Real-Time Mining Control Cockpit: a framework for interactive 3D visualization and optimized decision making support

Real-Time Mining is a research and development project within the European Union\'s Horizon 2020 initiative and consists of a consortium of thirteen European partners from five countries. The overall aim of Real-Time-Mining is to develop a real-time framework to decrease environmental impact and increase resource efficiency in the European raw material extraction industry. The key concept of the research conducted is to promote a paradigm shift from discontinuous to a continuous process monitoring and quality management system in highly selective mining operations. The Real-Time Mining Control Cockpit is a framework for the visualization of online data acquired during the extraction at the mining face as well as during material handling and processing. The modules include the visualization of the deposit-model, 3D extraction planning, integrated data of the positioning-system as well as the visualization of sensor and machine performance data. Different tools will be developed for supporting operation control and optimized decision making based on real-time data from the centralized database. This will also integrate results from the updated resource model and optimized mine plan. The developed Real-Time Mining cockpit software will finally be integrated into a wider central control and monitoring station of the whole mine

359 Gold-alloy tip electrode: A new "deep lesion" technology for catheter ablation?

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Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [F-18]-PSMA-1007 PET-CT

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [F-18]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians\u27 corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers

Best Practices for Improving Subgrade Drainage

This project developed a high-level best practices guidance document for the design/installation of subsurface drainage. This Tool serves as a guide to assist agencies in understanding drainage problems and options to consider for mitigating structural damage to pavements due to moisture. The weather and soil conditions vary drastically across Minnesota. Engineers should use their engineering judgment and seek expert guidance when necessary

C MSC 101-0695 Gift, Small pot with blue flowers

Cream colored urn decorated with blue and yellow flowers.https://commons.und.edu/pottery/1230/thumbnail.jp

C MSC 134-0727, Tall brown vase with brown relief

Brown vase made by Dona H. Bitzan.https://commons.und.edu/pottery/1404/thumbnail.jp

C MSC 104-0698 Gift, Dark blue capsule shaped vase

Heavy dark blue vase. Tubular in shape.https://commons.und.edu/pottery/1362/thumbnail.jp

C MSC 098-0692 Gift, Dark blue vase

Dark blue gloss vase.https://commons.und.edu/pottery/1246/thumbnail.jp

C MSC 099-0693 Gift, Green pot with berries and leaves

Orange-to-green colored sgraffito bowl, green leaves and brown berries or flowers. Dontaed by Elvira Bitzan Limberg 1935 and mother Dena H. Bitzen 1931.https://commons.und.edu/pottery/1344/thumbnail.jp

C MSC 102-0696 Gift, Howling coyote pot

Blue and white bowl with coyote silhouettes, howling pose. Donated by Elvira Bitzan Limberg 1935, gifted to her by her mother Dena H. Bitzen.https://commons.und.edu/pottery/1298/thumbnail.jp