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A lab experiment to investigate how time pressure, time delay, and cognitive load affect risk and ambiguity attitudes

The recombination of carriers at the photoanode-electrolyte interface is one of the crucial factors hindering the improvement of the photoelectric property of quantum dot-sensitized solar cells (QDSSCs). Passivation layers such as ZnS have been often used to suppress this charge loss, but the effect still needs to be improved. In this paper, Ga3+ is introduced into the traditional ZnS passivation of QDSSCs via a successive ion layer adsorption and reaction (SILAR) approach to further improve the photovoltaic performance. The power conversion efficiency (PCE) of QDSSCs using Zn-Cu-In-Se/Ga-ZnS QDs as sensitizers reaches 8.72%, significantly higher than the 7.7% of Zn-Cu-In-Se/ZnS QDs, demonstrating that the incorporation of Ga3+ in the ZnS layer can optimize the solar absorption and effectively reduce the appearance of carrier recombination, providing a novel means for QDSSCs optimization.

Pulmonary lobes segmentation of pulmonary CT images is important for assistant therapy and diagnosis of pulmonary disease in many clinical tasks. Recently supervised deep learning methods are applied widely in fast automatic medical image segmentation including pulmonary lobes segmentation of pulmonary CT images. However, they require plenty of ground truth due to their supervised learning scheme, which are always difficult to realize in practice. To address this issue, in this study we extend an existed unsupervised learning network with an extra pulmonary mask constraint to develop a deformable pulmonary lobes atlas and apply it for fast automatic segmentation of pulmonary lobes in pulmonary CT images. The experiment on 40 pulmonary CT images shows that our method can segment the pulmonary lobes in seconds, and achieve average Dice of 0.906 ± 0.044 and average surface distance of 0.495 ± 0.380 mm, which outperforms the state-of-the-art methods in segmentation accuracy. Our method successfully combines the advantages of both deformable atlas and unsupervised learning for automatic segmentation and ensures the consistent and topology preserving of pulmonary lobes without any postprocessing.

With the wide application of DC converters, various types of DC-DC converter modeling methods are also constantly proposed. This paper uses the state switch average concept to model the BUCK-BOOST in DCM mode. The simulation model is built on the SIMLINK to verify the matching between the switch network model and the simulation model, which explains the correctness of the small signal model and provides different way for the design of the controller.

Background and objectives Attenuation correction is important for PET image reconstruction. In clinical PET/CT scans, the attenuation information is usually obtained by CT. However, additional CT scans for delayed PET imaging may increase the risk of cancer. In this paper, we propose a novel CT generation method for attenuation correction in delayed PET imaging that requires no additional CT scans. Methods As only PET raw data is available for the delayed PET scan, routine image registration methods are difficult to use directly. To solve this problem, a reconstruction network is developed to produce pseudo PET images from raw data first. Then a second network is used to generate the CT image through mapping PET/CT images from the first scan to the delayed scan. The inputs of the second network are the two pseudo PET images from the first and delayed scans, and the CT image from the first scan. The labels are taken from the ground truth CT image in the delayed scan. The loss function contains an image similarity term and a regularization term, which reflect the anatomy matching accuracy and the smoothness of the non-rigid deformation field, respectively. Results We evaluated the proposed method with simulated and clinical PET/CT datasets. Standard Uptake Value was computed and compared with the gold standard (with coregistered CT for attenuation correction). The results show that the proposed supervised learning method can generate PET images with high quality and quantitative accuracy. For the test cases in our study, the average MAE and RMSE of the proposed supervised learning method were 4.61 and 22.75 respectively, and the average PSNR between the reconstructed PET image and the ground truth PET image was 62.13 dB. Conclusions The proposed method is able to generate accurate CT images for attenuation correction in delayed PET scans. Experiments indicate that the proposed method outperforms traditional methods with respect to quantitative PET image accuracy.