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This retrospective study evaluated a commercial deep learning (DL) software for chest radiographs and explored its performance in different scenarios. A total of 477 patients (284 male, 193 female, mean age 61.4 (44.7–78.1) years) were included. For the reference standard, two radiologists performed independent readings on seven diseases, thus reporting 226 findings in 167 patients. An autonomous DL reading was performed separately and evaluated against the gold standard regarding accuracy, sensitivity and specificity using ROC analysis. The overall average AUC was 0.84 (95%-CI 0.76–0.92) with an optimized DL sensitivity of 85% and specificity of 75.4%. The best results were seen in pleural effusion with an AUC of 0.92 (0.885–0.955) and sensitivity and specificity of each 86.4%. The data also showed a significant influence of sex, age, and comorbidity on the level of agreement between gold standard and DL reading. About 40% of cases could be ruled out correctly when screening for only one specific disease with a sensitivity above 95% in the exploratory analysis. For the combined reading of all abnormalities at once, only marginal workload reduction could be achieved due to insufficient specificity. DL applications like this one bear the prospect of autonomous comprehensive reporting on chest radiographs but for now require human supervision. Radiologists need to consider possible bias in certain patient groups, e.g., elderly and women. By adjusting their threshold values, commercial DL applications could already be deployed for a variety of tasks, e.g., ruling out certain conditions in screening scenarios and offering high potential for workload reduction. [ABSTRACT FROM AUTHOR]

Copyright of Business Administration Quarterly / Kwartalnik Nauk o Przedsiebiorstwie is the property of SGH Warsaw School of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates factors influencing the adoption of social media analytics (SMA) for citizen relationship management (CzRM). Three real-world cases of government departments, two in South Africa and one in Germany, were investigated, and focus group discussions were conducted. The technological, organizational, and environmental (TOE) theory and qualitative content analysis guided the data analysis. The findings revealed that in all cases, staff usually conducted a manual analysis of social media and SMA had not been implemented sufficiently to realize its full potential. Insights were obtained from TOE and factors were identified that should be considered for improving the planning of SMA adoption in government. Data quality, methods and tools for SMA, and resources (e.g., skills and budget) were the most important factors identified for achieving success in SMA projects in government. The contribution is an improved understanding of the adoption of SMA for CzRM and can lead to effective monitoring of social media posts by citizens to improve service delivery and, hence, lead to more citizencentric government. [ABSTRACT FROM AUTHOR]

Industry 4.0, initiated by Germany in 2013, focuses on digitization and process optimization in manufacturing. China's 'Made in China 2025' plan, aligned with Industry 4.0, was introduced in 2015. Motors are pivotal in industrial manufacturing, necessitating robust quality control. To enable real-time adjustments and enhance product quality, we propose an Online Quality Feedback (OQF) system using MySQL and big data. Implemented in H company's motor production line, this system gathers real-time data from sensor-equipped stations, processes it through MySQL and big data analysis, and presents outcomes on a web interface. This empowers process and equipment engineers with concise insights for effective decisionmaking, ultimately improving the manufacturing process. Results affirm the effectiveness of our method in enhancing production management. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

All EU nations and the rest of the globe have been undergoing a massive digital transformation since Germany introduced and put into practice the Industry 4.0 concept in 2011. This entails not only boosting production by incorporating all current means of production, such as modern ICT solutions and robotics, but also introducing new expert profiles and ways of working. And, as that process became increasingly intensive, a new phrase, Industry 5.0, emerged in 2015. This concept focuses on the human potential as well as various IoT and Big Data applications to enhance human job and talent. Its objective is to connect individuals who work with robots in automated industrial settings. It is obvious that protecting worker safety and wellbeing at work entails not just maintaining physical health but also maintaining mental health, autonomy, dignity, privacy, and inclusion. Thanks to new technologies and the concept of Industry 5.0, we can have safer and more productive workplaces where the skills and creativity of workers come to the fore. [ABSTRACT FROM AUTHOR]

While handball is characterized by repeated sprints and changes of direction, traditional player load models do not consider accelerations and decelerations. The aim of this study was to analyze the differences between metabolic power and speed zones for player load assessment with regard to the player role. Position data from 330 male individuals during 77 games from the 2019/20 German Men's Handball-Bundesliga (HBL) were analyzed, resulting in 2233 individual observations. Players were categorized into wings, backs and pivots. Distance covered in different speed zones, metabolic power, metabolic work, equivalent distance (metabolic work divided by energy cost of running), time spend running, energy spend running, and time over 10 and 20 W were calculated. A 2-by-3 mixed ANOVA was calculated to investigate differences and interactions between groups and player load models. Results showed that total distance was longest in wings (3568 ± 1459 m in 42 ± 17 min), followed by backs (2462 ± 1145 m in 29 ± 14 min), and pivots (2445 ± 1052 m in 30 ± 13 min). Equivalent distance was greatest in wings (4072.50 ± 1644.83 m), followed by backs (2765.23 ± 1252.44 m), and pivots (2697.98 ± 1153.16 m). Distance covered and equivalent distance showed moderate to large interaction effects between wings and backs (p < .01, ES = 0.73) and between wings and pivots (p < .01, ES = 0.86) and a small interaction effect between backs and pivots (p < .01, ES = 0.22). The results underline the need for individualized management of training loads and the potential of using information about locomotive accelerations and decelerations to obtain more precise descriptions of player load during handball game performance at the highest level of competition. Future studies should investigate the influence of physical performance on smaller match sequences, like ball possession phases. [ABSTRACT FROM AUTHOR]

We demonstrate how machine learning can be easily applied to support the analysis of large amounts of OH* airglow imager data. We use a TCN (temporal convolutional network) classification algorithm to automatically pre-sort images into the three categories "dynamic" (images where small-scale motions like turbulence are likely to be found), "calm" (clear-sky images with weak airglow variations) and "cloudy" (cloudy images where no airglow analyses can be performed). The proposed approach is demonstrated using image data of FAIM 3 (Fast Airglow IMager), acquired at Oberpfaffenhofen, Germany between 11 June 2019 and 25 February 2020, achieving a mean average precision of 0.82 in image classification. The attached video sequence demonstrates the classification abilities of the learned TCN. Within the "dynamic" category, we find a subset of 13 episodes of image series showing turbulence. As FAIM 3 exhibits a high spatial (23 m pixel-1) and temporal (2.8 s per image) resolution, turbulence parameters can be derived to estimate the energy diffusion rate. Similar to the results the authors found for another FAIM station (Sedlak et al., 2021), the values of energy dissipation rate range from 0.03 to 3.18 W kg-1. [ABSTRACT FROM AUTHOR]

Data security has become an increasingly important concern for organizations and individuals in the digital age. To address this issue, there has been growing interest in using bio-commercial technology and artificial intelligence (AI) to develop innovative security solutions. This paper presents a research study on the development of a data processing secure new entrants in the market for medical devices in Germany can use the use of biometric data, such as fingerprints, iris scans, and voice recognition, in combination with AI algorithms to enhance the security of data processing. The paper provides an overview of the technical framework and architecture of the proposed system, and evaluates its effectiveness in comparison to traditional security measures. The study uses a combination of quantitative and qualitative research methods, including surveys, interviews, and simulations, to collect and analyze data on the performance, accuracy, and usability of the proposed system. The results suggest that the data processing security system based on bio-commercial technology and AI is highly effective in preventing unauthorized access and enhancing data protection. The system provides a high level of accuracy, speed, and user-friendliness, making it a viable alternative to traditional security measures. The paper concludes with a discussion of the implications of the study for the field of data security and future research directions. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100–8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient ($R^{2}$) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA). [ABSTRACT FROM AUTHOR]

In the wake of the digital revolution and connected technologies, societies store an ever-increasing amount of data on humans, their preferences, and behavior. These modern technologies create a trust challenge, insofar as individuals have to trust data collectors such as private organizations, government institutions, and researchers that their data is not misused. Privacy regulations should increase trust because they provide laws that increase transparency and allow for punishment in cases in which the trustee violates trust. The introduction of the General Data Protection Regulation (GDPR) in May 2018 – a wide-reaching regulation in EU law on data protection and privacy that covers millions of individuals in Europe – provides a unique setting to study the impact of privacy regulation on trust in data collectors. We collected survey panel data in Germany around the implementation date and ran a survey experiment with a GDPR information treatment. Our observational and experimental evidence does not support the hypothesis that the GDPR has positively affected trust. This finding and our discussion of the underlying reasons are relevant for the wider research field of trust, privacy, and big data. [ABSTRACT FROM AUTHOR]

As the world has more urbanized, cities need to assess and manage their building energy performances in order to achieve energy-reduction goals. The urban digital twins (UDT) offer promising solutions to this demand by providing valuable insights with qualitative and quantitative information about the building environment. The urban building energy data is not only measured from the equipped sensor devices but can also be simulated based on the analysis software. In this research, we aim to explore the development of the spatial data infrastructure (SDI) for managing building energy in the UDT application by employing the Open Geospatial Consortium (OGC) standards which increases the data usability and efficiency. In our concept SDI, the big data in the UDT application is managed with the OGC specifications as follows: OGC SensorThings API (STA) for data with Spatio-temporal characteristics, OGC API 3D GeoVolumes for 3D geospatial content delivery, OGC CityGML for virtual 3D city models, OGC API Features, Web Feature Service (WFS), and Web Map Service (WMS) for other 2D geospatial contents. This concept enables broad interoperability between multiple data layers and client applications. As a proof of concept, we developed the UDT application called with a highly visual and intuitive user interface using the proposed SDI concept as a part of the EnSysLE project in the study area of three regions in Germany: Ludwigsburg, Dithmarschen, and Ilm-Kreis. The proposed concept can be expanded to other UDT domains and on a larger scale in future work. [ABSTRACT FROM AUTHOR]

The determination of odour in ambient air by field inspections using the grid method is a method that has been used in Germany for decades to determine the frequency of exposure to odours in an impact area. The method which is currently described in EN 16841-1:2016 is a statistical survey which is applied over a sufficiently long period of time. It delivers a representative map of the exposure to recognizable odour(s) with its distribution over the assessed area. Within this paper the principle of measurement will be briefly described, as well as the challenges that might occur during a project and the possibilities that arise from the detailed measurement of odour frequencies in the impact area. Often the question arises whether the sample of 104 single measurements per assessment square and year is sufficient. Olfasense GmbH has been carrying out yearly field surveys in the vicinity of an industrial plant since 2009. In contrast to the procedure described in EN 16841-1:2016-11, the field surveys take place on a daily basis in this project. This results in 365 single measurements per assessment square and year. The time of day is varied according to the standard. This extensive data set including 365 single measurements is used as reference survey. Subsequently, the same data was randomly divided into 3 individual data sets (sample surveys 1 to 3), each including 104 single measurements. For each data set the results including the measurement uncertainty according to EN 16841-1:2016 are evaluated for the single assessment squares. With a comparison of the reference survey (365 measurements/year) with each of the sample surveys (104 measurements/year) the reliability of each sample survey can be determined. As the sample surveys represent the required scope of measurement according to the European Standard EN 16841-1:2016 a validation of the usual extent could be performed. The results of this evaluation show that 104 single measurements per assessment square within one year, as reflected in the sample surveys, are sufficient to make a statement on the compliance with the permissible odour exposure in the impact area, taking into account the measurement uncertainty contributions. [ABSTRACT FROM AUTHOR]

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.

Over the last decade, the exponential growth in patient data volume and velocity has transformed it into a valuable resource for researchers. Yet, accessing comprehensive, unique patient data sets remains a challenge, particularly when individuals have received treatments across various practices and hospitals. Traditional record linkage methods fall short in adequately protecting patient privacy in these scenarios. Privacy Preserving Record Linkage (PPRL) offers a solution, employing techniques such as data cryptographic methods to identify common patients occurring in multiple datasets, while maintaining the privacy of other patients. This paper proposes an investigation into combined approaches of two common German PPRL tools, namely E-PIX and MainSEL. Each tool, while aiming for 'privacy preservation', employs distinct methods that offer unique advantages and drawbacks. Our research aims to explore these in a combined approach to leverage their respective strengths and mitigate their limitations. We anticipate that this synergistic approach will not only enhance data privacy but also allow for easier synchronisation of research data. This study is particularly pertinent in light of evolving privacy regulations and the increasing complexity of healthcare data management. By advancing PPRL methodologies, we aim to contribute to more robust, privacy-compliant data analysis practices in healthcare research.

Copyright of Industrielle Beziehungen is the property of Rainer Hampp Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Sociologia e Política is the property of Revista de Sociologia e Politica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. "Big Data" enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht./The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Intensive care units provide a data-rich environment with the potential to generate datasets in the realm of big data, which could be utilized to train powerful machine learning (ML) models. However, the currently available datasets are too small and exhibit too little diversity due to their limitation to individual hospitals. This lack of extensive and varied datasets is a primary reason for the limited generalizability and resulting low clinical utility of current ML models. Often, these models are based on data from single centers and suffer from poor external validity. There is an urgent need for the development of large-scale, multicentric, and multinational datasets. Ensuring data protection and minimizing re-identification risks pose central challenges in this process. The "Amsterdam University Medical Center database (AmsterdamUMCdb)" and the "Salzburg Intensive Care database (SICdb)" demonstrate that open access datasets are possible in Europe while complying with the data protection regulations of the General Data Protection Regulation (GDPR). Another challenge in building intensive care datasets is the absence of semantic definitions in the source data and the heterogeneity of data formats. Establishing binding industry standards for the semantic definition is crucial to ensure seamless semantic interoperability between datasets., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Christensen et al. criticized the application of Beals' index of sociological favourability to adjust for incomplete species lists when comparing repeated surveys. Their main argument was that using Beals' conditional occurrence probabilities would systematically underestimate biodiversity change compared to using observed frequencies. Although this might be the case for rare species, as we explicitly stated in our original publication, we here use a worked‐out example to show that this criticism is unjustified for species that are sufficiently represented in the reference data set. In our opinion, the misconception derives from ignoring one of the key requirements for applying Beal's index, which is the use of a sufficiently large reference data set to derive a reliable co‐occurrence matrix. We here show how the predicted probability for the occurrence of a species depends on the size of the reference data set and give recommendations on the premises for applying Beals' approach for monitoring purposes. [ABSTRACT FROM AUTHOR]

Objective: The frequency of aneurysmal subarachnoid hemorrhage (aSAH) presents complex fluctuations that have been attributed to weather and climate changes in the past. In the present long-term big data and deep learning analysis, we have addressed this long-held myth. Methods: Bleeding dates and basic demographic data for all consecutive patients (n = 1,271) admitted to our vascular center for treatment of aSAH between January 2003 and May 2020 (6,334 days) were collected from our continuously maintained database. The meteorological data of the local weather station, including 13 different weather and climate parameters, were retrieved from Germany's National Meteorological Service for the same period. Six different deep learning models were programmed using the Keras framework and were trained for aSAH event prediction with meteorological data from January 2003 to June 2017, with 10% of this dataset applied for data validation and model improvement. The dataset from July 2017 to May 2020 was tested for aSAH event prediction accuracy for all six models using the area under the receiver operating characteristic curve (AUROC) as the metric. Results: The study group comprised of 422 (33.2%) male and 849 (66.8%) female patients with an average age of 55 ± 14 years. None of the models showed an AUROC larger than 60.2. From the presented data, the influence of weather and climate on the occurrence of aSAH events is extremely unlikely. Conclusion: The myth of special weather conditions influencing the frequency of aSAH is disenchanted by this long-term big data and deep learning analysis. [ABSTRACT FROM AUTHOR]

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning. Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes, a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access. Concepts need to serve the evaluation of policy objectives, for example in regional or local area plans. In this study, we, therefore, extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns. To accomplish this, we link mobile phone records with urban classifications and transport network data, using both visual and computational approaches to mine the data. The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany, testing hypotheses of transit-oriented regional development, as well as testing for congestion risks in the transport network. The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data. [ABSTRACT FROM AUTHOR]

Movie exhibitors are faced with a challenging environment due to a trend of declining numbers of moviegoers in Germany. In the short term, they must therefore focus on the maximisation of concession profits with efficient personnel deployment to compensate for the declining box office gross. This paper attempts to create a more efficient personnel deployment plan in comparison to the status quo and to improve the decision-making basis of movie exhibitors regarding their personnel deployment. The data set on which this paper is based comprises selected figures of a German multiplex cinema over a period of 15 months. This includes the daily moviegoers and the corresponding work hours of the personnel. Based on these figures, the productivity per work hour is analysed to determine how the personnel deployment is calculated depending on the forecasted number of moviegoers. An analysis of variance is prepared to establish how profits at the concession can be maximised with an efficient personnel deployment. This paper presents a profit-maximising deployment of checkout and concession personnel depending on the forecasted number of moviegoers, which provides the possibility of reacting to a deviating number of moviegoers at short notice. [ABSTRACT FROM AUTHOR]

Multi-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation output, or statistics "datacubes". As classic database technology does not support arrays adequately, such data today are maintained mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for flexible ad-hoc analytics on large n-D arrays, similar to what SQL offers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison differentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the field; for example, subsetting tests were designed in a way that systems cannot be tuned to specifically these queries. It is hoped that this gives a representative overview to all who want to immerse into the field as well as a clear guidance to those who need to choose the best suited datacube tool for their application. This article presents results of the Research Data Alliance (RDA) Array Database Assessment Working Group (ADA:WG), a subgroup of the Big Data Interest Group. It has elicited the state of the art in Array Databases, technically supported by IEEE GRSS and CODATA Germany, to answer the question: how can data scientists and engineers benefit from Array Database technology? As it turns out, Array Databases can offer significant advantages in terms of flexibility, functionality, extensibility, as well as performance and scalability—in total, the database approach of offering "datacubes" analysis-ready heralds a new level of service quality. Investigation shows that there is a lively ecosystem of technology with increasing uptake, and proven array analytics standards are in place. Consequently, such approaches have to be considered a serious option for datacube services in science, engineering and beyond. Tools, though, vary greatly in functionality and performance as it turns out. [ABSTRACT FROM AUTHOR]

The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic - that is, artificial - data generated by AI can protect privacy.This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data., (© 2024. The Author(s).)

Education systems worldwide differ in their degree of eliteness. They range from being non‐elitist with dominant patterns of horizontal differentiation (e.g., Germany) to being strongly elitist and having established elite education sectors (e.g., France). Quantitatively, it remains relatively unclear how the eliteness of education systems can be assessed and evaluated. We identify several theoretical mechanisms that are assumed to increase or decrease vertical differentiation between education institutions. For the analyses, we use two large secondary datasets of student beginners' higher education choices in Germany and France and test different measures of inequality and concentration (Gini index, Duncan's D, inverse Pareto coefficient, percentile ratios) to describe the degree of eliteness. We estimated the shares of students from higher status groups (high achievers, upper‐tier service class academic milieu) in higher education institutions and fields of study and compared their distribution. Our findings indicate that analyses of percentiles, percentile ratios and inverse Pareto coefficient confirm an expected stronger concentration of high achieving students in the top of the distribution of higher education institutions and fields of study in France than in Germany. In comparison to students' achievement, social origin proved to be less important for country‐specific differences in concentration. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h -1 , more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study, we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference. This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever‐increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness of ML algorithms for inference remains elusive. In this study, we employ two popular ML algorithms, artificial neural networks and random forest, to analyze a large data set of flood events across Germany with the goals to analyze their predictive accuracy and their usability to provide insights to hydrologic system functioning. The results of the ML algorithms are contrasted against a parametric approach based on multiple linear regression. For analysis, we employ a model‐agnostic framework named Permuted Feature Importance to derive the influence of models' predictors. This allows us to compare the results of different algorithms for the first time in the context of hydrology. Our main findings are that (1) the ML models achieve higher prediction accuracy than linear regression, (2) the results reflect basic hydrological principles, but (3) further inference is hindered by the heterogeneity of results across algorithms. Thus, we conclude that the problem of equifinality as known from classical hydrological modeling also exists for ML and severely hampers its potential for inference. To account for the observed problems, we propose that when employing ML for inference, this should be made by using multiple algorithms and multiple methods, of which the latter should be embedded in a cross‐validation routine. Key Points: We investigate the use of machine learning methods for flood forecastingPredictive accuracy of ML methods is generally very highUsing ML methods for inference, however, is very elusive and potentially error prone [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4 x 105 trainable parameters. With a structure, inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time-series. We test the CNNs ability to generalize to CMLs and time periods outside the training data. Our CNN is trained on four months of data from 400 randomly selected CMLs and validated on two different months of data, once for all CMLs and once for the 3504 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set we use the gauge adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 87 % of all rainy and 91 % of all non-rainy periods. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall induced attenuation patterns in CML signal levels from a large CML data set covering entire Germany. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) provide an important frame of reference for the many organizations of the United Nations that utilize digital technologies, such as big data, blockchain, and artificial intelligence in their work. Flowever, UN organizations increasingly deal with the structural effects and systemic risks of digital technologies as the challenges they bear become more and more evident. Such a systemic approach to digital change should be strengthened within the UN to anticipate and better react to the current trajectories and unseen consequences of digitalization that could create and deepen inequalities and unsustainable development pathways. [ABSTRACT FROM AUTHOR]

Investment decisions in renewable energies are known to be influenced by many diverse drivers, e.g. social, political, geographic, economic and psychological. Non-comprehensive models are problematic since missed interactions might introduce bias. We implement a robust modelling approach by (1) using a large data set with 1.4 million solar installations and (2) three different machine learning algorithms (deep neural networks, gradient boosting, random forests). Generalized linear models serve as baseline and comparison. A high prediction accuracy can be achieved on the county level with deep neural networks (adjusted R2 = 0.86) and gradient boosting (adjusted R2 = 0.87). The most important drivers are population per county, followed by type of urbanisation and social variables like unemployment, with varying degree of importance for the different machine-learning algorithms. Our approach points out both differences and agreements across methods and therefore a higher confidence in their interpretation. • One of the first comprehensive models for investment decisions in photovoltaics. • Applies gradient boosting achieving very high explanatory power (adj. R2 = 0.87). • Provides robust estimates by using neural networks, random forests as comparison. • Most important drivers are demographic. [ABSTRACT FROM AUTHOR]

Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as “rewiring” coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case. [ABSTRACT FROM AUTHOR]

• Data-driven train delay forecasting using machine learning at scale. • Using operational data from Deutsche Bahn for 25,000 trains daily. • Online method combines machine learning and simulation. • Key pre-processing steps handled automatically. A large-scale ensemble prediction model to predict train delays is presented. The ensemble model uses a disparate set of models, two statistical and one simulation-based to generate forecasts of train delays. The first statistical model is a context-aware random forest that accounts for network traffic states, such as likely stretch conflicts and current headway's, exogenous weather, event, and work zone information. The second model is a kernel regression that captures train-specific dynamics. A mesoscopic simulation model that accounts for travel and dwell time variations as well as inferred track occupation conflicts, train connections and rolling stock rotations, is additionally considered. The models have been used in a proof of concept to forecast delays for nationwide passenger services network of Deutsche Bahn, which operates roughly 25,000 trains daily in Germany. Results demonstrate a 25% improvement potential in forecast correctness (fraction of predictions within one minute) and 50% reduction in root mean squared errors compared to the published schedule. The paper describes the models along with the big data challenges that were addressed in data storage, feature and model building, and computation. [ABSTRACT FROM AUTHOR]

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The new German minimum wage applies a specific exemption clause for internships, where internships that last up to three months are exempted while internships that exceed three months are due to the minimum wage. Negative minimum wage effects on internships are heavily debated as internships are mostly non-productive. Difference-in-difference analyses that exploit establishment and regional variation in the bite of the minimum wage do not show a reduction in the number of internships. In addition, we pursue an innovative approach by using Google search data to analyse how the search intensity for internships changed in course of the minimum wage introduction. Difference-in-difference comparisons with other countries in Europe do not reveal an effect on the search for internship positions in general, but we observe a significant reduction in Google search for 'generation internship'. This suggests that the underlying societal phenomenon of a generation entering internships without a perspective for regular jobs has lost in relevance. [ABSTRACT FROM AUTHOR]

This study pioneers the quantitative research of second generation Iranian graduates in Germany. Second generation immigrants in Germany, immigrants raised in Germany with at least one parent born abroad, are relevant as they are a crucial part of German society. Second generation immigrants in general as well as Iranians in particular are assumed to have high extrinsic aspirations. But a dataset big enough to study the employment situation of second generation Iranian graduates in Germany did not exist. Therefore, the article uses the KOAB dataset from 2011 to 2015, which contains data from 53,429 full-time employed higher education graduates in Germany (among those are 161 second generation Iranians). Their extrinsic job aspirations and career success (income, job satisfaction) are analyzed. Multivariate analyses reveal the higher extrinsic job aspirations of second generation Iranian graduates compared to many other migrant groups and Germans but similar to second generation Turkish graduates. Nevertheless, the extrinsic job aspiration is relevant, though only one factor among others to predict career success. [ABSTRACT FROM AUTHOR]

The self-government of physicians in Germany is an enormous privilege for the profession. The core tasks of medical associations are the design of the professional framework, specialist and continuing education, and quality assurance. A look at history reveals important developments within the profession and its changing relationships with politics and also the different systems of government and constantly changing professional policy orientations, which are continuously changing and must be permanently shaped by the medical profession. In particular, the relationship to health insurance companies, to the economy, and to politics should be mentioned here. What is new, on the other hand, are the changing expectations in the medical profession, the shortage of skilled workers, management and care structures, and new types of ownership, for example, in centers providing medical care. The basic ethical code of physicians-scientific knowledge, experience, personal attitude, and human compassion-continue to be of utmost importance. Taking into account the rapid developments within modern medicine and the increasing expectations of society, additional qualifications that go beyond the historically traditional characteristics of the "good physician" are needed today and in the future. These new demands complement and deepen the relationship between patients, society, and the medical profession. In order to practice personalized medicine, the profession needs to be free from all sociopolitical directives., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Recent shifts in technology have created a data-rich environment and made it necessary for firms to develop new capabilities to cope with these changes. To address this challenge, this study introduces marketing-enabled data analytics capability, a specific type of information technology (IT) capability that enables firms to better understand customer needs and achieve a competitive advantage in the digital era. Using empirical results collected via online survey, we argue that marketing-enabled data analytics capability, which comprises data analytics infrastructure, marketing-oriented analytics expertise, and IT-marketing social capital, positively influences a firm's organisational agility and innovation success. Moreover, the results show that organisational agility partially mediates the relationship between marketing-enabled data analytics capability and innovation success. By developing the construct of marketing-enabled data analytics capability, this paper lays a foundation for future research on this new type of IT capability, which is critical in the digitization process. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to examine the market liquidity (time-on-market (TOM)) and its determinants, for rental dwellings in the largest seven German cities, with big data.Design/methodology/approach The determinants of TOM are estimated with the Cox proportional hazards model. Hedonic characteristics, as well as socioeconomic and spatial variables, are combined with different fixed effects and controls for non-linearity, so as to maximise the explanatory power of the model.Findings Higher asking rent and larger living space decrease the liquidity in all seven markets, while the age of a dwelling, the number of rooms and proximity to the city centre accelerate the letting process. For the other hedonic characteristics heterogeneous implications emerge.Practical implications The findings are of interest for institutional and private landlords, as well as governmental organisations in charge of housing and urban development.Originality/value This is the first paper to deal with the liquidity of rental dwellings in the seven most populated cities of Europe’s second largest rental market, by applying the Cox proportional hazards model with spatial gravity variables. Furthermore, the German rental market is of particular interest, as approximately 60 per cent of all rental dwellings are owned by private landlords and the German market is organised polycentrically. [ABSTRACT FROM AUTHOR]

This study explores the impact of digitalization on technological (product and process) innovations in German small and medium-sized enterprises (SMEs). In particular, three forms of digitalization are considered – digitalization in production and logistics, digital value chains, and big data analytics. Empirical analysis utilizes the Mannheim Innovation Panel (MIP) dataset of German SMEs operating in manufacturing and service sectors. A binary probit model is separately estimated for micro, small and medium-sized firms to test if theoretically assumed positive innovation effects of digitalization occur in SMEs of different size. Empirical findings confirm that the impact of digitalization on innovation activities is heterogenous among SMEs. Furthermore, the innovation effects are modest and depend on the form of digitalization and a type of innovation. The study also explores a moderating effect of internal R&D activities. The findings suggest that the engagement in internal R&D weakness the innovation effects of digitalization. More specifically, the impact of digitalization on product and process innovations is absent in those SMEs that conduct intramural R&D, while in non-R&D SMEs, this impact is positive. Based on these findings, the study offers some theoretical and policy implications. • Use a bivariate probit model to analyze the effects of digitalization on SME innovation. • Micro, small and medium firms are analyzed separately. • Innovation effects of digitalization depends on firm size, forms of digitalization and internal R&D. • Internal R&D has a moderating effect on product and process innovations. • Digitalization stimulates a launch of new products and process in non-R&D SMEs. [ABSTRACT FROM AUTHOR]

Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as “invisible colleges” or “groups of collaborators” as well as academic “stars” that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types including journals and chapters in edited volumes, we analyze the complete co-authorship network of all 1,339 researchers in German political science. Through the use of consensus graph clustering techniques and descriptive centrality measures, we identify the ten largest research clusters, their research topics, and the most central researchers who act as bridges and connect these clusters. We also aggregate the findings at the level of research organizations and consider the inter-university co-authorship network. The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology. A small set of well-connected universities takes leading roles in these informal research groups. [ABSTRACT FROM AUTHOR]