Mixup data expansion. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

Marking example. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

PEDOT as a Flexible Organic Electrode for a Thin Film Acoustic Energy Harvester by Younghoon Kim (49041)

“…An AO-TEG with a PEDOT electrode (<i>R</i>_e = 200 Ω) showed instantaneous peak-to-peak voltage generation of 11 V under a sound pressure level (SPL) of 90–100 dB. …”

PEDOT as a Flexible Organic Electrode for a Thin Film Acoustic Energy Harvester by Younghoon Kim (49041)

“…An AO-TEG with a PEDOT electrode (<i>R</i>_e = 200 Ω) showed instantaneous peak-to-peak voltage generation of 11 V under a sound pressure level (SPL) of 90–100 dB. …”

PEDOT as a Flexible Organic Electrode for a Thin Film Acoustic Energy Harvester by Younghoon Kim (49041)

“…An AO-TEG with a PEDOT electrode (<i>R</i>_e = 200 Ω) showed instantaneous peak-to-peak voltage generation of 11 V under a sound pressure level (SPL) of 90–100 dB. …”

PEDOT as a Flexible Organic Electrode for a Thin Film Acoustic Energy Harvester by Younghoon Kim (49041)

“…An AO-TEG with a PEDOT electrode (<i>R</i>_e = 200 Ω) showed instantaneous peak-to-peak voltage generation of 11 V under a sound pressure level (SPL) of 90–100 dB. …”

Expression of FOXM1 promotes mucinous characteristics in mouse lung tumors induced either by urethane or Kras^G12D. by David Milewski (4710029)

“…<b>(B)</b> <i>Nkx2-1</i> mRNA was decreased in microdissected <i>epiFoxM1-ΔN</i> tumors (n = 5) compared to control tumors (n = 5) as shown by qRT-PCR. …”

Characterization of the p53 Response to Oncogene-Induced Senescence - Figure 5 by Lidia Ruiz (221142)

DataSheet_1_Multivariate Mendelian randomization provides no evidence for causal associations among both psoriasis and psoriatic arthritis, and skin cancer.pdf by Nianzhou Yu (14825545)

“…PsA showed a significant association with a decreased risk of BCC, with odds ratios (with corresponding 95% confidence intervals) of 1.00 (1.00, 1.00) (P_Ivw = 0.214) and 1.00 (1.00, 1.00) (P_Ivw = 0.477), respectively. …”

Comparison of mitochondrial number at MF and SC presynaptic bouton profiles. by Sang Hun Lee (564624)

<i>In vitro</i> titan cells generation is dependent on the negative regulator <i>PKR1</i> and <i>TSP2</i>. by Benjamin Hommel (523072)

Titan cells generation <i>in vitro</i> is influenced <i>in vitro</i> by host derived cues and ergosterol, protein and RNA inhibitors. by Benjamin Hommel (523072)

Drill image dataset for training part II. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

U²-Net network structure diagram [8]. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

RSU-7 structure diagram [8]. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

The N-terminal domain is sufficient for CMPK2 antiviral function. by Joanna B. Pawlak (5210639)

Drill image dataset for training part I. by Qingjun Yu (1649473)

“…A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. Overall, the model has high accuracy for segmenting structural planes and very low mean absolute error, indicating good segmentation accuracy and certain generalization of the network. …”

Supplementary file 5_8 specific Chinese herbal injections combined with chemotherapy for breast cancer: a systematic review and network meta-analysis of comparative safety and effi... by Yunze Shi (22364182)

“…Data analysis was performed using Stata 17.0 and R 4.2.1 software, with odds ratio (OR) and standardized mean difference (SMD) as effect size measures. The surface under the cumulative ranking curve (SUCRA) method was applied to rank the evaluated treatment regimens.…”

Transepithelial calcium flux of FeCl₃ and 1,25(OH)₂D₃-treated Caco-2 monolayer. by Sukpapohn Phummisutthigoon (13531576)

Evaluation of similarity-based and machine learning-based models in predicting <i>in vitro</i> effective drugs. by Szilvia Barsi (12400967)