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Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalised, plagued by delays, cost overruns and benefit shortfalls (Cantarelli et al. 2008; Flyvbjerg, 2007; Flyvbjerg et al., 2003; Flyvbjerg et al., 2004). The root cause is the prevailing fragmentation of the infrastructure sector (Fellows and Liu, 2012). To help overcome these challenges, integration of the value chain is needed. This could be achieved through a use-case-based creation of federated ecosystems connecting open and trusted data spaces and advanced services applied to infrastructure projects. Such digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. Digital federation enables secure and sovereign data exchange and thus collaboration across the silos within the infrastructure sector and between industries as well as within and between countries. Such an approach to infrastructure technology policy would not rely on technological solutionism but proposes the development of open and trusted data alliances. Federated data spaces provide access to the emerging data economy, especially for SMEs, and can foster the innovation of new digital services. Such responsible digital governance can help make the infrastructure sector more resilient, efficient and aligned with the realisation of ambitious decarbonisation and environmental protection targets. The European Union and the United States have already developed architectures for sovereign and secure data exchange.
Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.
Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient, and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalized, plagued by delays, cost overruns, and benefit shortfalls. The authors assessed trends and barriers in the planning and delivery of infrastructure based on secondary research, qualitative
interviews with internationally leading experts, and expert workshops. The analysis concludes that the root-cause of the industry’s problems is the prevailing fragmentation of the infrastructure value chain and a lacking long-term vision for infrastructure. To help overcome these challenges, an integration of the value chain is needed. The authors propose that this could be achieved through a use-case-based, as well as vision and governance-driven creation of federated digital platforms applied to infrastructure projects and outline a concept. Digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. This paper has contributed as policy recommendation to the Group of Twenty (G20) in 2021.
In Maun, Botswana, a self-sufficient, sustainable and future-oriented district will be created, the Maun Science Park. Within this project, several 5-8 storey smart homes shall be built in sustainable construction. The aim of this thesis is to develop a sustainable structural concept for those homes of the Maun Science Park. In a first step, the general basics for tall building structures and sustainable construction were established. Based on those fundamentals, criteria for the structural requirements, the ecological as well as the social sustainability of a structural design could be defined. Subsequently, four structural systems were drafted: a concrete core structure, a steel shear frame structure, a rammed earth shear wall structure and a wooden diagrid structure. In addition to the pre-dimensioning of the systems, a life cycle assessment was set up to evaluate the ecological sustainability of the designs. With the help of a utility value analysis, the wooden diagrid structure was determined as the preferred variant. The comparison of the designs also allows to draw general conclusions for the development of sustainable tall building structures. The results of the life cycle assessment show the advantage of wood as an ecological building material over industrially manufactured building materials, such as steel and concrete. Whereas rammed earth, a likewise ecological building material, is not convincing due to its low strength. In general, a balance is created in the life cycle assessment between ecological and industrially manufactured products in regard of strength and environmental impact. In terms of social sustainability, the design of the structure system can significantly influence the flexibility and use of local resources. However, due to the diversity of sustainable construction, the development of a structural system should be linked to an overarching sustainability concept that takes architecture and stakeholders into account.
A nonlinear mathematical model for the dynamics of permanent magnet synchronous machines with interior magnets is discussed. The model of the current dynamics captures saturation and dependency on the rotor angle. Based on the model, a flatness-based field-oriented closed-loop controller and a feed-forward compensation of torque ripples are derived. Effectiveness and robustness of the proposed algorithms are demonstrated by simulation results.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
With the increasing challenges of the 21st century, such as a rapidly growing population, increasing hunger and the destruction of the environment, the demand for sustainable and future-oriented ways of living is growing. To meet this demand, a residential district named Maun Science Park is being built in Botswana to develop a resilient society. In addition to the application of modern technology to optimise the use of resources, the environmentally friendly construction of the buildings is another goal of the project. This thesis investigates the prefabrication of rammed earth in terms of implementation and profitability for the Maun Science Park.
For this purpose, the specific properties, handling, as well as the application of the building material in prefabrication are first discussed.
This is followed by an investigation of how the work processes of prefabrication can be implemented in the Maun Science Park. Based on this, a profitability test is carried out using a break-even and sensitivity analysis.
The analyses showed that the investment in prefabrication is not profitable within the assumed production volume, which is due to the high fixed costs. These are primarily generated by the two main cost drivers, consisting of the new construction of the production hall and the rental of heavy construction equipment.
Lastly, recommendations for action were formulated that provide for a cost reduction in both the two main cost drivers as well as for other decisive factors.
Forecasting is crucial for both system planning and operations in the energy sector. With increasing penetration of renewable energy sources, increasing fluctuations in the power generation need to be taken into account. Probabilistic load forecasting is a young, but emerging research topic focusing on the prediction of future uncertainties. However, the majority of publications so far focus on techniques like quantile regression, ensemble, or scenario-based methods, which generate discrete quantiles or sets of possible load curves. The conditioned probability distribution remains unknown and can only be estimated when the output is post-processed using a statistical method like kernel density estimation.
Instead, the proposed probabilistic deep learning model uses a cascade of transformation functions, known as normalizing flow, to model the conditioned density function from a smart meter dataset containing electricity demand information for over 4,000 buildings in Ireland. Since the whole probability density function is tractable, the parameters of the model can be obtained by minimizing the negative loglikelihood through the state of the art gradient descent. This leads to the model with the best representation of the data distribution.
Two different deep learning models have been compared, a simple three-layer fully connected neural network and a more advanced convolutional neural network for sequential data processing inspired by the WaveNet architecture. These models have been used to parametrize three different probabilistic models, a simple normal distribution, a Gaussian mixture model, and the normalizing flow model. The prediction horizon is set to one day with a resolution of 30 minutes, hence the models predict 48 conditioned probability distributions.
The normalizing flow model outperforms the two other variants for both architectures and proves its ability to capture the complex structures and dependencies causing the variations in the data. Understanding the stochastic nature of the task in such detail makes the methodology applicable for other use cases apart from forecasting. It is shown how it can be used to detect anomalies in the power grid or generate synthetic scenarios for grid planning.
Botswana is a country in southern Africa with rich mineral resources, which has built its economy on mining. Due to challenges in the upcoming years caused by climate and demographic change, it aims to move away from a resource-based economy to a knowledge-based economy in the long term. In order to support the
process, the Maun Science Park, a centre for research and development is planned to be created in Maun, a town on the edge of the Okavango Delta. The project is initiated by the “International Resilience and Sustainability Partnership” (inRES), a non-governmental organization. The project is currently in the initiation phase.
The purpose of this thesis is to determine a cost framework with exemplary developer calculation and sensitivity analysis for the Maun Science Park Project in Botswana. Therefor, a source research was performed in a first step. Based on this, interviews were conducted with members of the inRES. Based on the data
obtained and further assumptions, a cost framework for the different project phases of the MSP project was established. Subsequently, a developer calculation
was exemplarily carried out on the basis of the project phase 2 and a sensitivity analysis was performed.
During the interviews, data was collected on the different project phases. It became clear that the interview partners had partly inconsistent perceptions
about different project phases. The calculation can be used as a basis for further calculation at the time of concretization of the planning data.
Modular arithmetic over integers is required for many cryptography systems. Montgomeryreduction is an efficient algorithm for the modulo reduction after a multiplication. Typically, Mont-gomery reduction is used for rings of ordinary integers. In contrast, we investigate the modularreduction over rings of Gaussian integers. Gaussian integers are complex numbers where the real andimaginary parts are integers. Rings over Gaussian integers are isomorphic to ordinary integer rings.In this work, we show that Montgomery reduction can be applied to Gaussian integer rings. Twoalgorithms for the precision reduction are presented. We demonstrate that the proposed Montgomeryreduction enables an efficient Gaussian integer arithmetic that is suitable for elliptic curve cryptogra-phy. In particular, we consider the elliptic curve point multiplication according to the randomizedinitial point method which is protected against side-channel attacks. The implementation of thisprotected point multiplication is significantly faster than comparable algorithms over ordinary primefields.