Study of the working body mechanism in forging-and-stamping equipment by K. O. Kobzev

“…The synthesized basic diagram of the frictional contact of solids in forging-and-stamping machines is considered. The possibility of obtaining the maximum load characteristics of the frictional contact within the variation interval of the friction factor is determined. …”
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Feedrate Fluctuation Minimization for NURBS Tool Path Interpolation Based on Arc Length Compensation and Iteration by Xing Liu, Pengxin Yu, Haiduo Chen, Bihui Peng, Zhao Wang, Fusheng Liang

“…Real-time parametric interpolation plays a crucial role in achieving high-speed and high-precision multi-axis CNC machining. In the interpolation cycle, the position of the next interpolation point is required to be calculated in real-time to guide the action of the machining process. …”
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An Experimental Investigation into the Enhancement of Surface Quality of Inconel 718 Through Axial Ultrasonic Vibration-Assisted Grinding in Dry and MQL Environments by Sreethul Das, Pandivelan Chinnaiyan, Joel Jayaseelan, Jeyapandiarajan Paulchamy, Andre Batako, Ashwath Pazhani

“…Ultrasonic vibration-assisted grinding (UVAG) has proven to be beneficial for grinding difficult-to-machine materials. This work attempts to enhance the grinding performance of Inconel 718 through a comprehensive study of UVAG characteristics. …”
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Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Ste... by Erik Gustafsson, Magnus Andersson

“…Physics-informed neural networks (PINNs) are gaining traction as surrogate models for fluid dynamics problems, combining machine learning with physics-based constraints. This study investigates the impact of labeled data on the performance of parameterized physics-informed neural networks (PINNs) for surrogate modeling and design optimization. …”
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An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis by Wei Xiao, Yingying Shen, Jiao Zhao, Luogeng Lv, Jiangtao Chen, Wei Zhao

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Meteorological Factors and the Spread of COVID‐19: A Territorial Analysis in Italy by Vito Telesca, Gianfranco Castronuovo, Gianfranco Favia, Mariarosaria Marra, Marica Rondinone, Alessandro Ceppi

“…The results reveal a significant correlation between specific atmospheric variables and the spread of COVID‐19, with dew point temperature as the most influential parameter at low air temperature values.…”
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Lithology Identification Method and Application Based on Generative Adversarial Neural Network by YIN Qiong

“…Lithology identification is the basis of reservoir evaluation and the key to reservoir parameter calculation and reservoir evaluation and development. …”
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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis by Fernanda Pereira Leite Aguiar, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. …”
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Intelligent text similarity assessment using Roberta with integrated chaotic perturbation optimization techniques by Esraa Hassan, Amira Samy Talaat, M. A. Elsabagh

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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…This paper presents a comparative analysis of machine learning models applied to stock price prediction using historical data from the Nasdaq stock index spanning the years 2015 to 2023. …”
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Enhanced Channel Estimation for RIS-Assisted OTFS Systems by Introducing ELM Network by Mintao Zhang, Zhiying Liu, Li Wang, Wenquan Hu, Chaojin Qing

“…Moreover, ML-based CE methods encounter numerous difficulties, including intricate parameter tuning and long training time. Motivated by the inherent advantages of the single-hidden layer feed-forward network structure, we introduce extreme learning machine (ELM) into RIS-assisted OTFS systems to improve CE accuracy. …”
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Prediction of minimum miscibility pressure of CO2-oil systems using grey-box modeling for carbon dioxide capture, utilization, storage, and enhanced oil recovery by Milad Asghari, Sajjad Moslehi, Mohammad Emami Niri, Shahin Kord

“…The current study introduces an innovative grey-box machine learning modeling method to bridge this gap. …”
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Characterization, identification and life prediction of acoustic emission signals of tensile damage for HSR gearbox housing material by Ai Yibo, Zhang Yuanyuan, Cui Hao, Zhang Weidong

“…Design/methodology/approach – In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. …”
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Model Input-Output Configuration Search With Embedded Feature Selection for Sensor Time-Series and Image Classification by Anh Tuan Hoang, Zsolt Janos Viharos

“…Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. …”
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Bearing Fault Diagnosis Based on IPOA-VMD and SSA-HKELM by Baoxian Chang, Xing Zhao, Dawei Guo, Siyu Zhao, Jiyou Fei, Hua Li, Xiaodong Liu

“…A novel comprehensive indicator is introduced as the fitness function during the parameter selection phase of IPOA. By utilizing IPOA, the optimal combination of VMD&#x2019;s parameters, including the mode component K and penalty factor <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, is determined. …”
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One-Dimensional Deep Residual Network with Aggregated Transformations for Internet of Things (IoT)-Enabled Human Activity Recognition in an Uncontrolled Environment by Sakorn Mekruksavanich, Anuchit Jitpattanakul

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Multi-task deep learning for simultaneous prediction of steel purity and carbon capture rate using membrane separation technology in integrated steelmaking processes by Somboon Sukpancharoen, Pakon Sakdee, Natacha Phetyim, Rinlada Sirisangsawang, Chayut Sungsook

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Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs by Joshua Paciocco, Ahmed Hasan, Jason Chan, Daisuke Taniguchi, Cristina Amon, Golnaz Karoubi, Aimy Bazylak

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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

“…OPTICALS incorporates a &#x201C;Look-back window&#x201D; hyperparameter, using recent historical prices to predict next-day trends through Moving Averages analysis. This parameter refines lagged feature engineering to enhance trend capture and predictive accuracy. …”
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Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms by Dichang Zhang, Christian Santoni, Zexia Zhang, Dimitris Samaras, Ali Khosronejad

“…To advance this field, a novel machine learning model has been developed to predict wind farm mean flow fields through an adaptive multi-fidelity framework. …”
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