A generalized machine learning framework to estimate fatigue life across materials with minimal data by Dharun Vadugappatty Srinivasan, Morteza Moradi, Panagiotis Komninos, Dimitrios Zarouchas, Anastasios P. Vassilopoulos

“…In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. …”
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Explainable Machine Learning for Radio Environment Mapping: An Intelligent System for Electric Field Strength Monitoring by Yiannis Kiouvrekis, Theodor Panagiotakopoulos, Efthymia Nousi, Ioannis Filippopoulos, Agapi Ploussi, Ellas Spyratou, Efstathios P. Efstathopoulos

“…The dataset includes geospatial and environmental features such as antenna distance, population density, urbanization level, and detailed built environment characteristics (e.g., volume, surface, and height). We evaluate multiple machine learning models—kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM—using a two-semester split for training and assessment. …”
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Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI by Nadesalingam Paripooranan, Warnakulasuriya Buddhini Nirasha, H. R. P. Perera, Sahan M. Vijithananda, P. Badra Hewavithana, Lahanda Purage Givanthika Sherminie, Mohan L. Jayatilake

“…Hyperparameters were tuned to optimize the model.ResultsThe model was able to differentiate IDC and ILC with an accuracy of 79% and an Area Under the Curve of 0.851 on the Receiver Operating Characteristic Curve. Among the morphological features, the total volume of the contralateral breast, surface area of the contralateral breast, breast density, and the ratio of the total volume of the contralateral breast to its surface area had higher F-scores, indicating that the dimensions of the contralateral breast could be an important factor in differentiating IDC and ILC.ConclusionThis study successfully developed and optimized a predictive model based on breast morphological features to differentiate IDC and ILC using machine learning methods.…”
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Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models by Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi

“…The application of five machine‐learning techniques in pluvial flood susceptibility mapping was investigated using the case study of two severe storms (2005 and 2013) in Toronto, Canada. …”
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Use of Machine Learning Algorithms to Predict Almen (Shot Peening) Intensity Values of Various Steel Materials by Murat İnce, Hatice Varol Özkavak

“…Wear, fatigue, and corrosion are just a few of the issues that mechanical components in engineering experience, leading to surface deterioration. Enhancing the surface characteristics of engineering components is therefore essential. …”
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Designing Learning Trajectory of Curved 3D Shapes using RME with The Context of Sesaji Rewanda Semarang by Lattifah Tuni'mah, Farida Nursyahidah, Irkham Ulil Albab, Maya Rini Rubowo

“…The subject in this research is the 9th grade of SMP Negeri 6 Semarang. The resulting learning trajectory comprised 5 sets of activities, namely: (1) identifying the types and characteristics of curved 3D shapes, (2) determining the beginning of the formulas for cylinder surface area and volume, (3) determining the beginning of the formulas for cone surface area and volume, (4) determining the beginning of the formulas for sphere surface area and volume, and (5) resolve contextual problems associated to curved shapes. …”
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Estimating volumetric water salinity in a Tibetan endorheic lake using machine learning and remote sensing by Pengju Xu, Kai Liu, Yaling Lin, Xuefei Fu, Chenyu Fan, Chunqiao Song

“…First, we developed a model using machine learning algorithms, with remote sensing data and hydrological and topographical features, to estimate surface water salinity. …”
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STRUCTURAL RELIABILITY ANALYSIS METHOD BASED ON COLLABORATION-MEAN POINT CONSTRAINED ACTIVE LEARNING SURROGATE MODEL

“…The reliability of mechanical structures is crucial for their safe operation,to address the problem of low accuracy and low efficiency in reliability analysis of complex mechanical structures,a new active learning surrogate model based reliability analysis method was proposed.The spatial location characteristics of excellent fitting samples were studied and three constraints,such as surface constraint,distance constraint,and domain constraint,were proposed accordingly.Correspondingly,three control functions were established to achieve the three constraints.Then,three control functions were organically collaborated,and an effective new learning function,collaboration-mean point constrained learning(CPCL)function was proposed.Combined with the augmented radial basis function(ARBF),a collaboration-mean point constrained active learning surrogate model(ARBF+CPCL)reliability analysis method was established.Finally,three cases were employed to verify the high computational accuracy and computational efficiency of ARBF+CPCL reliability analysis method,and the application ability of ARBF+CPCL method in practical engineering cases was proved through the reliability analysis example of the turbine disk.…”
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A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal by Yunpeng Ge, Kaiyang Ying, Guo Yu, Muhammad Ubaid Ali, Abubakr M. Idris, Abubakr M. Idris, Asfandyar Shahab, Habib Ullah, Habib Ullah

“…We examine key biochar characteristics, including physical (e.g., surface area, pore volume), chemical (e.g., ultimate/proximate analysis, aromatization), electrochemical (e.g., cation exchange capacity, electrical conductivity), and functional group properties, and their optimization for various contaminants. …”
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Exploring insights on deep learning-based photovoltaic fault detection for monofacial and bifacial modules using thermography by Eko Adhi Setiawan, Muhammad Fathurrahman

“…Afterward, these images are analyzed by a deep learning (DL) model known for its objec detection accuracy, identifying modules requiring further inspection. …”
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Experimenting with learning-based image orientation approaches for photogrammetric mapping of <em>Posidonia oceanica</em> meadows by F. Menna, A. Calantropio, A. Pansini, G. Ceccherelli, E. Nocerino

“…Delile (PO) is an endemic seagrass of the Mediterranean Sea, where it grows in the form of dense meadows extending from the surface up to 40 m depth. PO plays a key role in the underwater realm, providing numerous ecosystem services, but it is nowadays endangered by climate change and anthropogenic pressure. …”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…Consequently, this paper introduced a novel screening approach that integrates first principles with machine learning (ML) to rapidly predict the gas sensitivity of materials. …”
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Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach by Kourosh Hayatigolkhatmi, Chiara Soriani, Emanuel Soda, Elena Ceccacci, Oualid El Menna, Sebastiano Peri, Ivan Negrelli, Giacomo Bertolini, Gian Martino Franchi, Roberta Carbone, Saverio Minucci, Simona Rodighiero

“…In this study, we addressed this gap by combining a specialized surface to enhance cell attachment, the FUCCI(CA)2 sensor, an automated image analysis pipeline, and a custom machine learning algorithm. …”
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Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning by Saaketh Desai, Manish Jain, Sadhvikas J. Addamane, David P. Adams, Remi Dingreville, Frank W. DelRio, Brad L. Boyce

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Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

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On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning by Nansha Gao, Mou Wang, Xiao Liang, Guang Pan

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Regional, rural and remote medicine attracts students with a similar approach to learning in both the Northern and Southern hemisphere by Kylie J. Mansfield, Anita Iversen, Maja-Lisa Løchen

“…In both cohorts of medical students deep learning scores exceeded surface learning scores. Selection of students with a learning goal orientation and learning characteristics of curiosity, adaptability and conscientiousness could potentially help students to flourish in rural placement environments.…”
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A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry by Yashar Shahedi, Mohsen Zandi, Mandana Bimakr

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Simulation of lake underwater terrain based on the XGBoost model: a case study of typical lakes on the Tibetan Plateau by Kaixin Hu, Shanlong Lu, Qing Li, Cong Du, Bo Zhang, Peng Du, Harrison Odion Ikhumhen

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Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method by Xinyu Zou, Xinlong Li, Dali Wang, Ju Wang

“…Feature importance identifies NO₂ as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O₃ pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. …”
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