Filtern
Erscheinungsjahr
Dokumenttyp
- Wissenschaftlicher Artikel (237)
- Konferenzveröffentlichung (216)
- Teil eines Buches (Kapitel) (32)
- Sonstiges (31)
- Video (14)
- Buch (Monographie) (13)
- Preprint (12)
- Dissertation (4)
- Bericht (4)
- Arbeitspapier (4)
Sprache
- Englisch (572) (entfernen)
Schlagworte
- Robotik (8)
- Flugkörper (7)
- UAV (7)
- Rettungsrobotik (5)
- Dissipative Particle Dynamics (4)
- Polymer-Elektrolytmembran-Brennstoffzelle (4)
- adhesion (4)
- Bionik (3)
- Deep Learning (3)
- Erweiterte Realität <Informatik> (3)
Institut
- Westfälisches Institut für Gesundheit (115)
- Westfälisches Energieinstitut (61)
- Institut für Internetsicherheit (56)
- Informatik und Kommunikation (51)
- Elektrotechnik und angewandte Naturwissenschaften (50)
- Wirtschaft und Informationstechnik Bocholt (46)
- Institut für biologische und chemische Informatik (44)
- Maschinenbau Bocholt (37)
- Institut Arbeit und Technik (15)
- Wirtschaftsingenieurwesen (15)
- Maschinenbau und Facilities Management (13)
- Institut für Innovationsforschung und -management (11)
- Fachbereiche (9)
- Wirtschaftsrecht (9)
- Mechatronik-Institut Bocholt (2)
- Strategische Projekte (2)
- Institute (1)
Socio-cultural dynamics in spatial policy: explaining the on-going success of cluster politics
(2013)
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.
We propose a quantum-mechanical model to calculate the nonlinear differential conductance of a single molecular junction immersed in a solvent, either in pure form or as a binary mixture with varying volume fraction. The solvent mixture is captured by a dielectric continuum model for which the resulting spectral density is determined within the Gladstone-Dale approach. The conductance of the molecular junction is calculated by a real-time diagrammatic technique. We find a strong variation of the conductance maximum for varying volume fraction of the solvent mixture. Importantly, the calculated molecular nonlinear conductance shows a very good agreement with experimentally measured data for common molecular junctions in various polar solvent mixtures.
Earwig wings are highly foldable structures that lack internal muscles. The behaviour and shape changes of the wings during flight are yet unknown. We assume that they meet a great structural challenge to control the occurring deformations and prevent the wing from collapsing. At the folding structures especially, the wing could easily yield to the pressure. Detailed microscopy studies reveal adaptions in the structure and material which are not relevant for folding purposes. The wing is parted into two structurally different areas with, for example, a different trend or stiffness of the wing veins. The storage of stiff or more flexible material shows critical areas which undergo great changes or stress during flight. We verified this with high-speed video recordings. These reveal the extent of the occurring deformations and their locations, and support our assumptions. The video recordings reveal a dynamical change of a concave flexion line. In the static unfolded state, this flexion line blocks a folding line, so that the wing stays unfolded. However, during flight it extends and blocks a second critical folding line and prevents the wing from collapsing. With these results, more insight in passive wing control, especially within high foldable structures, is gained.
As vaccination campaigns are in progress in most countries, hopes to win back more normality are rising. However, the exact path from a pandemic to an endemic virus remains uncertain. While in the pre-vaccination phase many critical indoor situations were avoided by strict control measures, for the transition phase a certain mitigation of the effect of indoor situations seems advisable.
To better understand the mechanisms of indoor airborne transmissions, we present a new time-discrete model to calculate the level of exposure towards infectious SARS-CoV-2 aerosol and carry out a sensitivity analysis for the level of SARS-CoV-2 aerosol exposure in indoor settings. Time limitations and the use of any kind of masks were found to be strong mitigation measures, while how far the effort for a strict use of professional face pieces instead of simple masks can be justified by the additional reduction of the exposure dose remains unclear. Very good ventilation of indoor spaces is mandatory. The definition of sufficient ventilation in regard to airborne SARS-CoV-2 transmission follows other rules than the standards in ventilation design. This means that especially smaller rooms most likely require a significantly greater fresh air supply than usual. Further research on 50% group models in schools is suggested. The benefits of a model in which the students come to school every day, but for a limited time, should be investigated. In terms of window ventilation, it has been found that many short opening periods are not only thermally beneficial, they also reduce the exposure dose. The fresh air supply is driven by the temperature gradient and wind speed. However, the sensitivity towards these parameters is not very high and in times of low wind and temperature gradients, there are no arguments against keep windows open in order to make up for the reduced air flow rate. Long total opening periods and large window surfaces will strongly reduce the exposure. Additionally, the results underline the expectable fact that exposure doses will increase when hygiene and control measures are reduced. It seems advisable to investigate what this means for the infection rate and the fatality of infections in populations with partial immunity. Very basic considerations suggest that the value of aerosol reduction measures may be reduced with very infectious variants such as delta.
Segmentation of radio-angiographic images using morphological filters, thinning and region growing
(1997)
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
The purpose of the paper is to contribute to the inner workings of transformational leadership in the context of organizational change. According to the organizational role theory, role conflict is proposed as a mediator between transformational leadership and affective commitment to change and irritation. Cross-sectional data were collected in a German company in the textiles sector, undergoing a pervasive IT-related change. Confirmatory factor analysis and structural equation modeling was performed for validity and hypothesis testing. The findings suggest that role conflict acts as a full mediator in the relationship between transformational leadership and affective commitment to change, as well as irritation. Transformational leadership is often discussed in terms of change-oriented leadership. Surprisingly, only a few studies have examined the specific impact of transformational leadership on attitudinal outcomes during change processes, yet. Consequently, research on the underlying psychological mechanisms of the relationship is scarce, too.
Cone-Beam computed tomography (CBCT) has become the most important component of modern radiotherapy for positioning tumor patients directly before treatment. In this work we investigate alternations to standard acquisition protocol, called preset, for patients with a tumor in the thoracic region. The effects of the changed acquisition parameters on the image quality are evaluated using the Catphan Phantom and the image analysis software Smári. The weighted CT dose index (CTDIW) is determined in each case and the effects of the different acquisition protocols on the patient dose are classified accordingly. Additionally, the clinical suitability of alternative presets is tested by investigating correctness of image registration using the CIRS thorax phantom. The results show that a significant dose reduction can be achieved. It can be reduced by 51% for a full rotation by adjusting the gantry speed.
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.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
Quantum systems are typically subject to various environmental noise sources. Treating these environmental disturbances with a system-bath approach beyond weak coupling, one must refer to numerical methods as, for example, the numerically exact quasi-adiabatic path integral approach. This approach, however, cannot treat baths which couple to the system via operators, which do not commute. We extend the quasi-adiabatic path integral approach by determining the time discrete influence functional for such non-commuting fluctuations and by modifying the propagation scheme accordingly. We test the extended quasi-adiabatic path integral approach by determining the time evolution of a quantum two-level system coupled to two independent baths via non-commuting operators. We show that the convergent results can be obtained and agreement with the analytical weak coupling results is achieved in the respective limits.
Computational methods for the accurate prediction of protein folding based on amino acid sequences have been researched for decades. The field has been significantly advanced in recent years by deep learning-based approaches, like AlphaFold, RoseTTAFold, or ColabFold. Although these can be used by the scientific community in various, mostly free and open ways, they are not yet widely used by bench scientists in relevant fields such as protein biochemistry or molecular biology, who are often not familiar with software tools such as scripting notebooks, command-line interfaces or cloud computing. In addition, visual inspection functionalities like protein structure displays, structure alignments, and specific protein hotspot analyses are required as a second step to interpret and apply the predicted structures in ongoing research studies.
PySSA (Python rich client for visual protein Sequence to Structure Analysis) is an open Graphical User Interface (GUI) application combining the protein sequence to structure prediction capabilities of ColabFold with the open-source variant of the molecular structure visualisation and analysis system PyMOL to make both available to the scientific end-user. PySSA enables the creation of managed and shareable projects with defined protein structure prediction and corresponding alignment workflows that can be conveniently performed by scientists without specialised computer skills or programming knowledge on their local computers. Thus, PySSA can help make protein structure prediction more accessible for end-users in protein chemistry and molecular biology as well as be used for educational purposes. It is openly available on GitHub, alongside a custom graphical installer executable for the Windows operating system: https://github.com/urban233/PySSA/wiki/Installation-for-Windows-Operating-System.
To demonstrate the capabilities of PySSA, its usage in a protein mutation study on the protein drug Bone Morphogenetic Protein 2 (BMP2) is described: the structure prediction results indicate that the previously reported BMP2-2Hep-7M mutant, which is intended to be less prone to aggregation, does not exhibit significant spatial rearrangements of amino acid residues interacting with the receptor.
Psychological Capital as Mediator between Transformational Leadership and Adaptive Performance
(2013)
Protraction Effects in a Stochastic Cell-Cycle Tumor Model Exposed to Fractionated Radiotherapy
(2013)
Based on the fact that titanium and titanium alloys have poor fretting fatigue resistance and poor tribological properties, it is necessary to apply some surface engineering methods in order to increase the exploitation characteristics of these materials. One may either implement some surface treatment technologies or even deposit overlay coatings by thermal spraying.
The present study is focused on the achieved properties of the ceramic coatings (Al2O3 + 13 wt.% TiO2) deposited onto a titanium substrate using high velocity oxygen fuel (HVOF) and plasma spraying (APS) respectively.
The effect of the deposition method on the microstructure, phase constituents, and mechanical properties of the ceramic coatings was investigated by means of scanning electron microscopy (SEM), X-ray diffraction technique (XRD) and nanoindentation tests. The sliding wear performances of the Al2O3–TiO2 coatings were tested using a pin on disk wear tester.
In this study, a novel design concept for PEMFC (polymer electrolytemembrane fuel cell) stacks is presented with singlecells inserted in pockets surrounded by a hydraulic medium. Thehydraulic pressure introduces necessary compression forces to themembrane electrode assembly of each cell within a stack. Moreover, homogeneous cell cooling is achieved by this medium. First,prototypes presented in this work indicate that, upscaling of cells for the novelstack design is possible without significantperformancelosses. Due to its modularity and scalability, this stackdesign meets the requirements for large PEMFC units.
Preparation, catalytical activity and crystal structure of a heptanuclear zinc acetate cluster
(2017)
Hydrogen produced via water electrolysis powered by renewable electricity or green H2 offers new decarbonization pathways. Proton exchange membrane water electrolysis (PEMWE) is a promising technology although the current density, temperature, and H2 pressure of the PEMWE will have to be increased substantially to curtail the cost of green H2. Here, a porous transport layer for PEMWE is reported, that enables operation at up to 6 A cm−2, 90 °C, and 90 bar H2 output pressure. It consists of a Ti porous sintered layer (PSL) on a low‐cost Ti mesh (PSL/mesh‐PTL) by diffusion bonding. This novel approach does not require a flow field in the bipolar plate. When using the mesh‐PTL without PSL, the cell potential increases significantly due to mass transport losses reaching ca. 2.5 V at 2 A cm−2 and 90 °C.
Advanced Persistent Threats (APTs) are one of the main challenges in modern computer security. They are planned and performed by well-funded, highly-trained and often state-based actors. The first step of such an attack is the reconnaissance of the target. In this phase, the adversary tries to gather as much intelligence on the victim as possible to prepare further actions. An essential part of this initial data collection phase is the identification of possible gateways to intrude the target.
In this paper, we aim to analyze the data that threat actors can use to plan their attacks. To do so, we analyze in a first step 93 APT reports and find that most (80 %) of them begin by sending phishing emails to their victims. Based on this analysis, we measure the extent of data openly available of 30 entities to understand if and how much data they leak that can potentially be used by an adversary to craft sophisticated spear phishing emails. We then use this data to quantify how many employees are potential targets for such attacks. We show that 83 % of the analyzed entities leak several attributes of uses, which can all be used to craft sophisticated phishing emails.
The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public.
Performance enhancing study for large scale PEM electrolyzer cells based on hydraulic compression
(2017)
Biomimetics is the interdisciplinary co-operation of various scientific disciplines and fields of innovation, and it aims to solve practical problems using biological models. Biomimetic research and its fields of application are manifold, and the community is made up of a wide range of disciplines, from biologists and engineers to designers. Guidelines and standards can build a common ground for understanding of the field, communication across disciplines, present and future projects and implementation of biomimetic knowledge. Since 2015, three international standards have been published and defined terms and definitions, as well as specific applications. The scientific literature and patents in several databases were searched for citations of the published standards. Standards or technical guidelines on biomimetics are represented both in the scientific literature and in patents. However, taking into account the increasing number of publications in biomimetics, the number of publications (52) citing the international standards is low. This shows that the perception of technical rules is still underrepresented in the academic field. Greater awareness and acceptance of the importance of standards for quality assurance even in the academic environment is discussed, and active participation in the corresponding International Organization for Standardization committee on biomimetics is asked for.
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.
Due to high power density and superior efficiency, polymer electrolyte membrane fuel cells (PEMFC) are believed to play a significant role for carbon dioxide emissions free electrical energy systems in the future. Unlike in Carnot processes, chemical energy in the form of hydrogen and oxygen is converted directly into electrical energy without a further process step. One issue in the development of PEMFCs for mobile or stationary applications is the utilization of rare and expensive catalyst material like platinum within the membrane electrode assembly (MEA) see figure 1. In addition, the objective is to reduce production costs and to increase the lifetime of PEMFC. One approach to improve PEMFCs is the development of intelligent electrode architectures. However, cost effective high performance materials are necessary to reach the development targets.
This work deals with the preparation and investigation of polymer electrolyte membrane fuel cell (PEMFC) electrodes, which are obtained using gas diffusion layers coated with graphene related material (GRM) serving as a catalyst support for platinum nanoparticles. PEMFC electrocatalysts have been prepared by pulsed electrochemical deposition of platinum particles from hexachloroplatinic acid. Prior to GRM decoration with platinum, the graphene structures are functionalized by oxygen plasma treatment. This leads to oxygen containing functional groups on the GRM outer surface, providing an improved hydrophilic behavior, thus favoring the Pt deposition process. Membrane electrode assemblies (MEAs) with the so prepared electrodes are investigated in-situ in our fuel cell test system. Polarization plots (in-situ cell performance) using these MEAs have been tested under different operational conditions.
Patient specific simulation of brain stimulation using the unfitted discontinuous galerkin method
(2015)
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 investigated the formation of Artemia franciscana swarms of freshly hatched instar I nauplii larvae. Nauplii were released into light gradients but then interrupted by light-direction changes, small obstacles, or long barriers. All experiments were carried out horizontally. Each experiment used independent replicates. Freshly produced Artemia broods were harvested from independent incubators thus providing true replicate cohorts of Artemia subjected as replicates to the experimental treatments.
We discovered that Artemia nauplii swarms can: 1. repeatedly react to non-obstructed light gradients that undergo repeated direction-changes and do so in a consistent way, 2. find their way to a light source within maze-like arrangements made from small transparent obstacles, 3. move as a swarm around extended transparent barriers, following a light gradient. This paper focuses on the recognition of whole-swarm behaviors, the description thereof and the recognition of differences in whole-swarm movements comparing non-obstructed swarming with swarms encountering obstacles. Investigations of the within-swarm behaviors of individual Artemia nauplii and their interactions with neighboring nauplii are in progress, e.g. in order to discover the underlying swarming algorithms and differences
thereof comparing non-obstructed vs. obstructed pathways.
Software updates take an essential role in keeping IT environments secure. If service providers delay or do not install updates, it can cause unwanted security implications for their environments. This paper conducts a large-scale measurement study of the update behavior of websites and their utilized software stacks. Across 18 months, we analyze over 5.6M websites and 246 distinct client- and server-side software distributions. We found that almost all analyzed sites use outdated software. To understand the possible security implications of outdated software, we analyze the potential vulnerabilities that affect the utilized software. We show that software components are getting older and more vulnerable because they are not updated. We find that 95 % of the analyzed websites use at least one product for which a vulnerability existed.
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.
Bone morphogenetic protein 2 (BMP21) is a highly interesting therapeutic growth factor due to its strong osteogenic/osteoinductive potential. However, its pronounced aggregation tendency renders recombinant and soluble production troublesome and complex. While prokaryotic expression systems can provide BMP2 in large amounts, the typically insoluble protein requires complex denaturation-renaturation procedures with medically hazardous reagents to obtain natively folded homodimeric BMP2. Based on a detailed aggregation analysis of wildtype BMP2, we designed a hydrophilic variant of BMP2 additionally containing an improved heparin binding site (BMP2-2Hep-7M). Consecutive optimization of BMP2-2Hep-7M expression and purification enabled production of soluble dimeric BMP2-2Hep-7M in high yield in E. coli. This was achieved by a) increasing protein hydrophilicity via introducing seven point mutations within aggregation hot spots of wildtype BMP2 and a longer N-terminus resulting in higher affinity for heparin, b) by employing E. coli strain SHuffle® T7, which enables the structurally essential disulfide-bond formation in BMP2 in the cytoplasm, c) by using BMP2 variant characteristic soluble expression conditions and application of L-arginine as solubility enhancer. The BMP2 variant BMP2-2Hep-7M shows strongly attenuated although not completely eliminated aggregation tendency.
Optimization of the laser remelting process for HVOF-sprayed Stellite 6 wear resistant coatings
(2016)
Cobalt base alloys are used in all industrial areas due to their excellent wear resistance. Several studies have shown that Stellite 6 coatings are suitable not only for protection against sliding wear, but also in case of exposure to impact loading. In this respect, a possible application is the protection of hydropower plant components affected by cavitation. The main problem in connection with Stellite 6 is the deposition procedure of the protective layers, both welding and thermal spraying techniques requesting special measures in order to prevent the brittleness of the coating. In this study, Stellite 6 layers were HVOF thermally sprayed on a martensitic 13-4 stainless steel substrate, as usually used for hydraulic machinery components. In order to improve the microstructure of the HVOF-sprayed coatings and their adhesion to the substrate, laser remelting was applied, using a TRUMPF Laser type HL 124P LCU and different working parameters. The microstructure of the coatings, obtained for various remelting conditions, was evaluated by light microscopy, showing the optimal value of the pulse power, which provided a homogenous Stellite 6 layer with good adhesion to the substrate.
Platinum nanoparticles electrodeposition on carbon nanofibers (CNF) support has been performed with the purpose to obtain electrodes that can be further used especially in a polymer electrolyte membrane fuel cell (PEMFC). A pretreatment of CNF is required in order to enhance the surface energy, which simultaneously improves handling and wettability as well as interaction with the platinum cations. This step was performed using oxygen plasma functionalization. To produce CNF supported Pt catalysts, an electrochemical method was applied and the deposition parameters were adjusted to obtain nanosized platinum particles with a good distribution onto the graphitic surface. The morphology and structure of the obtained particles were investigated by scanning electron microscopy combined with energy dispersive X-Ray spectroscopy. The amount of deposited platinum was established using thermogravimetrical measurements. Cyclic voltammetry performed in 0.5 M H2SO4 solution was applied for determining the electrochemical surface area (ECSA) of the obtained electrodes.The functionalization degree of the CNF outer surface has a strong influence on the structure, distribution and amount of platinum particles. Moreover, the current densities, which were set for the deposition process influenced not only the particles size but also the platinum amount. Applying an oxygen plasma treatment of 80 W for 1800 s, the necessary degree of surface functionalization is achieved in order to deposit the catalyst particles. The best electrodes were prepared using a current density of 50 mA cm-2 during the deposition process that leads to a homogenous platinum distribution with particles size under 80 nm and ECSA over 6 cm2
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.
Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.
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.
Welding and joining of components processed by additive manufacturing (AM) to other AMas well as conventionally produced components is of high importance for industry as thisallows to combine advantages of either technique and to produce large-scale structures,respectively. One of the key influencing factors with respect to weldability and mechanicalproperties of AM components was found to be the inherent microstructural anisotropy ofthese components. In present work, the precipitation-hardenable AleSi10Mg was fabri-cated in different build orientations using selective laser melting (SLM) and subsequentlyjoined by friction stir welding (FSW) in different combinations. Microstructural analysisshowed considerable grain refinement in the friction stir zone, however, pronouncedsoftening occurred in this area. The latter can be mainly attributed to changes in themorphology and size of Si particles. Upon combination of different build orientations aremarkable influence on the tensile strength of FSW joints was seen. Cyclic deformationresponses of SLM and FSW samples were examined in depth. Fatigue properties of thisalloy in the low-cycle fatigue (LCF) regime imply that SLM samples with the building di-rection parallel to the loading direction show superior performance under cyclic loading ascompared to the other conditions and the FSW joints. From results presented solid process-microstructure-property relationships are drawn.
Metallic implants in magnetic resonance imaging (MRI) are a potential safety risk since the energy absorption may increase temperature of the surrounding tissue. The temperature rise is highly dependent on implant size. Numerical examinations can be used to calculate the energy absorption in terms of the specific absorption rate (SAR) induced by MRI on orthopaedic implants. This research presents the impact of titanium osteosynthesis spine implants, called spondylodesis, deduced by numerical examinations of energy absorption in simplified spondylodesis models placed in 1.5 T and 3.0 T MRI body coils. The implants are modelled along with a spine model consisting of vertebrae and disci intervertebrales thus extending previous investigations [1], [2]. Increased SAR values are observed at the ends of long implants, while at the center SAR is significantly lower. Sufficiently short implants show increased SAR along the complete length of the implant. A careful data analysis reveals that the particular anatomy, i.e. vertebrae and disci intervertebrales, has a significant effect on SAR. On top of SAR profile due to the implant length, considerable SAR variations at small scale are observed, e.g. SAR values at vertebra are higher than at disc positions.
Metallic implants in magnetic resonance imaging (MRI) are a potential safety risk since the energy absorption may increase temperature of the surrounding tissue. The temperature rise is highly dependent on implant size. Numerical examinations can be used to calculate the energy absorption in terms of the specific absorption rate (SAR) induced by MRI on orthopaedic implants. This research presents the impact of titanium osteosynthesis spine implants, called spondylodesis, deduced by numerical examinations of energy absorption in simplified spondylodesis models placed in 1.5 T and 3.0 T MRI body coils. The implants are modelled along with a spine model consisting of vertebrae and disci intervertebrales thus extending previous investigations [1, 2]. Increased SARvalues are observed at the ends of long implants, while at the center SAR is significantly lower. Sufficiently short implants show increased SAR along the complete length of the implant. A careful data analysis reveals that the particular anatomy, i.e. vertebrae and disci intervertebrales, has a significant effect on SAR. On top of SAR profile due to the implant length, considerable SAR variations at small scale are observed, e.g. SAR values at vertebra are higher than at disc positions.
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.
Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide’s charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.
The influence of molecular fragmentation and parameter settings on a mesoscopic dissipative particle dynamics (DPD) simulation of lamellar bilayer formation for a C10E4/water mixture is studied. A “bottom-up” decomposition of C10E4 into the smallest fragment molecules (particles) that satisfy chemical intuition leads to convincing simulation results which agree with experimental findings for bilayer formation and thickness. For integration of the equations of motion Shardlow’s S1 scheme proves to be a favorable choice with best overall performance. Increasing the integration time steps above the common setting of 0.04 DPD units leads to increasingly unphysical temperature drifts, but also to increasingly rapid formation of bilayer superstructures without significantly distorted particle distributions up to an integration time step of 0.12. A scaling of the mutual particle–particle repulsions that guide the dynamics has negligible influence within a considerable range of values but exhibits apparent lower thresholds beyond which a simulation fails. Repulsion parameter scaling and molecular particle decomposition show a mutual dependence. For mapping of concentrations to molecule numbers in the simulation box particle volume scaling should be taken into account. A repulsion parameter morphing investigation suggests to not overstretch repulsion parameter accuracy considerations.
Recent experimental results showing atypical nonlinear absorption and marked deviations from well known universality in the low temperature acoustic and dielectric losses in amorphous solids prove the need for improving the understanding of the nature of two-level systems (TLSs) in these materials. Here we suggest the study of TLSs focused on their properties which are nonuniversal. Our theoretical analysis shows that the standard tunneling model and the recently suggested two-TLS model provide markedly different predictions for the experimental outcome of these studies. Our results may be directly tested in disordered lattices, e.g KBr:CN, where there is ample theoretical support for the validity of the two-TLS model, as well as in amorphous solids. Verification of our results in the latter will significantly enhance understanding of the nature of TLSs in amorphous solids, and the ability to manipulate them and reduce their destructive effect in various cutting edge applications including superconducting qubits.
Environmental noise leads to dephasing and relaxation in a quantum system. Often, a rigorous treatment of multiple noise sources within a system-bath approach is not possible. We discuss the influence of environmental fluctuations on a quantum system whose dynamics is dephasing already due to a phenomenologically treated additional noise source. For this situation, we develop a path-integral approach, which allows us to treat the system-environment coupling in a numerically exact way, and additionally we extend standard perturbative approaches. We observe strong deviations between the numerically exact and the perturbative results even for weak system-bath coupling. This shows that standard perturbative approaches fail for additional, even weak, system-bath couplings if the system dynamics is already dissipative.
Commonly, nanosystems are characterized by their response to time-dependent external fields in the presence of inevitable environmental fluctuations. The direct impact of the external driving on the environment is generally neglected. While this approach is satisfactory for macroscopic systems, on the nanoscale, an interaction of external fields with the environment is often unavoidable on principle. We extend the standard linear response theory of quantum dissipative systems to strongly driven baths. Significant modifications are found for two paradigm examples. First, we evaluate the polarizability of a molecule immersed in a strongly polarizable medium that responds to terahertz radiation. We find an increase of the molecular polarizability by about 30%. Second, we determine the response of a semiconductor quantum dot in close proximity to a metallic nanoparticle. Both are placed in a polarizable medium and exposed to electromagnetic irradiation. We show that the response of the quantum dot is qualitatively modified by the driven nanoparticle, including the generation of an additional channel of stimulated emission.
The diffusion of hydrogen adsorbed inside layered MoS2 crystals has been studied by means of quasi- elastic neutron scattering, neutron spin-echo spectroscopy, nuclear reaction analysis, and X-ray photoelectron spectroscopy. The neutron time-of-flight and neutron spin-echo measurements demonstrate fast diffusion of hydrogen molecules parallel to the basal planes of the two dimensional crystal planes. At room temperature and above, this intra-layer diffusion is of a similar speed to the surface diffusion that has been observed in earlier studies for hydrogen atoms on Pt surfaces. A significantly slower hydrogen diffusion was observed perpendicular to the basal planes using nuclear reaction analysis.
The video shows a very high resolution 3D point cloud !!! of the outdoor area of the German Rescue Robotics Center. For the recording, a 25-second POI flight was performed with a Mavic 3. From the 4K video footage captured during this flight, 77 images were cropped and localized within 4 minutes using colmap and processed using Neural Radiance Fields (NeRF). The nerfacto model of Nerfstudio was trained on an Nvidia RTX 4090 for 8 minutes. In summary, a top 3D model is available to task forces after about 13 minutes. The calculation is performed locally on site by the RobLW of the DRZ. The video shown here shows a free camera path rendered at 60 hz (Full HD).
n-type silicon modules
(2023)
The photovoltaic industry is facing an exponential growth in the recent years fostered by a dramatic decrease in installation prices. This cost reduction is achieved by means of several mechanisms. First, because of the optimization of the design and installation process of current PV projects, and second, by the optimization, in terms of performance, in the manufacturing techniques and material combinations within the modules, which also has an impact on both, the installation process, and the levelized cost of electricity (LCOE).
One popular trend is to increase the power delivered by photovoltaic modules, either by using larger wafer sizes or by combining more cells within the module unit. This solution means a significant increase in the size of these devices, but it implies an optimization in the design of photovoltaic plants. This results in an installation cost reduction which turns into a decrease in the LCOE.
However, this solution does not represent a breakthrough in addressing the real challenge of the technology which affects the module requirements. The innovation efforts must be focused on improving the modules capability to produce energy without enlarging the harvesting area. This challenge can be faced by approaching some of the module characteristics which are summarized in this chapter.
Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the rules. This requires a computational workflow with data import, fragmentation algorithm integration, and result visualisation. The described workflow is normally unavailable for a new algorithm and must be set up individually. This work presents an open Java rich client Graphical User Interface (GUI) application to support the development of new in silico molecule fragmentation algorithms and make them readily available upon release. The MORTAR (MOlecule fRagmenTAtion fRamework) application visualises fragmentation results of a set of molecules in various ways and provides basic analysis features. Fragmentation algorithms can be integrated and developed within MORTAR by using a specific wrapper class. In addition, fragmentation pipelines with any combination of the available fragmentation methods can be executed. Upon release, three fragmentation algorithms are already integrated: ErtlFunctionalGroupsFinder, Sugar Removal Utility, and Scaffold Generator. These algorithms, as well as all cheminformatics functionalities in MORTAR, are implemented based on the Chemistry Development Kit (CDK).