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Advanced Inkjet Processes for Optoelectronics (Displays) and Related Applications
No full text405433In this chapter, the formulation and processing of inkjet inks for the fabrication of organic electronic devices are discussed. At first, basic requirements of ink formulations and printing processes are presented. Then, several examples of working ink formulations and processes for inkjet-printed organic light emitting diodes (OLED), organic photovoltaics (OPV) and quantum dot-light emitting diodes (QD-LED) are shown. The market for printed electronics is fast growing for flexible devices and health applications. Therefore, formulations of conducting inks for printed electrodes on highly flexible and stretchable devices are presented. An outlook is given on a nascent high-resolution printing technique, the electrostatic or electrohydrodynamic jetting, where the drop or jet formation is activated via high electric fields of several 100 V instead of piezo actuation. Thereby, inks of a large viscosity range are printable in dimensions as low as a few micrometer
Simulation Game for the Ultra-Efficient Factory Including a Moon Base Variant
No full text2226Das Ziel der Ultraeffizienzfabrik ist es, Firmen im Produktionssektor mit dem Wissen und den Fähigkeiten auszustatten, um ihre Fabriken zukunftsweisend zu gestalten. Sie stützt sich auf fünf Handlungsfelder: Energie, Material, Mensch, Emissionen und Organisation. In dieser Masterarbeit wurde ein Planspiel erarbeitet, welches innerhalb von Workshops als Lehrmaterial genutzt werden kann. Zusätzlich wurde eine Variante entwickelt, die die Handlungsfelder der Ultraeffizienzfabrik auf eine Mondbasis und extraterrestrische menschliche Besiedelung anwendet.The Ultra-Efficient Factory aims to provide producing firms with the capabilities to future-proof their factories. It is based on 5 key areas: energy, material, mission, organization and the human factor. A simulation game was developed for this Master’s thesis, which can be used in workshops as teaching material. Additionally, a variant that applies the key areas to a moon base and extraterrestrial human settlement was developed.1211-
Non-Invasive Detection and Characterization of Powdery Mildew in Strawberries Using Hyperspectral Imaging and Deep Learning under Poly-tunnel Conditions
No full textPowdery Mildew (Podosphaera aphanis, PM) presents a serious challenge to strawberry cultivation, causing significant yield losses if not controlled early. Globally, PM contributes to substantial reductions in strawberry production, posing economic threats to growers and raising food security concerns. Therefore, the development of accurate, noninvasive detection mechanisms is crucial for effective disease management. This study investigates the application of Hyperspectral Imaging (HSI) for nondestructive PM detection and classification of healthy and PM-infected strawberry leaves grown under a poly-tunnel, an environment that closely simulates real-world agricultural conditions. A hyperspectral camera (350-1000 nm), mounted on a mechanized rail system beneath the poly-tunnel, was used to capture leaf images as it moved linearly above the strawberry canopy. Spectral preprocessing included Savitzky-Golay smoothing (SGS), Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC), with the Isolation Forest algorithm applied for outlier removal. A one-dimensional Convolutional Neural Network (1D-CNN) was trained to classify healthy versus PM-infected leaves, achieving 75% accuracy and 84% precision. The model outperformed traditional classifiers such as Random Forest (RF), Decision Tree (DT), and Partial Least Squares Discriminant Analysis (PLS-DA). Among all tested pipelines, the SGS+MSC+1D-CNN combination yielded the highest performance. This study highlights the feasibility and effectiveness of integrating HSI with deep learning for robust disease detection under semi-controlled conditions, laying the groundwork for scalable, real-time plant health monitoring in precision agriculture
Polarimetric diversity in reference-free amplitude-based Wi-Fi sensing
No full textPassive sensing exploiting Wi-Fi as an illuminator of opportunity has attracted considerable interest for the ubiquitous presence of Wi-Fi systems in many environments, and the relative low cost & complexity of related passive solutions. In this paper, we consider a reference-free, amplitude-based sensing strategy exploiting Wi-Fi signals of opportunity and we investigate the potential advantage conveyed by the use of polarimetric diversity on receive. Specifically, we exploit the data collected by a dual-pol receiver to experimentally demonstrate that the joint availability of the signals collected by differently polarized antennas (H and V) could remarkably enhance the detection performance of the resulting Wi-Fi sensor, even if based on such a simple and cost-effective reference-free sensing approach. Moreover, we analyze the characteristics of the target signatures extracted by the considered amplitude-based approach for either single and dual-pol receivers, in order to investigate their suitability for detecting and recognizing different human movements, in view of future applications for automatic classification
Annotation-Efficient Strategy for Segmentation of 3D Body Composition
No full text11071127Body composition as a diagnostic and prognostic biomarker is gaining importance in various medical fields such as oncology. Therefore, accurate quantification methods are necessary, like analyzing CT images. While several studies introduced deep learning approaches to automatically segment a single slice, quantifying body composition in 3D remains understudied due to the high required annotation effort. This study proposes an annotation-efficient strategy using an iterative self-learning approach with sparse annotations to develop a segmentation model for the abdomen and pelvis, significantly reducing manual annotation needs. The developed model demonstrates outstanding performance with Dice scores for skeletal muscle (SM): 0.97+/-0.01, inter-/intra-muscular adipose tissue (IMAT): 0.83 +/- 0.07, visceral adipose tissue (VAT): 0.94 +/-0.04, and subcutaneous adipose tissue (SAT): 0.98 +/-0.02. A reader study supported these findings, indicating that most cases required negligible to no correction for accurate segmentation for SM, VAT and SAT. The variability in reader evaluations for IMAT underscores the challenge of achieving consensus on its quantification and signals a gap in our understanding of the precision required for accurately assessing this tissue through CT imaging. Moreover, the findings from this study offer advancements in annotation efficiency and present a robust tool for body composition analysis, with potential applications in enhancing diagnostic and prognostic assessments in clinical settings
Chronic nodular prurigo: Association between comorbidities, itch and quality of life
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Inspired by the gecko: How plastics adhere better Vom Gecko inspiriert: Wie Kunststoffe besser haften
No full text5455Mit einem patentierten Verfahren zur Mikro-Nano-Strukturierung hat ein Forschungsinstitut signifikante Verbesserungen der Haftungseigenschaften von Polymeren erzielt. Auch für die Benetzbarkeit, optische Reflexion, Hydrophobie sowie die Recyclingfähigkeit bringt das Verfahren, bei dem strukturierte Oberflächen mithilfe des Heißprägens oder Spritzgusses erzeugt werden, erhebliche Vorteile.641
CafeHD: A Charge-Domain FeFET-Based Compute-in-Memory Hyperdimensional Encoder with Hypervector Merging
No full textHyperdimensional computing (HDC) is an emerging paradigm that employs hypervectors (HV s) to emulate cognitive tasks. In HDC, the most time-consuming and power-hungry process is encoding, the first step that maps raw data into HV s. There have been non-volatile memory (NVM) based computing-in-memory (CiM) HDC encoding designs, which exploit the intrinsic HDC characteristics of high parallelism, massive data, and robustness. These NVM-based CiMs have shown great potential in reducing encoding time and power consumption. Among them, the ferroelectric field-effect transistor (FeFET) based designs show ultra-high energy efficiency. However, existing FeFET-based HDC encoding designs face the challenges of energy -consuming current-mode addition, inefficient HV storage, limited endurance, and single encoding method support. These challenges limit the energy efficiency, lifetime, and versatility of the designs. This work proposes an energy-efficient charge-domain FeFET-based in-memory HDC encoder, i.e., CafeHD, with extended lifetime, good versatility, and comparable accuracy. Area-efficient charge-domain computing is proposed in HDC encoding for the first time, which enables CafeHD with ultra-low power and high scalability. An HV merging technique is explored to improve the performance. A low-cost partial MAJ interface is also proposed to reduce writes. Besides, CafeHD also supports two widely used encoding methods. Results show that CafeHD on average achieves 10.9×/12.7×/3.5× speedup and 103.3×/21.9×/6.3× energy effi-ciency with 84 % write times reduction and similar accuracy compared with the state-of-the-art ReRAM/PCMlFeFET-based CiM design for HDC encoding, respectively
GA2LEN ANACARE consensus statement: Potential of omalizumab in food allergy management
No full textImmunoglobulin E (IgE)-mediated food allergies are the most common type of food allergy, often causing rapid symptoms after exposure to allergens posing a serious health risk and a high impact on patient's and caregiver's quality of life. Omalizumab, a humanized anti-IgE monoclonal antibody, reduces allergic reactions by binding to circulating IgE. Omalizumab has been successfully used in allergic asthma, chronic rhinosinusitis with nasal polyps, and chronic urticaria, and was recently approved for treating IgE-mediated food allergies by the US Food and Drug Administration (FDA). This GA2LEN ANACARE Consensus Statement presents our position on the use of omalizumab for treating IgE-mediated food allergies, based on a systematic review and meta-analysis, experience with use for other conditions, and expert consensus achieved via an eDelphi process. Following publication of the recent OUtMATCH study (stage 1) results and subsequent FDA approval, we propose that there is now sufficient evidence to recommend omalizumab as the only drug currently available that can mechanistically reduce IgE-mediated food allergic reactions. We acknowledge that the evidence does not reach the highest level of evidence which would be needed for a guideline recommendation.141
Morphologische Anpassung von Schichtsilicaten zum Einstellen des Permeationsverhaltens polymerer Beschichtungen
No full textDiese Arbeit befasst sich mit den synthetischen Herausforderungen beim Einsatz von Schichtsilicaten in Lacksystemen. Zu diesem Zweck wird zu Beginn die Notwendigkeit einer Modifikation untersucht, indem der Einfluss unmodifizierte und modifizierte Schichtsilicate auf die Permeationseigenschaften in einer Beschichtung untersucht wur-den. Dabei zeigten die Modifikation sowie die Art der Trocknung einen positiven Effkt auf die Barriereeigenschaften der Beschichtung. Durch das Anbinden von Dodecylamin und die Gefriertrocknung konnte eine deutliche Reduzierung der Permeation, um mehrals 90 %, erreicht werden