alexandria engineering journal最新文献

{"title":"Efficient population mean estimation of sensitive traits using robust quantile regression and scrambled response methodology","authors":"Abdulaziz S. Alghamdi ,&nbsp;Marwan H. Alhelali","doi":"10.1016/j.aej.2025.06.009","DOIUrl":"10.1016/j.aej.2025.06.009","url":null,"abstract":"<div><div>Using robust regression approach for mean estimation in survey sampling with a single supplementary information is a well-established practice when there are outliers in the data set. The most popular methods for sensitive studies are regression estimation methods that use standard regression coefficients. When it comes to survey researchers, mean estimation is an important issue. Many researchers introduced a class of mean estimation methods utilizing data on two additional variables of information under simple random sampling (SRS), with the help of several non-conventional location measures and typical OLS. In this article, we propose a novel method for computing the population mean using robust quantile regression-type estimators for scrambled response model (SRM) under SRS. According to the results, the suggested estimator performs better than other estimators in the literature.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 612-617"},"PeriodicalIF":6.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491137","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}

{"title":"Statistical analysis of disability: Utilizing the new extended Rayleigh inverted Weibull model","authors":"Mohamed A. Abdelkawy ,&nbsp;Safar M. Alghamdi ,&nbsp;Ibrahim Elbatal ,&nbsp;Atef F. Hashem ,&nbsp;Ahmed W. Shawki ,&nbsp;Mohammed Elgarhy","doi":"10.1016/j.aej.2025.06.003","DOIUrl":"10.1016/j.aej.2025.06.003","url":null,"abstract":"<div><div>In this article, we provide a novel distribution known as the new extended Rayleigh inverted Weibull (NERIW) distribution, which arises from the new extended family of distributions (NE-G). The shapes of the probability density function (PDF) can be decreasing, unimodal, or right-skewed, but the shapes of the hazard rate function (HRF) can be decreasing or upside down. The new model is investigated to identify its various statistical properties, including quantile function, median, moments, moment-generating function, incomplete, and conditional moments, mean deviation, and inequality measures. The model parameters are determined using the maximum likelihood estimation approach. A Monte Carlo simulation analysis is conducted to evaluate the effectiveness of maximum likelihood estimators. This study explores the effectiveness of the NERIW distribution in modeling health and disability statistics, focusing on two real-world datasets from Saudi Arabia. By comparing the NERIW distribution with alternative models and offering a more accurate representation of disability prevalence between age groups. The findings provide valuable information for policy makers and researchers in understanding disability trends and improving data-driven decision making in health planning.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 779-792"},"PeriodicalIF":6.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550061","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}

{"title":"Smart mobile application for real-time monitoring and prediction of pilgrim crowd activities in Hajj and Umrah using wearable sensors","authors":"Mahmoud Ragab","doi":"10.1016/j.aej.2025.06.043","DOIUrl":"10.1016/j.aej.2025.06.043","url":null,"abstract":"<div><div>Performing Hajj or Umrah is an Islamic pilgrim tradition and the fifth important Islam pillar. Preparing to implement Hajj or Umrah is very important, and in the middle of travelling to Mecca, learning all the rules, rituals, and obligatory. The recent mobile applications open to the public that are associated with Hajj or Umrah are less communicative, very easy, have boundaries, do not provide smart features to help the user, and do not encounter the users’ targeted needs. Even though the useful information offered by prior studies in crowd behavioural management and the growth of many crowd monitoring technologies, a significant gap remains in the knowledge of crowd dynamics, particularly in unique scenarios like the Hajj pilgrimage. This is primarily because of the absence of complete, multimodal data that can be responsible for the composite interplay between emotional levels, individual cognition, crowd behaviour, and physiological conditions. This study presents a novel Smart Mobile Application for Monitoring and Classification of Pilgrim Activities in Hajj and Umrah Using Wearable Sensors (SMAMCPA-HUWS) method. The main objective of the SMAMCPA-HUWS method is to classify pilgrim activities based on fatigue levels and emotional status using wearable sensors for real-time monitoring. The SMAMCPA-HUWS method performs data preprocessing using min-max normalization to standardize input data, ensuring consistent analysis across varied sensor readings. The waterwheel plant algorithm (WWPA) model is utilized for feature subset selection to identify the most relevant features by reducing data dimensionality and enhancing processing efficiency. In addition, an ensemble of three classifiers such as bidirectional long short-term memory (BiLSTM), bidirectional temporal convolutional network (BiTCN), and conditional variational autoencoder (CVAE), is employed for classifying pilgrim activities. Finally, the multi-strategy improved crested porcupine optimizer (MICPO) method performs hyperparameter tuning for the three classifiers. The experimental study of the SMAMCPA-HUWS technique is performed by utilizing a benchmark dataset from the Kaggle repository. The performance validation of the SMAMCPA-HUWS technique portrayed a superior accuracy value of 97.78 % over existing approaches.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 618-634"},"PeriodicalIF":6.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491138","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}

{"title":"Enhancing solar PV suitability mapping in the Middle East using an optimized deep learning framework","authors":"Sajaa Muhsein Khazael ,&nbsp;Khairul Nizam Abdul Maulud ,&nbsp;Mohamed Barakat A. Gibril ,&nbsp;Mourtadha Sarhan Sachit ,&nbsp;Othman A.Karim","doi":"10.1016/j.aej.2025.06.059","DOIUrl":"10.1016/j.aej.2025.06.059","url":null,"abstract":"<div><div>The shift toward sustainable energy has underscored the importance of optimizing PhotoVoltaic (PV) site selection through cutting-edge technological approaches. This study introduces an optimized Deep Learning (DL) framework for mapping PV suitability. The framework combines TabNet-an attentive and interpretable DL model-with the Optuna optimizer for efficient hyperparameter tuning and is further enhanced by an eXplainable Artificial Intelligence (XAI) approach. The study evaluates 12 key techno-economic factors along with an inventory of 612 PV solar stations to assess PV site suitability. The performance of the proposed approach was compared with two DL models-Tabular Prior Data Fitted Network (TabPFN) and Feature Tokenizer + Transformer (FT-Transformer)-as well as seven classical Machine Learning models, including Decision Tree, Random Forest, Gradient Boosting, CatBoost, Support Vector Machine, Naïve Bayes and K-Nearest Neighbors. Results demonstrated that the proposed architecture outperformed all other models on both validation and testing datasets, achieving classification accuracies of 0.875 and 0.886, respectively. The spatial suitability map indicated that 17.3 % (∼1231,254 km²) of the Middle East's land area is highly suitable for PV deployment, predominantly along the coasts and in the northern and northwestern regions. XAI, implemented via SHapley Additive exPlanation, revealed that proximity to infrastructure had the most significant impact on predictions.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 553-571"},"PeriodicalIF":6.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480423","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}

{"title":"An improved version of ZLindley distribution with mathematical properties and applications","authors":"Ali M. Mahnashi,&nbsp;Abdullah A. Zaagan","doi":"10.1016/j.aej.2025.06.048","DOIUrl":"10.1016/j.aej.2025.06.048","url":null,"abstract":"<div><div>The growing complexity of contemporary lifetime data necessitates the advancement of more adaptable probability models. To meet this demand, an advanced extension of the ZLindley distribution called the size-biased ZLindley (SBZL) distribution has been developed via a weighted approach. This new modification enhances the flexibility of the standard distribution by refining its functional shape and enabling it to model the most probable form of the hazard rate function effectively. The new model includes various distinct sub-models based on its parameter values. Here we discussed and studied their two variants: the length-biased ZLindley and area-biased ZLindley distributions. Key properties, such as moments, mean residual life function, moment-generating function, and entropy, along with their associated computational features, were described in depth. To estimate the model parameters, four different estimation methods were applied, and a detailed simulation study identified the most effective approach. The practicality and efficiency of the SBZL distribution model were validated using datasets from two distinct fields, where it was found to deliver superior results compared to other competing distributions. We also utilized a Bayesian approach to analyze both datasets.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 582-597"},"PeriodicalIF":6.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491134","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}

{"title":"Sol-gel synthesized TiO2-chitosan nanocomposite as antibacterial coating for orthopedic implants: Investigation of properties and antimicrobial mechanisms","authors":"Huan Min ,&nbsp;Yi Wu ,&nbsp;Jiabin Wen ,&nbsp;Jia Guo ,&nbsp;Sicong Jiang ,&nbsp;Jinhui Cheng","doi":"10.1016/j.aej.2025.06.034","DOIUrl":"10.1016/j.aej.2025.06.034","url":null,"abstract":"<div><div>This study investigates the development of surface-modified titanium alloy implants through sol-gel synthesis to prevent bacterial infections while maintaining biocompatibility. The optimized 50:50 TiO₂:chitosan nanocomposite coating exhibited superior performance characteristics, with AFM analysis revealing controlled surface topography Ra = 78 ± 7 nm) and uniform particle distribution. Electrochemical measurements demonstrated enhanced corrosion resistance, with impedance modulus increasing from 3.2 × 10⁵ Ω·cm² for uncoated Ti6Al4V to 2.8 × 10⁶ Ω·cm² for the optimized coating at 0.01 Hz. The coating showed excellent mechanical stability with an adhesion strength of 15.4 MPa and critical load of 18.5 N. Live/dead bacterial staining and CLSM analysis revealed significant reduction in biofilm formation, with time-dependent studies showing rapid initial bacterial inhibition within 6 h (68.7 % for <em>S. aureus</em>, 64.5 % for <em>E. coli</em>) and sustained antimicrobial activity over 72 h (95.2 % for <em>S. aureus</em>, 93.1 % for <em>E. coli</em>). The coating maintained structural integrity and performance after 30-day immersion in simulated body fluid, demonstrating its potential for long-term implant protection.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 572-581"},"PeriodicalIF":6.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480424","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}

{"title":"Soft sensor modeling method for Pichia pastoris fermentation process based on domain adaptation ensemble LSTM","authors":"Bo Wang ,&nbsp;Jun Wei ,&nbsp;Yongxian Song ,&nbsp;Hui Jiang","doi":"10.1016/j.aej.2025.05.078","DOIUrl":"10.1016/j.aej.2025.05.078","url":null,"abstract":"<div><div>To address the challenges of limited labeled sample information and the inability to effectively utilize public information across different fermentation batches in Pichia pastoris fermentation process, this paper propose a soft-sensing modeling method based on domain adaptation ensemble LSTM. Firstly, in order to effectively utilize unlabeled data and improve the generalization ability of the model, autoencoder technology is adopted to extract dynamic feature information of the fermentation process from unlabeled data using LSTM encoder decoder. Secondly, based on the LSTM encoder decoder framework, LSTM deep neural networks are used to establish corresponding sub-models for each fermentation batch. Finally, during the training of each LSTM sub-model, a domain adaptive ensemble learning strategy is introduced to incorporate the effective information of other fermentation batches under different operating conditions into the loss function of the sub model, in order to reduce the impact of distribution differences between different fermentation batches on the sub-model. The simulation results show that the mentioned method has the preponderance of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 530-537"},"PeriodicalIF":6.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471195","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}

{"title":"A global feature fusion and adaptive optimization method to enhance detection accuracy and computational efficiency based on YOLOv8","authors":"Hui Yan ,&nbsp;Huangbin Guo ,&nbsp;Liansuo Wei ,&nbsp;Xiaoqing Xu ,&nbsp;Yuan Liang ,&nbsp;Yong Li ,&nbsp;Shuaiting Chen ,&nbsp;Ping Yu","doi":"10.1016/j.aej.2025.06.025","DOIUrl":"10.1016/j.aej.2025.06.025","url":null,"abstract":"<div><div>Weed detection is vital for agricultural productivity but faces challenges like target scale diversity and leaf shading-induced asymmetry. To address these challenges, this paper proposes a global feature fusion adaptive optimization method based on YOLOv8 to enhance detection accuracy and computational efficiency. (Global Feature Fusion-YOLOv8, GFF-YOLOv8). First, to enhance the accuracy of small object detection, we propose the C2f-FADC (C2f-Frequency-Adaptive Dilated Convolution) to replace the traditional C2f method, thereby improving the backbone network of YOLOv8. Next, to improve information exchange between different dimensions within the network, we propose the Global Fusion Diffusion Pyramid Networks (GFDPN) to replace the Neck structure in YOLOv8. This is achieved through adaptive feature selection and global fusion diffusion methods. Finally, to improve the model's ability to learn features, we introduce an Adaptive Task-Aligned Dynamic Detection Head (ATDDH), which modifies the traditional detection head to enhance the model's robustness and accuracy in weed detection. Experiments on the DeepWeeds dataset show that GFF-YOLOv8 achieved a mAP@50 of 76.98 %, outperforming other YOLO-based weed detection models.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 538-552"},"PeriodicalIF":6.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471196","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}

{"title":"Effect of normal vibration on the dynamic flexoelectric response of a thin dielectric plate with symmetrical electrode thickness","authors":"A.R. El-Dhaba ,&nbsp;Zeinab Abouelnaga","doi":"10.1016/j.aej.2025.06.016","DOIUrl":"10.1016/j.aej.2025.06.016","url":null,"abstract":"<div><div>In this paper, we investigate the effect of thickness-normal vibration on the dynamical flexoelectric effect induced in a thin dielectric plate covered by two electrodes with either equal or unequal thicknesses. Firstly, we introduce the general form of the dynamical field equations and boundary conditions. Secondly, we transform these equations into a nondimensional form. Finally, we apply a low-frequency wave of mechanical or electric potential to the upper and lower surfaces of the plate, with the objective of studying the flexoelectric and converse flexoelectric effects. The results are plotted and analyzed graphically. The most relevant results are summarized as follows, the microinertia effect significantly influences the polarization within dielectrics. When microinertia is neglected, the polarization value is higher compared to when the microinertia effect is included. Additionally, the polarization value increases when the length scale parameter associated with the microinertia effect exceeds the length scale parameter of the internal structures of the material.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 459-471"},"PeriodicalIF":6.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366646","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}

{"title":"Fractional order unsteady mixed convection in porous-filled concentric pipes: Entropy analysis","authors":"Zafar Hayat Khan ,&nbsp;Oluwole D. Makinde ,&nbsp;Muhammad Usman ,&nbsp;Rashid Ahmad ,&nbsp;Waqar A. Khan","doi":"10.1016/j.aej.2025.06.047","DOIUrl":"10.1016/j.aej.2025.06.047","url":null,"abstract":"<div><div>This study presents a comprehensive analysis of entropy generation in unsteady mixed convection flow within the annular region of concentric cylindrical pipes filled with a porous medium, incorporating the effects of time fractional order derivatives. The model captures transient thermal-fluid systems' memory and nonlocal impact by employing the Caputo fractional derivative, offering a more accurate representation of real-world behavior. The governing equations are formulated to include temperature-dependent viscosity. They are solved numerically via an implicit finite difference method under appropriate initial and boundary conditions to investigate the influence of key thermophysical parameters. Graphical plots illustrate velocity and temperature profiles, enhancing understanding of fluid behavior. Entropy generation due to heat transfer, fluid friction, and porous media resistance is quantified to assess thermodynamic irreversibilities and identify regions of energy degradation. The results reveal that increasing the fractional order significantly alters the flow and thermal fields, intensifying entropy production and influencing system performance. This research provides valuable insights for optimizing and designing energy-efficient systems involving porous media and time-dependent heat transfer processes, such as geothermal applications, advanced heat exchangers, and thermal energy storage technologies.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 483-503"},"PeriodicalIF":6.2,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366644","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}