Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints by Guangming Mi, Guoqin Sun, Shuai Yang, Xiaodong Liu, Shujun Chen, Wei Kang

“…Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. …”
Get full text

Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing by Changqing Shu, Shasha Zhang, Peiheng Ding, Yaxin Sun, Xuewei Tao, Xiaolin Zhu, Qiuhao Gu, Liukai Hua, Song Xue, Zhengjun Yao

“…However, conventional models struggle to capture the complex interactions between process parameters, microstructure, and strength. This study presents a physics-enhanced machine learning framework to predict yield strength and ultimate tensile strength. …”
Get full text

Application of numerical analysis and machine learning techniques to improve drying performance and energy consumption of microwave-assisted convective dryer by Hany S. El-Mesery, Ahmed H. ElMesiry, Mohammad Kaveh, Zicheng Hu, Abdallah Elshawadfy Elwakeel, Sara Elhadad

“…Machine learning models, specifically Feedforward Backpropagation (FFBP) and Cascade Forward Backpropagation (CFBP) artificial neural networks, were developed to predict drying outcomes based on input variables. …”
Get full text

Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights by V. S. Shaisundaram, P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, S. Prabhakar

“…Biodiesel blends of 10%, 20%, and 30% Momordica seed biodiesel were enhanced with cerium oxide nano additives at 45 ppm and evaluated using a partially stabilized zirconia-coated piston and cylinder liner. Additionally, machine learning (ML) algorithms, including Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR), and Random Forest Regression (RF), were applied to predict thermal performance metrics using input parameters such as Fuel, Compression Ratio (CR), Load, and Peak Pressure (Bar). …”
Get full text

Spatial prediction and visualization of PM2.5 susceptibility using machine learning optimization in a virtual reality environment by Seyed Vahid Razavi-Termeh, Jalal Safari Bazargani, Abolghasem Sadeghi-Niaraki, X. Angela Yao, Soo-Mi Choi

“…This paper overcomes these shortcomings by combining state-of-the-art machine learning advancements with new visualization techniques. …”
Get full text

Modelling and Optimisation of Biodiesel Production from Margarine Waste Oil Using a Three-Dimensional Machine Learning Approach by Pascal Mwenge, Hilary Rutto, Tumisang Seodigeng

“…The effect of the process parameters methanol-to-oil ratio (3–15 mole), catalyst ratio (0.3–1.5 wt. %), reaction time (30–90 min), and reaction temperature (30–70 °C) were studied. …”
Get full text

Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study by Xiaofeng Jiang, Wenyong Peng, Jianbo Xu, Yanhong Zhu

“…The other main features include ventilator parameters and blood gas indicators. By applying machine learning to large datasets, we developed a new method for predicting extubation failure after cardiac surgery in critically ill patients. …”
Get full text

Establishment and Solution Test of Wear Prediction Model Based on Particle Swarm Optimization Least Squares Support Vector Machine by Xiao Huang, Yongguo Wang, Yuhui Mao

“…Traditional tool wear identification methods are usually based on the framework of “feature extraction + machine learning”, but these methods often have problems of low efficiency and low recognition accuracy. …”
Get full text

Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…The study extends beyond optimizing exfoliation parameters by employing machine learning (ML) and deep learning (DL) techniques to forecast the yield of hBNNs. …”
Get full text

Machining-induced burr distribution along hole contours in unidirectional carbon fibre-reinforced polymer (UD-CFRP) composites by Norbert Geier, Gergely Magyar

“…The coefficients of the models were determined using datasets of three previous research projects (i.e., 2 380 808 data points) and validated through a fourth one (208 571 data points) where hole machining experiments were carried out using different tools, parameters and setups. …”
Get full text

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine by Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du

“…Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. …”
Get full text

EXAM: Ex-vivo allograft monitoring dashboard for the analysis of hypothermic machine perfusion data in deceased-donor kidney transplantation. by Simon Schwab, Hélène Steck, Isabelle Binet, Andreas Elmer, Wolfgang Ender, Nicola Franscini, Fadi Haidar, Christian Kuhn, Daniel Sidler, Federico Storni, Nathalie Krügel, Franz Immer

“…Deceased-donor kidney allografts are exposed to ischemic injury during ex vivo transport due to the lack of blood oxygen supply. Hypothermic machine perfusion (HMP) effectively reduces the risk of delayed graft function in kidney transplant recipients compared to standard cold storage. …”
Get full text

Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

“…This study introduces an advanced calibration framework for SPFs using machine learning techniques, demonstrated through a case study of 20 urban roundabouts in Amman, Jordan. …”
Get full text

Evaluation of Image Processing Technique on Quality Properties of Chickpea Seeds (Cicer arietinum L.) Using Machine Learning Algorithms by Halil Kırnak, Necati Çetin, İhsan Serkan Varol

“…Chickpea is an important edible legume consumed worldwide because of rich nutrient composition. The physical parameters of chickpea are crucial attributes for design of processing and classification systems. …”
Get full text

Mitigating Climate Risk to China's Renewable Energy Generation by Power Plants Redeployment Using a Machine‐Learning Model by Lin Lv, Miaomiao Liu, Yuli Shan, Jinghang Xu, Jianxun Yang, Wen Fang, Zongwei Ma, Jun Bi

Get full text

Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care by Lin Wang, Shao-Bin Duan, Ping Yan, Xiao-Qin Luo, Ning-Ya Zhang

“…Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. …”
Get full text

TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD by Anju Chandran, Archa Santhosh, Claudio Pistidda, Paul Jerabek, Roland C. Aydin, Roland C. Aydin, Christian J. Cyron, Christian J. Cyron

“…In this work, we compare active and passive machine learning approaches for developing TiAlNb interatomic potentials using both deep potential molecular dynamics (DeePMD) and the moment tensor potential (MTP) methods. …”
Get full text

Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. …”
Get full text

Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning by Nora Rajs, Yinon Harush-Brosh, Ron Raisch, Ravit Yakobi Arancibia, Amani Zoabi, Guy Nevet Golan, Moshe Shpitzen, Sarena Wiesner, Michal Levin-Elad, Tommy Kaplan, Katherine Margulis

Get full text

Machinability and surface integrity for Mg AZ61A alloy composite by employing Taguchi integrated grey relational analysis by Vinoth Balu, Muruganandham Rajagopal, Rosewine Joy, Alagarsamy Sivasamy

“…The present experimental study seeking to identify the optimal processing parameters in WEDM of Mg AZ61A-ZrB2 composite using Taguchi integrated grey relational analysis (GRA). …”
Get full text