Stable Large-scale CO2 Storage in Defiance of an Energy System Based on Renewable Energy – Modelling the Impact of Varying CO2 Injection Rates on Reservoir Behavior

AbstractThe IPCC Report 2014 strengthens the need for CO2 storage as part of climate change mitigation options. The further expansion of electricity generation by solar and wind and its preferential usage in Germany is leading to strong fluctuations in the CO2 output from former base load coal fired power plants. This study takes a look at the feasibility of large scale industrial CO2 injection into a saline aquifer structure with the main focus on varying injection rates. By means of simulation the influence of the most important parameters is analyzed

Visual access to performance indicators in the mining sector

We introduce a visualization system that provides visual interactive access to information relevant for decision making in the mining sector. The mining sector is one of the most important industries in developing countries, especially in Africa. Stakeholders like governments, investors, and the civil society play an important role in the growth of the mining sector. They are interested in information reviewing individual country performances towards mining. The Mining Investment and Governance Review (MInGov) dataset explicitly addresses this issue. However, the complex data structure introduces challenges for the intuitive and easy understanding of the information. Together with mining sector experts, we conducted a design study with the goal to provide visual interactive access to investment- and policy-related information. We report on a domain characterization of the MInGov dataset, its potential users, and their tasks. Based on this analysis, we design a visualization system that supports mining-related decision making. Finally, we evaluate the visualization system in a user workshop with domain experts

Guiding Feature Subset Selection with an Interactive Visualization

We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features

A visual active learning system for the assessment of patient well-being in prostate cancer research

The assessment of patient well-being is highly relevant for the early detection of diseases, for assessing the risks of therapies, or for evaluating therapy outcomes. The knowledge to assess a patient's well-being is actually tacit knowledge and thus, can only be used by the physicians themselves. The rationale of this research approach is to use visual interfaces to capture the mental models of experts and make them available more explicitly. We present a visual active learning system that enables physicians to label the well-being state of patient histories suffering prostate cancer. The labeled instances are iteratively learned in an active learning approach. In addition, the system provides models and visual interfaces for a) estimating the number of patients needed for learning, b) suggesting meaningful learning candidates and c) visual feedback on test candidates. We present the results of two evaluation strategies that prove the validity of the applied model. In a representative real-world use case, we learned the feedback of physicians on a data collection of more than 16.000 prostate cancer histories

Visual analytics for radiomics: Combining medical imaging with patient data for clinical research

The visualization and analysis of electronic health records (EHRs) are becoming increasingly relevant for clinical researchers. While the digitization of medical images is general practice today, many clinics are just starting to build up database with the related patient data, patient histories, and treatment outcomes. This paper reports on a project with a medical group of ear, nose, and throat (ENT) specialists. It combines medical image analysis and Radiomics with visual analytics of patient data to build, analyze, and evaluate patient cohorts. The combined visual interface for both browsing and analyzing patient data was developed in collaboration with the medical researchers. In addition to offering a new way of cohort building, our approach also provides a first comprehensive view on the EHR, including the relevant anatomy of patients. This project triggered a new effort to extend the digitized patient database from around 100 patients to the entire patient population at our partner’s clinic

Visual interactive creation and validation of text clustering workflows to explore document collections

The exploration of text document collections is a complex and cumbersome task. Clustering techniques can help to group documents based on their content for the generation of overviews. However, the underlying clustering workflows comprising preprocessing, feature selection, clustering algorithm selection and parameterization offer several degrees of freedom. Since no "best" clustering workflow exists, users have to evaluate clustering results based on the data and analysis tasks at hand. In our approach, we present an interactive system for the creation and validation of text clustering workflows with the goal to explore document collections. The system allows users to control every step of the text clustering workflow. First, users are supported in the feature selection process via feature selection metrics-based feature ranking and linguistic filtering (e.g., part-of-speech filtering). Second, users can choose between different clustering methods and their parameterizations. Third, the clustering results can be explored based on the cluster content (documents and relevant feature terms), and cluster quality measures. Fourth, the results of different clusterings can be compared, and frequent document subsets in clusters can be identified. We validate the usefulness of the system with a usage scenario describing how users can explore document collections in a visual and interactive way

Single Fibril Growth Kinetics of α-Synuclein

Neurodegenerative disorders associated with protein misfolding are fatal diseases that are caused by fibrillation of endogenous proteins such as α-synuclein (α-syn) in Parkinson's disease (PD) or amyloid-β in Alzheimer's disease. Fibrils of α-syn are a major pathological hallmark of PD and certain aggregation intermediates are postulated to cause synaptic failure and cell death of dopaminergic neurons in the substantia nigra. For the development of therapeutic approaches, the mechanistic understanding of the fibrillation process is essential. Here we report real-time observation of α-syn fibril elongation on a glass surface, imaged by total internal reflection fluorescence microscopy using thioflavin T fluorescence. Fibrillation on the glass surface occurred in the same time frame and yielded fibrils of similar length as fibrillation in solution. Time-resolved imaging of fibrillation on a single fibril level indicated that α-syn fibril elongation follows a stop-and-go mechanism; that is, fibrils either extend at a homogenous growth rate or stop to grow for variable time intervals. The fibril growth kinetics were compatible with a model featuring two states, a growth state and a stop state, which were approximately isoenergetic and interconverted with rate constants of ~ 1.5 × 10− 4 s− 1. In the growth state, α-syn monomers were incorporated into the fibril with a rate constant of 8.6 × 103 M− 1 s− 1. Fibril elongation of α-syn is slow compared to other amyloidogenic proteins

Developing a Wearable Assistant for Hospital Ward Rounds

We describe the results of a three year effort to develop, deploy, and evaluate a wearable staff support system for hospital ward rounds. We begin by describing elaborate workplace studies and staff interviews and the resulting requirements. We then present a wearable system developed on the basis of those requirements. It consists of a belt worn PC (QBIC) for the doctor, wrist worn accelerometer for gesture recognition, a wrist worn RFID reader, a bedside display, and a PDA for the nurse. Results of evaluation of the system, including simulated (with dummy patient) ward rounds with 9 different doctors and accompanying nurses are given. The results of the evaluation have lead to a new system version aimed at deployment in real life ’production environment’ (doctors and nurses performing ward rounds with real patients). The paper concludes by describing this next generation system and initial experiences from a first two week test deployment in a real life hospital setting