YOLO11. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

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

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Comparative experimental results. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Algorithm operation steps. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

SCI-YOLO11. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Dataset for insulator defect detection. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

YOLOV8. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Faster-RCNN. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Results of ablation experiments. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Structure diagram of SPDConv. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Wise-IOU regression diagram. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Visualization of detection results. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Structure diagram of the SE attention mechanism. by Junyan Wang (4738518)

“…In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. …”

Rate-limiting enzymes tyrosine hydroxylase (catecholamines) and tryptophan hydroxylase (5-HT) post-stress. by C. Brad Wilson (468614)

“…<p>Tyrosine hydroxylase elevation in the PFC (A) and hippocampus (B) substantiated the findings of elevated levels of NE and DA, while down-regulated tryptophan hydroxylase in the PFC (C) and hippocampus (D) confirmed decreased levels of 5-HT. All data are presented as mean ± SEM. …”

RipE1 undergoes phosphorylation in <i>N. benthamiana</i>, which contributes to protein stability. by Wenjia Yu (9422059)

Heparanase activity in murine (A) and human (B) sepsis. by Lukas Martin (747245)

Heparanase level in murine (A) and human (B) sepsis. by Lukas Martin (747245)

MiR-512-5p is down-regulated in in telomerase positive HNSCC cell lines. by Jun Li (6494)

“…(B) qRT-PCR analysis of the expression levels of miR-512-5p in various telomerase status cell lines. All data are shown as mean±SD. …”

Microkinetic Simulations of Methanol-to-Olefin Conversion in H‑SAPO-34: Dynamic Distribution and Evolution of the Hydrocarbon Pool and Implications for Catalytic Performance by Jun Ke (1537273)

“…The dynamic distribution and evolution of the retained aromatic or olefinic HCPs and the origin of olefin products were revealed with respect to reaction conditions. We corroborate that the olefin-based cycle dominates the MTO conversion in H-SAPO-34 under most reaction conditions, and the contribution of the aromatic-based cycle increase with increasing temperature, decreasing pressure, and/or decreasing water partial pressure. …”

The impact of S2 mutations on Omicron SARS-CoV-2 cell surface expression and fusogenicity by Alba Escalera (17673661)

“…Our results showed that Omicron BA.1 subvariant is efficiently cleaved but it is poorly fusogenic compared to previous SARS-CoV-2 strains. To understand the basis of this phenotype, we generated chimeric S protein using combinations of the S1 and S2 domains from WA1, Delta and Omicron BA.1 variants. …”