Results 81 to 90 of about 88,634 (286)

Evaluation of Modification of the Upper Batavia Dam on the Fox River, Illinois [PDF]

, 2004
Progress Report, Federal Aid Project F-136-R Segment 6Report issued on: August 2004Submitted to Office of Water Resources, Illinois Department of Natural ...
Butler, S.E., Dodd, Hope R., Wahl, David H.   +2 more
core  

Decoupling Intrinsic Molecular Efficacy From Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery

Advanced Science, EarlyView.
This study establishes an interpretable machine learning framework that disentangles the intrinsic molecular efficacy of passivators from experimental platform effects—enabling unbiased, high‐throughput discovery of effective perovskite surface modifiers.
Jing Zhang, Ziyuan Li, Shan Gao, Zhen Zhu, Jing Wang, Xiangmei Duan   +5 more
wiley   +1 more source

Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease

Frontiers in Medicine
IntroductionCardiovascular diseases (CVDs) are complex and affect a large part of the world’s population; early accurate and timely prediction is also complicated.
Anas Bilal, Abdulkareem Alzahrani, Khalid Almohammadi, Muhammad Saleem, Muhammad Sajid Farooq, Raheem Sarwar   +5 more
doaj   +1 more source

Liquid propellant sloshing in mobile tanks of arbitrary shape [PDF]

Equations for dynamic behavior of liquid propellants sloshing in mobile tanks of arbitrary ...
Lomen, D. O.
core   +1 more source

Mechanism‐Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy

Advanced Science, EarlyView.
MISPOP integrates ensemble learning with membrane‐active physicochemical priors to identify Dermaseptin‐S9, a natural oncolytic peptide that disrupts tumor membranes, triggers immunogenic cell death, and shows strong antitumor activity. The study illustrates a mechanism‐informed route from peptide sequence data to cancer immunotherapy leads.
Wen Zhang, Shengxin Lu, Guangyong Zheng, Shensuo Li, Hongyu Chen, Mei Hong, Xiangru Zhou, Ruotian Tang, Ye Wu, Weidong Zhang, Dong Lu, Xin Luan   +11 more
wiley   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

基于可解释机器学习模型的电信行业客户流失预测研究

Dianxin kexue
在电信行业中，客户流失的准确预测对于相关企业维持市场竞争力和增加收益至关重要。为此提出一个结合CatBoost算法和SHAP（shapley additive explanations）模型的客户流失预测框架，旨在提高预测的准确性，同时增强模型的可解释性。利用新疆某通信公司的实际营业数据，通过数据预处理及特征工程，构建预测模型，选取5种主要关键性能指标评估模型性能。实验结果显示，所提出模型在选取的评价指标上均优于当前主流机器学习预测模型。最后引入SHAP框架增强模型可解释性，揭示影响客户流失的关键因素 ...
王圣节, 张庆红
doaj   +1 more source

Wave propagation in graphite/epoxy laminates due to impact [PDF]

The low velocity impact response of graphite-epoxy laminates is investigated theoretically and experimentally. A nine-node isoparametric finite element in conjunction with an empirical contact law was used for the theoretical investigation.
Sun, C. T., Tan, T. M.
core   +1 more source