Background and objective: This study was conducted to find out the status of plant utilization in the current textbooks by analyzing the plants by grade and subject in the national textbooks for all elementary school grades in the 2015 revised curriculum in Korea. Methods: The data collected was analyzed using Microsoft Office Excel to obtain the frequency and ratio of collected plant data and SPSS for Windows 26.0 to determine learning content areas by grade and the R program was used to visualize the learning content areas. Results: A total of 232 species of plants were presented 1,047 times in the national textbooks. Based on an analysis of the plants presented by grade, the species that continued to increase in the lower grades tended to decrease in the fifth and sixth grades, the upper grades of elementary school. As for the number and frequency of plant species by subject, Korean Language had the highest number and frequency of plant species. The types of presentation of plants in textbooks were mainly text, followed by illustrations and photos of plants, which were largely used in first grade textbooks. In addition, as for the area of learning contents in which plants are used, in the lower grades, plants were used in the linguistic domain, and in the upper grades, in the botanical and environmental domains of the natural sciences. Herbaceous plants were presented more than woody plants, and according to an analysis of the plants based on the classification of crops, horticultural crops were presented the most, followed by food crops. Out of horticultural crops, flowering plants were found the most diversity with 63 species, but the plants that appeared most frequently were fruit trees that are commonly encountered in real life. Conclusion: As a result of this study, various plant species were included in elementary school textbooks, but most of them were horticultural crops encountered in real life depending on their use. Nevertheless, plant species with high frequency have continued a similar trend of frequency from the previous curriculums. Therefore, in the next curriculum, plant learning materials should be reflected according to social changes and students' preference for plants.
Eum Han;Giok Park;Heejin Kang;Yoseph Lee;Ilsoo Yun
The Journal of The Korea Institute of Intelligent Transport Systems
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v.21
no.6
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pp.36-56
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2022
Traffic accidents occur in Korea are calculated with the 「Automobile Accident Negligence Ratio Certification Standard」 prepared by the 'General Insurance Association of Korea' and the insurance company's agreement or judgment is made. However, disputes are frequently occurring in calculating the negligence ratio. Therefore, it is thought that a more effective response would be possible if accident type according to the standard could be quickly identified using traffic accident information prepared by police. Therefore, this study aims to develop a model that learns the accident information prepared by the police and classifies it to match the accident type in the standard. In particular, through data mining, keywords necessary to classify the accident types of the standard were extracted from the accident data of the police. Then, models were developed to derive the types of accidents by learning the extracted keywords through decision trees and random forest models.
Xanthomonas arboricola pv. juglandis (Xaj) is a globally important bacterial pathogen of walnut trees that causes substantial economic losses in commercial walnut production. Although prophages are common in bacterial plant pathogens and play important roles in bacterial diversity and pathogenicity, there has been limited investigation into the distribution and function of prophages in Xaj. In this study, we identified and characterized 13 predicted prophages from the genomes of 12 Xaj isolates from around the globe. These prophages ranged in length from 11.8 kb to 51.9 kb, with between 11-75 genes and 57.82-64.15% GC content. The closest relatives of these prophages belong to the Myoviridae and Siphoviridae families of the Caudovirales order. The phylogenetic analysis allowed the classification of the prophages into five groups. The gene constitution of these predicted prophages was revealed via Roary analysis. Amongst 126 total protein groups, the most prevalent group was only present in nine prophages, and 22 protein groups were present in only one prophage (singletons). Also, bioinformatic analysis of the 13 identified prophages revealed the presence of 431 genes with an average length of 389.7 bp. Prokka annotation of these prophages identified 466 hypothetical proteins, 24 proteins with known function, and six tRNA genes. The proteins with known function mainly comprised prophage integrase IntA, replicative DNA helicase, tyrosine recombinase XerC, and IS3 family transposase. There was no detectable insertion site specificity for these prophages in the Xaj genomes. The identified Xaj prophage genes, particularly those of unknown function, merit future investigation.
Background: Free-living amoebae (FLA) are widely distributed in freshwater, seawater, soil, and extreme environments, and play a critical role as feeders on diverse preys in the ecosystem. Also, some FLA can become opportunistic pathogens in animals including humans. The taxa Amoebozoa and Heterolobosea are important amoeboid groups associated with human pathogens. However, the identification and habitat of amoebae belonging to Amoebozoa and Heterolobosea remain poorly reported in the Republic of Korea. This study highlights the first record for identification and source of four amoebae including putative pathogens in the Republic of Korea. Results: In the present study, four previously reported FLA were isolated from freshwaters in Sangju Gonggeomji Reservoir (strain GO001), one of the largest reservoirs during the Joseon Dynasty period, and along the Nakdong River, the largest river in the Republic of Korea (strains NR5-2, NR12-1, and NR14-1) for the first time. Microscopic observations and 18S rDNA phylogenetic trees revealed the four isolated strains to be Acanthamoeba polyphaga (strains NR5-2 and NR12-1), Tetramitus waccamawensis (strain GO001), and Naegleria australiensis (strain NR14-1). Strains NR5-2 and NR12-1 might be the same species and belonged to the morphological Group 2 and the T4 genotype of Acanthamoeba. Strain GO001 formed a clade with T. waccamawensis in 18S rDNA phylogeny, and showed morphological characteristics similar to previously recorded strains, although the species' flagellate form was not observed. Strain NR14-1 had the typical morphology of Naegleria and formed a strongly supported clade with previously recorded strains of N. australiensis in phylogenetic analysis of 18S rDNA sequences. Conclusions: On the bases of morphological and molecular analyses, four strains of FLA were newly observed and classified in the Republic of Korea. Three strains belonging to the two species (A. polyphaga and N. australiensis) isolated from the Nakdong River have the potential to act as opportunistic pathogens that can cause fatal diseases (i.e. granulomatous amoebic encephalitis, Acanthamoeba Keratitis, and meningoencephalitis) in animals including humans. The Nakdong River in the Republic of Korea may provide a habitat for potentially pathogenic amoebae, but additional research is required to confirm the true pathogenicity of these FLA now known in the Republic of Korea.
Sulaiman Sulmi Almutairi;Rehmat Ullah;Qazi Zia Ullah;Habib Shah
KSII Transactions on Internet and Information Systems (TIIS)
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v.18
no.6
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pp.1478-1499
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2024
Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.
Journal of the Institute of Convergence Signal Processing
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v.24
no.4
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pp.186-191
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2023
Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.
This study categorized the damaged trees by Supervised Classification using time-series-aerial photographs of Bukhan, Cheonggae and Suri mountains because oak wilt disease seemed to be concentrated in the metropolitan regions. In order to analyze the spatial characteristics of the damaged areas, the geographical characteristics such as elevation and slope were statistically analyzed to confirm their strong correlation. Based on the results from the statistical analysis of Moran's I, we have retrieved the following: (i) the value of Moran's I in Bukhan mountain is estimated to be 0.25, 0.32, and 0.24 in 2009, 2010 and 2012, respectively. (ii) the value of Moran's I in Cheonggye mountain estimated to be 0.26, 0.32 and 0.22 in 2010, 2012 and 2014, respectively and (iii) the value of Moran's I in Suri mountain estimated to be 0.42 and 0.42 in 2012 and 2014. respectively. These numbers suggest that the damaged trees are distributed in clusters. In addition, we conducted hotspot analysis to identify how the damaged tree clusters shift over time and we were able to verify that hotspots move in time series. According to our research outcome from the analysis of the entire hotspot areas (z-score>1.65), there were 80 percent probability of oak wilt disease occurring in the broadleaf or mixed-stand forests with elevation of 200~400 m and slope of 20~40 degrees. This result indicates that oak wilt disease hotspots can occur or shift into areas with the above geographical features or forest conditions. Therefore, this research outcome can be used as a basic resource when predicting the oak wilt disease spread-patterns, and it can also prevent disease and insect pest related harms to assist the policy makers to better implement the necessary solutions.
This study aims to show the changes of characteristics of vegetation structure for 20 years(1991~2010) in Naejangsan National Park. As a result of analysis of actual vegetation, the mixed community of Quercus variabilis and Quercus serrata was distributed with 56.1%, and Q. variabilis community showed in southern steep slope with 17.6%. Pinus densiflora community(5.8%) was observed on the ridge and Carpinu tschonoskii community distributed in the slope of the valley with 6.6%. Zelkova serrata and Prunus sargentii community were distributed in valley. The classification by TWINSPAN, ordination by DCA considering importance percentage and property of vegetation class were divided into 4 communities, which are community I(P. densiflora-Q. variabilis community), community II(Q. variabilis community), community III(C. tschonoskii community) and community IV(Mixed deciduous broad-leaved trees community). The age of Pinus densiflora was 32years old and Q. serrata was 36 years old in the community I, that of Q. variabilis was 64 years old in the community II, Q. serrata was 46 years old and C. tschonoskii was 45 years old in the community III, and Acer palmatum was 54 years old and Cornus controversa was 47 years old in the community IV. As the result of Shannon's index of species diversity, the community Iwas ranged from 0.9751 to 1.4199, community II was ranged from 1.0765 to 1.3278, community III was ranged from 1.0353 to 1.2881, and community IV was ranged from 1.1412 to 1.3807. The change of vegetation structure analyzed through the comparison with results of studies carried out 20 years ago were natural selection of P. densiflora, expansion of Quercus spp. and increase of C. tschonoskii. Especially, A. palmatum is dominated by Q. variabilis in canopy layer like the result of study 20 years ago. A. palmatum was analysed by 14.6% in the canopy layer of only mixed deciduous broad-leaved trees community. As a result of analysis of habitat property of Q. variabilis and A. palmatum, Q. variabilis was distributed in dry area with the low value of pH, O.M., exchangeable cations and Avail. P, and A. palmatum was located in the wet valley with huge value of nourishment. The tendency of reduction of bio-diversity by Sasa borealis is same as previous study but, the distributed areas were reduced in Naejangsan area.
The objectives of this study were to investigate the possibility of establishing a tree nursery at Saemangeum reclaimed land and to classify landscape trees based on the salt tolerance. A tree nursery (2.0 ha) was made in Gunsan Okgu area in 2012 with underground drain lines on the reclaimed land established in 2010. Salt content of the nursery soil within the 60 cm depth in 2013 was 5.13 dS/m and 8.20 dS/m for the pre-desalinated and non-desalinated lands, respectively. Thirty-six woody plant species (22 tree species and 14 shrub species at ages of 1 to 4) with a total of 3,943 individuals were planted in early April, 2013 and their growth performance was monitored until September of the same year. The average survival rate of the transplanted plants was 71.4% in late September, suggesting the high possibility of establishing a tree nursery at the reclaimed land. Based on the survival rate and tree vigor (amount of healthy leaves and crown development), the following 17 species with some salt tolerance were classified into three groups: "salt tolerant group" (3 species, Tamarix chinensis, Cudrania tricuspidata, Ilex serrata), "recommended group" (5 species, Pinus thunbergii, Albizia julibrissin, Ligustrum obtusifolium, Rosa rugosa, Pleioblatus pygmaeus), "plantable group" (9 species, Zelkova serrata, Hibiscus syriacus, Elaeagnus umbellata, Sorbus alnifolia, Sophora japonica, Metasequoia glyptostroboides, Quercus acutissima, Ulmus parvifolia, Robinia pseudoacacia). Seven tree species that had been adapted to the reclaimed land for three to four years before being transplanted to new reclaimed land in Gunsan Okgu area showed average survival rate of 98%, suggesting that pre-conditioned trees would survive well in the reclaimed land.
Microclimatic data were measured, and the human thermal sensation was analyzed at 10 local climate zones based on the major land cover classification to investigate the thermal environment of urban areas during summer nighttime. From the results, the green infrastructure areas (GNIAs) showed an average air temperature of 1.6℃ and up to 2.4℃ lower air temperature than the gray infrastructure areas (GYIAs), and the GNIAs showed an average relative humidity of 9.0% and up to 15.0% higher relative humidity. The wind speed of the GNIAs and GYIAs had minimal difference and showed no significance at all locations, except for the forest location, which had the lowest wind speed owing to the influence of trees. The local winds and the surface roughness, which was determined based on the heights of buildings and trees, appeared to be the main factors that influenced wind speed. At the mean radiant temperature, the forest location showed the maximum value, owing to the influence of trees. Except at the forest location, the GNIAs showed an average decrease of 5.5℃ compared to GYIAs. The main factor that influenced the mean radiant temperature was the sky view factor. In the analysis of the human thermal sensation, the GNIAs showed a "neutral" thermal perception level that was neither hot nor cold, and the GYIAs showed a "slightly warm" level, which was a level higher than those of the GNIAs. The GNIAs showed a 3.2℃ decrease compared to the GYIAs, except at the highest forest location, which indicated a half-level improvement in the human thermal environment.
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