Filtern
Erscheinungsjahr
Dokumenttyp
- Wissenschaftlicher Artikel (1015) (entfernen)
Sprache
- Deutsch (790)
- Englisch (223)
- Französisch (1)
- Spanisch (1)
Volltext vorhanden
- nein (1015) (entfernen)
Schlagworte
- Geldpolitik (6)
- Building Information Modeling (4)
- Kühllastberechnung (4)
- Qualitätsplan (3)
- Reinraumtechnik (3)
- VDI 2078 (3)
- CDK (2)
- Deutschlandwetter (2)
- Energiepolitik (2)
- Industry Foundation Classes (2)
Institut
- Wirtschaftsrecht (382)
- Institut für Internetsicherheit (160)
- Wirtschaft und Informationstechnik Bocholt (68)
- Institut für Innovationsforschung und -management (55)
- Westfälisches Energieinstitut (55)
- Westfälisches Institut für Gesundheit (47)
- Wirtschaft Gelsenkirchen (35)
- Elektrotechnik und angewandte Naturwissenschaften (33)
- Wirtschaftsingenieurwesen (23)
- Informatik und Kommunikation (20)
- Institut für biologische und chemische Informatik (19)
- Maschinenbau und Facilities Management (16)
- Institut Arbeit und Technik (13)
- Maschinenbau Bocholt (8)
- Fachbereiche (6)
- Strategische Projekte (4)
- Institute (2)
- Mechatronik-Institut Bocholt (1)
- Sonstige (1)
Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture
(2024)
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.
It is well-known that protein-modified implant surfaces such as TiO2 show a higher bioconductivity. Fibronectin is a glycoprotein from the extracellular matrix (ECM) with a major role in cell adhesion. It can be applied on titanium oxide surfaces to accelerate implant integration. Not only the surface concentration but also the presentation of the protein plays an important role for the cellular response. We were able to show that TiOX surfaces modified with biotinylated fibronectin adsorbed on a streptavidin-silane self-assembly multilayer system are more effective regarding osteoblast adhesion than surfaces modified with nonspecifically bound fibronectin. The adsorption and conformation behavior of biotinylated and nonbiotinylated (native) fibronectin was studied by surface plasmon resonance (SPR) spectroscopy and atomic force microscopy (AFM). Imaging of the protein modification revealed that fibronectin adopts different conformations on nonmodified compared to streptavidin-modified TiOX surfaces. This conformational change of biotinylated fibronectin on the streptavidin monolayer delivers a fibronectin structure similar to the conformation inside the ECM and therefore explains the higher cell affinity for these surfaces.
Under ambient conditions, almost all metals are coated by an oxide. These coatings, the result of a chemical reaction, are not passive. Many of them bind, activate and modify adsorbed molecules, processes that are exploited, for example, in heterogeneous catalysis and photochemistry. Here we report an effect of general importance that governs the bonding, structure formation and dissociation of molecules on oxidic substrates. For a specific example, methanol adsorbed on the rutile TiO2(110) single crystal surface, we demonstrate by using a combination of experimental and theoretical techniques that strongly bonding adsorbates can lift surface relaxations beyond their adsorption site, which leads to a sig- nificant substrate-mediated interaction between adsorbates. The result is a complex super- structure consisting of pairs of methanol molecules and unoccupied adsorption sites. Infrared spectroscopy reveals that the paired methanol molecules remain intact and do not depro- tonate on the defect-free terraces of the rutile TiO2(110) surface.
Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantl when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.