Results 141 to 150 of about 213,445 (314)

Lessons From Drug Discovery for Cryoprotective Agent Design: An AI‐Oriented Perspective

Advanced Science, EarlyView.
Cryoprotectant design is reframed through the lens of drug discovery as a multiparameter optimization problem. This perspective highlights how AI and systematic design strategies could enable safer, more effective cryoprotectants, while identifying key limitations that currently constrain predictive progress in cryobiology. ABSTRACT Cryopreservation is
Dominika Wilczok, Jesús Valdés‐Hernández, Varinia Bernales, Alán Aspuru‐Guzik, Alex Zhavoronkov   +4 more
wiley   +1 more source

Monte Carlo Techniques in Studying Robust Estimators [PDF]

Recent work on robust estimation has led to many procedures, which are easy to formulate and straightforward to program but difficult to study analytically.
David C. Hoaglin
core  

Sparse Pd–Te Covalent Bridges Drive Anomalous Bulk‐to‐Monolayer Electronic and Magnetic Evolution in FePd2Te2

Advanced Science, EarlyView.
Bulk FePd2Te2 contains sparse interlayer Pd–Te covalent bonds, giving it unexpectedly low exfoliation energy and enabling van der Waals‐like exfoliation. Cleaving these bonds during exfoliation makes the monolayer magnetically distinct from the bulk: magnetic anisotropy energy increases, and the strain‐response coefficient of the magnetic moment ...
Huaiyuan Zhao, Jianwen Fang, Mohan Luo, Yinuo Ye, Yulin Wang, Jianhui Yang, Hongwen Zhang, Peng Cheng   +7 more
wiley   +1 more source

Taming Flexibility: Synergistic Pore and Polarity Engineering in a MOF for High‐Efficiency Xe/Kr Separation

Advanced Science, EarlyView.
Through a mixed‐ligand strategy that precisely regulates pore size and framework polarity, the Xe adsorption behavior is transformed from flexible to near‐rigid. ZIF‐7‐Cl(20) achieves sensitive recognition, efficient capture, and high selectivity for Xe, enabling high‐efficiency separation from Xe/Kr mixtures.
Tao Zhao, Xue Wang, Youjin Gong, Siqi Dong, Shunshun Xiong, Hui Xu, Aziz Bakhtiyarovich Ibragimov, Thamraa Alshahrani, He Zheng, Lin Li, Junkuo Gao   +10 more
wiley   +1 more source

Monte Carlo methods for the estimation of value-at-risk and related risk measures

, 2011
Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss.
Marks, Dean
core  

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Design and Analysis of Monte Carlo Experiments

monte carlo experiments;simulation models;mathematical analysis;sensitivity analysis;experimental ...
Kleijnen, J.P.C.
core  

Combining Monte Carlo Simulations and Options to Manage the Risk of Real Estate Portfolios [PDF]

This paper aims to show that the accuracy of real estate portfolio valuations can be improved through the simultaneous use of Monte Carlo simulations and options theory.
Michel Baroni, Fabrice Barthélémy, Etienne Dupuy, Charles-Olivier Amédée-Manesme   +3 more
core  

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

APPLICATIONS OF MONTE CARLO SIMULATION TO STRUCTURAL ENGINEERING PROBLEMS

Nonconventional Technologies Review
This paper investigates the application of Monte Carlo simulations in structural engineering to address various design optimization and uncertainty analysis.
Abdullah Azbah
doaj