Refine
Departments, institutes and facilities
- Fachbereich Informatik (64)
- Fachbereich Angewandte Naturwissenschaften (49)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (42)
- Fachbereich Ingenieurwissenschaften und Kommunikation (33)
- Fachbereich Wirtschaftswissenschaften (22)
- Institut für Cyber Security & Privacy (ICSP) (18)
- Institut für funktionale Gen-Analytik (IFGA) (17)
- Institut für Verbraucherinformatik (IVI) (16)
- Institute of Visual Computing (IVC) (16)
- Institut für Sicherheitsforschung (ISF) (8)
Document Type
- Conference Object (96)
- Article (88)
- Preprint (9)
- Doctoral Thesis (6)
- Part of a Book (5)
- Report (4)
- Book (monograph, edited volume) (3)
- Master's Thesis (3)
- Conference Proceedings (1)
- Research Data (1)
Year of publication
- 2019 (217) (remove)
Language
- English (217) (remove)
Keywords
- lignin (4)
- Navigation (3)
- security (3)
- work engagement (3)
- Aminoacylase (2)
- Design (2)
- Drosophila (2)
- Exergame (2)
- Extrusion blow molding (2)
- FPGA (2)
- HCI (2)
- HTTP (2)
- Hyperspectral image (2)
- ICT (2)
- Mass spectrometry (2)
- Microorganisms (2)
- Privacy (2)
- Raman microscopy (2)
- Ray tracing (2)
- UAV (2)
- Virtual Reality (2)
- Wizard of Oz (2)
- aerodynamics (2)
- antimicrobial activity (2)
- bone tissue engineering (2)
- burnout (2)
- chemometrics (2)
- deep learning (2)
- dynamic vector fields (2)
- employee well-being (2)
- evaluation (2)
- flight zone (2)
- geofence (2)
- image fusion (2)
- inborn error of metabolism (2)
- injection moulding (2)
- ketone body (2)
- knowledge learning (2)
- learning outcomes (2)
- mental health (2)
- metabolic acidosis (2)
- metabolic decompensation (2)
- modeling of complex systems (2)
- neural networks (2)
- organic aciduria (2)
- pansharpening (2)
- perseverative cognition (2)
- principal component analysis (2)
- psychological detachment (2)
- rumination (2)
- simulation (2)
- thriving (2)
- usable privacy (2)
- virtual reality (2)
- visualization (2)
- vitality (2)
- work reflection (2)
- 802.11 (1)
- ACAT1 (1)
- ACPYPE (1)
- ALPS (1)
- ANN controller (1)
- Accuracy (1)
- Acetylcholinesterase (AChE) (1)
- Active Learning (1)
- Active healthy ageing (1)
- Active site mapping (1)
- Acylpeptide hydrolase (1)
- Adams-Moulton (1)
- Affinity proteomics (1)
- Africa (1)
- Aircraft (1)
- Analytical pyrolysis (1)
- Anoplophora glabripennis (1)
- Antibodies* (1)
- Antifuse memory (1)
- Antioxidant activity (1)
- Antioxidant assays (1)
- Assay development (1)
- Attitudes (1)
- Augmented Reality (1)
- Augmented reality (1)
- Automated PyMS (1)
- Autonomous Driving (1)
- B-splines (1)
- BDF (1)
- BLAST (1)
- Ball Tracking (1)
- Ball tracking (1)
- Basiswerkstoff (1)
- Batch Normalization (1)
- Benchmark (1)
- Benchmarking (1)
- Biomass (1)
- Blockchain (1)
- Blood glucose meter (1)
- Bond graph (1)
- Bond strength (1)
- Browser cache (1)
- Bulk fill (1)
- Business Support System (1)
- CIBERSORT (1)
- CPACS (1)
- Cache Poisoning (1)
- Canola (1)
- Car Interior Design (1)
- Carbapenem (1)
- Carbohydrate (1)
- Carsharing (1)
- Cathepsin B (1)
- Chemical imaging (1)
- Chemometrics (1)
- Chip ID (1)
- Citizen Journalism (1)
- Claim personal data (1)
- Co-Design (1)
- CoAP (1)
- Communication (1)
- Comparative Analysis (1)
- Comparative analysis (1)
- Computer graphics (1)
- Computergrafik (1)
- Consumer (1)
- Conversational Interface (1)
- Counterfeit protection (1)
- Critique (1)
- Crystallinity (1)
- Culture in Education (1)
- Curie-point pyrolysis (1)
- Customization (1)
- Cutting sticks problem (1)
- Cybersecurity (1)
- Cytokine-induced killer (CIK) cells (1)
- DFA Lab (1)
- DNA typing (1)
- DPA Lab (1)
- DSGVO (1)
- Data takeout (1)
- Degraded DNA (1)
- Dementia (1)
- Denial of Service (1)
- Design-Fiction (1)
- Dinarides (1)
- Directional antennas (1)
- Discriminant analysis (1)
- Distributed Robot Systems (1)
- Draw ratio (1)
- Driving Simulator (1)
- Dynamic motion primitives (1)
- EM leakage (1)
- Entrepreneurship (1)
- Enzyme activity assays (1)
- European horse chestnut (1)
- Everyday object manipulation (1)
- Ewing´s Sarcoma Family of Tumors (1)
- Experiment design (1)
- Explainability (1)
- Explainable artificial intelligence (1)
- FOXP3 (1)
- Factor and Cluster analyses (1)
- Fast Moving Consumer Goods Company (1)
- Fault Channel Watermarking Lab (1)
- Fault analysis (1)
- Field sequential imaging (1)
- Flexible robots (1)
- Flow direction (1)
- Fluorescence-quenched substrates (1)
- Folin–Ciocalteu assay (1)
- Force and tactile sensing (1)
- Force field (1)
- Forensic genomics (1)
- Furnace pyrolyzer (1)
- GDPR (1)
- Geometry (1)
- Germany (1)
- Ghana (1)
- Glycam06 (1)
- Gordon surface (1)
- Grid Control (1)
- Grid Stability (1)
- Gromacs (1)
- Group behavior analysis (1)
- HIF1α (1)
- HMGCL (1)
- HSQC NMR (1)
- Haco Tiger Brands (1)
- Head Mounted Display (1)
- Healthcare (1)
- Healthcare logistics (1)
- Heart Rate Prediction (1)
- Higher Education (1)
- Hochschule Bonn-Rhein-Sieg (1)
- Hospitality and wine (1)
- Hough Forests (1)
- Human-agent interaction (1)
- Hybrid offering (1)
- IC identification (1)
- Identification (1)
- Image-based rendering (1)
- Immersive analytics (1)
- Immunology* (1)
- Indirect Encodings (1)
- Information and Communication Technologies (1)
- Information needs (1)
- Innovation (1)
- Instantaneous assignment (1)
- Integrative simulation (1)
- Intelligence Amplification (1)
- Intelligence Augmentation (1)
- Interactive Object Detection (1)
- Interference (1)
- Internet of Things (1)
- Interview (1)
- Inverter (1)
- IoT services security (1)
- K/BxN (1)
- Kenya (1)
- Kenya Vision 2030 (1)
- Ketogenesis (1)
- Ketolysis (1)
- Kinect (1)
- Kraft lignin (1)
- LC-MS/MS (1)
- Large display interaction (1)
- Lattice Boltzmann method (1)
- Lead userness (1)
- Leakage circuits (1)
- Learning Culture (1)
- Learning Culture Survey (1)
- Learning from demonstration (1)
- Ligand -Receptor Interactions* (1)
- Light curing (1)
- Light measurement (1)
- Lignocellulose feedstock (1)
- Long-Term Autonomy (1)
- Longitudinal Study (1)
- Low-power digital design (1)
- MAP-Elites (1)
- MOOC (1)
- Magnetic resonance imaging (MRI) (1)
- Marketing practices (1)
- Measurement (1)
- Media Reporting (1)
- Methods (1)
- Middleware and Programming Environments (1)
- Miscanthus (1)
- Miscanthus x giganteus (1)
- Mobile technologies (1)
- Model-based engineering approaches to AI safety (1)
- Mold temperature (1)
- Mully Model of Applied Entrepreneurship Teaching (1)
- Multi-Modal Interaction (1)
- Multi-robot systems (1)
- Multilayer interaction (1)
- Multimodal hyperspectral data (1)
- Multivariate analysis (1)
- Nanofibers (1)
- Natural language understanding (1)
- Naturkautschuk (1)
- Networked Robots (1)
- Neuroevolution (1)
- Neurometabolic disease (1)
- Next Generation Sequencing (NGS) (1)
- Non-covalent interaction MS* (1)
- Nonbonded scaling factor (1)
- OER (1)
- Object Detection (1)
- Observation (1)
- Offer design (1)
- Older people (1)
- Open source firmware (1)
- Optical Flow (1)
- Optical flow (1)
- Organic aciduria (1)
- Organosolv lignin (1)
- Orthotropic material behavior (1)
- PHC (1)
- Participatory Journalism (1)
- Pattern recognition (1)
- Paulownia (1)
- Peer-to-Peer (1)
- Peptidomimetic inhibitors (1)
- Perception (1)
- Performance (1)
- Persistence (1)
- Pharmacogenetics (1)
- Phenolic acids (1)
- Plasmid DNA (pBR322) (1)
- Polymers (1)
- Polymorphism (1)
- Poultry meat (1)
- Prediction of physiological responses to strain (1)
- Principal Components Analysis (1)
- Principal component analysis (1)
- Privatsphäre (1)
- Probabilistic methods (1)
- Process Automation (1)
- Process dependent material parameters (1)
- Product Innovation (1)
- Protein complex analysis (1)
- Prunus avium L. (1)
- Public Opinion (1)
- Py-EGA-MS (1)
- Pyrolysis mass spectrometry (PyMS) (1)
- Pyrolysis-evolved gas analysis-mass spectrometry (1)
- Qualitative analysis (1)
- Qualitative study (1)
- Quality Diversity (1)
- RACS (1)
- REST (1)
- ROPOD (1)
- Raman spectroscopy (1)
- Raman-microspectroscopy (1)
- Rapeseed pomace (1)
- Rapid prototyping (1)
- Real-Time Image Processing (1)
- Real-time image processing (1)
- Remote laboratory (1)
- Research Trajectories (1)
- Research reproducibility and replicability (1)
- Research through Design (1)
- Resins (1)
- Rheometer (1)
- RhoA GTPases (1)
- Robot commands (1)
- Robotics (1)
- Runtime AI safety monitoring (1)
- Rural communities (1)
- SELU (1)
- SOA (1)
- Schwartz's portrait value questionnaire (PVQ) (1)
- Self-Driving Cars (1)
- Service robot (1)
- Set partition problem (1)
- Short tandem repeat (STR) (1)
- Side Channel Watermarking Lab (1)
- Side channels (1)
- Side-channel analysis (1)
- Silphium (1)
- Simulation (1)
- Sinapine (1)
- Smart metering (1)
- Software (1)
- Software Acquisition (1)
- Software IP protection (1)
- Spatio-Temporal (1)
- Spherical Treadmill (1)
- Spherical treadmill (1)
- Spoilage (1)
- Spoilage bacteria (1)
- State machines (1)
- Statistical methods (1)
- Storage modulus (1)
- Strategy (1)
- Stream cipher (1)
- Student Administration (1)
- Student Life Cycle (1)
- Student Self-Service (1)
- Substrate specificity (1)
- Supervised classification (1)
- Support vector machines (1)
- Survey (1)
- Survey research (1)
- Sustainable Development Goals 2030 (1)
- TD-GC/MS (1)
- Targeted mass spectrometry (1)
- Task allocation (1)
- Taylor-Green (1)
- Telogen hair (1)
- Temporal constraints (1)
- Testing (1)
- Therapeutic antibodies* (1)
- Time extended assignment (1)
- Total phenol content (1)
- Tourism Destination Development (1)
- Tourism Destination Management (1)
- Toyota HSR (1)
- Training Approaches (1)
- Trust (1)
- UV spectrum (1)
- University–industry linkage (1)
- User interfaces (1)
- Valproic acid (1)
- Values (1)
- Vibrational microspectroscopy (1)
- Videogame (1)
- Virtual reality (1)
- Virtuelle Realität (1)
- Visualization (1)
- Visuelle Wahrnehmung (1)
- Volatile organic compounds (1)
- Vulkanisation (1)
- V˙CO2 prediction (1)
- V˙O2 prediction (1)
- Web (1)
- Web-Tracking (1)
- West Africa (1)
- Whole body motion (1)
- Wi-Fi (1)
- WiAFirm (1)
- Wireless sensor networks (1)
- XAI (1)
- Xenopus (1)
- YOLO v3 (1)
- Yersinia toxins (1)
- accelerometer (1)
- accompanied refugee children (1)
- acetylcholine (1)
- acute (1)
- additive (1)
- adoptive cell transfer (1)
- advanced applications (1)
- affective events (1)
- agarose (1)
- allopurinol (1)
- allosteric regulation (1)
- amino acid transport (1)
- amorphous 2D polymer (1)
- angiogenesis (1)
- anomaly detection (1)
- antioxidant (1)
- antiradical activity (1)
- applications (1)
- arthritis (1)
- artificial neural networks (1)
- asylum laws (1)
- atmospheric aerosol (1)
- audio-tactile feedback (1)
- autoinflammatory diseases (1)
- automated sensor-screening (1)
- autonomous systems (1)
- back-of-device interaction (1)
- beta-ketothiolase (1)
- biocomposite (1)
- biomass (1)
- brightfield microscopy (1)
- brilliant green (1)
- brush cells (1)
- brushless motors (1)
- bulk and local viscoelastic properties (1)
- caching (1)
- cancer (1)
- cell harvesting (1)
- cell migration (1)
- childhood (1)
- chitosan (1)
- climate change (1)
- closed kinematic chain (1)
- coefficient of thermal expansion (1)
- collaborative learning (1)
- composites (1)
- control (1)
- creativity (1)
- cross-disciplinary (1)
- cross-evaluation (1)
- crystal violet (1)
- data base search (1)
- delta-subunit (1)
- demethylation (1)
- depth perception (1)
- design (1)
- design probe (1)
- digital platform ecosystem (1)
- distributed systems (1)
- diversity (1)
- drone video quality (1)
- drug release (1)
- emissions (1)
- emotion recognition (1)
- endothelial cells (1)
- energy deposition (1)
- energy efficiency (1)
- energy meteorology (1)
- engineering education (1)
- entrepreneurship education (1)
- epithelial sodium channel (ENaC) (1)
- epitope mapping (1)
- evolution (1)
- evolutionary development (1)
- experience sampling (1)
- experimental design (1)
- external faults (1)
- extraction (1)
- extrusion blow molding (1)
- familial Mediterranean fever (1)
- fault handling (1)
- first-semester students (1)
- fitness-fatigue model (1)
- fouling (1)
- fruit quality (1)
- gamification (1)
- gas transport networks (1)
- genes (1)
- genetics (1)
- genotype (1)
- globalisation (1)
- globally convergent solvers (1)
- governance (1)
- green economy (1)
- growth curve modeling (1)
- guidance (1)
- hands-on experiences (1)
- haptic feedback (1)
- haptic interfaces (1)
- healthcare (1)
- helical drilling (1)
- high degree of diagnostic coverage and reliability (1)
- holistic learning (1)
- human computer interaction (1)
- human security (1)
- hydrogel (1)
- hydroxyapatite (1)
- hydroxypropylmethylcellulose (1)
- hyperammonemia (1)
- hypoglycemia (1)
- immunhistochemistry (1)
- immunotherapy (1)
- industrial relations (1)
- innovative work behavior (1)
- interactive-learning (1)
- intercultural learning (1)
- international (1)
- international labour standards (1)
- international teams (1)
- isoleucine (1)
- job and wealth creation (1)
- job demands-resources model (1)
- learning-based fault detection and diagnosis (1)
- leucine (1)
- leukemia (1)
- lignocellulosic feedstock (1)
- low-input crops (1)
- lymphocytic (1)
- machine learning (1)
- mathematical chemistry (1)
- mathematical modeling (1)
- mechanical thinning (1)
- metabolic integration (1)
- micro processing (1)
- microindentation (1)
- mobile applications (1)
- modelling methodology (1)
- molecular dynamics (1)
- molecular evolution (1)
- monolignol ratio (1)
- motion estimation (1)
- motor drive (1)
- mountain tourism (1)
- mucociliary clearance (1)
- multi-channel power sourcing (1)
- multibody system (1)
- multibond graphs (1)
- multidimensional (1)
- multidisciplinary (1)
- multiresolution analysis (1)
- multistep (1)
- multivariate data analysis (1)
- mutation (1)
- naive physics (1)
- neural-networks (1)
- object detection (1)
- observational data and simulations (1)
- occupational e-mental health (1)
- optical flow (1)
- organosolv (1)
- osteogenesis (1)
- pH (1)
- pain recognition (1)
- pathogenic microorganisms (1)
- pause (1)
- pen interaction (1)
- peptide sequencing (1)
- performance measure (1)
- performance modeling (1)
- performance prediction (1)
- peripheral vision (1)
- permanent magnet motors (1)
- physiological measure (1)
- plastic manufacturing (1)
- polyphenols (1)
- polysaccharide (1)
- polytunnel (1)
- polyurethane coatings (1)
- posture analysis (1)
- practical learning (1)
- precision (1)
- privacy preferences (1)
- privacy settings (1)
- proanthocyanidins (1)
- processing-structure-property relationship (1)
- project-based learning (1)
- prototype apparatus (1)
- pyrin inflammasome (1)
- qualitative reasoning (1)
- radio transceivers (1)
- recovery from work (1)
- recovery intervention (1)
- reference dataset (1)
- refugee protection (1)
- remote lab (1)
- requirements analysis (1)
- rest break (1)
- rheumatoid arthritis (1)
- ring-size statistics (1)
- ripening (1)
- risk taking (1)
- robotics (1)
- scanning tunnelling microscopy (1)
- scratch assay (1)
- seed coat (1)
- self-assessment (1)
- self-report measure (1)
- semiconducting metal oxide gas sensor array (1)
- sensemaking (1)
- sensitization-satiation effects (1)
- sensor systems (1)
- sensor-based fault detection and diagnosis (1)
- serine-threonine kinase (1)
- serious games (1)
- service robots (1)
- services (1)
- short-range correlation (1)
- single-cell RNA-seq (1)
- size exclusion chromatography (1)
- smart agriculture (1)
- smart meters (1)
- smartphone-based intervention (1)
- social representations of tourism (1)
- sodium self-inhibition (1)
- solar power (1)
- spinal posture (1)
- stability (1)
- stem cells (1)
- stress detection (1)
- structural equation modeling (1)
- superhydrophobic surfaces (1)
- surface technologies (1)
- sustainability (1)
- symbiosis (1)
- taste (1)
- temperature control (1)
- temporal discretization (1)
- temporomandibular joint (1)
- tetrapod (1)
- time integration (1)
- topological reduction (1)
- tourism geography (1)
- training performance relationship (1)
- trapezoidal rule (1)
- tripartism (1)
- triphenylmethane dyes (1)
- tumor microenvironment (1)
- tumor-infiltrating immune cells (1)
- ultrashort pulse laser (1)
- user acceptance (1)
- user study (1)
- vacations (1)
- virtual-reality (1)
- water-to-land transition (1)
- wearable sensor (1)
- web caching (1)
- web services security (1)
- welfare technology (1)
- work break (1)
- wound healing assay (1)
- yield (1)
- µCT (1)
- ß-OHB (1)
- ß-hydroxybutyrate (1)
- “Big Four” agenda (1)
This work presents the preliminary research towards developing an adaptive tool for fault detection and diagnosis of distributed robotic systems, using explainable machine learning methods. Autonomous robots are complex systems that require high reliability in order to operate in different environments. Even more so, when considering distributed robotic systems, the task of fault detection and diagnosis becomes exponentially difficult.
To diagnose systems, models representing the behaviour under investigation need to be developed, and with distributed robotic systems generating large amount of data, machine learning becomes an attractive method of modelling especially because of its high performance. However, with current day methods such as artificial neural networks (ANNs), the issue of explainability arises where learnt models lack the ability to give explainable reasons behind their decisions.
This paper presents current trends in methods for data collection from distributed systems, inductive logic programming (ILP); an explainable machine learning method, and fault detection and diagnosis.
Healing of large bone defects requires implants or scaffolds that provide structural guidance for cell growth, differentiation, and vascularization. In the present work, an agarose-hydroxyapatite composite scaffold was developed that acts not only as a 3D matrix, but also as a release system. Hydroxyapatite (HA) was incorporated into the agarose gels in situ in various ratios by a simple procedure consisting of precipitation, cooling, washing, and drying. The resulting gels were characterized regarding composition, porosity, mechanical properties, and biocompatibility. A pure phase of carbonated HA was identified in the scaffolds, which had pore sizes of up to several hundred micrometers. Mechanical testing revealed elastic moduli of up to 2.8 MPa for lyophilized composites. MTT testing on Lw35human mesenchymal stem cells (hMSCs) and osteosarcoma MG-63 cells proved the biocompatibility of the scaffolds. Furthermore, scaffolds were loaded with model drug compounds for guided hMSC differentiation. Different release kinetic models were evaluated for adenosine 5′-triphosphate (ATP) and suramin, and data showed a sustained release behavior over four days.
Bone tissue engineering is an ever-changing, rapidly evolving, and highly interdisciplinary field of study, where scientists try to mimic natural bone structure as closely as possible in order to facilitate bone healing. New insights from cell biology, specifically from mesenchymal stem cell differentiation and signaling, lead to new approaches in bone regeneration. Novel scaffold and drug release materials based on polysaccharides gain increasing attention due to their wide availability and good biocompatibility to be used as hydrogels and/or hybrid components for drug release and tissue engineering. This article reviews the current state of the art, recent developments, and future perspectives in polysaccharide-based systems used for bone regeneration.
This work introduces a semi-Lagrangian lattice Boltzmann (SLLBM) solver for compressible flows (with or without discontinuities). It makes use of a cell-wise representation of the simulation domain and utilizes interpolation polynomials up to fourth order to conduct the streaming step. The SLLBM solver allows for an independent time step size due to the absence of a time integrator and for the use of unusual velocity sets, like a D2Q25, which is constructed by the roots of the fifth-order Hermite polynomial. The properties of the proposed model are shown in diverse example simulations of a Sod shock tube, a two-dimensional Riemann problem and a shock-vortex interaction. It is shown that the cell-based interpolation and the use of Gauss-Lobatto-Chebyshev support points allow for spatially high-order solutions and minimize the mass loss caused by the interpolation. Transformed grids in the shock-vortex interaction show the general applicability to non-uniform grids.
Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional implicit features during password entry such as device or geolocation information, and requests additional authentication factors if a certain risk level is detected. RBA is recommended by the NIST digital identity guidelines, is used by several large online services, and offers protection against security risks such as password database leaks, credential stuffing, insecure passwords and large-scale guessing attacks. Despite its relevance, the procedures used by RBA-instrumented online services are currently not disclosed. Consequently, there is little scientific research about RBA, slowing down progress and deeper understanding, making it harder for end users to understand the security provided by the services they use and trust, and hindering the widespread adoption of RBA.
In this paper, with a series of studies on eight popular online services, we (i) analyze which features and combinations/classifiers are used and are useful in practical instances, (ii) develop a framework and a methodology to measure RBA in the wild, and (iii) survey and discuss the differences in the user interface for RBA. Following this, our work provides a first deeper understanding of practical RBA deployments and helps fostering further research in this direction.
The limited sodium availability of freshwater and terrestrial environments was a major physiological challenge during vertebrate evolution. The epithelial sodium channel (ENaC) is present in the apical membrane of sodium-absorbing vertebrate epithelia and evolved as part of a machinery for efficient sodium conservation. ENaC belongs to the degenerin/ENaC protein family and is the only member that opens without an external stimulus. We hypothesized that ENaC evolved from a proton-activated sodium channel present in ionocytes of freshwater vertebrates and therefore investigated whether such ancestral traits are present in ENaC isoforms of the aquatic pipid frog Xenopus laevis. Using whole-cell and single-channel electrophysiology of Xenopus oocytes expressing ENaC isoforms assembled from alpha beta gamma- or delta beta gamma-subunit combinations, we demonstrate that Xenopus delta beta gamma-ENaC is profoundly activated by extracellular acidification within biologically relevant ranges (pH 8.0-6.0). This effect was not observed in Xenopus alpha beta gamma-ENaC or human ENaC orthologs. We show that protons interfere with allosteric ENaC inhibition by extracellular sodium ions, thereby increasing the probability of channel opening. Using homology modeling of ENaC structure and site-directed mutagenesis, we identified a cleft region within the extracellular loop of the delta-subunit that contains several acidic amino acid residues that confer proton-sensitivity and enable allosteric inhibition by extracellular sodium ions. We propose that Xenopus delta beta gamma-ENaC can serve as a model for investigating ENaC transformation from a proton-activated toward a constitutively-active ion channel. Such transformation might have occurred during the evolution of tetrapod vertebrates to enable bulk sodium absorption during the water-to-land transition.
Although work events can be regarded as pivotal elements of organizational life, only a few studies have examined how positive and negative events relate to and combine to affect work engagement over time. Theory suggests that to better understand how current events affect work engagement (WE), we have to account for recent events that have preceded these current events. We present competing theoretical views on how recent and current work events may affect employees (e.g., getting used to a high frequency of negative events or becoming more sensitive to negative events). Although the occurrence of events implies discrete changes in the experience of work, prior research has not considered whether work events actually accumulate to sustained mid-term changes in WE. To address these gaps in the literature, we conducted a week-level longitudinal study across a period of 15 consecutive weeks among 135 employees, which yielded 849 weekly observations. While positive events were associated with higher levels of WE within the same week, negative events were not. Our results support neither satiation nor sensitization processes. However, high frequencies of negative events in the preceding week amplified the beneficial effects of positive events on WE in the current week. Growth curve analyses show that the benefits of positive events accumulate to sustain high levels of WE. WE dissipates in the absence of continuous experience of positive events. Our study adds a temporal component and informs research that has taken a feature-oriented perspective on the dynamic interplay of job demands and resources.
In the literature on occupational stress and recovery from work several facets of thinking about work in off-job time have been conceptualized. However, research on the focal concepts is currently rather disintegrated. In this study we take a closer look at the five most established concepts, namely (1) psychological detachment, (2) affective rumination, (3) problem-solving pondering, (4) positive work reflection, and (5) negative work reflection. More specifically, we scrutinized (1) whether the five facets of work-related rumination are empirically distinct, (2) whether they yield differential associations with different facets of employee well-being (burnout, work engagement, thriving, satisfaction with life, and flourishing), and (3) to what extent the five facets can be distinguished from and relate to conceptually similar constructs, such as irritation, worry, and neuroticism. We applied structural equation modeling techniques to cross-sectional survey data from 474 employees. Our results provide evidence that (1) the five facets of work-related rumination are highly related, yet empirically distinct, (2) that each facet contributes uniquely to explain variance in certain aspects of employee well-being, and (3) that they are distinct from related concepts, albeit there is a high overlap between (lower levels of) psychological detachment and cognitive irritation. Our study contributes to clarify the structure of work-related rumination and extends the nomological network around different types of thinking about work in off-job time and employee well-being.
In the literature on occupational stress and recovery from work, several facets of thinking about work during off-job time have been conceptualized. However, research on the focal concepts is currently rather diffuse. In this study we take a closer look at the five most well-established concepts: (1) psychological detachment, (2) affective rumination, (3) problem-solving pondering, (4) positive work reflection, and (5) negative work reflection. More specifically, we scrutinized (1) whether the five facets of work-related rumination are empirically distinct, (2) whether they yield differential associations with different facets of employee well-being (burnout, work engagement, thriving, satisfaction with life, and flourishing), and (3) to what extent the five facets can be distinguished from and relate to conceptually similar constructs, such as irritation, worry, and neuroticism. We applied structural equation modeling techniques to cross-sectional survey data from 474 employees. Our results provide evidence for (1) five correlated, yet empirically distinct facets of work-related rumination. (2) Each facet yields a unique pattern of association with the eight aspects of employee well-being. For instance, detachment is strongly linked to satisfaction with life and flourishing. Affective rumination is linked particularly to burnout. Problem-solving pondering and positive work reflection yield the strongest links to work engagement. (3) The five facets of work-related rumination are distinct from related concepts, although there is a high overlap between (lower levels of) psychological detachment and cognitive irritation. Our study contributes to clarifying the structure of work-related rumination and extends the nomological network around different types of thinking about work during off-job time and employee well-being.
Computer graphics research strives to synthesize images of a high visual realism that are indistinguishable from real visual experiences. While modern image synthesis approaches enable to create digital images of astonishing complexity and beauty, processing resources remain a limiting factor. Here, rendering efficiency is a central challenge involving a trade-off between visual fidelity and interactivity. For that reason, there is still a fundamental difference between the perception of the physical world and computer-generated imagery. At the same time, advances in display technologies drive the development of novel display devices. The dynamic range, the pixel densities, and refresh rates are constantly increasing. Display systems enable a larger visual field to be addressed by covering a wider field-of-view, due to either their size or in the form of head-mounted devices. Currently, research prototypes are ranging from stereo and multi-view systems, head-mounted devices with adaptable lenses, up to retinal projection, and lightfield/holographic displays. Computer graphics has to keep step with, as driving these devices presents us with immense challenges, most of which are currently unsolved. Fortunately, the human visual system has certain limitations, which means that providing the highest possible visual quality is not always necessary. Visual input passes through the eye’s optics, is filtered, and is processed at higher level structures in the brain. Knowledge of these processes helps to design novel rendering approaches that allow the creation of images at a higher quality and within a reduced time-frame. This thesis presents the state-of-the-art research and models that exploit the limitations of perception in order to increase visual quality but also to reduce workload alike - a concept we call perception-driven rendering. This research results in several practical rendering approaches that allow some of the fundamental challenges of computer graphics to be tackled. By using different tracking hardware, display systems, and head-mounted devices, we show the potential of each of the presented systems. The capturing of specific processes of the human visual system can be improved by combining multiple measurements using machine learning techniques. Different sampling, filtering, and reconstruction techniques aid the visual quality of the synthesized images. An in-depth evaluation of the presented systems including benchmarks, comparative examination with image metrics as well as user studies and experiments demonstrated that the methods introduced are visually superior or on the same qualitative level as ground truth, whilst having a significantly reduced computational complexity.
Kenya, like all other developing countries in the world, is faced with the task of working strategically towards the achievement of the Sustained Development Goals (SDGs) 2030. These goals whose due date of accomplishment coincides with those of the national development blueprint, namely, the Kenya Vision 2030, have become a major focus of attention in the country. Conferences, workshops, and seminars are organized throughout the country on regular bases by joint multiplicity of organizations to address modalities of ensuring a timely achievement of SDGs in the country. Universities either individually or jointly are working towards this same target. More specifically, there are great areas of concern or priority areas that the country is focusing on as a strategic focus towards the achievement of the Kenya Vision 2030 and SDGs 2030. These strategic areas of focus have been isolated and declared by the President of the Republic of Kenya, His Excellency Uhuru Kenyatta, as the country’s “big four priority areas”, namely, affordable housing, affordable health care, food security, and manufacturing as a grandiose effort towards achievement of the SDGs, Kenya Vision 2030 as well as job and wealth creation. Similarly, Mount Kenya University’s top management established the Graduate Enterprise Academy (GEA) in 2013 under the direct Patronage of the university’s Founder with the primary aim of assisting graduates to be job and wealth creators rather than being job seekers. So far, over twenty start-ups are running throughout the country under Graduate Enterprise Academy (GEA). Incidentally, although the Graduate Enterprise Academy’s diverse areas of focus extend beyond the President of Kenya’s “Big Four” to include ICT and creative arts, among others, there are justifiable cases to indicate that GEA’s activities are also in support of the national “Big Four” agenda. This paper gives an exposition of different start-ups under MKU’s Graduate Enterprise Academy and are show-cased as evidence of MKU’s support towards the achievement of the national “Big Four” agenda. The paper covers a part of an ongoing program through desk-top analyses of reports, with an objective of show-casing MKU’s contribution to the national agenda through the Graduate Enterprise Academy for possible scale - up.
Due to increased emissions of palladium nanoparticles in recent years, it is important to develop analytical techniques to characterize these particles. The synthesis of defined and stable particles plays a key role in this process, as there are not many materials commercially available yet which could act as reference materials. Polyvinylpyrrolidone- (PVP-) stabilized palladium nanoparticles were synthesized through the reduction of palladium chloride by tetraethylene glycol (TEG) in the presence of KOH. Four different methods were used for particle size analysis of the palladium nanoparticles. Palladium suspensions were analyzed by scanning electron microscopy (SEM), small angle X-ray scattering (SAXS), single-particle ICP-MS (SP-ICP-MS), and X-ray diffraction (XRD). Secondary particles between 30 nm and 130 nm were detected in great compliance with SAXS and SP-ICP-MS. SEM analysis showed that the small particulates tend to form agglomerates.
Interactive Object Detection
(2019)
The success of state-of-the-art object detection methods depend heavily on the availability of a large amount of annotated image data. The raw image data available from various sources are abundant but non-annotated. Annotating image data is often costly, time-consuming or needs expert help. In this work, a new paradigm of learning called Active Learning is explored which uses user interaction to obtain annotations for a subset of the dataset. The goal of active learning is to achieve superior object detection performance with images that are annotated on demand. To realize active learning method, the trade-off between the effort to annotate (annotation cost) unlabeled data and the performance of object detection model is minimised.
Random Forests based method called Hough Forest is chosen as the object detection model and the annotation cost is calculated as the predicted false positive and false negative rate. The framework is successfully evaluated on two Computer Vision benchmark and two Carl Zeiss custom datasets. Also, an evaluation of RGB, HoG and Deep features for the task is presented.
Experimental results show that using Deep features with Hough Forest achieves the maximum performance. By employing Active Learning, it is demonstrated that performance comparable to the fully supervised setting can be achieved by annotating just 2.5% of the images. To this end, an annotation tool is developed for user interaction during Active Learning.
Background: Virtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.
New Method: We developed an optical flow-based method for tracking treadmil ball motion in real-time using a single high-resolution camera.
Results: Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms.
Comparison: with Existing Method(s) Optical flow based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration.
Conclusions: A fast system for rotational ball motion tracking suitable for virtual reality animal behavior across different scales was developed and characterized.
The aim of this study was to investigate whether beneficial vacation effects can be strengthened and prolonged with a smartphone-based intervention. In a four-week longitudinal study among 79 Finnish teachers, we investigated the development of recovery, well-being, and job performance before, during, and after a one-week vacation in three groups: non-users (n = 51), passive (n = 18) and active (n = 10) users. Participants were instructed to actively use a recovery app (called Holidaily) and complete five digital questionnaires. Most recovery experiences and well-being indicators increased during the vacation. Job performance and concentration capacity showed no significant time effects. Among active app users, creativity at work increased from baseline to after the vacation, whereas among non-users it decreased and among passive users it decreased a few days after the vacation but increased again one and a half weeks after the vacation. The fading of beneficial vacation effects on negative affect seems to have been slower among active app users. Only few participants used the app actively. Still, results suggest that a smartphone-based recovery intervention may support beneficial vacation effects.
In this paper, we provide a participatory design study of a mobile health platform for older adults that provides an integrative perspective on health data collected from different devices and apps. We illustrate the diversity and complexity of older adults’ perspectives in the context of health and technology use, the challenges which follow on for the design of mobile health platforms that support active and healthy ageing (AHA) and our approach to addressing these challenges through a participatory design (PD) process. Interviews were conducted with older adults aged 65+ in a two-month study with the goal of understanding perspectives on health and technologies for AHA support. We identified challenges and derived design ideas for a mobile health platform called “My-AHA”. For researchers in this field, the structured documentation of our procedures and results, as well as the implications derived provide valuable insights for the design of mobile health platforms for older adults.
Mass Spectrometry: Pyrolysis
(2019)
Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.
PosturePairsDB19
(2019)
The design of self-driving cars is one of the most exciting and ambitious challenges of our days and every day, new research work is published. In order to give an orientation, this article will present an overview of various methods used to study the human side of autonomous driving. Simplifying roughly, you can distinguish between design science-oriented methods (such as Research through Design, Wizard of Oz or driving simulator ) and behavioral science methods (such as survey, interview, and observation). We show how these methods are adopted in the context of autonomous driving research and dis-cuss their strengths and weaknesses. Due to the complexity of the topic, we will show that mixed method approaches will be suitable to explore the impact of autonomous driving on different levels: the individual, the social interaction and society.
The alternative use of travel time is one of the widely discussed benefits of driverless cars. We therefore conducted 14 co-design sessions to examine how people manage their time, to determine how they perceive the value of time in driverless cars and to derive design implications. Our findings suggest that driverless mobility will affect both people’s use of travel time as well as their time management in general. The participants repeatedly stated the desire of completing tasks while traveling to save time for activities that are normally neglected in their everyday life. Using travel time efficiently requires using car space efficiently, too. We found out that the design concept of tiny houses could serve as common design pattern to deal with the limited space within cars and support diverse needs.
Towards self-explaining social robots. Verbal explanation strategies for a needs-based architecture
(2019)
In order to establish long-term relationships with users, social companion robots and their behaviors need to be comprehensible. Purely reactive behavior such as answering questions or following commands can be readily interpreted by users. However, the robot's proactive behaviors, included in order to increase liveliness and improve the user experience, often raise a need for explanation. In this paper, we provide a concept to produce accessible “why-explanations” for the goal-directed behavior an autonomous, lively robot might produce. To this end we present an architecture that provides reasons for behaviors in terms of comprehensible needs and strategies of the robot, and we propose a model for generating different kinds of explanations.
Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of traffic signs, but in our experiments it does not reach high accuracy, and we believe this is due to lack of data, requiring data augmentation. Generative adversarial networks can learn the high dimensional distribution of empirical data, allowing the generation of new data points. In this paper we apply pix2pix GANs architecture to generate new traffic sign images and evaluate the use of these images in data augmentation. We were motivated to use pix2pix to translate symbolic sign images to real ones due to the mode collapse in Conditional GANs. Through our experiments we found that data augmentation using GAN can increase classification accuracy for circular traffic signs from 92.1% to 94.0%, and for triangular traffic signs from 93.8% to 95.3%, producing an overall improvement of 2%. However some traditional augmentation techniques can outperform GAN data augmentation, for example contrast variation in circular traffic signs (95.5%) and displacement on triangular traffic signs (96.7 %). Our negative results shows that while GANs can be naively used for data augmentation, they are not always the best choice, depending on the problem and variability in the data.
Large display environments are highly suitable for immersive analytics. They provide enough space for effective co-located collaboration and allow users to immerse themselves in the data. To provide the best setting - in terms of visualization and interaction - for the collaborative analysis of a real-world task, we have to understand the group dynamics during the work on large displays. Among other things, we have to study, what effects different task conditions will have on user behavior.
In this paper, we investigated the effects of task conditions on group behavior regarding collaborative coupling and territoriality during co-located collaboration on a wall-sized display. For that, we designed two tasks: a task that resembles the information foraging loop and a task that resembles the connecting facts activity. Both tasks represent essential sub-processes of the sensemaking process in visual analytics and cause distinct space/display usage conditions. The information foraging activity requires the user to work with individual data elements to look into details. Here, the users predominantly occupy only a small portion of the display. In contrast, the connecting facts activity requires the user to work with the entire information space. Therefore, the user has to overview the entire display.
We observed 12 groups for an average of two hours each and gathered qualitative data and quantitative data. During data analysis, we focused specifically on participants' collaborative coupling and territorial behavior.
We could detect that participants tended to subdivide the task to approach it, in their opinion, in a more effective way, in parallel. We describe the subdivision strategies for both task conditions. We also detected and described multiple user roles, as well as a new coupling style that does not fit in either category: loosely or tightly. Moreover, we could observe a territory type that has not been mentioned previously in research. In our opinion, this territory type can affect the collaboration process of groups with more than two collaborators negatively. Finally, we investigated critical display regions in terms of ergonomics. We could detect that users perceived some regions as less comfortable for long-time work.
The number of studies on work breaks and the importance of this subject is growing rapidly, with research showing that work breaks increase employees’ wellbeing and performance and workplace safety. However, comparing the results of work break research is difficult since the study designs and methods are heterogeneous and there is no standard theoretical model for work breaks. Based on a systematic literature search, this scoping review included a total of 93 studies on experimental work break research conducted over the last 30 years. This scoping review provides a first structured evaluation regarding the underlying theoretical framework, the variables investigated, and the measurement methods applied. Studies using a combination of measurement methods from the categories “self-report measures,” “performance measures,” and “physiological measures” are most common and to be preferred in work break research. This overview supplies important information for ergonomics researchers allowing them to design work break studies with a more structured and stronger theory-based approach. A standard theoretical model for work breaks is needed in order to further increase the comparability of studies in the field of experimental work break research in the future.
The pyrin inflammasome has evolved as an innate immune sensor to detect bacterial toxin-induced Rho guanosine triphosphatase (Rho GTPase)-inactivation, a process that is similar to the "guard" mechanism in plants. Rho GTPases act as molecular switches to regulate a variety of signal transduction pathways including cytoskeletal organization. Pathogens can modulate Rho GTPase activity to suppress host immune responses such as phagocytosis. Pyrin is encoded by MEFV, the gene that is mutated in patients with familial Mediterranean fever (FMF). FMF is the prototypic autoinflammatory disease characterized by recurring short episodes of systemic inflammation and is a common disorder in many populations in the Mediterranean basin. Pyrin specifically senses modifications in the activity of the small GTPase RhoA, which binds to many effector proteins including the serine/threonine-protein kinases PKN1 and PKN2 and actin-binding proteins. RhoA activation leads to PKN-mediated phosphorylation-dependent pyrin inhibition. Conversely, pathogen virulence factors downregulate RhoA activity in a variety of ways, and these changes are detected by the pyrin inflammasome irrespective of the type of modifications. MEFV pathogenic variants favor the active state of pyrin and elicit proinflammatory cytokine release and pyroptosis. They can be inherited either as a dominant or recessive trait depending on the variant's location and effect on the protein function. Mutations in the C-terminal B30.2 domain are usually considered recessive, although heterozygotes may manifest a biochemical or even a clinical phenotype. These variants are hypomorphic in regard to their effect on intramolecular interactions, but ultimately accentuate pyrin activity. Heterozygous mutations in other domains of pyrin affect residues critical for inhibition or protein oligomerization, and lead to constitutively active inflammasome. In healthy carriers of FMF mutations who have the subclinical inflammatory phenotype, the increased activity of pyrin might have been protective against endemic infections over human history. This finding is supported by the observation of high carrier frequencies of FMF-mutations in multiple populations. The pyrin inflammasome also plays a role in mediating inflammation in other autoinflammatory diseases linked to dysregulation in the actin polymerization pathway. Therefore, the assembly of the pyrin inflammasome is initiated in response to fluctuations in cytoplasmic homeostasis and perturbations in cytoskeletal dynamics.
Systemic autoinflammatory diseases (SAIDs) are a group of inflammatory disorders caused by dysregulation in the innate immune system that leads to enhanced immune responses. The clinical diagnosis of SAIDs can be difficult since individually these are rare diseases with considerable phenotypic overlap. Most SAIDs have a strong genetic background, but environmental and epigenetic influences can modulate the clinical phenotype. Molecular diagnosis has become essential for confirmation of clinical diagnosis. To date there are over 30 genes and a variety of modes of inheritance that have been associated with monogenic SAIDs. Mutations in the same gene can lead to very distinct phenotypes and can have different inheritance patterns. In addition, somatic mutations have been reported in several of these conditions. New genetic testing methods and databases are being developed to facilitate the molecular diagnosis of SAIDs, which is of major importance for treatment, prognosis and genetic counselling. The aim of this review is to summarize the latest advances in genetic testing for SAIDs and discuss potential obstacles that might arise during the molecular diagnosis of SAIDs.
Namibia’s hunting industry is increasingly threatened by animal rightists and opponent groups whose adversarial mindset is mostly based on emotion orientated information. The fatal consequences if closing hunting tourism in a country like Namibia are expounded in this study by critically investigating the input of well-regulated hunting tourism towards conservation in Namibia. Different factors have to be taken into consideration, regarding the country’s attributes that differ significantly from other countries and their methods to achieve successful conservation management strategies. By conducting an in-depth interview with Mr. Volker Grellmann and by obtaining secondary data from local authorities and organizations, the current research investigates how well-regulated hunting tourism in Namibia is an important part of biodiversity conservation. The results outline that hunting tourism is crucial for the value of wildlife and yields for wildlife to have a greater benefit than livestock and crop farming in Namibia. Likewise, the country takes care of their valuable natural recourse. As a result, natural habitats are induced, and subsequently a steeply growing number of wildlife was recorded over the last 50 years in Namibia. Among others hunting tourism favors the development of rural areas and yields incentives to fight poaching and the illegal trade of wild animal products.
Energy Profiles of the Ring Puckering of Cyclopentane, Methylcyclopentane and Ethylcyclopentane
(2019)
Application developers constitute an important part of a digital platform’s ecosystem. Knowledge about psychological processes that drive developer behavior in platform ecosystems is scarce. We build on the lead userness construct which comprises two dimensions, trend leadership and high expected benefits from a solution, to explain how developers’ innovative work behavior (IWB) is stimulated. We employ an efficiencyoriented and a social-political perspective to investigate the relationship between lead userness and IWB. The efficiency-oriented view resonates well with the expected benefit dimension of lead userness, while the social-political view might be interpreted as a reflection of trend leadership. Using structural equation modeling, we test our model with a sample of over 400 developers from three platform ecosystems. We find that lead userness is indirectly associated with IWB and the performance-enhancing view to be the stronger predictor of IWB. Finally, we unravel differences between paid and unpaid app developers in platform ecosystems.
Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews ofstatic scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.
Tell Your Robot What To Do: Evaluation of Natural Language Models for Robot Command Processing
(2019)
The use of natural language to indicate robot tasks is a convenient way to command robots. As a result, several models and approaches capable of understanding robot commands have been developed, which however complicates the choice of a suitable model for a given scenario. In this work, we present a comparative analysis and benchmarking of four natural language understanding models - Mbot, Rasa, LU4R, and ECG. We particularly evaluate the performance of the models to understand domestic service robot commands by recognizing the actions and any complementary information in them in three use cases: the RoboCup@Home General Purpose Service Robot (GPSR) category 1 contest, GPSR category 2, and hospital logistics in the context of the ROPOD project.
Digital transformation in Higher Education and Science is a mission-critical demand to prepare educational institutions for their future competition on the international market. In many cases, the digitization goes along with the search for and acquisition of new software. For easily exchangeable software, wrong product decisions, in the worst case, lead to calculable financial losses. However, if a planned software requires a lot of technological adjustments and is to be applied as central component of a business- and/or security-critical environment, wrong decisions during the software acquisition process might lead to hardly calculable damage. Questions arising are how to decide for a product and how many resources should be invested for the acquisition process.
We planned to apply a commercial Business Support System, which should replace the currently used in-house developed software. Our goals were the increase of our university’s level of data security, to ease the interaction between stakeholders, to eliminate media discontinuities, to improve the process management and transparency, and to reduce the execution time of automated processes. Alongside with the introduction of the electronic case file, our agenda stipulates the digitization (and automation) of administrative university processes, especially, but not limited to, the student self-service and the administrative student life cycle. Usual tools and practices, commonly applied to (simple) software acquisition, failed in our scenario.
With the case study introduced in this paper, we address all persons, involved within software acquisition processes: From our experiences, we strongly recommend to place greater value on an exhaustively completed acquisition process, than on short-termed economic advantages.
The Learning Culture Survey (LCS) is a questionnaire-based research, investigating students’ perceptions of and expectations towards Higher Education (HE). The aim of this survey is to improve our understanding about the sources of cultural conflicts in educational scenarios. This understanding, shell help us to predict potential conflict situations and develop supportive measures.
After three years of development, the LCS was initialized in 2010 in South Korea and Germany. During the following years, the investigations were extended to further countries. The results, on the one hand, provided insights about the cultural context of HE in general and on the other hand, about specific (national / regional) characteristics of learners in HE. Most issues targeted with the questionnaire were directly linked to value systems. Thus, we expected from the beginning that the collected data would keep valid over longer periods of time. However, we had no evidence regarding the actual persistence of learning culture. For a study, designed to being implemented on a global scope and providing input for further applications, persistence is a basic condition to justify related investigations.
To answer the question on persistence, we repeated the LCS in our university every four years, between 2010 to 2018/19. Besides a small number of slight changes, explainable out of their situational context, the overall results kept consistent over the investigated years. In this paper, after an introduction of the LCS’ concept, setting and its general results from the past years, we present the insights from our most recently finalized longitudinal study on learning culture.
This paper stresses the importance of entrepreneurship education towards enhancing sustainable development in Kenya. The problems facing the country ranging from high rate of poverty, youth and graduate unemployment; overdependence on foreign goods and technology.
This paper therefore argues that entrepreneurship education will equip the students with the skills with which to not only be self-reliant, but to become wealth creators. The intervention level of entrepreneurship education has been at tertiary institutions and universities. This paper argues that attitudes and values are acquired at formative stage in life. Based on literature review of the models that have been used and yielded positive results, this paper proposes an innovative approach to the teaching of entrepreneurship education that is inclusive of pre-school, primary, secondary, tertiary and university levels. This paper explores the “Mully Model of Applied Entrepreneurship Teaching” as a case study, using interviews, surveys and reviewing relevant MCF data. The organization’s success factors within the Kenyan context are discussed.
The paper also recommended that educational programs at all levels of education should be made relevant to provide the youth the needed entrepreneurial skills. Further, it recommends that experiential learning methodologies be emphasized in the delivery of entrepreneurship education.
Process-dependent thermo-mechanical viscoelastic properties and the corresponding morphology of HDPE extrusion blow molded (EBM) parts were investigated. Evaluation of bulk data showed that flow direction, draw ratio, and mold temperature influence the viscoelastic behavior significantly in certain temperature ranges. Flow induced orientations due to higher draw ratio and higher mold temperature lead to higher crystallinities. To determine the local viscoelastic properties, a new microindentation system was developed by merging indentation with dynamic mechanical analysis. The local process-structure-property relationship of EBM parts showed that the cross-sectional temperature distribution is clearly reflected by local crystallinities and local complex moduli. Additionally, a model to calculate three-dimensional anisotropic coefficients of thermal expansion as a function of the process dependent crystallinity was developed based on an elementary volume unit cell with stacked layers of amorphous phase and crystalline lamellae. Good agreement of the predicted thermal expansion coefficients with measured ones was found up to a temperature of 70 °C.
Quantifying Interference in WiLD Networks using Topography Data and Realistic Antenna Patterns
(2019)
Avoiding possible interference is a key aspect to maximize the performance in Wi-Fi based Long Distance networks. In this paper we quantify self-induced interference based on data derived from our testbed and match the findings against simulations. By enhancing current simulation models with two key elements we significantly reduce the deviation between testbed and simulation: the usage of detailed antenna patterns compared to the cone model and propagation modeling enhanced by license-free topography data. Based on the gathered data we discuss several possible optimization approaches such as physical separation of local radios, tuning the sensitivity of the transmitter and using centralized compared to distributed channel assignment algorithms. While our testbed is based on 5 GHz Wi-Fi, we briefly discuss the possible impact of our results to other frequency bands.
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.
The Peren-Clement index (PCI) is a methodology to analyze country-specific risk for businesses engaged in international trade and direct investment. This index, established in 1998, provides a guideline when deciding which foreign markets offer the possibility for additional business engagement and investment, and to what extent existing engagement or investment can be increased or should be reduced.
Humankind, it can be argued, lives beyond its means and often at the expense of future generations. This paper starkly demonstrates, with the aid of a mathematical model, the imperative for a sustainable existence. In the model, consumption of resources is represented as a closed system, just like our planet. Long-term survival is only possible if consumption is below the ability of the system to regenerate.
This article examines similarities and differences in the attitudes and social representations of destination managers towards implementing sustainable tourism between the mountain regions of the Alps and the Dinarides. Bearing in mind the transnational impacts (i.e., environmental, economic and social) of the tourism industry the research methodology adopted an international perspective by sending a questionnaire to tourism organizations in fourteen different countries in the Alps and the Dinarides. The research is interdisciplinary in nature, because it integrates knowledge from sustainability and management science with tourism geography and social psychology. The findings confirm that social representations of sustainable tourism differ significantly in the two mountain regions.
This work aims to create a natural language generation (NLG) base for further development of systems for automatic examination questions generation and automatic summarization in Hochschule Bonn-Rhein-Sieg and Fraunhofer IAIS, respectively. Nowadays both tasks are very relevant. The first can significantly simplify the university teachers' work and the second to be of assistance for a faster retrieval of knowledge from an excessively large amount of information that people often work with. We focus on the search for an efficient and robust approach to the controlled NLG problem. Therefore, though the initial idea of the project was the usage of the generative adversarial neural networks (GANs), we switched our attention to more robust and easily-controllable autoencoders. Thus, in this work we implement an autoencoder for unsupervised discovery of latent space representations of text, and show the ability of the system to generate new sentences based on this latent space. Apart from that, we apply Gaussian mixture techniques in order to obtain meaningful text clusters and thereby try to create a tool that would allow us to generate sentences relevant to the semantics of the Gaussian clusters, e.g. positive or negative reviews or examination questions on certain topic. The developed system is tested on several datasets and compared to GANs' performance.
Beyond HCI and CSCW: Challenges and Useful Practices Towards a Human-Centred Vision of AI and IA
(2019)
This research was conducted to determine the relationship between entrepreneurship educations, venture intention on venture creation among entrepreneurial graduate in Kenya focusing on selected universities in Kenya. The study was grounded on the economic entrepreneurship theory, an attitude-based view on entrepreneurship education and resource-based theory. This research embraced a cross-sectional descriptive survey design. Study population was 2500 student taking entrepreneurship course in various universities of whom a sample of 345 students was chosen using purposive and simple random sampling technique. The study used both primary and secondary data. Statistical Package for Social Sciences (SPSS Version 21) was used to analyse quantitative date. The findings of the study revealed that entrepreneurial education had a noteworthy influence on venture creation (r= 0. 512, p = .001<0.05, t= 10.904) increase in entrepreneurial education would lead to significant increase in venture creation. The study revealed that entrepreneurial training has significance influence in venture creation among graduate as indicated by β1=-0.670, p=0.002<0.05, t= 10.304. Study established that increase in entrepreneurial orientation would lead to increase in venture creation among graduates by a factor of 0.519 with P value of 0.002 (r =0.519, P=0.03< 0.05). The research conclusion was that entrepreneurial knowledge acquisition, entrepreneurial training and entrepreneurial orientation combined have important and positive relationship with venture creation among the graduates.
Atmospheric aerosols affect the power production of solar energy systems. Their impact depends on both the atmospheric conditions and the solar technology employed. By being a region with a lack in power production and prone to high solar insolation, West Africa shows high potential for the application of solar power systems. However, dust outbreaks, containing high aerosol loads, occur especially in the Sahel, located between the Saharan desert in the north and the Sudanian Savanna in the south. They might affect the whole region for several days with significant effects on power generation. This study investigates the impact of atmospheric aerosols on solar energy production for the example year 2006 making use of six well instrumented sites in West Africa. Two different solar power technologies, a photovoltaic (PV) and a parabolic through (PT) power plant, are considered. The daily reduction of solar power due to aerosols is determined over mostly clear-sky days in 2006 with a model chain combining radiative transfer and technology specific power generation. For mostly clear days the local daily reduction of PV power (at alternating current) (PVAC) and PT power (PTP) due to the presence of aerosols lies between 13 % and 22 % and between 22 % and 37 %, respectively. In March 2006 a major dust outbreak occurred, which serves as an example to investigate the impact of an aerosol extreme event on solar power. During the dust outbreak, daily reduction of PVAC and PTP of up to 79 % and 100 % occur with a mean reduction of 20 % to 40 % for PVAC and of 32 % to 71 % for PTP during the 12 days of the event.
The link between universities and the industry has been of concern both locally as well as globally for a long time, for the obvious reason that it is perceived to enhance organizational performance. The gap between universities and the industry has been widening in developing countries leading to lost opportunities for joint research, product development and job creation. Marketing and entrepreneurship could play a pivotal role in reversing the weakened linkages by building mutual relationship and strengthening bonds between universities and industry. This study sought to examine the role of marketing and entrepreneurship as important tools for enhancing the university industry linkages. The study sought to determine the aspects of marketing and entrepreneurship that have the highest influence on enhancing the university industry linkages. It considered the nexus of entrepreneurship and marketing exemplified by the attributes of innovativeness, creativity, risk taking; proactive orientation and value creation as crucial for creating, nurturing and developing sustained linkages between universities and industry. The study targeted 150 small and medium sized enterprises in Nairobi City County, out of which 143 responded, giving a response rate of 95 %. Data was collected using structured questionnaire administered to managers of small and medium sized enterprises engaged in manufacturing, retail, banking and hospitals. Survey data collected from small and medium enterprises will be analyzed through descriptive statistics including mean scores and standard deviation. We will test our hypothesis through regression analysis. The study found that marketing practices especially those focused on the product, promotion and distribution were key in enhancing University industry linkage. With regards to entrepreneurial orientation, risk taking, and creativity indicators were found to be more important than innovation in enhancing university-industry linkages.
Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm
(2019)
This paper presents a modification of the data-driven sensor-based fault detection and diagnosis (SFDD) algorithm for online robot monitoring. Our version of the algorithm uses a collection of generative models, in particular restricted Boltzmann machines, each of which represents the distribution of sliding window correlations between a pair of correlated measurements. We use such models in a residual generation scheme, where high residuals generate conflict sets that are then used in a subsequent diagnosis step. As a proof of concept, the framework is evaluated on a mobile logistics robot for the problem of recognising disconnected wheels, such that the evaluation demonstrates the feasibility of the framework (on the faulty data set, the models obtained 88.6% precision and 75.6% recall rates), but also shows that the monitoring results are influenced by the choice of distribution model and the model parameters as a whole.
When developing robot functionalities, finite state machines are commonly used due to their straightforward semantics and simple implementation. State machines are also a natural implementation choice when designing robot experiments, as they generally lead to reproducible program execution. In practice, the implementation of state machines can lead to significant code repetition and may necessitate unnecessary code interaction when reparameterisation is required. In this paper, we present a small Python library that allows state machines to be specified, configured, and dynamically created using a minimal domain-specific language. We illustrate the use of the library in three different use cases - scenario definition in the context of the RoboCup@Home competition, experiment design in the context of the ROPOD project, as well as specification transfer between robots.
For robots acting - and failing - in everyday environments, a predictable behaviour representation is important so that it can be utilised for failure analysis, recovery, and subsequent improvement. Learning from demonstration combined with dynamic motion primitives is one commonly used technique for creating models that are easy to analyse and interpret; however, mobile manipulators complicate such models since they need the ability to synchronise arm and base motions for performing purposeful tasks. In this paper, we analyse dynamic motion primitives in the context of a mobile manipulator - a Toyota Human Support Robot (HSR)- and introduce a small extension of dynamic motion primitives that makes it possible to perform whole body motion with a mobile manipulator. We then present an extensive set of experiments in which our robot was grasping various everyday objects in a domestic environment, where a sequence of object detection, pose estimation, and manipulation was required for successfully completing the task. Our experiments demonstrate the feasibility of the proposed whole body motion framework for everyday object manipulation, but also illustrate the necessity for highly adaptive manipulation strategies that make better use of a robot's perceptual capabilities.
Treatment options for acute myeloid leukemia (AML) remain extremely limited and associated with significant toxicity. Nicotinamide phosphoribosyltransferase (NAMPT) is involved in the generation of NAD+ and a potential therapeutic target in AML. We evaluated the effect of KPT-9274, a p21-activated kinase 4/NAMPT inhibitor that possesses a unique NAMPT-binding profile based on in silico modeling compared with earlier compounds pursued against this target. KPT-9274 elicited loss of mitochondrial respiration and glycolysis and induced apoptosis in AML subtypes independent of mutations and genomic abnormalities. These actions occurred mainly through the depletion of NAD+, whereas genetic knockdown of p21-activated kinase 4 did not induce cytotoxicity in AML cell lines or influence the cytotoxic effect of KPT-9274. KPT-9274 exposure reduced colony formation, increased blast differentiation, and diminished the frequency of leukemia-initiating cells from primary AML samples; KPT-9274 was minimally cytotoxic toward normal hematopoietic or immune cells. In addition, KPT-9274 improved overall survival in vivo in 2 different mouse models of AML and reduced tumor development in a patient-derived xenograft model of AML. Overall, KPT-9274 exhibited broad preclinical activity across a variety of AML subtypes and warrants further investigation as a potential therapeutic agent for AML.
Opportunities for Sustainable Mobility: Re-thinking Eco-feedback from a Citizen's Perspective
(2019)
In developed nations, a growing emphasis is being placed on the promotion of sustainable behaviours amongst individuals, or ‘citizen-consumers’. In HCI, various eco-feedback tools have been designed as persuasive instruments, with a strong normative appeal geared to encouraging citizens to conduct a more sustainable mobility. However, many critiques have been formulated regarding this ‘paternalistic’ stance. In this paper, we switched the perspective from a designer’s to a citizen’s point of view and explored how people would use eco-feedback tools to support sustainable mobility in their city. In the study, we conducted 14 interviews with citizens who had used eco-feedback previously. The findings indicate new starting points that could inform future eco-feedback tools. These encompass: (1) better information regarding how sustainable mobility is measured and monitored; (2) respect for individual mobility situations and preferences; and (3) the scope for participation and the sharing of responsibility between citizens and municipal city services.