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A stereoselective synthesis of isoindolo[2,1-a]quinolin-11(5H)-ones containing three contiguous stereogenic centers is described. This Lewis-acid mediated reaction of enamides with N-aryl-acylimines affords the desired fused heterocyclic isoindolinones in high yields and diastereoselectivities. Scope and limitations of this method are discussed. The stereochemical outcome of this transformation indicates a stepwise reaction pathway.
The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures.
We compute three-dimensional displacement vector fields to estimate the deformation of microstructural data sets in mechanical tests. For this, we extend the well-known optical flow by Brox et al. to three dimensions, with special focus on the discretization of nonlinear terms. We evaluate our method first by synthetically deforming foams and comparing against this ground truth and second with data sets of samples that underwent real mechanical tests. Our results are compared to those from state-of-the-art algorithms in materials science and medical image registration. By a thorough evaluation, we show that our proposed method is able to resolve the displacement best among all chosen comparison methods.
Coastal port-industrial areas are becoming increasingly significant due to urban shrinkage, population
decline, and climate change. To address social and economic issues and enhance climate resilience, it
is crucial to anticipate urban shrinkage in both stable and growing coastal areas that are undergoing
economic transformation. Urban planning can better understand the dynamics of planning for urban
shrinkage and climate resilience, as port-industrial areas have a large economic impact on nearby
coastal communities.
This dissertation examines the long-term implications of urban shrinkage in coastal port-industrial
areas in the context of climate change and sea level rise in England. The research problem is that
current urban policy does not adequately address the challenges of urban shrinkage and climate
resilience in these areas. The research questions are: What are the population changes in local areas
in England? What effect does population decline have on changing urbanisation patterns in older
industrial areas? What type of adaptation efforts were made in North East Lincolnshire, England, and
Bremerhaven, Germany, in response to the 2013 tidal surge, and how did this affect urban
shrinkage?
The dissertation applies an integrated concept of Shrinkage-Resilience as a framework for analysis.
The methodology includes a review of existing models and frameworks, as well as case studies of
international and local contexts. The findings suggest that between 2013-2019, 68% of older
industrial areas (including coastal ports) in England are undergoing changing urbanisation patterns
relative to population, land use, and green belt areas, and are key areas for urban policy, such as the
Levelling Up agenda. One of the areas, North East Lincolnshire is discussed and compared to
Bremerhaven. These examples demonstrate the link between Shrinkage-Resilience approaches and
their practical implementation in coastal port-industrial areas affected by urban shrinkage.
This research advances the scientific practice of urban planning and policy-making for shrinking cities
by introducing the approach of Shrinkage-Resilience, which emphasises the importance of
considering long-term social, economic, and environmental impacts in urban shrinkage contexts. This
approach is crucial in the transition to a more sustainable and inclusive society, where the welfare of
present and future generations, the environment, and economic development are taken into
account. The dissertation provides recommendations for urban planning to incorporate policy
changes for shrinking cities and coastal port-industrial areas worldwide, to include disaster risk
reduction and climate change adaptation approaches.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
We study the sensor fault estimation and accommodation problems in a data-driven \(\mathcal{H}_\infty\) setting, leading to a data-driven sensor fault-tolerant control scheme. First, we formulate the fault estimation problem as a finite-horizon minimax \(\mathcal{H}_\infty\)-optimization problem in a data-driven setup, whose solution yields the fault estimate. The estimated fault is then used for output compensation. This compensated output and the experimental input are used to achieve certain control objectives in a data-driven \(\mathcal{H}_\infty\) setting. Next, the data-driven \(\mathcal{H}_\infty\) fault estimation and control problems are solved using a subspace predictor-based approach. Finally, the proposed algorithm is applied to the steering subsystem of the remotely operated underwater vehicle.
Opposition parties under minority governments find themselves in a fundamental dilemma. They are competing with other parties, including the government, for electoral support while also having a common responsibility to make stable government work. This dilemma is especially pronounced for opposition parties signing support agreements with the government. While not formally in a coalition, they nonetheless publicly commit to supporting a government. They may thus be concerned about losing distinctiveness and have an interest in strategically timing cooperation with the minority government. The present paper tests whether this is the case using data on opposition party voting on committee proposals from 23 years of Swedish minority governments between 1991 and 2018. The findings indicate that support parties are less likely to support the government towards the beginning and end of the election cycle, that is, when public attention is intense – a pattern that is not observable for other opposition parties.
With direct laser writing micro structures can be manufactured by solidifying a photo resist when the laser beam triggers a photochemical reaction in the focal voxel. We have used direct laser writing to fabricate a thermally actuated microgripper, which can move its two cantilever like arms to grip micro-objects. One cantilever consists thereby of two strips with different coefficients of thermal expansion such that both cantilevers bends towards each other for an increasing temperature like a welded bimetal.This work investigates the impact of each cantilever's geometry on the gripping performance of the micro gripper theoretically. The tip deflection of the gripper is calculated by the analytical model of Timoshenko's theory of elasticity. After fabricaiton of the microgripper, its gripping performance is observed under the microscope while heated by a heating element.
The quality of risk reports: Integrating requirement levels of standard setters into text analysis
(2021)
The intention of this paper is to shed light on the analysis of financial disclosure through the integration of requirement levels. This in return will lead to the development of a general applicable evaluation methodology based on Bloom's taxonomy system. Therefore, it will be possible to explicitly consider the relevance of the given information. To underline the appropriateness of our method, we combine the requirement levels with a qualitative content analysis. Based on the German accounting standard DRS 20, we clarify the respective application of the requirement levels in the context of the qualitative content analysis. Hence, we will discuss the limitations of our developed approach. In addition, we analyze further areas of application in the context of qualitative analysis of financial disclosure. All things considered, it is evident that our chosen approach, through the integration of a taxonomy system, contributes to the validity of established text analyzing methods.
Firn describes the interstage product between snow and ice in cold regions of the earth, where annual snow fall exceeds the amount of snow melting. The continuing accumulation of snow leads to its densificiation due to overburden stress until it becomes ice. In the field of glaciology various attempts on simulating firn densification have been made and new models are still developed, as the knowledge of the firn column's density structure allows important derivations.
The presented study reassesses a model description for low density firn based on the process of grain boundary sliding presented by Alley in 1987 [1] using an optimisation approach. By comparing simulation results to 159 measured firn density profiles from Greenland and Antarctica it finds a possible additional dependency of the constitutive relation on the mean surface mass balance. This result is interpreted as an insufficient description of the stress regime.