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Invasive species play increasing roles worldwide. Invasions are considered successful when species establish and spread in their exotic range. Subsequently, dispersal is a major determinant of species’ range dynamics. Mermessus trilobatus, native to North America, has rapidly spread in Europe via aerial dispersal. Here we investigated the interplay of ecological and evolutionary processes behind its colonisation success.
First, we examined two possible ecological mechanisms. Similar to other invasive invertebrates, the colonisation success of Mermessus trilobatus might be related to human-induced habitat disturbance. Opposite to this expectation, our results showed that densities of Mermessus trilobatus decreased with soil disturbance in grasslands suggesting that its invasion success was not connected to a ruderal strategy. Further, invasive species often escape the ecological pressures from novel enemies in their exotic ranges. Unexpectedly, invasive Mermessus trilobatus was more sensitive to a native predator than native Erigone dentipalpis during our predator susceptibility trials. This indicates that the relation between the invasive spider and its native predator is dominated by prey naïveté rather than enemy release.
The remaining three chapters of the thesis investigated the dispersal behaviour of this invasive species. Hitherto, studies of passive aerial dispersal used wind as the primary dispersal-initiating factor despite a recent demonstration of the effects of the atmospheric electric fields on spiders’ pre-dispersal behaviour. During our experiments, only the wind facilitated the flight, although electric fields induced pre-dispersal behaviour in spiders. Consequently, studies around passive aerial dispersal should control electric fields but use wind as a stimulating factor.
Rapidly expanding species might be disproportionately distributed in their exotic range, with an accumulation of dispersive genotypes at the leading edge of their range. Such imbalanced spatial segregation is possible when the dispersal behaviour of expanding species is heritable. Our results showed that the dispersal traits of Mermessus trilobatus were heritable through both parents and for both sexes with recessive inheritance of high dispersal ability in this species.
Following the heritability experiments, we documented an accelerated spread of Mermessus trilobatus in Europe and tested whether dispersal, reproduction or competing ability was at the source of this pattern. Our results showed that the accumulation of more mobile but not reproductive or competitive genotypes at the expansion front of this invasive species gave rise to an accelerated range expansion by more than 1350 km in under 45 years.
Invasive Mermessus trilobatus is inferior to native sympatric species with respect to competing ability (Eichenberger et al., 2009), disturbance tolerance and predation pressure. Nevertheless, the species successfully established in its exotic range and spread by accelerating its expansion rate. Rapid reproduction that balances the high ecological pressures might be the other potential mechanism behind its colonisation success in Europe and deserves further investigation.
Agricultural intensification is leading to a severe decline in farmland biodiversity worldwide. The resulting landscape simplification through the expansion of monocultures and removal of non-crop habitats has a major impact on arthropod communities in agricultural landscapes. While arable fields are often highly disturbed and ephemeral habitats that are unsuitable for many species, non-crop habitats in agroecosystems can provide important refugia. The creation of non-crop habitats through agri-environmental schemes (AES) in intensive agricultural landscapes, such as the ‘Maifeld’ region in western Germany, is intended to mitigate the negative effects of agricultural intensification, although the effectiveness of these measures for nature conservation is still controversial. Therefore, this work focuses on the taxonomic and functional diversity of beetles (Coleoptera) and spiders (Araneida), being important providers of ecosystem services, between wheat fields and different non-crop habitats, namely grassy field margins adjacent to wheat and oilseed rape fields, small- and large-scale set-aside areas sown with wildflowers, and permanent grassland fallows. Arthropods were collected between 2019 and 2020 using pitfall traps and suction sampling. Land-use type influenced beetle and spider diversity in the study area, with significantly higher values in grassland fallows than wheat fields. Surprisingly, species diversity differed little among all non-crop habitats, but all harboured distinct species assemblages. In particular, large long-term grassland fallows showed the largest within-group variation of beetle and spider assemblages and represented important habitats, especially for habitat specialists and threatened species, likely due to their variable soil moisture and complex habitat structure. In contrast, the homogeneous arthropod assemblages of wheat fields exhibited lower trait richness and were dominated by a few predatory species adapted to such disturbed, man-made habitats. Interestingly, all conservation measures complemented each other in that they contributed in different ways to supporting beetles and spiders in agricultural landscapes. Even small-scale non-crop habitats and existing habitat boundaries in an agricultural matrix appear to be valuable habitats for farmland arthropods by enhancing taxonomic diversity. Field margins and small wildflower-sown patches can link isolated non-crop habitats and contribute to a heterogeneous agricultural landscape. Consequently, a combination of various small- and large-scale greening measures leads to increased compositional and configurational landscape heterogeneity, resulting in improved beetle and spider diversity. Considering the ongoing loss of farmland biodiversity worldwide, agri-environmental schemes should be promoted in the future, as they are particularly important for arthropod conservation in intensive agricultural landscapes such as the Maifeld region.
Remote Working Study 2022
(2022)
The Remote Working Study 2022 is focused on the transition to work from home (WFH) triggered by the stay at home directives of 2020. These directives required employees to work in their private premises wherever possible to reduce the transmission of the coronavirus. The study, conducted by the Center for Enterprise Information Research (CEIR) at the University of Koblenz from December 2021 to January 2022, explores the transition to remote working.
The objective of the survey is to collect baseline information about organisations’ remote work experiences during and immediately following the COVID-19 lockdowns. The survey was completed by the key persons responsible for the implementation and/or management of the digital workplace in 19 German and Swiss organisations.
The data presented in this report was collected from member organisations of the IndustryConnect initiative. IndustryConnect is a university-industry research programme that is coordinated by researchers from the University of Koblenz. It focuses on research in the areas of the digital workplace and enterprise collaboration technologies, and facilitates the generation of new research insights and the exchange of experiences among user companies.
The ongoing loss of species is a global threat to biodiversity, affecting ecosystems worldwide. This also concerns arthropods such as insects and spiders, which are especially endangered in agricultural ecosystems. Here, one of the main causing factors is management intensification. In areas with a high proportion of traditionally managed grassland, extensive hay meadows that are cut only once per year can still hold high levels of biodiversity, but are threatened by conversion into highly productive silage grassland. The Westerwald mountain range, western Germany, is such a region. In this thesis, I compare the local diversity of bees, beetles, hoverflies, leafhoppers, and spiders of five grassland management regimes along a gradient of land-use intensity. These comprise naturally occurring grassland fallows, three types of traditionally managed hay meadows, and intensively used silage grassland. By using three different sampling methods, I recorded ground-dwelling, flower-visiting, and vegetation-dwelling species. The results show that in most cases species richness and diversity are highest on fallows, whereas variation among different managed grassland types is very low. Also, for most sampled taxa, fallows harbour the most distinct species assemblages, while that of other management regimes are largely overlapping. Management has the largest effect on species composition, whereas environmental parameters are of minor importance. Long-term grassland fallows seem to be highly valuable for arthropod conservation, even in a landscape with a low overall land-use intensity, providing structural heterogeneity. In conclusion, such fallows should be subsidized agri-environmental schemes, to preserve insect and spider diversity.
Agriculture requires a sustainable intensification to feed the growing world population without exacer-bating soil degradation and threatening soil quality. Globally, plastic mulching (PM) is increasingly used to improve crop growth and yields and consequently agronomic productivity. However, recent literature reported also critical aspects of PM for soil quality and showed contradictory outcomes. This might result from the numerous applications of PM in different climates across various crops, soils and agri-cultural techniques. Thus, a closer look is necessary on how PM influences soil processes under certain climate and cultivation conditions to obtain a comprehensive understanding of its effects, which is im-portant to evaluate PM in terms of a sustainable agriculture.
The aim of this PhD thesis was to understand how multiannual PM influences soil properties and pro-cesses under the temperate, humid Central European cultivation conditions and to evaluate the resulting consequences for soil quality. I designed a three-year field study to investigate the influence of PM (black polyethylene, 50 μm) on microclimate, structural stability, soil organic matter (SOM) and the concentrations of selected fungicides and mycotoxins in three soil layers (0–10, 10–30 and 30–60 cm) compared to straw mulching (SM). Both mulching types were applied in a drip-irrigated ridge-furrow system in strawberry cultivation.
PM shifted the soil microclimate to higher soil temperatures and lower soil moistures. The higher soil temperature seems thus to be the key factor for the increased crop growth and yields under the present humid climate. The reduced soil moisture under PM indicated that under PM the impeded rainfall infil-tration had a stronger effect on the water balance than the reduced evaporation. This indicate an ineffi-cient rainwater use in contrast to arid climates. PM changed the water cycling in the ridges from down-ward directed water flows to lateral water flows from furrows to ridges. This reduced nitrogen leaching in the topsoil (0–10 cm) in the strawberry establishment period. The plastic mulches avoided aggregate breakdown due to rapid soil wetting and excess water during rainfalls and thus maintained a loose and stable soil structure in the surface soil, which prevents soil compaction and made soil less prone to erosion. PM changed carbon fluxes and transformation so that a larger total and more stable SOM was observed. Thus, the higher belowground biomass productivity under PM compensated the impeded aboveground biomass input and the temperature-induced SOM decomposition. However, SM increased the labile and total SOM in the topsoil after the first experiment year and promoted microbial growth due to the aboveground biomass incorporation. PM reduced fungicide entry into soil compared to SM and reduced consequently the fungal biomass reduction and the biosynthesis of the mycotoxin deoxyni-valenol. The modified microclimate under PM did not increase mycotoxin occurrence. In this context, PM poses no risk for an increased soil contamination, impairing soil quality. This PhD thesis demon-strated that the PM effects on soil can vary depending on time, season and soil depth, which emphasizes the importance to include soil depth and time in future studies.
Compared to semiarid and arid regions, the PM effects found in this PhD thesis were small, absent or in another way. I attributed this to the fact that PM under humid climate reduced instead of increased soil moisture and that SM had due to straw und strawberry canopy a similar ‘covering effect’ as PM. Thus, generalizing the PM effects on soil across different climates seems hardly possible as they differ in type and extent depending on climate. A differentiated consideration is hence necessary to evaluate the PM effects on soil quality. I conclude that PM under temperate, humid climate might contribute to reduce soil degradation (e.g., SOM depletion, erosion, nutrient leaching, soil compaction and soil contamina-tion), which sustains soil quality and helps to enable a sustainable agricultural intensification. However, further research is necessary (1) to support my findings on a larger scale, longer time periods and across various soil and crop types, (2) to address remaining open questions and (3) to develop optimization to overcome the critical aspects of PM (e.g. macro- and microplastic waste in soil, mulch disposal).
The decline of biodiversity can be observed worldwide and its consequences are alarming. It is therefore crucial that nature must be protected and, where possible, restored. A wide variety of different project options are possible. Yet in the context of limited availability of resources, the selection of the most efficient measures is increasingly important. For this purpose, there is still a lack of information. This pertains, as outlined in the next paragraph, in particular, to information at different scales of projects.
Firstly, there is a lack of information on the concrete added value of biodiversity protection projects. Secondly, there is a lack of information on the actual impacts of such projects and on the costs and benefits associated with a project. Finally, there is a lack of information on the links between the design of a project, the associated framework conditions and the perception of specific impacts. This paper addresses this knowledge gap by providing more information on the three scales by means of three empirical studies on three different biodiversity protection projects in order to help optimize future projects.
The first study “Assessing the trade-offs in more nature-friendly mosquito control in the Upper Rhine region” examines the added value of a more nature-friendly mosquito control in the Upper Rhine Valley of Germany using a contingent valuation method. Recent studies show that the widely used biocide Bti, which is used as the main mosquito control agent in many parts of the world, has more negative effects on nature than previously expected. However, it is not yet clear whether the population supports a more nature-friendly mosquito control, as such an adaptation could potentially lead to higher nuisance. This study attempts to answer this question by assessing the willingness to pay for an adapted mosquito control strategy that reduces the use of Bti, while maintaining nuisance protection within settlements. The results show that the majority of the surveyed population attaches a high value to a more nature-friendly mosquito control and is willing to accept a higher nuisance outside of the villages.
The second study “Inner city river restoration projects: the role of project components for acceptance” examines the acceptance of a river restoration project in Rhineland-Palatinate, Germany. Despite much effort, many rivers worldwide are still in poor condition. Therefore, a rapid implementation of river restoration projects is of great importance. In this context, acceptance by society plays a fundamental role, however, the factors determining such acceptance are still poorly understood. In particular, the complex interplay between the acceptance or rejection of specific project components and the acceptance of the overall project require further exploration. This study addresses this knowledge gap by assessing the acceptance of the project, its various ecological and social components, and the perception of real and fictitious costs as well as the benefits of the components. Our findings demonstrate that while acceptance of the overall project is generally rather high, many respondents reject one or more of the project's components. Complementary social project components, like a playground, find less support than purely ecological components. Overall, our research shows that complementary components may increase or decrease acceptance of the overall project. We, furthermore, found that differences in the acceptance of the individual components depend on individual concerns, such as perceived flood risk, construction costs, expected noise and littering as well as the quality of communication, attachment to the site, and the age of the respondents.
The third study “What determines preferences for semi-natural habitats in agrarian landscapes? A choice-modelling approach across two countries using attributes characterizing vegetation” investigates people's aesthetic preferences for semi-natural habitats in agricultural landscapes. The EU-Common Agricultural Policy promotes the introduction of woody and grassy semi-natural habitats (SNH) in agricultural landscapes. While the benefits of these structures in terms of regulating ecosystem services are already well understood, the effects of SNH on visual landscape quality is still not clear. This study investigates the factors determining people’s visual preferences in the context of grassy and woody SNH elements in Swiss and Hungarian landscapes using picture-based choice experiments. The results suggest that respondents’ choices strongly depend on specific vegetation characteristics that appear and disappear over the year. In particular, flowers as a source of colours and green vegetation as well as ordered structure and the proportion of uncovered soil in the picture play an important role regarding respondents’ aesthetic perceptions of the pictures.
The three empirical studies can help to make future projects in the study areas of biodiversity protection more efficient. While this thesis highlights the importance of exploring biodiversity protection projects at different scales, further analyses of the different scales of biodiversity protection projects are needed to provide a sound basis to develop guidance on identifying the most efficient biodiversity protection projects.
Objectives: Crayfish plague disease, caused by the oomycete pathogen Aphanomyces astaci represents one of the greatest risks for the biodiversity of the freshwater crayfish. This data article covers the de novo transcriptome assembly and annotation data of the noble crayfish and the marbled crayfish challenged with Ap. astaci. Following the controlled infection experiment (Francesconi et al. in Front Ecol Evol, 2021, https://doi.org/10.3389/fevo.2021.647037), we conducted a differential gene expression analysis described in (Boštjančić et al. in BMC Genom, 2022, https://doi.org/10.1186/s12864-022-08571-z) Data description: In total, 25 noble crayfish and 30 marbled crayfish were selected. Hepatopancreas tissue was isolated, followed by RNA sequencing using the Illumina NovaSeq 6000 platform. Raw data was checked for quality with FastQC, adapter and quality trimming were conducted using Trimmomatic followed by de novo assembly with Trinity. Assembly quality was assessed with BUSCO, at 93.30% and 93.98% completeness for the noble crayfish and the marbled crayfish, respectively. Transcripts were annotated using the Dammit! pipeline and assigned to KEGG pathways. Respective transcriptome and raw datasets may be reused as the reference transcriptome assemblies for future expression studies.
Predictive Process Monitoring is becoming more prevalent as an aid for organizations to support their operational processes. However, most software applications available today require extensive technical know-how by the operator and are therefore not suitable for most real-world scenarios. Therefore, this work presents a prototype implementation of a Predictive Process Monitoring dashboard in the form of a web application. The system is based on the PPM Camunda Plugin presented by Bartmann et al. (2021) and allows users to easily create metrics, visualizations to display these metrics, and dashboards in which visualizations can be arranged. A usability test is with test users of different computer skills is conducted to confirm the application’s user-friendliness.
Challenges of Implementing Innovation Strategies at Large Organizations: A case of Lotte Group
(2023)
For many decades, one of the most important focuses of research has been on determining whether or not there is a correlation between the size of an organization and its level of innovation. Unlike small companies, large companies often have well-established structure that are hard to change and change managements seems to be much more difficult especially related to innovation. Nevertheless, there are many examples to prove the opposites. Some large organization like Apple, Amazon... always show great innovation efforts and keep changing in a much positive way. Therefore, the aim of this thesis is to discuss of how large organization can be able to implement innovation when having much drawbacks compare to SMEs. Through the use of a qualitative research approach, researcher was able to explore essential information on the innovation strategies that large companies are using in order to innovate and how they could overcome existing challenges by studying the working process of Lotte Group – one of the biggest companies in Korea.
In the last decade, policy-makers around the world have turned their attention toward the creative industry as the economic engine and significant driver of employments. Yet, the literature suggests that creative workers are one of the most vulnerable work-forces of today’s economy. Because of the highly deregulated and highly individuated environment, failure or success are believed to be the byproduct of individual ability and commitment, rather than a structural or collective issue. This thesis taps into the temporal, spatial, and social resolution of digital behavioural data to show that there are indeed structural and historical issues that impact individuals’ and
groups’ careers. To this end, this thesis offers a computational social science research framework that brings together the decades-long theoretical and empirical knowledge of inequality studies, and computational methods that deal with the complexity and scale of digital data. By taking music industry and science as use cases, this thesis starts off by proposing a novel gender detection method that exploits image search and face-detection methods.
By analysing the collaboration patterns and citation networks of male and female computer scientists, it sheds lights on some of the historical biases and disadvantages that women face in their scientific career. In particular, the relation of scientific success and gender-specific collaboration patterns is assessed. To elaborate further on the temporal aspect of inequalities in scientific careers, this thesis compares the degree of vertical and horizontal inequalities among the cohorts of scientists that started their career at different point in time. Furthermore, the structural inequality in music industry is assessed by analyzing the social and cultural relations that breed from live performances and musics releases. The findings hint toward the importance of community belonging at different stages of artists’ careers. This thesis also quantifies some of the underlying mechanisms and processes of inequality, such as the Matthew Effect and the Hipster Paradox, in creative careers. Finally, this thesis argues that online platforms such as Wikipedia could reflect and amplify the existing biases.
The diversity within amphibian communities in cultivated areas in Rwanda and within two selected, taxonomically challenging groups, the genera Ptychadena and Hyperolius, were investigated in this thesis. The amphibian community of an agricultural wetland near Butare in southern Rwanda comprised 15 anuran species. Rarefaction and jackknife analyses corroborated that the complete current species richness of the assemblage had been recorded, and the results of acoustic niche analysis suggested species saturation of the community. Surveys at many other Rwandan localities showed that the species recorded in Butare are widespread in cultivated and pristine wetlands. The species were readily distinguishable using morphological, bioacoustic, and molecular (DNA barcoding) features, but only eight of the 15 species could be assigned unambiguously to nominal species. The remaining represented undescribed or currently unrecognized taxa, including three species of Hyperolius, two Phrynobatrachus species, one Ptychadena species, and one species of Amietia. The diversity of the Ridged Frogs in Rwanda was investigated in two studies (Chapters III and IV). Three species of Ptychadena were recorded in wetlands in the catchment of the Nile. They can be distinguished by morphological characters (morphometrics and qualitative features) as well as by their advertisement calls and genetics. The Rwandan species of the P. mascareniensis group was shown to differ from the topotypic population as well as from other genetic lineages in sub-Saharan Africa and an old available name, P. nilotica, was resurrected from synonymy for this lineage. Two further Ptychadena species were identified among voucher specimens from Rwanda deposited in the collection of the RMCA, P. chrysogaster and P. uzungwensis. Morphologically they can be unambiguously distinguished from each other and the three other Rwandan species. A key based on qualitative morphological characters was developed, which allows unequivocal identification of specimens of all species that have been recorded from Rwanda. DNA was isolated from a Rwandan voucher specimen of P. chrysogaster, and the genetic analysis corroborated the species" distinct status.
A species of Hyperolius collected in the Nyungwe National Park was compared to all other Rwandan species of the genus and to morphologically or genetically similar species from neighbouring countries. Its distinct taxonomic status was justified by morphological, bioacoustic, and molecular evidence and it was described as a new species, H. jackie. A species of the H. nasutus group collected at agricultural sites in Rwanda was described as a new species in the course of a revision of the species of the Hyperolius nasutus group. The group was shown to consist of 15 distinct species which can be distinguished from each other genetically, bioacoustically, and morphologically.
The aerial performance, i.e. parachuting, of the Disc-fingered Reed Frog, Hyperolius discodactylus, was described. It represents a novel observation of a behaviour that has been known from a number of Southeast Asian and Neotropical frog species. Parachuting frogs, including H. discodactylus, exhibit certain morphological characteristics and, while airborne, assume a distinct posture which is best-suited for maneuvering in the air. Another study on the species addressed the validity of the taxon H. alticola which had been considered either a synonym of H. discodactylus or a distinct species. Type material of both taxa was re-examined and the status of H. alticola reassessed using morphological data from historic and new collections, call recordings, and molecular data from animals collected on recent expeditions. A northern and a southern genetic clade were identified, a divide that is weakly supported by diverging morphology of the vouchers from the respective localities. No distinction in advertisement call features could be recovered to support this split and both genetic and morphological differences between the two geographic clades are marginal and not always congruent and more likely reflect population-level variation. Therefore it was concluded that H. alticola is not a valid taxon and should be treated as a synonym of H. discodactylus.
On the recognition of human activities and the evaluation of its imitation by robotic systems
(2023)
This thesis addresses the problem of action recognition through the analysis of human motion and the benchmarking of its imitation by robotic systems.
For our action recognition related approaches, we focus on presenting approaches that generalize well across different sensor modalities. We transform multivariate signal streams from various sensors to a common image representation. The action recognition problem on sequential multivariate signal streams can then be reduced to an image classification task for which we utilize recent advances in machine learning. We demonstrate the broad applicability of our approaches formulated as a supervised classification task for action recognition, a semi-supervised classification task for one-shot action recognition, modality fusion and temporal action segmentation.
For action classification, we use an EfficientNet Convolutional Neural Network (CNN) model to classify the image representations of various data modalities. Further, we present approaches for filtering and the fusion of various modalities on a representation level. We extend the approach to be applicable for semi-supervised classification and train a metric-learning model that encodes action similarity. During training, the encoder optimizes the distances in embedding space for self-, positive- and negative-pair similarities. The resulting encoder allows estimating action similarity by calculating distances in embedding space. At training time, no action classes from the test set are used.
Graph Convolutional Network (GCN) generalized the concept of CNNs to non-Euclidean data structures and showed great success for action recognition directly operating on spatio-temporal sequences like skeleton sequences. GCNs have recently shown state-of-the-art performance for skeleton-based action recognition but are currently widely neglected as the foundation for the fusion of various sensor modalities. We propose incorporating additional modalities, like inertial measurements or RGB features, into a skeleton-graph, by proposing fusion on two different dimensionality levels. On a channel dimension, modalities are fused by introducing additional node attributes. On a spatial dimension, additional nodes are incorporated into the skeleton-graph.
Transformer models showed excellent performance in the analysis of sequential data. We formulate the temporal action segmentation task as an object detection task and use a detection transformer model on our proposed motion image representations. Experiments for our action recognition related approaches are executed on large-scale publicly available datasets. Our approaches for action recognition for various modalities, action recognition by fusion of various modalities, and one-shot action recognition demonstrate state-of-the-art results on some datasets.
Finally, we present a hybrid imitation learning benchmark. The benchmark consists of a dataset, metrics, and a simulator integration. The dataset contains RGB-D image sequences of humans performing movements and executing manipulation tasks, as well as the corresponding ground truth. The RGB-D camera is calibrated against a motion-capturing system, and the resulting sequences serve as input for imitation learning approaches. The resulting policy is then executed in the simulated environment on different robots. We propose two metrics to assess the quality of the imitation. The trajectory metric gives insights into how close the execution was to the demonstration. The effect metric describes how close the final state was reached according to the demonstration. The Simitate benchmark can improve the comparability of imitation learning approaches.
FinTech is deemed to be an underexplored phenomenon even in academic and real environments. Among (1) “Sustainable FinTech” – the application of information technology as innovation in established financial services providers’ business operation; and (2) “Disruptive FinTech” – the provision of financial products and services by non-incumbents which in most cases are information technology entrepreneurs, the former receives more attention. In order to contribute to Disruptive FinTech category, the thesis strive to examine Entrepreneurial Strategy framework applied for technology players taking part in Vietnam financial market.
Digital Transformation Maturity of Vietnam Aviation Industry: The Effect of Organizational Readiness
(2023)
The paper studies the digital transformation maturity in the context of the aviation industry in Vietnam. Digital transformation can mean enhancing existing processes, finding new opportunities within existing business domains, or finding new opportunities outside existing business domains. In the era of post Covid-19, digital transformation will play a vital role in the recovery with the support from digital technology to leverage the communication and implementation of new projects or changes.
Digital transformation and digital transformation maturity sometimes are used indistinguishing, but they are two different definitions. This paper will further explain the differences and will apply digital transformation maturity as a scale for the digital transformation in the report.
Due to the lack of experiment in the relationship between digital transformation maturity and the organizational readiness, the study will explore four components of organizational readiness, including digital leadership, digital culture, digital capabilities, and digital partnering.
The paper is a study focusing on exploring which factors and examining the impact of those factors influencing the entrepreneurial intention among students in the Construction industry, specifically among students of Hanoi Construction University and Hanoi Architecture University. The study also mentions some solution of this findings for entrepreneurship in the Construction field in Vietnam that the author might think of based on this research work for future study. The Theory of planned behavior is used as the theoritical framework for this study. Both qualitative and quantitative methods are employed. The questionaire will be conducted among students of the two universities mentioned above. Then, an exploratory factor analysis (EFA) will performed to test the validity of the constructs. The research findings provide factors and their impact factors influencing the entrepreneurial intention and propose some solutions to improve the entrepreneurship in the Construction field in Vietnam.
Digital transformation is a prevailing trend in the world, especially in dynamic Asia. Vietnam has recorded remarkable changes in the economy as domestic enterprises have made new strides in the digital transformation process. MB Bank, one of the prestigious financial groups in Vietnam, also takes advantage of digital transformation to have the opportunity to break through to become a large-scale technology enterprise with many factors such as improving customer experience, increasing customer base and increasing customer satisfaction. enhance competitiveness, build trust and loyalty for customers. However, in the process of converting MB, there are also many challenges that require banks to have appropriate policies to handle. It can be said that MB Bank is a typical case study of digital transformation in the banking sector in Vietnam.
With the increasing importance and urgency of climate change, companies are challenged to contribute to sustainable development, especially by younger generations. However, existing corporate contributions have been criticized as insufficient, which could be particularly caused by a lack of employee engagement in corporate sustainability. In this context, gamification has been proposed and increasingly investigated in recent years as a promising, innovative tool to motivate sustainable employee behaviors in the workplace. However, there are few studies and applicable gamification solutions that address more than one specific sustainability issue and thus take a holistic perspective on sustainable behaviors in the workplace. Moreover, previous research lacks a comprehensive understanding of how different gamification elements elicit specific psychological effects, how these manifest in behavioral changes, and how these, in turn, cumulatively result in measurable corporate outcomes. The path from gamification as ”input” to corporate sustainability as ”output” thus remains unexplored.
This dissertation fills this gap by conceptualizing, designing, and evaluating a holistic gamified intervention that supports employees in various sustainable behaviors in their daily activities. The project uses a design science research approach that closely involves employees in the incremental development of the solution. As part of the iterative design process, this dissertation presents six studies to extend the theoretical understanding of gamification for sustainable employee behaviors. First, a comprehensive review of existing research on gamification for sustainable employee behavior is provided, analyzing gamification designs and results of previous studies and outlining an agenda for further research (Study 1). Theoretical foundations of research on gamification, serious games, and game-based learning (Study 2) and empirical design principles for gamification and persuasive systems (Study 3) are then systematically reviewed as a basis for the successful design of gamified applications. Subsequently, empirical studies explore employees’ motivations for sustainable behavior and illuminate their expectations for design features (Study 4), and identify contextual challenges and design dilemmas when implementing gamification in an organizational context (Study 5). Finally, a quantitative field study (Study 6) explores how different gamification designs influence sustainable employee behavior and corporate sustainability in organizations. Based on the findings, this dissertation presents a comprehensive framework of gamification for sustainable employee behavior that incorporates design, individual behavior, and organizational perspectives. Finally, building on these insights, it provides practical recommendations for designing gamification to encourage sustainable employee behavior at work.
Counts of SARS-CoV-2-related deaths have been key numbers for justifying severe political, social and economical measures imposed by authorities world-wide. A particular focus thereby was the concomitant excess mortality (EM), i.e. fatalities above the expected all-cause mortality (AM). Recent studies, inter alia by the WHO, estimated the SARS-CoV-2-related EM in Germany between 2020 and 2021 as high as 200 000. In this study, we attempt to scrutinize these numbers by putting them into the context of German AM since the year 2000. We propose two straightforward, age-cohort-dependent models to estimate German AM for the ‘Corona pandemic’ years, as well as the corresponding flu seasons, out of historic data. For Germany, we find overall negative EM of about −18 500 persons for the year 2020, and a minor positive EM of about 7000 for 2021, unveiling that officially reported EM counts are an exaggeration. In 2022, the EM count is about 41 200. Further, based on NAA-test-positive related death counts, we are able to estimate how many Germans have died due to rather than with CoViD-19; an analysis not provided by the appropriate authority, the RKI. Through 2020 and 2021 combined, our due estimate is at no more than 59 500. Varying NAA test strategies heavily obscured SARS-CoV-2-related EM, particularly within the second year of the proclaimed pandemic. We compensated changes in test strategies by assuming that age-cohort-specific NAA-conditional mortality rates during the first pandemic year reflected SARS-CoV-2-characteristic constants.
X-ray computer tomography (XRT) is a three-dimensional, nondestructive, and thus reproducible examination method that allows for the investigation of internal and external structures of objects. Due to its characteristics, the XRT technique has increasingly established itself as an alternative examination method and is also applied in the field of mineral processing. Within this work, XRT is used to investigate the influence of hydrochloric acid leaching of iron-rich bauxites on grain composition. Acid leaching is a promising method for the beneficiation of iron-rich bauxites for refractories. Many studies have already established that leaching with hydrochloric acid can reduce the Fe₂O₃ content in bauxites. However, apart from the influence of the leaching process on the composition of the bauxites, aspects such as the influence of the acid on the exact grain constitution or the porosity behavior have rarely been considered so far. To address these open questions, XRT analysis was used to examine and characterize various bauxites. By comparing identical grains before and after leaching, it was observed that in gibbsite bauxites the acid penetration is deeper, and the volume decreases significantly. In diasporic and boehmitic bauxites, clear leaching edges can be seen in which the iron content has been reduced.
Leichte Sprache (LS, easy-to-read German) is a simplified variety of German. It is used to provide barrier-free texts for a broad spectrum of people, including lowliterate individuals with learning difficulties, intellectual or developmental disabilities (IDD) and/or complex communication needs (CCN). In general, LS authors are proficient in standard German and do not belong to the aforementioned group of people. Our goal is to empower the latter to participate in written discourse themselves. This requires a special writing system whose linguistic support and ergonomic software design meet the target group’s specific needs. We present EasyTalk a system profoundly based on natural language processing (NLP) for assistive writing in an extended variant of LS (ELS). EasyTalk provides users with a personal vocabulary underpinned with customizable communication symbols and supports in writing at their individual level of proficiency through interactive user guidance. The system minimizes the grammatical knowledge needed to produce correct and coherent complex contents by intuitively formulating linguistic decisions. It provides easy dialogs for selecting options from a natural-language paraphrase generator, which provides context-sensitive suggestions for sentence components and correctly inflected word forms. In addition, EasyTalk reminds users to add text elements that enhance text comprehensibility in terms of audience design (e.g., time and place of an event) and improve text coherence (e.g., explicit connectors to express discourse-relations). To tailor the system to the needs of the target group, the development of EasyTalk followed the principles of human-centered design (HCD). Accordingly, we matured the system in iterative development cycles, combined with purposeful evaluations of specific aspects conducted with expert groups from the fields of CCN, LS, and IT, as well as L2 learners of the German language. In a final case study, members of the target audience tested the system in free writing sessions. The study confirmed that adults with IDD and/or CCN who have low reading, writing, and computer skills can write their own personal texts in ELS using EasyTalk. The positive feedback from all tests inspires future long-term studies with EasyTalk and further development of this prototypical system, such as the implementation of a so-called Schreibwerkstatt (writing workshop)
In the last years, the public interest in epidemiology and mathematical modeling of disease spread has increased - mainly caused by the COVID-19 pandemic, which has emphasized the urgent need for accurate and timely modelling of disease transmission. However, even prior to that, mathematical modelling has been used for describing the dynamics and spread of infectious diseases, which is vital for developing effective interventions and controls, e.g., for vaccination campaigns and social restrictions like lockdowns. The forecasts and evaluations provided by these models influence political actions and shape the measures implemented to contain the virus.
This research contributes to the understanding and control of disease spread, specifically for Dengue fever and COVID-19, making use of mathematical models and various data analysis techniques. The mathematical foundations of epidemiological modelling, as well as several concepts for spatio-temporal diffusion like ordinary differential equation (ODE) models, are presented, as well as an originally human-vector model for Dengue fever, and the standard (SEIR)-model (with the potential inclusion of an equation for deceased persons), which are suited for the description of COVID-19. Additionally, multi-compartment models, fractional diffusion models, partial differential equations (PDE) models, and integro-differential models are used to describe spatial propagation of the diseases.
We will make use of different optimization techniques to adapt the models to medical data and estimate the relevant parameters or finding optimal control techniques for containing diseases using both Metropolis and Lagrangian methods. Reasonable estimates for the unknown parameters are found, especially in initial stages of pandemics, when little to no information is available and the majority of the population has not got in contact with the disease. The longer a disease is present, the more complex the modelling gets and more things (vaccination, different types, etc.) appear and reduce the estimation and prediction quality of the mathematical models.
While it is possible to create highly complex models with numerous equations and parameters, such an approach presents several challenges, including difficulties in comparing and evaluating data, increased risk of overfitting, and reduced generalizability. Therefore, we will also consider criteria for model selection based on fit and complexity as well as the sensitivity of the model with respect to specific parameters. This also gives valuable information on which political interventions should be more emphasized for possible variations of parameter values.
Furthermore, the presented models, particularly the optimization using the Metropolis algorithm for parameter estimation, are compared with other established methods. The quality of model calculation, as well as computational effort and applicability, play a role in this comparison. Additionally, the spatial integro-differential model is compared with an established agent-based model. Since the macroscopic results align very well, the computationally faster integro-differential model can now be used as a proxy for the slower and non-traditionally optimizable agent-based model, e.g., in order to find an apt control strategy.
In a world where language defines the boundaries of one's understanding, the words of Austrian philosopher Ludwig Wittgenstein resonate profoundly. Wittgenstein's assertion that "Die Grenzen meine Sprache bedeuten die Grenzen meiner Welt" (Wittgenstein 2016: v. 5.6) underscores the vital role of language in shaping our perceptions. Today, in a globalized and interconnected society, fluency in foreign languages is indispensable for individual success. Education must break down these linguistic barriers, and one promising approach is the integration of foreign languages into content subjects.
Teaching content subjects in a foreign language, a practice known as Content Language Integrated Learning (CLIL), not only enhances language skills but also cultivates cognitive abilities and intercultural competence. This approach expands horizons and aligns with the core principles of European education (Leaton Gray, Scott & Mehisto 2018: 50). The Kultusministerkonferenz (KMK) recognizes the benefits of CLIL and encourages its implementation in German schools (cf. KMK 2013a).
With the rising popularity of CLIL, textbooks in foreign languages have become widely available, simplifying teaching. However, the appropriateness of the language used in these materials remains an unanswered question. If textbooks impose excessive linguistic demands, they may inadvertently limit students' development and contradict the goal of CLIL.
This thesis focuses on addressing this issue by systematically analyzing language requirements in CLIL teaching materials, emphasizing receptive and productive skills in various subjects based on the Common European Framework of Reference. The aim is to identify a sequence of subjects that facilitates students' language skill development throughout their school years. Such a sequence would enable teachers to harness the full potential of CLIL, fostering a bidirectional approach where content subjects facilitate language learning.
While research on CLIL is extensive, studies on language requirements for bilingual students are limited. This thesis seeks to bridge this gap by presenting findings for History, Geography, Biology, and Mathematics, allowing for a comprehensive understanding of language demands. This research endeavors to enrich the field of bilingual education and CLIL, ultimately benefiting the academic success of students in an interconnected world.
Potential impacts of invasive crayfish on native
benthic fish: shelter use and agonistic behaviour
(2023)
Spinycheek crayfish (Faxonius limosus) and signal crayfish (Pacifastacus leniusculus) are successful North American invasive crayfish species distributed throughout Europe. Both species compete with native benthic fish for shelter. In a laboratory approach, we assessed competition for shelter and antagonistic interactions between these invasive crayfish species and the native benthic fish species, stone loach (Barbatula barbatula) and bullhead (Cottus gobio). This allows for studying the potential impacts of invasive crayfish on native benthic fish. Spinycheek crayfish and signal crayfish were able to gain control of the shelter and could successfully displace both benthic fish species. For stone loach, the presence of crayfish significantly decreased their shelter use and caused several behavioural changes such as reduced activity and increased hiding behaviour outside the shelter. Although the shelter use by bullheads was not reduced, they displayed similar behavioural changes, if less intense. Invasive crayfish species showed remarkable combative interactions against both species of benthic fishes, evidenced by the high number of aggressive interactions, especially concerning stone loach. Our results highlight the pronounced dominance of invasive crayfish over benthic fish in terms of shelter competition and aggressive interactions under laboratory conditions, which consequently might promote the latter’s exposure to predation.
Increasingly, problematic smartphone use behavior (PSU) and excessive consumption are reported. In this study, an experiment was developed to investigate the influence of screen coloration using the grayscale setting on smartphone usage time in repeated measurements. We also investigated how individuals perceived suffering correlates with smartphone usage time and PSU, and whether differences exist by smartphone usage type (social, process, habitual). 240 subjects completed a questionnaire about smartphone usage time, PSU, perceived suffering, and smartphone usage types. Afterward, their smartphones were switched to grayscale setting for at least 24h, and thereafter 92 of these participants completed the second questionnaire. Analyses showed that grayscale setting decreases usage time and that there is a positive correlation between PSU, smartphone usage duration, and perceived suffering. The types of use (process and habitual) influence one’s perceived suffering. Thus, it shows that individuals are aware of their PSU and suffer from it. Using grayscale setting is effective in reducing smartphone use time.
Artificial neural networks is a popular field of research in artificial intelli-
gence. The increasing size and complexity of huge models entail certain
problems. The lack of transparency of the inner workings of a neural net-
work makes it difficult to choose efficient architectures for different tasks.
It proves to be challenging to solve these problems, and with a lack of in-
sightful representations of neural networks, this state of affairs becomes
entrenched. With these difficulties in mind a novel 3D visualization tech-
nique is introduced. Attributes for trained neural networks are estimated
by utilizing established methods from the area of neural network optimiza-
tion. Batch normalization is used with fine-tuning and feature extraction to
estimate the importance of different parts of the neural network. A combi-
nation of the importance values with various methods like edge bundling,
ray tracing, 3D impostor and a special transparency technique results in a
3D model representing a neural network. The validity of the extracted im-
portance estimations is demonstrated and the potential of the developed
visualization is explored.
The trends of industry 4.0 and the further enhancements toward an ever changing factory lead to more mobility and flexibility on the factory floor. With that higher need of mobility and flexibility the requirements on wireless communication rise. A key requirement in that setting is the demand for wireless Ultra-Reliability and Low Latency Communication (URLLC). Example use cases therefore are cooperative Automated Guided Vehicles (AGVs) and mobile robotics in general. Working along that setting this thesis provides insights regarding the whole network stack. Thereby, the focus is always on industrial applications. Starting on the physical layer, extensive measurements from 2 GHz to 6 GHz on the factory floor are performed. The raw data is published and analyzed. Based on that data an improved Saleh-Valenzuela (SV) model is provided. As ad-hoc networks are highly depended onnode mobility, the mobility of AGVs is modeled. Additionally, Nodal Encounter Patterns (NEPs) are recorded and analyzed. A method to record NEP is illustrated. The performance by means of latency and reliability are key parameters from an application perspective. Thus, measurements of those two parameters in factory environments are performed using Wireless Local Area Network (WLAN) (IEEE 802.11n), private Long Term Evolution (pLTE) and 5G. This showed auto-correlated latency values. Hence, a method to construct confidence intervals based on auto-correlated data containing rare events is developed. Subsequently, four performance improvements for wireless networks on the factory floor are proposed. Of those optimization three cover ad-hoc networks, two deal with safety relevant communication, one orchestrates the usage of two orthogonal networks and lastly one optimizes the usage of information within cellular networks.
Finally, this thesis is concluded by an outlook toward open research questions. This includes open questions remaining in the context of industry 4.0 and further the ones around 6G. Along the research topics of 6G the two most relevant topics concern the ideas of a network of networks and overcoming best-effort IP.
This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.
Focusing on the triangulation of detective fiction, masculinity studies and disability studies, "Investigating the Disabled Detective – Disabled Masculinity and Masculine Disability in Contemporary Detective Fiction" shows that disability challenges common ideals of (hegemonic) masculinity as represented in detective fiction. After a theoretical introduction to the relevant focal points of the three research fields, the dissertation demonstrates that even the archetypal detectives Dupin and Holmes undermine certain nineteenth-century masculine ideals with their peculiarities. Shifting to contemporary detective fiction and adopting a literary disability studies perspective, the dissertation investigates how male detectives with a form of neurodiversity or a physical impairment negotiate their masculine identity in light of their disability in private and professional contexts. It argues that the occupation as a detective supports the disabled investigator to achieve ‘masculine disability’. Inversing the term ‘disabled masculinity’, predominantly used in research, ‘masculine disability’ introduces a decisively gendered reading of neurodiversity and (acquired) physical impairment in contemporary detective fiction. The term implies that the disabled detective (re)negotiates his masculine identity by implementing the disability in his professional investigations and accepting it as an important, yet not defining, characteristic of his (gender) identity. By applying this approach to five novels from contemporary British and American detective fiction, the dissertation demonstrates that masculinity and disability do not negate each other, as commonly assumed. Instead, it emphasises that disability allows the detective, as much as the reader, to rethink masculinity.
Empirical studies in software engineering use software repositories as data sources to understand software development. Repository data is either used to answer questions that guide the decision-making in the software development, or to provide tools that help with practical aspects of developers’ everyday work. Studies are classified into the field of Empirical Software Engineering (ESE), and more specifically into Mining Software Repositories (MSR). Studies working with repository data often focus on their results. Results are statements or tools, derived from the data, that help with practical aspects of software development. This thesis focuses on the methods and high order methods used to produce such results. In particular, we focus on incremental methods to scale the processing of repositories, declarative methods to compose a heterogeneous analysis, and high order methods used to reason about threats to methods operating on repositories. We summarize this as technical and methodological improvements. We contribute the improvements to methods and high-order methods in the context of MSR/ESE to produce future empirical results more effectively. We contribute the following improvements. We propose a method to improve the scalability of functions that abstract over repositories with high revision count in a theoretically founded way. We use insights on abstract algebra and program incrementalization to define a core interface of highorder functions that compute scalable static abstractions of a repository with many revisions. We evaluate the scalability of our method by benchmarks, comparing a prototype with available competitors in MSR/ESE. We propose a method to improve the definition of functions that abstract over a repository with a heterogeneous technology stack, by using concepts from declarative logic programming and combining them with ideas on megamodeling and linguistic architecture. We reproduce existing ideas on declarative logic programming with languages close to Datalog, coming from architecture recovery, source code querying, and static program analysis, and transfer them from the analysis of a homogeneous to a heterogeneous technology stack. We provide a prove-of-concept of such method in a case study. We propose a high-order method to improve the disambiguation of threats to methods used in MSR/ESE. We focus on a better disambiguation of threats, operationalizing reasoning about them, and making the implications to a valid data analysis methodology explicit, by using simulations. We encourage researchers to accomplish their work by implementing ‘fake’ simulations of their MSR/ESE scenarios, to operationalize relevant insights about alternative plausible results, negative results, potential threats and the used data analysis methodologies. We prove that such way of simulation based testing contributes to the disambiguation of threats in published MSR/ESE research.
This thesis explores and examines the effectiveness and efficacy of traditional machine learning (ML), advanced neural networks (NN) and state-of-the-art deep learning (DL) models for identifying mental distress indicators from the social media discourses based on Reddit and Twitter as they are immensely used by teenagers. Different NLP vectorization techniques like TF-IDF, Word2Vec, GloVe, and BERT embeddings are employed with ML models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) followed by NN models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to methodically analyse their impact as feature representation of models. DL models such as BERT, DistilBERT, MentalRoBERTa and MentalBERT are end-to-end fine tuned for classification task. This thesis also compares different text preprocessing techniques such as tokenization, stopword removal and lemmatization to assess their impact on model performance. Systematic experiments with different configuration of vectorization and preprocessing techniques in accordance with different model types and categories have been implemented to find the most effective configurations and to gauge the strengths, limitations, and capability to detect and interpret the mental distress indicators from the text. The results analysis reveals that MentalBERT DL model significantly outperformed all other model types and categories due to its specific pretraining on mental data as well as rigorous end-to-end fine tuning gave it an edge for detecting nuanced linguistic mental distress indicators from the complex contextual textual corpus. This insights from the results acknowledges the ML and NLP technologies high potential for developing complex AI systems for its intervention in the domain of mental health analysis. This thesis lays the foundation and directs the future work demonstrating the need for collaborative approach of different domain experts as well as to explore next generational large language models to develop robust and clinically approved mental health AI systems.