Results 171 to 180 of about 475 (217)

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Triple Junctions as Dislocation‐Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation

Advanced Engineering Materials, EarlyView.
Grain boundary triple junctions are an essential ingredient of the microstructure of polycrystalline materials. In this study, a triple junction is observed using atomic‐resolution scanning transmission electron microscopy and characterized. Computer simulations reveal that the junction has a dislocation character that is determined by the joining ...
Tobias Brink, Saba Saood, Peter Schweizer, Jörg Neugebauer, Gerhard Dehm   +4 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

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Net Asset Value in Private Equity

SSRN Electronic Journal, 2023
<div> The valuation of seasoned closed-end drawdown&nbsp;<span>funds, which include private equity buyout and&nbsp;</span><span>venture capital (VC) funds, plays a crucial role in&nbsp;</span><span>portfolio management and investment decision making.&nbsp;</span><span>Despite the lack of ...
Gregory W. Brown, Maria Nykyforovych Borysoff   +1 more
openaire   +1 more source

Clustering Mutual Funds by Net Asset Value Change Ratios

2020 2nd International Conference on E-Business and E-commerce Engineering, 2020
The traditional factors of the clustering mutual fund (such as Net Asset Value (NAV)) are not always an efficient measure in both maximizing returns and minimizing portfolio risk. This research presents a novel measure, Net Asset Value Change Ratios for some of time durations N (NAVCR-N), to assist the mutual fund clustering. We proved the usage of the
Laor Boongasame, Akan Narabin
openaire   +1 more source

A stateless deep learning framework to predict net asset value

Neural Computing and Applications, 2020
Recurrent neural networks (RNN) such as Long Short-Term Memory and Gated Recurrent Unit have recently emerged as a state-of-art neural network architectures to process sequential data efficiently. Thereby, they can be used to model prediction of time series data, since time series values are also a sequence of discrete time data.
Koffi Mawuna Koudjonou, Minakhi Rout
openaire   +1 more source

Portfolio Characteristics and Net Asset Values in REITs

The Canadian Journal of Economics, 1996
The most dramatic change in the real estate industry in recent years has been the rapid increase in securitization through real estate investment trusts (REITs). Many factors have contributed to the growth of REIT initial and secondary public offerings, including changes in capital requirements for commercial lenders making mortgage loans more costly ...
Dennis R. Capozza, Sohan Lee
openaire   +1 more source

Decoupled Net Present Value—An Alternative to the Long-Term Asset Value in the Evaluation of Ship Investments?

2018
The aftermath of the financial crisis has threatened the stability of several financial institutions over the past years. Most heavily hit were banks with a notable exposure to ship finance, who saw the collateral value of many loans being diminished. Industry observers trace back the rare occurrence of actual defaults of ship loans to the use of the ...
Philipp Schrader, Jan-Hendrik Piel, Michael H. Breitner   +2 more
openaire   +1 more source