Results 161 to 170 of about 288,536 (295)

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Bubbles Acting as Micro End‐Effectors for Dexterous Manipulation and Sensing in Aqueous Environment

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Inspired by bubbles, this article proposes a low‐cost method for multifunctional manipulation and sensing using microbubbles in aqueous environments. Bubbles are easily generated in situ, enabling the safe and adaptive handling of microobjects and sensing of microforces and surface textures.
Zichen Xu, Qingsong Xu
wiley   +1 more source

Fused Ring Engineering Induced Topology Control in Covalent Organic Frameworks: Unlocking Promoted Photocatalytic H2O2 Production and Selective Methane Oxidation

Angewandte Chemie, EarlyView.
By modulating the topological structures of two N‐heterocycle‐based covalent organic frameworks, Phen‐TTA with a kgd‐v topology exhibits narrower optical bandgap, lower exciton binding energy, and faster charge‐carrier kinetics compared to O‐TTA with an hcb topology.
Lei Wang, Zhipeng Xie, Xinyi Zeng, Hetao Xu, Mingyue Wang, Jie Yang, Chao Lin, Wandong Xing, Zhengxiao Guo, Xiong Chen   +9 more
wiley   +2 more sources

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Quantifying roadless areas and fragmentation in the context of wildlife-vehicle collision risk in Great Britain. [PDF]

Sci Rep
Raymond S, Chadwick EA, Perkins SE.
europepmc   +1 more source

Relationship between truck driver fatigue and rear-end collision risk. [PDF]

PLoS One, 2020
Mizuno K, Ojiro D, Tanaka T, Minusa S, Kuriyama H, Yamano E, Kuratsune H, Watanabe Y.   +7 more
europepmc   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang, Zichao Wang, Zhiwei He, Qingquan Wei, Yong Zuo, Linzhen Nie, Zhishuai Yin   +6 more
wiley   +1 more source

Collision Risk Analysis for HSC [PDF]

, 1999
Pedersen, Preben Terndrup, Simonsen, Bo Cerup, Urban, Jesper   +2 more
core   +1 more source

Increasing canopy cover elevates vehicle collision risk for barbastelle bats (Barbastella barbastellus) at roads. [PDF]

Sci Rep
O'Malley KD, Schofield HW, Wright PGR, Hargreaves D, Corcoran AJ, Mathews F.   +5 more
europepmc   +1 more source

Debris-cloud collision risk assessment with GSOC Collision Avoidance System [PDF]

After an in-orbit break-up event, tracking and cataloging fragments takes time, leading to a blackout period where debris is not yet cataloged, and Conjunction Data Messages (CDMs) cannot be issued. Traditional 1-vs-1 collision assessments are ineffective in the short-term phase of debris cloud evolution, which typically lasts some hours after the ...
Annarita Trombetta, Andrea Zollo, Giancarmine Fasano, Roberto Opromolla, Giorgio Isoletta   +4 more
openaire   +2 more sources