Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. https://www.selleckchem.com/peptide/lysipressin-acetate.html The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.Hearing aids are essential for people with hearing loss, and noise estimation and classification are some of the most important technologies used in devices. This paper presents an environmental noise classification algorithm for hearing aids that uses convolutional neural networks (CNNs) and image signals transformed from sound signals. The algorithm was developed using the data of ten types of noise acquired from living environments where such noises occur. Spectrogram images transformed from sound data are used as the input of the CNNs after processing of the images by a sharpening mask and median filter. The classification results of the proposed algorithm were compared with those of other noise classification methods. A maximum correct classification accuracy of 99.25% was achieved by the proposed algorithm for a spectrogram time length of 1 s, with the correct classification accuracy decreasing with increasing spectrogram time length up to 8 s. For a spectrogram time length of 8 s and using the sharpening mask and median filter, the classification accuracy was 98.73%, which is comparable with the 98.79% achieved by the conventional method for a time length of 1 s. The proposed hearing aid noise classification algorithm thus offers less computational complexity without compromising on performance.Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.While the weight of epidemiological evidence does not support a causal link with influenza vaccination evaluated over the last 30 years, Guillain-Barré syndrome (GBS) has been considered a vaccine-associated adverse event of interest since 1976. To investigate the existence of GBS risk after vaccination against seasonal influenza, a systematic review and meta-analysis have been conducted based on 22 eligible epidemiological studies from 1981 to 2019 reporting 26 effect sizes (ESs) in different influenza seasons. The primary result of our meta-analysis pointed to no risk of vaccine-associated GBS, as documented by a pooled ES of 1.15 (95% CI 0.97-1.35). Conversely, an obvious high risk of GBS was observed in patients with previous influenza-like illness (ILI), as demonstrated by a pooled ES of 9.6 (95% CI 4.0-23.0) resulting from a supplementary analysis. While the meta-analysis did not confirm the putative risk of vaccine-associated GBS suggested by many epidemiological studies, vaccination against seasonal influenza reduced the risk of developing ILI-associated GBS by about 88%. However, to obtain strong evidence, more epidemiological studies are warranted to establish a possible coincidence between vaccination and ILI prior to GBS onset.Most of the current serological diagnosis of pertussis is based on pertussis toxin (PT) IgG antibodies and does not differentiate between vaccination and infection-induced antibodies. PT is included in all of acellular pertussis vaccines available in the world. Multiplex testing of non-vaccine antigen-related antibodies has the potential to improve the diagnostic outcome of these assays. In this study, we developed a quantitatively spatial multiplex lateral flow immunoassay (LFIA) for the detection of IgG antibodies directed against PT, pertactin (PRN), and filamentous hemagglutinin (FHA). The assay was evaluated with serum samples with varying anti-PT, anti-PRN, and anti-FHA IgG levels and the result was compared to those obtained with standardized ELISA. The developed assay showed good specificity with PT and PRN antibodies and semiquantification throughout the antigen combinations. This exploratory study indicates that the multiplex LFIA is specific and sensitive, and a similar test platform with alternative antigens could be suitable for new type of pertussis serology.


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Last-modified: 2025-01-23 (木) 06:34:20 (19d)