Hagelsimulation an Vitis vinifera L. cv. Müller-Thurgau unter besonderer Berücksichtigung des Erziehungssystems Minimalschnitt im Spalier

Der Wandel des Klimas und die damit einhergehende Zunahme an Wetterextremen stellt den Winzer vor neue Herausforderungen, die sowohl den An- als auch Ausbau von Wein betreffen (SCHULTZ et al. 2012; GÖMANN et al. 2015). Diese Ausarbeitung untersuchte, inwiefern eine Minimalschnitt-im-Spalier-Anlage einen Baustein im Risikomanagement des Pflanzenbaus bezüglich der Hagelgefährdung darstellen kann. Die Verwendung eines Trockeneisstrahlgerätes ermöglichte eine standardisier-, reproduzier- und skalierbare künstliche Nachbildung eines natürlichen Hagelschlages an Vitis vinifera L. cv. Müller-Thurgau. Die Ergebnisse zeigten auf, dass in der SHB-Anlage ein Großteil des hagelbedingten Ernteverlustes durch die Verrieselung – die bei steigender Hagelintensität zunahm – und einzelne an- bzw. abgeschlagene Beeren zu erklären war. In der MSS-Anlage wurden hingegen durch die Hagelsimulation mehr ganze Gescheine bzw. Trauben abgeschlagen. Um die Ertragsverluste der beiden Anlagen zu relativieren und vergleichen zu können, wurden die Schadensquoten der einzelnen Versuchsglieder gebildet. So konnte dargelegt werden, dass der identisch simulierte Hagelschlag zu denselben Entwicklungsstadien in der MSS-Erziehung durchschnittlich über alle Versuchsglieder 28 % bis 36 % weniger Ertragsverlust verursachte. Eine Kompensation des Ernteverlustes von ≈ 6 % bzw. ≈ 20 % durch ein verstärktes Dickenwachstum der Beeren, konnte nur tendenziell für Schäden vor der Blüte in einzelnen Versuchsvarianten festgestellt werden. Zur Beurteilung hagelbedingter qualitativer Unterschiede zwischen den Versuchsgliedern wurden im Frischmost die folgenden Inhaltstoffe mittels FTIR-Messung untersucht: Mostgewicht, Gesamtsäure, Äpfelsäure, Weinsäure, Kalium und YAN. Darüber hinaus wurden die Versuchsweine durch einen Dreieckstest und eine Intensitätsbewertung organoleptisch geprüft. Statistisch nachzuweisende Unterschiede in der Traubenqualität bzw. der Ogranoleptik konnten nur bedingt durch die Hagelsimulation erklärt werden, da die verschiedenen Hagelintensitäten meist keinen eindeutigen Trend hervorriefen.In this study the adaption of a minimal pruned vineyard was investigated in relation to avoid negative influence, which might be triggered by climate change, particularly damage by hail. To cause a standardised, repeat- and scalable hail damage on two different types of trellis systems (vertical shoot position: SHB, minimal pruning: MSS) a dry ice blasting machine was used. The results indicate, that the greatest effect of this artificial hail were yield losses. In the SHB-trellis the most yield losses can be determined by bad flowering (increasing by higher level of hail damage) and single berry losses. In contrast with the MSS-trellis more whole grapes were hew off. In order to be able to compare the yield losses of this two trellis systems, the loss ratio of each treatment was compared with the control unit. It could be shown, that the simulated hail at the same developmental stages in MSS caused an average yield loss from 28 % up to 36 %. A compensation of yield loss determined by elevated berry growth was only observed for pre flowering treatments with no statistically significant influence. To evaluate qualitative differences between the treatments the following grape juice ingredients were tested by FTIR: sugar concentration (°Oechsle), total acid, malic acid, tartaric acid, potassium and YAN. In addition the wines of each treatment were organoleptically examined by an triangle and intensity test. Statistically proven differences in grape quality or organoleptic could only be verified for a limited degree by the simulated hail damage, since the various treatments by hail intensity caused no clear trend in most cases

Assessing the optical configuration of a structured light scanner in metrological use

Structured light scanners for three-dimensional surface acquisition (SL scanners) are increasingly used for dimensional metrology. The optical configuration of SL scanners (focal length and baseline distance) influences the triangulation process, on which the scanners\u27 measurement principle relies. So far, only a limited number of studies has investigated the optical configuration\u27s influence on the accuracy of a SL scanner. To close this gap, this work presents a design of experiment in which the optical configuration of a SL scanner is systematically varied and its influence on the accuracy evaluated. Further, tactile reference measurements allow to separate random from systematical errors, while a special test specimen is used in two different configurations to ensure general applicability of the findings. Thus, this work provides support when designing a SL scanner by highlighting which optical configuration maximizes accuracy

β-Amyloid (1–42) Levels in Cerebrospinal Fluid and Cerebral Atrophy in Mild Cognitive Impairment and Alzheimer's Disease

Background: Recent studies consistently reported Alzheimer’s disease (AD) and, to a lower extent, mild cognitive impairment (MCI) to be accompanied by reduced cerebrospinal fluid (CSF) levels of β-amyloid. However, how these changes are related to brain morphological alterations is so far only partly understood. Methods: CSF levels of β-amyloid (1–42) were examined with respect to cerebral atrophy in 23 subjects with MCI, 16 patients with mild-to-moderateAlzheimer’s disease (AD) and 15 age-matched controls by using magnetic resonance imaging and voxel-based morphometry (VBM). Results: When contrasted with the controls, β-amyloid (1–42) levels were significantly lower (p Conclusion: Our finding confirms the results of previous studies and suggests that both the decrease in β-amyloid (1–42) and the development of hippocampal atrophy coincide in the disease process

Metallic conductivity in Na-deficient structural domain walls in the spin-orbit Mott insulator Na2IrO3

Honeycomb Na2IrO3 is a prototype spin-orbit Mott insulator and Kitaev magnet. We report a combined structural and electrical resistivity study of Na2IrO3 single crystals. Laue back-scattering diffraction indicates twinning with ±120◦ rotation around the c∗ axis while scanning electron microscopy displays nanothin lines parallel to all three b-axis orientations of twin domains. Energy dispersive x-ray analysis line scans across such domain walls indicate no change of the Ir signal intensity, i.e., intact honeycomb layers, while the Na intensity is reduced down to ∼2/3 of its original value at the domain walls, implying significant hole doping. Utilizing focused-ion-beam microsectioning, the temperature dependence of the electrical resistance of individual domain walls is studied. It demonstrates the tuning through the metal-insulator transition into a correlated-metal ground state by increasing hole doping

Anomaly Detection in Li-ion cell Contacting – Innovative Anomaly Detection in Laser Welding: A Pipeline Based on Radiation Emission Analysis and Machine Learning.; [Anomalieerkennung bei der Li-Ionen-Zellkontaktierung]

Die vorliegende Studie untersucht die KI-basierte Anomalieerkennung beim Kontaktierprozess von Li-Ionen-Batterieelektroden. Zur Datenge- nerierung wurden Schweißproben mit zwei gezielt eingebrachten De- fekten hergestellt. Auf Basis der aufgezeichneten Strahlungsemissio- nen können die Fehlertypen aus den Zeitreihendaten durch Merkmals- extraktion und Clusterbildung voneinander unterschieden und gegen- über den defektfreien Referenzproben erfolgreich abgegrenzt werden

Editorial: IPPS 2022 - plant phenotyping for a sustainable future

Plants are a venue for addressing the challenges facing humanity. The need for a reliable supply of food, feed, materials, chemicals and energy as well as ways to manage agroecology and climate change are among the challenges that we can address through the sustainable use of plants and plant ecosystems. The research community needs to integrate plant systems approaches, from molecular to organismal to applications in the field and ecosystems, to increase productivity sustainably while using fewer land, water, and nutrient resources. In the past two decades, plant phenotyping research has developed a highly valuable portfolio of technologies, processes and infrastructures to address these questions (Pieruschka and Schurr, 2019). In the past, the creation of datasets was limited by low throughput sensing and image analysis (Tsaftaris et al., 2016). However, through the development of digital image analysis the previous phenotyping “bottleneck” has shifted towards a capacity problem, making it difficult to interpret vast datasets (especially in the face of plant x environment interactions), leading to an “interpretation bottleneck” (Smith et al., 2021). Innovative plant phenotyping approaches that reveal and target relevant traits are thus still needed to identify and quantify key traits and processes and to understand the dynamic interactions between genetics, molecular and biochemical processes, and the physiological responses to changes in the environment that lead to the development of a phenotype

Multimodal Image Captioning for Marketing Analysis

Automatically captioning images with natural language sentences is an important research topic. State of the art models are able to produce human-like sentences. These models typically describe the depicted scene as a whole and do not target specific objects of interest or emotional relationships between these objects in the image. However, marketing companies require to describe these important attributes of a given scene. In our case, objects of interest are consumer goods, which are usually identifiable by a product logo and are associated with certain brands. From a marketing point of view, it is desirable to also evaluate the emotional context of a trademarked product, i.e., whether it appears in a positive or a negative connotation. We address the problem of finding brands in images and deriving corresponding captions by introducing a modified image captioning network. We also add a third output modality, which simultaneously produces real-valued image ratings. Our network is trained using a classification-aware loss function in order to stimulate the generation of sentences with an emphasis on words identifying the brand of a product. We evaluate our model on a dataset of images depicting interactions between humans and branded products. The introduced network improves mean class accuracy by 24.5 percent. Thanks to adding the third output modality, it also considerably improves the quality of generated captions for images depicting branded products.Comment: 4 pages, 1 figure, accepted at MIPR201

Single walled carbon nanotubes (SWCNT) affect cell physiology and cell architecture

Single walled carbon nanotubes (SWCNT) find their way in various industrial applications. Due to the expected increased production of various carbon nanotubes and nanoparticle containing products, exposure to engineered nanoparticles will also increase dramatically in parallel. In this study the effects of SWCNT raw material and purified SWCNT (SWCNT bundles) on cell behaviour of mesothelioma cells (MSTO-211H) and on epithelial cells (A549) had been investigated. The effect on cell behaviour (cell proliferation, cell activity, cytoskeleton organization, apoptosis and cell adhesion) were dependent on cell type, SWCNT quality (purified or not) and SWCNT concentratio

Designing an adaptive production control system using reinforcement learning

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited