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Effect of physiological temperature on the mechanical properties and network structure of biodegradable poly(propylene fumarate)
      
These two polymers were chemically photo-crosslinked to generate a three-dimensional network structure, which were characterized by FT-IR, DSC and SEM.
      
As the PEGDA component increased, the rate of formation of a loose three-dimensional (3D) network structure increased; consequently, the sustained rate and extent of BSA release increased.
      
Subsequently, swelling properties, sol fraction and polymer network structure (as indicated by differential scanning calorimetry), as well as attachment of human dermal fibroblasts to these hydrogels at 4 and 24 h was examined.
      
FT-IR spectra of the blended films showed a new band, implying that a surface physical interpenetrating network structure had formed.
      
FT-IR, TGA and DSC revealed the semi-interpenetrating polymer network structure formed in the hydrogel.
      
Depending on the feed ratio of these two precursors, the resultant FPBe-G hydrogels showed different crosslinking levels of network structure, mesh sizes (ξ) and matrix morphology.
      
This variation in porous network structure of NDF hydrogels led to a wide range of swelling, deswelling and biodegradation processes.
      
This method embodies the network structure of the free area of environment with less nodes, so the complexity of path planning problem is reduced largely.
      
The results show that the particles of tin oxide-based materials form an interconnected network structure like mesoporous material.
      
The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight.
      
SEM analysis shows that KGMSB possesses cross-linked porous network structure.
      
They include those systems with interdependent sensor observations and any network structure.
      
In order to characterize networks in the scale-free network class we study the frequency of cycles of length h that indicate the ordering of network structure and the multiplicity of paths connecting two nodes.
      
The present analytical study clarifies that there is no phase transition independent of network structure if temperature T = 0, threshold h = 0 and the probability distribution degree P(k) satisfies P(0) = D = 0.
      
Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory.
      
The results showed that after the inducement, many MSCs turned into bipolar, multipolar and taper, and then intersected as network structure.
      
The network structure of the immune system implicates the possibility of a selective immunization by the activation of idiotypic cascades.
      
Computational experience shows that the underlying network structure of the transformed problem often leads to integral solutions when solved with a standard linear programming code.
      
The first one identifies the time delay vector for a given neural network structure.
      
 

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