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The bio-insecticide Bacillus thuringiensis israelensis (Bti) has worldwide become the most commonly used agentin mosquito control programs that pursue two main objectives: the control of vector-borne diseases and the reduction of nuisance, mainly coming frommosquitoes that emerge in large quantities from seasonal wetlands. The Upper Rhine Valley, a biodiversity hotspot in Germany, has been treated withBti for decades to reduce mosquito-borne nuisance and increase human well-being.Although Btiis presumed to be an environmentally safe agent,adverse effects on wetland ecosystems are still a matter of debate especially when it comes to long-term and indirect effects on non-target organisms. In light of the above, this thesis aims at investigating direct and indirect effects of Bti-based mosquito control on non-target organisms within wetland food chains.Effects were examinedin studies with increasingeco(toxico)logical complexity, ranging from laboratory over mesocosm to field approaches with a focus on the non-biting Chironomidae and amphibian larvae (Rana temporaria, Lissotriton sp.).In addition, public acceptance of environmentally less invasive alternative mosquito control methods was evaluated within surveys among the local population.
Chironomids were the most severely affected non-target aquatic invertebrates. Bti substantially reduced larval and adult chironomid abundances and modified their species composition. Repeated exposures to commonly used Bti formulations induced sublethal alterations of enzymatic biomarkers activityin frog tadpoles. Bti-induced reductions of chironomid prey availability indirectly decreased body size of newts at metamorphosis and increased predation on newt larvae in mesocosm experiments. Indirect effects of severe reductions in midge biomassmight equally be passed through aquatic but also terrestrial food chains influencing predators of higher trophic levels. The majority ofaffectedpeople in the Upper Rhine Valley expressed a high willingness to contributefinancially to environmentally less harmful mosquito control.Alternative approaches could still include Bti applications excepting treatment of ecologically valuable areas. Potentially rising mosquito levels could be counteracted with local acting mosquito traps in domestic and urban areas because mosquito presence was experienced as most annoying in the home environment.
As Bti-based mosquito control can adversely affect wetland ecosystems, its large-scale applications, including nature conservation areas, should be considered more carefully to avoid harmful consequences for the environmentat the Upper Rhine Valley.This thesis emphasizesthe importance to reconsiderthe current practice of mosquito control and encourage research on alternative mosquito control concepts that are endorsed by the local population. In the context ofthe ongoing amphibian and insect declinesfurther human-induced effects onwetlands should be avoided to preserve biodiversity in functioning ecosystems.
Thesis is devoted to the topic of challenges and solutions for human resources management (HRM) in international organizations. The aim is to investigate methodological approaches to assessment of HRM challenges and solutions, and to apply them on practice, to develop ways of improvement of HRM of a particular enterprise. The practical research question investigated is “Is the Ongoing Professional Development – Strategic HRM (OPD-SHRM) model a better solution for HRM system of PrJSC “Philip Morris Ukraine”?”
To achieve the aim of this work and to answer the research question, we have studied theoretical approaches to explaining and assessing HRM in section 1, analyzed HRM system of an international enterprise in section 2, and then synthesized theory and practice to find intersection points in section 3.
Research findings indicate that the main challenge of HRM is to balance between individual and organizational interests. Implementation of OPD-SHRM is one of the solutions. Switching focus from satisfaction towards success will bring both tangible and intangible benefits for individuals and organization. In case of PrJSC “Philip Morris Ukraine”, the maximum forecasted increase is 330% in net profit, 350% in labor productivity, and 26% in Employee Development and Engagement Index.
Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.
This paper describes the robots TIAGo and Lisa used by
team homer@UniKoblenz of the University of Koblenz-Landau, Germany,
for the participation at the RoboCup@Home 2019 in Sydney,
Australia. We ended up first at RoboCup@Home 2019 in the Open Platform
League and won the competition in our league now three times
in a row (four times in total) which makes our team the most successful
in RoboCup@Home. We demonstrated approaches for learning from
demonstration, touch enforcing manipulation and autonomous semantic
exploration in the finals. 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, gesture recognition
and imitation learning. The packages are available (and new packages
will be released) on http://homer.uni-koblenz.de.
This dissertation deals with the opportunities and restrictions that parties face in an election campaign at the supranational level of the EU. Using communication science concepts of agenda-setting (focus: media) and agenda-building (focus: political parties), the first part of the study is based on the election campaign for the European Parliament (EP) in 2014. It analyses to what extent political parties put the EU on the agenda. Second, it is examined whether parties have used their structural advantage of being able to influence the media agenda at the supranational level during the election campaign in the context of the EP election campaign. Third, it is examined whether parties can gain an advantage for the visibility of their campaigns by rejecting EU integration and the associated conflictual communication. Fourth and final, it will be explored whether agenda-building can influence the rankings of specific policy issues on the media agenda in the European context.
First, the analyses show that a European political focus of election campaign communication can no longer be found only on the part of the small (eurosceptic) parties. Second, parties have a good chance of being present in media coverage if the they pursue a European political focus in their campaign communication. Third, a negative tone in party communication turns out not to be decisive for the parties' visibility in the election campaign. Fourth, a clear positioning on political issues also prepares parties for restrictions of the further development of a European thematic agenda. After a discussion of these results, the paper concludes with an assessment of the analysis limitations and an outlook on further research approaches.
Software systems have an increasing impact on our daily lives. Many systems process sensitive data or control critical infrastructure. Providing secure software is therefore inevitable. Such systems are rarely being renewed regularly due to the high costs and effort. Oftentimes, systems that were planned and implemented to be secure, become insecure because their context evolves. These systems are connected to the Internet and therefore also constantly subject to new types of attacks. The security requirements of these systems remain unchanged, while, for example, discovery of a vulnerability of an encryption algorithm previously assumed to be secure requires a change of the system design. Some security requirements cannot be checked by the system’s design but only at run time. Furthermore, the sudden discovery of a security violation requires an immediate reaction to prevent a system shutdown. Knowledge regarding security best practices, attacks, and mitigations is generally available, yet rarely integrated part of software development or covering evolution.
This thesis examines how the security of long-living software systems can be preserved taking into account the influence of context evolutions. The goal of the proposed approach, S²EC²O, is to recover the security of model-based software systems using co-evolution.
An ontology-based knowledge base is introduced, capable of managing common, as well as system-specific knowledge relevant to security. A transformation achieves the connection of the knowledge base to the UML system model. By using semantic differences, knowledge inference, and the detection of inconsistencies in the knowledge base, context knowledge evolutions are detected.
A catalog containing rules to manage and recover security requirements uses detected context evolutions to propose potential co-evolutions to the system model which reestablish the compliance with security requirements.
S²EC²O uses security annotations to link models and executable code and provides support for run-time monitoring. The adaptation of running systems is being considered as is round-trip engineering, which integrates insights from the run time into the system model.
S²EC²O is amended by prototypical tool support. This tool is used to show S²EC²O’s applicability based on a case study targeting the medical information system iTrust.
This thesis at hand contributes to the development and maintenance of long-living software systems, regarding their security. The proposed approach will aid security experts: It detects security-relevant changes to the system context, determines the impact on the system’s security and facilitates co-evolutions to recover the compliance with the security requirements.
Belief revision is the subarea of knowledge representation which studies the dynamics of epistemic states of an agent. In the classical AGM approach, contraction, as part of the belief revision, deals with the removal of beliefs in knowledge bases. This master's thesis presents the study and the implementation of concept contraction in the Description Logic EL. Concept contraction deals with the following situation. Given two concept C and D, assuming that C is subsumed by D, how can concept C be changed so that it is not subsumed by D anymore, but is as similar as possible to C? This approach of belief change is different from other related work because it deals with contraction in the level of concepts and not T-Boxes and A-Boxes in general. The main contribution of the thesis is the implementation of the concept contraction. The implementation provides insight into the complexity of contraction in EL, which is tractable since the main inference task in EL is also tractable. The implementation consists of the design of five algorithms that are necessary for concept contraction. The algorithms are described, illustrated with examples, and analyzed in terms of time complexity. Furthermore, we propose an new approach for a selection function, adapt for the concept contraction. The selection function uses metadata about the concepts in order to select the best from an input set. The metadata is modeled in a framework that we have designed, based on standard metadata frameworks. As an important part of the concept contraction, the selection function is responsible for selecting the best concepts that are as similar as possible to concept C. Lastly, we have successfully implemented the concept contraction in Python, and the results are promising.
To construct a business process model manually is a highly complex and error-prone task which takes a lot of time and deep insights into the organizational structure, its operations and business rules. To improve the output of business analysts dealing with this process, different techniques have been introduced by researchers to support them during construction with helpful recommendations. These supporting recommendation systems vary in their way of what to recommend in the first place as well as their calculations taking place under the hood to recommend the most fitting element to the user. After a broad introduction into the field of business process modeling and its basic recommendation structures, this work will take a closer look at diverse proposals and descriptions published in current literature regarding implementation strategies to effectively and efficiently assist modelers during their business process model creation. A critical analysis of presentations in the selected literature will point out strengths and weaknesses of their approaches, studies and descriptions of those. As a result, the final concept matrix in this work will give a precise and helpful overview about the key features and recommendation methods used and implemented in previous research studies to pinpoint an entry into future works without the downsides already spotted by fellow researchers.
Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.