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Although the official founding date of the Prussian Geological Survey (PGL) is January 1, 1873, the establishment of this state geological survey gradually developed from the geological mapping responsibilities of the Prussian mining administration. The PGL was at first attached to the Royal Mining Academy in Berlin before becoming an independent organisation in 1907. Besides mapping the territory and safeguarding the geoscientific data of Prussia, the PGL developed special expertise concerning public health and raw material issues, the latter stimulated by war requirements during World War I. Germany, through the Imperial central government, administered colonies in Africa, Oceania and Asia up to World War I, and the PGL was then responsible for geological issues. When the Nazi Party gained government control in 1933, the federal system in Germany was changed to a centralised government. As a result, in 1938 the PGL was transformed into the Imperial Agency for Geological Investigations ("Reichsstelle", later "Reichsamt für Bodenforschung" -- RfB), and all the state geological surveys became branches of the RfB. The work of the RfB was then primarily focussed on efforts to achieve self-sufficiency, the autarchy, including the search for ore deposits and, especially, oil to fuel the military machinery for the war preparations of the Nazi regime. After the German capitulation in 1945, and following the intermediate period of allied occupied zones in Germany, the federal government system was re-established, however with a more rational state structure than before 1933. Because Lower Saxony included the principal hydrocarbon region of Germany, a strong branch of the RfB had existed in Hannover/Lower Saxony since 1934. This branch became the nucleus for the Lower Saxony Geological Survey and, later in 1958, for the German Federal Geological Survey, named the Federal Institute for Geosciences and Natural Resources ("Bundesanstalt für Geowissenschaften und Rohstoffe" -- BGR). The separation between state tasks and federal tasks in Germany is very strict. The responsibility for geology and raw materials lies with the states. The task of the BGR is to fulfil international geoscientific obligations of Germany, such as training the geological surveys of developing countries or being the National Data Centre for the Comprehensive Nuclear Test Ban Treaty Organisation CTBTO, which is based in Vienna. Other tasks include providing advice to the Federal Government and undertaking research and development (comprising about 40% of BGR's funds). The only tasks of the BGR within Germany relate to the overall responsibility of the Federal Government for geoscientific matters that are a state obligation, and for geoscientific aspects of the nuclear waste disposal. [ABSTRACT FROM AUTHOR]

During recent years, maintaining scientific and medical databases has turned out to be a major challenge in the medical sector. The medical data regarding the patients comprise diverse diagnostic medical reports and features for such disease need to be recorded carefully for providing high-quality treatments. Therefore, this paper proposes a hybrid deep convolutional neural network (HDCNN)-based COOT approach to predict the risk of diseases. Initially, an improved crossover-based levy flight optimization algorithm (ICLFDO) algorithm is employed in selecting and processing the unstructured textual data. Then later, the HDCNN-COOT approach predicts the diseases more accurately. In addition to this, the classifier predicts whether the patient is at risk of disease in the future or not. The efficiency of the proposed model is evaluated by using a dataset obtained from University Hospital of Ludwig Maximilian University of Munich, Germany. The data collected in the data center include 29,477,035 data items from 36,082 patients. The proposed model is capable of using structured data and uses textual data of the patients. Finally, the higher classification accuracy and improved performance of the classifiers can be viewed using the experimental results conducted on five different datasets. [ABSTRACT FROM AUTHOR]

Introduction: As part of the development of the evidence-based (S3) clinical practice guidelines for kidney and bladder cancer by the German Guideline Program in Oncology, quality indicators (QIs) were defined to measure the quality of care. Based on these guidelines and QIs, the German Cancer Society (DKG) developed two new certification systems. The aim of this article is to show the process of development and implementation of QIs in certified cancer centres. Methods: Based on strong recommendations of each guideline and an additional systematic literature review for national and international QIs, two sets of QIs were derived in a multistep standardized approach. These QIs were implemented in the centres in certification data sheets to measure their outcomes. First results of treatment years 2018 and 2019 are available. Results: The final sets include 9 QIs for kidney cancer and 12 QIs for bladder cancer. Two-thirds of the QIs were transferred to the data sheets. In 2018 and 2019, the results of all but one QI are within the plausibility limits. From 2020 on, they are replaced by stricter target values that will challenge centres to improve their outcomes. Conclusions: Guideline-derived QIs make relevant aspects of patient care measurable and consequently improvable. The first QI results are encouraging. However, the DKG certification system and the methods of measuring quality are under ongoing development. Systematic QI implementation and evaluation may help to generate broader databases and thus expand knowledge. [ABSTRACT FROM AUTHOR]

Introduction: Due to their euphoric and sedative effects, opioid analgesics have high potential for abuse and dependence. In the last decade in the USA and many Western European Countries the prescription rates of opioid analgesics have steadily increased. Objective: This study describes 5-year trends in the prescription of opioid analgesics and risk indicators such as duration, dose and 'doctor shopping' in Northern Germany. Methods: The annual rates of opioid analgesic prescriptions have been analysed for patients with statutory health insurance processed by the North German Pharmacy Data Center for the years 2011–2015. A distinction was made between non-cancer patients and cancer patients, and different groups according to prescription duration and dose level. Results: Between 2011 and 2015, the annual number of patients with opioid analgesic prescriptions increased from 500,000 to 550,000. About half of non-cancer-patients (85% of the total sample) and cancer patients received opioid analgesics for 90 days or less. The rates for long-term prescriptions (⩾9 months) ranged between 6–7% for non-cancer patients and 7–8% for cancer patients. Between 1.2–1.8% received opioid analgesics in doses of more than two defined daily doses. The majority of non-cancer patients with opioid analgesic prescriptions were female. The average age of non-cancer patients was 66 years. About 80% of non-cancer patients with first opioid analgesic prescription received World Health Organization step II medication. Conclusion: For the first time, this study provides comprehensive patient-related analyses of opioid analgesic prescriptions in Germany over a 5-year period. Despite a slight increase in the overall number of opioid analgesic prescriptions, an epidemic spread of opioid analgesics cannot be observed. [ABSTRACT FROM AUTHOR]

To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power management strategies in the face of any set of markets for power flexibility. A part of the framework is then instantiated and applied to the workload of a real high-performance data center. It uses workload shifting and frequency scaling in order to market their flexibility on the EPEX spot market and the secondary reserve market in Germany. The results show that by using the inherent flexibility of their power profile on the EPEX spot market the considered data center in 2014 could have earned savings of 7.3% of their power bill. [ABSTRACT FROM AUTHOR]

The electric energy demand of data centers in Germany has grown rapidly from 10.5 TWh/a in 2010 to 13.2 TWh/a in 2017, an average of 25% of which are used to fulfill the data centers' cooling demand. In order to increase its energy efficiency, TU Darmstadt applies a new cooling concept in the next generation of its high‐performance computing data center "Lichtenberg II." Instead of the current air‐cooled servers with water‐cooled rear doors at 17‐24°C, the new data center will be equipped with direct hot‐water cooling for the high‐performance computer, supplying heat at a temperature of 45°C. The high‐temperature waste heat is used for heating purposes on the university's campus Lichtwiese. For waste heat utilization, two concepts are presented, either integrating the heat in the return line of the district heating network or using it locally in buildings located near the data center. Reductions in CO2 emission and annuity are generated both by decreased compression cooling demand for the data center and by decreased heat generation due to waste heat utilization. Depending on the scenario, a total of 20%‐50% of the waste heat emitted by the high‐performance computer can be used for heating purposes, while the remaining heat is dissipated efficiently via free cooling without additional energy demand for mechanical chillers. CO2 emission can be decreased by up to 720 tCO2/a, representing a reduction of about 4% of the total emission at campus Lichtwiese. TU Darmstadt is currently implementing the waste heat integration into its district heating network and will benefit from this concept starting in 2020. [ABSTRACT FROM AUTHOR]

Data centers are estimated to have the fastest growing carbon footprint from across the whole information and communication technology (ICT) sector. Evaluating the performance of data centers in terms of energy efficiency and sustainability is becoming an increasingly important matter for organizations and governments (e.g., for regulation or reputation purposes). It nonetheless remains difficult to achieve such evaluation, as data centers imply to take into consideration a wide range of dimensions and stakeholders. Even though a wide range of sustainability performance indicators exist in the literature, there is still a lack of frameworks to help data center stakeholders (spanning from data center owners, governmental regulators to engineers/field operators) to evaluate and understand how a data center performs in terms of sustainable development/behavior. Our research work proposes such a framework, whose originality lies in the combination of state-of-the-art sustainability metrics with the biomimicry commandments of eco-mature system, which enables holistic sustainability assessment of data centres. From a theoretical perspective, the proposed model is designed based on a benefit-cost analysis using the Analytic Hierarchy Process (AHP) technique. This approach allows data center stakeholders for specifying their own preferences and/or expertise in the comparison process, whose practicability is demonstrated in this paper considering three data center candidates, which are respectively located in France, Germany and Sweden. • A new model for comparing data center sustainability performance is proposed. • The model makes use of state-of-the-art sustainability metrics together with biomimicry lessons. • The model is designed based on a benefit-cost approach using the AHP technique. • A comparison study is carried out with three distinct data center candidates. [ABSTRACT FROM AUTHOR]

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. [ABSTRACT FROM AUTHOR]