Staats- und Universitätsbibliothek Bremen

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines as physics, mathematics, computer science, social psychology and philosophy. Agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte-Carlo style, but they can also be reformulated on the agent’s density in the opinion space in a master-equation style. This paper is to present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence; second, to give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions; third to review the several extensions and the evolving phenomena which have been studied so far; and fourth to state some basic open questions.

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.

Aqueous zinc-ion batteries are realistic candidates as stationary storage systems for power-grid applications. However, to accelerate their commercialization, some important challenges must be specifically tackled, and appropriate experimental practices need to be embraced to align the academic research efforts with the realistic industrial working conditions for stationary storage. Within this commentary article, both the open challenges and the good experimental practices are discussed in relation to their impact on the future development of the aqueous Zn-ion technology. Aqueous Zn-based batteries represent a viable and cost-effective technology for electricity grid storage. Here, the authors discuss the most challenging aspects to bridge academic and industrial research and accelerate the adoption of this class of devices on a large scale.

Due to the ubiquitous presence of lithium-ion batteries in portable applications, and their implementation in the transportation and large-scale energy sectors, the future cost and availability of lithium is currently under debate. Lithium demand is expected to grow in the near future, up to 900 ktons per year in 2025. Lithium utilization would depend on a strong increase in production. However, the currently most extended lithium extraction method, the lime-soda evaporation process, requires a period of time in the range of 1-2 years and depends on weather conditions. The actual global production of lithium by this technology will soon be far exceeded by market demand. Alternative production methods have recently attracted great attention. Among them, electrochemical lithium recovery, based on electrochemical ion-pumping technology, offers higher capacity production, it does not require the use of chemicals for the regeneration of the materials, reduces the consumption of water and the production of chemical wastes, and allows the production rate to be controlled, attending to the market demand. Here, this technology is analyzed with a special focus on the methodology, materials employed, and reactor designs. The state-of-the-art is reevaluated from a critical perspective and the viability of the different proposed methodologies analyzed.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.

The production of biodegradable plastic is increasing. Given the augmented littering of these products an increasing input into the sea is expected. Previous laboratory experiments have shown that degradation of plastic starts within days to weeks. Little is known about the early composition and activity of biofilms found on biodegradable and conventional plastic debris and its correlation to degradation in the marine environment. In this study we investigated the early formation of biofilms on plastic shopper bags and its consequences for the degradation of plastic. Samples of polyethylene and biodegradable plastic were tested in the Mediterranean Sea for 15 and 33 days. The samples were distributed equally to a shallow benthic (sedimentary seafloor at 6 m water depth) and a pelagic habitat (3 m water depth) to compare the impact of these different environments on fouling and degradation. The amount of biofilm increased on both plastic types and in both habitats. The diatom abundance and diversity differed significantly between the habitats and the plastic types. Diatoms were more abundant on samples from the pelagic zone. We anticipate that specific surface properties of the polymer types induced different biofilm communities on both plastic types. Additionally, different environmental conditions between the benthic and pelagic experimental site such as light intensity and shear forces may have influenced unequal colonisation between these habitats. The oxygen production rate was negative for all samples, indicating that the initial biofilm on marine plastic litter consumes oxygen, regardless of the plastic type or if exposed in the pelagic or the benthic zone. Mechanical tests did not reveal degradation within one month of exposure. However, scanning electron microscopy (SEM) analysis displayed potential signs of degradation on the plastic surface, which differed between both plastic types. This study indicates that the early biofilm formation and composition are affected by the plastic type and habitat. Further, it reveals that already within two weeks biodegradable plastic shows signs of degradation in the benthic and pelagic habitat.

BACKGROUND: In this review we aimed to determine the economic impact of epilepsy and factors associated with costs to individuals and health systems. METHODS: A narrative systematic review of incidence and case series studies with prospective consecutive patient recruitment and economic outcomes published before July 2014 were retrieved from Medline, Embase and PsycInfo. RESULTS: Of 322 studies reviewed, 22 studies met the inclusion criteria and 14 were from high income country settings. The total costs associated with epilepsy varied significantly in relation to the duration and severity of the condition, response to treatment, and health care setting. Where assessed, 'out of pocket' costs and productivity losses were found to create substantial burden on households which may be offset by health insurance. However, populations covered ostensibly for the upfront costs of care can still bear a significant economic burden. CONCLUSIONS: Epilepsy poses a substantial economic burden for health systems and individuals and their families. There is uncertainty over the degree to which private health insurance or social health insurance coverage provides adequate protection from the costs of epilepsy. Future research is required to examine the role of different models of care and insurance programs in protecting against economic hardship for this condition, particularly in low and middle income settings.

INTRODUCTION: Intensive care unit (ICU) costs account for up to 20% of a hospital's costs. We aimed to analyse the individual patient-related cost of intensive care at various hospital levels and for different groups of disease. METHODS: Data from 51 ICUs all over Germany (15 primary care hospitals and 14 general care hospitals, 10 maximal care hospitals and 12 focused care hospitals) were collected in an observational, cross-sectional, one-day point prevalence study by two external study physicians (January-October 2003). All ICU patients (length of stay > 24 hours) treated on the study day were included. The reason for admission, severity of illness, surgical/diagnostic procedures, resource consumption, ICU/hospital length of stay, outcome and ICU staffing structure were documented. RESULTS: Altogether 453 patients were included. ICU (hospital) mortality was 12.1% (15.7%). The reason for admission and the severity of illness differed between the hospital levels of care, with a higher amount of unscheduled surgical procedures and patients needing mechanical ventilation in maximal care hospital and focused care hospital facilities. The mean total costs per day were euro 791 +/- 305 (primary care hospitals, euro 685 +/- 234; general care hospitals, euro 672 +/- 199; focused care hospitals, euro 816 +/- 363; maximal care hospitals, euro 923 +/- 306), with the highest cost in septic patients (euro 1,090 +/- 422). Differences were associated with staffing, the amount of prescribed drugs/blood products and diagnostic procedures. CONCLUSION: The reason for admission, the severity of illness and the occurrence of severe sepsis are directly related to the level of ICU cost. A high fraction of costs result from staffing (up to 62%). Specialized and maximum care hospitals treat a higher proportion of the more severely ill and most expensive patients.

Abstract In this paper we describe an investigation into the application of deep learning methods for low-dose and sparse angle computed tomography using small training datasets. To motivate our work we review some of the existing approaches and obtain quantitative results after training them with different amounts of data. We find that the learned primal-dual method has an outstanding performance in terms of reconstruction quality and data efficiency. However, in general, end-to-end learned methods have two deficiencies: (a) a lack of classical guarantees in inverse problems and (b) the lack of generalization after training with insufficient data. To overcome these problems, we introduce the deep image prior approach in combination with classical regularization and an initial reconstruction. The proposed methods achieve the best results in the low-data regime in three challenging scenarios.

The ever-increasing amount of batteries used in today's society has led to an increase in the demand of lithium in the last few decades. While mining resources of this element have been steadily exploited and are rapidly depleting, water resources constitute an interesting reservoir just out of reach of current technologies. Several techniques are being explored and novel materials engineered. While evaporation is very time-consuming and has large footprints, ion sieves and supramolecular systems can be suitably tailored and even integrated into membrane and electrochemical techniques. This review gives a comprehensive overview of the available solutions to recover lithium from water resources both by passive and electrically enhanced techniques. Accordingly, this work aims to provide in a single document a rational comparison of outstanding strategies to remove lithium from aqueous sources. To this end, practical figures of merit of both main groups of techniques are provided. An absence of a common experimental protocol and the resulting variability of data and experimental methods are identified. The need for a shared methodology and a common agreement to report performance metrics are underlined.

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19-74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2-3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4-5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.

The adoption of Cyber-Physical Systems (CPS) in production networks enables new potential for improved efficiency, accountability, sustainability and scalability. In terms of production and transport processes, materialising this potential requires customised technological concepts, planning and control methods as well as business models. Even though CPS strongly rely on technological advancements, the creativity, flexibility and problem solving competence of human stakeholders is strongly needed for their operation. This paper introduces and reviews the social aspects of CPS and motivates future research towards Socio-Cyber-Physical Systems (SCPS) applied to production networks. In this frame, context-dependent behavioural aspects and implications related to the human stakeholders are delimitated. As a showcase for the relevance of these aspects the deficits arising from an insufficient communication among stakeholders in SCPS are analysed by means of a simulation experiment. The obtained results substantiate the dependence of SCPS on properly considering the aspects related to human stakeholders together with technology.

In the past two decades, high-intensity focused ultrasound (HIFU) in combination with diagnostic ultrasound (USgFUS) or magnetic resonance imaging (MRgFUS) opened new ways of therapeutic access to a multitude of pathologic conditions. The therapeutic potential of HIFU lies in the fact that it enables the localized deposition of high-energy doses deep within the human body without harming the surrounding tissue. The addition of diagnostic ultrasound or in particular MRI with HIFU allows for planning, control and direct monitoring of the treatment process. The clinical and preclinical applications of HIFU range from the thermal treatment of benign and malign lesions, targeted drug delivery, to the treatment of thrombi (sonothrombolysis). Especially the therapy of prostate cancer under US-guidance and the ablation of benign uterine fibroids under MRI monitoring are now therapy options available to a larger number of patients. The main challenges for an abdominal application of HIFU are posed by partial or full occlusion of the target site by bones or air filled structures (e.g. colon), as well as organ motion. In non-trivial cases, the implementation of computer based modeling, simulation and optimization is desirable. This article describes the principles of HIFU, ultrasound and MRI therapy guidance, therapy planning and simulation, and gives an overview of the current and potential future applications. Hochenergetischer fokussierter Ultraschall (HIFU) appliziert unter US-diagnostischer (USgFUS) oder Magnetresonanz-tomographischer (MRgFUS) Kontrolle hat in den letzen 20 Jahren neuartige therapeutische Ansätze für eine Vielzahl von Erkrankungen geschaffen. Das therapeutische Potential von HIFU besteht darin, dass hohe Mengen Energie sehr lokalisiert in der Tiefe des menschlichen Körpers appliziert werden können ohne dabei die umgebenden Strukturen zu schädigen. Die Kombination mit der Ultraschall- und insbesondere der MR-Bildgebung erlaubt die Planung, Kontrolle und das direkte Monitoring des Behandlungsfortschritts. Die Anwendungen von HIFU reichen von der thermischen Behandlung benigner und maligner Läsionen über die gezielte Verabreichung von Medikamenten (targeted drug delivery) bis hin zur Behandlung von Thrombosen (Sonothrombolyse). So stehen nun mit der Therapie des Prostatakarzinoms unter US-Kontrolle und der Ablation von Uterusmyomen unter MRT-Kontrolle zwei HIFU-Therapiemodalitäten einer größeren Patientenzahl zur Verfügung. Eine große Herausforderung bleibt die Therapie abdomineller Organe die sich einerseits bewegen und andererseits durch Knochen oder luftgefüllte Strukturen, wie den Darm, partiell oder total verdeckt werden. Für solche Fälle wird die Kombination mit anderen bildgebenden Verfahren und Computerunterstützung zur Modellierung, Simulation und Optimierung in der Planung und Durchführung der HIFU-Behandlung untersucht. Der Artikel beschreibt die Grundlagen von HIFU, die Therapieüberwachung mit diagnostischem Ultraschall und MRT, die Therapieplanung und die Simulation. Zudem wird ein Überblick der aktuellen und in Zukunft möglichen Anwendungen gegeben.

Over the recent years Convolutional Neural Networks (CNN) have become the primary choice for many image-processing problems. Regarding industrial applications, they are hence especially interesting for automated optical quality inspection. However, with well-optimized processes is it often not possible to obtain a sufficiently large set of defective samples for CNN-based classification and the training objective shifts from defect classification to anomaly detection. Here we approach this problem with deep metric learning using triplet networks. Our evaluation shows promising results that even translate to novel surface/defect classes, which were not part of the training data.

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametric filtering methods do not require specification of model and are popular because of their simplicity in application. We discuss the Henderson, LOESS, and Hodrick–Prescott filters and their versions derived by exploiting the Reproducing Kernel Hilbert Space methodology. In addition to these prominent approaches, we consider SSA and wavelet methods. SSA is widespread in the geosciences; its algorithm is similar to that of principal components analysis, but SSA is applied to time series. Wavelet methods are the de facto standard for denoising in signal procession, and recent works revealed their potential in trend analysis.

Developing the capacity to digitally transform through AI is becoming increasingly important for public organizations, as a constantly growing number of their activities is now becoming AI-driven. This prompts an understanding of how public organizations should organize in order to derive value from AI, as well as in which forms can value be realized. Against this background, this paper examines how AI capabilities can lead to organizational performance by inducing change in key organizational activities. Using a survey-based study, we collected data from European public organizations regarding the indirect effect AI capabilities have on organizational performance. Data was collected from 168 municipalities from three European countries (Norway, Germany, and Finland) and analyzed by means of structural equation modeling. Our findings show that AI capabilities have a positive effect on process automation, cognitive insight generation, and cognitive engagement. While process automation and cognitive insights are having a positive effect on organizational performance, we found that cognitive engagement negatively affects organizational performance. Our findings document the key resources that constitute an AI capability and showcase the effects of fostering such capabilities on key organizational activities, and in turn organizational performance.

Living beings have an unsurpassed range of ways to manipulate objects and interact with them. They can make autonomous decisions and can heal themselves. So far, a conventional robot cannot mimic this complexity even remotely. Classical robots are often used to help with lifting and gripping and thus to alleviate the effects of menial tasks. Sensors can render robots responsive, and artificial intelligence aims at enabling autonomous responses. Inanimate soft robots are a step in this direction, but it will only be in combination with living systems that full complexity will be achievable. The field of biohybrid soft robotics provides entirely new concepts to address current challenges, for example the ability to self-heal, enable a soft touch, or to show situational versatility. Therefore, "living materials" are at the heart of this review. Similarly to biological taxonomy, there is a recent effort for taxonomy of biohybrid soft robotics. Here, an expansion is proposed to take into account not only function and origin of biohybrid soft robotic components, but also the materials. This materials taxonomy key demonstrates visually that materials science will drive the development of the field of soft biohybrid robotics.

Growth rates and stable carbon isotope compositions were determined for cultivated polar and cold-temperate macroalgae of both hemispheres. Growth in macrothatli of endemic Antarctic Desmarestiales was Light-saturated at lower irradiances compared to Arctic cold-temperate Laminaria species. Moreover, photoinhibition of growth was more strongly expressed in most Antarctic algae.

By a simple extension of the bounded confidence model, it is possible tomodel the influence of a radical group, or a charismatic leader on theopinion dynamics of `normal' agents that update their opinions underboth, the influence of their normal peers, and the additional influenceof the radical group or a charismatic leader. From a more abstract pointof view, we model the influence of a signal, that is constant, may havedifferent intensities, and is `heard' only by agents with opinions, that arenot too far away. For such a dynamic a Constant Signal Theorem is proven.In the model we get a lot of surprising effects. For instance,the more intensive signal may have less effect; more radicals may lead to less radicalization of normal agents. The model is an extremely simple conceptual model. Undersome assumptions the whole parameter space can be analyzed. The modelinspires new possible explanations, new perspectives for empirical studies,and new ideas for prevention or intervention policies.