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Faculty / Organisational entity
- Kaiserslautern - Fachbereich Informatik (18)
- Kaiserslautern - Fachbereich Mathematik (18)
- Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik (13)
- Kaiserslautern - Fachbereich Biologie (11)
- Kaiserslautern - Fachbereich Elektrotechnik und Informationstechnik (7)
- Kaiserslautern - Fachbereich Physik (7)
- Kaiserslautern - Fachbereich Sozialwissenschaften (6)
- Kaiserslautern - Fachbereich Chemie (5)
- Kaiserslautern - Fachbereich Wirtschaftswissenschaften (3)
- Kaiserslautern - Fachbereich Raum- und Umweltplanung (2)
Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis.
This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals.
I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115
people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.
This research explores the development of web based reference software for
characterisation of surface roughness for two-dimensional surface data. The reference software used for verification of surface characteristics makes the evaluation methods easier for clients. The algorithms used in this software
are based on International ISO standards. Most software used in industrial measuring
instruments may give variations in the parameters calculated due to numerical changes in
calculation. Such variations can be verified using the proposed reference software.
The evaluation of surface roughness is carried out in four major steps: data capture, data
align, data filtering and parameter calculation. This work walks through each of these steps
explaining how surface profiles are evaluated by pre-processing steps called fitting and
filtering. The analysis process is then followed by parameter evaluation according to DIN EN
ISO 4287 and DIN EN ISO 13565-2 standards to extract important information from the
profile to characterise surface roughness.
Due to the steadily growing flood of data, the appropriate use of visualizations for efficient data analysis is as important today as it has never been before. In many application domains, the data flood is based on processes that can be represented by node-link diagrams. Within such a diagram, nodes may represent intermediate results (or products), system states (or snapshots), milestones or real (and possibly georeferenced) objects, while links (edges) can embody transition conditions, transformation processes or real physical connections. Inspired by the engineering sciences application domain and the research project “SinOptiKom: Cross-sectoral optimization of transformation processes in municipal infrastructures in rural areas”, a platform for the analysis of transformation processes has been researched and developed based on a geographic information system (GIS). Caused by the increased amount of available and interesting data, a particular challenge is the simultaneous visualization of several visible attributes within one single diagram instead of using multiple ones. Therefore, two approaches have been developed, which utilize the available space between nodes in a diagram to display additional information.
Motivated by the necessity of appropriate result communication with various stakeholders, a concept for a universal, dashboard-based analysis platform has been developed. This web-based approach is conceptually capable of displaying data from various data sources and has been supplemented by collaboration possibilities such as sharing, annotating and presenting features.
In order to demonstrate the applicability and usability of newly developed applications, visualizations or user interfaces, extensive evaluations with human users are often inevitable. To reduce the complexity and the effort for conducting an evaluation, the browser-based evaluation framework (BREF) has been designed and implemented. Through its universal and flexible character, virtually any visualization or interaction running in the browser can be evaluated with BREF without any additional application (except for a modern web browser) on the target device. BREF has already proved itself in a wide range of application areas during the development and has since grown into a comprehensive evaluation tool.
Nowadays, the increasing demand for ever more customizable products has emphasized the need for more flexible and fast-changing manufacturing systems. In this environment, simulation has become a strategic tool for the design, development, and implementation of such systems. Simulation represents a relatively low-cost and risk-free alternative for testing the impact and effectiveness of changes in different aspects of manufacturing systems.
Systems that deal with this kind of data for its use in decision making processes are known as Simulation-Based Decision Support Systems (SB-DSS). Although most SB-DSS provide a powerful variety of tools for the automatic and semi-automatic analysis of simulations, visual and interactive alternatives for the manual exploration of the results are still open to further development.
The work in this dissertation is focused on enhancing decision makers’ analysis capabilities by making simulation data more accessible through the incorporation of visualization and analysis techniques. To demonstrate how this goal can be achieved, two systems were developed. The first system, viPhos – standing for visualization of Phos: Greek for light –, is a system that supports lighting design in factory layout planning. viPhos combines simulation, analysis, and visualization tools and techniques to facilitate the global and local (overall factory or single workstations, respectively) interactive exploration and comparison of lighting design alternatives.
The second system, STRAD - standing for Spatio-Temporal Radar -, is a web-based systems that considers the spatio/attribute-temporal analysis of event data. Since decision making processes in manufacturing also involve the monitoring of the systems over time, STRAD enables the multilevel exploration of event data (e.g., simulated or historical registers of the status of machines or results of quality control processes).
A set of four case studies and one proof of concept prepared for both systems demonstrate the suitability of the visualization and analysis strategies adopted for supporting decision making processes in diverse application domains. The results of these case studies indicate that both, the systems as well as the techniques included in the systems can be generalized and extended to support the analysis of different tasks and scenarios.
The aim of this dissertation is to explain processes in recruitment by gaining a better understanding of how perceptions evolve and how recruitment outcomes and perceptions are influenced. To do so, this dissertation takes a closer look at the formation of fit perceptions, the effects of top employer awards on pre-hire recruitment outcomes, and on how perceptions about external sources are influenced.
Arctic, Antarctic and alpine biological soil crusts (BSCs) are formed by adhesion of soil particles to exopolysaccharides (EPSs) excreted by cyanobacterial and green algal communities, the pioneers and main primary producers in these habitats. These BSCs provide and influence many ecosystem services such as soil erodibility, soil formation and nitrogen (N) and carbon (C) cycles. In cold environments degradation rates are low and BSCs continuously increase soil organic C; therefore, these soils are considered to be CO2 sinks. This work provides a novel, nondestructive and highly comparable method to investigate intact BSCs with a focus on cyanobacteria and green algae and their contribution to soil organic C. A new terminology arose,basedonconfocallaserscanningmicroscopy(CLSM) 2-D biomaps, dividing BSCs into a photosynthetic active layer (PAL) made of active photoautotrophic organisms and a photosynthetic inactive layer (PIL) harbouring remnants of cyanobacteria and green algae glued together by their remaining EPSs. By the application of CLSM image analysis (CLSM–IA) to 3-D biomaps, C coming from photosynthetic activeorganismscouldbevisualizedasdepthprofileswithC peaks at 0.5 to 2mm depth. Additionally, the CO2 sink character of these cold soil habitats dominated by BSCs could be highlighted, demonstrating that the first cubic centimetre of soil consists of between 7 and 17% total organic carbon, identified by loss on ignition.
We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.
The size congruity effect involves interference between numerical magnitude and physical size of visually presented numbers: congruent numbers (either both small or both large in numerical magnitude and physical size) are responded to faster than incongruent ones (small numerical magnitude/large physical size or vice versa). Besides, numerical magnitude is associated with lateralized response codes, leading to the Spatial Numerical Association of Response Codes (SNARC) effect: small numerical magnitudes are preferably responded to on the left side and large ones on the right side. Whereas size congruity effects are ascribed to interference between stimulus dimensions in the decision stage, SNARC effects are understood as (in)compatibilities in stimulus-response combinations. Accordingly, size congruity and SNARC effects were previously found to be independent in parity and in physical size judgment tasks. We investigated their dependency in numerical magnitude judgment tasks. We obtained independent size congruity and SNARC effects in these tasks and replicated this observation for the parity judgment task. The results confirm and extend the notion that size congruity and SNARC effects operate in different representational spaces. We discuss possible implications for number representation.
European economic, social and territorial cohesion is one of the fundamental aims of the European Union (EU). It seeks to both reduce the effects of internal borders and enhance European integration. In order to facilitate territorial cohesion, the linkage of member states by means of efficient cross-border transport infrastructures and services is an important factor. Many cross-border transport challenges have historically existed in everyday life. They have hampered smooth passenger and freight flows within the EU.
Two EU policies, namely European Territorial Cooperation (ETC) and the Trans-European Transport Networks (TEN-T), promote enhancing cross-border transport through cooperation in soft spaces. This dissertation seeks to explore the influence of these two EU policies on cross-border transport and further European integration.
Based on an analysis of European, national and cross-border policy and planning documents, surveys with TEN-T Corridor Coordinators and INTERREG Secretariats and a high number of elite interviews, the dissertation will investigate how the objectives of the two EU policies were formally implemented in both soft spaces and the EU member states as well as which practical implementations have taken place. Thereby, the initiated Europeanisation and European integration processes will be evaluated. The analysis is conducted in nine preliminary case studies and two in-depth case studies. The cases comprise cross-border regions funded by the ETC policy that are crossed by a TEN-T corridor. The in-depth analysis explores the Greater Region Saar-Lor-Lux+ and the Brandenburg-Lubuskie region. The cases are characterised by different initial situations.
The research determined that the two EU policies support cross-border transport on different levels and, further, that they need to be better intertwined in order to make effective use of their complementarities. Moreover, it became clear that the EU policies have a distinct influence on domestic policy and planning documents of different administrative levels and countries as well as on the practical implementation. The final implementation of the EU objectives and the cross-border transport initiatives was strongly influenced by the member states’ initial situations – particularly, the regional and local transport needs. This dissertation concludes that the two EU policies cannot remove the entirety of the cross-border transport-related challenges. However, in addition to their financial investments in concrete projects, they promote the importance of cross-border transport and facilitate cooperation, learning and exchange processes. These are all of high relevance to cross-border transport development, driven by member states, as well as to further European integration.
The dissertation recommends that the transport planning competences of the EU in addition to the TEN-T network should not be enlarged in the future, but rather further transnational transport development tasks should be decentralised to transnational transport planning committees that are aware of regional needs and can coordinate a joint transport development strategy. The latter should be implemented with the support of additional EU funds for secondary and tertiary cross-border connections. Moreover, the potential complementarities of the transnational regions and transport corridors as well as the two EU policy fields should be made better use of by improving communication. This means that soft spaces, the TEN-T and ETC Policy as well as the domestic transport ministries and the domestic administrations that are responsible for the two EU policies need to intensify their cooperation. Furthermore, a focus of future ETC projects on topics that are of added value for the whole cross-border region or else that can be applied in different territorial contexts is recommended rather than investing in small-scale scattered expensive infrastructures and services that are only of benefit for a small part of the region. Additionally, the dissemination of project results should be enhanced so that the developed tools can be accessed by potential users and benefits become more visible to a wider society, despite the fact that they might not be measurable in numbers. In addition, the research points at another success factor for more concrete outputs: the frequent involvement of transport and spatial planners in transnational projects could increase the relation to planning practice. Besides that, advanced training regarding planning culture could reduce cooperation barriers.
The core muscles play a central role in stabilizing the head during headers in soccer. The objective of this study was to examine the influence of a fatigued core musculature on the acceleration of the head during jump headers and run headers. Acceleration of the head was measured in a pre-post-design in 68 soccer players (age: 21.5 ± 3.8 years, height: 180.0 ± 13.9 cm, weight: 76.9 ± 8.1 kg). Data were recorded by means of a telemetric 3D acceleration sensor and with a pendulum header. The treatment encompassed two exercises each for the ventral, lateral, and dorsal muscle chains. The acceleration of the head between pre- and post-test was reduced by 0.3 G (p = 0.011) in jump headers and by 0.2 G (p = 0.067) in run headers. An additional analysis of all pretests showed an increased acceleration in run headers when compared to stand headers (p < 0.001) and jump headers (p < 0.001). No differences were found in the sub-group comparisons: semi-professional vs. recreational players, offensive vs. defensive players. Based on the results, we conclude that the acceleration of the head after fatiguing the core muscles does not increase, which stands in contrast to postulated expectations. More tests with accelerated soccer balls are required for a conclusive statement.
The research problem is that the land-use (re-)planning process in the existing Egyptian cities
does not attain sustainability. This is because of the unfulfillment of essential principles within
their land-use structures, lack of harmony between the added and old parts in the cities, and
other reasons. This leads to the need for developing an assessment system, which is a
computational spatial planning support system-SPSS. This SPSS is used for identifying the
degree of sustainability attainment in land-uses plans, predicting probable problems, and
suggesting modifications in the evaluated plans.
The main goal is to design the SPSS for supporting sustainability in the Egyptian cities. The
secondary goals are: studying the Egyptian planning and administrative systems for designing
the technical and administrative frameworks for the SPSS, the development of an assessment
model from the SPSS for assessing sustainability in land-use structures of urban areas, as well
as the identification of the improvements required in the model and the recommendations for
developing the SPSS.
The theoretical part aims to design each of the administrative and technical frameworks of the
SPSS. This requires studying each of the main planning approaches, the sustainability in urban
land-use planning, and the significance of using efficient assessment tools for evaluating the
sustainability in this process. The added value of the planning support systems-PSSs for
planning and their role in supporting sustainability attainment in urban land-use planning are
discussed. Then, a group of previous examples in the sustainability assessment from various
countries (developed and developing countries) are selected, which have used various
assessment tools. This is to extract some learned lessons to be guides for the SPSS. And so,
the comprehensive technical framework for the SPSS is designed, which includes the suggested
methods and techniques that perform various stages of the assessment process.
The Egyptian context is studied regarding the planning and administration systems within the
Egyptian cities, as well as the spatial and administrative problems facing the sustainable
development. And so, the administrative framework for the SPSS is identified, which includes
the entities that should be involved in the assessment process.
The empirical part focuses on the design of a selected assessment model from the
comprehensive technical framework of the SPSS to be established as a minimized version from
it. This model is programmed in the form of a new toolbox within the ArcGIS™ software through
geoscripting using Python programming language to be applied for assessing the sustainability
attainment in the land-use structure of urban areas. The required assessing criteria for the model
specialized for the Egyptian and German cities are identified, for applying it on German and
Egyptian study areas.
The conclusions regarding each of PSSs, the Egyptian local administration and planning
systems, sustainability attainment in the land-use planning process in Egyptian Cities, as well as
the proposed SPSS and the developed toolbox are drawn. The recommendations are regarding
each of challenges facing the development and application of PSSs, the Egyptian local
administration and planning systems, the spatial problems in Egyptian cities, the establishment
of the SPSS, and the application of the toolbox. The future agenda is in the fields of sustainable urban land-use planning, planning support science, and the development process in the
Egyptian cities.
Background: Aneuploidy, or abnormal chromosome numbers, severely alters cell physiology and is widespread in
cancers and other pathologies. Using model cell lines engineered to carry one or more extra chromosomes, it has
been demonstrated that aneuploidy per se impairs proliferation, leads to proteotoxic as well as replication stress
and triggers conserved transcriptome and proteome changes.
Results: In this study, we analysed for the first time miRNAs and demonstrate that their expression is altered in
response to chromosome gain. The miRNA deregulation is independent of the identity of the extra chromosome
and specific to individual cell lines. By cross-omics analysis we demonstrate that although the deregulated miRNAs
differ among individual aneuploid cell lines, their known targets are predominantly associated with cell development,
growth and proliferation, pathways known to be inhibited in response to chromosome gain. Indeed, we show that up
to 72% of these targets are downregulated and the associated miRNAs are overexpressed in aneuploid cells, suggesting
that the miRNA changes contribute to the global transcription changes triggered by aneuploidy. We identified
hsa-miR-10a-5p to be overexpressed in majority of aneuploid cells. Hsa-miR-10a-5p enhances translation of a
subset of mRNAs that contain so called 5’TOP motif and we show that its upregulation in aneuploids provides
resistance to starvation-induced shut down of ribosomal protein translation.
Conclusions: Our work suggests that the changes of the microRNAome contribute on one hand to the adverse
effects of aneuploidy on cell physiology, and on the other hand to the adaptation to aneuploidy by supporting
translation under adverse conditions.
Keywords: Aneuploidy, Cancer, miRNA, miR-10a-5p, Trisomy
Poor posture in childhood and adolescence is held responsible for the occurrence
of associated disorders in adult age. This study aimed to verify whether body
posture in adolescence can be enhanced through the improvement of neuromuscular
performance, attained by means of targeted strength, stretch, and body perception
training, and whether any such improvement might also transition into adulthood. From
a total of 84 volunteers, the posture development of 67 adolescents was checked
annually between the age of 14 and 20 based on index values in three posture
situations. 28 adolescents exercised twice a week for about 2 h up to the age of 18, 24
adolescents exercised continually up to the age of 20. Both groups practiced other
additional sports for about 1.8 h/week. Fifteen persons served as a non-exercising
control group, practicing optional sports of about 1.8 h/week until the age of 18,
after that for 0.9 h/week. Group allocation was not random, but depended on the
participants’ choice. A linear mixed model was used to analyze the development
of posture indexes among the groups and over time and the possible influence of
anthropometric parameters (weight, size), of optional athletic activity and of sedentary
behavior. The post hoc pairwise comparison was performed applying the Scheffé test.
The significance level was set at 0.05. The group that exercised continually (TR20)
exhibited a significant posture parameter improvement in all posture situations from
the 2nd year of exercising on. The group that terminated their training when reaching
adulthood (TR18) retained some improvements, such as conscious straightening of the
body posture. In other posture situations (habitual, closed eyes), their posture results
declined again from age 18. The effect sizes determined were between Eta² = 0.12 and
Eta² = 0.19 and represent moderate to strong effects. The control group did not exhibit
any differences. Anthropometric parameters, additional athletic activities and sedentary
behavior did not influence the posture parameters significantly. An additional athletic
training of 2 h per week including elements for improved body perception seems to
have the potential to improve body posture in symptom free male adolescents and
young adults.
In this study, the dependence of the cyclic deformation behavior on the surface morphology of metastable austenitic HSD® 600 TWinning Induced Plasticity (TWIP) steel was investigated. This steel—with the alloying concept Mn-Al-Si—shows a fully austenitic microstructure with deformation-induced twinning at ambient temperature. Four different surface morphologies were analyzed: as-received with a so-called rolling skin, after up milling, after down milling, and a reference morphology achieved by polishing. The morphologies were characterized by X-Ray Diffraction (XRD), Focused Ion Beam (FIB), Scanning Electron Microscopy (SEM) as well as confocal microscopy methods and show significant differences in initial residual stresses, phase fractions, topographies and microstructures. For specimens with all variants of the morphologies, fatigue tests were performed in the Low Cycle Fatigue (LCF) and High Cycle Fatigue (HCF) regime to characterize the cyclic deformation behavior and fatigue life. Moreover, this study focused on the frequency-dependent self-heating of the specimens caused by cyclic plasticity in the HCF regime. The results show that both surface morphology and specimen temperature have a significant influence on the cyclic deformation behavior of HSD® 600 TWIP steel in the HCF regime.
Cyanobacteria of biological soil crusts (BSCs) represent an important part of circumpolar
and Alpine ecosystems, serve as indicators for ecological condition and climate
change, and function as ecosystem engineers by soil stabilization or carbon and nitrogen
input. The characterization of cyanobacteria from both polar regions remains
extremely important to understand geographic distribution patterns and community
compositions. This study is the first of its kind revealing the efficiency of combining
denaturing gradient gel electrophoresis (DGGE), light microscopy and culture-based
16S rRNA gene sequencing, applied to polar and Alpine cyanobacteria dominated
BSCs. This study aimed to show the living proportion of cyanobacteria as an extension
to previously published meta-transcriptome
data of the same study sites.
Molecular fingerprints showed a distinct clustering of cyanobacterial communities
with a close relationship between Arctic and Alpine populations, which differed from
those found in Antarctica. Species richness and diversity supported these results,
which were also confirmed by microscopic investigations of living cyanobacteria
from the BSCs. Isolate-based
sequencing corroborated these trends as cold biome
clades were assigned, which included a potentially new Arctic clade of Oculatella.
Thus, our results contribute to the debate regarding biogeography of cyanobacteria
of cold biomes.
Multiphase materials combine properties of several materials, which makes them interesting for high-performing components. This thesis considers a certain set of multiphase materials, namely silicon-carbide (SiC) particle-reinforced aluminium (Al) metal matrix composites and their modelling based on stochastic geometry models.
Stochastic modelling can be used for the generation of virtual material samples: Once we have fitted a model to the material statistics, we can obtain independent three-dimensional “samples” of the material under investigation without the need of any actual imaging. Additionally, by changing the model parameters, we can easily simulate a new material composition.
The materials under investigation have a rather complicated microstructure, as the system of SiC particles has many degrees of freedom: Size, shape, orientation and spatial distribution. Based on FIB-SEM images, that yield three-dimensional image data, we extract the SiC particle structure using methods of image analysis. Then we model the SiC particles by anisotropically rescaled cells of a random Laguerre tessellation that was fitted to the shapes of isotropically rescaled particles. We fit a log-normal distribution for the volume distribution of the SiC particles. Additionally, we propose models for the Al grain structure and the Aluminium-Copper (\({Al}_2{Cu}\)) precipitations occurring on the grain boundaries and on SiC-Al phase boundaries.
Finally, we show how we can estimate the parameters of the volume-distribution based on two-dimensional SEM images. This estimation is applied to two samples with different mean SiC particle diameters and to a random section through the model. The stereological estimations are within acceptable agreement with the parameters estimated from three-dimensional image data
as well as with the parameters of the model.
In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base.
Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving.
This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases.
The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research.
The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.
SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness
(2018)
We deduce cell population models describing the evolution of a tumor (possibly interacting with its
environment of healthy cells) with the aid of differential equations. Thereby, different subpopulations
of cancer cells allow accounting for the tumor heterogeneity. In our settings these include cancer
stem cells known to be less sensitive to treatment and differentiated cancer cells having a higher
sensitivity towards chemo- and radiotherapy. Our approach relies on stochastic differential equations
in order to account for randomness in the system, arising e.g., by the therapy-induced decreasing
number of clonogens, which renders a pure deterministic model arguable. The equations are deduced
relying on transition probabilities characterizing innovations of the two cancer cell subpopulations,
and similarly extended to also account for the evolution of normal tissue. Several therapy approaches
are introduced and compared by way of tumor control probability (TCP) and uncomplicated tumor
control probability (UTCP). A PDE approach allows to assess the evolution of tumor and normal
tissue with respect to time and to cell population densities which can vary continuously in a given set
of states. Analytical approximations of solutions to the obtained PDE system are provided as well.
Cutting-edge cancer therapy involves producing individualized medicine for many patients at the same time. Within this process, most steps can be completed for a certain number of patients simultaneously. Using these resources efficiently may significantly reduce waiting times for the patients and is therefore crucial for saving human lives. However, this involves solving a complex scheduling problem, which can mathematically be modeled as a proportionate flow shop of batching machines (PFB). In this thesis we investigate exact and approximate algorithms for tackling many variants of this problem. Related mathematical models have been studied before in the context of semiconductor manufacturing.
Collaboration aims to increase the efficiency of problem solving and decision making by bringing diverse areas of expertise together, i.e., teams of experts from various disciplines, all necessary to come up with acceptable concepts. This dissertation is concerned with the design of highly efficient computer-supported collaborative work involving active participation of user groups with diverse expertise. Three main contributions can be highlighted: (1) the definition and design of a framework facilitating collaborative decision making; (2) the deployment and evaluation of more natural and intuitive interaction and visualization techniques in order to support multiple decision makers in virtual reality environments; and (3) the integration of novel techniques into a single proof-of-concept system.
Decision making processes are time-consuming, typically involving several iterations of different options before a generally acceptable solution is obtained. Although, collaboration is an often-applied method, the execution of collaborative sessions is often inefficient, does not involve all participants, and decisions are often finalized with- out the agreement of all participants. An increasing number of computer-supported cooperative work systems (CSCW) facilitate collaborative work by providing shared viewpoints and tools to solve joint tasks. However, most of these software systems are designed from a feature-oriented perspective, rather than a human-centered perspective and without the consideration of user groups with diverse experience and joint goals instead of joint tasks. The aim of this dissertation is to bring insights to the following research question: How can computer-supported cooperative work be designed to be more efficient? This question opens up more specific questions like: How can collaborative work be designed to be more efficient? How can all participants be involved in the collaboration process? And how can interaction interfaces that support collaborative work be designed to be more efficient? As such, this dissertation makes contributions in:
1. Definition and design of a framework facilitating decision making and collaborative work. Based on examinations of collaborative work and decision making processes requirements of a collaboration framework are assorted and formulated. Following, an approach to define and rate software/frameworks is introduced. This approach is used to translate the assorted requirements into a software’s architecture design. Next, an approach to evaluate alternatives based on Multi Criteria Decision Making (MCDM) and Multi Attribute Utility Theory (MAUT) is presented. Two case studies demonstrate the usability of this approach for (1) benchmarking between systems and evaluates the value of the desired collaboration framework, and (2) ranking a set of alternatives resulting from a decision-making process incorporating the points of view of multiple stake- holders.
2. Deployment and evaluation of natural and intuitive interaction and visualization techniques in order to support multiple diverse decision makers. A user taxonomy of industrial corporations serves to create a petri network of users in order to identify dependencies and information flows between each other. An explicit characterization and design of task models was developed to define interfaces and further components of the collaboration framework. In order to involve and support user groups with diverse experiences, smart de- vices and virtual reality are used within the presented collaboration framework. Natural and intuitive interaction techniques as well as advanced visualizations of user centered views of the collaboratively processed data are developed in order to support and increase the efficiency of decision making processes. The smartwatch as one of the latest technologies of smart devices, offers new possibilities of interaction techniques. A multi-modal interaction interface is provided, realized with smartwatch and smartphone in full immersive environments, including touch-input, in-air gestures, and speech.
3. Integration of novel techniques into a single proof-of-concept system. Finally, all findings and designed components are combined into the new collaboration framework called IN2CO, for distributed or co-located participants to efficiently collaborate using diverse mobile devices. In a prototypical implementation, all described components are integrated and evaluated. Examples where next-generation network-enabled collaborative environments, connected by visual and mobile interaction devices, can have significant impact are: design and simulation of automobiles and aircrafts; urban planning and simulation of urban infrastructure; or the design of complex and large buildings, including efficiency- and cost-optimized manufacturing buildings as task in factory planning. To demonstrate the functionality and usability of the framework, case studies referring to factory planning are demonstrated. Considering that factory planning is a process that involves the interaction of multiple aspects as well as the participation of experts from different domains (i.e., mechanical engineering, electrical engineering, computer engineering, ergonomics, material science, and even more), this application is suitable to demonstrate the utilization and usability of the collaboration framework. The various software modules and the integrated system resulting from the research will all be subjected to evaluations. Thus, collaborative decision making for co-located and distributed participants is enhanced by the use of natural and intuitive multi-modal interaction interfaces and techniques.
We isolated an encysted ciliate from a geothermal field in Iceland. The morphological features of this isolate fit the descriptions of Dexiotricha colpidiopsis (Kahl, 1926) Jankowski, 1964 very well. These comprise body shape and size in vivo, the number of somatic kineties, and the positions of macronucleus and contractile vacuole. Using state-of-the-art taxonomic methods, the species is redescribed, including phylogenetic analyses of the small subunit ribosomal RNA (SSU rRNA) gene as molecular marker. In the phylogenetic analyses, D. colpidiopsis clusters with the three available SSU rRNA gene sequences of congeners, suggesting a monophyly of the genus Dexiotricha. Its closest relative in phylogenetic analyses is D. elliptica, which also shows a high morphological similarity. This is the first record of a Dexiotricha species from a hot spring, indicating a wide temperature tolerance of this species at least in the encysted state. The new findings on D. colpidiopsis are included in a briefly revision of the scuticociliate genus Dexiotricha and an identification key to the species.
Słowa kluczowe: Dexiotricha, hot spring, morphology, phylogeny, SSU rRNA gene
Field-effect transistor (FET) sensors and in particular their nanoscale variant of silicon nanowire transistors are very promising technology platforms for label-free biosensor applications. These devices directly detect the intrinsic electrical charge of biomolecules at the sensor’s liquid-solid interface. The maturity of micro fabrication techniques enables very large FET sensor arrays for massive multiplex detection. However, the direct detection of charged molecules in liquids faces a significant limitation due to a charge screening effect in physiological solutions, which inhibits the realization of point-of-care applications. As an alternative, impedance spectroscopy with FET devices has the potential to enable measurements in physiological samples. Even though promising studies were published in the field, impedimetric detection with silicon FET devices is not well understood.
The first goal of this thesis was to understand the device performances and to relate the effects seen in biosensing experiments to device and biomolecule types. A model approach should help to understand the capability and limitations of the impedimetric measurement method with FET biosensors. In addition, to obtain experimental results, a high precision readout device was needed. Consequently, the second goal was to build up multi-channel, highly accurate amplifier systems that would also enable future multi-parameter handheld devices.
A PSPICE FET model for potentiometric and impedimetric detection was adapted to the experiments and further expanded to investigate the sensing mechanism, the working principle, and effects of side parameters for the biosensor experiments. For potentiometric experiments, the pH sensitivity of the sensors was also included in this modelling approach. For impedimetric experiments, solutions of different conductivity were used to validate the suggested theories and assumptions. The impedance spectra showed two pronounced frequency domains: a low-pass characteristic at lower frequencies and a resonance effect at higher frequencies. The former can be interpreted as a contribution of the source and double layer capacitances. The latter can be interpreted as a combined effect of the drain capacitance with the operational amplifier in the transimpedance circuit.
Two readout systems, one as a laboratory system and one as a point-of-care demonstrator, were developed and used for several chemical and biosensing experiments. The PSPICE model applied to the sensors and circuits were utilized to optimize the systems and to explain the sensor responses. The systems as well as the developed modelling approach were a significant step towards portable instruments with combined transducer principles in future healthcare applications.
The Power and Energy Student Summit (PESS) is designed for students, young professionals and PhD-students in the field of power engineering. PESS offers the possibility to gain first experience in presentation, publication and discussion with a renowned audience of specialists. Therefore, the conference is accompanied and supervised by established scientists and experts. The venue changes every year. In 2018, the University of Kaiserslautern held the eighth PESS conference. This document presents the submissions of this conference.
We studied the development of cognitive abilities related to intelligence and creativity
(N = 48, 6–10 years old), using a longitudinal design (over one school year), in order
to evaluate an Enrichment Program for gifted primary school children initiated by
the government of the German federal state of Rhineland-Palatinate (Entdeckertag
Rheinland Pfalz, Germany; ET; Day of Discoverers). A group of German primary school
children (N = 24), identified earlier as intellectually gifted and selected to join the
ET program was compared to a gender-, class- and IQ- matched group of control
children that did not participate in this program. All participants performed the Standard
Progressive Matrices (SPM) test, which measures intelligence in well-defined problem
space; the Creative Reasoning Task (CRT), which measures intelligence in ill-defined
problem space; and the test of creative thinking-drawing production (TCT-DP), which
measures creativity, also in ill-defined problem space. Results revealed that problem
space matters: the ET program is effective only for the improvement of intelligence
operating in well-defined problem space. An effect was found for intelligence as
measured by SPM only, but neither for intelligence operating in ill-defined problem space
(CRT) nor for creativity (TCT-DP). This suggests that, depending on the type of problem
spaces presented, different cognitive abilities are elicited in the same child. Therefore,
enrichment programs for gifted, but also for children attending traditional schools,
should provide opportunities to develop cognitive abilities related to intelligence,
operating in both well- and ill-defined problem spaces, and to creativity in a parallel,
using an interactive approach.
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
(2018)
Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.
The extraction kinetics of polyphenols, which are leached from red vine leaves, are studied and evaluated using a laboratory robot and nonconventional processing techniques such as ultrasonic (US)-, microwave (MW)-, and pulsed electric field (PEF)-assisted extraction processes. The robotic high-throughput screening reveals optimal extraction conditions at a pH value of 2.5, a temperature of 56 °C, and a solvent mixture of methanol:water:HCl of 50:49:1 v/v/v. Nonconventional processing techniques, such as MW- and US-assisted extraction, have the fastest kinetics and produce the highest polyphenol yield. The non-conventional techniques yield is 2.29 g/L (MW) resp. 2.47 g/L (US) for particles that range in size from 450 to 2000 µm and 2.20 g/L (MW) resp. 2.05 g/L (US) for particles that range from 2000 to 4000 µm. PEF has the lowest yield of polyphenols with 0.94 g/L (450–2000 µm), resp. 0.64 g/L (2000–4000 µm) in comparison to 1.82 g/L (2000 to 4000 µm) in a standard stirred vessel (50 °C). When undried red vine leaves (2000 to 4000 µm) are used the total phenol content is 1.44 g/L with PEF.
A fast numerical method for an advanced electro-chemo-mechanical model is developed which is able to capture phase separation processes in porous materials. This method is applied to simulate lithium-ion battery cells, where the complex microstructure of the electrodes is fully resolved. The intercalation of ions into the popular cathode material LFP leads to a separation into lithium-rich and lithium-poor phases. The large concentration gradients result in high mechanical stresses. A phase-field method applying the Cahn-Hilliard equation is used to describe the diffusion. For the sake of simplicity, the linear elastic case is considered. Numerical tests for fully resolved three-dimensional granular microstructures are discussed in detail.
The phase field approach is a powerful tool that can handle even complicated fracture phenomena within an apparently simple framework. Nonetheless, a profound understanding of the model is required in order to be able to interpret the obtained results correctly. Furthermore, in the dynamic case the phase field model needs to be verified in comparison to experimental data and analytical results in order to increase the trust in this new approach. In this thesis, a phase field model for dynamic brittle fracture is investigated with regard to these aspects by analytical and numerical methods
The N-containing heterocycles have received strong attention from the organic synthesis field because of their importance for pharmaceutical and material sciences. Nitrogen element plays an important role between inorganic salts and biomolecules, to search convenient methods combine C-N bond together become a hot topic in recent decades.
Since the early beginning of 20th century, transition-metal-catalyzed coupling reactions had been well-known and world widely spread in organic researchs, achieved abundant significant progress. In the other side, the less toxic and more challenging transition metal free coupling method remained further potential value.
With the evolution of amination reactions and oxidants, more and more effective, simplified, and atom economic organic synthesis methods will come soon. And these stories also drove me to think about investigating the novel cross-dehydrogenative-coupling amination methods development as the topics of my PhD research.
Thus, we selected the phenothiazine derivatives as the N-nucleophile reagents and the phenols as the C-nucleophile reagents. To achieve the transition metal-free CDC aminations of phenols with phenothiazines, we scanned the chemical toolbox and tested a series of both common and uncommon oxidants.
Firstly, we start the condition in the presence of cumene and O2. The proposed mechanism initiated by a Hock process, which would form in situ peroxo-species as initiator of the reaction. And the initial infra-red analysis predicted there is a strong O-H..N interaction.
In the second method, a series of iodines with different valance have been tested to achieve the C-N bond formation of phenols with phenothiazines. This time, a simplified and more efficient method had been developed, which also provides a wider scope of phenols. Several controlling experiments had been conducted for the plausible pathway research. Large-scale synthesis of target molecular was also successfully performed.
And then, we focus the research on the cross-coupling reaction of pre-oxidized(iminated) phenothiazine with ubiquitous phenols and indoles. In this task, we first regio-selectively synthesized the novel iminated phenothiazine derivatives with the traditional biocide and mild disinfectant, Chloramine T. Then the phenothiazinimine performed an ultra-simple condensation technique with phenol or indole coupling partners in a simplified condition. Parallel reactions were also performed to investigate the plausible pathway.
We report on generation of pulsed broadband terahertz radiation utilizing the inverse spin hall effect in Fe/Pt bilayers on MgO and sapphire substrates. The emitter was optimized with respect to layer thickness, growth parameters, substrates and geometrical arrangement. The experimentally determined optimum layer thicknesses were in qualitative agreement with simulations of the spin current induced in the ferromagnetic layer. Our model takes into account generation of spin polarization, spin diffusion and accumulation in Fe and Pt and electrical as well as optical properties of the bilayer samples. Using the device in a counterintuitive orientation a Si lens was attached to increase the collection efficiency of the emitter. The optimized emitter provided a bandwidth of up to 8 THz which was mainly limited by the low-temperature-grown GaAs (LT-GaAS) photoconductive antenna used as detector and the pulse length of the pump laser. The THz pulse length was as short as 220 fs for a sub 100 fs pulse length of the 800 nm pump laser. Average pump powers as low as 25 mW (at a repetition rate of 75 MHz) have been used for terahertz generation. This and the general performance make the spintronic terahertz emitter compatible with established emitters based on optical rectification in nonlinear crystals.
Optimal control of partial differential equations is an important task in applied mathematics where it is used in order to optimize, for example, industrial or medical processes. In this thesis we investigate an optimal control problem with tracking type cost functional for the Cattaneo equation with distributed control, that is, \(\tau y_{tt} + y_t - \Delta y = u\). Our focus is on the theoretical and numerical analysis of the limit process \(\tau \to 0\) where we prove the convergence of solutions of the Cattaneo equation to solutions of the heat equation.
We start by deriving both the Cattaneo and the classical heat equation as well as introducing our notation and some functional analytic background. Afterwards, we prove the well-posedness of the Cattaneo equation for homogeneous Dirichlet boundary conditions, that is, we show the existence and uniqueness of a weak solution together with its continuous dependence on the data. We need this in the following, where we investigate the optimal control problem for the Cattaneo equation: We show the existence and uniqueness of a global minimizer for an optimal control problem with tracking type cost functional and the Cattaneo equation as a constraint. Subsequently, we do an asymptotic analysis for \(\tau \to 0\) for both the forward equation and the aforementioned optimal control problem and show that the solutions of these problems for the Cattaneo equation converge strongly to the ones for the heat equation. Finally, we investigate these problems numerically, where we examine the different behaviour of the models and also consider the limit \(\tau \to 0\), suggesting a linear convergence rate.
In this thesis, we deal with the finite group of Lie type \(F_4(2^n)\). The aim is to find information on the \(l\)-decomposition numbers of \(F_4(2^n)\) on unipotent blocks for \(l\neq2\) and \(n\in \mathbb{N}\) arbitrary and on the irreducible characters of the Sylow \(2\)-subgroup of \(F_4(2^n)\).
S. M. Goodwin, T. Le, K. Magaard and A. Paolini have found a parametrization of the irreducible characters of the unipotent subgroup \(U\) of \(F_4(q)\), a Sylow \(2\)-subgroup of \(F_4(q)\), of \(F_4(p^n)\), \(p\) a prime, for the case \(p\neq2\).
We managed to adapt their methods for the parametrization of the irreducible characters of the Sylow \(2\)-subgroup for the case \(p=2\) for the group \(F_4(q)\), \(q=p^n\). This gives a nearly complete parametrization of the irreducible characters of the unipotent subgroup \(U\) of \(F_4(q)\), namely of all irreducible characters of \(U\) arising from so-called abelian cores.
The general strategy we have applied to obtain information about the \(l\)-decomposition numbers on unipotent blocks is to induce characters of the unipotent subgroup \(U\) of \(F_4(q)\) and Harish-Chandra induce projective characters of proper Levi subgroups of \(F_4(q)\) to obtain projective characters of \(F_4(q)\). Via Brauer reciprocity, the multiplicities of the ordinary irreducible unipotent characters in these projective characters give us information on the \(l\)-decomposition numbers of the unipotent characters of \(F_4(q)\).
Sadly, the projective characters of \(F_4(q)\) we obtained were not sufficient to give the shape of the entire decomposition matrix.
In this paper, we demonstrate the power of functional data models for a statistical analysis of stimulus-response experiments which is a quite natural way to look at this kind of data and which makes use of the full information available. In particular, we focus on the detection of a change in the mean of the response in a series of stimulus-response curves where we also take into account dependence in time.
Composite materials are used in many modern tools and engineering applications and
consist of two or more materials that are intermixed. Features like inclusions in a matrix
material are often very small compared to the overall structure. Volume elements that
are characteristic for the microstructure can be simulated and their elastic properties are
then used as a homogeneous material on the macroscopic scale.
Moulinec and Suquet [2] solve the so-called Lippmann-Schwinger equation, a reformulation of the equations of elasticity in periodic homogenization, using truncated
trigonometric polynomials on a tensor product grid as ansatz functions.
In this thesis, we generalize their approach to anisotropic lattices and extend it to
anisotropic translation invariant spaces. We discretize the partial differential equation
on these spaces and prove the convergence rate. The speed of convergence depends on
the smoothness of the coefficients and the regularity of the ansatz space. The spaces of
translates unify the ansatz of Moulinec and Suquet with de la Vallée Poussin means and
periodic Box splines, including the constant finite element discretization of Brisard and
Dormieux [1].
For finely resolved images, sampling on a coarser lattice reduces the computational
effort. We introduce mixing rules as the means to transfer fine-grid information to the
smaller lattice.
Finally, we show the effect of the anisotropic pattern, the space of translates, and the
convergence of the method, and mixing rules on two- and three-dimensional examples.
References
[1] S. Brisard and L. Dormieux. “FFT-based methods for the mechanics of composites:
A general variational framework”. In: Computational Materials Science 49.3 (2010),
pp. 663–671. doi: 10.1016/j.commatsci.2010.06.009.
[2] H. Moulinec and P. Suquet. “A numerical method for computing the overall response
of nonlinear composites with complex microstructure”. In: Computer Methods in
Applied Mechanics and Engineering 157.1-2 (1998), pp. 69–94. doi: 10.1016/s00457825(97)00218-1.
Multifacility location problems arise in many real world applications. Often, the facilities can only be placed in feasible regions such as development or industrial areas. In this paper we show the existence of a finite dominating set (FDS) for the planar multifacility location problem with polyhedral gauges as distance functions, and polyhedral feasible regions, if the interacting facilities form a tree. As application we show how to solve the planar 2-hub location problem in polynomial time. This approach will yield an ε-approximation for the euclidean norm case polynomial in the input data and 1/ε.
Epoxy belongs to a category of high-performance thermosetting polymers which have been used extensively in industrial and consumer applications. Highly cross-linked epoxy polymers offer excellent mechanical properties, adhesion, and chemical resistance. However, unmodified epoxies are prone to brittle fracture and crack propagation due to their highly crosslinked structure. As a result, epoxies are normally toughened to ensure the usability of these materials in practical applications.
This research work focuses on the development of novel modified epoxy matrices, with enhanced mechanical, fracture mechanical and thermal properties, suitable to be processed by filament winding technology, to manufacture composite based calender roller covers with improved performance in comparison to commercially available products.
In the first stage, a neat epoxy resin (EP) was modified using three different high functionality epoxy resins with two type of hardeners i.e. amine-based (H1) and anhydride-based (H2). Series of hybrid epoxy resins were obtained by systematic variation of high functionality epoxy resin contents with reference epoxy system. The resulting matrices were characterized by their tensile properties and the best system was chosen from each hardener system i.e. amine and anhydride. For tailored amine based system (MEP_H1) 14 % improvement was measured for bulk samples similarly, for tailored anhydride system (MEP_H2) 11 % improvement was measured when tested at 23 °C.
Further, tailored epoxy systems (MEP_H1 and MEP_H2) were modified using specially designed block copolymer (BCP), and core-shell rubber nanoparticles (CSR). Series of nanocomposites were obtained by systematic variation of filler contents. The resulting matrices were extensively characterized qualitatively and quantitatively to reveal the effect of each filler on the polymer properties. It was shown that the BCP confer better fracture properties to the epoxy resin at low filler loading without losing the other mechanical properties. These characteristics were accompanied by ductility and temperature stability. All composites were tested at 23 °C and at 80 °C to understand the effect of temperature on the mechanical and fracture properties.
Examinations on fractured specimen surfaces provided information about the mechanisms responsible for reinforcement. Nanoparticles generate several energy dissipating mechanisms in the epoxy, e.g. plastic deformation of the matrix, cavitation, void growth, debonding and crack pinning. These were closely related to the microstructure of the materials. The characteristic of the microstructure was verified by microscopy methods (SEM and AFM). The microstructure of neat epoxy hardener system was strongly influenced by the nanoparticles and the resulting interfacial interactions. The interaction of nanoparticles with a different hardener system will result in different morphology which will ultimately influence the mechanical and fracture mechanical properties of the nanocomposites. Hybrid toughening using a combination of the block-copolymer / core-shell rubber nanoparticles and block copolymer / TiO2 nanoparticles has been investigated in the epoxy systems. It was found out that addition of rigid phase with a soft phase recovers the loss of strength in the nanocomposites caused by a softer phase.
In order to clarify the relevant relationships, the microstructural and mechanical properties were correlated. The Counto’s, Halpin-Tsai, and Lewis-Nielsen equations were used to calculate the modulus of the composites and predicted modulus fit well with the measured values. Modeling was done to predict the toughening contribution from block copolymers and core-shell rubber nanoparticles. There was good agreement between the predicted values and the experimental values for the fracture energy.
The complexity of modern real-time systems is increasing day by day. This inevitable rise in complexity predominantly stems from two contradicting requirements, i.e., ever increasing demand for functionality, and required low cost for the final product. The development of modern multi-processors and variety of network protocols and architectures have enabled such a leap in complexity and functionality possible. Albeit, efficient use of these multi-processors and network architectures is still a major problem. Moreover, the software design and its development process needs improvements in order to support rapid-prototyping for ever changing system designs. Therefore, in this dissertation, we provide solutions for different problems faced in the development and deployment process of real-time systems. The contributions presented in this thesis enable efficient utilization of system resources, rapid design & development and component modularity & portability.
In order to ease the certification process, time-triggered computation model is often used in distributed systems. However, time-triggered scheduling is NP-hard, due to which the process of schedule generation for complex large systems becomes convoluted. Large scheduler run-times and low scalability are two major problems with time-triggered scheduling. To solve these problems, we present a modular real-time scheduler based on a novel search-tree pruning technique, which consumes less time (compared to the state-of-the-art) in order to schedule tasks on large distributed time-triggered systems. In order to provide end-to-end guarantees, we also extend our modular scheduler to quickly generate schedules for time-triggered network traffic in large TTEthernet based networks. We evaluate our schedulers on synthetic but practical task-sets and demonstrate that our pruning technique efficiently reduces scheduler run-times and exhibits adequate scalability for future time-triggered distributed systems.
In safety critical systems, the certification process also requires strict isolation between independent components. This isolation is enforced by utilizing resource partitioning approach, where different criticality components execute in different partitions (each temporally and spatially isolated from each other). However, existing partitioning approaches use periodic servers or tasks to service aperiodic activities. This approach leads to utilization loss and potentially leads to large latencies. On the contrary to the periodic approaches, state-of-the-art aperiodic task admission algorithms do not suffer from problems like utilization loss. However, these approaches do not support partitioned scheduling or mixed-criticality execution environment. To solve this problem, we propose an algorithm for online admission of aperiodic tasks which provides job execution flexibility, jitter control and leads to lower latencies of aperiodic tasks.
For safety critical systems, fault-tolerance is one of the most important requirements. In time-triggered systems, modes are often used to ensure survivability against faults, i.e., when a fault is detected, current system configuration (or mode) is changed such that the overall system performance is either unaffected or degrades gracefully. In literature, it has been asserted that a task-set might be schedulable in individual modes but unschedulable during a mode-change. Moreover, conventional mode-change execution strategies might cause significant delays until the next mode is established. In order to address these issues, in this dissertation, we present an approach for schedulability analysis of mode-changes and propose mode-change delay reduction techniques in distributed system architecture defined by the DREAMS project. We evaluate our approach on an avionics use case and demonstrate that our approach can drastically reduce mode-change delays.
In order to manage increasing system complexity, real-time applications also require new design and development technologies. Other than fulfilling the technical requirements, the main features required from such technologies include modularity and re-usability. AUTOSAR is one of these technologies in automotive industry, which defines an open standard for software architecture of a real-time operating system. However, being an industrial standard, the available proprietary tools do not support model extensions and/or new developments by third-parties and, therefore, hinder the software evolution. To solve this problem, we developed an open-source AUTOSAR toolchain which supports application development and code generation for several modules. In order to exhibit the capabilities of our toolchain, we developed two case studies. These case studies demonstrate that our toolchain generates valid artifacts, avoids dirty workarounds and supports application development.
In order to cope with evolving system designs and hardware platforms, rapid-development of scheduling and analysis algorithms is required. In order to ease the process of algorithm development, a number of scheduling and analysis frameworks are proposed in literature. However, these frameworks focus on a specific class of applications and are limited in functionality. In this dissertation, we provide the skeleton of a scheduling and analysis framework for real-time systems. In order to support rapid-development, we also highlight different development components which promote code reuse and component modularity.
Mobility has become an integral feature of many wireless networks. Along with this mobility comes the need for location awareness. A prime example for this development are today’s and future transportation systems. They increasingly rely on wireless communications to exchange location and velocity information for a multitude of functions and applications. At the same time, the technological progress facilitates the widespread availability of sophisticated radio technology such as software-defined radios. The result is a variety of new attack vectors threatening the integrity of location information in mobile networks.
Although such attacks can have severe consequences in safety-critical environments such as transportation, the combination of mobility and integrity of spatial information has not received much attention in security research in the past. In this thesis we aim to fill this gap by providing adequate methods to protect the integrity of location and velocity information in the presence of mobility. Based on physical effects of mobility on wireless communications, we develop new methods to securely verify locations, sequences of locations, and velocity information provided by untrusted nodes. The results of our analyses show that mobility can in fact be exploited to provide robust security at low cost.
To further investigate the applicability of our schemes to real-world transportation systems, we have built the OpenSky Network, a sensor network which collects air traffic control communication data for scientific applications. The network uses crowdsourcing and has already achieved coverage in most parts of the world with more than 1000 sensors.
Based on the data provided by the network and measurements with commercial off-the-shelf hardware, we demonstrate the technical feasibility and security of our schemes in the air traffic scenario. Moreover, the experience and data provided by the OpenSky Network allows us to investigate the challenges for our schemes in the real-world air traffic communication environment. We show that our verification methods match all
requirements to help secure the next generation air traffic system.
Tables or ranked lists summarize facts about a group of entities in a concise and structured fashion. They are found in all kind of domains and easily comprehensible by humans. Some globally prominent examples of such rankings are the tallest buildings in the World, the richest people in Germany, or most powerful cars. The availability of vast amounts of tables or rankings from open domain allows different ways to explore data. Computing similarity between ranked lists, in order to find those lists where entities are presented in a similar order, carries important analytical insights. This thesis presents a novel query-driven Locality Sensitive Hashing (LSH) method, in order to efficiently find similar top-k rankings for a given input ranking. Experiments show that the proposed method provides a far better performance than inverted-index--based approaches, in particular, it is able to outperform the popular prefix-filtering method. Additionally, an LSH-based probabilistic pruning approach is proposed that optimizes the space utilization of inverted indices, while still maintaining a user-provided recall requirement for the results of the similarity search. Further, this thesis addresses the problem of automatically identifying interesting categorical attributes, in order to explore the entity-centric data by organizing them into meaningful categories. Our approach proposes novel statistical measures, beyond known concepts, like information entropy, in order to capture the distribution of data to train a classifier that can predict which categorical attribute will be perceived suitable by humans for data categorization. We further discuss how the information of useful categories can be applied in PANTHEON and PALEO, two data exploration frameworks developed in our group.
Initiated by a task in tunable microoptics, but not limited to this application, a microfluidic droplet array in an upright standing module with 3 × 3 subcells and droplet actuation via electrowetting is presented. Each subcell is filled with a single (of course transparent) water droplet, serving as a movable iris, surrounded by opaque blackened decane. Each subcell measures 1 × 1 mm ² and incorporates 2 × 2 quadratically arranged positions for the droplet. All 3 × 3 droplets are actuated synchronously by electrowetting on dielectric (EWOD). The droplet speed is up to 12 mm/s at 130 V (Vrms) with response times of about 40 ms. Minimum operating voltage is 30 V. Horizontal and vertical movement of the droplets is demonstrated. Furthermore, a minor modification of the subcells allows us to exploit the flattening of each droplet. Hence, the opaque decane fluid sample can cover each water droplet and render each subcell opaque, resulting in switchable irises of constant opening diameter. The concept does not require any mechanically moving parts or external pumps.
The transfer of substrates between to enzymes within a biosynthesis pathway is an effective way to synthesize the specific product and a good way to avoid metabolic interference. This process is called metabolic channeling and it describes the (in-)direct transfer of an intermediate molecule between the active sites of two enzymes. By forming multi-enzyme cascades the efficiency of product formation and the flux is elevated and intermediate products are transferred and converted in a correct manner by the enzymes.
During tetrapyrrole biosynthesis several substrate transfer events occur and are prerequisite for an optimal pigment synthesis. In this project the metabolic channeling process during the pink pigment phycoerythrobilin (PEB) was investigated. The responsible ferredoxin-dependent bilin reductases (FDBR) for PEB formation are PebA and PebB. During the pigment synthesis the intermediate molecule 15,16-dihydrobiliverdin (DHBV) is formed and transferred from PebA to PebB. While in earlier studies a metabolic channeling of DHBV was postulated, this work revealed new insights into the requirements of this protein-protein interaction. It became clear, that the most important requirement for the PebA/PebB interaction is based on the affinity to their substrate/product DHBV. The already high affinity of both enzymes to each other is enhanced in the presence of DHBV in the binding pocket of PebA which leads to a rapid transfer to the subsequent enzyme PebB. DHBV is a labile molecule and needs to be rapidly channeled in order to get correctly further reduced to PEB. Fluorescence titration experiments and transfer assays confirmed the enhancement effect of DHBV for its own transfer.
More insights became clear by creating an active fusion protein of PebA and PebB and comparing its reaction mechanism with standard FDBRs. This fusion protein was able to convert biliverdin IXα (BV IXα) to PEB similar to the PebS activity, which also can convert BV IXα via DHBV to PEB as a single enzyme. The product and intermediate of the reaction were identified via HPLC and UV-Vis spectroscopy.
The results of this work revealed that PebA and PebB interact via a proximity channeling process where the intermediate DHBV plays an important role for the interaction. It also highlights the importance of substrate channeling in the synthesis of PEB to optimize the flux of intermediates through this metabolic pathway.
The scales of white beetles strongly scatter light within a thin disordered network of
chitin filaments. There is no comparable artificial material achieving such a high scat-
tering strength within a thin layer of low refractive index material. Several analyses
investigated the scattering but could not explain the underlying concept. Here a model
system is described, which has the same optical properties as the white beetles’ scales
in the visible wavelength range. With some modification, it also explains the behavior
of the structures in the near infrared range. The comparison of the original structure and
the model system is done by finite-difference time-domain calculations. The calcula-
tions show excellent agreement with the beetles’ scales with respect to the reflectance,
the time-of-flight, and the intensity distribution in the far-field.
Based on the Lindblad master equation approach we obtain a detailed microscopic model of photons in a dye-filled cavity, which features condensation of light. To this end we generalise a recent non-equilibrium approach of Kirton and Keeling such that the dye-mediated contribution to the photon-photon interaction in the light condensate is accessible due to an interplay of coherent and dissipative dynamics. We describe the steady-state properties of the system by analysing the resulting equations of motion of both photonic and matter degrees of freedom. In particular, we discuss the existence of two limiting cases for steady states: photon Bose-Einstein condensate and laser-like. In the former case, we determine the corresponding dimensionless photon-photon interaction strength by relying on realistic experimental data and find a good agreement with previous theoretical estimates. Furthermore, we investigate how the dimensionless interaction strength depends on the respective system parameters.
Road accidents remain as one of the major causes of death and injuries globally. Several million people die every year due to road accidents all over the world. Although the number of accidents in European region have reduced in the past years, road safety still remains a major challenge. Especially in case of commercial trucks, due to the size and load of the vehicle, even minor collisions with other road users would lead to serious injuries or death. In order to reduce number of accidents, automotive industry is rapidly developing advanced driver assistance systems (ADAS) and automated driving technologies. Efficient and reliable solutions are required for these systems to sense, perceive and react to different environmental conditions. For vehicle safety applications such as collision avoidance with vulnerable road users (VRUs), it is not only important for the system to efficiently detect and track the objects in the vicinity of the vehicle but should also function robustly.
An environment perception solution for application in commercial truck safety systems and for future automated driving is developed in this work. Thereby a method for integrated tracking and classification of road users in the near vicinity of the vehicle is formulated. The drawbacks in conventional multi-object tracking algorithms with respect to state, measurement and data association uncertainties have been addressed with the recent advancements in the field of unified multi-object tracking solutions based on random finite sets (RFS). Gaussian mixture implementation of the recently developed labeled multi-Bernoulli (LMB) filter [RSD15] is used as the basis for multi-object tracking in this work. Measurement from an high-resolution radar sensor is used as the main input for detecting and tracking objects.
On one side, the focus of this work is on tracking VRUs in the near vicinity of the truck. As it is beneficial for most of the vehicle safety systems to also know the category that the object belongs to, the focus on the other side is also to classify the road users. All the radar detections believed to originate from a single object are clustered together with help of density based spatial clustering for application with noise (DBSCAN) algorithm. Each cluster of detections would have different properties based on the respective object characteristics. Sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A machine learning based classifier is constructed, trained and parameterised for distinguishing the road users based on the extracted features.
The class information derived from the radar detections can further be used by the tracking algorithm, to adapt the model parameters used for precisely predicting the object motion according to the category of the object. Multiple model labeled multi-Bernoulli filter (MMLMB) is used for modelling different object motions. Apart from the detection level, the estimated state of an object on the tracking level also provides information about the object class. Both these informations are fused using Dempster-Shafer theory (DST) of evidence, based on respective class probabilities Thereby, the output of the integrated tracking and classification with MMLMB filter are classified tracks that can be used by truck safety applications with better reliability.
The developed environment perception method is further implemented as a real-time prototypical system on a commercial truck. The performance of the tracking and classification approaches are evaluated with the help of simulation and multiple test scenarios. A comparison of the developed approaches to a conventional converted measurements Kalman filter with global nearest neighbour association (CMKF-GNN) shows significant advantages in the overall accuracy and performance.
Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N = 41 physics majors. We found that students’ performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding.Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixed method research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.
Influence of the Crystal Surface on the Austenitic and Martensitic Phase Transition in Pure Iron
(2018)
Using classical molecular dynamics simulations, we studied the influence that free
surfaces exert on the austenitic and martensitic phase transition in iron. For several single-indexed
surfaces—such as (100)bcc and (110)bcc as well as (100)fcc and (110)fcc surfaces—appropriate
pathways exist that allow for the transformation of the surface structure. These are the Bain,
Mao, Pitsch, and Kurdjumov–Sachs pathways, respectively. Tilted surfaces follow the pathway
of the neighboring single-indexed plane. The austenitic transformation temperature follows the
dependence of the specific surface energy of the native bcc phase; here, the new phase nucleates at
the surface. In contrast, the martensitic transformation temperature steadily decreases when tilting
the surface from the (100)fcc to the (110)fcc orientation. This dependence is caused by the strong
out-of-plane deformation that (110)fcc facets experience under the transformation; here, the new
phase also nucleates in the bulk rather than at the surface.
Embedded reactive systems underpin various safety-critical applications wherein they interact with other systems and the environment with limited or even no human supervision. Therefore, design errors that violate essential system specifications can lead to severe unacceptable damages. For this reason, formal verification of such systems in their physical environment is of high interest. Synchronous programs are typically used to represent embedded reactive systems while hybrid systems serve to model discrete reactive system in a continuous environment. As such, both synchronous programs and hybrid systems play important roles in the model-based design of embedded reactive systems. This thesis develops induction-based techniques for safety property verification of synchronous and hybrid programs. The imperative synchronous language Quartz and its hybrid systems’ extensions are used to sustain the findings.
Deductive techniques for software verification typically use Hoare calculus. In this context, Verification Condition Generation (VCG) is used to apply Hoare calculus rules to a program whose statements are annotated with pre- and postconditions so that the validity of an obtained Verification Condition (VC) implies correctness of a given proof goal. Due to the abstraction of macro steps, Hoare calculus cannot directly generate VCs of synchronous programs unless it handles additional label variables or goto statements. As a first contribution, Floyd’s induction-based approach is employed to generate VCs for synchronous and hybrid programs. Five VCG methods are introduced that use inductive assertions to decompose the overall proof goal. Given the right assertions, the procedure can automatically generate a set of VCs that can then be checked by SMT solvers or automated theorem provers. The methods are proved sound and relatively complete, provided that the underlying assertion language is expressive enough. They can be applied to any program with a state-based semantics.
Property Directed Reachability (PDR) is an efficient method for synchronous hardware circuit verification based on induction rather than fixpoint computation. Crucial steps of the PDR method consist of deciding about the reachability of Counterexamples to Induction (CTIs) and generalizing them to clauses that cover as many unreachable states as possible. The thesis demonstrates that PDR becomes more efficient for imperative synchronous programs when using the distinction between the control- and dataflow. Before calling the PDR method, it is possible to derive additional program control-flow information that can be added to the transition relation such that less CTIs will be generated. Two methods to compute additional control-flow information are presented that differ in how precisely they approximate the reachable control-flow states and, consequently, in their required runtime. After calling the PDR method, the CTI identification work is reduced to its control-flow part and to checking whether the obtained control-flow states are unreachable in the corresponding extended finite state machine of the program. If so, all states of the transition system that refer to the same program locations can be excluded, which significantly increases the performance of PDR.
Increasing costs due to the rising attrition of drug candidates in late developmental phases alongside post-marketing withdrawal of drugs challenge the pharmaceutical industry to further improve their current preclinical safety assessment strategies. One of the most common reasons for the termination of drug candidates is drug induced hepatotoxicity, which more often than not remains undetected in early developmental stages, thus emphasizing the necessity for improved and more predictive preclinical test systems. One reason for the very limited value of currently applied in vitro test systems for the detection of potential hepatotoxic liabilities is the lack of organotypic and tissue-specific physiology of hepatocytes cultured in ordinary monolayer culture formats.
The thesis at hand primarily deals with the evaluation of both two- and three-dimensional cell culture approaches with respect to their relative ability to predict the hepatotoxic potential of drug candidates in early developmental phases. First, different hepatic cell models, which are routinely used in pharmaceutical industry (primary human hepatocytes as well as the three cell lines HepG2, HepaRG and Upcyte hepatocytes), were investigated in conventional 2D monolayer culture with respect to their ability to detect hepatotoxic effects in simple cytotoxicity studies. Moreover, it could be shown that the global protein expression levels of all cell lines substantially differ from that of primary human hepatocytes, with the least pronounced difference in HepaRG cells.
The introduction of a third dimension through the cultivation of spheroids enables hepatocytes to recapitulate their typical native polarity and furthermore dramatically increases the contact surface of adjacent cells. These differences in cellular architecture have a positive influence on hepatocyte longevity and the expression of drug metabolizing enzymes and transporters, which could be proven via immunofluorescent (IF) staining for at least 14 days in PHH and at least 28 days in HepaRG spheroids, respectively. Additionally, the IF staining of three different phase III transporters (MDR1, MRP2 and BSEP) indicated a bile canalicular network in spheroids of both cell models. A dose-dependent inducibility of important cytochrome P450 isoenzymes in HepaRG spheroids could be shown on the protein level via IF for at least 14 days. CYP inducibility of HepaRG cells cultured in 2D and 3D was compared on the mRNA level for up to 14 days and inducibility was generally lower in 3D compared to 2D under the conditions of this study. In a comparative cytotoxicity study, both PHH and HepaRG spheroids as well as HepaRG monolayers have been treated with five hepatotoxic drugs for up to 14 days and viability was measured at three time points (days 3, 7 and 14). A clear time- and dose-dependent onset of the drug-induced hepatotoxic effects was observable in all conditions tested, indicated by a shift of the respective EC50 value towards lower doses by increasing exposure. The observed effects were most pronounced in PHH spheroids, thus indicating those as the most sensitive cell model in this study. Moreover, HepaRG cells were more sensitive in spheroid culture compared to monolayers, which suggests a potential application of spheroids as long-term test system for the detection of hepatotoxicities with slow onset. Finally, the basal protein expression levels of three antigens (CYP1A2, CYP3A4 and NAT 1/2) were analyzed via Western Blotting in HepaRG cells cultured in three different cell culture formats (2D, 3D and QV) in order to estimate the impact of the cell culture conditions on protein expression levels. In the QV system enables a pump-driven flow of cell culture media, which introduces both mechanical stimuli through shear and molecular stimuli through dynamic circulation to the monolayer. Those stimuli resulted in a clearly positive effect on the expression levels of the selected antigens by an increased expression level in comparison to both 2D and 3D. In contrast, HepaRG spheroids showed time-dependent differences with the overall highest levels at day 7.
The studies presented in this thesis delivered valuable information on the increased physiological relevance in dependence on the cell culture format: three-dimensionality as well as the circulation of media lead to a more differentiated phenotype in hepatic cell models. Those cell culture formats are applicable in preclinical drug development in order to obtain more relevant information at early developmental stages and thus help to create a more efficient drug development process. Nonetheless, further studies are necessary to thoroughly characterize, validate and standardize such novel cell culture approaches prior to their routine application in industry.
Computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important
subject of study in programming languages. Recent advances in self-adjusting
computation made progress towards achieving efficient incremental computation by providing algorithmic language abstractions to express computations that respond automatically to dynamic changes in their inputs. Selfadjusting programs have been shown to be efficient for a broad range of problems via an explicit programming style, where the programmer uses specific
primitives to identify, create and operate on data that can change over time.
This dissertation presents implicit self-adjusting computation, a type directed technique for translating purely functional programs into self-adjusting
programs. In this implicit approach, the programmer annotates the (toplevel) input types of the programs to be translated. Type inference finds
all other types, and a type-directed translation rewrites the source program
into an explicitly self-adjusting target program. The type system is related to
information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting
programs and preserves the source program’s input-output behavior, guaranteeing that translated programs respond correctly to all changes to their
data. Using a cost semantics, we also prove that the translation preserves the
asymptotic complexity of the source program.
As a second contribution, we present two techniques to facilitate the processing of large and dynamic data in self-adjusting computation. First, we
present a type system for precise dependency tracking that minimizes the
time and space for storing dependency metadata. The type system improves
the scalability of self-adjusting computation by eliminating an important assumption of prior work that can lead to recording spurious dependencies.
We present a type-directed translation algorithm that generates correct selfadjusting programs without relying on this assumption. Second, we show a
probabilistic-chunking technique to further decrease space usage by controlling the fundamental space-time tradeoff in self-adjusting computation.
We implement implicit self-adjusting computation as an extension to Standard ML with compiler and runtime support. Using the compiler, we are able
to incrementalize an interesting set of applications, including standard list
and matrix benchmarks, ray tracer, PageRank, sparse graph connectivity, and
social circle counts. Our experiments show that our compiler incrementalizes existing code with only trivial amounts of annotation, and the resulting
programs bring asymptotic improvements to large datasets from real-world
applications, leading to orders of magnitude speedups in practice.
Motivation: Mathematical models take an important place in science and engineering.
A model can help scientists to explain dynamic behavior of a system and to understand
the functionality of system components. Since length of a time series and number of
replicates is limited by the cost of experiments, Boolean networks as a structurally simple
and parameter-free logical model for gene regulatory networks have attracted interests
of many scientists. In order to fit into the biological contexts and to lower the data
requirements, biological prior knowledge is taken into consideration during the inference
procedure. In the literature, the existing identification approaches can only deal with a
subset of possible types of prior knowledge.
Results: We propose a new approach to identify Boolean networks fromtime series data
incorporating prior knowledge, such as partial network structure, canalizing property,
positive and negative unateness. Using vector form of Boolean variables and applying
a generalized matrix multiplication called the semi-tensor product (STP), each Boolean
function can be equivalently converted into a matrix expression. Based on this, the
identification problem is reformulated as an integer linear programming problem to
reveal the system matrix of Boolean model in a computationally efficient way, whose
dynamics are consistent with the important dynamics captured in the data. By using
prior knowledge the number of candidate functions can be reduced during the inference.
Hence, identification incorporating prior knowledge is especially suitable for the case of
small size time series data and data without sufficient stimuli. The proposed approach is
illustrated with the help of a biological model of the network of oxidative stress response.
Conclusions: The combination of efficient reformulation of the identification problem
with the possibility to incorporate various types of prior knowledge enables the
application of computational model inference to systems with limited amount of time
series data. The general applicability of thismethodological approachmakes it suitable for
a variety of biological systems and of general interest for biological and medical research.