Journal of Radiation Research and Applied Sciences最新文献

{"title":"A new modified Sine-Weibull distribution for modeling medical data with dynamic structures","authors":"Hatem Semary ,&nbsp;Ahmad Abubakar Suleiman ,&nbsp;Aliyu Ismail Ishaq ,&nbsp;Jamilu Yunusa Falgore ,&nbsp;Umar Kabir Abdullahi ,&nbsp;Hanita Daud ,&nbsp;Mohamed A. Abd Elgawad ,&nbsp;Mohammad Elgarhy","doi":"10.1016/j.jrras.2025.101427","DOIUrl":"10.1016/j.jrras.2025.101427","url":null,"abstract":"<div><div>Biomedical data often exhibits complex structures characterized by skewness, kurtosis, and high variability, which pose challenges for traditional statistical models. To adequately address these challenges, asymmetrical statistical models are essential. This paper proposes a novel family of statistical distributions formed by Sine-G transformations. The family will be known as the Modified Sine-G (MoS-G) family. The new MoS-G family is utilized to propose the Modified Sine-Weibull (MoS-Weibull) distribution, as an extension of the classical Weibull distribution, which encompasses a greater variety of data features. The aim is to integrate a wider range of data features than the conventional Weibull distribution. The MoS-Weibull distribution demonstrates enhanced variability regarding skewness and kurtosis, rendering it appropriate for capturing diverse shapes. The shapes include reversed-J-shaped, right-skewed, heavy-tailed, and left-skewed distributions, commonly found in biomedical data. The maximum likelihood method is utilized to carry out the process of parameter estimation. It has been demonstrated through simulation tests conducted in various environments that the parameter estimates are reliable. Three real biological datasets demonstrate the practical applicability of the MoS-Weibull distribution. These datasets are the mortality statistics for the pathological clinic records, the COVID-19, and the acute bone cancer data. The results of this study suggest that the MoS-Weibull distribution has an edge over other models in terms of its capacity to capture the intricate patterns and variability that are present in these datasets. The findings of this study demonstrate that the MoS-Weibull distribution is an efficient tool for analysing complex biomedical data. It also offers significant insights and works to improve statistical modeling approaches in the field of biomedical research.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101427"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Alpha-Beta-Power Family of distributions: Properties and applications","authors":"H.E. Semary ,&nbsp;Aleena Thampi ,&nbsp;Safar M. Alghamdi ,&nbsp;Vasili B.V. Nagarjuna","doi":"10.1016/j.jrras.2025.101426","DOIUrl":"10.1016/j.jrras.2025.101426","url":null,"abstract":"<div><div>In this article, a new family of distributions is introduced called the Generalized Alpha-Beta-Power family, applying the concept of Exponentiated family to the Alpha-Beta Power family. The family introduces three parameters to an existing model, increasing its flexibility. A special model of the family, taking exponential distribution as the baseline distribution, is studied in detail. The density curves of the distribution are found to accommodate decreasing and right-skewed heavy-tailed shapes with changes in values of the shape and scale parameters. The hazard plots can be increasing, decreasing or bath-tub shaped. Statistical properties of the distribution, like moments, moment generating function, order statistics and entropy are mathematically derived. Parameter estimation is done using the maximum likelihood estimation method, and simulation studies are used to assess the performance of the parameters. The proposed model is applied to the data sets in the field of reliability, engineering and medicine, and is compared to existing models to understand its practicality in the modeling of the probability of observations.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101426"},"PeriodicalIF":1.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligent-powered detection of breast cancer","authors":"Misbahu Koramar Boko Lawal ,&nbsp;May Almousa ,&nbsp;Abdullahi Umar Ibrahim ,&nbsp;Pwadubashiyi Coston Pwavodi ,&nbsp;Abdullahi Garba Usman ,&nbsp;Badr Aloraini","doi":"10.1016/j.jrras.2025.101422","DOIUrl":"10.1016/j.jrras.2025.101422","url":null,"abstract":"<div><div>Breast cancer (BC) is characterized as uncontrollable growth of breast cells. Accurate screening of patients suspected with BC is crucial for timely treatment and minimizing cost. Medical expert relies on several techniques such as biopsy, ultrasound, mammography, Magnetic Resonance Imaging (MRI) etc. Despite reliance on these techniques, majority have drawbacks which include high cost, miss-diagnosis and miss-interpretation, false positive results. As result of the growing number of patients diagnosed with BC, radiologists are facing increase in workload which can delay diagnosis and increase susceptibility or prone to error. Integrating Artificial intelligence techniques into routine pathology practice have shown to reduce workload, errors and improve diagnostic efficiency. Therefore, in this study, we proposed the application of ensemble Deep Learning and Machine Learning approach for the detection of BC from both histopathological and ultrasound images. We curated 4 datasets from Kaggle repository which include 2 histopathological datasets (Breast Cancer Dataset (BCD) with 7783 images and BreaKHis 400X dataset (BH400X D) with 1693 histopathological images) and 2 ultrasound datasets (Ultrasound Breast Classification Dataset (UBCD) with 9016 images and Breast Ultrasound Images Dataset (BUID) with 1578 images. The acquired images are processed via resizing and colour enhancement and trained using customized CNN (D-ResNet) and 5 pre-trained models (ResNet101, ResNet50, VGG16, VGG19 and MobileNet) coupled with Random Forest (RF). Evaluation of the models based on unseen dataset resulted in 95.98 % accuracy using D-ResNet-RF validated on BCD, 92.34 % accuracy using D-ResNet-RF validated on BHX400, 84.82 % accuracy using ResNet101-RF validated on UBCD and 94.32 % accuracy using VGG19-RF validated on BUID.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101422"},"PeriodicalIF":1.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Military expenditure and economic growth in the largest military spending country: Using machine learning analysis","authors":"Abdallah Sayed Mossalem Ahmed Elshafei ,&nbsp;Mansour Shrahili ,&nbsp;Mohamed Kayid ,&nbsp;Shahid Mohammad","doi":"10.1016/j.jrras.2025.101429","DOIUrl":"10.1016/j.jrras.2025.101429","url":null,"abstract":"<div><div>The research conducted a comprehensive analysis of the relationship between military expenditure and economic growth in the United States. The findings revealed a significant negative correlation between increased military spending and GDP growth, indicating that as military expenditure rises, the rate of economic growth tends to decline. In contrast, the study identified a positive relationship between GDP growth and gross national expenditure when military spending is excluded. This suggests that investments in civilian sectors may yield better returns for economic expansion. Additionally, the analysis highlighted that inflation serves as a detrimental factor for GDP growth, often eroding purchasing power and destabilizing economic conditions. On the other hand, real interest rates and gross savings were found to positively influence GDP growth, suggesting that higher levels of savings and favorable interest rates can create a conducive environment for economic development. Based on these findings, the study recommends a reevaluation of military spending policies, advocating for a reduction in military expenditure to a level not exceeding 3.5 % of GDP. Such a shift could potentially redirect resources towards more productive sectors of the economy, fostering sustainable growth and enhancing overall economic stability.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101429"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of fluctuations in global oil prices on Saudi Arabia's gross domestic product: A machine learning analysis","authors":"Abdallah Sayed Mossalem Ahmed Elshafei ,&nbsp;Mansour Shrahili ,&nbsp;Mohamed Kayid ,&nbsp;Shahid Mohammad","doi":"10.1016/j.jrras.2025.101436","DOIUrl":"10.1016/j.jrras.2025.101436","url":null,"abstract":"<div><div>Saudi Arabia's economy is highly dependent on oil exports. This paper examines the relationship between fluctuations in global the oil prices and Saudi Arabia's Gross Domestic Product (GDP) from 1970 to 2022, utilizing machine learning techniques for analysis. The results reveal a strong positive correlation between the oil prices and GDP growth, particularly during periods of high the oil prices, which increase government revenues and stimulate investment. However, this relationship is complex and influenced by various factors, including global oil price changes, geopolitical events, and national economic policies. The study emphasizes the urgent need for Saudi Arabia to diversify its economy to reduce its dependence on oil. By doing so, the kingdom can better manage the risks associated with oil price volatility and enhance its long-term economic resilience, preparing for a more balanced and sustainable future.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101436"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the clinical benefits of regular breath-holding training utilizing surface guided radiation therapy technology for patients with left breast cancer","authors":"Hongming Li,&nbsp;Zheng Miao,&nbsp;Jie Shen,&nbsp;Jianing Xiao,&nbsp;Zhiwei Yang,&nbsp;Wei Tian,&nbsp;Xiansong Sun,&nbsp;Zhen Zhou,&nbsp;Jing Shen,&nbsp;Jie Qiu","doi":"10.1016/j.jrras.2025.101393","DOIUrl":"10.1016/j.jrras.2025.101393","url":null,"abstract":"<div><h3>Background</h3><div>The deep inspiration breath-hold (DIBH) technique has indeed been widely applied in breast cancer radiotherapy, with numerous studies demonstrating its significant advantages in reducing radiation doses to the heart and lungs. However, previous research has primarily focused on the dosimetric benefits of the DIBH technique, with relatively little attention given to the stability and duration of breath-holding. This study aims to evaluate the practical benefits of regular breath-holding training in the DIBH workflow by quantitatively analyzing how such training reduces setup and treatment times and improves workflow efficiency.</div></div><div><h3>Materials and methods</h3><div>This single-center randomized, self-controlled clinical trial involved fifty patients diagnosed with left breast cancer. Twenty-five patients received regular breath-holding training throughout their treatment (trained group), while twenty-five control patients were provided only with basic instructions during their initial treatment session (untrained group). Data on breath-holding duration, radiotherapy setup time for the trained group, and overall treatment time for both groups were collected. Breath-holding stability was assessed by comparing Real Time Coach (RTC) from AlignRT at the setup stage, X-Ray Volume Imaging (XVI), and during treatment.</div></div><div><h3>Results</h3><div>In the training group, 22 patients (88%) completed regular breathing training, resulting in the collection of 363 sets of breathing training data. Regular training resulted in a gradual increase in breath-holding duration; specifically, setup time decreased from over 320 s during the first session to approximately 200 s by the fifth session. The trained group exhibited a significantly shorter treatment duration (233.85 ± 51.36 s) compared to the untrained group (323.71 ± 104.75 s; p &lt; 0.05). RTC results at the setup stage, XVI, and during treatment showed no significant differences between groups (p &gt; 0.05).</div></div><div><h3>Conclusions</h3><div>Regular breath-holding training enhances both breath-holding duration and patient cooperation while strengthening doctor-patient relationships and reducing both setup and treatment times. It is recommended to incorporate regular breathing training into the workflow of peer DIBH treatment and to implement three breath-holding sessions during the setup process for fine-tuning and validation, aiming to optimize breath-holding stability.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101393"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-enhanced deep learning and machine learning framework for knee osteoarthritis severity detection in football players using X-ray images","authors":"Xu Wang ,&nbsp;Tianpeng Wang ,&nbsp;Zhanguo Su","doi":"10.1016/j.jrras.2025.101428","DOIUrl":"10.1016/j.jrras.2025.101428","url":null,"abstract":"<div><h3>Objective</h3><div>To develop a highly accurate, interpretable, and scalable framework for automated knee osteoarthritis (OA) classification, integrating attention-enhanced autoencoders for feature extraction and advanced machine learning techniques for robust and clinically reliable severity grading based on X-ray images.</div></div><div><h3>Materials and methods</h3><div>This study analyzed 5987 knee X-ray images from football athletes (18–45 years) using the Kellgren-Lawrence (KL) grading system. Preprocessing involved resizing, normalization, and quality checks, with data augmentation (rotations, flips, brightness adjustments) to enhance model robustness. A convolutional autoencoder (CAE) with attention mechanisms extracted key features, improving interpretability and accuracy. Machine learning classifiers (SVM, XGBoost, Stacking) processed features from the bottleneck layer, while dimensionality reduction (PCA, LDA, RFE) optimized feature selection, enhancing classification performance.</div></div><div><h3>Results</h3><div>The study assessed knee OA classification using autoencoders with and without attention mechanisms. Without attention, dimensionality reduction techniques like PCA and RFE performed well, particularly when combined with ensemble classifiers. RFE + Stacking achieved the highest F1-score (82.99 %), while PCA + SVM and PCA + XGBoost delivered high accuracy (86.57 % and 85.52 %, respectively). Incorporating attention mechanisms significantly boosted performance, with RFE + Stacking attaining the best overall results (AUC: 96.5 %, F1-score: 93.5 %). Additionally, PCA + Stacking and PCA + XGBoost demonstrated strong accuracy (92.99 % and 92.12 %, respectively). End-to-end autoencoders with attention outperformed their non-attentive counterparts, reaching an accuracy of 0.94 and an AUC of 0.95. These findings underscore the critical role of attention mechanisms in enhancing model robustness, accuracy, and interpretability, making them highly applicable for clinical decision-making.</div></div><div><h3>Conclusion</h3><div>This study introduces a highly interpretable AI framework for knee OA classification, integrating attention-enhanced autoencoders to highlight key diagnostic regions in X-ray images. By incorporating attention mechanisms, our model improves transparency and clinical relevance, ensuring that classification decisions are guided by meaningful radiological features rather than arbitrary patterns.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101428"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cobalt and iron oxide nanoparticles flow and exponential heat transfer over an elaborated surface","authors":"Huda Alfannakh,&nbsp;Basma Souayeh","doi":"10.1016/j.jrras.2025.101437","DOIUrl":"10.1016/j.jrras.2025.101437","url":null,"abstract":"<div><div>The current communication concentrates on the cobalt-capped iron oxide nanoparticles on Darcy flow and exponential heat transfer over an elaborated surface. The nature of cobalt capped iron oxide nanoparticles with ethylene glycol base fluid is executed in this analysis. The Darcy's law of porosity is used for the modelling of porous medium. The energy equation is explored in the occurrence of thermal radiation and exponential heat source. The boundary is enhanced with the help of thermal stratification condition. Using a group of similar variables, the problem being modeled will be converted into a set of ODEs. The dimensionless equations are numerically solved by utilizing the RKF-45 solver with shooting technique. To compute the code of modelling computational software Maple is used. The behavior of the various significant flow parameters will be analysed and presented through graphical representations. The major outcomes include that, the higher values of Darcy Forchheimer parameter cause the fluid velocity to fall. Additionally, the heat transfer is more controllable in the case of cobalt capped iron oxide nanoparticles than that of cobalt nanoparticles.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101437"},"PeriodicalIF":1.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical properties of the Odd Lomax Burr Type X distribution with applications to failure rate and radiation data","authors":"Ahmed R. El-Saeed ,&nbsp;Nooruldeen A. Noori ,&nbsp;Mundher A. Khaleel ,&nbsp;Safar M. Alghamdi","doi":"10.1016/j.jrras.2025.101421","DOIUrl":"10.1016/j.jrras.2025.101421","url":null,"abstract":"<div><div>This article introduces the Odd Lomax Burr Type X (OLoBX) distribution, an extension of the four-parameter Burr Type X model. The Quantile function, moments, the function that generates the moments, Rényi entropy, and the ordered statistics are some of the new distribution's essential statistical aspects. For the purpose of estimating the parameters of the new distribution, the technique of maximum likelihood estimation is used. Monte Carlo simulation examines the estimators' performance and reveals that the maximum likelihood technique is effective in parameter estimation. The OLoBX distribution was used on the electrical relay failure time, fatigue fracture life, and radiation data sets to test its adaptability and flexibility, it outperformed its sub models and other popular distributions. The new distribution showed high flexibility in representing heavy-tailed and asymmetric data, outputforming well-known distributions such as TEEBX, BeBX, and WeBX. Monte Carlo simulations demonstrated that MLE estimation of OLoBX parameters is satable and accurate, especially at large sample size (n = 300), where the RMSE and Abias values decreased significantly. The OLoBX distribution outperformed competing distributions in terms of goodness-of-fit criteria, confirming its effectiveness in modeling real data.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101421"},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermally Darcy-Forchheimer flow of tri-hybrid nanomaterials with temperature-dependent fluid characteristics","authors":"Masood Khan ,&nbsp;Gohar Rehman ,&nbsp;Mudassar Qamar ,&nbsp;A.S. Alqahtani ,&nbsp;M.Y. Malik","doi":"10.1016/j.jrras.2025.101404","DOIUrl":"10.1016/j.jrras.2025.101404","url":null,"abstract":"<div><div>Trihybrid nanofluids (THNF) are prepared by combining three distinct types of nanoparticles in a regular fluid to improve the heat efficiency of fluid in several fields, including electronics cooling, solar energy, heat exchangers, automobile radiators, biomedicine, and solar power systems. Further, Darcy-Forchheimer flow through porous materials is widely used in distinct industrial and manufacturing processes, such as waste storage, foam and ceramics, oil purification, food processing, and many others. In line with this, explore the heat transfer characteristics of the magnetized ternary hybrid nanofluid flow of the Darcy-Forchheimer model over a porous shrinking horizontal cylinder incorporating variable thermal conductivity, thermal radiation and Joule heating with first-order velocity and thermal slip conditions. The leading equations are reform into dimensionless notation by using the application of similarity transformations. We altered a non-dimensional set of ordinary differential equations using the numerical method, namely MATLAB function bvp4c. Graphical and tabular analyses are carried out for the emergent variables against drag friction, heat transportation rate, flow field, and thermal distribution. Dual solution obtained under specific ranges of several physical factors. The major outcome reveals that drag friction efficiency of the ternary hybrid nanofluid is more superior than that of the hybrid nanofluid and simple nanofluid. Histrogram analysis also reveal that friction force improved with the increment in tri-hybrid nanofluid for stable branch solution. The variation in the velocity slip factor increases the velocity field. Furthermore, heat transport rate boosts with the variation of suction factor. Additionally, thermal distribution is also enhanced with an increment in the thermal slip parameter and variable thermal conductivity factor. This model of tri-hybrid nanofluid with stretching (shrinking) cases has numerous daily life applications such as X-ray process, bundle wrapping, cooling of thermal reactors, tumor therapy, hot roll, laser diodes, heat exchangers, cooling of gadgets, nuclear fusion, sheet material extrusion, polymer production, pharmaceuticals, sheet material extrusion, computer chips, aluminum bottles manufacturing, turbine blades, hybrid vehicles, and many others.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 2","pages":"Article 101404"},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}