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Figure 15 Achieved sum-rate versus the number of antennas for selected scenarios: (a) TSD-MIMO, (b) SVD-MIMO, and (c) spatial waterfilling reference line (LP: Linear Programming, RM: Regret Matching).
LP, linear predictor.
DAG: directed acyclic graph; LP: linear program(ming); QP: quadratic program(ming); RMS: root-mean-squared; SNP: single nucleotide polymorphism; SE: standard error.
Based on this probabilistic model, LP (Linear Programming -based methods were develoProgramming -basedn et al. imethods the accuracy of interaction strength prediction by APM (Association Probabilistic Method) [ 12].
RNP: Root-non-produced; RNC: Root-non-consumed; DNP: Downstream-non-produced; UNC: Upstream-non-consume; UM: Unconnected module; FBA: Flux balance analysis; CBM: Constraint based modeling; GSM: Genome scale model; RS: Reactions subset; LP: Linear programming; MILP: Mixed integer linear programming; GPR: Gene-protein-reaction; EC: Enzyme commission number; TC: Transport commission number.
AHP (Analytical Hierarchy Process) DEA (Data envelopment analysis) LP (Linear program) LP1 (Linear Program 1) Linearinear Program 2) Linearinear Program 3) WHO (World Health Organization) JAL developed methods inspired by the benefit-of-the-doubt concept and drafted an earlier version of the manuscript.
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duration LLD has positive curvature Gain of linear prediction (LP), LP coefficients 1 5 Mean, max, min, std.
To determine changes in maximal strength between two different resistance training progression models, linear (LP) and daily undulating (DUP), over a 12-week resistance training programme in sub-elite adolescent rugby union players.
The ZH supplementation increased (P ≤ 0.023) longissimus muscle area (LM) and leg perimeter (LP) with a linear improvement (P < 0.001) of LM area by 12.2% and 21.0% as ZH level increased in diet.
In the case of the standard Lambek systems and the extended systems discussed in §3 above, the abstract syntax is a directional type logic; in the case of ACG, one finds LP and the linear lambda calculus both at the source and at the target end.
Due to quantity times quality nonlinear terms inherent in the oil-refining industry, performing industrial-sized capital investment planning (CIP) in this field is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions.
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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