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Total number of subsystems; The set of components in the i-th subsystem; Number of type j component in subsystem i; Total number of component in subsystem i; Failure rate of component j in subsystem i; Repair rate of component j in subsystem i; Cost of component j in subsystem i; Weight of component j in subsystem i; Total weight of system; Availability of system; Cost of system.
1: {Initilization :} x I n d e x = b l o c k I d x. x × b l o c k s i z e × W b + t h r e a d I d x. x × W b I n d e x = b l o c k I d x. x × b l o c k s i z e + t h r e a d I d x. x. 2: for i weight of bit Node M e m o r y [ Z a d d r [ x I n d e x + i ] ] = I n i t [ I n d e x ]. 3: end for.
1: {Check Node Update :} x I n d e x = b l o c k I d x. x × b l o c k s i z e × W c + t h r e a d I d x. x × W c. 2: for i weight of Check Node M e m o r y [ x I n d e x + i ] = C h e c k N o d e _ C o m p [ x I n d e x + i ]. 3: end for.
1: {Bit Node Update :} x I n d e x = b l o c k I d x. x × b l o c k s i z e × W b + t h r e a d I d x. x × W b I n d e x = b l o c k I d x. x × b l o c k s i z e + t h r e a d I d x. x. 2: for i weight of bit Node M e m o r y [ Z a d d r [ x I n d e x + i ] ] = B i t N o d e _ C o m p [ Z a d d r [ x I n d e x + i ] ]. 3: end for.
1: {Parity Check :} x I n d e x = b l o c k I d x. x × b l o c k s i z e × W c + t h r e a d I d x. x × W c I n d e x = b l o c k I d x. x × b l o c k s i z e + t h r e a d I d x. x. 2: for i weight of Check Node c h e c k = D e c o d e [ int ( L a d d r [ x I n d e x + i ] ∕ W b ) ] + c h e c k. 3: end for c h e c k = int ( c h e c k % 2 ).
where: score, signature score; N, number of genes in the signature; i, gene; w i, weight of gene i; exp i, gene expression of gene i.
Similar(54)
Goodness-of-fit was tested by two parameters: σ (connected with the objective function U = n i = 1 n W i r i 2, where W i - weight of the i-th experimental point of n and r i - i-th residual in EMF (E exp - E theoret )) as well as by the χ2 statistics (test of randomness).
W i = Weight of each i core health status group.
Further, let x i be the within-study treatment effect estimate (e.g., a log odds ratio), s i 2 the within-study variance of x i, and w i the weight of study i (i = 1,..., k).
When I woke, I felt the weight of illness on me before I opened my eyes.
Of course, the question begs to be asked: Is the weight of that knowledge sometimes a burden?
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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