Filters

2015 - 2019172020 - 202448

More filters

2023 - 202465
Reset filters

Settings

Start date: 2023 × DDC: 333.7 ×

Search Results

Now showing 1 - 10 of 65
  • Thumbnail Image
    Item
    Mechanically Stable, Binder‐Free, and Free‐Standing Vanadium Trioxide/Carbon Hybrid Fiber Electrodes for Lithium‐Ion Batteries
    (Weinheim : Wiley-VCH, 2023) Bornamehr, Behnoosh; Gallei, Markus; Husmann, Samantha; Presser, Volker
    Binder is a crucial component in present-day battery electrodes but commonly contains fluorine and requires coating processing using organic (often toxic) solvents. Preparing binder-free electrodes is an attractive strategy to make battery electrode production and its end-of-use waste greener and safer. Herein, electrospinning is employed to prepare binder-free and self-standing electrodes. Such electrodes often suffer from low flexibility, and the correlation between performance and flexibility is usually overlooked. Processing parameters affect the mechanical properties of the electrodes, and for the first time it is reported that mechanical flexibility directly influences the electrochemical performance of the electrode. The importance is highlighted when processing parameters advantageous to powder materials, such as a higher heat treatment temperature, harm self-standing electrodes due to deterioration of fiber flexibility. Other strategies, such as conductive carbon addition, can be employed to improve the cell performance, but their effect on the mechanical properties of the electrodes must be considered. Rapid heat treatment achieves self-standing V2O3 with a capacity of 250 mAh g−1 at 250 mA g−1 and 390 mAh g−1 at 10 mA g−1
  • Thumbnail Image
    Item
    Climate change impacts on European arable crop yields: Sensitivity to assumptions about rotations and residue management
    (Amsterdam [u.a.] : Elsevier, 2022) Faye, Babacar; Webber, Heidi; Gaiser, Thomas; Müller, Christoph; Zhang, Yinan; Stella, Tommaso; Latka, Catharina; Reckling, Moritz; Heckelei, Thomas; Helming, Katharina; Ewert, Frank
    Most large scale studies assessing climate change impacts on crops are performed with simulations of single crops and with annual re-initialization of the initial soil conditions. This is in contrast to the reality that crops are grown in rotations, often with sizable proportion of the preceding crop residue to be left in the fields and varying soil initial conditions from year to year. In this study, the sensitivity of climate change impacts on crop yield and soil organic carbon to assumptions about annual model re-initialization, specification of crop rotations and the amount of residue retained in fields was assessed for seven main crops across Europe. Simulations were conducted for a scenario period 2040–2065 relative to a baseline from 1980 to 2005 using the SIMPLACE1 framework. Results indicated across Europe positive climate change impacts on yield for C3 crops and negative impacts for maize. The consideration of simulating rotations did not have a benefit on yield variability but on relative yield change in response to climate change which slightly increased for C3 crops and decreased for C4 crops when rotation was considered. Soil organic carbon decreased under climate change in both simulations assuming a continuous monocrop and plausible rotations by between 1% and 2% depending on the residue management strategy.
  • Thumbnail Image
    Item
    Deep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps
    (Berlin ; Heidelberg : Springer, 2022) Salamut, Christian; Kohnert, Iris; Landwehr, Niels; Pflanz, Michael; Schirrmann, Michael; Zare, Mohammad
    Insect populations appear with a high spatial, temporal and type-specific diversity in orchards. One of the many monitoring tools for pest management is the manual assessment of sticky traps. However, this type of assessment is laborious and time-consuming so that only a few locations can be controlled in an orchard. The aim of this study is to test state-of-the art object detection algorithms from deep learning to automatically detect cherry fruit flies (Rhagoletis cerasi), a common insect pest in cherry plantations, within images from yellow sticky traps. An image annotation database was built with images taken from yellow sticky traps with more than 1600 annotated cherry fruit flies. For better handling in the computational algorithms, the images were augmented to smaller ones by the known image preparation methods “flipping” and “cropping” before performing the deep learning. Five deep learning image recognition models were tested including Faster Region-based Convolutional Neural Network (R-CNN) with two different methods of pretraining, Single Shot Detector (SSD), RetinaNet, and You Only Look Once version 5 (YOLOv5). R‑CNN and RetinaNet models outperformed other ones with a detection average precision of 0.9. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. Therefore, this can reduce the time for repetitive and laborious trap assessment but also increase the observed amount of sticky traps
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Improving Deep Learning-based Plant Disease Classification with Attention Mechanism
    (Berlin ; Heidelberg : Springer, 2022) Alirezazadeh, Pendar; Schirrmann, Michael; Stolzenburg, Frieder
    In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.
  • Thumbnail Image
    Item
    A Pixel-wise Segmentation Model to Identify Bur Chervil (Anthriscus caucalis M. Bieb.) Within Images from a Cereal Cropping Field
    (Berlin ; Heidelberg : Springer, 2022) Karimi, Hadi; Navid, Hossein; Dammer, Karl-Heinz
    Because of insufficient effectiveness after herbicide application in autumn, bur chervil (Anthriscus caucalis M. Bieb.) is often present in cereal fields in spring. A second reason for spreading is the warm winter in Europe due to climate change. This weed continues to germinate from autumn to spring. To prevent further spreading, a site-specific control in spring is reasonable. Color imagery would offer cheap and complete monitoring of entire fields. In this study, an end-to-end fully convolutional network approach is presented to detect bur chervil within color images. The dataset consisted of images taken at three sampling dates in spring 2018 in winter wheat and at one date in 2019 in winter rye from the same field. Pixels representing bur chervil were manually annotated in all images. After a random image augmentation was done, a Unet-based convolutional neural network model was trained using 560 (80%) of the sub-images from 2018 (training images). The power of the trained model at the three different sampling dates in 2018 was evaluated at 141 (20%) of the manually annotated sub-images from 2018 and all (100%) sub-images from 2019 (test images). Comparing the estimated and the manually annotated weed plants in the test images the Intersection over Union (Jaccard index) showed mean values in the range of 0.9628 to 0.9909 for the three sampling dates in 2018, and a value of 0.9292 for the one date in 2019. The Dice coefficients yielded mean values in the range of 0.9801 to 0.9954 for 2018 and a value of 0.9605 in 2019.
  • Thumbnail Image
    Item
    Methods for Recognition of Colorado Beetle (Leptinotarsa decemlineata (Say)) with Multispectral and Color Camera-sensors
    (Berlin ; Heidelberg : Springer, 2022) Dammer, Karl-Heinz
    At the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides). In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed.
  • Thumbnail Image
    Item
    NaOH protective layer for a stable sodium metal anode in liquid electrolytes
    (Amsterdam [u.a.] : Elsevier, 2024) Thomas, Alexander; Pohle, Björn; Schultz, Johannes; Hantusch, Martin; Mikhailova, Daria
    Sodium is known as a soft metal that can easily change its particle morphology. It can form outstretched and rolled fibers with plastic or brittle behavior, and cubes. In Na-batteries, metallic Na anodes demonstrate a high reactivity towards the majority of electrolyte solutions, volume change and a random deposition process from the electrolyte, accompanied by dendrite formation. In order to smooth the electrochemical Na deposition, we propose NaOH as a simple artificial protective layer for sodium, formed by its exposure to ambient conditions for a certain period of time. The formed NaOH layer on top of the metallic sodium suppresses the volume change and dendrite growth on the sodium surface. Additionally, the protected sodium does not change its morphology after a prolonged contact with carbonate-based electrolytes. In symmetric Na-batteries, the NaOH layer increases the lifetime of the electrochemical cell by eight times in comparison to non-protected Na. In the full-cell with a layered sodium oxide cathode, the NaOH-protected sodium anode also leads to a high cycling stability, providing 81 % of the initial cell capacity after 500 cycles with a 1C current rate. In contrast, batteries with a non-protected Na-anode reach only 20 % of their initial capacity under the same conditions. Therefore, the main benefits of the NaOH artificial layer are the chemical compatibility with the carbonate-based electrolytes, the protection of Na metal against reaction with the electrolyte solution, the rapid Na-ion diffusion through the layer and the formation of a mechanical barrier, mitigating Na-dendrite growth. This work presents an easily scalable method to protect sodium without any additional chemicals or a special environment for this reaction.
  • Thumbnail Image
    Item
    The influence of Arctic amplification on mid-latitude summer circulation
    ([London] : Nature Publishing Group UK, 2018) Coumou, D.; Di Capua, G.; Vavrus, S.; Wang, L.; Wang, S.
    Accelerated warming in the Arctic, as compared to the rest of the globe, might have profound impacts on mid-latitude weather. Most studies analyzing Arctic links to mid-latitude weather focused on winter, yet recent summers have seen strong reductions in sea-ice extent and snow cover, a weakened equator-to-pole thermal gradient and associated weakening of the mid-latitude circulation. We review the scientific evidence behind three leading hypotheses on the influence of Arctic changes on mid-latitude summer weather: Weakened storm tracks, shifted jet streams, and amplified quasi-stationary waves. We show that interactions between Arctic teleconnections and other remote and regional feedback processes could lead to more persistent hot-dry extremes in the mid-latitudes. The exact nature of these non-linear interactions is not well quantified but they provide potential high-impact risks for society.
  • Thumbnail Image
    Item
    Diverging importance of drought stress for maize and winter wheat in Europe
    ([London] : Nature Publishing Group UK, 2018) Webber, Heidi; Ewert, Frank; Olesen, Jørgen E.; Müller, Christoph; Fronzek, Stefan; Ruane, Alex C.; Bourgault, Maryse; Martre, Pierre; Ababaei, Behnam; Bindi, Marco; Ferrise, Roberto; Finger, Robert; Fodor, Nándor; Gabaldón-Leal, Clara; Gaiser, Thomas; Jabloun, Mohamed; Kersebaum, Kurt-Christian; Lizaso, Jon I.; Lorite, Ignacio J.; Manceau, Loic; Moriondo, Marco; Nendel, Claas; Rodríguez, Alfredo; Ruiz-Ramos, Margarita; Semenov, Mikhail A.; Siebert, Stefan; Stella, Tommaso; Stratonovitch, Pierre; Trombi, Giacomo; Wallach, Daniel
    Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.