Vickfogh5607

001) and [La] values were higher at post-combat measurements (p less then 0.001). Moreover, tension and fatigue were higher in 6 × 6 m compared with 8 × 8 m (p = 0.022 and p = 0.023, respectively) and anger was higher in 6 × 6 m and 8 × 8 m in comparison with 4 × 4 m (p = 0.012 and p = 0.043, respectively). Confusion increased from before to after bouts (p less then 0.001), from 4 × 4 m and 6 × 6 m area sizes to 8 × 8 m (p = 0.001 and p = 0.018, respectively), and from 1vs.1 to 1vs.2 (p less then 0.001). Furthermore, vigour decreased from before to after bouts (p less then 0.01). Taekwondo combat sessions are a specific conditioning exercise for athletes. Thus, coaches can use the 1vs.1 condition to elicit higher HR responses and 6 × 6 m area size to induce higher psychological stress, mimicking what occurs during a competition.The use of reclaimed or treated water from urban wastewater treatment plants for irrigation has been proposed as an alternative water source to address water scarcity issues in Europe. In this paper using agro-economic modelling, we analyse if treated water available for agriculture has the potential to reduce freshwater abstraction and, consequently, water stress. Implementing exogenous treated water quantities as an additional water supply at NUTS 2 level in the CAPRI model, we found that treated water reuse is a possible alternative supply source to address water shortages with a very negligible effect on farmers' income and food production in the EU. Androgen Receptor Antagonist nmr However, the actual water reuse and water stress reduction is very limited due to high costs. Even climate change effects on water availability and precipitation failed to induce higher use. The one-size-fits-all approach modelled via a flat rate water price only encourages the reuse of treated water in a limited number of EU member states. Thus, in order to maximise the potential of reused water to address water scarcity, different rates should be used so as to ensure higher treated water volumes at lower costs.Surgical tool segmentation is becoming imperative to provide detailed information during intra-operative execution. These tools can obscure surgeons' dexterity control due to narrow working space and visual field-of-view, which increases the risk of complications resulting from tissue injuries (e.g. tissue scars and tears). This paper demonstrates a novel application of segmenting and removing surgical instruments from laparoscopic/endoscopic video using digital inpainting algorithms. To segment the surgical instruments, we use a modified U-Net architecture (U-NetPlus) composed of a pre-trained VGG11 or VGG16 encoder and redesigned decoder. The decoder is modified by replacing the transposed convolution operation with an up-sampling operation based on nearest-neighbor (NN) interpolation. This modification removes the artifacts generated by the transposed convolution, and, furthermore, these new interpolation weights require no learning for the upsampling operation. The tool removal algorithms use the tool segmentation mask and either the instrument-free reference frames or previous instrument-containing frames to fill-in (i.e., inpaint) the instrument segmentation mask with the background tissue underneath. We have demonstrated the performance of the proposed surgical tool segmentation/removal algorithms on a robotic instrument dataset from the MICCAI 2015 EndoVis Challenge. We also showed successful performance of the tool removal algorithm from synthetically generated surgical instruments-containing videos obtained by embedding a moving surgical tool into surgical tool-free videos. Our application successfully segments and removes the surgical tool to unveil the background tissue view otherwise obstructed by the tool, producing visually comparable results to the ground truth.Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging, the current benchmark for assessment of myocardium viability, enables the identification and quantification of the compromised myocardial tissue regions, as they appear hyper-enhanced compared to the surrounding, healthy myocardium. However, in LGE CMR images, the reduced contrast between the left ventricle (LV) myocardium and LV blood-pool hampers the accurate delineation of the LV myocardium. On the other hand, the balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images ideal for accurate segmentation of the cardiac chambers. In the interest of generating patient-specific hybrid 3D and 4D anatomical models of the heart, to identify and quantify the compromised myocardial tissue regions for revascularization therapy planning, in our previous work, we presented a spatial transformer network (STN) based convolutional neural network (CNN) architecture for registration of LGE and bSSFP cine CMR image dble to the registration results obtained by stand-alone STN based CNN model by training 35 of the available 45 image datasets and also shows significant improvement in registration performance when compared to the results achieved by the stand-alone STN based CNN model by training 25 of the available 45 image datasets.The worldwide outbreak of the COVID-19 has significantly increased the fear of individuals, which brings severe psychosocial stress and adverse psychological consequences, and become a serious public health problem. Based on the imprinting theory, this study investigates whether childhood experiences of SARS have an imprinting effect that significantly influences the fear of COVID-19. Furthermore, we propose that this effect is contingent on the applications of AI and big data. We test our framework with a sample of 1871 questionnaires that covered students in universities across all provincial regions in China, and the results suggest that the imprinting of SARS increases the individuals' fear of COVID-19, and this effect is reduced with the applications of AI and big data. Overall, this study provides a novel insight of the fear caused by the childhood experience of the similar health crisis and the unique role of AI and big data applications into fighting against COVID-19.