Differences in the muscle force of anterior (A), middle (B), and posterior (C) deltoids in models with and without cuff tears. by Shobu Nakashima (22537717)

Differences in the muscle force of teres minor (A) and long head biceps (B) in models with and without cuff tears. by Shobu Nakashima (22537717)

Presentation_1_Multifaceted neuroprotective approach of Trolox in Alzheimer's disease mouse model: targeting Aβ pathology, neuroinflammation, oxidative stress, and synaptic dysfunc... by Muhammad Tahir (741682)

“…Aβ_{1 − 42} 5μL/5min/mouse was injected intracerebroventricularly (i.c.v.) into wild-type adult mice brain to induce AD-like neurotoxicity. …”

Seedling and hairy roots of <i>Echinacea purpurea.</i> by Samane Khalili (21560106)

“…<div><p><i>Echinacea purpurea</i> (L.) Moench, commonly known as purple coneflower, is a significant medicinal plant renowned for its therapeutic properties, which are attributed to various phytochemical compounds, including caffeic acid derivatives (CADs). …”

S1 Data - by Zerfu Bazie (17301622)

“…A 10% yield decrease was observed from the P omitted treatment in the rainy season. …”

Hardness preference in <i>Drosophila</i> larvae, as a relevant cue for varying feeding substrates. by Nikita Komarov (11903342)

“…Different letters (A or B) denote <i>p</i> < 0.05. C’: Two-choice navigation preference between 2.5% agarose and 0.1%, 0.5%, and 1% agarose, respectively. …”

PfIleRS-SANC456 delta <i>CC.</i> by Curtis Chepsiror (21368103)

Differences in glenohumeral joint stability (A) and muscle force of the infraspinatus (B), and subscapularis (C) in models with and without cuff tears. by Shobu Nakashima (22537717)

RFAConv working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

PConv working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Improvement of SPPF to SPPF-R process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

PR comparison on RSOD dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Ablation study on the RSOD dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Structure and working principle of LI-YOLOv8. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

C2f-E improvement process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Structure of Detect and GP-Detect. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

YOLOv8 structure and working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Improvement of CBS to CBR process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

EMA attention mechanism working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Ablation study on the NWPU VHR-10 dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”