alexandria engineering journal最新文献

{"title":"Lipid-based nanomedicine for colorectal cancer: Progress and prospects","authors":"Fereshteh Asgharzadeh ,&nbsp;Maryam Moradi Binabaj ,&nbsp;Sahar Fanoudi ,&nbsp;William C. Cho ,&nbsp;Haneul Kang ,&nbsp;Zahra Elyasi ,&nbsp;Bahareh Farasati Far ,&nbsp;Ali Pourmolaei ,&nbsp;Marzieh Ramezani Farani ,&nbsp;Yun Suk Huh","doi":"10.1016/j.aej.2025.02.023","DOIUrl":"10.1016/j.aej.2025.02.023","url":null,"abstract":"<div><div>Colorectal cancer (CRC) stands as a common disease and a primary associated to cancer-related mortality. Treatment options for CRC encompass surgical procedures, targeted therapies, radiation treatments, and chemotherapy. Optimal treatment outcomes are attained through the judicious combination of two or more treatment modalities, tailored to the specific stage of CRC. A diverse range of therapeutic medications exists for CRC treatment. However, achieving the necessary therapeutic concentration often mandates the administration of elevated doses of chemotherapeutic agents, resulting in a variety of side effects such as severe gastrointestinal reactions, hematological issues, neurological complications, cardiac challenges, and dermatological reactions. Hence, there exist a critical requirement for an intelligent delivery system capable of enhancing therapeutic efficacy while safeguarding and delivering high doses of chemotherapeutic agents. Colon-specific drug delivery systems, especially those rooted in nanotechnology, hold promise as a viable solution to tackle these obstacles. This review aims to examine the advancements in lipid-based nanotechnologies for treating CRC, while also delving into the associated challenges and potential prospects of these therapeutic methodologies.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 385-398"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641974","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}

{"title":"The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model","authors":"Md Galal Uddin ,&nbsp;Apoorva Bamal ,&nbsp;Mir Talas Mahammad Diganta ,&nbsp;Abdul Majed Sajib ,&nbsp;Azizur Rahman ,&nbsp;Mohamed Abioui ,&nbsp;Agnieszka I. Olbert","doi":"10.1016/j.aej.2025.03.022","DOIUrl":"10.1016/j.aej.2025.03.022","url":null,"abstract":"<div><div>Lake water is considered as one of the most precious freshwater resources for human consumption and thus, it is required to monitor the lake water quality (WQ) to maintain the good water state. For these purposes, the practice of integrating data-driven models in assessing and predicting lake WQ has received wider application. Nevertheless, the optimal performance of the data-driven models depends on the model hyperparameters and hyperparameter optimization techniques have crucial role in this regard. Therefore, the research aimed to assess the impact of various optimization techniques on these prediction models in order to predict the lake WQ. For the purposes of reliable lake water state assessment, this study presents a comprehensive analysis of lake WQ prediction, integrating sophisticated Root Mean Squared (RMS)-Water Quality Index (WQI) model with eight machine learning algorithms (ML), two Artificial Intelligence (AI) techniques, and five optimization methods. Comparing five optimization methods—Grid Search (GS), Random Search (RS), Bayesian Optimization (BO), Optuna (OPT), and Tree-based Pipeline Optimization Tool (TPOT)—revealed their significant influence on model performance and computational efficiency. Based on the analysis, random forest (RF) integrated with TPOT consistently demonstrated robust performance compared to other models, achieving an impressive R² value of 0.94 during training (Root mean squared error-RMSE= 0.85, Mean squared error-MSE= 0.73, Mean absolute error-MAE= 0.48, and Percentage of absolute bias error-PABE= 2.35), testing (RMSE= 0.43, MSE= 0.18, MAE= 0.15, and PABE= 0.19), and validation (RMSE= 0.96, MSE= 0.96, MAE= 0.823, and PABE= 1.08), indicating its precision in predicting lake WQ. The results reveal that TPOT and OPT show remarkable effectiveness in optimizing the hyperparameter space to enhance model accuracy across various ML/AI model combinations. The research findings suggest that RMS-WQI with RF-TPOT integration model could be effective to predict lake WQ that can be helpful approach for sustainable water resource management in terms of computational-cost efficiency and reliable WQ assessment. However, by addressing these complexities and highlighting the impact of various optimization techniques, the research provides crucial guidance for researchers, advancing the field of lake WQ prediction and ensuring effective utilization of ML/AI integration models.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 411-435"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643213","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}

{"title":"The effect of intelligent monitoring of physical exercise on executive function in children with ADHD","authors":"Liwen Lin ,&nbsp;Nan Li ,&nbsp;Shuchen Zhao","doi":"10.1016/j.aej.2025.02.095","DOIUrl":"10.1016/j.aej.2025.02.095","url":null,"abstract":"<div><div>Children with ADHD often struggle with executive function (EF) and motor skills, impacting their academics and social life. While medications are commonly used, they have side effects, leading to interest in non-drug treatments. Physical activity (PA) has shown promise in improving cognitive and motor skills in children with ADHD. This study examined the short- and long-term effects of three PA interventions: a specific skill training group (EG1), a low-demand exercise group (EG2), and a control group (CG) over 12 weeks. EG1 showed significant improvements in motor tasks and working memory (15% improvement, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>), while EG2 and CG showed smaller changes. Long-term PA improved working memory, but short-term PA had limited effects on balance and manual dexterity. These findings suggest that skill training has an immediate impact on motor performance, while more complex motor skills require longer interventions. Smart devices tracked progress, confirming sustained engagement and improvement in EG1. This research highlights PA as a promising non-pharmacological treatment for ADHD, warranting further exploration of its effects on other cognitive domains.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 355-363"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641961","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}

{"title":"Simulation of pharmaceutical supply chain resilience in Hebei Province: An impact factor-capability perspective","authors":"Yanxin Zhu ,&nbsp;Zhe Yang ,&nbsp;Qi He","doi":"10.1016/j.aej.2025.03.049","DOIUrl":"10.1016/j.aej.2025.03.049","url":null,"abstract":"<div><div>The production and supply of pharmaceuticals are critical to safeguarding public health and national security. Enhancing the resilience of the pharmaceutical supply chain is essential for mitigating uncertain pharmaceutical risks and maintaining social stability. This study focuses on the pharmaceutical supply chain in Hebei Province and, based on an impact factor-capability-resilience framework, develops an evaluation system for the resilience factors of the pharmaceutical supply chain in terms of tolerance capacity, recovery capacity, and risk resistance capacity. The study utilizes the system dynamics (SD) method to perform simulation analysis. Sensitivity analysis reveals that the resilience of the pharmaceutical supply chain in Hebei Province is most influenced by risk tolerance capacity, followed by recovery capacity. Among the various influencing factors, supply chain risk tolerance capacity is predominantly affected by factors such as operating income, inventory, and social environment. Supply chain recovery capacity is primarily influenced by factors such as total profit and partner credibility, while supply chain risk resistance capacity is chiefly influenced by operating profit margin and logistics support level.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 436-452"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643212","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}

{"title":"Error probability analysis under H-noise scenarios in diffusive molecular communication","authors":"Gunjal Chauhan ,&nbsp;Nitin Rakesh ,&nbsp;Monali Gulhane ,&nbsp;Ghanshyam Singh ,&nbsp;S. Pratap Singh ,&nbsp;Akhil Gupta ,&nbsp;Sudeep Tanwar ,&nbsp;Giovanni Pau ,&nbsp;Osama Alfarraj ,&nbsp;Fahad Alblehai","doi":"10.1016/j.aej.2025.03.041","DOIUrl":"10.1016/j.aej.2025.03.041","url":null,"abstract":"<div><div>Diffusive Molecular Communication (DMC) represents a critical paradigm in nanoscale communication, yet various noise models significantly influence its performance. This paper presents an analytical framework for evaluating error probability under H-noise. This novel noise model accounts for anomalous diffusion scenarios, including sub-diffusion, super-diffusion, and normal diffusion. Unlike conventional noise models that primarily focus on normal diffusion, H-noise provides a unified characterization of uncertainty in molecular propagation across diverse diffusion environments. The study introduces a mathematical formulation of error probability, integrating parameters such as decision thresholds, binary transmission probability, and diffusion coefficients. Numerical simulations validate the theoretical analysis, demonstrating the impact of scenario parameters on error probability and the statistical behavior of molecular arrival times. In addition to error analysis, this study explores broader applications of DMC in biomedical systems, environmental monitoring, and nanoscale computing, highlighting its potential beyond intelligent transportation systems. This work enhances the understanding of DMC under complex noise conditions by delineating different evaluation metrics and extending the discussion to a broader spectrum of applications. It provides insights into optimizing molecular communication systems for future nano-networking applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 484-495"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643216","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}

{"title":"A multi-objective dynamic detection model in autonomous driving based on an improved YOLOv8","authors":"Chaoran Li ,&nbsp;Yinghui Zhu ,&nbsp;Manyao Zheng","doi":"10.1016/j.aej.2025.03.020","DOIUrl":"10.1016/j.aej.2025.03.020","url":null,"abstract":"<div><div>How to efficiently identify and accurately track multiple targets in complex traffic scenes has become a key technical challenge that urgently needs to be solved. Therefore, this study developed a lightweight model that combines attention mechanism with convolutional neural network and YOLOv8 to achieve dynamic detection algorithm for multiple targets. Firstly, in the data preprocessing stage, mosaic image enhancement technology is used to highlight the features of small targets and complex scenes. Subsequently, MobileNetV3_CA network was designed to integrate lightweight MobileNetV3 and Coordinate Attention module as the backbone feature extraction network of YOLOv8, thereby enhancing the accuracy and specificity of feature extraction. To further enrich feature information, multi-scale feature layers are input into PANet for fusion. The experimental results show that the proposed method, which combines attention convolution and YOLOv8 for multi-target dynamic detection in autonomous driving scenes, significantly improves performance in multi-target detection tasks. Compared to the traditional YOLOv8 model and its variants without attention mechanism, this method outperforms the detection accuracy on multiple autonomous driving datasets. This research achievement provides a strong technical support for the perception module of the auto drive system, and helps to promote the development of autopilot technology to a higher level.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 453-464"},"PeriodicalIF":6.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643214","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}

{"title":"Rheological behavior of two immiscible hybrid nanofluids with modified thermal conductivity models through a heated curved pipe","authors":"H. Shahzad,&nbsp;Z. Abbas,&nbsp;M.Y. Rafiq","doi":"10.1016/j.aej.2025.03.039","DOIUrl":"10.1016/j.aej.2025.03.039","url":null,"abstract":"<div><div>The immiscible fluids can help design more efficient systems for drug delivery and understanding the blood flow through diseased arteries. It can also have implications for the design of flow control devices or medical implants that involve curved geometries. Therefore, the current article scrutinizes the characteristics of heat transfer on the flow of two immiscible hybrid nanofluids and Newtonian fluids through a curved pipe induced due to a pressure gradient in an axial direction. Hybrid nanofluids are formulated by combining single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) as nanoparticles, with water serving as the base fluid. The pipe is divided into two separate regions: (i) Region I (core), which is filled with a Newtonian fluid, and (ii) Region II (periphery), containing water as the base fluid mixed with hybrid nanofluids composed of both single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs). Mathematical modeling was developed by modifying the Navier-Stokes equations and simplifying the complex problem using non-dimensional parameters. Nonlinear coupled differential equations were analyzed with suitable assumptions and the perturbation technique to gain analytical solutions. The energy equation also incorporates the effects of viscous dissipation and thermal radiation. Graphical representations highlight the effects of critical parameters, such as Prandtl number, Reynolds number, Eckert number, curvature, viscosity and density ratios, thermal radiation, and the thermal conductivity parameter. Additionally, streamlines for variations in the velocity parameters and isotherms of the temperature parameter are presented. The shear stress and volumetric flow rate are also provided to assess the impact of numerous parameters. The results show that the axial velocity profile demonstrates a notable shift from the core region to the peripheral region, especially when the curvature ratio and Reynolds number are higher. As the viscosity ratio increases, the axial velocity of the immiscible fluid flow also increases. Conversely, the temperature of the fluid decreases as the conductivity ratio parameter rises. Furthermore, a comparison of temperature profiles reveals that the Yamada-Ota model predicts higher temperatures than the Xue model.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 318-333"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641970","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}

{"title":"Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour","authors":"Nuha Alruwais ,&nbsp;Ghada Moh. Samir Elhessewi ,&nbsp;Muhammad Kashif Saeed ,&nbsp;Menwa Alshammeri ,&nbsp;Othman Alrusaini ,&nbsp;Abdulwhab Alkharashi ,&nbsp;Samah Al Zanin ,&nbsp;Yahia Said","doi":"10.1016/j.aej.2025.03.027","DOIUrl":"10.1016/j.aej.2025.03.027","url":null,"abstract":"<div><div>In order to get a correct diagnosis and choose the best treatment options before it becomes deadly, early detection and classification of pancreatic tumours are essential. Grading can be a tedious and time-consuming process for experts and doctors when the case is complex. In such cases, experts usually look at the tumour and pinpoint its exact position. Moreover, it could be required to compare the tumor's cells to those in the vicinity. The end goal is to confirm that the growth is a tumour and, if possible, to ascertain the exact type and grade of the tumour. However, due to the high amounts of weights sent and received from the client-side trained models, federated learning techniques incur substantial communication overhead. This study aims to address this problem by introducing a unified framework that integrates the inherent capabilities of Federated Learning (FL) with the unique characteristics of the Grey Wolf Optimisation algorithm. The pancreatic tumour dataset is used to evaluate the GWO-enabled FL framework. The proposed model was more network efficient, performed better in data imbalance scenarios, and led to lower communication costs than the currently available federated average model. Following validation, the proposed framework attained a prediction accuracy of 98.9 %. For pancreatic tumour classification, the data obtained from the proposed system can be a useful component.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 344-354"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641972","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}

{"title":"Object detection algorithm for autonomous driving: Design and real-time performance analysis of AttenRetina model","authors":"Gang Liu ,&nbsp;Weiqiang Jiang ,&nbsp;Changlin Sun ,&nbsp;Na Ning ,&nbsp;Rui Wang ,&nbsp;Abudhahir Buhari","doi":"10.1016/j.aej.2025.02.063","DOIUrl":"10.1016/j.aej.2025.02.063","url":null,"abstract":"<div><div>With the continuous advancement of autonomous driving technology, how to efficiently and accurately detect objects (such as pedestrians, cyclists, traffic signs, etc.) has become a core challenge to improve the safety and reliability of the system. Existing object detection models still face the problem of insufficient accuracy and robustness when dealing with complex backgrounds and occlusions. To this end, this paper proposes the AttenRetina object detection model for autonomous driving, which combines the multi-scale feature fusion module (FPN) and the attention mechanism to significantly improve the detection ability of the model in various scenarios. Experimental results show that AttenRetina performs well on the KITTI and MS COCO datasets, and significantly outperforms other mainstream models in key indicators such as Precision, Recall and mAP. The mAP on the KITTI dataset reaches 0.86, which is more than 12% higher than the basic model, showing its great potential in autonomous driving object detection. The research in this paper provides an effective solution to the object detection problem in autonomous driving systems, and provides an important reference for future algorithm optimization and application.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"123 ","pages":"Pages 392-402"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705360","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}

{"title":"Weed detection in cornfields based on improved lightweight neural network model","authors":"Haicheng Wan,&nbsp;Shanping Wang","doi":"10.1016/j.aej.2025.03.016","DOIUrl":"10.1016/j.aej.2025.03.016","url":null,"abstract":"<div><div>Accurate weed detection in cornfields is a basis for their prevention and control. Machine vision technology is an effective method for accurate weed detection. Thus, this paper selects weeds growing with corn at the seedling stage as the research object. Based on the original YOLOv4, an improved lightweight weed detection model, YOLOv4-weeds, is proposed. By changing the main YOLOv4 feature extraction network into MobileNetV3-Small, combined with the depthwise separable convolution and inverse residual structure, and introducing a lightweight attention mechanism, this model reduces the image processing memory size and improves detection efficiency. The YOLOv4-weeds mAP reaches 88.5 %, which is significantly higher than that of YOLOv3, YOLOv4 and YOLOv4-tiny. The innovation of this research lies in the idea of \"replacing the backbone extraction network of the detection model\". Specifically, the original CSPDarknet-53 backbone extraction network of the YOLOv4 model structure is replaced with MobileNetV3-Small. By improving the original YOLOv4 model structure, the complexity of the original YOLOv4 model is simplified. While ensuring the precise detection of small target objects such as corn seedlings and 13 types of weeds in the image, the model structure becomes more lightweight, better meeting the configuration requirements of embedded devices. Therefore, this research not only provides a theoretical basis and technical guidance for corn yield prediction and the development of a visual system for disease identification but also sets a good example for identifying other species.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"122 ","pages":"Pages 334-343"},"PeriodicalIF":6.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641971","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}