alexandria engineering journal最新文献

{"title":"Detection of diabetic nephropathy biomarker cystatin C using bimetallic-based immunosensor","authors":"Meilian Du,&nbsp;Shanbao Zhang,&nbsp;Xiaoyu Wang,&nbsp;Chen Liu,&nbsp;Chunying Yao,&nbsp;Xiao Chen,&nbsp;Liqun Fan,&nbsp;Yinghui Qi","doi":"10.1016/j.aej.2025.06.032","DOIUrl":"10.1016/j.aej.2025.06.032","url":null,"abstract":"<div><div>In this research, a new electrochemical immunosensor was created to detect Cystatin C with high sensitivity, utilizing Ag-Au bimetallic nanoparticles synthesized through eco-friendly methods. The nanoparticles, synthesized using waste tea leaves extract, exhibited uniform spherical morphology with an average diameter of 28.5 ± 4.2 nm and demonstrated excellent conductivity, increasing the electrode's effective surface area by 2.25-fold compared to the bare GCE. Cyclic voltammetry and electrochemical impedance spectroscopy confirmed successful stepwise assembly of the immunosensor, with the peak-to-peak separation (ΔEp) increasing from 111 mV at bare GCE to 136 mV after antibody immobilization and 230 mV upon antigen binding, and a charge-transfer resistance (Rct) trend consistent with interface modification. Under optimized conditions (pH 7.4, 37°C, 35-minute incubation), the immunosensor displayed dual linear ranges of 0.1–10 ng/mL and 10–100 ng/mL. The sensor achieved a remarkable limit of detection of 0.034 ng/mL and demonstrated excellent reproducibility with RSDs of 3.8 % for inter-sensor and 2.9 % for intra-assay precision. The immunosensor retained 94.3 % of its original reactivity following a 30-day storage period and exhibited excellent specificity in distinguishing against typical interferences. Validation using clinical samples revealed excellent correlation with standard ELISA (R² = 0.9943) and impressive recovery rates ranging from 96.8 % to 103.5 %. This environmentally friendly, cost-effective platform offers significant advantages in terms of analysis time (35 min versus 3.5 h for ELISA) and demonstrates great potential for point-of-care CyC detection in clinical settings.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 302-311"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321739","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":"M-CNN-RF: A hybrid deep learning model for accurate pediatric skeletal age estimation using hand bone radiographs","authors":"Hazrat Bilal ,&nbsp;Yibin Tian ,&nbsp;Inam Ullah ,&nbsp;Sahil Garg ,&nbsp;Bong Jun Choi ,&nbsp;Mohammad Mehedi Hassan","doi":"10.1016/j.aej.2025.05.090","DOIUrl":"10.1016/j.aej.2025.05.090","url":null,"abstract":"<div><div>Precise and accurate skeletal age estimation using medical imaging is a pivotal and challenging task in the healthcare sector, particularly for identifying potential bone growth issues in infants and newborns. Therefore, this study addresses the pervasive challenges associated with assessing bone abnormalities in pediatric patients, including injuries and infections. Given the importance of early and precise detection of skeletal development, a novel hybrid model is proposed that integrates a modified convolutional neural network (M-CNN) with a robust machine learning (ML) model, specifically random forest (RF), resulting in the M-CNN-RF framework. This model is designed to enhance pediatric bone health assessment by providing an effective method for skeletal age estimation. The M-CNN-RF model is tailored to accurately evaluate hand bone maturation, overcoming the inherent difficulties in skeletal age assessment. The model utilizes the bone age dataset from the Radiological Society of North America that includes 14,236 left-hand radiological images, focusing on the development of a robust model for a precise evaluation based on hand skeleton guidelines. In addition, to enhance the prediction and generalization of the model, data augmentation techniques were employed to increase the size of the dataset. The M-CNN-RF exhibits exceptional performance using numerous performance measures, achieving an accuracy of 97% and precision and recall exceeding 94%. In addition, the model reaches an F1 score of 97%, highlighting the ability of the model to ensure a balance between precision and recall. Furthermore, low mean absolute error (MAE) and mean square error (MSE) values of 0.0141 and 0.0327, respectively, were computed for the proposed model, which demonstrates its notable efficacy in predicting skeletal age. The findings of this study not only contribute valuable information for clinical applications but also underscore the potential of the adopted approach to address the challenges associated with pediatric bone health assessment.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 289-301"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321738","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":"Solving the Lane–Emden equation by using Bernstein operational matrix","authors":"Nurul Izzatie Najwa Kamarul Bahrim,&nbsp;Md Yushalify Misro","doi":"10.1016/j.aej.2025.06.001","DOIUrl":"10.1016/j.aej.2025.06.001","url":null,"abstract":"<div><div>Lane–Emden equations model a wide range of problems in physics and astrophysics, with applications including stellar structure, radiative cooling, and the modeling of galaxy clusters. This study proposes an efficient numerical method to solve the Lane–Emden equation using Bernstein polynomials. By deriving the Bernstein Operational Matrix of Derivative, it can be applied to solve linear Lane–Emden type Ordinary Differential Equations. Various types of collocation points are being used in this study to determine which collocation points have the highest efficiency. The method’s efficiency has also been determined by calculating the infinity norm of the absolute errors at the collocation points. Several illustrative examples are provided to demonstrate the efficiency and validity of the method.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 278-288"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321737","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":"Cross-cluster precision-guided knowledge fusion for fair and personalized federated learning","authors":"Anam Nawaz Khan ,&nbsp;Atif Rizwan ,&nbsp;Rashid Ahmad ,&nbsp;Qazi Waqas Khan ,&nbsp;DoHyeun Kim","doi":"10.1016/j.aej.2025.05.055","DOIUrl":"10.1016/j.aej.2025.05.055","url":null,"abstract":"<div><div>We propose a Personalized Precision-Centric Clustered Federated Learning (PPCFL) framework to address statistical heterogeneity and model personalization challenges in Federated Learning. PPCFL introduces a layer-wise, angular dissimilarity-based clustering mechanism that groups nodes with similar optimization trajectories, guiding both intra- and inter-cluster weight aggregation. A class precision-centric aggregation strategy assigns weights to model updates based on each node’s class-wise predictive performance, ensuring a more representative and personalized global model. To preserve local model fidelity after aggregation, we adopt a bias-aware model consolidation approach using functional parameter decoupling, aggregating feature extraction and classification layers separately based on dataset size and class-wise precision. We further introduce a cross-cluster knowledge fusion strategy that leverages shared hidden layer weights to integrate generalizable representations across clusters while retaining domain-specific adaptations. To support scalability and real-time coordination, we incorporate a secure Digital-Twin based orchestration layer that virtualizes edge nodes for efficient management. Unlike traditional simulation-driven digital twins, our implementation provides a web-based control interface enabling real-time node registration, training configuration, and performance monitoring. Empirical evaluations across heterogeneous data modalities demonstrate improved convergence and enhanced generalization under non-IID conditions. This work lays a practical foundation for scalable, secure, and adaptive federated learning systems in real-world decentralized environments.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 255-270"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312539","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":"Rectifier array with wide input power and frequency ranges using harmonic controllable impedance compression network","authors":"Hyunjae Jeon,&nbsp;Seongick Jo,&nbsp;Juntaek Oh","doi":"10.1016/j.aej.2025.06.038","DOIUrl":"10.1016/j.aej.2025.06.038","url":null,"abstract":"<div><div>This paper presents a rectifier array with a harmonic-controllable impedance compression network (HCICN) designed to achieve a high power conversion efficiency (PCE) across wide input power and frequency ranges. The HCICN of the proposed rectifier array comprises two harmonic reflection networks (HRNs), an impedance compression network (ICN), and two sub-rectifiers. The HRN is connected to each diode of the sub-rectifiers to recycle the second-harmonic signal generated during the rectification process, thereby enhancing the PCE by reusing the reflected signal. The characteristic impedances of the transmission lines in the network are adjusted to configure the input impedances seen by each rectifier asymmetrically according to the input power and frequency. The ICN then transforms the input impedance of one sub-rectifier to maintain a conjugate relationship with the other over broad input power and frequency ranges, ensuring a high PCE throughout this operating region. Measurement results showed that the prototype achieved a peak PCE of 76 % at an input power of 28.8 dBm, with a PCE above 50 % over an input power range of 10.6–30.1 dBm at 2.45 GHz. Moreover, at an input power of 26 dBm, the frequency range for a PCE exceeding 70 % spanned from 1.7 to 2.6 GHz.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 271-277"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321736","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":"Cell fate regulation by hyperthermia: Cancer, stem cells, and immune system","authors":"Madineh Moradialvand ,&nbsp;Sajad Farashi ,&nbsp;Asmita Deka Dey ,&nbsp;Arun Kumar ,&nbsp;Ibrahim Sharifi ,&nbsp;Mahnaz Hassanpour ,&nbsp;Mohammad Chehelgerdi ,&nbsp;Chander Prakash ,&nbsp;Heemin Kang ,&nbsp;Esmaeel Sharifi ,&nbsp;Geriletu Ao","doi":"10.1016/j.aej.2025.06.022","DOIUrl":"10.1016/j.aej.2025.06.022","url":null,"abstract":"<div><div>Temperature, as an affecting factor, influences physiological systems in point of their structure and function. Hyperthermia has gained extensive research due to its promising results in disease diagnosis and treatment. Despite its importance, comprehensive knowledge about the way that temperature perturbation might affect cellular function and behavior is not available. This review highlights the effect of temperature elevation (hyperthermia) on cancer cells, including the molecular mechanisms underlying hyperthermia-induced cancer therapy. Due to the importance of stem cells in biology and medicine, the impact of temperature elevation on several aspects of stem cell functions and behavior is also discussed. In addition, the effect of hyperthermia on innate and adaptive immune systems regulation is reviewed. Finally, the challenges and perspectives of hyperthermia-based therapies for biomedical applications are presented.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 240-254"},"PeriodicalIF":6.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312538","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":"Temporal analysis of ternary-hybrid nanomaterial flow in the vicinity of Howarth stagnation point: Insights from the Cattaneo-Christov heat flux model","authors":"Mariam Sheikh ,&nbsp;Zainab Bibi ,&nbsp;Jafar Hasnain","doi":"10.1016/j.aej.2025.06.017","DOIUrl":"10.1016/j.aej.2025.06.017","url":null,"abstract":"<div><div>The cooling of nuclear reactors and spacecraft, the production of energy, the use of magnetic fields for medication targeting in medicine, the conduction of heat within tissues, and countless other industrial and technical processes rely on heat transfer. This study deals with the effect of C-C heat flux for nodal/saddle SP flow in the presence of ternary hybrid nanomaterial. C-C heat flux is used to analyze thermal relaxation time. Trihybrid nanofluids have an increased thermal conductivity as compared to hybrid nanofluids. By transforming the fundamental flow equations, partial differential equations become ODEs. Numerical solutions are displayed graphically after solving the system. The results show that a reduction in the nanofluid velocity is caused by increasing concentrations of <span><math><mrow><mi>T</mi><mi>i</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>,</mo></mrow></math></span><span><math><mrow><mi>C</mi><mi>o</mi><mi>F</mi><msub><mrow><mi>e</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mi>O</mi></mrow><mrow><mn>4</mn></mrow></msub></mrow></math></span> and <span><math><mrow><mi>M</mi><mi>g</mi><mi>O</mi></mrow></math></span> in ethylene glycol. The results also show that in contrast to the saddle SP, the nodal stagnation point has a larger magnitude of velocity components. It was found that the temperature of the fluid decreased due to the thermal relaxation time parameter. A greater thermal relaxation time parameter results in higher values of the Nusselt number for both saddle and nodal stagnation points. However, when comparing the nodal SP to the saddle SP, the Nusselt number is consistently larger. Applications in biology and industry, such as food packaging, fog creation, clay coating, and dispersion, are all extremely intriguing in this area. Additionally, this study has a wide range of technological and technical applications. By maximizing heat transport in complicated fluids, it can enhance thermal management systems in industrial machinery, electronics, and aircraft. It facilitates the construction of effective geothermal and solar thermal collectors in the field of renewable energy.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 226-239"},"PeriodicalIF":6.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307540","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":"Dual-branch skeleton–RGB fusion for human action recognition with spatio-temporal attention","authors":"Xiaoping Lan ,&nbsp;Jian Zhang ,&nbsp;Jin Zhou","doi":"10.1016/j.aej.2025.05.083","DOIUrl":"10.1016/j.aej.2025.05.083","url":null,"abstract":"<div><div>Skeleton-based action recognition has gained attention due to the simplicity and robustness of skeletal data. However, the lack of fine-grained human information in skeleton data limits its performance. To overcome this limitation, We introduce a video modality and proposes a dual-branch network that models RGB video and skeleton data separately. For skeletal data, a graph convolutional network based on a self-attention mechanism is employed, achieving state-of-the-art performance in skeleton-based action recognition tasks. For video data, a 3D convolutional network is used for feature extraction. Additionally, we design a deep supervision method to oversee the intermediate features of both modalities, enhancing feature coupling and improving network efficiency. Experimental results demonstrate that the proposed dual-branch network shows significant advantages in jointly modeling video and skeleton data, achieving higher recognition accuracy and stronger generalization ability compared to traditional unimodal methods.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 205-215"},"PeriodicalIF":6.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307538","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":"Design and implementation of a decentralized trustless data standardization framework for blockchain interoperability using smart contracts","authors":"Abdulrahman Alzahrani ,&nbsp;Amin Noaman ,&nbsp;Ahmed A.A. Gad-Elrab ,&nbsp;Fathy Eassa ,&nbsp;Maher Khemakhem ,&nbsp;Faisal Albalwy ,&nbsp;Hosam Aljihani","doi":"10.1016/j.aej.2025.06.011","DOIUrl":"10.1016/j.aej.2025.06.011","url":null,"abstract":"<div><div>Interoperability among heterogeneous blockchain platforms remains a significant challenge in decentralized ecosystems, especially with the growing adoption of blockchain across various industries. This research introduces the Data Standardization Module (DSM). This framework facilitates seamless communication between platforms like Ethereum and Hyperledger Fabric using a unified schema for data transformation and exchange. DSM leverages Concise Binary Object Representation (CBOR) encoding, which reduces data size by 60 % and achieves a compression ratio (CR) of 2.5. This outperforms conventional JSON-based methods, typically achieving only 20–30 % compression with lower CR values. The system ensures data security and integrity through end-to-end encryption, access control, and validation mechanisms. Performance evaluations show that DSM supports an average throughput of 250 transactions per second (TPS). In comparison, interoperability frameworks like Cosmos and Polkadot typically achieve 100–150 TPS, while early implementations of Hyperledger Fabric reported TPS as low as 300. However, optimized settings of Hyperledger Fabric now achieve over 100,000 TPS. DSM strikes a balance by providing high throughput with minimal resource overhead, making it suitable for real-world applications. This work advances blockchain interoperability by offering a lightweight, secure, and scalable framework, ideal for high-frequency use cases in healthcare, finance, and supply chains.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 168-191"},"PeriodicalIF":6.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297167","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 fitness with YOLO-Fit IoT: Real-time pose analysis and personalized training via IoT and RL","authors":"Jingyu Liu ,&nbsp;Yan Zhou","doi":"10.1016/j.aej.2025.05.068","DOIUrl":"10.1016/j.aej.2025.05.068","url":null,"abstract":"<div><div>With the development of smart technology, smart fitness is becoming an integral part of modern fitness. Traditional fitness methods depend on coaches or predetermined training plans, often lacking real-time feedback and personalization, which limits their ability to meet the evolving needs of users. Although current smart fitness systems combine various technologies, they still struggle with issues such as inaccurate data fusion, delayed real-time feedback, and insufficient personalization. To address these challenges, this paper proposes the YOLO-Fit IoT model, which integrates IoT technology, YOLO-based posture estimation, and reinforcement learning for real-time exercise posture analysis and personalized training recommendations. By fusing multimodal data, the model processes physiological data and exercise status in real time, dynamically adjusting the training plan based on user feedback. Experimental results, compared to baseline models such as MediaPipe Pose, MS-G3D, FitLSTM, YOLOv7-Pose, and YOLOv10, show that YOLO-Fit IoT significantly outperforms existing systems, offering improvements in accuracy, real-time feedback, and training effectiveness. The combination of IoT devices, deep learning, and reinforcement learning allows for personalized, dynamically optimized training, leading to enhanced fitness outcomes. This research presents a new solution for smart fitness systems, with potential for future optimization in computational efficiency and application expansion to various exercise scenarios.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 216-225"},"PeriodicalIF":6.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307539","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}