Pub Date : 2025-12-01DOI: 10.1016/j.jer.2024.10.015
Johnny Salazar-Cardona , Francisco Luis Gutiérrez Vela , Jeferson Arango-Lopez , Patricia Paderewski , Fernando Moreira
Older adults are now actively participating in game-based systems. They find in these technological solutions not only a form of entertainment and socialization, but also a way to stimulate themselves physically and cognitively. Although, their participation is increasing, they face challenges such as the digital divide, since most of the games are oriented to a young audience with different tastes and motivations. Therefore, although positive results are obtained in terms of participation, there is still potential for improvement, since current game experiences are not fully adapted to the tastes and needs of older adults. Consequently, the objective of this paper is to propose a characterization of the types of players in older adults, based on the motivations previously identified. This is to better understand this population and design more appropriate game experiences. It is hoped that from the results of this research it will be possible to create attractive and fun game environments for older adults, thus improving their experience as players in these systems.
{"title":"Older adults and game-based systems: Engagement model and player types of characterization based on their motivations","authors":"Johnny Salazar-Cardona , Francisco Luis Gutiérrez Vela , Jeferson Arango-Lopez , Patricia Paderewski , Fernando Moreira","doi":"10.1016/j.jer.2024.10.015","DOIUrl":"10.1016/j.jer.2024.10.015","url":null,"abstract":"<div><div>Older adults are now actively participating in game-based systems. They find in these technological solutions not only a form of entertainment and socialization, but also a way to stimulate themselves physically and cognitively. Although, their participation is increasing, they face challenges such as the digital divide, since most of the games are oriented to a young audience with different tastes and motivations. Therefore, although positive results are obtained in terms of participation, there is still potential for improvement, since current game experiences are not fully adapted to the tastes and needs of older adults. Consequently, the objective of this paper is to propose a characterization of the types of players in older adults, based on the motivations previously identified. This is to better understand this population and design more appropriate game experiences. It is hoped that from the results of this research it will be possible to create attractive and fun game environments for older adults, thus improving their experience as players in these systems.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3107-3120"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates machine learning-based quality control for ZnO electrode manufacturing via screen printing. Traditional machine learning models such as Random Forest (RF), XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), known for their speed, are rarely used for image-based machine learning since they are designed for numerical and categorical data. Here, images of ZnO electrodes were converted to numerical form via feature extraction using filters, allowing these models to achieve competitive accuracy and recall values of up to 96 % and 95 %, respectively. Images were labeled according to conductivity tests and print quality analysis via optical microscopy and visual inspection. The initial dataset, which contained 356 “bad” and 100 “good” electrode images, was balanced using Synthetic Minority Over-sampling Technique and image augmentation to reduce overfitting. The best performing model was the RF due to its highest testing accuracy (0.96), F1 score (0.96), and low overfitting. The worst model was LR due to its lowest testing accuracy (0.85) and F1 score (0.81), despite being the fastest model. The RF model balanced performance (accuracy of 96 %) and speed, classifying 100 images in under 2 ms.
{"title":"Zinc electrode manufacturing quality control with machine learning: Using SMOTE & image augmentation to prevent overfitting","authors":"Lola Azoulay-Younes , Anesu Nyabadza , Mercedes Vazquez , Dermot Brabazon","doi":"10.1016/j.jer.2025.01.002","DOIUrl":"10.1016/j.jer.2025.01.002","url":null,"abstract":"<div><div>This study investigates machine learning-based quality control for ZnO electrode manufacturing via screen printing. Traditional machine learning models such as Random Forest (RF), XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), known for their speed, are rarely used for image-based machine learning since they are designed for numerical and categorical data. Here, images of ZnO electrodes were converted to numerical form via feature extraction using filters, allowing these models to achieve competitive accuracy and recall values of up to 96 % and 95 %, respectively. Images were labeled according to conductivity tests and print quality analysis via optical microscopy and visual inspection. The initial dataset, which contained 356 “bad” and 100 “good” electrode images, was balanced using Synthetic Minority Over-sampling Technique and image augmentation to reduce overfitting. The best performing model was the RF due to its highest testing accuracy (0.96), F1 score (0.96), and low overfitting. The worst model was LR due to its lowest testing accuracy (0.85) and F1 score (0.81), despite being the fastest model. The RF model balanced performance (accuracy of 96 %) and speed, classifying 100 images in under 2 ms.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3822-3832"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2024.12.013
Ming Zhao, Sitao Li, Hao Chen, Min Ling, Hong Chang
Solar photovoltaic (PV) power prediction is easily affected by weather factors. In order to reduce the solar photovoltaic (PV) power prediction deviation and improve the prediction accuracy, a distributed solar photovoltaic (PV) power prediction algorithm based on deep neural network is proposed. By deeply exploring the working principle of photovoltaic power generation, constructing a photovoltaic power generation system model, and systematically analyzing various factors that affect photovoltaic power generation, detailed classification of weather types can be achieved. On this basis, outlier detection, standardization processing, and normalization techniques are used to deeply clean and optimize the raw data, effectively avoiding the problem of neuron saturation. The use of wavelet packet decomposition method to decompose the photovoltaic power generation sequence into multiple sub sequences significantly reduces the difficulty of prediction. The effective fusion of LSTM (Long Short-Term Memory) and BPNN (Back Propagation Neural Network), and the fine adjustment of the fusion ratio parameter through genetic algorithm, ultimately achieved high-precision prediction of distributed photovoltaic power under complex and variable weather conditions. The experimental results show that the proposed method can accurately predict photovoltaic power under different weather conditions, and the prediction results are reliable.
{"title":"Distributed solar photovoltaic power prediction algorithm based on deep neural network","authors":"Ming Zhao, Sitao Li, Hao Chen, Min Ling, Hong Chang","doi":"10.1016/j.jer.2024.12.013","DOIUrl":"10.1016/j.jer.2024.12.013","url":null,"abstract":"<div><div>Solar photovoltaic (PV) power prediction is easily affected by weather factors. In order to reduce the solar photovoltaic (PV) power prediction deviation and improve the prediction accuracy, a distributed solar photovoltaic (PV) power prediction algorithm based on deep neural network is proposed. By deeply exploring the working principle of photovoltaic power generation, constructing a photovoltaic power generation system model, and systematically analyzing various factors that affect photovoltaic power generation, detailed classification of weather types can be achieved. On this basis, outlier detection, standardization processing, and normalization techniques are used to deeply clean and optimize the raw data, effectively avoiding the problem of neuron saturation. The use of wavelet packet decomposition method to decompose the photovoltaic power generation sequence into multiple sub sequences significantly reduces the difficulty of prediction. The effective fusion of LSTM (Long Short-Term Memory) and BPNN (Back Propagation Neural Network), and the fine adjustment of the fusion ratio parameter through genetic algorithm, ultimately achieved high-precision prediction of distributed photovoltaic power under complex and variable weather conditions. The experimental results show that the proposed method can accurately predict photovoltaic power under different weather conditions, and the prediction results are reliable.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3352-3359"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2025.03.005
Khaled Alkhaledi
Musculoskeletal disorder affects many workers in jobs that involve frequent bending and heavy lifting. Dentists are no exception. Musculoskeletal lower back pain is a serious problem for dentists who perform frequent physical activities while treating patients and it can arise from a broad range of causes. Dentists were ranked among occupations with high levels of physical stresses. The first objective of this study is to define and evaluate work related movements factors that are causing the musculoskeletal lower back pain for dentists. The second objective is to look for possible ergonomic solutions to prevent lower back pain and to enhance dentists’ occupational safety and physical health. Volunteered dentists from different gender participated in this study. Time duration, lift rate, average twisting velocity, maximum moment, maximum sagittal flexion, maximum lateral velocity, gender and dental specialties were measured using lumbar motion monitoring device. Analysis of variance was used to analyze the output data. The results of this study showed that average twisting velocity, maximum sagittal flexion, and maximum lateral velocity physical movements had a significant effect on lower back pain. Also when dentists spend more time treating patients with awkward posture, the lower back pain increases leading to lower working quality and reduces productivity. A correct posture during patient treatments and taking more break time were recommended to prevent musculoskeletal lower back pain.
{"title":"Assessment of work-related musculoskeletal lower back pain for dentists in Kuwait","authors":"Khaled Alkhaledi","doi":"10.1016/j.jer.2025.03.005","DOIUrl":"10.1016/j.jer.2025.03.005","url":null,"abstract":"<div><div>Musculoskeletal disorder affects many workers in jobs that involve frequent bending and heavy lifting. Dentists are no exception. Musculoskeletal lower back pain is a serious problem for dentists who perform frequent physical activities while treating patients and it can arise from a broad range of causes. Dentists were ranked among occupations with high levels of physical stresses. The first objective of this study is to define and evaluate work related movements factors that are causing the musculoskeletal lower back pain for dentists. The second objective is to look for possible ergonomic solutions to prevent lower back pain and to enhance dentists’ occupational safety and physical health. Volunteered dentists from different gender participated in this study. Time duration, lift rate, average twisting velocity, maximum moment, maximum sagittal flexion, maximum lateral velocity, gender and dental specialties were measured using lumbar motion monitoring device. Analysis of variance was used to analyze the output data. The results of this study showed that average twisting velocity, maximum sagittal flexion, and maximum lateral velocity physical movements had a significant effect on lower back pain. Also when dentists spend more time treating patients with awkward posture, the lower back pain increases leading to lower working quality and reduces productivity. A correct posture during patient treatments and taking more break time were recommended to prevent musculoskeletal lower back pain.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3563-3567"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2024.12.007
Amina Tabet Zatla , Amina Hammoudi , Mamoun Fellah , Dunya Zeki Mohammed , Joëlle Pérard , Gamal A. El-Hiti
Our study is a strategy aimed at improving the therapeutic index of the molecule by reducing its toxicity while maintaining its activity. To this end, we will investigate the use of Salvia officinalis L. and Curcuma longa L. essential oils, which are rich in molecules with potential therapeutic activities. These oils will be tested and evaluated in vitro on the red blood cells by measuring three biological parameters, including intracellular LDH concentration, potassium concentration, and extra cellular hemoglobin level, to assess their antihemolytic activity. Additionally, we will examine markers of oxidative stress, including malondialdehyde, glutathione, catalase, and protein carbonylation, to determine their antioxidant activity. Curcuma longa L. and Salvia officinalis L. essential oils have been demonstrated to decrease the destruction of red blood cells significantly. Thereby protecting erythrocytes from hemolysis induced by glucantime. Specifically, the study observed a progressive decrease in intracellular lactate dehydrogenase (LDH) concentrations, with levels dropping from 77 U/L to 44 U/L in suspensions with Salvia officinalis L. and from 89 U/L to 47 U/L for Curcuma longa L. over time. However, these decreases were not as pronounced as the reduction seen with glucantime alone, which lowered LDH levels from 67 U/L to 21 U/L. Moreover, glucantime treatment led to a significant decrease in intracellular potassium concentration, falling from 2.03 mmol/L to 1.38 mmol/L. In contrast, the presence of the essential oils diminished this effect; potassium levels decreased to 2.1 mmol/L with Curcuma longa L. and to 1.69 mmol/L with Salvia officinalis L. Furthermore, hemoglobin was detected at a low initial concentration of 0.0021 g/L in suspensions containing glucantime, which escalated to 0.201 g/L by the end of the observation period. Significantly, the hemoglobin levels were lower when the essential oils were present. Additionally, the results indicated that Curcuma longa L. essential oil possesses a relatively greater antihemolytic capacity compared to that of Salvia officinalis L. Both essential oils demonstrated antioxidant properties by mitigating oxidative stress effects caused by glucantime, as evidenced by reductions in the levels of catalase, glutathione (GSH), malondialdehyde, and carbonylated proteins. Our results are suggestive that Salvia officinalis L. and Curcuma longa L. essential oils can protect erythrocytes against glucantime-induced hemolytic phenomena. This effect is due to these essential oils' antioxidant activities.
{"title":"In vitro study of the antihemolytic and antioxidant potential of two essential oils from Salvia officinalis L. and Curcuma longa L. against glucantime® toxicity","authors":"Amina Tabet Zatla , Amina Hammoudi , Mamoun Fellah , Dunya Zeki Mohammed , Joëlle Pérard , Gamal A. El-Hiti","doi":"10.1016/j.jer.2024.12.007","DOIUrl":"10.1016/j.jer.2024.12.007","url":null,"abstract":"<div><div>Our study is a strategy aimed at improving the therapeutic index of the molecule by reducing its toxicity while maintaining its activity. To this end, we will investigate the use of <em>Salvia officinalis</em> L. and <em>Curcuma longa</em> L. essential oils, which are rich in molecules with potential therapeutic activities. These oils will be tested and evaluated <em>in vitro</em> on the red blood cells by measuring three biological parameters, including intracellular LDH concentration, potassium concentration, and extra cellular hemoglobin level, to assess their antihemolytic activity. Additionally, we will examine markers of oxidative stress, including malondialdehyde, glutathione, catalase, and protein carbonylation, to determine their antioxidant activity. <em>Curcuma longa</em> L. and <em>Salvia officinalis</em> L. essential oils have been demonstrated to decrease the destruction of red blood cells significantly. Thereby protecting erythrocytes from hemolysis induced by glucantime. Specifically, the study observed a progressive decrease in intracellular lactate dehydrogenase (LDH) concentrations, with levels dropping from 77 U/L to 44 U/L in suspensions with <em>Salvia officinalis</em> L. and from 89 U/L to 47 U/L for <em>Curcuma longa</em> L. over time. However, these decreases were not as pronounced as the reduction seen with glucantime alone, which lowered LDH levels from 67 U/L to 21 U/L. Moreover, glucantime treatment led to a significant decrease in intracellular potassium concentration, falling from 2.03 mmol/L to 1.38 mmol/L. In contrast, the presence of the essential oils diminished this effect; potassium levels decreased to 2.1 mmol/L with <em>Curcuma longa</em> L. and to 1.69 mmol/L with <em>Salvia officinalis</em> L. Furthermore, hemoglobin was detected at a low initial concentration of 0.0021 g/L in suspensions containing glucantime, which escalated to 0.201 g/L by the end of the observation period. Significantly, the hemoglobin levels were lower when the essential oils were present. Additionally, the results indicated that <em>Curcuma longa</em> L. essential oil possesses a relatively greater antihemolytic capacity compared to that of <em>Salvia officinalis</em> L. Both essential oils demonstrated antioxidant properties by mitigating oxidative stress effects caused by glucantime, as evidenced by reductions in the levels of catalase, glutathione (GSH), malondialdehyde, and carbonylated proteins. Our results are suggestive that <em>Salvia officinalis</em> L. and <em>Curcuma longa</em> L. essential oils can protect erythrocytes against glucantime-induced hemolytic phenomena. This effect is due to these essential oils' antioxidant activities.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 2839-2850"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2024.12.012
Mohamed Omri , Fatih Selimefendigil , Hatem Besbes , Lotfi Ladhar , Badr M. Alshammari , Lioua Kolsi
In this study, effects of using wall corrugation and nano-enhanced magnetic field in a channel with area expansion and elastic interface on the phase change and thermal process are examined by using finite element method with ALE (Arbitrary Lagrangian-Eulerian). A range of values for the relevant parameters are included in the simulations: the flow Reynolds number (Re) between 100 and 1000; the elasticity of the flexible partition (E between 105 and 109); the Hartmann number (Ha) between 0 and 60; the amplitude of the wavy wall (A between 0.01 and 0.35); and the wave number of corrugation (N between 2 and 20). Complete phase transition (tF) with Re shows non-monotonic behavior while variations of tF up to 57 % and 50 % are obtained for the upper and lower PCM under wavy wall with varying Re. For upper PCM, the variation of tF with elastic modulus becomes 21 %-25 %. When E is changed, the average Nu increment with a corrugated wall is 11 %. When the magnetic field is applied with maximal strength, thermal performance is enhanced and the phase transition process is accelerated. For the upper and lower PCM zones, reduction of tF with Ha yields 44 % and 33 %, respectively. For the upper and lower PCM zones, the full transition time decreases with higher corrugation amplitudes by 14.7 % and 12.5 %, respectively. Average Nu increments of 10 % and 7.5 % are found by raising the corrugation amplitude and wave number to their maximum values. A significant reduction of tF, around 54 %, is obtained with the introduction of wavy walls with magnetic field and nanofluid when compared to the reference case (flat channel using base fluid and without magnetic field effects). Although the upper wall’s corrugation further enhances thermal performance, magnetic field has a bigger impact on thermal performance than wavy shape.
{"title":"Analysis of MHD nanofluid forced convection and phase change process in a PCM mounted corrugated and partly elastic partitioned channel system with area expansion","authors":"Mohamed Omri , Fatih Selimefendigil , Hatem Besbes , Lotfi Ladhar , Badr M. Alshammari , Lioua Kolsi","doi":"10.1016/j.jer.2024.12.012","DOIUrl":"10.1016/j.jer.2024.12.012","url":null,"abstract":"<div><div>In this study, effects of using wall corrugation and nano-enhanced magnetic field in a channel with area expansion and elastic interface on the phase change and thermal process are examined by using finite element method with ALE (Arbitrary Lagrangian-Eulerian). A range of values for the relevant parameters are included in the simulations: the flow Reynolds number (Re) between 100 and 1000; the elasticity of the flexible partition (E between 10<sup>5</sup> and 10<sup>9</sup>); the Hartmann number (Ha) between 0 and 60; the amplitude of the wavy wall (A between 0.01 and 0.35); and the wave number of corrugation (N between 2 and 20). Complete phase transition (<em>t</em><sub><em>F</em></sub>) with Re shows non-monotonic behavior while variations of <em>t</em><sub><em>F</em></sub> up to 57 % and 50 % are obtained for the upper and lower PCM under wavy wall with varying Re. For upper PCM, the variation of <em>t</em><sub><em>F</em></sub> with elastic modulus becomes 21 %-25 %. When E is changed, the average Nu increment with a corrugated wall is 11 %. When the magnetic field is applied with maximal strength, thermal performance is enhanced and the phase transition process is accelerated. For the upper and lower PCM zones, reduction of <em>t</em><sub><em>F</em></sub> with Ha yields 44 % and 33 %, respectively. For the upper and lower PCM zones, the full transition time decreases with higher corrugation amplitudes by 14.7 % and 12.5 %, respectively. Average Nu increments of 10 % and 7.5 % are found by raising the corrugation amplitude and wave number to their maximum values. A significant reduction of <em>t</em><sub><em>F</em></sub>, around 54 %, is obtained with the introduction of wavy walls with magnetic field and nanofluid when compared to the reference case (flat channel using base fluid and without magnetic field effects). Although the upper wall’s corrugation further enhances thermal performance, magnetic field has a bigger impact on thermal performance than wavy shape.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3807-3821"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2024.11.004
Zhenyang Jin , Sanglin Zhao , Siyu Fan , Hamed Javdanian
Accurate slope stability analysis of earth embankments under ground shaking is of great importance for practical use in earthquake geotechnics. This study aims to predict soil slope displacements of earth embankments subjected to earthquake loading using evolutionary algorithms. Comprehensive real case histories of slope displacement of earth embankments under past earthquakes in different areas of the world were gathered and analyzed. A robust model was then developed to predict earthquake induced soil slope displacements using gene expression programming (GEP). Characteristics of earthquake ground motion including earthquake magnitude, earthquake predominant period, maximum earthquake acceleration and also geotechnical specifications of earth embankment including yield acceleration and fundamental period of earth embankment were taken as most influential factors on the slope displacements of earth embankments under earthquakes. Subsequently, performance of developed GEP-based predictive model was assessed using a sensitivity analysis under various effective factors. Finally, the accuracy of the predictive model was evaluated through comparison with the available relationships for estimation of seismic soil slope displacements. The results clearly indicate favorable accuracy of developed GEP-based model to predict slope displacements of earth embankments subjected to earthquake ground motions.
{"title":"An evolutionary approach to predict slope displacement of earth embankments under earthquake ground motions","authors":"Zhenyang Jin , Sanglin Zhao , Siyu Fan , Hamed Javdanian","doi":"10.1016/j.jer.2024.11.004","DOIUrl":"10.1016/j.jer.2024.11.004","url":null,"abstract":"<div><div>Accurate slope stability analysis of earth embankments under ground shaking is of great importance for practical use in earthquake geotechnics. This study aims to predict soil slope displacements of earth embankments subjected to earthquake loading using evolutionary algorithms. Comprehensive real case histories of slope displacement of earth embankments under past earthquakes in different areas of the world were gathered and analyzed. A robust model was then developed to predict earthquake induced soil slope displacements using gene expression programming (GEP). Characteristics of earthquake ground motion including earthquake magnitude, earthquake predominant period, maximum earthquake acceleration and also geotechnical specifications of earth embankment including yield acceleration and fundamental period of earth embankment were taken as most influential factors on the slope displacements of earth embankments under earthquakes. Subsequently, performance of developed GEP-based predictive model was assessed using a sensitivity analysis under various effective factors. Finally, the accuracy of the predictive model was evaluated through comparison with the available relationships for estimation of seismic soil slope displacements. The results clearly indicate favorable accuracy of developed GEP-based model to predict slope displacements of earth embankments subjected to earthquake ground motions.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 2940-2949"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2025-12-01DOI: 10.1016/j.jer.2024.12.014
Ammar T. Al-Sayegh , Nasim Shakouri Mahmoudabadi , Faisal Shabbir , Fatma J. Alkandari , Saba Saghir , Afaq Ahmad
This study presents a comparative analysis of predicting the load-bearing capacity of Reinforced Concrete (RC) columns using contemporary design codes and alternative methodologies, specifically Artificial Neural Networks (ANN) and the Compressive Force Path (CFP) method. The ANN models were trained on a hybrid enriched experimental dataset (HEXP). Comparisons with current design codes, CFP, and ANN models reveal that the ANN predictions most accurately reflect the experimental results. The CFP method also provides estimates that closely match actual experimental outcomes. These comparative analyses identified and evaluated critical parameters width of columns in x-direction =b, width of columns in y-direction=d, Shear span ratio=av/d, Longitudinal steel ratio, Tensile strength of steel=fyl, Compressive strength of concrete=fc, Transverse steel ratio=pw, Axial Load=N, Flexural moment=Mf, and Shear Capacity of member=Vu, affecting RC column performance. The VANN model demonstrated superior stability and reliability with a Coefficient of Variation (CV) of 0.73, outperforming other models with higher CVs. The ANN model's predictions closely align with test data due to their derivation from experimental results. Furthermore, predictions from both ANN and CFP models were validated against ABAQUS simulations, with ANN predictions showing excellent agreement with ABAQUS outcomes.
{"title":"Prediction of load-bearing capacity of RC columns (CWA) using Artificial Neural Networks (ANN) trained on a hybrid experimental database HEXP","authors":"Ammar T. Al-Sayegh , Nasim Shakouri Mahmoudabadi , Faisal Shabbir , Fatma J. Alkandari , Saba Saghir , Afaq Ahmad","doi":"10.1016/j.jer.2024.12.014","DOIUrl":"10.1016/j.jer.2024.12.014","url":null,"abstract":"<div><div>This study presents a comparative analysis of predicting the load-bearing capacity of Reinforced Concrete (RC) columns using contemporary design codes and alternative methodologies, specifically Artificial Neural Networks (ANN) and the Compressive Force Path (CFP) method. The ANN models were trained on a hybrid enriched experimental dataset (HEXP). Comparisons with current design codes, CFP, and ANN models reveal that the ANN predictions most accurately reflect the experimental results. The CFP method also provides estimates that closely match actual experimental outcomes. These comparative analyses identified and evaluated critical parameters width of columns in x-direction =b, width of columns in y-direction=d, Shear span ratio=av/d, Longitudinal steel ratio, Tensile strength of steel=fyl, Compressive strength of concrete=fc, Transverse steel ratio=pw, Axial Load=N, Flexural moment=M<sub>f</sub>, and Shear Capacity of member=V<sub>u</sub>, affecting RC column performance. The VANN model demonstrated superior stability and reliability with a Coefficient of Variation (CV) of 0.73, outperforming other models with higher CVs. The ANN model's predictions closely align with test data due to their derivation from experimental results. Furthermore, predictions from both ANN and CFP models were validated against ABAQUS simulations, with ANN predictions showing excellent agreement with ABAQUS outcomes.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3007-3025"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gearbox power density plays a key role in wind energy competitiveness. As wind turbines have become increasingly larger, gear surface durability and flank fracture load carrying capacity have become key elements in gearbox sizing. While shot peening and superfinishing have been studied independently as potential techniques to improve surface durability, few reported studies have combined both of these processes to increase the gear flank load-carrying capacity. In this work, spot pitting tests were carried out to evaluate the expected load-carrying capacity increase for wind gears through this combination of finishing processes. The results are consistent with previous research's conclusions and highlight the need for further investigations to evaluate potential increased flank fracture risk related to the near-surface residual stress distribution.
{"title":"Improving the surface durability of wind gears via shot peening and superfinishing","authors":"Ruben Carranza Fernandez , Sascha Rommel , Thomas Tobie , Joaquin Collazo","doi":"10.1016/j.jer.2024.10.008","DOIUrl":"10.1016/j.jer.2024.10.008","url":null,"abstract":"<div><div>Gearbox power density plays a key role in wind energy competitiveness. As wind turbines have become increasingly larger, gear surface durability and flank fracture load carrying capacity have become key elements in gearbox sizing. While shot peening and superfinishing have been studied independently as potential techniques to improve surface durability, few reported studies have combined both of these processes to increase the gear flank load-carrying capacity. In this work, spot pitting tests were carried out to evaluate the expected load-carrying capacity increase for wind gears through this combination of finishing processes. The results are consistent with previous research's conclusions and highlight the need for further investigations to evaluate potential increased flank fracture risk related to the near-surface residual stress distribution.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3662-3672"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.
{"title":"Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system","authors":"Mahdi Hermassi , Saber Krim , Youssef Kraiem , Mohamed Ali Hajjaji","doi":"10.1016/j.jer.2024.09.017","DOIUrl":"10.1016/j.jer.2024.09.017","url":null,"abstract":"<div><div>This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R<sup>2</sup>) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 4","pages":"Pages 3266-3288"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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