Mediating the German Ideology: Ordoliberal Framing in European Press Coverage of the Eurozone Crisis

The German Government has played a leading role in the Eurozone crisis management, largely characterised by a commitment to fiscal austerity and supply-side structural reforms. The legitimation of these measures in the European policy arenas as well as in the public domain has partly rested on an ordoliberal economic policy framing, which has presented the Eurozone crisis as one of public indebtedness and loss of competitiveness. To study the public legitimation of the crisis management, we analyse the press coverage of the crisis in 8 Eurozone member states, with a total of 7986 newspaper articles included in the sample. Focusing on ‘problem definitions’ and ‘treatment recommendations’ as two key elements of issue framing, we find that an ordoliberal framing of the crisis prevails in all the studied countries, while a competing Keynesian policy frame is mostly undermined. Significant variation between the countries emerges, however, on the question of EU federalisation and on the framing of the sovereign bailout loans. We discuss the implications of these findings for the success of the German Government to maintain the austerity orthodoxy across the Eurozone and crowd out economic policy alternatives.Peer reviewe

A lightweight, industrially-validated instrument to measure user satisfaction and service quality experienced by the users of a UML modeling tool

The research community has delivered many comprehensive instruments to measure user satisfaction and service quality. However, they may be tedious to deploy in industrial settings, often leading to low response rates. Industrial organizations are thus looking for simpler and more cost effective ways to measure both user satisfaction and service quality. This paper presents and validates a lightweight 8-item instrument to measure the user satisfaction and the quality of service experienced by the users of a Unified Modeling Language tool. The instrument merges ease of use and service-related items. The analysis of the results of two surveys, conducted in a global high-tech corporation, indicates that the instrument has adequate reliability and validity. It is short, easy to use, and appropriate for both practical and research purposes. Future research is needed to validate the instrument in the context of other organizations and other classes of information systems

Effects of Climate Change on CO2 Concentration and Efflux in a Humic Boreal Lake : A Modeling Study

Climate change may have notable impacts on carbon cycling in freshwater ecosystems, especially in the boreal zone. Higher atmospheric temperature and changes in annual discharge patterns and carbon loading from the catchment affect the thermal and biogeochemical conditions in a lake. We developed an extension of a one-dimensional process-based lake model MyLake for simulating carbon dioxide (CO2 ) dynamics of a boreal lake. We calibrated the model for Lake Kuivajarvi, a small humic boreal lake, for the years 2013-2014, using the extensive data available on carbon inflow and concentrations of water column CO2 and dissolved organic carbon. The lake is a constant source of CO2 to the atmosphere in the present climate. We studied the potential effects of climate change-induced warming on lake CO2 concentration and air-water flux using downscaled air temperature data from three recent-generation global climate models with two alternative representative concentration pathway forcing scenarios. Literature estimates were used for climate change impacts on the lake inflow. The scenario simulations showed a 20-35% increase in the CO2 flux from the lake to the atmosphere in the scenario period 2070-2099 compared to the control period 1980-2009. In addition, we estimated possible implications of different changes in terrestrial inorganic and organic carbon loadings to the lake. The scenarios with plausible increases of 10% and 20% in CO2 and dissolved organic carbon loadings, respectively, produced increases of 2.1-2.5% and 2.2-2.3% in the annual CO2 flux.Peer reviewe

Restorative effects of urban green environments and the role of urban-nature orientedness and noise sensitivity: A field experiment

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Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.Comment: 29 pages, 10 figure

Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.Comment: 26 pages, 5 figure