Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş, Mehmet Masum Tünçay

“…In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based on boriding parameters such as temperature, time, boriding media, method, and alloy composition. …”
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Industrial multi-machine data aggregation, AI-ready data preparation, and machine learning for virtual metrology in semiconductor wafer and slider production by Feiyang Ou, Julius Suherman, Chao Zhang, Henrik Wang, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…Smart Manufacturing is rooted in AI, Machine Learned (ML), and Data Synchronized (DS) modeling to tap into invaluable operating data. …”
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Condition-Aware Autoencoder and Transfer Learning-Based Estimation of Milling Cutting Forces from Spindle Vibration Signals by Je-Doo Ryu, Jungmin Lee, Sung-Ryul Kim, Min Cheol Lee

“…To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. …”
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Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare by Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi, Fenglin Liu, Andrew Soltan, Patrick Schwab, Kim Branson, David A. Clifton

“…This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. …”
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Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT by Sinan Andan Diwan

“…This smart hospitality framework considers inputs like reception facilities, room amenities, and restaurant services, providing a comprehensive customer satisfaction model. …”
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Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride by Mohamed AbdulHameed, Benjamin Beeler, Antoine Claisse

“…MD simulations of stress–strain behavior were used to estimate the nanoindentation hardness, revealing that the Kocevski potential accurately predicts hardness even though it fails to model dynamic plasticity. …”
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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products by Ehsan Mohsennia, Alireza Javid, Vahab Toufigh

“…This novel approach is aligned with environmental management principles, aiming to maximize the effective use of diverse IBPs, reduce waste, and enhance resource efficiency. …”
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Evaluating present-day and future impacts of agricultural ammonia emissions on atmospheric chemistry and climate by M. Beaudor, M. Beaudor, D. Hauglustaine, J. Lathière, M. Van Damme, M. Van Damme, L. Clarisse, N. Vuichard

“…Ammonia sources are, however, challenging to quantify because of their dependencies on environmental variables and agricultural practices and represent a crucial input for chemistry–climate models. In this study, we use the chemistry–climate model LMDZ–INCA (Laboratoire de Météorologie Dynamique–INteraction with Chemistry and Aerosols) with agricultural and natural soil ammonia emissions from a global land surface model ORCHIDEE (ORganising Carbon and Hydrology In Dynamic Ecosystems), together with the integrated module CAMEO (Calculation of AMmonia Emissions in ORCHIDEE), for the present-day and 2090–2100 period under two divergent Shared Socioeconomic Pathways (SSP5-8.5 and SSP4-3.4). …”
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