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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.

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.

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.

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.

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.

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.