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Amaranth is a nutritionally valuable crop, as it contains phenolic acids and flavonoids, yielding diverse plant secondary metabolites (PSMs) like phytosterol, tocopherols, and carotenoids. This study explored the variations in the contents of seventeen polyphenolic compounds within the leaves of one hundred twenty Amaranthus accessions representing nine Amaranthus species. The investigation entailed the analysis of phenolic content across nine Amaranthus species, specifically A. hypochondriacus, A. cruentus, A. caudatus, A. tricolor, A. dubius, A. blitum, A. crispus, A. hybridus, and A. viridis, utilizing ultra performance liquid chromatography with photodiode array detection (UPLC-PDA). The results revealed significant differences in polyphenolic compounds among accessions in which rutin content was predominant in all Amaranthus species in both 2018 and 2019. Among the nine Amaranthus species, the rutin content ranged from 95.72 ± 199.17 μg g−1 (A. dubius) to 1485.09 ± 679.51 μg g−1 (A. viridis) in 2018 and from 821.59 ± 709.95 μg g−1 (A. tricolor) to 3166.52 ± 1317.38 μg g−1 (A. hypochondriacus) in 2019. Correlation analysis revealed, significant positive correlations between rutin and kaempferol-3-O-β-rutinoside (r = 0.93), benzoic acid and ferulic acid (r = 0.76), and benzoic acid and kaempferol-3-O-β-rutinoside (r = 0.76), whereas gallic acid showed consistently negative correlations with each of the 16 phenolic compounds. Wide variations were identified among accessions and between plants grown in the two years. The nine species and one hundred twenty Amaranthus accessions were clustered into six groups based on their seventeen phenolic compounds in each year. These findings contribute to expanding our understanding of the phytochemical traits of accessions within nine Amaranthus species, which serve as valuable resources for Amaranthus component breeding and functional material development. [ABSTRACT FROM AUTHOR]

Rice paddies are dynamic areas for microbe-mediated nitrogen cycling and this could be driven by the long-term alteration of paddy soil edaphic factors. The objective of this study is to evaluate the lasting impact of long-term compost fertilization on the soil chemical properties of paddy fields, determining the size of the microbial guilds involved in nitrification and denitrification. Soil sampling was done on paddy fields without fertilizer, with NPK + compost, and with compost application, and the soil chemical properties of the fields were determined from 2018 to 2021. The abundance of genes related to nitrogen cycling was measured using quantitative PCR (qPCR). Annual analyses showed that the studied soils have attained stable, baseline chemical properties with significantly increased phosphorus (P2O5), potassium (K), SOM, and total nitrogen (TN) due to decades of fertilization with NPK + compost and compost. Consequently, the genes related to bacterial amoA, nosZI, and nosZII were significantly increased in Com- and NPKCom-amended soils compared to the NF paddy field. The nirK gene abundance was unaffected after long-term amendment with compost. A positive correlation was found between the archaeal amoA gene abundance and DOC, while SOM and TN were consistently positively correlated with the abundances of bacterial amoA, nosZI, and nosZII genes, in addition to interactions with potassium and DOC. Principal component analysis (PCA) indicated soil variabilities across treatments, where the unfertilized paddy field contained lower SOM and nutrient contents with a characteristic nirK gene abundance. Similar variabilities in terms of the SOM, TN, K, nosZI, and nosZII gene abundances were observed in the Com and NPKCom paddy fields. Long-term amendment with NPK + compost and compost created soil paddy fields with stable soil chemical properties with higher SOM and nutrient contents, which established higher abundances of genes associated with denitrification and nitrification that were observed during the fallow period. [ABSTRACT FROM AUTHOR]

Algal blooms have been observed worldwide and have had a serious impact on industries that use water resources, which is a problem for people and the environment. For this reason, an algae warning system is used to count the number of cyanobacterial cells and the concentration of chlorophyll-a. Several studies using multispectral or hyperspectral data to estimate chlorophyll concentration have recently been carried out. In the present study, a comparative approach was applied to estimate the concentration of chlorophyll-a at Paldang Dam, South Korea using hyperspectral data. We developed a framework for estimating chlorophyll-a using dimension reduction methods, such as principal component analysis and partial least squares, and various machine learning algorithms. We analyzed hyperspectral data collected during a field survey to locate peaks in the chlorophyll-a spectrum. The framework that used support vector regression achieved the highest R2 of 0.99, a mean square error (MSE) of 1.299 μg/cm3, and showed a small discrepancy between observed and real values relative to other frameworks. These findings suggest that by combining hyperspectral data with dimension reduction and a machine learning algorithm, it is possible to provide an accurate estimation of chlorophyll-a. Using this, chlorophyll-a can be obtained in real time through hyperspectral sensor data input from drones or unmanned aerial vehicles using the learned machine learning algorithm. [ABSTRACT FROM AUTHOR]

Odor is a very serious problem worldwide. Thus, odor prediction research has been conducted consistently to help prevent odor. Odor substances that are complex odors are known, but complex odors and odor substances do not have a linear dependence. In addition, depending on the combination of odor substances, the causal relationships, such as synergy and antagonism, are different for complex odors. Research is needed to know this, but the situation is incomplete. Therefore, in this study, research was conducted through data-based research. The complex odor was predicted using various machine learning methods, and the effect of odor substances on the complex odor was verified using an explainable artificial intelligence method. In this study, according to the Malodor Prevention Act in Korea, complex odors are divided into two categories: acceptable and unacceptable. Analysis of variance and correlation analysis were used to determine the relationships between variables. Six machine learning methods (k-nearest neighbor, support vector classification, random forest, extremely randomized tree, eXtreme gradient boosting, and light gradient boosting machine) were used as predictive classification models, and the best predictive method was chosen using various evaluation metrics. As a result, the support vector machine that performed best in five out of six evaluation metrics was selected as the best model (f1-score = 0.7722, accuracy = 0.8101, sensitivity = 0.7372, specificity = 0.8656, positive predictive value = 0.8196, and negative predictive value = 0.8049). In addition, the partial dependence plot method from explainable artificial intelligence was used to understand the influence and interaction effects of odor substances. [ABSTRACT FROM AUTHOR]

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment. [ABSTRACT FROM AUTHOR]

Diatoms have traditionally been used to assess water quality; however, current research suggests that physical factors, such as habitat and landscape, may be linked to the organization of diatom assemblages in streams. The aim of this study was to determine the environmental factors affecting the physiological and ecological changes of epilithic diatoms. To this end, the dominant diatom species were used, and a strategy based on epilithic diatom habitat characteristics was investigated for river restoration. The classification and regression tree (CART) and weighted averaging (WA) regression models were used to determine the habitat preference of epilithic diatoms and physicochemical environmental factors. The 35 environmental variables and six epilithic diatom species that were dominant at 58 sites along the Han River in South Korea were used. The species abundance and composition of adherent diatoms were affected by nutrient concentration and a variety of physicochemical environmental factors. These results suggest that when evaluating water quality, various factors that affect the abundance of epilithic diatoms should be considered. Research on the autecological characteristics and environmental preferences of indicator diatom species could help establish river restoration policies and quantitative evaluation criteria for biological assessments. [ABSTRACT FROM AUTHOR]

The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and the method of imputing missing data was found to be more effective. Then, in order to recommend a suitable model, we created a total of 126 models (missing imputation: single imputation, multiple imputations, K-nearest neighbor imputation; data preprocessing: standardization, principal component analysis, partial least square; and predictive method: multiple regression, machine learning, deep learning) and compared them using R2 and mean absolute error (MAE) values. Finally, we investigated variable importance using the best prediction model. The results identified the best model as a combination of multivariate imputation using Bayesian ridge as the missing imputation method, standardization for data preprocessing, and an extremely randomized tree as the predictive method. Among the odor compounds, Methyl mercaptan, acetic acid, and dimethyl sulfide were identified as the most important odor compounds in predicting complex odors. [ABSTRACT FROM AUTHOR]

Maintaining rice yield, soil function, and fertility are essential components of long-term compost fertilization. However, paddy fields are major sources of anthropogenic methane emissions. The aim of the study is to evaluate the changes in soil chemical properties and their concurrent impact on the abundance of methanogenesis (mcrA) and methane oxidation (pmoA) related genes among compost (Com), NPK+Compost (NPKCom), and unfertilized (NF) fallow paddy fields under long-term compost fertilization. Results showed that compost and NPK+Compost fertilization altered the soil chemical properties of paddy fields with a significant increase in the functional gene abundance potentially associated with Methanobacteriaceae for mcrA (1.23 × 106 to 3.84 × 106 copy number g−1 dry soil) and methane oxidizing bacteria such as Methylomonas and Methylobacter for pmoA (1.65 × 106 to 4.3 × 106 copy number g−1 dry soil). Ordination plots visualized these changes, where treatments clustered distinctly indicating that Com and NPKCom treatments were characterized by paddy soils with elevated OM, TN, K and P content and higher abundances of methanogenesis and methane oxidation related genes. The study showed that long-term compost fertilization resulted in paddy fields with high nutrient content and high gene abundance, attributed to methanogens and methane oxidizing bacteria that responded well with compost fertilization. These results indicated the potential of these fallow paddy fields for methane emission and methane oxidation and that they are 'primed', potentially influencing subsequent paddy field responses to long-term compost application. [ABSTRACT FROM AUTHOR]

Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZIG model) and the zero-inflated log-normal regression model (ZILN model) for the semi-continuous measurements. The models are employed for the evaluation of the railway accidents on Korea Railroad, considering the accident damages, such as the train delay time, the number of trains delayed and the cost of considering the accident count responses, for the period 2008 to 2016. From the results obtained, we found that the human-related factors, the high-speed railway system or the Korea Train Express (KTX) and the number of casualties, are the main cost-escalating factors. The number of trains delayed and the amount of delay time tend to increase both the probability of incurring costs and the amount of cost. For better evaluation, the railway accident data should contain accurate information with less recurrence of zeros. [ABSTRACT FROM AUTHOR]

Featured Application: Hard shoulder running (HSR) has been increasingly used in recent years as a more sustainable way to alleviate congestion and increase the efficiency of freeways in many countries. We found that freeways experienced more crashes after installing HSR, but most of the crashes were less severe. Hence, caution should be exercised when designing and implementing hard shoulder running to reflect the trade-off between improvements in efficiency and a reduction in safety. Also, HSR installation on lengthy freeways with two travel lane configurations (without HSR) should be avoided. Hard shoulder running (HSR) has been increasingly used as a sustainable and viable way to increase road capacity. This study investigated the safety effect of HSR on freeways in South Korea using the empirical Bayes method. This study found an increase in the total number of crashes. In terms of crash severity, a higher proportion of crashes (25.3%) on 2(3)-lane sections were found to be serious (involving injuries and/or fatalities) compared to those on 4(5)-lane sections (3.6%). Also, a positive relationship was found between the length of the hard shoulder running and changes in crash frequencies. Thus, hard shoulder running on lengthy 2(3)-lane freeways should be avoided. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is being implemented in many areas of medicine, such as patient-customized diagnosis. Growth in the artificial intelligence medical device (AIMD) field is expected in the coming years. Major countries are currently establishing systems and policies to gain a leading position in the medical artificial intelligence market. The Republic of Korea has initiated the Act on Nurturing the Medical Devices Industry and Supporting Innovative Medical Devices for the development of AIMDs and is implementing it preemptively. As a result, the country has achieved an effective strategy for coping with the COVID-19 pandemic, an increase in the number of AIMD approvals (85 approved as of September 2021), and the creation of a document pertaining to internationally harmonized guidelines on AIMD-related terms and definitions. However, in order to develop and activate more AIMD products, it is necessary to improve post-market management such as product change and quality control in addition to approval. Here, we review the current regulatory status of AIMD in the Republic of Korea and what needs to be improved for AIMD to be more developed and activated. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist's performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases. [ABSTRACT FROM AUTHOR]

The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models' practical applicability. [ABSTRACT FROM AUTHOR]

It is essential to monitor water quality for river water management because river water is used for various purposes and is directly related to the health and safety of a population. Proper network installation and removal is an important part of water quality monitoring and network operation efficiency. To do this, cluster analysis based on calculated similarity between measuring stations can be used. In this study, we measured the similarities between 12 water quality monitoring stations of the Bukhan River. River water quality data always have a station-dependent time lag because water flows from upstream to downstream; therefore, we proposed a Dynamic Time Warping (DTW) algorithm that searches for the minimum distance by changing and comparing time-points, rather than using the Euclidean algorithm, which compares the same time-point. Both Euclidean and DTW algorithms were applied to nine water quality variables to identify similarities between stations, and K-medoids cluster analysis were performed based on the similarity. The Clustering Validation Index (CVI) was used to select the optimal number of clusters. Our results show that the Euclidean algorithm formed clusters by mixing mainstream and tributary stations; the mainstream stations were largely divided into three different clusters. In contrast, the DTW algorithm formed clear clusters by reflecting the characteristics of water quality and watershed. Furthermore, because the Euclidean algorithm requires the lengths of the time series to be the same, data loss was inevitable. As a result, even where clusters were the same as those obtained by DTW, the characteristics of the water quality variables in the cluster differed. The DTW analysis in this study provides useful information for understanding the similarity or difference in water parameter values between different locations. Thus, the number and location of required monitoring stations can be adjusted to improve the efficiency of field monitoring network management. [ABSTRACT FROM AUTHOR]

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices. [ABSTRACT FROM AUTHOR]

Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), and Long–Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1–step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold–out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1–step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance. [ABSTRACT FROM AUTHOR]

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices. [ABSTRACT FROM AUTHOR]

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS. [ABSTRACT FROM AUTHOR]

Microalgal–bacterial consortia are considered an alternative method to conventional wastewater treatment processes with several benefits, such as low oxygen production cost and reduced emission of carbon dioxide resulting from photosynthetic activity. Besides, microalgae effectively remove various emerging contaminants and heavy metals that are hardly removed by conventional wastewater treatment processes. The purpose of this study is finding optimal operation conditions (e.g., light wavelengths, light intensity, microalgal–bacterial consortia biomass) when applying microalgae in wastewater treatment system. Firstly, reduced transmittance was monitored at four different wavelengths (i.e., blue, green, red, and white light) and at various concentrations of microalgal–bacterial consortia. Light transmittance rates were rapidly reduced as the biomass increased, where the highest transmittance was observed in green light. Secondly, the reduction of oxygen production over time, by the inhibition of the photosynthetic activity, was tested as the light intensity increased at four different wavelengths and at low (100 mg L−1) and high (500 mg L−1) concentrations of microalgal–bacterial consortia. The observations and subsequent statistical analyses verify that microalgal–bacterial consortia show the strongest resistance to the inhibition of the photosynthetic activity in green light, with white coming next, when the intensity of light is increased. [ABSTRACT FROM AUTHOR]

Pairs of ionic group dependence of the structure of a complex coacervate core micelle (C3M) in an aqueous solution was investigated using DLS, cryo-TEM, and SANS with a contrast matching technique and a detailed model analysis. Block copolyelectrolytes were prepared by introducing an ionic group (i.e., ammonium, guanidinium, carboxylate, and sulfonate) to poly(ethylene oxide-b-allyl glycidyl ether) (NPEO = 227 and NPAGE = 52), and C3Ms were formed by simple mixing of two oppositely-charged block copolyelectrolyte solutions with the exactly same degree of polymerization. All four C3Ms are spherical with narrow distribution of micelle dimension, and the cores are significantly swollen by water, resulting in relatively low brush density of PEO chains on the core surface. With the pair of strong polyelectrolytes, core radius and aggregation number increases, which reflects that the formation of complex coacervates are significantly sensitive to the pairs of ionic groups rather than simple charge pairing. [ABSTRACT FROM AUTHOR]