• ALGAE
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• v.39 no.2
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• pp.75-96
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• 2024

Rhodomonas (Cryptophyceae) and species assigned to this genus have undergone numerous taxonomic revisions. This also applies to R. marina studied here as it was originally assigned as a species of Cryptomonas and later considered a variation of R. baltica, the type species. Despite being described more than 130 years ago, R. marina still lacks a comprehensive characterization. Light and electron microscopy were employed to delineate a strain from western Greenland. The living cells were 18 ㎛ long and 9 ㎛ wide, elliptical in shape with a pointed to rounded posterior and truncated anterior in lateral view. Two sub-equal flagella emerged from a vestibulum, where also a furrow extended. In transmission electron microscopy, the furrow was associated with a tubular gullet and the pyrenoid embedded in a deeply lobed chloroplast. The chloroplast contained DNA in perforations and was surrounded by starch grains. A tubular nucleomorph was enclosed within the pyrenoid matrix. In scanning electron microscopy, the inner periplast consisted of rectangular plates with rounded edges and posteriorly these were replaced by a sheet-like structure. The water-soluble pigment was Crypto-Phycoerythrin type I (Cr-PE 545). A phylogenetic inference based on SSU rDNA confirmed the identity of strain S18 as a species of Rhodomonas as it clustered with congeners but also Rhinomonas, Storeatula, and Pyrenomonas. These genera formed a monophyletic clade separated from a diverse assemblage of other cryptophyte genera. To further explore the phylogeny of R. marina a concatenated phylogenetic analysis based on the SSU rDNA-ITS1-5.8S rDNA-ITS2-LSU rDNA region was performed but included only closely related species. The secondary structure of nuclear internal transcribed spacer 2 was predicted and compared to similar structures in related species. Using morphological and molecular signatures as diagnostic features the description of R. marina was emended.

• The Plant Pathology Journal
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• v.40 no.2
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• pp.171-191
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• 2024

Identification of Helicotylenchus species is very challenging due to phenotypic plasticity and existence of cryptic species complexes. Recently, the use of rDNA barcodes has proven to be useful for identification of Helicotylenchus. Molecular markers are a quick diagnostic tool and are crucial for discriminating related species and resolving cryptic species complexes within this speciose genus. However, DNA barcoding is not an error-free approach. The public databases appear to be marred by incorrect sequences, arising from sequencing errors, mislabeling, and misidentifications. Herein, we provide a comprehensive analysis of the newly obtained, and published DNA sequences of Helicotylenchus, revealing the potential faults in the available DNA barcodes. A total of 97 sequences (25 nearly full-length 18S-rRNA, 12 partial 28S-rRNA, 16 partial internal transcribed spacer [ITS]-rRNA, and 44 partial cytochrome c oxidase subunit I [COI] gene sequences) were newly obtained in the present study. Phylogenetic relationships between species are given as inferred from the analyses of 103 sequences of 18S-rRNA, 469 sequences of 28S-rRNA, 183 sequences of ITS-rRNA, and 63 sequences of COI. Remarks on suggested corrections of published accessions in GenBank database are given. Additionally, COI gene sequences of H. dihystera, H. asiaticus and the contentious H. microlobus are provided herein for the first time. Similar to rDNA gene analyses, the COI sequences support the genetic distinctness and validity of H. microlobus. DNA barcodes from type material are needed for resolving the taxonomic status of the unresolved taxonomic groups within the genus.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.