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Institute
This paper examines the communication channels used by innovation projects at the ProtoSpace Hamburg, when engaging with stakeholders, and tries to answer the thesis question whether new media channels improve the chances of success for innovation projects, when used for this communication. Expert interviews with eight experts in com-munication, innovation and stakeholder management were conducted and then analyzed through the application of Mayring´s qualitative content analysis, in order to answer the posed question.
Drought is one of the most common and dangerous threats plants have to face, costing the global agricultural sector billions of dollars every year and leading to the loss of tons of harvest. Until people drastically reduce their consumption of animal products or cellular agriculture comes of age, more and more crops will need to be produced to sustain the ever growing human population. Even then, as more areas on earth are becoming prone to drought due to climate change, we may still have to find or breed plant varieties more suitable to grow and prosper in these changing environments.
Plants respond to drought stress with a complex interplay of hormones, transcription factors, and many other functional or regulatory proteins and mapping out this web of agents is no trivial task. In the last two to three decades or so, machine learning has become immensely popular and is increasingly used to find patterns in situations that are too complex for the human mind to overlook. Even though much of the hype is focused on the latest developments in deep learning, relatively simple methods often yield superior results, especially when data is limited and expensive to gather.
This Master Thesis, conducted at the IPK in Gatersleben, develops an approach for shedding light on the phenotypic and transcriptomic processes that occur when a plant is subjected to stress. It centers around a random forest feature selection algorithm and although it is used here to illuminate drought stress response in Arabidopsis thaliana, it can be applied to all kinds of stresses in all kinds of plants.
This paper analyses the status quo of large-scale decision making combined with the possibility of blockchain as an underlying decentralized architecture to govern common pool resources in a collective manner and evaluates them according to their requirements and features (technical and non-technical). Due to an increasing trend in the distribution of knowledge and an increasing amount of information, the combination of these decentralized technologies and approaches, can not only be beneficial for consortial governance using blockchain but can also help communities to govern common goods and resources. Blockchain and its trust-enhancing properties can potenitally be a catalysator for more collaborative behavior among participants and may lead to new insights about collective action and CPRs.
Gold cyanidation is a process by which gold is removed from low-grade ore. Due to its efficiency it has found widespread application around the world, including Peru. The process requires free cyanide in high concentration. After the gold extraction is completed, free cyanide as well as metal cyanide complexes remain in the effluent of gold mines and refineries. Often these effluents are kept in storage ponds where they pose considerable risk to health and environ-ment. Thus, it is preferable to degrade cyanide to minimize the risk of exposure. In the context of this thesis cyanide degradation was explored in a UV-light based prototype. Degradation with a combination of hydrogen peroxide and UV-light has proven to be very effective at degrading cyanide concentrations of 100 mg/L and 1000 mg/L. Furthermore, the presence of ammonia as a degradation product could also be confirmed. Membrane distillation may provide an alternative to cyanide destruction in the form of cyanide recovery. Promising results were gathered from several membrane experiment.
This paper looks at current projects in the field of Blockchain in education, their specific areas of application, possible advantages and weaknesses. Three examples developed by the team of authors are introduced in detail. First: Gallery-Defender a Serious Game, which was adapted to serve as a demonstrator in a stand-alone version to show the possibility to carry out exams directly from within the game and store the grades and meta-data on Blockchain. Second: Art-Quiz, an e-learning tool, which can be integrated into existing LMS systems and map exam results and further data using Blockchain technologies. Both were developed following an iterative design process. And third: The results of a focus group, which simulated the assignment of grades after an oral online exam. The three examples presented here are based on the Blockchain system Ardor/Childchain Ignis, but each demonstrator has a different set of features and approaches.
In addition, the integration of various Blockchain solutions was conceptually designed to make a Multi-Chain model possible.
Procurement processes are deemed to lack supporting digital technologies that raise efficiency and automation.
Blockchain solutions are piloted in procurement in order to offer a decentralized IT infrastructure covering these needs. This paper aims at identifying current blockchain approaches in the field of procurement and presenting affected business processes. In order to get an overview of the current state of the art, a systematic literature mapping is conducted.
Moreover, the out-comes are gathered and categorized in a classification scheme. Based on the analysis, systematic maps are presented to showcase relevant findings. Within the findings, several blockchain use cases in the field of procurement are identified and information about addressed challenges, utilized blockchain frameworks and affected business processes are extracted.
In response to prevailing environmental conditions, Arabidopsis thaliana plants must increase their photosynthetic capacity to acclimate to potential harmful environmental high light stress. In order to measure these changes in acclimation capacity, different high throughput imaging-based methods can be used. In this master thesis we studied different Arabidopsis thaliana knockout mutants-and accessions in their capacity to acclimate to potential harmful environmental high light and cold temperature conditions using a high throughput phenotyping system with an integrated chlorophyll fluorescence measurement system. In order to determine the acclimation capacity, Arabidopsis thaliana knockout mutants of previously not high light assigned genes as well as accessions of two different haplotype groups with a reference and alternative allele from different countries of origin were grown under switching high light and temperature environmental conditions. Photosynthetic analysis showed that knockout mutant plants did differ in their Photosystem II operating efficiency during an increased light irradiance switch but did not significantly differ a week later under the same circumstances from the wildtype. High throughput phenotyping of haplotype accessions revealed significant better acclimation capacity in non-photochemical quenching and steady-state photosynthetic efficiency in Russian domiciled accessions with an altered SPPA gene during high light and cold stress.
We present dimensionality reduction methods like autoencoders and t-SNE for visualization of high-dimensional data into a two-dimensional map. In this thesis, we initially implement basic and deep autoencoders using breast cancer and mushroom datasets. Next, we build another dimensionality reduction method t-SNE using the same datasets. The obtained visualization results of the datasets using the dimensionality reduction methods are documented in the experiments section of the thesis. The evaluation of classification and clustering for the dimensionality reduction techniques is also performed. The visualization and evaluation results of t-SNE are significantly better than the other dimensionality reduction techniques.
Convolutional Neural network (CNN) has been one of most powerful and popular preprocessing techniques employed for image classification problems. Here, we use other signal processing techniques like Fourier transform and wavelet transform to preprocess the images in conjunction with different classifiers like MLP, LVQ, GLVQ and GMLVQ and compare its performance with CNN.
In an era of global climate change and fast growing cities, local governments are in an urgent need for adopting sustainable urban growth concepts for tackling a liveable and prosperous urban future. Against this background, the smart city notion progressively gained popularity as an urban development concept, which heavily relies on technology and urban data use for fostering sustainable urban growth. However, so far, the understandingof the smart city term is ambiguous, and little scientific research has been done on developing comprehensive conceptual frameworks to support local governments in the making of smarter cities. This paper aims at presenting the current state-of-the-art of smart city research in order to support the making of smart city best practices and to promote a comprehensive understanding of the smart city notion. In doing so, the role of technology in the making of smarter cities and critical success factors in transforming cities are elaborated, following the methodological approach of a multidimensional conceptual framework. The research findings and an expert interview with a representative of the state capital will then serve for the assessment of the weak points and best practices in the smart city pursuit of the German city Munich, providing urban policymaking with valuable insights and fostering the development of a comprehensive smart city conceptualism.