YOLO11. by Junyan Wang (4738518)

Subjects:

SCI-YOLO11. by Junyan Wang (4738518)

Subjects:

Structure of the SCI-YOLO11 network. by Junyan Wang (4738518)

Subjects:

Inter-assay parameters correlations. by Ekaterina M. Koltsova (5464523)

Data_Sheet_1_Physiological synchrony in electrodermal activity predicts decreased vigilant attention induced by sleep deprivation.docx by Ivo V. Stuldreher (11897870)

Intra- and inter-day precision and accuracy. by Ewa Paszkowska (21246702)

“…<div><p>Purpose</p><p>Statins are the most commonly used drugs worldwide. Besides a significant decrease in cardiovascular diseases (CVDs) risk, the use of statins is also connected with a broad beneficial pleiotropic effect. …”

Significant inter-relationships between baseline variables and MSCC or MCS. by Harshit Arora (22008280)

Shuffling identity decreases behavior trajectory uniqueness. by Catalin Mitelut (5587493)

Following supporting information attached to this manuscript: *Calculations of BMI changes before and after experiment by Tukey-Kramer’s inter group test; * Calculations of TP, Cho... by Fatima Dikhanbayeva (11501962)

Subjects:

The network structure of FasterNet--YOLO. by Shiwei Yu (6060308)

Subjects:

Inter-operator reliability^b'*'. by Lotte van Dam (17347733)

“…<div><p>This observational study aimed to evaluate the intra- and inter-operator reliability of a digital palpation device in measuring compressive stiffness of the patellar tendon at different knee angles in talent and elite volleyball players. …”

Treatment with ICs causes a significant decrease in podocyte proliferation. by George Haddad (470789)

Detection effect diagram of YOLO11. by Jianming Meng (22209039)

Subjects:

PR diagram of YOLOv8n and BGS-YOLO: (a) YOLOv8n and (b) BGS-YOLO. by Zeyu Liang (4813593)

The architecture of the proposed BGM-YOLO model. by Chenghai Yu (21440036)

Subjects:

Overall network architecture of FCMI-YOLO. by Junjie Lu (160350)

Subjects:

Structure of UAS-YOLO network. by Ju Liang (4277062)

“…These characteristics impose high demands on detection algorithms in terms of fine-grained feature extraction, cross-scale fusion capability, and occlusion resistance.The YOLOv11s model has significant limitations in practical applications: its feature extraction module has a single semantic representation, the traditional feature pyramid network has limited capability to detect multi-scale targets, and it lacks an effective feature compensation mechanism when targets are occluded.To address these issues, we propose a UAV aerial small target detection algorithm named UAS-YOLO (Universal Inverted Bottleneck with Adaptive BiFPN and Separated and Enhancement Attention module YOLO), which incorporates three key optimizations. …”

Comparison of YOLO-DSTD model with other models. by Jianming Meng (22209039)

Subjects:

Architecture of GOLD-YOLO. by Xichun Chen (22001791)

“…The network architecture integrates the GOLD-YOLO module, an advanced object detection approach, alongside the updated deformable convolutional networks (DCNv3). …”

(a) YOLOv5s; (b) FasterNet-YOLO. by Shiwei Yu (6060308)

Subjects: