Results 171 to 180 of about 242,470 (301)

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Long-term impact and biological recovery in a deep-sea mining track. [PDF]

Nature
Jones DOB, Arias MB, Van Audenhaege L, Blackbird S, Boolukos C, Bribiesca-Contreras G, Copley JT, Dale A, Evans S, Fleming BFM, Gates AR, Grant H, Hartl MGJ, Huvenne VAI, Jeffreys RM, Josso P, King LD, Simon-Lledó E, Le Bas T, Norman L, O'Malley B, Peacock T, Shimmield T, Stewart ECD, Sweetman AK, Wardell C, Aleynik D, Glover AG.   +27 more
europepmc   +1 more source

Biological effects 26 years after simulated deep-sea mining. [PDF]

Sci Rep, 2019
Simon-Lledó E, Bett BJ, Huvenne VAI, Köser K, Schoening T, Greinert J, Jones DOB.   +6 more
europepmc   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Open-Source Marine Biodiversity Data Quality in the Norwegian Sea Spanning 149 Years: Knowledge Gaps in the Deep-Sea Mining Opening Area. [PDF]

Ecol Evol
Paiba-García LC, Johnsen G, Perrin SW, Bakken T.   +3 more
europepmc   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses. [PDF]

PLoS One
Alam L, Pradhoshini KP, Flint RA, Sumaila UR.   +3 more
europepmc   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

Probabilistic ecological risk assessment for deep-sea mining: A Bayesian network for Chatham Rise, Pacific Ocean. [PDF]

Ecol Appl
Kaikkonen L, Clark MR, Leduc D, Nodder SD, Rowden AA, Bowden DA, Beaumont J, Cummings V.   +7 more
europepmc   +1 more source

Deep-Sea mining: Fairer shares [PDF]

Nature, 1982
openaire   +1 more source