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Renewable energy production is one of the strongest rising markets and further extreme growth can be anticipated due to desire of increased sustainability in many parts of the world. With the rising adoption of renewable power production, such facilities are increasingly attractive targets for cyber attacks. At the same time higher requirements on a reliable production are raised. In this paper we propose a concept that improves monitoring of renewable power plants by detecting anomalous behavior. The system does not only detect an anomaly, it also provides reasoning for the anomaly based on a specific mathematical model of the expected behavior by giving detailed information about various influential factors causing the alert. The set of influential factors can be configured into the system before learning normal behaviour. The concept is based on multidimensional analysis and has been implemented and successfully evaluated on actual data from different providers of wind power plants.
Integrated Risk and Opportunity Management (IROM) goes far beyond what is found in organizations today. However, it offers the best opportunity not only to keep pace with the VUCA world, but to actually profit from it. Accordingly, the introduction of opportunity-based thinking in addition to risk-based thinking is part of the design specification for ISO 9000 and ISO 9001. The prerequisite for the successful design of an IROM is the individual definition, control and integration of risk and opportunity management processes, considering eight success factors, the "8 C". Top management benefits directly from the result: better, coordinated decision memos enable faster and more appropriate decisions.
Autonomous and integrated passenger and freight transport (APFIT) is a promising approach to tackle both, traffic and last-mile-related issues such as environmental emissions, social and spatial conflicts or operational inefficiencies. By conducting an agent-based simulation, we shed light on this widely unexplored research topic and provide first indications regarding influential target figures of such a system in the rural area of Sarstedt, Germany. Our results show that larger fleets entail inefficiencies due to suboptimal utilization of monetary and material resources and increase traffic volume while higher amounts of unused vehicles may exacerbate spatial conflicts. Nevertheless, to fit the given demand within our study area, a comparatively large fleet of about 25 vehicles is necessary to provide reliable service, assuming maximum passenger waiting times of six minutes to the expense of higher standby times, rebalancing effort, and higher costs for vehicle acquisition and maintenance.
Pathologists need to identify abnormal changes in tissue. With the developing digitalization, the used tissue slides are stored digitally. This enables pathologists to annotate the region of interest with the support of software tools. PathoLearn is a web-based learning platform explicitly developed for the teacher-student scenario, where the goal is that students learn to identify potential abnormal changes. Artificial intelligence (AI) and machine learning (ML) have become very important in medicine. Many health sectors already utilize AI and ML. This will only increase in the future, also in the field of pathology. Therefore, it is important to teach students the fundamentals and concepts of AI and ML early in their studies. Additionally, creating and training AI generally requires knowledge of programming and technical details. This thesis evaluates how this boundary can be overcome by comparing existing end-to-end AI platforms and teaching tools for AI. It was shown that a visual programming editor offers a fitting abstraction for creating neural networks without programming. This was extended with real-time collaboration to enable students to work in groups. Additionally, an automatic training feature was implemented, removing the necessity to know technical details about training neural networks.
On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
We present an approach towards a data acquisition system for digital twins that uses a 5G net- work for data transmission and localization. The current hardware setup, which utilizes stereo vision and LiDAR for 3D mapping, is explained together with two recorded point cloud data sets. Furthermore, a resulting digital twin comprised of voxelized point cloud data is shown. Ideas for future applications and challenges regarding the system are discussed and an outlook on further development is given.
In this paper we describe methods to approximate functions and differential operators on adaptive sparse (dyadic) grids. We distinguish between several representations of a function on the sparse grid and we describe how finite difference (FD) operators can be applied to these representations. For general variable coefficient equations on sparse grids, genuine finite element (FE) discretizations are not feasible and FD operators allow an easier operator evaluation than the adapted FE operators. However, the structure of the FD operators is complex. With the aim to construct an efficient multigrid procedure, we analyze the structure of the discrete Laplacian in its hierarchical representation and show the relation between the full and the sparse grid case. The rather complex relations, that are expressed by scaling matrices for each separate coordinate direction, make us doubt about the possibility of constructing efficient preconditioners that show spectral equivalence. Hence, we question the possibility of constructing a natural multigrid algorithm with optimal O(N) efficiency. We conjecture that for the efficient solution of a general class of adaptive grid problems it is better to accept an additional condition for the dyadic grids (condition L) and to apply adaptive hp-discretization.
The paper presents a comprehensive model of a banking system that integrates network effects, bankruptcy costs, fire sales, and cross-holdings. For the integrated financial market we prove the existence of a price-payment equilibrium and design an algorithm for the computation of the greatest and the least equilibrium. The number of defaults corresponding to the greatest price-payment equilibrium is analyzed in several comparative case studies. These illustrate the individual and joint impact of interbank liabilities, bankruptcy costs, fire sales and cross-holdings on systemic risk. We study policy implications and regulatory instruments, including central bank guarantees and quantitative easing, the significance of last wills of financial institutions, and capital requirements.
Background:
Many patients with cardiovascular disease also show a high comorbidity of mental disorders, especially such as anxiety and depression. This is, in turn, associated with a decrease in the quality of life. Psychocardiological treatment options are currently limited. Hence, there is a need for novel and accessible psychological help. Recently, we demonstrated that a brief face-to-face metacognitive therapy (MCT) based intervention is promising in treating anxiety and depression. Here, we aim to translate the face-to-face approach into digital application and explore the feasibility of this approach.
Methods:
We translated a validated brief psychocardiological intervention into a novel non-blended web app. The data of 18 patients suffering from various cardiac conditions but without diagnosed mental illness were analyzed after using the web app over a two-week period in a feasibility trial. The aim was whether a nonblended web app based MCT approach is feasible in the group of cardiovascular patients with cardiovascular disease.
Results:
Overall, patients were able to use the web app and rated it as satisfactory and beneficial. In addition, there was first indication that using the app improved the cardiac patients’ subjectively perceived health and reduced their anxiety. Therefore, the approach seems feasible for a future randomized controlled trial.
Conclusion:
Applying a metacognitive-based brief intervention via a nonblended web app seems to show good acceptance and feasibility in a small target group of patients with CVD. Future studies should further develop, improve and validate digital psychotherapy approaches, especially in patient groups with a lack of access to standard psychotherapeutic care.
In the last years generative models have gained large public attention due to their high level of quality in generated images. In short, generative models learn a distribution from a finite number of samples and are able then to generate infinite other samples. This can be applied to image data. In the past generative models have not been able to generate realistic images, but nowadays the results are almost indistinguishable from real images.
This work provides a comparative study of three generative models: Variational Autoencoder (VAE), Generative Adversarial Network (GAN) and Diffusion Models (DM). The goal is not to provide a definitive ranking indicating which one of them is the best, but to qualitatively and where possible quantitively decide which model is good with respect to a given criterion. Such criteria include realism, generalization and diversity, sampling, training difficulty, parameter efficiency, interpolating and inpainting capabilities, semantic editing as well as implementation difficulty. After a brief introduction of how each model works on the inside, they are compared against each other. The provided images help to see the differences among the models with respect to each criterion.
To give a short outlook on the results of the comparison of the three models, DMs generate most realistic images. They seem to generalize best and have a high variation among the generated images. However, they are based on an iterative process, which makes them the slowest of the three models in terms of sample generation time. On the other hand, GANs and VAEs generate their samples using one single forward-pass. The images generated by GANs are comparable to the DM and the images from VAEs are blurry, which makes them less desirable in comparison to GANs or DMs. However, both the VAE and the GAN, stand out from the DMs with respect to the interpolations and semantic editing, as they have a latent space, which makes space-walks possible and the changes are not as chaotic as in the case of DMs. Furthermore, concept-vectors can be found, which transform a given image along a given feature while leaving other features and structures mostly unchanged, which is difficult to archive with DMs.