Comparative Analysis of Chills Experience Across Three Groups: This table presents a detailed comparison of three groups (A, B, and C) in terms of their experiences with chills. It... by Felix Alexandre Schoeller (20988295)

In BC tissue samples and BC cell lines, the expression of let-7b-5p decreased while the expression of AURKB increased. by Murat Kaya (578380)

Patients’ demographics and clinical parameters. by Zi-Rong Chen (9227618)

Subjects:

Presentation 1_Prehospital tranexamic acid decreases early mortality in trauma patients: a systematic review and meta-analysis.pdf by Yi Li (1144)

“…</p>Conclusion<p>Prehospital TXA decreases early (24-h) mortality in trauma patients without a significant increase in the risk of VTE and other complications, and further studies are still needed to improve and optimize its management strategy.…”

<i>In vivo</i> treatment decreases bacterial burden and colonization of vital organs in a skin abscess biofilm infection model. by Robert L. Beckman I.V (22784303)

Pan-cancer analyses of ANGPTL4 expression and its prognostic significance in the TCGA database. by Yu Zhang (12946)

Pan-cancer analyses of NFKB2 expression and its prognostic significance in the TCGA database. by Yu Zhang (12946)

Raw data. by Jia Zhu (135506)

“…The remaining working conditions did not exhibit a significant difference. However, the observed decreasing trend was consistent with previously documented research. …”

T-DNA insertions in <i>AtB’’α</i>, <i>AtB’’β</i>, <i>AtB’’γ</i>, and <i>AtB’’δ</i> do not affect touch-induced root bending. by Hyuk Sung Yoon (20208672)

Subjects:

Fig 4 - by Hyuk Sung Yoon (20208672)

Subjects:

Top view of the experimental setup. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Parameters of energy harvesting. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Graph for Max Amplitude/Length at G_y = 0. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Graph for maximum Frequency at G_y = 0. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Graph for maximum Power at G_y = 0. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Summary of experimentation results. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Piezoelectric eel. by Muhammad Hammad Bucha (21736111)

“…By increasing the surface roughness of the bluff body, the lock-in region decreases and as a result, the harvested power from that bluff body is reduced. …”

Effect of aging on cellular senescence in the mouse kidney. by Lihua Qiu (178071)

Geometric manifold comparison visualization by Eloy Geenjaar (21533195)

“…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”

Hyperparameter ranges by Eloy Geenjaar (21533195)

“…While many tr-FC approaches have been proposed, most are linear approaches, e.g. computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”