Results 81 to 90 of about 72,345 (262)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Assessment of seasonal variations in the air quality index (2019–2022) in Al-Jahra city, Kuwait

Kuwait Journal of Science
The daily air quality indices (AQIs) for pollutants, including particulate matter (PM10 and PM2.5), carbon monoxide (CO), nitrogen oxides (NO2), ozone (O3), and sulfur dioxide (SO2), were evaluated for the period of 2019–2022 in Al-Jahra City, Kuwait ...
Alsowaidan S., Al-Hurban A., Alsaber A.
doaj   +1 more source

Beyond Descriptor‐Based AI Design: Sp2‐Hybridized Branched Side Chains Enable Pre‐Aggregation–Driven Seeding Effects in Green‐Solvent‐Processed Organic Solar Cells

Advanced Energy Materials, EarlyView.
sp2‐hybridized branched side chains are introduced as a new molecular design for NFAs, YBOV, inducing strong solution‐state pre‐aggregation. This pre‐aggregation enables universal seeding motifs, highly ordered film growth, and overcoming the intrinsic current–voltage trade‐off, achieving 19.67% efficiency via green‐solvent processing beyond descriptor‐
Seokhwan Jeong, Donghoo Won, Zhe Sun, Chihyung Lee, Jaewook Kim, Seunglok Lee, Sangjin Yang, Jieun Kim, Keonho Yoon, Dong Young Kim, Yongjoon Cho, Seung‐Jae Shin, Hee‐Seung Lee, Doo‐Hyun Ko, Changduk Yang   +14 more
wiley   +1 more source

XGBOOST model test set verification results description.

, 2023
XGBOOST model test set verification results description.
Wenzhu Zhang (154845), Xiaoling Feng (10909845), Guanhai Gu (15188006), Rucheng Lu (15188009), Bin Wu (138543), Wenhui Liao (468450), Caiping Pang (15188012), Shengquan Lu (15188015)   +7 more
core   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

Pemodelan Pohon Keputusan Menggunakan Algoritma Xgboost Dan Smote Xgboost Dengan Preprocessing Dalam Memprediksi Mahasiswa

, 2021
UNISLA adalah perguruan tinggi dengan tingkat akreditasi yang belum maksimal. Dalam penilaian akreditasi mahasiswa memiliki poin 13,16% yang ditinjau dari berbagai aspek, salah satunya adanya mahasiswa lulus terlambat.
Achmad Efendi, S.Si., M.Sc., Ph.D, Fadhelan, Achmad Nadjih   +1 more
core  

Feature importance using DT, RF, and XGBoost.

, 2022
Feature importance using DT, RF, and XGBoost.
Hong Siang Chua (11979210), Asoke K. Nandi (11979204), Chee Peng Lim (11979216), Kuldeep Kaur Randhawa (11979207), Manjeevan Seera (11979213)   +4 more
core   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

XGBoost model summaries of anderson darling tests.

, 2023
XGBoost model summaries of anderson darling tests.
Samuel Y. Huang (14670660), Alexander A. Huang (14670657)   +1 more
core   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source