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The main challenges (eg, complexity of food matrices) that make the commercialization of these devices difficult are also presented.
The maintenance of normal joint architecture and function requires equilibrium between the synthesis and degradation of the specialized extracellular matrices that make up bone, cartilage, and tendons.
With the proposed source prior in Equation (11), we derive a new learning algorithm to find a set of linear transformation matrices that make the components as statistically independent as possible, such that { W b * } = arg max { W b } L ( { W b } ), (13).
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In contrast, the inner membrane is far less permeable, allowing only very small molecules to cross into the gel-like matrix that makes up the organelle's central mass.
The first approach is aimed at blocking enzymes that generate the collagen matrix that makes up fibrotic tissue.
For solving the problem with the PCG, the most important thing is to find a preconditioning matrix that makes the system matrix have low condition number.
Models of cell surfaces on solid substrates can serve as a quasi two‐dimensional matrix that makes it possible to connect biological and artificial materials.
This paper considers the problem of robust eigenstructure assignment, which involves finding a feedback gain matrix that makes the closed-loop system insensitive to perturbations or parameter variations.
All bones contain living cells embedded in a mineralized organic matrix that makes up the main bone material (Boskey 2007; Glimcher 2006; Boskey and Roy 2008).
Different from these transfer learning approaches mentioned above, we propose to transfer the parameters from a precomputed projection matrix that makes our network can make full use of the image prior and improve SR performance.
Inserting the weights to the matrix in Fig. 2a yields the distance matrix that makes it easier to calculate many analysis functions that are important such as clustering coefficient, degree distribution, the distance matrix created in Fig. 3 is an appropriate way to represent the dataset and make it much more uncomplicated and simpler to analyze the dataset and to visualize it.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com