Results 51 to 60 of about 3,353 (215)

Selecting the best distribution for modeling trends in low, medium, and extreme daily precipitation under climate change [PDF]

Advances in Statistical Climatology, Meteorology and Oceanography
Changes in mean precipitation and the frequency and intensity of extreme precipitation represent one of the most consequential aspects of anthropogenic climate change. This study evaluates a set of statistical distributions for modeling trends across the
A. Haruna, J. Blanchet, G. Evin, E. Paquet   +3 more
doaj   +1 more source

Discriminator‐Guided Inverse Folding for Multi‐Property Protein Design

Advanced Science, EarlyView.
Discriminator‐Guided Inverse Folding (DGIF) integrates multiple property predictors trained from single‐property datasets to guide protein sequence generation from a backbone structure. DGIF enables simultaneous improvement of thermostability and solubility without requiring multi‐property annotated datasets and generates designs that move toward the ...
Yuchuan Zheng, Chuyi Liu, Zhaoming Liu, Mao Su, Chenyu Tang, Xiang Zheng, Hao Zhang, Jingyuan Li   +7 more
wiley   +1 more source

Estimation of Beta-Pareto Distribution Based on Several Optimization Methods

Mathematics, 2020
This paper is concerned with the maximum likelihood estimators of the Beta-Pareto distribution introduced in Akinsete et al. (2008), which comes from the mixing of two probability distributions, Beta and Pareto.
Badreddine Boumaraf, Nacira Seddik-Ameur, Vlad Stefan Barbu   +2 more
doaj   +1 more source

Large Language Model‐Informed Dual‐Track AI Framework for the Synergistic Design of Crack‐Free and High‐Strength Superalloys

Advanced Science, EarlyView.
This paper illustrates a knowledge‐augmented dual‐track AI framework for advanced superalloy design. First, Large Language Models translate metallurgical heuristics into explicit rules to rapidly prune a vast compositional search space. Subsequently, LLM‐distilled priors safely guide a reinforcement learning agent during autonomous process optimization,
Jian Yao, Junkang Wu, Jie Su, Fuzhu Wang, Zi Wang, Li Wang, Liming Tan, Lan Huang, Feng Liu, Yong Liu   +9 more
wiley   +1 more source

Estimation and Bayesian Prediction of the Generalized Pareto Distribution in the Context of a Progressive Type-II Censoring Scheme

Applied Sciences
The generalized Pareto distribution plays a significant role in reliability research. This study concentrates on the statistical inference of the generalized Pareto distribution utilizing progressively Type-II censored data.
Tianrui Ye, Wenhao Gui
doaj   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Frequency Analysis of Annual Maximum Flood for Segamat River

MATEC Web of Conferences, 2017
Several major floods had occurred in the last few decades in Segamat, causing extensive damage to properties and harm local community. For the purpose of flood risk management, this study estimated the average recurrence interval (ARI) and peak flows ...
Romali Noor Suraya, Yusop Zulkifli
doaj   +1 more source

A Generative Neuro‐Symbolic AI for Protein Sequence Design

Advanced Science, EarlyView.
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar‐Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis F. Garcia‐Alles, Thomas Schiex, Sophie Barbe   +12 more
wiley   +1 more source

A Closed‐Loop Framework for Inverse Design: Dynamic Training and Intelligent Optimization for Heterostructured Materials

Advanced Science, EarlyView.
To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong, Xueru Zheng, Xiao Zhou, Zhengheng Tao, Wan Han, Jingyi Pan, Dian Wu, Ning Gao, Lei Liu, Zhongyang Wang, Fanchao Meng, Tongxiang Fan   +11 more
wiley   +1 more source

Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations

Advanced Science, EarlyView.
Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford, Matthew Tamasi, Elena Di Mare, Adam J. Gormley   +3 more
wiley   +1 more source