• Korean Journal of Veterinary Service
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• v.44 no.2
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• pp.119-124
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• 2021

The report describes an avian mycobacteriosis in a captive wild bird. A 7-year-old female Indian peafowl (Pavo cristatus) maintained in a zoo of Korea presented a gradual cachexia and eventually was found dead. At necropsy, severely atrophied pectoral muscles exposing the keel bone were noticed. Yellowish thick nodules in varying sizes were scattered in all lobes of lungs, liver and spleen, suggesting mycobacteriosis. Histopathologically, multifocal to coalescing granulomas surrounded by multinucleated giant cells were observed. Numbers of acid-fast bacilli were revealed in granulomas. Then, a series of molecular diagnostic techniques were followed: a nested PCR, DNA sequencing and bioinformatics analysis. It resulted as Mycobacterium genavense. The identification of M. genavense as an etiological agent suggested that it might serve as a risk factor for other captive wild animals, and for a potential zoonotic risk since M. genavense have been a definite cause of disseminated mycobacterial infection in immunocompromised people. To the authors' knowledge, this is the first report of avian mycobacteriosis with M. genavense in a captive Indian peafowl.

• Journal of The Korean Astronomical Society
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• v.55 no.4
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• pp.131-138
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• 2022

We report the discovery of four quasars with M₁₄₅₀ ≳ -25.0 mag at z ~ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000-8000 Å, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its C _IV λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ~10⁸ M_⊙ and ~0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (L_Bol < 10⁴⁶ erg s^-1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.

• Smart Structures and Systems
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• v.30 no.4
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• pp.411-420
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• 2022

Monitoring large structures is a significant issue involving public health on which new studies are constantly carried out. Although the Global Navigation Satellite System (GNSS) is the most preferable method for measuring structural displacements, total stations, one of the classical geodetic instruments, are the first devices that come to mind in cases that require complementary usage and auxiliary measurement methods. In this study, the relative displacements of the structural movements of a tower were determined using robotic total stations (RTS) and GNSS. Two GNSS receivers and two RTS observations were carried out simultaneously for 10 hours under normal weather conditions. The spectral analysis of the GNSS data was performed using fast Fourier transform (FFT), and while the dominant modal frequencies were determined, the total station data were balanced with the least-squares technique, and the position and position errors were calculated for each measurement epoch. It has been observed that low-frequency structural movements can be determined by both methods. This result shows that total station measurements are a helpful alternative method for monitoring large structures in situations where measurements are not possible due to the basic handicaps of GNSS or where it is necessary to determine displacements with short observations.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2373-2379
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• 2023

In sodium systems, leakage is one of the safety concerns; it can cause chemical reactions, which may result in fires. There are contact and non-contact types of leak detectors, and the conventional method of non-contact type detection is by gas sampling. Because of the complexity of this method, there has always been a need for a simple gas sensor, and the resistive-type nanostructure ZnO sensor is a promising option with various advantages. In this study, a ZnO sensor was fabricated, and the concept was tested as a leak detector using a dedicated experiment facility. The experiment results showed distinctive changes in resistance with the presence of sodium aerosol under various conditions. Replacing the conventional gas sampling with the ZnO sensors is expected to enable identification of the leakage location if used as a point-wise instrumentation and to greatly reduce the total cost, making the system simple, light, and effective. For further study, more tests will be performed to evaluate the sensitivity of key parameters under various conditions.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2022.09a
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• pp.29-29
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• 2022

The fast-changing climatic conditions make plants to be vulnerable to many abiotic stresses. Drought stress is one of the limiting factors that affect pepper production in water deficient regions. It affects plant growth and development by altering physiological, morphological, and metabolic processes. Breeding drought tolerant varieties is one of the mitigation strategies to overcome the ever increasing drought disaster. Hence, screening of new drought tolerant pepper genotypes is essential. The current study was aimed to identify new drought tolerant genotypes among the collection of pepper genetic resources. In total, 70 pepper genotypes were screened for drought tolerance after exposure to drought stress condition. The pepper genotypes were classified as highly tolerant, intermediate, or severely sensitive to drought stress based on the phenotypic analysis. Consequently, 13 genotypes significantly exhibited higher recovery rate after drought stress and were classified as highly tolerant. Comparative analysis of morphological and physiological parameters and expression of drought responsive genes between tolerant and susceptible pepper genotypes will be presented and discussed. The identified tolerant genotypes will be useful resources for breeding drought tolerant pepper cultivars.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of Distribution Science
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• v.22 no.6
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• pp.107-114
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• 2024

Purpose: The current research investigates historical and future trends of tourist distribution channels in the tourism services business. The research examines historical patterns, current shifts, and new technologies in electricity distribution to offer insight into the distribution dynamics and advice for companies and regulators. Research design, data and methodology: The research in this case specifically employed the PRISMA approach when it comes to the data collection and research methodology. (PRISMA). The process is specifically made up of four steps, such as (1) Identification of Relevant Studies, (2) Screening and Selection Procedures, (3) Data Synthesis and Analysis, and (4) Reporting of Findings. Results: The fast-changing technology offers all opportunities to innovate the sector of tourism services. These upcoming technologies are not just reconstructing the way customers interact and operate but they are also creating room for development. Besides "the utilization of new technologies such as artificial intelligence, augmented reality, virtual reality, and blockchain, the current state of tourism distribution channels also implies some other possible consequences. Conclusions: These research results show that we should not be reluctant about adopting new technologies, we should expand direct booking systems, promote eco-friendly tourism, and use data analytics in order to provide personalized experiences.

• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.

• Wind and Structures
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• v.38 no.5
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• pp.381-398
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• 2024

This study presents a mode analysis of 3D turbulent velocity data around a square-section building model to identify the dynamic system for Kármán-type vortex shedding. Proper orthogonal decomposition (POD) was first performed to extract the significant 3D modes. Magnitude-squared coherence was then applied to detect the phase consistency between the modes, which were roughly divided into three groups. Group 1 (modes 1-4) depicted the main vortex shedding on the wake of the building, with mode 2 being controlled by the inflow fluctuation. Group 2 exhibited complex wake vortexes and single-sided vortex phenomena, while Group 3 exhibited more complicated phenomena, including flow separation. Subsequently, a third-order polynomial regression model was used to fit the dynamics system of modes 1, 3, and 4, which revealed average trend of the state trajectory. The two limit cycles of the regression model depicted the two rotation directions of Kármán-type vortex. Furthermore, two characteristic periods were identified from the trajectory generated by the regression model, which indicates fast and slow motions of the wake vortex. This study provides valuable insights into 3D mode morphology and dynamics of Kármán-type vortex shedding that helps to improve design and efficiency of structures in turbulent flow.