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Purpose: Hospital information systems (HIS) play a critical role in modern healthcare by facilitating the management and delivery of patient care services. We aimed to evaluate the current landscape of HIS in the specialty of gynecology and obstetrics in Germany. Methods: An anonymous questionnaire was distributed via the German Society of Gynecology and Obstetrics newsletter in December 2022. The questionnaire covered the domains baseline demographic information, satisfaction with daily use, satisfaction with implementation, and degree of digitization. Results: Ninety-one participants completed the survey. Median age was 34 years; 67.4% (60 of 89) were female, and 32.6% (29 of 89) were male. Of the survey participants, 47.7% (42 of 88) were residents, 26.1% (23 of 91) senior physicians, and 9.1% (8 of 88) medical directors. The degree of digitization of clinical documentation is mainly mixed digital and paper-based (64.0%, 57 of 89) while 16.9% (15 of 89) operate mainly paper-based. The current HIS has been in use on average for 9 years. The median number of different software systems used in daily routine is 4. About 33.7% (30 of 89) would likely or very likely recommend their current HIS to a colleague. Conclusions: The current landscape of HIS in gynecology and obstetrics in Germany is characterized by a high heterogeneity of systems with low interoperability and long service life; thus, many healthcare professionals are not satisfied. There is both a need to enhance and an interest in modernizing the technological infrastructure to meet today's requirements for patient care. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. Methods: We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results: Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion: Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care. [ABSTRACT FROM AUTHOR]

Fourth Industrial Revolution (4IR) technologies have elevated the capabilities and possibilities of improvement and efficiency in the energy sector. This paper interrogates how energy companies in South Africa, Germany and China apply 4IR technologies. A total of 26 energy companies in those countries were surveyed. An analysis was carried out using the Cronbach Alpha, Kruskal-Wallis and Mann-Whitney tests. Survey results indicate that 85% of companies acknowledge good levels of participation in the 4IR, and were clear about which 4IR technologies are important, although few companies develop these themselves. Technologies enabling access to big, real-time data (BRTD) and BRTD analysis software, are valued the most in measured importance, efficiency, reliability and ability to be integrated across the energy system. The transfer of data using the Internet of things ranked highly as a 4IR technology, whereas artificial intelligence, robotics and machine-human integration (also referred to as machine-human interaction) are considered less important, efficient, and reliable. China rates 4IR technologies as more important than South Africa and Germany do. For South Africa to be competitive in the global energy sector it needs to engage with and embrace 4IR technologies to a greater extent. [ABSTRACT FROM AUTHOR]

The issue of the "Mitteilungen der DMV" (Announcements of the German Mathematical Society) from March 1, 2023, contains various articles on mathematical topics. One article by Jessica Wynne deals with the beauty of mathematical chalkboard images. The president of the DMV, Joachim Escher, gives a greeting. Other articles discuss topics such as the Tilings Encyclopedia, the development of ChatGPT in AI research, and mathematics in the digital society. There is also an article on the geometry of negotiation tables and a contribution on life and work in Germany from an external perspective. The position paper by DMV, GDM, and MNU on combating the shortage of mathematics teachers is also presented. The issue concludes with information about new members, appointments, promotions, and the imprint. [Extracted from the article]

Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the "supply and demand ratio" (SDR) and "mid-market rate pricing" (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model's initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility. [ABSTRACT FROM AUTHOR]

Meeting the energy goals of the European Union requires new ways of managing energy. Decentralized energy management, cross-commodity energy production and usage optimization are promising means. Future neighbourhoods will include multiple forms of energy such as electricity, heat, and cooling. According to our vision, the smart neighbourhoods, can optimize energy across different vectors by sharing resources in a controlled way. The first contribution of this paper is a comprehensive review on market, regulatory and technological status to support the transition towards distributed cross-commodity energy management with focus on Germany and Finland. Markets disruption could lead to more decentralized structures. Mechanisms therefore have been proposed, but mostly without sector integration. Clean Energy Package includes legislation that is supportive towards cross-commodity energy sharing solutions. Corresponding implementation in Germany and Finland may be successful in both countries even though it differs. Regarding technology, the article identifies required technical components (IoT, AI and blockchain) focusing on their support decentralized and cross-sector energy systems. Key components of IoT are wireless communication and interoperability middleware. AI provides key technologies for modelling and optimization of sector-integrated and distributed energy systems. Even if there has been lots of research, practical implementations are still lacking. Furthermore, many technical challenges still exist for blockchain based solutions in decentralized energy systems. Moreover, organizational, and legal responsibilities need to be clarified to support the adoption of blockchains in decentralized energy systems. Finally, the article gives recommendations regarding an increasing implementation of cross-commodity sharing and how it can contribute to the energy transition. • Giving an overview of cross-commodity energy sharing in Finland and Germany. • It is shown how recent energy market development support energy transition. • Comparing the regulatory framework for smart components in energy markets. • Combination of IoT, AI and Blockchain to enable decentralized energy markets. [ABSTRACT FROM AUTHOR]

Industry 4.0, a term invented by Wolfgang Wahlster in Germany, is celebrating its 10th anniversary in 2021. Still, the digitalization of the production environment is one of the hottest topics in the computer science departments at universities and companies. Optimization of production processes or redefinition of the production concepts is meaningful in light of the current industrial and research agendas. Both the mentioned optimization and redefinition are considered in numerous subtopics and technologies. One of the most significant topics in these areas is the newest findings and applications of artificial intelligence (AI)—machine learning (ML) and deep convolutional neural networks (DCNNs). The authors invented a method and device that supports the wiring assembly in the control cabinet production process, namely, the Wire Label Reader (WLR) industrial system. The implementation of this device was a big technical challenge. It required very advanced IT technologies, ML, image recognition, and DCNN as well. This paper focuses on an in-depth description of the underlying methodology of this device, its construction, and foremostly, the assembly industrial processes, through which this device is implemented. It was significant for the authors to validate the usability of the device within mentioned production processes and to express both advantages and challenges connected to such assembly process development. The authors noted that in-depth studies connected to the effects of AI applications in the presented area are sparse. Further, the idea of the WLR device is presented while also including results of DCNN training (with recognition results of 99.7% although challenging conditions), the device implementation in the wire assembly production process, and its users' opinions. The authors have analyzed how the WLR affects assembly process time and energy consumption, and accordingly, the advantages and challenges of the device. Among the most impressive results of the WLR implementation in the assembly process one can be mentioned—the device ensures significant process time reduction regardless of the number of characters printed on a wire. [ABSTRACT FROM AUTHOR]