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Wikipedia is the biggest, free online encyclopaedia that can be expanded by any-one. For the users, who create content on a specific Wikipedia language edition, a social network exists. In this social network users are categorised into different roles. These are normal users, administrators and functional bots. Within the networks, a user can post reviews, suggestions or send simple messages to the "talk page" of another user. Each language in the Wikipedia domain has this type of social network.
In this thesis characteristics of the three different roles are analysed in order to learn how they function in one language network of Wikipedia and apply them to another Wikipedia network to identify bots. Timestamps from created posts are analysed to reveal noticeable characteristics referring to continuous messages, message rates and irregular behaviour of a user are discovered. Through this process we show that there exist differences between the roles for the mentioned characteristics.
The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts.
We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons.
Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW.
Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things.
Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.
This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.
The Internet of Things (IoT) is a concept in which connected physical objects are integrated into the virtual world to become active partakers of businesses and everyday processes (Uckelmann, Harrison and Michahelles, 2011; Shrouf, Ordieres and Miragliotta, 2014). It is expected to have a major impact on businesses (Council, Nic and Intelligence, 2008), but small and medium enterprises’ business models are threatened if they do not adopt the new concept (Sommer, 2015). Thus, this thesis aims to showcase a sample implementation of connected devices in a small enterprise, demonstrating its added benefits for the business.
Design Science Research (DSR) is used to develop a prototype based on a use case provided by a carpentry. The prototype comprises a hardware sensor and a web application which can be used by the wood shop to improve their processes. The thesis documents the iterative process of developing a prototype from the grounds up to useable hard- and software.
This contribution provides an example of how IoT can be used and implemented at a small business.
This Master Thesis is an exploratory research to determine whether it is feasible to construct a subjectivity lexicon using Wikipedia. The key hypothesis is that that all quotes in Wikipedia are subjective and all regular text are objective. The degree of subjectivity of a word, also known as ''Quote Score'' is determined based on the ratio of word frequency in quotations to its frequency outside quotations. The proportion of words in the English Wikipedia which are within quotations is found to be much smaller as compared to those which are not in quotes, resulting in a right-skewed distribution and low mean value of Quote Scores.
The methodology used to generate the subjectivity lexicon from text corpus in English Wikipedia is designed in such a way that it can be scaled and reused to produce similar subjectivity lexica of other languages. This is achieved by abstaining from domain and language-specific methods, apart from using only readily-available English dictionary packages to detect and exclude stopwords and non-English words in the Wikipedia text corpus.
The subjectivity lexicon generated from English Wikipedia is compared against other lexica; namely MPQA and SentiWordNet. It is found that words which are strongly subjective tend to have high Quote Scores in the subjectivity lexicon generated from English Wikipedia. There is a large observable difference between distribution of Quote Scores for words classified as strongly subjective versus distribution of Quote Scores for words classified as weakly subjective and objective. However, weakly subjective and objective words cannot be differentiated clearly based on Quote Score. In addition to that, a questionnaire is commissioned as an exploratory approach to investigate whether subjectivity lexicon generated from Wikipedia could be used to extend the coverage of words of existing lexica.
Fresh water resources like rivers and reservoirs are exposed to a drastically changing world. In order to safeguard these lentic ecosystems, they need stronger protection in times of global change and population growth. In the last years, the exploitation pressure on drinking water reservoirs has increased steadily worldwide. Besides securing the demands of safe drinking water supply, international laws especially in Europe (EU Water Framework Directive) stipulate to minimize the impact of dams on downstream rivers. In this study we investigate the potential of a smart withdrawal strategy at Grosse Dhuenn Reservoir to improve the temperature and discharge regime downstream without jeopardizing drinking water production. Our aim is to improve the existing withdrawal strategy for operating the reservoir in a sustainable way in terms of water quality and quantity. First, we set-up and calibrated a 1D numerical model for Grosse Dhuenn Reservoir with the open-source community model “General Lake Model” (GLM) together with its water quality module “Aquatic Ecodynamics” library (AED2). The reservoir model reproduced water temperatures and hypolimnetic dissolved oxygen concentrations accurately over a 5 year period. Second, we extended the model source code with a selective withdrawal functionality (adaptive offtake) and added operational rules for a realistic reservoir management. Now the model is able to autonomously determine the best withdrawal height according to the temperature and flow requirements of the downstream river and the raw water quality objectives. Criteria for the determination of the withdrawal regime are selective withdrawal, development of stratification and oxygen content in the deep hypolimnion. This functionality is not available in current reservoir models, where withdrawal heights are generally provided a priori to the model and kept fixed during the simulation. Third, we ran scenario simulations identifying an improved reservoir withdrawal strategy to balance the demands for downstream river and raw water supply. Therefore we aimed at finding an optimal parallel withdrawal ratio between cold hypolimnetic water and warm epilimnetic or metalimnetic water in order to provide a pre-defined temperature in the downstream river. The reservoir model and the proposed withdrawal strategy provide a simple and efficient tool to optimize reservoir management in a multi-objective view for mastering future reservoir management challenges.
The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.
The term “Software Chrestomaty” is defined as a collection of software systems meant to be useful in learning about or gaining insight into software languages, software technologies, software concepts, programming, and software engineering. 101companies software chrestomathy is a community project with the attributes of a Research 2.0 infrastructure for various stakeholders in software languages and technology communities. The core of 101companies combines a semantic wiki and confederated open source repositories. We designed and developed an integrated ontology-based knowledge base about software languages and technologies. The knowledge is created by the community of contributors and supported with a running example and structured documentation. The complete ecosystem is exposed by using Linked Data principles and equipped with the additional metadata about individual artifacts. Within the context of software chrestomathy we explored a new type of software architecture – linguistic architecture that is targeted on the language and technology relationships within a software product and based on the megamodels. Our approach to documentation of the software systems is highly structured and makes use of the concepts of the newly developed megamodeling language MegaL. We “connect” an emerging ontology with the megamodeling artifacts to raise the cognitive value of the linguistic architecture.
Social entrepreneurship is a form of entrepreneurship that marries a social mission to a competitive value proposition. Notably, social entrepreneurship fosters a more equitable society by addressing social issues and trying to achieve an ongoing sustainable impact through a social mission rather than purely profit maximization. The topic of social entrepreneurship has appealed considerably to many different streams of research. The focus on understanding how and why entrepreneurs think and act is a significant justification for future research. Nevertheless, the theoretical examination of this phenomenon is in its infancy. Social entrepreneurship research is still largely phenomenon-driven. Specifically, Social Entrepreneurial Intention is in an early stage and lacks quantitative research. Therefore, this thesis proposes to address this need. The thesis’ objectives are twofold: (1) develop a formation model for Social Entrepreneurial Intentions in general and (2) test the model by conducting an empirical study. Based on these objectives, the two research questions guiding the thesis are (1) what factors influence the intention of a person to become a social entrepreneur and (2) what relationships exist among these factors.
In order to answer these two research questions, this thesis uses purposeful research design, which is a combination of literature review and empirical study. The literature review is based on a comprehensive range of books, articles, and research papers published in leading academic journals and conference proceedings in different disciplines such as entrepreneurship, social entrepreneurship, entrepreneurship education, management, social psychology, and social economics. The empirical study is conducted via a survey of 600 last-year students from four universities in three regions in Vietnam: Hanoi, Da Nang, and Ho Chi Minh. The data are analyzed with SPSS-AMOS version 24, using screening data, scale development, exploratory factor analysis, and confirmation factor analysis. The thesis ascertains that Entrepreneurship Experience/Extra-curricular Activity, Role Model, Social Entrepreneurial Self-Efficacy, and Social Entrepreneurial Outcome Expectation directly and positively affect the intention of the Vietnamese students to be social entrepreneurs. Entrepreneurship Education also influences the Social Entrepreneurial Intention, but not directly, otherwise indirectly via Social Entrepreneurial Self-Efficacy and Social Entrepreneurial Outcome Expectation. Similarly, Perceived Support has no direct relationship to Social Entrepreneurial Intention; however, it shows an indirect link via the mediator ‘Social Entrepreneurial Outcome Expectation’. Furthermore, the dissertation brings new insights to the social entrepreneurship literature and provides important implications for practice. Limitations and future directions are also provided in the thesis.
This thesis connects the endeavors of the winemaker’s intention in perfect and profitable wine making with an innovative technological application to use Internet of Things. Thereby the winemaker’s work may be supported and enriched – and enables until recent years still unthinkable optimization of managing and planning of his business, including close state control of different areas of his vineyard, and more than that, not ending up with the single grapevine. It is exemplarily shown in this thesis how to measure, transmit, store and make data available, exemplarily demonstrated with “live” temperature, air and soil humidity values from the vineyard. A modular architecture was designed for the system presented, which allows the use of current sensors, and similar low-voltage sensors, which will be developed in the future.
By using IoT devices in the vineyard, the winemaker advances to a new quality of precision of forecasted data, starting from live data of his vineyard. Of more and more importance, the winemaker can start immediate action, when unforeseen heavy weather conditions occur. Immediate use of current data enabled by a Cloud Infrastructure. For this system, an open service infrastructure is employed. In contrast to other published commercial approaches, the described solution is based on open source.
As an alone-standing part of this work, a physical prototype for measuring relevant parameters in the vineyard was de-novo designed and developed until fulfilling the set of specifications. The outlined features and requirements for a functioning data collection and autonomously transmitting device was developed, described, and the fulfilment by the prototype device were demonstrated. Through literature research and supportive orientationally live interviews of winemakers, the theory and the practical application were synchronized and qualified.
For the development of the prototype the general principles of development of an electronic device were followed, in particular the Design Science Research development rules, and principles of Quality Function Deployment. As a characteristic of the prototype, some principles like re-use of approved construction and material price of the building blocks of the device were taken into consideration as well (e.g. housing; Arduino; PCB). Parts reduction principles, decomplexation and simplified assembly, testing and field service were integrated to the development process by the modular design of the functional vineyard device components, e.g. with partial reference to innovative electrical cabinet construction system Modular-3.
The software architectural concept is based on a three-layer architecture inclusive the TTN infrastructure. The front end is realized as a rich web client, using a WordPress plugin. WordPress was chosen due to the wide adoption through the whole internet, enabling fast and easy user familiarization. Relevant quality issues have been tested and discussed in the view of exemplary functionality, extensibility, requirements fulfilment, as usability and durability of the device and the software.
The prototype was characterized and tested with success in the laboratory and in field exposition under different conditions, in order to allow a measurement and analysis of the fulfilment of all requirements by the selected and realized electronic construction and layout.
The solution presented may serve as a basis for future development and application in this special showcase and within similar technologies. A prognosis of future work and applications concludes this work.
Estuaries are characterized by a longitudinal salinity gradient. This gradient is one of the main environmental factors responsible for the distribution of organisms. Distinguishing salinity zones is of crucial importance, e.g., for the development of tools for the assessment of ecological quality. The methods most often applied for classifying water according to salinity are the Venice System and the method of Bulger et al. (1993), both of which determine zone boundaries using species occurrences relative to mean salinity. However, although these methods were developed for homoiohaline waters, they have also been routinely applied to poikilohaline systems. I tested the applicability of both methods using salinity and macroinvertebrate data for the poikilohaline Elbe Estuary (Germany). My results showed that the mid-estuary distribution of macro-invertebrates is determined by variation in salinity rather than by mean salinity. Consequently, neither of the two methods is applicable for defining salinity zones in the Elbe Estuary. Cluster analysis combined with a significance test, by contrast, was a better tool for identifying the boundaries of salinity zones in poikilohaline systems.
In many estuaries, such as the Elbe Estuary, a maximum turbidity zone (MTZ) develops, where suspended matter accumulates owing to circulation processes. It is assumed that the MTZ is a stressful environment with an excess of organic matter, high deposition rates, large variations in salinity, and dredging activities. Under such harsh conditions, populations might remain below the carrying capacity, and it is assumed that competition is of little importance, as predicted by the stress gradient hypothesis. I tested whether competition for food is important in the MTZ of the Elbe Estuary using stable isotope analysis of the macroinvertebrate community. The isotopic niches of no two taxa within a feeding group overlapped, which indicated different resource use and the absence of competition. The main reasons for the lack of overlap of isotopic niches were differences in habitat, feeding behavior, and migration behavior.
The Elbe Estuary is nowadays highly industrialized and has long been subjected to a plethora of human-caused alterations. However, it is largely unknown what changes occurred in benthic communities in the last century. Hence, I considered taxonomic and functional aspects of macrobenthic invertebrates of the Elbe Estuary given in data from 1889 (most natural state), 1985 and 1986 (highly polluted state), and 2006 (recent state) to assess benthic community shifts. Beta-diversity analysis showed that taxonomic differences between the sampling dates were mainly due to species turnover, whereas functional differences were predominantly a result of functional nestedness. Species number (S), functional richness (FRic), and functional redundancy reached minimum values in 1985 and 1986 and were highest and rather similar in 1889 and 2006. The decline in FRic from 1889 to 1985/1986 was non-random, consistent with habitat filtering. FRic, functional beta diversity, and S data suggested that the state of the estuary from 1889 was almost re-established in 2006. However, the community in 1889 significantly differed from that in 2006 owing to species replacement. My results indicate that FRic and FR in 1889 could have promoted ecosystem resilience and stability.
Companies try to utilise Knowledge Management (KM) to gain more efficiency and effectiveness in business. The major problem is that most of these KM projects are not or rarely based on sustainable analyses or established theories about KM. Often there is a big gap between the expectations and the real outcome of such KM initiatives. So the research question to be answered is: What challenges arise in KM projects, which KM requirements can be derived from them and which recommendations support the goal of meeting the requirements for KM? As theoretical foundation a set of KM frameworks is examined. Subsequently KM challenges from literature are analysed and best practices from case studies are used to provide recommendations for action on this challenges. The main outcome of this thesis is a best practice guideline,which allows Chief Knowledge Officers (CKOs) and KM project managers to examine the challenges mentioned in this thesis closely, and to find a suitable method to master these challenge in an optimal way. This guideline shows that KM can be positively and negatively influenced in a variety of ways. Mastering Knowledge Management (KM) in a company is a big and far-reaching venture and that technology respectively Information Technology (IT) is only a part of the big picture.
Topic models are a popular tool to extract concepts of large text corpora. These text corpora tend to contain hidden meta groups. The size relation of these groups is frequently imbalanced. Their presence is often ignored when applying a topic model. Therefore, this thesis explores the influence of such imbalanced corpora on topic models.
The influence is tested by training LDA on samples with varying size relations. The samples are generated from data sets containing a large group differences i.e language difference and small group differences i.e. political orientation. The predictive performance on those imbalanced corpora is judged using perplexity.
The experiments show that the presence of groups in training corpora can influence the prediction performance of LDA. The impact varies due to various factors, including language-specific perplexity scores. The group-related prediction performance changes for groups when varying the relative group sizes. The actual change varies between data sets.
LDA is able to distinguish between different latent groups in document corpora if differences between groups are large enough, e.g. for groups with different languages. The proportion of group-specific topics is under-proportional to the share of the group in the corpus and relatively smaller for minorities.
The primary aims of the study are (1) to identify classroom instructional factors which have a crucial effect on the academic growth of ninth-graders in EFL in Vietnam, and (2) to gain insight into their interplay with each other and with context factors. Besides, this study has a strong focus on methodological approaches: (a) using multiple methods in order to deal with the “large p, small n” problem, (b) to understand the relevance of the scaling model used for the results.
Data from a research project carried out in Vietnam during the school year 2006–2007 were used in this study. Besides a longitudinal design with two measurement points (MPs) using adapted English tests and questionnaires from the DESI-study in Germany, a video study was conducted in the middle of the school year between two MPs. The recorded video data were transcribed, micro-analytically coded, and lessons were rated to gain indicators of classroom instruction. Different IRT scaling models were chosen to estimate student ability in the pretest and posttest. For the C-test, the unidimensional 1PL and 2PL models, the Rasch testlet model, and testlet 2PL model were selected to model student ability. To estimate student ability via the listening comprehension test (LC-test), the Rasch model, the unidimensional 2PL, and 3PL models were applied. The student ability estimates at the two MPs were linked to one common scale using the concurrent calibration approach with different a priori ability distributions. The plausible values (PVs) were generated and treated as student ability estimates for all analyses. To understand the relationship between the instructional variables and student growth, we explored the hypothesized linear and nonlinear, additive and interactive effects of classroom instructional factors. To examine these hypothetical effects, OLS and regularized regression models using lasso (least absolute shrinkage and selection operators) were applied, including main effects as well as quadratic and interaction terms of instructional variables. Initial student ability and the socioeconomic status of students were treated as context variables.
The results show, on the one hand, a positive view of important general instructional quality dimensions of teaching effectiveness and, on the other hand, a strongly teacher-centered and textbook-driven instruction and poor instructional quality from the point of view of EFL didactics. The most important instructional factors of student growth in the C-test were quality aspects of motivation in instruction as well as aspects related to the teaching language. Regarding the LC-test results, language-related aspects together with the relative frequency of repeated questions were the most important predictors of student growth. While the findings confirmed all the hypothesized instructional effects on student growth, aptitude treatment interaction effects of instruction were only confirmed with regard to student growth in the C-test. The different scaling models produced significant differences in the results regarding instructional effects on student growth.
Soil organic matter (SOM) is a key component responsible for sequestration of organic molecules in soil and regulation of their mobility in the environment. The basic structure of SOM is a supramolecular assembly responding dynamically to the environmental factors and the presence of interacting molecules. Despite of the advances in the understanding of sorption processes, the relation between sorbate molecules, SOM supramolecular structure and its dynamics is limited. An example of a dynamic nature of SOM is a physicochemical matrix aging that is responsible for SOM structural arrangement. The underlying process of the physicochemical aging is the formation of water molecule bridges (WaMB) between functional groups of molecular segments. Since WaMB influence the stiffness of SOM structure, it was hypothesized that formation of WaMB contributes to the sequestration of organic molecules. However, this hypothesis has not been tested experimentally until now. Furthermore, the knowledge about the influence of organic molecules on WAMB is based solely on computer modeling studies. In addition, the influence of organic molecules on some physical phases forming SOM is not well understood. Especially, the interactions between organic molecules and crystalline phases represented by aliphatic crystallites, are only presumed. Thus, the investigation of those interactions in unfractioned SOM is of high importance.
In order to evaluate the involvement of WaMB in the sequestration of organic molecules and to increase our understanding about interactions of organic chemicals with WaMB or aliphatic crystallites, the following hypotheses were tested experimentally. 1) Similarly to crystalline phases in synthetic polymers, aliphatic crystallites, as a part of SOM, cannot be penetrated by organic molecules. 2) The stability of WaMB is determined by the ability of surrounding molecules to interact with water forming WaMB. 3) WaMB prevent organic molecules to leave the SOM matrix and contribute thus to their immobilization. In order to test the hypotheses 1 and 2, a set of experiments including treatment of soils with chosen chemicals was prepared. Interaction abilities of these chemicals were characterized using interaction parameters from the Linear Solvation Energy Relationship theory. WaMB characteristics were monitored using Differential Scanning Calorimetry (DSC) allowing to measure the WaMB thermal stability and the rigidity of SOM matrix; which in turn was determined by the heat capacity change. In addition, DSC and 13C NMR spectroscopy assessed thermal properties and the structure of aliphatic crystallites. The spiking of samples with a model compound, phenol, and measurements of its desorption allowed to link parameters of the desorption kinetics with WaMB characteristics.
The investigation showed that the WaMB stability is significantly reduced by the presence of molecules with H-donor/acceptor interaction abilities. The matrix rigidity associated with WaMB was mainly influenced by the McGowan’s volume of surrounding molecules, suggesting the importance of dispersion forces. The desorption kinetics of phenol followed a first order model with two time constants. Both of them showed a relation with WaMB stability, which supports the hypothesis that WaMB contribute to the physical immobilization of organic molecules. The experiments targeted to the crystallites revealed their structural change from the ordered to the disordered state, when in contact with organic chemicals. This manifested in their melting point depression and the decrease of overall crystallinity. Described structural changes were caused by molecules interacting with specific as well as non-specific forces, which suggests that aliphatic crystallites can be penetrated and modified by molecules with a broad range of interaction abilities.
This work shows that chosen organic molecules interact with constituents of SOM as exemplified on WaMB and aliphatic crystallites, and cause measurable changes of their structure and properties. These findings show that the relevance of aliphatic crystallites for sorption in soil may have been underestimated. The results support the hypothesis that physicochemical matrix aging significantly contributes to the immobilization of organic chemicals in SOM.
During the last couple of years the extension of the internet into the real world, also referred to as the Internet of Things (IoT), was positively affected by an ongoing digitalization (Mattern and Floerkemeier, 2010; Evans, 2013). Furthermore, one of the most active IoT domains is the personal health ecosystem (Steele and Clarke, 2013). However, this thesis proposes a gamification framework which is supported and enabled by IoT to bring personal health and IoT together in the context of health-insurances. By examining gamification approaches and identifying the role of IoT in such, a conceptual model of a gamification approach was created which indicates where and how IoT is ap-plicable to it. Hence, IoT acts as enabler and furthermore as enhancer of gamified activities. Especial-ly the necessity of wearable devices was highlighted. A stakeholder analysis shed light on respective benefits which concluded in the outcome, that IoT enabled two paradigm shifts for both, the insur-ance and their customer. While taking the results of the examination and the stakeholder analysis as input, the previously made insights were used to develop an IoT supported gamification framework. The framework includes a multi-level structure which is meant to guide through the process of creat-ing an approach but also to analyze already existing approaches. Additionally, the developed frame-work was instantiated based on the application Pokémon Go to identify occurring issues and explain why it failed to retain their customer in the long term. The thesis provides a foundation on which fur-ther context related research can be orientated.
The physical-biological interactions that affect the temporal variability of benthic oxygen fluxes were investigated to gain improved understanding of the factors that control these processes. This study, for the first time is able to resolve benthic diffusive boundary layer (DBL) dynamics using the newly developed lifetime-based laser induced fluorescence (τLIF) oxygen imaging system, which enables study of the role of small-scale fluid mechanics generated by benthic organism activity, and hence a more detailed analysis of oxygen transport mechanisms across the sediment-water interface (SWI).
The net benthic oxygen flux across the sediment-water interface is controlled by sediment oxygen uptake and oxygen transport. While the oxygen transport is largely influenced by turbulence driven by large-scale flows, sediment oxygen uptake is mainly affected by oxygen production and biological- and chemical-oxygen degradation of organic matter. Both processes can be enhanced by the presence of fauna and are intimately coupled. The benthic oxygen flux can be influenced by fauna in two ways, i.e. by modulating the availability of oxygen, which enhances the sediment oxygen uptake, and by enhancing the transport of oxygen.
In-situ and a series of laboratory measurements were conducted to estimate the short- and seasonal variability of benthic fluxes including the effects of burrow ventilation activity by tube-dwelling animals using eddy correlation (EC) and τLIF oxygen imaging techniques, respectively.
The in-situ benthic oxygen fluxes showed high variability at hourly and seasonal timescales, where statistical analysis indicated that current velocity and water depth were the most significant predictors of benthic oxygen flux at the waterside, which co-varied with the discharge, temperature, and oxygen concentration. The range of variability of seasonal fluxes corresponded to the friction velocities which were driven by large-scale flows. Application of a simplified analytical model that couples the effect of hydrodynamic forcing of the diffusive boundary layer with a temperature-dependent oxygen consumption rate within the sediment showed that friction velocity and temperature cause similar variability of the steady-state benthic oxygen flux.
The application of τLIF oxygen imaging system in bioturbation experiments enabled the investigation and discovery of insights into oxygen transport mechanisms across the sediment-water interface. Distinct oxygen structures above burrow openings were revealed, these were associated with burrow ventilation. The DBL was degraded in the presence of burrow ventilation. Advective transport generated by the energetic plumes released at burrow outlets was the dominant transport driving mechanism. The contribution of diffusive flux to the total estimated decreased with increasing larval density. For a range of larvae densities, commonly observed in ponds and lakes, sediment oxygen uptake rates increased up to 2.5-fold in the presence of tube-dwelling animals, and the oxygen transport rate exceeded chironomid respiration by up to a factor of 4.
The coupled physical-biological factors affecting net benthic oxygen flux can be represented by temperature, which is a prominent factor that accounts for both oxygen transport and sediment oxygen uptake. Low oxygen transport by flow coincided with high summer temperatures, amplified by a reduction of benthic population density and pupation. It can also, however, be offset by increased ventilation activity. In contrast, low temperature coincided with high oxygen concentrations, an abundance of larvae, and higher flow is offset by less burrow ventilation activity. Investigation of the effect of hydrodynamics on oxygen transport alone suggested that the expected increase of benthic oxygen flux under global warming can be offset by a reduction in flow velocity, which could ultimately lead to increasing carbon burial rates, and in a growing importance of anaerobic mineralization pathways with increasing emission rates of methane.
This study suggests a significant contribution of biological induced benthic oxygen flux to physical transport driven by large-scale flow-fields contributing to bottom-boundary layer turbulence.
This paper describes the robot Lisa used by team homer@UniKoblenz of the University of Koblenz Landau, Germany, for the participation at the RoboCup@Home 2017 in Nagoya, Japan. A special focus is put on novel system components and the open source contributions of our team. We have released packages for object recognition, a robot face including speech synthesis, mapping and navigation, speech recognition interface via android and a GUI. The packages are available (and new packages will be released) on
http://wiki.ros.org/agas-ros-pkg.
This paper describes the robot Lisa used by team
homer@UniKoblenz of the University of Koblenz Landau, Germany, for the participation at the RoboCup@Home 2016 in Leipzig, Germany. A special focus is put on novel system components and the open source contributions of our team. We have released packages for object recognition, a robot face including speech synthesis, mapping and navigation, speech recognition interface via android and a GUI. The packages are available (and new packages will be released) on http://wiki.ros.org/agas-ros-pkg.
Assessment of renewable energy potentials based on GIS. A case study in southwest region of Russia
(2018)
In the present thesis, the initial conditions for the development of RES potentials for the production of wind, solar and biomass energy in the Krasnodar region (southwestern region of the Russian Federation) are examined using a multi-criteria assessment methodology. For the assessment of the RES potentials at regional scale, the prosed multi-criteria methodology based on the geographic information systems (GIS) and has been complemented by the evaluation and analysis of primary and secondary data as well as economic calculations relevant related to economic feasibility of RES projects.