Discover applied sciences最新文献

{"title":"Application of machine learning for predicting the incubation period of water droplet erosion in metals.","authors":"Khaled AlHammad, Mamoun Medraj, Moussa Tembely","doi":"10.1007/s42452-025-07268-8","DOIUrl":"10.1007/s42452-025-07268-8","url":null,"abstract":"<p><p>Water droplet erosion (WDE) is a critical degradation phenomenon that significantly affects component lifespan and performance in power generation, aerospace, and wind energy industries. The incubation period-the initial phase before visible material loss occurs-is particularly crucial for maintenance planning and material selection yet remains challenging to predict accurately due to the complex interplay of material properties and impact conditions. Traditional empirical models have shown limited predictive capability due to their reliance on numerous adjustable parameters with insufficient physical interpretation. This study aimed to develop and validate a machine learning (ML) approach for accurately predicting the WDE incubation period across different metallic materials and impact conditions. The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. A range of ML models-linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)-were trained and validated using experimental data from five different alloys under various impact conditions. Data transformation methods significantly enhanced model performance, with the LR model using Box-Cox transformation achieving the highest accuracy (R² > 90%, low MAE), followed by the ANN model with Yeo-Johnson transformation (R² > 85%). Feature importance analysis through SHAP values revealed that impact velocity and surface hardness were the most influential factors affecting incubation period, providing valuable physical insights into the erosion mechanism. Hyperparameter optimization techniques showed minimal improvement in model performance, suggesting that the transformations effectively captured the underlying relationships in the data. This research represents the first comprehensive application of ML techniques to WDE incubation period prediction, establishing a methodological framework that integrates experimental data, statistical analysis, and advanced ML algorithms. Unlike previous approaches, our methodology (1) systematically evaluates multiple ML algorithms and transformation techniques for WDE prediction, (2) provides quantitative assessment of feature importance that aligns with physical understanding of erosion mechanisms, (3) demonstrates superior predictive accuracy compared to traditional empirical models, and (4) offers a generalizable approach applicable across different metallic materials and impact conditions. This work bridges the gap between data-driven modeling and physical understanding of WDE, providing a valuable tool for engineers to optimize material selection and maintenance strategies in erosion-prone applications.</p>","PeriodicalId":520292,"journal":{"name":"Discover applied sciences","volume":"7 7","pages":"712"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early diagnosis of Alzheimer's disease and mild cognitive impairment using MRI analysis and machine learning algorithms.","authors":"Helia Givian, Jean-Paul Calbimonte","doi":"10.1007/s42452-024-06440-w","DOIUrl":"10.1007/s42452-024-06440-w","url":null,"abstract":"<p><p>Early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42452-024-06440-w.</p>","PeriodicalId":520292,"journal":{"name":"Discover applied sciences","volume":"7 1","pages":"27"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying and establishing the critical elements of a human cardiac in-vitro model for studying type-II diabetes.","authors":"Ivana Hernandez, Gobinath Chithiravelu, Andie E Padilla, Binata Joddar","doi":"10.1007/s42452-025-07442-y","DOIUrl":"10.1007/s42452-025-07442-y","url":null,"abstract":"<p><p>This study aimed to elucidate the impact of advanced glycation end products (AGEs) and glucose shock on cardiomyocyte viability, gene expression, cardiac biomarkers, and cardiac contractility. Firstly, AGEs were generated in-house, and their concentration was confirmed using absorbance measurements. AC16 cardiomyocytes were then exposed to varying doses of AGEs, resulting in dose-dependent decreases in cell viability. The maximum tolerated dose of AGEs was determined, revealing significant downregulation of the cardiac gene gap junction alpha 1 (GJA1). Furthermore, the study assessed the effects of AGEs, glucose shock, and their combination on biomarkers, cardiac myosin heavy chain (MHC) and connexin-43 (Cx-43), in AC16 cells. It was found that AGEs supplementation induced an increase in MHC expression while reducing Cx-43 expression, potentially contributing to cardiac dysfunction. Glucose shock also affected cardiomyocyte contractility, highlighting the complex interplay between AGEs, glucose levels, and cardiac function. Additionally, human iPSC-derived cardiomyocytes were subjected to varying doses of AGEs, revealing dose-dependent cytotoxicity and alterations in contractility. Immunostaining confirmed upregulation of MYH7, a cardiac gene associated with muscle contraction, in response to AGEs. However, the expression of Cx-43 was minimal in these cells. This investigation sheds light on the intricate relationship between AGEs, glucose shock, and cardiomyocyte function, providing insights into potential mechanisms underlying cardiac dysfunction associated with metabolic disorders such as diabetic cardiomyopathy (DCM).</p><p><strong>Graphical abstract: </strong></p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42452-025-07442-y.</p>","PeriodicalId":520292,"journal":{"name":"Discover applied sciences","volume":"7 7","pages":"788"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laser induced forward transfer imaging using deep learning.","authors":"James A Grant-Jacob, Michalis N Zervas, Ben Mills","doi":"10.1007/s42452-025-06679-x","DOIUrl":"10.1007/s42452-025-06679-x","url":null,"abstract":"<p><p>A novel approach for improving the accuracy and efficiency of laser-induced forward transfer (LIFT), through the application of deep learning techniques is presented. By training a neural network on a dataset of images of donor and receiver substrates, the appearance of copper droplets deposited onto the receiver was predicted directly from images of the donor. The results of droplet image prediction using LIFT gave an average RMSE of 9.63 compared with the experimental images, with the SSIM ranging from 0.75 to 0.83, reflecting reliable structural similarity across predictions. These findings underscore the model's predictive potential while identifying opportunities for refinement in minimising error. This approach has the potential to transform parameter optimisation for LIFT, as it enables the visualization of the deposited material without the time-consuming requirement of removing the donor from the setup to allow inspection of the receiver. This work therefore represents an important step forward in the development of LIFT as an additive manufacturing technology to create complex 3D structures on the microscale.</p>","PeriodicalId":520292,"journal":{"name":"Discover applied sciences","volume":"7 4","pages":"254"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural analysis and fatigue prediction of harrow tines used in Canadian prairies.","authors":"Arafater Rahman, Mohammad Abu Hasan Khondoker","doi":"10.1007/s42452-024-06310-5","DOIUrl":"10.1007/s42452-024-06310-5","url":null,"abstract":"<p><p>The Canadian prairies are renowned for their substantial agricultural contributions to the global food market. Harrow tines are indispensable in farming equipment, especially for soil preparation and weed control before planting crops. During operation, these tines are exposed to repetitive cyclic loading, which eventually causes fatigue failure. Commercially available three different harrow tines named 0.562HT, 0.625HT, and 0.500HT undergo an experimental fatigue evaluation and are validated through Finite Element Analysis (FEA). Fatigue life estimation for different deflections under various real-field deflections was carried out where 0.562HT showed groundbreaking life compared with others. The study results showed that the fatigue life is highly dependent on geometry, number of coils, pitch angle, leg length, and coil diameter. The 0.354HT model, developed to investigate the effect of wire diameter, closely resembles the 0.500HT model. The harrowing ability of the four different harrow tine models against identical deflections has been analyzed. Experimental fractured surfaces went through morphological investigation. This research has an impeccable impact on prairies' agricultural acceleration by saving time and mitigating unpredictable fatigue failure often faced by farmers. Even the observed failure phenomena can serve as motivation to develop more reliable and durable harrow tines, which could increase agricultural efficiency.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42452-024-06310-5.</p>","PeriodicalId":520292,"journal":{"name":"Discover applied sciences","volume":"6 11","pages":"613"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11564268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}