The optimal number of sentinel plants to include in the sample depends on the sample size, sample interval and the total number of sentinels in the population. by Francesca A. Lovell-Read (14378253)

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

“…Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. …”

Bayesian network for BMV_OD model. by Xinchi Dong (18554525)

“…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”

Bayesian network for BMV_C1 model. by Xinchi Dong (18554525)

“…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”

Bayesian network for BMV_C3 model. by Xinchi Dong (18554525)

“…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”

Bayesian network for BMV_C2 model. by Xinchi Dong (18554525)

“…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Study flowchart. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Risk of bias graph. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Results of network meta-analysis. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Characteristics of included studies. by Kaiyu Zhang (198799)

“…With respect to the total effective rate, α-RBs+ needling was most likely to be the optimal treatment. …”

Optimising the number of sentinels to include in the population. by Francesca A. Lovell-Read (14378253)

The baseline case–the model in the absence of sentinel plants. by Francesca A. Lovell-Read (14378253)

An overview of the experimental paradigm. by Nienke E. R. van Bueren (11414330)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation by Xinkai Zhou (9346175)

“…Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. …”

Colored non-motorized lanes. by Xinchi Dong (18554525)

“…Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. …”