is established, and the damage mechanism is revealed. Compared with the immersion time, reducing the initial immersion age is the key factor to improve the structure stability of CPB.
{"title":"Investigation on structure stability and damage mechanism of cemented paste backfill under the coupling effect of water-static load","authors":"Zhiyi Liu , Deqing Gan , Haikuan Sun , Zhenlin Xue , Youzhi Zhang","doi":"10.1016/j.aej.2024.11.002","DOIUrl":"10.1016/j.aej.2024.11.002","url":null,"abstract":"<div><div>Cemented paste backfill (CPB) is easy to withstand the coupling effect of the mining operation equipment's crushing, water immersion, forming all kinds of intrinsic or extrinsic defects affecting its load-bearing capacity. In this paper, the initial immersion age and immersion time were used as variables, the damage and uniaxial compression characteristics of CPB under the coupling effect of water-static load were explored. Results show that the damage of water-immersed CPB under static load are mainly affected by water lubrication and pore water pressure and it improves the plastic deformation of CPB and weakens the energy storage capacity. When the initial immersion age was 3d, the effect is more significant. Water immersion increases the rate of damage with strain before peak strain and decreases the rate of damage with strain after peak strain. The strength of CPB varies from 0.3 MPa to 0.8 MPa at the same initial immersion age. The damage constitutive model of CPB under water-static load coupling is established, and the damage mechanism is revealed. Compared with the immersion time, reducing the initial immersion age is the key factor to improve the structure stability of CPB.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 307-318"},"PeriodicalIF":6.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593085","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}
Pub Date : 2024-11-06DOI: 10.1016/j.aej.2024.10.010
Rongbao Huang , Bo Zhang , Zhixin Yao , Bojun Xie , Jia Guo
With the rapid development of IoT technology, real-time human pose estimation has become increasingly important in sports training feedback systems. However, current methods often fall short in balancing high accuracy with low computational resource requirements, especially in resource-constrained environments. Deep learning has shown significant potential in enhancing computer vision tasks, including human pose estimation. In this study, we propose DESNet, an improved EfficientHRNet model that integrates IoT technology. DESNet combines Dynamic Multi-Scale Context (DMC) modules and Squeeze-and-Excitation (SE) modules, and utilizes IoT for real-time data collection, transmission, and processing. Experimental results show that DESNet achieves an average precision (AP) of 74.8% on the COCO dataset and a PCKh (Percentage of Correct Keypoints with head-normalized) of 90.9% on the MPII dataset, outperforming existing lightweight models. The integration of deep learning and IoT technology not only improves the accuracy and efficiency of human pose estimation but also significantly enhances the timeliness and robustness of feedback in sports training applications. Our findings demonstrate that DESNet is a powerful tool for real-time human pose analysis, offering promising solutions for intelligent sports training and rehabilitation systems.
{"title":"DESNet: Real-time human pose estimation for sports applications combining IoT and deep learning","authors":"Rongbao Huang , Bo Zhang , Zhixin Yao , Bojun Xie , Jia Guo","doi":"10.1016/j.aej.2024.10.010","DOIUrl":"10.1016/j.aej.2024.10.010","url":null,"abstract":"<div><div>With the rapid development of IoT technology, real-time human pose estimation has become increasingly important in sports training feedback systems. However, current methods often fall short in balancing high accuracy with low computational resource requirements, especially in resource-constrained environments. Deep learning has shown significant potential in enhancing computer vision tasks, including human pose estimation. In this study, we propose DESNet, an improved EfficientHRNet model that integrates IoT technology. DESNet combines Dynamic Multi-Scale Context (DMC) modules and Squeeze-and-Excitation (SE) modules, and utilizes IoT for real-time data collection, transmission, and processing. Experimental results show that DESNet achieves an average precision (AP) of 74.8% on the COCO dataset and a PCKh (Percentage of Correct Keypoints with head-normalized) of 90.9% on the MPII dataset, outperforming existing lightweight models. The integration of deep learning and IoT technology not only improves the accuracy and efficiency of human pose estimation but also significantly enhances the timeliness and robustness of feedback in sports training applications. Our findings demonstrate that DESNet is a powerful tool for real-time human pose analysis, offering promising solutions for intelligent sports training and rehabilitation systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 293-306"},"PeriodicalIF":6.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593084","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}
Pub Date : 2024-11-05DOI: 10.1016/j.aej.2024.11.006
Yi Nie , Chenlong Xu , Zhongkui Liu , Lihang Yang , Tianqi Li , Yinfeng He
The Selective Laser Melting (SLM), as a widely used metallic Additive manufacturing (AM) process, relies heavily on support structures. This study investigated the impact of different support structure configurations on the quality of In718 samples fabricated through SLM. On the basis of a comprehensive review of existing support structures configurations from the literature, three typical configurations: block, cone, and lattice, were designed to support cantilever parts for performance comparison. A coupled thermo-structural finite element simulation using ANSYS was performed to evaluate the temperature, deformation, and thermal stress evolution during the printing process of the three supported cantilever structures. The residual stress and deformation of the printed In718 cantilevers with different support structures were measured for validation. The results showed that block support exhibits the best strength and heat dissipation capability, making it the most effective support configuration for the SLM of In718 material. This research provides a fundamental procedure for evaluating the supporting performances among various support structures for the SLM process.
{"title":"Investigation of support structure configurations for selective laser melting of In718","authors":"Yi Nie , Chenlong Xu , Zhongkui Liu , Lihang Yang , Tianqi Li , Yinfeng He","doi":"10.1016/j.aej.2024.11.006","DOIUrl":"10.1016/j.aej.2024.11.006","url":null,"abstract":"<div><div>The Selective Laser Melting (SLM), as a widely used metallic Additive manufacturing (AM) process, relies heavily on support structures. This study investigated the impact of different support structure configurations on the quality of In718 samples fabricated through SLM. On the basis of a comprehensive review of existing support structures configurations from the literature, three typical configurations: block, cone, and lattice, were designed to support cantilever parts for performance comparison. A coupled thermo-structural finite element simulation using ANSYS was performed to evaluate the temperature, deformation, and thermal stress evolution during the printing process of the three supported cantilever structures. The residual stress and deformation of the printed In718 cantilevers with different support structures were measured for validation. The results showed that block support exhibits the best strength and heat dissipation capability, making it the most effective support configuration for the SLM of In718 material. This research provides a fundamental procedure for evaluating the supporting performances among various support structures for the SLM process.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"112 ","pages":"Pages 281-292"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593083","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}
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