Decrease in loss during the training process. by Muhammad Idrees (92773)

Youthful systemic milieu in young recipients decreased apoptosis and oxidative stress damage in allografts, and promoted proliferation. by Chengjun Yu (12136227)

Boxplot rate of influenza positivity value by period. by Nadia Nisar (311028)

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Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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Block diagram for Average Value-based irrigation system. by Gourab Saha (8987405)

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Flow chart of Average Value-based control system. by Gourab Saha (8987405)

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Oviduct Glycoprotein 1 (OVGP1) Diagnoses Polycystic Ovary Syndrome (PCOS) Based on Machine Learning Algorithms by Fengjuan Wang (768949)

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TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

P values of Kruskal–Wallis test at 95% significance level. by Fahd Saeed Alakbari (10701871)

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Sensor value scenario for average value algorithm. by Gourab Saha (8987405)

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The refracted opposition-based learning mechanism. by Yang Guo (399435)

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Figure 6 from Ammonia Suppresses the Antitumor Activity of Natural Killer Cells and T Cells by Decreasing Mature Perforin by Joanna Domagala (21643925)

Comparison of environmental perception time results at different learning rates. by Wenhui Wang (34265)

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Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

Differences between extreme values of different indicators. by Weijia Zhu (65481)

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Table of values for <i>p.</i> by Long Di (9977453)

“…Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Statistic STD values of different satellite systems. by Jiang Li (272926)

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