A flow diagram to explain the algorithm named 2BDP (Biomarker Discovery Process at Binomial Decision Point) that we used to find the best fitting model to predict RRiF. by Nabarun Chakraborty (535772)

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

Mean RMSE and R² for the training and test phases of RBF by different training algorithms for temperature and humidity prediction. by Caixia Yan (6647072)

Mean RMSE and R² for the training and test phases of MLP by different training algorithms for temperature and humidity prediction. by Caixia Yan (6647072)

^aTwo HIV-1 protease mutants bound to inhibitor darunavir (G48V: PDB ID 3cyw [81] & L90M: PDB ID 2f81 [82]). by Yuxi Long (11024307)

The result for the risk assessment on A, B, C bank using different features. by Jiaqing Huang (5018492)

Membership function of error E1 for tank 1. by Tamiru Demelash Kassie (20678515)

Membership function of error E2 for tank 2. by Tamiru Demelash Kassie (20678515)

Metapopulation model notation. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Estimates of for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

“…We provide a theoretical explanation for this effectiveness by showing that the approximation factor (a measure of how well the algorithmic output for a problem instance compares to its theoretical optimum) of these algorithms depends on the <i>submodularity ratio</i> of the objective function <i>g</i>. …”

An example of an aSNP affecting G4 formation. by Rongxin Zhang (1618159)

VEP annotation of the aSNPs listed in S1 Table. by Rongxin Zhang (1618159)

“…However, there is currently a lack of software tools specifically designed to assess such effects. …”

Assessment of the level of difficulty of the task from subjects (A) and therapists (B) when using the tablet compared to the paper-based support during the item 18, 19 and 22 compl... by Dominique Vincent-Genod (20552594)

(a) Comparison between SDNN and Traditional Methods. The horizontal axis represents the prediction horizon, while the vertical axis denotes computation time; red denotes traditiona... by Kai Sun (140584)

Schematic diagram of the siting of a tiered fire station. by Junjie He (700642)

Wilcoxon signed-rank test results comparing IFOX with other optimization algorithms across CL, C17, C19, C21, C22, and RWPs. by Mahmood A. Jumaah (22272371)

Computational efficiency of the DySCo framework. by Giuseppe de Alteriis (20846230)

“…<p>i) Comparison of computational speed of the TCEVD algorithm compared to naïve numerical methods (the MATLAB <i>eigs</i> function, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012795#sec026" target="_blank">Investigation of computational efficiency of the TCEVD in the DySCo framework</a>), using randomly generated covariance matrices in a window of size 10. …”

Overview of the research process. by Pedro Fong (2378413)

“…We used the automated docking suite GOLD v5.5 with the genetic algorithm to simulate molecular docking and predict the protein-ligand binding modes, and the ChemPLP empirical scoring function to estimate the binding affinities of 2,115 FDA-approved drugs to the human Ca_v3.1 channel. …”

Example of how the Resnet34UNet model functions. by Henrik Mustonen (20378529)