Results 71 to 80 of about 5,971,608 (284)

Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials

Advanced Engineering Materials, EarlyView.
Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley   +1 more source

Programación con restricciones dinámica

Ingenieria Industrial, 2009
Este trabajo presenta algoritmos de resolución de Problemas de Satisfacción de Restricciones, capaces de medir el desempeño de su proceso a través de indicadores relevantes, posibilitando su auto-ajuste.
Broderick Crawford, Carlos Castro, Eric Monfroy   +2 more
doaj  

Landscape analysis of constraint satisfaction problems [PDF]

Physical Review E, 2007
We discuss an analysis of Constraint Satisfaction problems, such as Sphere Packing, K-SAT and Graph Coloring, in terms of an effective energy landscape. Several intriguing geometrical properties of the solution space become in this light familiar in terms of the well-studied ones of rugged (glassy) energy landscapes.
Florent Krzakala, Jorge Kurchan
openaire   +4 more sources

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Solving Boolean Satisfiability Problems With The Quantum Approximate Optimization Algorithm

PRX Quantum
One of the most prominent application areas for quantum computers is solving hard constraint satisfaction and optimization problems. However, detailed analyses of the complexity of standard quantum algorithms have suggested that outperforming classical ...
Sami Boulebnane, Ashley Montanaro
doaj   +1 more source

Fabrication Routes for Ionic Conducting Fiber Strain Sensors

Advanced Engineering Materials, EarlyView.
Ionic conducting fiber strain sensors (ICFSs) offer compliant, textile‐integrable sensing. Thus far, the commercialization of ICFSs has been constrained by fiber fabrication routes. This review provides a fabrication‐centric analysis of ICFSs correlating processing strategies with material properties and scalability.
Leo John Kershaw, Zhanhong Yang, Anne Ladegaard Skov, Magdalena Skowyra   +3 more
wiley   +1 more source

Solving non-binary constraint satisfaction problems using GHD and restart.

ITEGAM-JETIA
The non-binary instances of the Constraint Satisfaction Problem (CSP) could be efficiently solved if their constraint hypergraphs have small generalized hypertree widths.
Fatima AIT HATRIT, Kamal AMROUN, Professor   +1 more
doaj   +1 more source

A Novel Strategy of Combining Variable Ordering Heuristics for Constraint Satisfaction Problems

IEEE Access, 2018
Variable ordering heuristic plays a central role in solving constraint satisfaction problems. Many heuristics have been proposed and well-studied.
Hongbo Li, Zhanshan Li
doaj   +1 more source

Decomposing Constraint Satisfaction Problems by Means of Meta Constraint Satisfaction Optimization Problems

Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019
This paper describes a new approach to decompose constraint satisfaction problems (CSPs) using an auxiliary constraint satisfaction optimization problem (CSOP) that detects sub-CSPs which share only few common variables. The purpose of this approach is to find sub-CSPs which can be solved in parallel and combined to a complete solution of the original ...
Sven Löffler, Ke Liu 0006, Petra Hofstedt   +2 more
openaire   +2 more sources

Robustly Solvable Constraint Satisfaction Problems [PDF]

SIAM Journal on Computing, 2016
An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least $(1-g(\varepsilon))$-fraction of the constraints given a $(1-\varepsilon)$-satisfiable instance, where $g(\varepsilon) \rightarrow 0$ as $\varepsilon \rightarrow 0$.
Libor Barto, Marcin Kozik
openaire   +5 more sources