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Autonomous surface vessels are a promising building block of the future’s transport sector and are investigated by research groups worldwide. This paper presents a comprehensive and systematic overview of the autonomous research vessel Solgenia including the latest investigations and recently presented methods that contributed to the fields of autonomous systems, applied numerical optimization, nonlinear model predictive control, multi-extended-object-tracking, computer vision, and collision avoidance. These are considered to be the main components of autonomous water taxi applications. Autonomous water taxis have the potential to transform the traffic in cities close to the water into a more efficient, sustainable, and flexible future state. Regarding this transformation, the test platform Solgenia offers an opportunity to gain new insights by investigating novel methods in real-world experiments. An established test platform will strongly reduce the effort required for real-world experiments in the future.
Numerous studies have demonstrated that energy demand in the building sector, particularly for heating, ventilation, and air conditioning systems, can be reduced by forecasting future indoor temperatures and applying targeted control strategies. Accurate indoor temperature forecasts depend on understanding random variables such as occupancy and the number of active electrical devices. However, detecting these random influences is challenging, leading existing methods to be overly specific, reliant on expensive sensors, and poorly generalizable across different buildings. Moreover, prevalent point forecasting methods fail to account for the uncertainty surrounding future outcomes. In this paper, we propose that instead of attempting to eliminate naturally occurring random disturbances, it is more effective to incorporate these uncertainties into the modeling process. We introduce a deep learning methodology for probabilistic forecasting that predicts future temperatures as a probability distribution, integrating the inherent randomness of the data without requiring direct measurements. The proposed model is based on normalizing flows with flexible Bernstein polynomials and is compared to a Gaussian baseline. This approach enables the estimation of complex distributions via the maximum likelihood principle, with only mild assumptions on its shape. Due to the lack of high-quality real-world data, we use simulated data from various rooms with differing characteristics and evaluate both models in terms of robustness and flexibility. Our results indicate that our model accurately predicts indoor temperature distributions and generalizes well to different and previously unseen rooms. The dataset and code are published along with this paper, to provide reproducible results and benchmark data to the community.
Infolge des Klimawandels werden Hitzeperioden häufiger und intensiver, was insbesondere in Städten zu einer Überwärmung des Straßenraums führt. Erhöhte Gesundheitsrisiken für vulnerable Gruppen sowie eine Minderung der Aufenthalts- und Lebensqualität sind die Folgen. Für die Stadtplanung ergibt sich die Notwendigkeit, dem Urban-Heat-Island-(UHI-)Effekt durch geeignete Klimaanpassungsmaßnahmen zu begegnen. Bisherigen Ansätzen zur Lokalisierung überwärmungsgefährdeter Bereiche fehlt oft die Detailtiefe, um einen direkten Straßenbezug herzustellen, Ursachen zu analysieren und geeignete Anpassungsmaßnahmen im Straßenraum abzuleiten. In diesem Beitrag wird daher ein Ansatz vorgestellt, der die Daten eines Mobile-Mapping-Systems nutzt, um UHI-Risikobereiche im städtischen Straßennetz präzise zu kartieren und zu bewerten. Das Bewertungskonzept ist so ausgelegt, dass gezielt Maßnahmen zur Verbesserung des Mikroklimas empfohlen werden können.
The class of square matrices of order nhaving a positive determinant and all their minors up to order n−1nonpositive is considered. A characterization of these matrices based on the Cauchon algorithm is presented, which provides an easy test for their recognition. Furthermore, it is shown that all matrices lying between two matrices of this class with respect to the checkerboard ordering are contained in this class, too. For both results, we require that the entry in the bottom-right position is negative.
With the help of a new, comprehensive sanctions database and utilizing the latest developments in the structural gravity literature, we estimate the effects of economic sanctions on international trade. We demonstrate that the average effects of sanctions hide significant heterogeneity depending on the type of sanctions considered, their duration, objectives and sender types. We also zoom in on the sanctions against Iran. We find that their effects are significant but also widely heterogeneous across sanctioning countries, even within the European Union, and depend on the direction of trade. We complement the aggregate analysis with estimates for 170 sectors, showing that sanctions have been effective in decreasing bilateral trade at the sectoral level while the effects vary significantly across sectors and across complete versus partial trade sanctions.
Form-finding is an essential task in the design of efficient lightweight structures. It is based on the crucial assumption of one single shape-determining load case, usually represented by self-weight. Adaptive components integrated into the structure open a way to even more efficient lightweight designs, as such structures can adapt their shapes to varying external loads and redistribute internal forces. This article presents a method for form-finding of adaptive truss structures subject to multiple, independently acting load cases, also incorporating possible design constraints. To ensure the consistency of the manufacturing lengths of passive elements in all load cases, special constraints are considered. The method enables to reduce sensitivity of the structural shape with respect to various different loads by means of actuation to meet design and serviceability requirements with a lower structural mass compared to conventional design strategies. This is demonstrated within a replaced real-world-like setting of an adaptive suspension truss bridge.
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow back end with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
Cities need to adapt to climate change in an increasingly rapid pace. Data and information on the existing and expected climate impact and the effectiveness of adaptive measures can support the planning and implementation of resilient urban planning. To inform urban climate change adaptation (CCA) in Germany a diverse landscape of climate services exists. However, the literature on usability gaps shows different barriers impeding the use potential of climate services. This study empirically analyzes the needs and barriers of municipal staff of different departments in Constance with regard to utilizing climate data and information. Surveying 72 and interviewing 10 municipal staffers, we found that climate data and information hold great potential for different public services but its handling poses many challenges. Furthermore, we found that a strategic approach mainstreaming climate data and information into cross-departmental work practices on urban CCA is crucial to anchor its usage in complex decision-making systems. The co-development of data-sensitive workflows, decision support tools, and capacity trainings can foster such integration. Based on the survey and interview results we designed a workflow on how to integrate such data and information strategically in municipal work processes.
Environmental negotiations are complex, and conveying the interaction between science and policy in traditional teaching methods is challenging. To address this issue, innovative educational approaches like serious gaming and role-playing games have emerged. These methods allow students to actively explore the roles of different stakeholders in environmental decision-making and weigh for instance between sometimes conflicting UN Sustainable Development Goals or other dilemmas. In this work the phosphorus negotiation game (P-Game) is for the first time introduced. We present the initial quantitative and qualitative findings derived from engaging 788 students at various academic levels (Bachelor, Master, PhD, and Postdoc) across three continents and spanning 22 different countries. Quantitative results indicate that female participants and MSc students benefitted the most significantly from the P-Game, with their self-reported knowledge about phosphorus science and negotiation science/practice increasing by 71–93% (overall), 86–100% (females), and 73–106% (MSc students in general). Qualitative findings reveal that the P-Game can be smoothly conducted with students from diverse educational and cultural backgrounds. Moreover, students highly value their participation in the P-Game, which can be completed in just 2–3 h. This game not only encourages active engagement among participants but also provides valuable insights into the complex environmental issues associated with global phosphorus production. We strongly believe that the underlying methodology described here could also be used for other topics.
The efficient and reliable operation of power grids is of great importance for ensuring a stable and uninterrupted supply of electricity. Traditional grid operation techniques have faced challenges due to the increasing integration of renewable energy sources and fluctuating demand patterns caused by the electrification of the heat and mobility sector. This paper presents a novel application of convolutional neural networks in grid operation, utilising their capabilities to recognise fault patterns and finding solutions. Different input data arrangements were investigated to reflect the relationships between neighbouring nodes as imposed by the grid topology. As disturbances we consider voltage deviations exceeding 3% of the nominal voltage or transformer and line overloads. To counteract, we use tab position changes of the transformer stations as well as remote controllable switches installed in the grid. The algorithms are trained and tested on a virtual grid based on real measurement data. Our models show excellent results with test accuracy of up to 99.06% in detecting disturbances in the grid and suggest a suitable solution without performing time-consuming load flow calculations. The proposed approach holds significant potential to address the challenges associated with modern grid operation, paving the way for more efficient and sustainable energy systems.
