Thermal annealing is widely applied to enhance the mechanical performance of PLLA monofilaments, which brings in a variety of expected strengths through different constrained methods. In this work, samples with constrained and unconstrained annealing process were both prepared and characterized, including mechanical performance, surface morphology, radial supporting performance and axial flexibility. Experimental results revealed that the monofilaments under constrained annealing showed higher elastic modulus with 6.4 GPa, which were higher than those without any constraint. While the maximal elongation at break with 51.11% were observed in unconstrained annealed monofilaments. Few changes were presented in the molecular weight between the two types of samples. Moreover, the springs under constrained annealing inhibited the most reliable radial supporting performance with higher radial compression force and chronic outward force, 0.665 /mm and 0.14 respectively. However, unconstrained annealing springs showed better flexibility with 0.178 bending stiffness and 1.58 maximum bending force. These results suggested that filaments and springs with various properties can be obtained under different annealing conditions.
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http://dx.doi.org/10.1177/08853282221095926 | DOI Listing |
Brief Bioinform
September 2024
Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Da'an District, Taipei City 106319, Taiwan.
The digital annealer (DA) leverages its computational capabilities of up to 100 000 bits to address the complex nondeterministic polynomial-time (NP)-complete challenge inherent in elucidating complex structures of natural products. Conventional computational methods often face limitations with complex mixtures, as they struggle to manage the high dimensionality and intertwined relationships typical in natural products, resulting in inefficiencies and inaccuracies. This study reformulates the challenge into a Quadratic Unconstrained Binary Optimization framework, thereby harnessing the quantum-inspired computing power of the DA.
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October 2024
School of Business and Economics, Maastricht University, Minderbroedersberg 4, 6211 LK, Maastricht, The Netherlands.
Quantum Computing has emerged as a promising alternative, utilising quantum mechanics for faster computations. This paper explores the nearest neighbour compliance (NNC) Problem in Gate-based Quantum Computers, where quantum gates are constrained to operate on physically adjacent qubits. The NNC problem aims to optimise the insertion of SWAP-gates to ensure compliance with these constraints while minimising their count.
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October 2024
Electrical Sustainable Energy, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands.
Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others.
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September 2024
Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, DE-52074, Aachen, Germany.
Depending on their aspect ratio, rod-shaped particles exhibit a much richer 2D and 3D phase behavior than their spherical counterparts, with additional nematic and smectic phases accompanied by defined orientational ordering. While the phase diagram of colloidal hard rods is extensively explored, little is known about the influence of softness in such systems, partly due to the absence of appropriate model systems. Additionally, investigating higher volume fractions for long rods is usually complicated because non-equilibrium dynamical arrest is likely to precede the formation of more defined states.
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July 2024
CNRS, Centrale Lille, Junia, Univ. Polytechnique Hauts-de-France, UMR 8520 - IEMN, Univ. Lille, 41 Bd Vauban, 59000, Lille, France.
Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.
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