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Upgrade of Bioreactor System Providing Physiological Stimuli
to Engineered Musculoskeletal Tissues
(2017)
A novel central control interface (CCI) is developed to improve the modular bioreactor system with regard to extendability and modifiability in Tissue Engineering (TE) applications. This paper presents the results developed in the project with open-source hardware and the graphical programming system LabVIEW. A new platform independent User Interface was further developed to contribute to the new flexibility of the device.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
Web advertisements are the primary financial source for many online services, but also for cybercriminals. Successful ad campaigns rely on good online profiles of their potential customers. The financial potentials of displaying ads have led to the rise of malware that injects or replaces ads on websites, in particular, so-called adware. This development leads to always further optimized and customized advertising. For these customization's, various tracking methods are used. However, only sparse work has gone into privacy issues emerging from adware. In this paper, we investigate the tracking capabilities and related privacy implications of adware and potentially unwanted programs (PUPs). Therefore, we developed a framework that allows us to analyze any network communication of the Firefox browser on the application level to circumvent encryption like TLS. We use this to dynamically analyze the communication streams of over 16,000 adware or potentially unwanted programs samples that tamper with the users' browser session. Our results indicate that roughly 37% of the requests issued by the analyzed samples contain private information and are accordingly able to track users. Additionally, we analyze which tracking techniques and services are used.
Thermal Stress at the Surface of Thick Conductive Plates Induced by Sinusoidal Current Pulses
(2016)
The Unfitted Discontinuous Galerkin Method for Solving the EEG Forward Problem: A Second Order Study
(2016)
This paper reveals various approaches undertaken over more than two decades of teaching undergraduate programming classes at different Higher Education Institutions, in order to improve student activation and participation in class and consequently teaching and learning effectiveness.
While new technologies and the ubiquity of smartphones and internet access has brought new tools to the classroom and opened new didactic approaches, lessons learned from this personal long-term study show that neither technology itself nor any single new and often hyped didactic approach ensured sustained improvement of student activation. Rather it needs an integrated yet open approach towards a participative learning space supported but not created by new tools, technology and innovative teaching methods.
This Article introduces two research projects towards assistive robotic arms for people with severe body impairments. Both projects aim to develop new control and interaction designs to promote accessibility and a better performance for people with functional losses in all four extremities, e.g. due to quadriplegic or multiple sclerosis. The project MobILe concentrates on using a robotic arm as drinking aid and controlling it with smart glasses, eye-tracking and augmented reality. A user oriented development process with participatory methods were pursued which brought new knowledge about the life and care situation of the future target group and the requirements a robotic drinking aid needs to meet. As a consequence the new project DoF-Adaptiv follows an even more participatory approach, including the future target group, their family and professional caregivers from the beginning into decision making and development processes within the project. DoF-Adaptiv aims to simplify the control modalities of assistive robotic arms to enhance the usability of the robotic arm for activities of daily living. lo decide on exemplary activities, like eating or open a door, the future target group, their family and professional caregivers are included in the decision making process. Furthermore all relevant stakeholders will be included in the investigation of ethical, legal and social implications as well as the identification of potential risks. This article will show the importance of the participatory design for the development and research process in MobILe and DoF-Adaptiv.
The DECIMER.ai Project
(2024)
Over the past few decades, the number of publications describing chemical structures and their metadata has increased significantly. Chemists have published the majority of this information as bitmap images along with other important information as human-readable text in printed literature and have never been retained and preserved in publicly available databases as machine-readable formats. Manually extracting such data from printed literature is error-prone, time-consuming, and tedious. The recognition and translation of images of chemical structures from printed literature into machine-readable format is known as Optical Chemical Structure Recognition (OCSR). In recent years, deep-learning-based OCSR tools have become increasingly popular. While many of these tools claim to be highly accurate, they are either unavailable to the public or proprietary. Meanwhile, the available open-source tools are significantly time-consuming to set up. Furthermore, none of these offers an end-to-end workflow capable of detecting chemical structures, segmenting them, classifying them, and translating them into machine-readable formats.
To address this issue, we present the DECIMER.ai project, an open-source platform that provides an integrated solution for identifying, segmenting, and recognizing chemical structure depictions within the scientific literature. DECIMER.ai comprises three main components: DECIMER-Segmentation, which utilizes a Mask-RCNN model to detect and segment images of chemical structure depictions; DECIMER-Image Classifier EfficientNet-based classification model identifies which images contain chemical structures and DECIMER-Image Transformer which acts as an OCSR engine which combines an encoder-decoder model to convert the segmented chemical structure images into machine-readable formats, like the SMILES string.
A key strength of DECIMER.ai is that its algorithms are data-driven, relying solely on the training data to make accurate predictions without any hand-coded rules or assumptions. By offering this comprehensive, open-source, and transparent pipeline, DECIMER.ai enables automated extraction and representation of chemical data from unstructured publications, facilitating applications in chemoinformatics and drug discovery.
Purpose
So far, there are several approaches of measuring the Dark Triad traits, but still all of them are
personality questionnaires with at least questionable usability for applied contexts such as Human
Resource Management.
The purpose of the study is the development of a structured interview with the aim of measuring the Dark Triad in a rather qualitative way that increases social validity for the respondents.
Design/Methodology/Approach/Intervention
In the present study, 15 executives from the telecommunications industry were interviewed on their personal evaluation of management success and derailment. Afterwards, their personality traits of the Dark Triad were measured with the help of the Short Dark Triad Scale. Subsequently, the data from qualitative and quantitative research were examined for correlations using the mixed-method approach.
Results
The results of the mixed-method approach showed a statistically significant correlation between the Short Dark Triad Scale and the ratings for narcissism, Machiavellianism and subclinical psychopathy in the Dark Triad interview.
Limitations
Replicating the results in a bigger sample and a deeper investigation of the criterion-related validity as well as an integration of multiple raters can provide more confidence in our results.
Research/Practical Implications
Structured interviews allow the measurement of personality traits in a more convenient way especially in personnel selection and development processes. Identifying subclinical traits in leadership candidates can, e.g. prevent management derailment.
Originality/Value
The present study advances the measurement methods of the Dark Triad.
Purpose
Although the systemic approach to the leadership concept seems to fit well into our modern complex and dynamic work environment, only little research has been conducted to define and assess systemic leadership. In this study we therefore developed and assessed criterion validity of the
multidimensional systemic leadership inventory (SLI, Sülzenbrück & Externbrink, 2017).
Methodology
We conducted two cross-sectional survey among managers and employees of various organizations (N = 143 and N = 150).
Results
We found a robust five-factor structure of the SLI, comprising systemic thinking, self-knowledge, solution-oriented communication, creating meaning and delegation. Regarding criterion validity, a significant positive correlation of systemic leadership was found with affective commitment, while a significant negative correlation with emotional strain in occupational contexts occurred. These overall positive outcomes for employees were not undermined by negative personality traits of the employee (Machiavellianism), while strong growth need strength further enhanced positive effects on affective commitment.
Limitations
Since all variables were measured as self-reports, common method variance could limit our findings.
Practical Implications
Systemic leadership is a very promising new approach for leaders to ensure committed and less strained employees.
Value
Systemic leadership, especially in terms of a leaders’ understanding of organizational and private systems influencing work behaviour of all members of an organization, is a promising novel leadership model suitable to address challenges of complex and dynamic work environments.
Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.
As a first step toward this goal, we devise an in situ pipeline tailored to the neuroscientific use case. It is capable of recording and transferring simulation data to an analysis/visualization process, while the simulation is still running. The developed libraries are made publicly available as open source projects. We provide a proof-of-concept integration, coupling the neuronal simulator NEST to basic 2D and 3D visualization.
Steps Towards an Open All-in-one Rich-Client Environment for Particle-Based Mesoscopic Simulation
(2018)
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.
Segmentation of radio-angiographic images using morphological filters, thinning and region growing
(1997)
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
Recommendations for the Development of a Robotic Drinking and Eating Aid - An Ethnographic Study
(2021)
Being able to live independently and self-determined in one’s own home is a crucial factor or human dignity and preservation of self-worth. For people with severe physical impairments who cannot use their limbs for every day tasks, living in their own home is only possible with assistance from others. The inability to move arms and hands makes it hard to take care of oneself, e.g. drinking and eating independently. In this paper, we investigate how 15 participants with disabilities consume food and drinks. We report on interviews, participatory observations, and analyzed the aids they currently use. Based on our findings, we derive a set of recommendations that supports researchers and practitioners in designing future robotic drinking and eating aids for people with disabilities.
Psychological Capital as Mediator between Transformational Leadership and Adaptive Performance
(2013)
Performance enhancing study for large scale PEM electrolyzer cells based on hydraulic compression
(2017)
This experimental work deals with the preparation and investigation of PEM fuel cell electrodes, which are obtained using Graphene Related Material (GRM) serving as catalyst support material for platinum nanoparticles. The applied GRM belong to the group of carbon nanofibers and exhibits a helical-ribbon structure with dimensions of 50 nm in diameter and an average length up to a few µm. Furthermore, utilized GRM provide a superior graphitisation degree of about 100 %, which leads to both high corrosion resistance and low ohmic resistance. Material stability plays one of the main roles for long term fuel cell operation, whereby a great electrical catalyst contact combined with high specific surface area yields in high fuel cell performances.
Prior to GRM dispersion and deposition onto a gas diffusion layer, the graphene structures are functionalized by oxygen plasma treatment. Through this step, functional oxygen groups are generated onto the GRM outer surface providing an improved hydrophilic behaviour and facilitating the GRM suspension preparation. In addition, the oxygen groups act as anchors for platinum nanoparticles which are subsequently deposited onto the GRM surface through a pulse electrodeposition process.
Membrane electrode assemblies produced with the prepared electrodes are investigated in-situ in a PEM fuel cell test bench.
Since the 1980’s, against the backdrop of global warming and the decline of conventional energy resources, low emission and renewable energy systems have gotten into the focus of politics as well as research and development. In order to decrease the emission of greenhouse gases Germany intents to generate 80% of its electrical energy from renewable and low emission sources by 2050. For low emission electricity generation hydrogen operated fuel cells are a potential solution. However, although fuel cell technology has been well known since the 19th century cost effective materials are needed to achieve a breakthrough in the market.
Proton Exchange Membrane Fuel Cells with Carbon Nanotubes as Electrode Material
At the Westphalian Energy Institute of the Wesphalian University of Applied Sciences one main focus is on the research of proton exchange membrane fuel cells (PEMFC). PEMFC membrane electrode assemblies (MEA) consist of a polymer membrane with electrolytic properties covered on both sides by a catalyst layer (CL) as well as a porous and electrical conductive gas diffusion layer (GDL).
For PEMFC carbon nanotubes (CNT) have ideal properties as electrode material concerning electrical conductivity, oxidation resistance and media transport. CNTs are suitable for the use as catalyst support material within the CL due to their large surface in comparison to conventional carbon supports. Furthermore, oxygen plasma treated CNTs show electrochemical activity referred to hydrogen adsorption and desorption, which has been shown by cyclic voltammetry in 0.5 M sulfuric acid solution. According to the PEMFCs anode a GDL coated with oxygen plasma activated CNTs has promising properties to significantly reduce catalyst content (e.g. platinum) of the anodic CL.
To further increase platinum utilisation in PEM fuel cells CNFs are investigated as catalyst support material due to the CNF’s high specific surface area. Furthermore, CNFs provide suitable properties concerning corrosion resistance as well as electrical conductivity in contrast to conventional carbon supports.
This work presents the results of an electrode preparation procedure based on O2 plasma activated CNFs. The plasma treatment leads to CNF dispersibility in alcohol/water for a spray coating process. Furthermore, O2 plasma activation enhances metal deposition on the CNF’s surface. Pulse plating procedure as well as wet chemical metal synthesis have been used for particle deposition. For pulse plating a potentiostat/galvanostat type MMates 510 AC from Materials Mates, Italy has been used. Electrode morphology has been determined in SEM type XL 30 ESEM from Philips, The Netherlands.
We investigate the possibility to use update propagation methods for optimizing the evaluation of continuous queries. Update propagation allows for the efficient determination of induced changes to derived relations resulting from an explicitly performed base table update. In order to simplify the computation process, we propose the propagation of updates with different degrees of granularity which corresponds to an incremental query evaluation with different levels of accuracy. We show how propagation rules for diferent update granularities can be systematically derived, combined and further optimized by using Magic Sets. This way, the costly evaluation of certain subqueries within a continuous query can be systematically circumvented allowing for cutting down on the number of pipelined tuples considerably.
Opportunities and Challenges in Mixed-Reality for an Inclusive Human-Robot Collaboration Environment
(2018)
This paper presents an approach to enhance robot control using Mixed-Reality. It highlights the opportunities and challenges in the interaction design to achieve a Human-Robot Collaborative environment. In fact, Human-Robot Collaboration is the perfect space for social inclusion. It enables people, who suffer severe physical impairments, to interact with the environment by providing them movement control of an external robotic arm. Now, when discussing about robot control it is important to reduce the visual-split that different input and output modalities carry. Therefore, Mixed-Reality is of particular interest when trying to ease communication between humans and robotic systems.
Measurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such studies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in
the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘dependency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results.
A simplified model for spondylodesis, ie fixation of vertebrae by osteosynthesis, is developed for virtual magnetic resonance imaging (MRI) examinations to numerically calculate energy absorption. This paper presents results of calculated energy absorption in body tissue surrounding titanium rod implants. In general each wire or rod behaves like an antenna in electromagnetic fields. The specific absorption rate (SAR) profile describes dependence of implant size. SAR hotspots appear near the rod edges. Depending of the size of implant fixation SAR is 62%(small fixation) up to 90.95%(large fixation) higher than without implants. In addition, local SAR profile displays local dependency on tissue: SAR is lower between the vertebrae.
An energy economy with high share of renewable but volatile energy sources is dependent on storage strategies in order to ensure sufficient energy delivery in periods of e.g. low wind and/or low solar radiation. Hydrogen as environmental friendly energy carrier is thought to be an appropriate solution for large scale energy storage. In 2011 the NOW (national organisation for hydrogen in Germany) calculated the demand for hydrogen energy systems as positive (0.8 GW to 5.25 GW) and negative supply for varying power demand (0.68 to 4.3 GW) for the German energy economy in 2025. Due to its dynamic behaviour on load changes polymer electrolyte membrane fuel cells (PEMFC) as well as water electrolyser systems (PEMEL) can play a significant role for large scale hydrogen based storage systems. In this work a novel design concept for modular fuel cell and electrolyser stacks is presented with single cells in pockets surrounded by a hydraulic medium. This hydraulic medium introduces necessary compression forces on the membrane electrode assembly (MEA) of each cell within a stack. Furthermore, ideal stack cooling is achieved by this medium. Due to its modularity and scalability the modular stack design with hydraulic compression meets the requirements for large PEMFC as well as PEMEL units. Small scale prototypes presented in this work illustrate the potential of this design concept.
Moderating Role of Self-control Strength with Transformational Leadership and Adaptive Performance
(2013)
Based on a longitudinal sample of employees from the U.S. financial services industry (N=121), the present research examined the impact of transformational leadership on followers’ adaptive performance in change processes. Follower personality was taken into account as boundary condition by testing, if follower self-control strength as an individual trait moderated the relationship between transformational leadership and adaptive performance. In line with the developed hypothesis, results from a latent moderated structural equation model showed that followers’ self-control strength attenuated the relationship between transformational leadership and adaptive performance. Implications for research and practice are discussed.
Renewable and sustainable energy production by many small and distributed producers is revolutionizing the energy landscape as we know it. Consumers produce energy, making them to prosumers in the smart grid. The interaction between prosumers and other entities in the grid and the optimal utilization of new smart grid components (electric cars, freezers, solar panels, etc.) are crucial for the success of the smart grid. The Power Trading Agent Competition is an open simulation platform that allows researchers to conduct low risk studies in this new energy market. In this work we present Maxon16, an autonomous energy broker and champion of the 2016's Power Trading Agent Competition. We present the strategies the broker used in the final round and evaluate the effectiveness of the strategies by analyzing the tournament's results.
Many fluids transported by pipelines are in some sense hazardous. It is therefore often necessary to install leak detection (and locating) systems (LDS), especially due to legal regulations like the "Code for Federal Regulations (CFR) Title 49 Part 195", API 1130 2nd Ed., both for the USA, or the "Technische Regeln für Fernleitungen" (TRFL) (Technical Rules for Pipelines) in Germany. This paper gives a survey of methodologies, methods and techniques for leak detection and locating. The survey starts with some remarks concerning (legal) regulations both for the USA and for Germany. Some few words about externally based systems (due to API 1130 2nd Ed.) follow next. A significant part of the paper deals with internally based systems (also due to API 1130 2nd Ed.) like balancing systems (line balance, volume balance, compensated mass balance etc.), Real Time Transient Model LDS (RTTM-LDS), pressure/flow monitoring and statistical analysis LDS. Different methods for leak locating (gradient intersection method, wave propagation analysis etc.) will also be shown. The presentation of an Extended RTTM approach (E-RTTM) combining advantages of conventional RTTM LDS and statistical analysis follows next, together with the demonstration of applicability by means of two examples, a liquid multi-batch pipeline, and a gas pipeline. Sketching future work and the conclusion conclude the survey.