Characteristics of Gasless Combustion of Core–Shell Al@NiO Microparticles with Boosted Exothermic Performance by Shina Maini (19413980)

“…The PM composite was not able to be ignited at all by a 5 W laser, while the core–shell counterpart ignited at 2.55 ms and was completely combusted within 6.50 ms accompanying a violent impulse.…”

Characteristics of Gasless Combustion of Core–Shell Al@NiO Microparticles with Boosted Exothermic Performance by Shina Maini (19413980)

“…The PM composite was not able to be ignited at all by a 5 W laser, while the core–shell counterpart ignited at 2.55 ms and was completely combusted within 6.50 ms accompanying a violent impulse.…”

Maintenance of weight loss or stability in subjects with obesity: a retrospective longitudinal analysis of a real-world population by Maral DerSarkissian (504144)

“…</p> <p><b>Methods:</b> A retrospective observational longitudinal study of subjects with obesity was conducted using the General Electric Centricity electronic medical record database. …”

IG feature selection process. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

RFE feature selection process. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

CICID2017 dataset information. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Shows the basic architecture of an autoencoder. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Architecture of deep neural networks. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Proposed model framework. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

WUSTL-EHMS-2020 dataset information. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”

Natural Derivatives of Selective HDAC8 Inhibitors with Potent <i>in Vivo</i> Antitumor Efficacy against Breast Cancer by Xiaoming Chen (230202)

“…XZB108, selectively inhibited HDAC8 (IC₅₀ = 0.90 ± 0.014 μM), suggesting that it may be a promising nonhydroxamate HDAC8 inhibitor. …”