Probabilistic Analysis of Surface Integrity in CNC Turning: Influence of Thermal Conductivity and Hardness on Roughness and Waviness Distributions by Mohammad S. Alsoufi, Saleh A. Bawazeer

“…High thermal conductivity materials exhibited narrower parameter distributions (σ_<i>R</i>_a ≤ 0.40 µm), while harder materials showed increased variability (σ_<i>R</i>_a ≥ 0.60 µm). Probabilistic models, validated with high confidence (<i>R</i>² > 0.95), offer predictive insights for optimizing machining strategies. …”
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Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems by Sarucha Yanyong, Somyot Kaitwanidvilai

“…This paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. …”
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Estimating Shape Parameters of Piecewise Linear-Quadratic Problems by Zheng, Peng, Ramamurthy, Karthikeyan Natesan, Aravkin, Aleksandr Y.

“…Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. …”
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Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation by Bowei Jiang, Tongyuan Bai, Peng Zheng, Tieru Wu, Rui Ma

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Improved Crack Detection and Recognition Based on Convolutional Neural Network by Keqin Chen, Amit Yadav, Asif Khan, Yixin Meng, Kun Zhu

“…Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.…”
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Embedded feature selection using dual-network architecture by Abderrahim Abbassi, Arved Dörpinghaus, Niklas Römgens, Tanja Grießmann, Raimund Rolfes

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Application of ANN in Milling Process: A Review by Salah Al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron

“…In recent years the trends were towards modeling of machining using artificial intelligence. …”
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The evolving role of programming and LLMs in the development of self-driving laboratories by John R. Kitchin

“…This device enables users to explore automation at multiple levels, from basic programming and experimental design to machine learning-driven optimization. We demonstrate the application of Claude-Light in structured automation approaches, including traditional scripting, statistical design of experiments, and active learning methods. …”
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Simulation of the Effective Distribution of Biofuel Droplets in a Reacting Flow by Aliya Askarova, Saltanat Bolegenova, Shynar Ospanova, Karlygash Bolegenova, Alfiya Nurmukhanova

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Graph-aware isomorphic attention for adaptive dynamics in transformers by Markus J. Buehler

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Determining the Strength of Structural Materials by Solving Inverse Problems of Thermoelasticity by Natalia A. Guk, Yurii M. Matsevity, Volodymyr O. Povhorodnii, Mykola O. Safonov

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Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches by Honglei Yao, Andrew Topper

“…It enhances these models with two cutting-edge optimization methods: Crystal Structure Algorithm and Reptile Search Algorithm. …”
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Introducing a Novel Method to Identify the Future Trend of Nikkei 225 Stock Price in Order to Reduce Investment Risk by Freyr Björgvinsson

“…This study proposes a new incorporation of hyperparameter optimization algorithms into machine learning techniques, such as Genetic Algorithms, Battle Royale Optimization, and Grey Wolf Optimization, for stock price prediction. …”
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ASKSQL: Enabling cost-effective natural language to SQL conversion for enhanced analytics and search by Arpit Bajgoti, Rishik Gupta, Rinky Dwivedi

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Are queries and keys always relevant? A case study on transformer wave functions by Riccardo Rende, Luciano Loris Viteritti

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WEISS: Wasserstein efficient sampling strategy for LLMs in drug design by Riccardo Tedoldi, Junyong Li, Ola Engkvist, Andrea Passerini, Annie M Westerlund, Alessandro Tibo

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Neural architecture codesign for fast physics applications by Jason Weitz, Dmitri Demler, Luke McDermott, Nhan Tran, Javier Duarte

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Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions by Nathaniel Tamminga, Scott Feister, Kyle D. Frische, Ronak Desai, Joseph Snyder, John J. Felice, Joseph R. Smith, Chris Orban, Enam A. Chowdhury, Michael L. Dexter, Anil K. Patnaik

“…An important approach to controlling and optimizing ultra-intense laser interactions involves gathering large datasets and using these data to train statistical and machine learning models. …”
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Opening New Opportunities For Aeronautic, Naval And Train Large Components Realization With Hybrid And Twin Manufacturing by Matthieu Rauch, Jean-Yves Hascoet

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A prediction rigidity formalism for low-cost uncertainties in trained neural networks by Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli

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