Results 221 to 230 of about 5,165 (285)
Novel level and edge-triggered universal shift registers with low latency in QCA technology. [PDF]
Gholamnia Roshan M, Gholami M.
europepmc +1 more source
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Testing the Conjecture That Quantum Processes Create Conscious Experience. [PDF]
Neven H +8 more
europepmc +1 more source
Flexible Memory: Progress, Challenges, and Opportunities
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan +5 more
wiley +1 more source
Novel parallel inputs shift registers with set/reset terminals in QCA nanotechnology. [PDF]
Gholami M, Movahedi M, Amirzadeh Z.
europepmc +1 more source
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk +4 more
wiley +1 more source
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi +3 more
wiley +1 more source
Sub‐Micrometer‐Precision Path Following of Piezo‐Actuated Mobile Robot
This article reports on the Holonomic‐Beetle (HB), a palm‐sized robot that achieves sub‐micrometer (sub‐µm) precision path tracking across spatial ranges from 100 µm to 10 mm. Using proportional‐integral‐derivative (PID) control, the HB accurately tracks both complex and straight paths with sub‐µm path errors, surpassing existing robots.
Eiji Kusui +9 more
wiley +1 more source

