Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Smoothing curve from GAM model in which the survival of smaller cluster has been modeled as a smooth function of the sample size. by Muhammad Atif (2575789)

Smoothing curve from GAM model in which the survival of smaller cluster has been modeled as a smooth function of the sample size. by Muhammad Atif (2575789)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

The SSIM for the different algorithms. by Bingbing Li (461702)

“…The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. …”

In this example the MPC algorithm perfectly tracks the noisy signal , which results in many false spikes. by Nicholas A. Rondoni (21573261)

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. …”

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

“…Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. …”

How fries chooses which sequences to keep and which sequences to prune. by Anna U. Lowegard (8051597)

Plot from a GAM model in which the survival of smaller cluster has been modeled as a smooth function of the sample size of smaller segment for aggregate data over all levels of cov... by Muhammad Atif (2575789)

Plot from a GAM model in which the survival of smaller cluster has been modeled as a smooth function of the sample size of smaller segment for specific values of the Separation bet... by Muhammad Atif (2575789)

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…<p>The primary objective of this study is to address the challenge posed by the increasing number of variants of unknown clinical significance (VUS) within the GABRD gene, which encodes the δ subunit of γ-Aminobutyric acid type A receptors. …”

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

Comparative analysis of algorithms. by Xumin Zhao (18261643)

“…At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. …”

Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

Subjects:

Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query in g:Profiler [16]. Columns include source, term name, GO id, and adjusted p-values across each phenotype definition. P-values were adjusted to maintain an experiment-wide type 1 error rate of 0.05 using the g:SCS algorithm which accounts for the dependent structure of functionally annotated gene sets. by Jenna C. Carlson (3950024)

“…P-values were adjusted to maintain an experiment-wide type 1 error rate of 0.05 using the g:SCS algorithm which accounts for the dependent structure of functionally annotated gene sets.…”