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Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification, however, there is little research on suitable ConvFilters for sound classification. Therefore, we compare the performances of three convolution filters, 1D-ConvFilter, 3×1 2D-ConvFilter, and 3×3 2D-ConvFilter, in two different network configurations, when classifying mosquitoes using audio data. In insect sound classification, most machine learning researchers use only audio data as input. However, a classification model, which combines other information such as activity circadian rhythm, should intuitively yield improved classification results. To utilize such relevant additional information, we propose a method that defines this information as a priori probabilities and combines them with CNN outputs. Of the networks, VGG13 with 3×3 2D-ConvFilter showed the best performance in classifying mosquito species, with an accuracy of 80.8%. Moreover, adding activity circadian rhythm information to the networks showed an average performance improvement of 5.5%. The VGG13 network with 1D-ConvFilter achieved the highest accuracy of 85.7% with the additional activity circadian rhythm information.

Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.

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.

Salinization of agricultural lands, particularly rice paddies, results in the drastic decline of crop yields. Soil salinization impacts the plant physiology by inducing salt stress which may leads to osmotic stress, ionic stress and water-related nutrient imbalance. These imbalances necessitate the need for plants to produce osmolytes including proline and glycine betaine. This study aimed to elucidate the dynamic changes in proline and glycine betaine accumulation modulated by the inoculation of Brevibacterium linens RS16 in salt-sensitive and moderately salt-tolerant rice plants under salt stress conditions. This study showed the interaction of four major factors including rice genotypes with differing tolerance to salt stress, length of exposure to salt stress, level of salt stress and effects of inoculation. Salt stress resulted in significant reduction in plant growth parameters with the salt-sensitive rice genotype (IR29) having a more significant growth reduction. Both the salt-sensitive and salt-tolerant rice genotypes increased in total proline and glycine betaine accumulation at 3 days and 10 days after subjecting under 50 mM and 150 mM salt stress conditions. A significant increase in proline and glycine betaine was observed in the salt-sensitive genotype after 10 days under 50 mM and 150 mM salt stress conditions. Inoculation of the rice genotypes with B. linens RS16 resulted in the improvement of plant growth parameters in both rice genotypes, and total proline and glycine betaine accumulation, especially in IR29. This study showed that proline and glycine betaine are compatible osmolytes of rice under salt stress, and that inoculation of rice genotypes with B. linens RS16 mediated salt tolerance through improvement of plant growth parameters and proline and glycine betaine accumulation in rice plants.

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.

The tight regulation of local auxin homeostasis and signalling maxima in xylem precursor cells specifies the organising activity of the vascular cambium and consequently promotes xylem differentiation and wood formation. However, the molecular mechanisms underlying the local auxin signalling maxima in the vascular cambium are largely unknown. Here, we reveal that brassinosteroid (BR)-activated WALLS ARE THIN1 (WAT1) facilitates wood formation by enhancing local auxin signalling in the vascular cambium in Solanum lycopersicum. Growth defects and low auxin signalling readouts in the BR-deficient tomato cultivar, Micro-Tom, were associated with a novel recessive allele, Slwat1-copi, created by the insertion of a retrotransposon in the last exon of the SlWAT1 locus. Molecular and genetic studies by generating the gain-of-function and loss-of-function tomato mutants revealed that SlWAT1 is a critical regulator for fine tuning local auxin homeostasis and signalling outputs in vascular cambium to facilitate secondary growth. Finally, we discovered that BR-regulated SlBZR1/2 directly activated downstream auxin responses by SlWAT1 upregulation in xylem precursor cells to facilitate xylem differentiation and subsequent wood formation. Our data suggest that the BR-SlBZR1/2-WAT1 signalling network contributes to the high level of auxin signalling in the vascular cambium for secondary growth.

Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.

In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of ‘large’ particles (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document}≥0.5 μm) in the slurry, which is considerably larger in size than the main abrasives (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx $$\end{document}≈ 0.1 μm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument’s operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.

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&ndash, Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1&ndash, 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&ndash, 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&ndash, step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance.

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.

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.

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.

The prediction of anaerobic digestion (AD) performance using numerical models, which are based on mathematics and kinetics, is being challenged by poor mechanistic understanding and the non-linear relationships between performance and operational parameters. This study demonstrated that various machine learning (ML) models using the 1-step ahead with the retraining method, which utilized AD performance data from prior states, can improve the prediction accuracy of ML models. For the four types of ML models studied, the 1-step ahead with the retraining method could improve the root mean square errors by 32–49% compared to the conventional multi-step ahead method, which was particularly noteworthy during the transition period when AD reactors were faced with loading shocks and showed inhibited methane yields. Moreover, the 1-step ahead with the retraining method showed the potential of achieving accurate predictions using a single input parameter, pH, which was considerably less labor-intensive to monitor than the other parameters often required in AD models (e.g., VSS). As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.

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.

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.

Microalgal&ndash, 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&ndash, 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&ndash, 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&minus, 1) and high (500 mg L&minus, 1) concentrations of microalgal&ndash, bacterial consortia. The observations and subsequent statistical analyses verify that microalgal&ndash, 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.

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.

The effects of hydrodynamics and alum dose on particle growth were investigated by monitoring particle counts in a rapid mixing process. Experiments were performed to measure the particle growth and breakup under various conditions. The rapid mixing scheme consisted of the following operating parameters: Velocity gradient (G) (200-300 s -1 ), alum dose (10-50 mg/L) and mixing time (30-180 s). The Poisson regression model was applied to assess the effects of the doses and velocity gradient with mixing time. The mechanism for the growth and breakup of particles was elucidated. An increase in alum dose was found to accelerate the particle count reduction. The particle count at a G value of 200 s -1 decreased more rapidly than those at 300 s -1 . The growth and breakup of larger particles were more clearly observed at higher alum doses. Variations of particles due to aggregation and breakup of micro-flocs in rapid mixing step were interactively affected by G, mixing time and alum dose. Micro-flocculation played an important role in a rapid mixing process.

Sensitivity and uncertainty analysis of contaminant fate and transport modeling have received considerable attention in the literature. In this study, our objective is to elucidate the uncertainty pertaining to micropollutant modeling in the sediment-water column interface. Our sensitivity analysis suggests that not only partitioning coefficients of metals but also critical stress values for cohesive sediment affect greatly the predictions of suspended sediment and metal concentrations. Bayesian Monte Carlo is used to quantify the propagation of parameter uncertainty through the model and obtain the posterior parameter probabilities. The delineation of periods related to different river flow regimes allowed optimizing the characterization of cohesive sediment parameters and effectively reducing the overall model uncertainty. We conclude by offering prescriptive guidelines about how Bayesian inference techniques can be integrated with contaminant modeling and improve the methodological foundation of uncertainty analysis. Sensitivity and uncertainty analysis was performed for sediment-metal modeling.Suspended sediment predictions are sensitive to critical erosion stress.Sediment bed-water partitioning coefficient is critical for metal predictions.River flow dynamics affect contaminant fate and model parameter sensitivity.Strategies to improve uncertainty analysis of sediment-metal modeling are discussed.

Summary The recent increase in demand for performance-driven and outcome-based transportation planning makes accurate and reliable performance measures essential. Vehicle miles traveled (VMT), the total miles traveled by all vehicles on roadways, has been utilized widely as a proxy for traffic impact assessment, vehicle emissions, gasoline consumption, and crashes. Accordingly, a number of studies estimate VMT using diverse data sources. This study estimates VMT in the urban area of Bucheon, South Korea, by predicting the annual average daily traffic for unmeasured locations using spatial interpolation techniques (i.e., regression kriging and linear regression). The predictive performance of this method is compared with that of the existing Highway Performance Monitoring System (HPMS) method. The results show that regression kriging could provide more accurate VMT estimates than the HPMS method and linear regression, especially with a small sample size. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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&rsquo, practical applicability.

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

The sdu1+ gene encodes Sdu1, a PPPDE superfamily member of deubiquitinating enzymes (DUBs) in Schizosaccharomyces pombe. Sdu1 was previously shown to contain an actual ubiquitin C-terminal hydrolase (UCH) activity using the recombinant plasmid pYSTP which harbors the sdu1+ gene. This work was designed to assess a thermotolerant role of Sdu1 against high incubation temperatures. In the temperature-shift experiments, the S. pombe cells harboring pYSTP grew much better after the shifts to 37˚C and 42˚C, when compared with the vector control cells. After being shifted to 37˚C and 42˚C for 6 h, the S. pombe cells harboring pYSTP contained lower reactive oxygen species (ROS) levels, compared with the vector control cells. The nitric oxide (NO) levels of the S. pombe cells harboring pYSTP were slightly lower than those of the vector control cells in the absence or presence of the temperature shifting. The total glutathione (GSH) levels of the S. pombe cells harboring pYSTP were significantly higher than those of the vector control cells. Total superoxide dismutase (SOD) and GSH peroxidase activities were also higher in the S. pombe cells harboring pYSTP after the temperature shifts than in the vector control cells. In brief, the S. pombe Sdu1 plays a thermotolerant role against high incubation temperature through the down-regulation of ROS and NO and the up-regulation of total GSH content, total SOD and GSH peroxidase activities.

Background and Purpose cAMP as a second messenger stimulates expression of microphthalmia-associated transcription factor (MITF) or the tyrosinase gene in UVB-induced skin pigmentation. Diphenylmethylene hydrazinecarbothioamide (QNT 3-80) inhibits α-melanocyte-stimulating hormone (α-MSH)-induced melanin production in B16 murine melanoma cells but its molecular basis remains to be defined. Here, we investigated the mechanism underlying the amelioration of skin hyperpigmentation by QNT 3-80. Experimental Approach We used melanocyte cultures with raised levels of cAMP and UVB-irradiated dorsal skin of guinea pigs for pigmentation assays. Immunoprecipitation, kemptide phosphorylation, fluorescence analysis and docking simulation were applied to elucidate a molecular mechanism of QNT 3-80. Key Results QNT 3-80 inhibited melanin production in melanocyte cultures with elevated levels of cAMP, including those from human foreskin. This compound also ameliorated hyperpigmentation in vivo in UVB-irradiated dorsal skin of guinea pigs. As a mechanism, QNT 3-80 directly antagonized cAMP binding to the regulatory subunit of PKA, nullified the dissociation and activation of inactive PKA holoenzyme in melanocytes and fitted into the cAMP-binding site on the crystal structure of human PKA under the most energetically favourable simulation. QNT 3-80 consequently inhibited cAMP- or UVB-induced phosphorylation (activation) of cAMP-responsive element-binding protein in vitro and in vivo, thus down-regulating expression of genes for MITF or tyrosinase in the melanogenic process. Conclusions and Implications Our data suggested that QNT 3-80 could contribute significantly to the treatment of skin disorders with hyperpigmented patches with the cAMP-binding site of PKA as its molecular target.

Purpose: The purpose of this study is to develop the statistical test for process capability index   based on mixture normal process.Methods: This study uses Bootstrap method to calculate the approximate P-value for various simulation con-ditions under mixture normal process. Results: This study indicates that our proposed method is effective way to test for process capability index   based on mixture normal process.Conclusion: This study finds out that statistical test for process capability index   based on mixture normal process is useful for real application.Key Words: Process Capability Index, Mixture Normal Distribution, Limiting Distribution, Bootstrap ● Received 30 April 2014, revised 3 June 2014, accepted 11 June 2014Corresponding Author(jjcho@chungbuk.ac.kr) ⓒ 2014, The Korean Society for Quality ManagementThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-Commercial use, distribution, and reproduction i n any medium, provided the original work is properly cited.* 이 논문은 2012년도 충북대학교 학술연구지원 사업의 연구비 지원에 의하여 연구되었음.

In this study, we analyze the effect on the smoking status in the Seoul Metropolitan area using a multilevel logistic model with Community Health Survey data from the Korea Centers for Disease Control and Prevention. Intraclass correlation coefficient (ICC), profiling analysis and two types of predicted value were used to determine the appropriate multilevel analysis level. Sensitivity, specificity, percentage of correctly classified observations (PCC) and ROC curve evaluated model performance. We showed the applicability for multilevel analysis allowed for the possibility that different factors contribute to within group and between group variability using survey data.

This study suggests a measurement error model based on skewed normal distribution instead of normal distribution to identify slope parameter properties in a simple liner regression model. We prove that the slope parameter in a simple linear regression model is underestimated.

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.

In this study, we provide empirical evidence of whether a spatial correlation among mode choices at the TAZ(Traffic Analysis Zone) level exists based on transit smart card data observed in Seoul, Korea. The results show that the areas with a higher probability that passengers choose to take a bus are clustered and that those regions have fewer metro stations than bus stations. We also found that the spatial correlation turned out to be statistically meaningful and provided an opportunity for the potential use of the spatial correlation in modeling mode choices. A reliable spatial interaction would constitute valuable information for transportation agencies in terms of their route planning and scheduling based on the transit smart card data.