Results 151 to 160 of about 9,785 (214)

A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links' weight to increase network stability. [PDF]

ScientificWorldJournal, 2014
Karimi A, Afsharfarnia A, Zarafshan F, Al-Haddad SA.   +3 more
europepmc   +1 more source

Chemical Bond‐Orchestrated Energy Depletion to Augment Lung Cancer Chemo‐Immunotherapy

Advanced Science, EarlyView.
This study grafted dichloroacetic acid (DCA) into perylenediimide (PDI) skeleton via ionic or covalent bond to create novel metabolic agent PDIC‐AC and PDIC‐NAC, explicitly elaborated the underlying energy metabolism and therapeutic mechanism, and developed a promising strategy for energy metabolism‐targeting chemo‐immunotherapy by chemical bond ...
Xuejie Zhao, Yuting Liu, Xiao Huang, Liwen Zhao, Chunli Li, Yongwei Huang   +5 more
wiley   +1 more source

A Multi-User Game-Theoretical Multipath Routing Protocol to Send Video-Warning Messages over Mobile Ad Hoc Networks. [PDF]

Sensors (Basel), 2015
Mezher AM, Igartua MA, de la Cruz Llopis LJ, Pallarès Segarra E, Tripp-Barba C, Urquiza-Aguiar L, Forné J, Sanvicente Gargallo E.   +7 more
europepmc   +1 more source

Programmed Interfacial Phase Separation for Spatiotemporal Fungicide Delivery via Tunable Micro/Nano Architectures

Advanced Science, EarlyView.
ABSTRACT Translating dynamic liquid‐liquid phase separation into robust solid‐state architectures remains a challenge in materials engineering. Here, we report a composition‐driven interfacial assembly strategy that couples supramolecular coacervation with metal‐phenolic coordination to engineer tunable fungicide carriers.
Rui Zhang, Tongfang Jing, Leiming He, Kaidi Cui, Yuying Song, Xuewei Mao, Lidong Cao, Lin Zhou   +7 more
wiley   +1 more source

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

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Effective route maintenance and restoration schemes in mobile ad hoc networks. [PDF]

Sensors (Basel), 2010
Kang BS, Ko IY.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source