Results 101 to 110 of about 1,535,797 (279)

Internal Resonance Mismatch‐Controlled Dissipative Bistability Collapse in Coupled Micromechanical Oscillators

Advanced Science, EarlyView.
This paper reveals internal resonance mismatch‐controlled dissipative bistability collapse in a coupled micromechanical resonator. Tuning δIR changes both internal‐mode amplitude and phase‐dependent feedback. For negative mismatch, Duffing bistability survives.
Jianlin Chen, Xin Zhou, Takashiro Tsukamoto, Nan Wang, Shuji Tanaka   +4 more
wiley   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Mutation testing from probabilistic and stochastic finite state machines [PDF]

, 2009
Specification mutation involves mutating a specification, and for each mutation a test is derived that distinguishes the behaviours of the mutated and original specifications. This approach has been applied with finite state machine based models.
García Merayo, María De Las Mercedes, Hierons, Robert M., Hierons, RM, Merayo, MG   +3 more
core   +1 more source

Organic Electrochemical Transistors for Energy‐Autonomous Bioelectronics: Materials, Devices, and System Integration

Advanced Electronic Materials, EarlyView.
This review surveys organic electrochemical transistors as core building blocks for energy‐autonomous bio‐integrated electronics. We outline device physics, OMIEC materials, and electrolyte effects that enable sub‐volt, high‐gain operation, and discuss direct coupling with mechanical, thermal, and optical energy harvesters to realize battery‐free ...
Jonggeun Park, Yeong Joo Jo, Yong Hyun Kwon, Sang‐Woo Kim   +3 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

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Implementasi Model Deterministic Finite Automaton untuk Interpretasi Regular Expression pada Studi Kasus Permasalahan SPOJ Klasik 10354 [PDF]

, 2015
Regular expression merupakan salah satu bentuk pola yang banyak digunakan untuk melakukan pencarian dan validasi string. Namun pada implementasinya sering kali regular expression hanya ditransformasi menjadi sebuah model Nondeterministic Finite ...
Bahari, Muhammad Yunus
core  

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Matching a graph with a non-deterministic finite automaton [PDF]

, 2018
The problem of matching a graph with a non-deterministic finite automaton (NFA) is of importance in various domains of computer science. An example is regular-expression matching, which can be formulated as a graph-matching problem. Current techniques of
Boulgakov, Alexandre
core   +1 more source