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  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Social Sciences Vol. 12, No. 10 ( 2023-09-29), p. 547-
    In: Social Sciences, MDPI AG, Vol. 12, No. 10 ( 2023-09-29), p. 547-
    Abstract: Against the background of considerable regional disparities, we test the “discouraged worker” hypothesis, which postulates that poor regional socioeconomic conditions foster students’ aspirations for more education, ultimately leading to an extension of their educational careers. Our two dependent variables are (i) whether students aspire to prolong their general school careers or enter vocational training and (ii) whether they in fact prolong their school careers. To that end, we link regional-level data to individual-level data from the German National Educational Panel Study (NEPS). To describe regional conditions adequately, we illustrate geographical patterns in socioeconomic conditions relevant for school-to-work transitions (e.g., labour market conditions and availability of vocational training opportunities). We compare two operationalisations of regional areas: (i) administrative districts and (ii) public transport areas. Our results show that students are more likely to aspire to prolong their general school careers in socioeconomically deprived regions. Moreover, the effects are stronger when school-based vocational training opportunities are scarce. The effects on actual transitions vary according to the school track attended and the availability of educational alternatives in the general school system. Finally, the operationalisation of regions varies regarding effect sizes and corresponding levels of statistical significance.
    Type of Medium: Online Resource
    ISSN: 2076-0760
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2663343-7
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  • 2
    In: Diagnostics, MDPI AG, Vol. 13, No. 13 ( 2023-06-28), p. 2201-
    Abstract: This study was designed to investigate the image quality of ultra-high-resolution ankle arthrography employing a photon-counting detector CT. Bilateral arthrograms were acquired in four cadaveric specimens with full-dose (10 mGy) and low-dose (3 mGy) scan protocols. Three convolution kernels with different spatial frequencies were utilized for image reconstruction (ρ50; Br98: 39.0, Br84: 22.6, Br76: 16.5 lp/cm). Seven radiologists subjectively assessed the image quality regarding the depiction of bone, hyaline cartilage, and ligaments. An additional quantitative assessment comprised the measurement of noise and the computation of contrast-to-noise ratios (CNR). While an optimal depiction of bone tissue was achieved with the ultra-sharp Br98 kernel (S ≤ 0.043), the visualization of cartilage improved with lower modulation transfer functions at each dose level (p ≤ 0.014). The interrater reliability ranged from good to excellent for all assessed tissues (intraclass correlation coefficient ≥ 0.805). The noise levels in subcutaneous fat decreased with reduced spatial frequency (p 〈 0.001). Notably, the low-dose Br76 matched the CNR of the full-dose Br84 (p 〉 0.999) and superseded Br98 (p 〈 0.001) in all tissues. Based on the reported results, a photon-counting detector CT arthrography of the ankle with an ultra-high-resolution collimation offers stellar image quality and tissue assessability, improving the evaluation of miniscule anatomical structures. While bone depiction was superior in combination with an ultra-sharp convolution kernel, soft tissue evaluation benefited from employing a lower spatial frequency.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 3
    In: Microorganisms, MDPI AG, Vol. 8, No. 11 ( 2020-10-22), p. 1629-
    Abstract: Public health concerns and the potential for food-borne zoonotic transmission have made Salmonella a subject of surveillance programs in food-producing animals. Forty-two piglets (25 d of age and initially 7.48 kg) were used in a 28 d infection period to evaluate the effects of a high proportion of rye on reducing Salmonella Typhimurium. Piglets were divided into two diet groups: control diet (wheat 69%) and experimental diet (rye 69%). After a one-week adaptation period, all piglets were orally infected with Salmonella Typhimurium (107 log CFU/mL; 2mL/pig). Salmonella in fecal shedding were evaluated at day 1, 3, 5, 7 and then weekly after infection. At the end of the experimental period (at day 28 after infection), the piglets were euthanized to sample feces, cecal digesta contents and ileocecal lymph nodes to determine the bacterial counts of Salmonella. The results suggest that the bacterial counts in the experimental group fed rye diets showed evidence of reducing Salmonella fecal shedding from day 14 onwards and decreasing the number of Salmonella in cecal digesta. However, the translocation of Salmonella in ileocecal lymph nodes was not affected. Furthermore, feed intake, weight gain and feed conversion did not differ between the groups (p 〉 0.05).
    Type of Medium: Online Resource
    ISSN: 2076-2607
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2720891-6
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  • 4
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 24, No. 2 ( 2023-01-13), p. 1650-
    Abstract: In addition to the classic functions of proteins, such as acting as a biocatalyst or binding partner, the conformational states of proteins and their remodeling upon stimulation need to be considered. A prominent example of a protein that undergoes comprehensive conformational remodeling is transglutaminase 2 (TGase 2), the distinct conformational states of which are closely related to particular functions. Its involvement in various pathophysiological processes, including fibrosis and cancer, motivates the development of theranostic agents, particularly based on inhibitors that are directed toward the transamidase activity. In this context, the ability of such inhibitors to control the conformational dynamics of TGase 2 emerges as an important parameter, and methods to assess this property are in great demand. Herein, we describe the application of the switchSENSE® principle to detect conformational changes caused by three irreversibly binding Nε-acryloyllysine piperazides, which are suitable radiotracer candidates of TGase 2. The switchSENSE® technique is based on DNA levers actuated by alternating electric fields. These levers are immobilized on gold electrodes with one end, and at the other end of the lever, the TGase 2 is covalently bound. A novel computational method is introduced for describing the resulting lever motion to quantify the extent of stimulated conformational TGase 2 changes. Moreover, as a complementary biophysical method, native polyacrylamide gel electrophoresis was performed under similar conditions to validate the results. Both methods prove the occurrence of an irreversible shift in the conformational equilibrium of TGase 2, caused by the binding of the three studied Nε-acryloyllysine piperazides.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 21 ( 2021-11-04), p. 10375-
    In: Applied Sciences, MDPI AG, Vol. 11, No. 21 ( 2021-11-04), p. 10375-
    Abstract: The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual assessment of a cooled weld seam does not provide any information about its strength. However, based on the behavior during welding, especially about spattering, conclusions can be made about the quality of the weld. In addition, spatter on the component can have serious consequences. In this paper, we present in-process monitoring of laser-based hairpin welding. Using an in-process image analyzed by a neural network, we present a spatter detection method that allows conclusions to be drawn about the quality of the weld. In this way, faults caused by spattering can be detected at an early stage and the affected components sorted out. The implementation is based on a small data set and under consideration of a fast process time on hardware with limited computing power. With a network architecture that uses dilated convolutions, we obtain a large receptive field and can therefore consider feature interrelation in the image. As a result, we obtain a pixel-wise classifier, which allows us to infer the spatter areas directly on the production lines.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 6
    In: Molecules, MDPI AG, Vol. 17, No. 12 ( 2012-12-11), p. 14685-14699
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2012
    detail.hit.zdb_id: 2008644-1
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  • 7
    In: Agronomy, MDPI AG, Vol. 9, No. 5 ( 2019-05-09), p. 237-
    Abstract: Miscanthus is one of the most promising perennial herbaceous industrial crops worldwide mainly due to its high resource-use efficiency and biomass yield. However, the extent of miscanthus cultivation across Europe is still lagging far behind its real potential. Major limiting factors are high initial costs and low biomass yields in the crop establishment period, especially the first year. This study explores the possibility of establishing miscanthus under maize to generate yields from the first year of cultivation onwards. A field trial with mono-cropped maize and two miscanthus establishment procedures, ‘under maize’ (MUM) and ‘standard’ (REF), was established in southwest Germany in 2016. Annual aboveground biomass was harvested in autumn (2016–2018). In 2016 and 2017, the miscanthus dry matter yield (DMY) was significantly lower in MUM than REF. However, the accumulated DMY of miscanthus and maize was as high in MUM as in maize cultivation alone. In 2018, there was no significant difference between the miscanthus DMY of REF (7.86 ± 0.77 Mg ha−1) and MUM (6.21 ± 0.77 Mg ha−1). The accumulated DMY over the three years was 31.7 Mg ha−1 for MUM, of which 10.1 Mg ha−1 were miscanthus-based, compared to 17.7 Mg ha−1 for REF. These results indicate that miscanthus establishment under maize could compensate for its lack of yield in the first year.
    Type of Medium: Online Resource
    ISSN: 2073-4395
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2607043-1
    SSG: 23
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  • 8
    In: Agriculture, MDPI AG, Vol. 13, No. 8 ( 2023-08-21), p. 1639-
    Abstract: This study aimed to demonstrate the application of process mining on video data of pigs, facilitating the analysis of behavioral patterns. Video data were collected over a period of 5 days from a pig pen in a mechanically ventilated barn and used for analysis. The approach in this study relies on a series of individual steps to allow process mining on this data set. These steps include object detection and tracking, spatiotemporal activity recognition in video data, and process model analysis. Each step gives insights into pig behavior at different time points and locations within the pen, offering increasing levels of detail to describe typical pig behavior up to process models reflecting different behavior sequences for clustered datasets. Our data-driven approach proves suitable for the comprehensive analysis of behavioral sequences in conventional pig farming.
    Type of Medium: Online Resource
    ISSN: 2077-0472
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2651678-0
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 17 ( 2022-08-25), p. 6425-
    In: Sensors, MDPI AG, Vol. 22, No. 17 ( 2022-08-25), p. 6425-
    Abstract: Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise. This work deals with deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. The potential of 3D reconstruction can be used for quality control because the height values contain relevant information that is not visible in 2D data. Instead of 3D scans, estimated depth maps based on a 2D input image can be used with the advantage of a simple setup and a short recording time. Determining a 3D reconstruction from a single input image is a difficult task for which many algorithms and methods have been proposed in the past decades. In this work, three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets are investigated, evaluated and compared for 3D reconstruction from a 2D grayscale image of laser-welded components. In this work, different variants of GANs are tested, with the conclusion that Wasserstein GANs (WGANs) are the most robust approach among them. To the best of our knowledge, the present paper considers for the first time the U-Net, which achieves outstanding results in semantic segmentation, in the context of 3D reconstruction tasks. Unlike the U-Net, which uses standard convolutions, the stacked dilated U-Net (SDU-Net) applies stacked dilated convolutions. Of all the 3D reconstruction approaches considered in this work, the SDU-Net shows the best performance, not only in terms of evaluation metrics but also in terms of computation time. Due to the comparably small number of trainable parameters and the suitability of the architecture for strong data augmentation, a robust model can be generated with only a few training data.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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