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Microstructure-Based Flow Stress Model to Predict Machinability of Inconel 718.
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- Materials (1996-1944), 2024, v. 17, n. 17, p. 4206, doi. 10.3390/ma17174206
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Behavioral biometrics to detect fake expert profiles during negotiation.
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- Multimedia Tools & Applications, 2024, v. 83, n. 32, p. 78293, doi. 10.1007/s11042-024-18644-8
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- Article
Improving Urban Forest Expansion Detection with LandTrendr and Machine Learning.
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- Forests (19994907), 2024, v. 15, n. 8, p. 1452, doi. 10.3390/f15081452
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- Article
The Virtual Rotor Kit Project: A virtual rotor test rig for balancing experiments.
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- International Journal of Mechanical Engineering Education, 2024, v. 52, n. 4, p. 429, doi. 10.1177/03064190231197119
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- Article
New Precedence Theorems for One-Machine Weighted Tardiness.
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- Mathematics of Operations Research, 2007, v. 32, n. 3, p. 579, doi. 10.1287/moor.1070.0255
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Combining Algorithmic Information Dynamics Concepts and Machine Learning for Electroencephalography Analysis: What Can We Get?
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- Complex Systems, 2022, v. 31, n. 4, p. 389, doi. 10.25088/ComplexSystems.31.4.389
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- Article
Coolant Volume Prediction for Spindle Cooler with Adaptive Neuro-fuzzy Inference System Control Method.
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- Sensors & Materials, 2022, v. 34, n. 6, Part 4, p. 2447, doi. 10.18494/SAM3794
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- Article
Parametric Vibration Analysis of a Six-Degree-of-Freedom Electro-Hydraulic Stewart Platform.
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- Shock & Vibration, 2021, p. 1, doi. 10.1155/2021/9994786
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Information processing via physical soft body.
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- Scientific Reports, 2015, p. 10487, doi. 10.1038/srep10487
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Spatially and Temporally Distributed Complexity—A Refreshed Framework for the Study of GRN Evolution.
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- Cells (2073-4409), 2022, v. 11, n. 11, p. 1790, doi. 10.3390/cells11111790
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Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design.
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- Catalysts (2073-4344), 2024, v. 14, n. 1, p. 40, doi. 10.3390/catal14010040
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Molecular Dynamics and Machine Learning in Catalysts.
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- Catalysts (2073-4344), 2021, v. 11, n. 9, p. 1129, doi. 10.3390/catal11091129
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Computational Assessments on Dragline Bucket Oscillations.
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- Romanian Journal of Acoustics & Vibration, 2018, v. 15, n. 2, p. 149
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- Article
Hydrodynamic spin-orbit coupling in asynchronous optically driven micro-rotors.
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- Nature Communications, 2023, v. 14, n. 1, p. 1, doi. 10.1038/s41467-023-39582-3
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- Article
Racetrack Microtron—Pushing the Limits.
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- Symmetry (20738994), 2021, v. 13, n. 12, p. 2244, doi. 10.3390/sym13122244
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Chatter Stability Prediction and Process Parameters' Optimization of Milling Considering Uncertain Tool Information.
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- Symmetry (20738994), 2021, v. 13, n. 6, p. 1071, doi. 10.3390/sym13061071
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An Analysis of the Dynamical Behaviour of Systems with Fractional Damping for Mechanical Engineering Applications.
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- Symmetry (20738994), 2019, v. 11, n. 12, p. 1499, doi. 10.3390/sym11121499
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- Article
Corrected Inertial Torques of Gyroscopic Effects.
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- Advances in Mathematical Physics, 2022, p. 1, doi. 10.1155/2022/3479736
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Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning.
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- Frontiers in Behavioral Neuroscience, 2021, v. 15, p. 1, doi. 10.3389/fnbeh.2021.681771
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Machine learning and dynamics based error‐index method for the detection of monsoon onset vortex over the Arabian Sea: Climatology and composite structures.
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- Quarterly Journal of the Royal Meteorological Society, 2023, v. 149, n. 751, p. 537, doi. 10.1002/qj.4422
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State, global, and local parameter estimation using local ensemble Kalman filters: Applications to online machine learning of chaotic dynamics.
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- Quarterly Journal of the Royal Meteorological Society, 2022, v. 148, n. 746, p. 2167, doi. 10.1002/qj.4297
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- Article
Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.
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- PLoS Computational Biology, 2023, v. 19, n. 3, p. 1, doi. 10.1371/journal.pcbi.1010999
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Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine.
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- Water Resources Research, 2023, v. 59, n. 5, p. 1, doi. 10.1029/2022WR032690
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The Digital Diversity in Russian Regional Dynamics: Analysis by Machine Learning Methods.
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- Nordic Journal of Business, 2020, v. 69, n. 1, p. 7
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A STUDY OF THE DYNAMIC PERFORMANCE OF TILTING PAD BEARING CONSIDERING THERMOHYDRODYNAMIC AND PERTURBATION FREQUENCY EFFECTS.
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- Journal of the Balkan Tribological Association, 2016, v. 22, n. 2-II, p. 1452
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Investigation of water desalination/purification with molecular dynamics and machine learning techniques.
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- AIMS Materials Science, 2022, v. 9, n. 6, p. 919, doi. 10.3934/matersci.2022054
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Theoretical and Experimental Study of a Cement Finish Mill.
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- Acta Physica Polonica: A, 2017, v. 131, n. 3, p. 507, doi. 10.12693/APhysPolA.131.507
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Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions.
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- Infectious Diseases of Poverty, 2022, v. 11, n. 1, p. 1, doi. 10.1186/s40249-022-00981-1
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Analysis of urban sprawl dynamics using machine learning, CA-Markov chain, and the Shannon entropy model: a case study in Mbombela City, South Africa.
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- Environmental Systems Research, 2024, v. 13, n. 1, p. 1, doi. 10.1186/s40068-024-00348-5
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Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques.
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- Water (20734441), 2022, v. 14, n. 23, p. 3941, doi. 10.3390/w14233941
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Model-based analysis of the mechanical subsystem of an air brake system.
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- International Journal of Automotive Technology, 2011, v. 12, n. 5, p. 697, doi. 10.1007/s12239-011-0081-1
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COMPARATIVE ANALYSIS OF THE ENERGIES OF NEEDLE BAR MECHANISMS OF THE SEWING MACHINES.
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- Trakia Journal of Sciences, 2020, v. 18, n. 3, p. 256, doi. 10.15547/tjs.2020.03.012
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Design and Implementation of a Virtual Laboratory for Machine Dynamics.
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- International Journal of Online Engineering, 2010, v. 6, n. 2, p. 15, doi. 10.3991/ijoe.v6i2.1184
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Kinematic and Kinetic Patterns Related to Free-Walking in Parkinson's Disease.
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- Sensors (14248220), 2018, v. 18, n. 12, p. 4224, doi. 10.3390/s18124224
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Autophagy and SARS-CoV-2-Old Players in New Games.
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- International Journal of Molecular Sciences, 2023, v. 24, n. 9, p. 7734, doi. 10.3390/ijms24097734
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CellDynaMo–stochastic reaction-diffusion-dynamics model: Application to search-and-capture process of mitotic spindle assembly.
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- PLoS Computational Biology, 2022, v. 18, n. 6, p. 1, doi. 10.1371/journal.pcbi.1010165
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Model predictive force control in milling based on an ensemble Kalman filter.
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- Journal of Intelligent Manufacturing, 2022, v. 33, n. 7, p. 1907, doi. 10.1007/s10845-022-01931-2
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Real-time machining data application and service based on IMT digital twin.
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- Journal of Intelligent Manufacturing, 2020, v. 31, n. 5, p. 1113, doi. 10.1007/s10845-019-01500-0
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The tricriteria flowshop scheduling problem.
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- International Journal of Advanced Manufacturing Technology, 2008, v. 36, n. 11/12, p. 1210, doi. 10.1007/s00170-007-0931-1
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- Article
Characterization of Oscillatory Response of Light-Weight Wind Turbine Rotors under Controlled Gust Pulses.
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- Fluids, 2024, v. 9, n. 4, p. 83, doi. 10.3390/fluids9040083
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- Article
Migrating Virtual Machine in Heterogen and Homogen Cloud Data Center with Efficient Electricity Consumption Using Fuzzy C-meams.
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- International Journal of Intelligent Engineering & Systems, 2019, v. 12, n. 6, p. 199, doi. 10.22266/ijies2019.1231.19
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- Article
Reference trajectory tracking for a multi-DOF robot arm.
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- Archives of Control Sciences, 2015, v. 25, n. 4, p. 513, doi. 10.1515/acsc-2015-0033
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- Article
Surrogate-Based Multisource Sensitivity Analysis of TBM Driving System.
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- Shock & Vibration, 2018, p. 1, doi. 10.1155/2018/5187535
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- Article
A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics.
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- Scientific Reports, 2024, v. 14, n. 1, p. 1, doi. 10.1038/s41598-024-67940-8
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- Article
Predicting Mechanical Properties of Boron Nitride Nanosheets Obtained from Molecular Dynamics Simulation: A Machine Learning Method.
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- Crystals (2073-4352), 2024, v. 14, n. 1, p. 52, doi. 10.3390/cryst14010052
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Power Consumption Simulator of Data Center by using Computational Fluid Dynamics and Machine Learning.
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- ASHRAE Transactions, 2019, v. 125, n. Part 1, p. 116
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Iterative learning control for integrated system of robot and machine tool.
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- Asian Journal of Control, 2023, v. 25, n. 2, p. 807, doi. 10.1002/asjc.2924
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An off-line application that determines the maximum accuracy of the realization of reference points from G-code for given parameters of CNC machine dynamics.
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- Bulletin of the Polish Academy of Sciences: Technical Sciences, 2024, v. 72, n. 1, p. 1, doi. 10.24425/bpasts.2024.147345
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Selected problems of rotating machinery dynamics.
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- Bulletin of the Polish Academy of Sciences: Technical Sciences, 2023, v. 71, n. 6, p. 1, doi. 10.24425/bpasts.2023.148442
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Dynamics of rotating machinery.
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- Bulletin of the Polish Academy of Sciences: Technical Sciences, 2021, v. 69, n. 6, p. 1, doi. 10.24425/bpasts.2021.139792
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