Refine
H-BRS Bibliography
- yes (373) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (80)
- Fachbereich Wirtschaftswissenschaften (69)
- Fachbereich Ingenieurwissenschaften und Kommunikation (65)
- Fachbereich Angewandte Naturwissenschaften (64)
- Fachbereich Sozialpolitik und Soziale Sicherung (59)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (53)
- Institut für funktionale Gen-Analytik (IFGA) (29)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (27)
- Institut für Cyber Security & Privacy (ICSP) (23)
- Institut für Verbraucherinformatik (IVI) (23)
Document Type
- Article (133)
- Conference Object (87)
- Part of a Book (44)
- Book (monograph, edited volume) (25)
- Preprint (15)
- Working Paper (13)
- Contribution to a Periodical (11)
- Report (9)
- Video (8)
- Research Data (6)
Year of publication
- 2021 (373) (remove)
Keywords
- Lehrbuch (7)
- DGQ (6)
- Melcher (6)
- Augmented Reality (4)
- Machine Learning (4)
- Usable Security (4)
- Big Data Analysis (3)
- Digitalisierung (3)
- Grundwerkzeug des Qualitätsmanagements (3)
- radiation (3)
When users in virtual reality cannot physically walk and self-motions are instead only visually simulated, spatial updating is often impaired. In this paper, we report on a study that investigated if HeadJoystick, an embodied leaning-based flying interface, could improve performance in a 3D navigational search task that relies on maintaining situational awareness and spatial updating in VR. We compared it to Gamepad, a standard flying interface. For both interfaces, participants were seated on a swivel chair and controlled simulated rotations by physically rotating. They either leaned (forward/backward, right/left, up/down) or used the Gamepad thumbsticks for simulated translation. In a gamified 3D navigational search task, participants had to find eight balls within 5 min. Those balls were hidden amongst 16 randomly positioned boxes in a dark environment devoid of any landmarks. Compared to the Gamepad, participants collected more balls using the HeadJoystick. It also minimized the distance travelled, motion sickness, and mental task demand. Moreover, the HeadJoystick was rated better in terms of ease of use, controllability, learnability, overall usability, and self-motion perception. However, participants rated HeadJoystick could be more physically fatiguing after a long use. Overall, participants felt more engaged with HeadJoystick, enjoyed it more, and preferred it. Together, this provides evidence that leaning-based interfaces like HeadJoystick can provide an affordable and effective alternative for flying in VR and potentially telepresence drones.
Die digitale Transformation verändert die internationale Kooperation der Hochschulen massiv. Über die Möglichkeiten der virtuellen Mobilität hinaus entstehen neue Themenfelder, die internationale Lern- und Lehrerlebnisse mit digitaler Unterstützung verändern, ergänzen oder neu ermöglichen. Dazu sind im Bereich der Förderung der Internationalisierung (DAAD, Erasmus+, BMBF u.a.) Projekte und Förderformate entstanden, die Digitalisierung und Internationalisierung kombinieren und die neuen Themenstellungen adressieren, z.B. didaktische Formate, administrative Prozesse (auch im Kontext OZG und DSGVO), virtuelle und hybride Mobilität, internationale Projekt- und Teamformate sowie schlussendlich auch Inhalte, die internationale, interkulturelle und interdisziplinäre Kompetenzen mit digitalen Kompetenzen verbinden. Der vorgeschlagene Workshop soll entsprechende Projekte zusammenbringen und die Themen strukturieren, um einen Überblick der Entwicklungen zu schaffen und somit einen Beitrag zur Definition des Themenfelds „Digitalisierung & Internationalisierung“ zu leisten.
BWL für Dummies
(2021)
Ziel der vorliegenden Forschungsarbeit ist es, den Einfluss von Persönlichkeit auf nachhaltige Maßnahmen anhand des Streamingkonsums zu eruieren. Der allgemein steigende Streamingkonsum und die damit einhergehenden Umweltschäden einerseits und ein wachsendes gesellschaftliches Umweltbewusstsein andererseits stellen einen Widerspruch dar. An einer Online-Umfrage zu diesen und weiterführenden Aspekten nahmen 204 Probanden teil. Während sich die Eigenschaften Verträglichkeit und Offenheit in hoher Ausprägung positiv auf die Umwelteinstellung, das Umweltverhalten und die Umweltbesorgnis auswirkten, wurden die umweltfreundlichen Maßnahmen in einer Clusteranalyse hingegen stärker von der Gruppe bevorzugt, deren Verträglichkeit und Offenheit verhältnismäßig schwach ausgeprägt waren. Ein geringes Wissen über die streamingbedingten Umweltfolgen lag grundsätzlich vor und dient als möglicher Erklärungsansatz des genannten Widerspruchs. Die Probanden forderten, ein Bewusstsein für diese Thematik zu schaffen. Um Streamingkonsum umweltfreundlicher zu gestalten empfiehlt es sich, alle am Prozess beteiligten Akteure einzubeziehen. Die befragten Konsumenten bevorzugten dabei vor allem die Verwendung von Ökostrom und lehnten eine Umstellung der Bezahlstruktur vorwiegend ab.
This study investigates the effects of four multifunctional chain-extending cross-linkers (CECL) on the processability, mechanical performance, and structure of polybutylene adipate terephthalate (PBAT) and polylactic acid (PLA) blends produced using film blowing technology. The newly developed reference compound (M·VERA® B5029) and the CECL modified blends are characterized with respect to the initial properties and the corresponding properties after aging at 50 °C for 1 and 2 months. The tensile strength, seal strength, and melt volume rate (MVR) are markedly changed after thermal aging, whereas the storage modulus, elongation at the break, and tear resistance remain constant. The degradation of the polymer chains and crosslinking with increased and decreased MVR, respectively, is examined thoroughly with differential scanning calorimetry (DSC), with the results indicating that the CECL-modified blends do not generally endure thermo-oxidation over time. Further, DSC measurements of 25 µm and 100 µm films reveal that film blowing pronouncedly changes the structures of the compounds. These findings are also confirmed by dynamic mechanical analysis, with the conclusion that tris(2,4-di-tert-butylphenyl)phosphite barely affects the glass transition temperature, while with the other changes in CECL are seen. Cross-linking is found for aromatic polycarbodiimide and poly(4,4-dicyclohexylmethanecarbodiimide) CECL after melting of granules and films, although overall the most synergetic effect of the CECL is shown by 1,3-phenylenebisoxazoline.
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
Solving transport network problems can be complicated by non-linear effects. In the particular case of gas transport networks, the most complex non-linear elements are compressors and their drives. They are described by a system of equations, composed of a piecewise linear ‘free’ model for the control logic and a non-linear ‘advanced’ model for calibrated characteristics of the compressor. For all element equations, certain stability criteria must be fulfilled, providing the absence of folds in associated system mapping. In this paper, we consider a transformation (warping) of a system from the space of calibration parameters to the space of transport variables, satisfying these criteria. The algorithm drastically improves stability of the network solver. Numerous tests on realistic networks show that nearly 100% convergence rate of the solver is achieved with this approach.
Target meaning representations for semantic parsing tasks are often based on programming or query languages, such as SQL, and can be formalized by a context-free grammar. Assuming a priori knowledge of the target domain, such grammars can be exploited to enforce syntactical constraints when predicting logical forms. To that end, we assess how syntactical parsers can be integrated into modern encoder-decoder frameworks. Specifically, we implement an attentional SEQ2SEQ model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure. Compared to other approaches to grammar-guided decoding that modify the underlying neural network architecture or attempt to derive full parse trees, our approach is conceptually simpler, adds less computational overhead during inference and integrates seamlessly with current SEQ2SEQ frameworks. We present preliminary evaluation results against a recurrent SEQ2SEQ baseline on GEOQUERY and ATIS and demonstrate improved performance while enforcing grammatical constraints.
Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.
In view of the rapid growth of solar power installations worldwide, accurate forecasts of photovoltaic (PV) power generation are becoming increasingly indispensable for the overall stability of the electricity grid. In the context of household energy storage systems, PV power forecasts contribute towards intelligent energy management and control of PV-battery systems, in particular so that self-sufficiency and battery lifetime are maximised. Typical battery control algorithms require day-ahead forecasts of PV power generation, and in most cases a combination of statistical methods and numerical weather prediction (NWP) models are employed. The latter are however often inaccurate, both due to deficiencies in model physics as well as an insufficient description of irradiance variability.
The rapid increase in solar photovoltaic (PV) installations worldwide has resulted in the electricity grid becoming increasingly dependent on atmospheric conditions, thus requiring more accurate forecasts of incoming solar irradiance. In this context, measured data from PV systems are a valuable source of information about the optical properties of the atmosphere, in particular the cloud optical depth (COD). This work reports first results from an inversion algorithm developed to infer global, direct and diffuse irradiance as well as atmospheric optical properties from PV power measurements, with the goal of assimilating this information into numerical weather prediction (NWP) models.
Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments
(2021)
This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.
In diesem Paper wird ein Modell eines Photovoltaik(PV)-Diesel-Hybrid-Systems aufgebaut. Dieses System besitzt neben einer PV-Anlage einen Batteriespeicher und ist an das öffentliche Stromnetz angeschlossen. Bei einem Ausfall aller drei Energiequellen stellt ein Dieselgenerator die Stromversorgung sicher. Mit Hilfe des erstellten Modells wird der Einfluss der unterschiedlichen Jahreszeiten und Wetterbedingungen auf den PV-Ertrag und das gesamte System im Zeitraum von Februar 2016 bis Februar 2017 untersucht. Die Messdaten dafür stammen von einem Krankenhaus in Akwatia, Ghana. Das Krankenhaus besitzt bereits eine PV-Anlage und einen Dieselgenerator als Backup.
Ein weiterer Aspekt der Untersuchung ist der Einfluss der Stromausfälle, die in dieser Region häufig vorkommen, auf den Einsatz des Generators.
Resultat der Untersuchung ist die Relevanz saisonaler und infrastruktureller Einflüsse auf die Betriebsweise des Systems. Mit Hilfe des erstellten Modells wurde analysiert, dass besonders während der Regenzeit im August die PV-Leistung sinkt und folglich viel Energie durch das öffentliche Stromnetz und den Generator bereitgestellt werden muss. Ein weiterer signifikanter Einbruch im PV-Ertrag ist zur Zeit des Harmattans im Januar zu verzeichnen.
In this contribution, we perform computer simulations to expedite the development of hydrogen storages based on metal hydride. These simulations enable in-depth analysis of the processes within the systems which otherwise could not be achieved. That is, because the determination of crucial process properties require measurement instruments in the setup which are currently not available. Therefore, we investigate the reliability of reaction values that are determined by a design of experiments.
Specifically, we first explain our model setup in detail. We define the mathematical terms to obtain insights into the thermal processes and reaction kinetics. We then compare the simulated results to measurements of a 5-gram sample consisting of iron-titanium-manganese (FeTiMn) to obtain the values with the highest agreement with the experimental data. In addition, we improve the model by replacing the commonly used Van’t-Hoff equation by a mathematical expression of the pressure-composition-isotherms (PCI) to calculate the equilibrium pressure.
Finally, the parameters’ accuracy is checked in yet another with an existing metal hydride system. The simulated results demonstrate high concordance with experimental data, which advocate the usage of approximated kinetic reaction properties by a design of experiments for further design studies. Furthermore, we are able to determine process parameters like the entropy and enthalpy.
The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent studies have not addressed possible connections between machine learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As a proof of concept for combining machine learning with the LBM, a neural collision model is developed, trained on a doubly periodic shear layer and then transferred to a different flow, a decaying turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and optimization. To this end, the spectrum of a forced isotropic turbulence is maintained without further constraining the velocity field.
Public preferences
(2021)
For reforms to be acceptable and sustainable in the long run, they should be aligned with public preferences. ‘Preferences’ is a technical term used in social sciences or humanities including for example disciplines such as economics, philosophy or psychology. Broadly speaking, preferences refer to an individual’s judgements on liking one alternative more than others. More specifically, preferences are ‘subjective comparative evaluations, in the form of “Agent prefers X to Y”’ (Hansson and Grüne-Yanoff 2018). Here, we are particularly interested in people’s policy preferences concerning social protection, an area which deserves more attention in policy debates and research.
As a low-input crop, Miscanthus offers numerous advantages that, in addition to agricultural applications, permits its exploitation for energy, fuel, and material production. Depending on the Miscanthus genotype, season, and harvest time as well as plant component (leaf versus stem), correlations between structure and properties of the corresponding isolated lignins differ. Here, a comparative study is presented between lignins isolated from M. x giganteus, M. sinensis, M. robustus and M. nagara using a catalyst-free organosolv pulping process. The lignins from different plant constituents are also compared regarding their similarities and differences regarding monolignol ratio and important linkages. Results showed that the plant genotype has the weakest influence on monolignol content and interunit linkages. In contrast, structural differences are more significant among lignins of different harvest time and/or season. Analyses were performed using fast and simple methods such as nuclear magnetic resonance (NMR) spectroscopy. Data was assigned to four different linkages (A: β-O-4 linkage, B: phenylcoumaran, C: resinol, D: β-unsaturated ester). In conclusion, A content is particularly high in leaf-derived lignins at just under 70% and significantly lower in stem and mixture lignins at around 60% and almost 65%. The second most common linkage pattern is D in all isolated lignins, the proportion of which is also strongly dependent on the crop portion. Both stem and mixture lignins, have a relatively high share of approximately 20% or more (maximum is M. sinensis Sin2 with over 30%). In the leaf-derived lignins, the proportions are significantly lower on average. Stem samples should be chosen if the highest possible lignin content is desired, specifically from the M. x giganteus genotype, which revealed lignin contents up to 27%. Due to the better frost resistance and higher stem stability, M. nagara offers some advantages compared to M. x giganteus. Miscanthus crops are shown to be very attractive lignocellulose feedstock (LCF) for second generation biorefineries and lignin generation in Europe.
Kinder – unsere Zukunft!
(2021)
Using Visual and Auditory Cues to Locate Out-of-View Objects in Head-Mounted Augmented Reality
(2021)
Intercultural Management
(2021)
An der Hochschule Bonn-Rhein-Sieg fand am Donnerstag, den 23.9.21 das erste Verbraucherforum für Verbraucherinformatik statt. Im Rahmen der Online-Tagesveranstaltung diskutierten mehr als 30 Teilnehmer:innen über Themen und Ideen rund um den Bereich Verbraucherdatenschutz. Dabei kamen sowohl Beiträge aus der Informatik, den Verbraucher- und Sozialwissenschaften sowie auch der regulatorischen Perspektive zur Sprache. Der folgende Beitrag stellt den Hintergrund der Veranstaltung dar und berichtet über Inhalte der Vorträge sowie Anknüpfungspunkte für die weitere Konstituierung der Verbraucherinformatik. Veranstalter waren das Institut für Verbraucherinformatik an der H-BRS in Zusammenarbeit mit dem Lehrstuhl IT-Sicherheit der Universität Siegen sowie dem Kompetenzzentrum Verbraucherforschung NRW der Verbraucherzentrale NRW e. V. mit Förderung des Bundesministeriums der Justiz und für Verbraucherschutz.
This book shows in a comprehensive presentation how Bond Graph methodology can support model-based control, model-based fault diagnosis, fault accommodation, and failure prognosis by reviewing the state-of-the-art, presenting a hybrid integrated approach to Bond Graph model-based fault diagnosis and failure prognosis, and by providing a review of software that can be used for these tasks.
Die vorliegende Forschungsarbeit behandelt die Filtrierung von sozialen Medien durch die Content Moderatoren. Die Content Moderatoren sind Menschen, die unter schlechten Arbeitsbedingungen und hoher psychischer Belastung Plattformen wie Facebook tagtäglich von strafbaren Inhalten filtern. Durch dieses Löschregime werden zwar gewaltzeigende Inhalte gelöscht, aber auch aufklärende oder künstlerische Inhalte zensiert.
Mithilfe von durchgeführten Fokusgruppendiskussionen wurde der Einfluss der Gruppenzusammensetzung und Darbietung positiver und negativer Informationen auf die individuelle und eindimensionale Wahrnehmung der Teilnehmer bezüglich sozialer Medien, Content Moderatoren und Löschrichtlinien erforscht.
Die Ergebnisse zeigen, dass die Darbietung der Informationen keinen Einfluss auf die mehrdimensionale Wahrnehmung der Probanden hatte und sie unabhängig von der Gruppenzusammensetzung nonkonforme Meinungen vertraten. Trotz des erweiterten Wissenstandes und der entwickelten Alternativlösungen äußerten die meisten Probanden nicht die Absicht, ihr Nutzungsverhalten künftig zu ändern. Trotz der Annahme, dass die meisten Probanden eine eindimensionale Wahrnehmung sozialer Medien haben, zeigten die Ergebnisse, dass viele Probanden eine ähnlich positive und kritische Haltung gegenüber den Plattformen hatten. Darüber hinaus wird deutlich, dass es einen starken Forschungsbedarf in Bezug auf die Langzeitfolgen der Arbeit als Content Moderator und Auswirkungen von Zensur und Filtrierung auf die Nutzer sozialer Medien gibt.
Konzept zum Umgang mit Prüfungsstress und Lernblockaden bei Studierenden in der Studieneingangsphase
(2021)
Steuerlehre für Dummies
(2021)
The molecular weight properties of lignins are one of the key elements that need to be analyzed for a successful industrial application of these promising biopolymers. In this study, the use of 1H NMR as well as diffusion-ordered spectroscopy (DOSY NMR), combined with multivariate regression methods, was investigated for the determination of the molecular weight (Mw and Mn) and the polydispersity of organosolv lignins (n = 53, Miscanthus x giganteus, Paulownia tomentosa, and Silphium perfoliatum). The suitability of the models was demonstrated by cross validation (CV) as well as by an independent validation set of samples from different biomass origins (beech wood and wheat straw). CV errors of ca. 7–9 and 14–16% were achieved for all parameters with the models from the 1H NMR spectra and the DOSY NMR data, respectively. The prediction errors for the validation samples were in a similar range for the partial least squares model from the 1H NMR data and for a multiple linear regression using the DOSY NMR data. The results indicate the usefulness of NMR measurements combined with multivariate regression methods as a potential alternative to more time-consuming methods such as gel permeation chromatography.
Less is Often More: Header Whitelisting as Semantic Gap Mitigation in HTTP-Based Software Systems
(2021)
The web is the most wide-spread digital system in the world and is used for many crucial applications. This makes web application security extremely important and, although there are already many security measures, new vulnerabilities are constantly being discovered. One reason for some of the recent discoveries lies in the presence of intermediate systems—e.g. caches, message routers, and load balancers—on the way between a client and a web application server. The implementations of such intermediaries may interpret HTTP messages differently, which leads to a semantically different understanding of the same message. This so-called semantic gap can cause weaknesses in the entire HTTP message processing chain.
In this paper we introduce the header whitelisting (HWL) approach to address the semantic gap in HTTP message processing pipelines. The basic idea is to normalize and reduce an HTTP request header to the minimum required fields using a whitelist before processing it in an intermediary or on the server, and then restore the original request for the next hop. Our results show that HWL can avoid misinterpretations of HTTP messages in the different components and thus prevent many attacks rooted in a semantic gap including request smuggling, cache poisoning, and authentication bypass.
Low-Cost In-Hand Slippage Detection and Avoidance for Robust Robotic Grasping with Compliant Fingers
(2021)
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
In this paper, the electrochemical alkaline methanol oxidation process, which is relevant for the design of efficient fuel cells, is considered. An algorithm for reconstructing the reaction constants for this process from the experimentally measured polarization curve is presented. The approach combines statistical and principal component analysis and determination of the trust region for a linearized model. It is shown that this experiment does not allow one to determine accurately the reaction constants, but only some of their linear combinations. The possibilities of extending the method to additional experiments, including dynamic cyclic voltammetry and variations in the concentration of the main reagents, are discussed.
Psychische Belastungen führen im Gegensatz zu physikalischen, chemischen und biologischen Risiken häufig noch ein Schattendasein bei der Beurteilung möglicher Risikofaktoren für Sicherheit und Gesundheit bei der Arbeit. Die Hinweise auf Zusammenhänge mit Sicherheit und Gesundheit führen aber langsam zu einem Umdenken.