Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market

Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models. The forecasts are combined to improve forecasting performance. Experimental results on the Korean film market show that the forecasting accuracy in early screening periods can be significantly improved by considering competition. In addition, WOM has a stronger influence on total box office forecasting. Considering both competition and WOM improves forecasting performance to a larger extent than when only one of them is considered

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area

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Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.clos

Distribution of Heavy Metals and Organic Compounds: Contamination and Associated Risk Assessment in the Han River Watershed, South Korea

Given water pollution increases in aquatic ecosystems resulting from industrialization and rapid urbanization, appropriate treatment strategies to alleviate water pollution are crucial. The spatiotemporal distribution, sources, and potential risk of heavy metals and organic compounds were determined in surface water from the Han River watershed (n = 100) in wet and dry seasons. The inductively coupled plasma mass spectrometer (Cr and As), mercury analyzer (Hg), and ultra-high performance liquid chromatography tandem mass spectrometer (organic compounds) were used to analyze the target compounds. Total concentration and detection frequency were in the order: Cr (2.375 µg/L, 100%) > As (1.339 µg/L, 100%) > Hg (0.007 µg/L, 100%) for heavy metals, and carbofuran (0.051 µg/L, 75%) > bisphenol A (0.040 µg/L, 47%) > quinoline (0.020 µg/L, 32%) for organic compounds. The target compounds showed the highest concentration in the area near industrial facilities. High concentrations and risk levels of all target compounds, except quinoline, were observed during the wet season. Principal component analysis indicated anthropogenic activities were the primary source of pollution. Cr showed the most prominent environmental impact in the wet season, suggesting its ecological risk. Additional monitoring is required for clear risk pollutant assessments in aquatic ecosystems to aid policy implementation

Assessment of Water Quality Target Attainment and Influencing Factors Using the Multivariate Log-Linear Model in the Nakdong River Basin, Republic of Korea

Because identifying the factors affecting water quality is challenging, water quality assessment of an individual component based on the arithmetic mean method cannot adequately support management policies. Therefore, in this study, we assessed the water quality target attainment at 24 sites in the Nakdong River Basin by applying multivariate log-linear models to identify factors influencing water quality, including flow and season. The temporal and seasonal water quality trend and flow were also analyzed using the calculated model coefficients. Specifically, weekly data on biological oxygen demand (BOD), total phosphorous (TP), and flow during 2013–2018 were used to investigate the 2018 water quality target attainment level for this river. The significance and suitability of the models were analyzed using the F-test, root mean squared error (RMSE), mean absolute percent error (MAPE), and adjusted R2 values. All 24 models applied in this study showed statistical significance and suitability for the prediction of BOD and TP concentrations. Moreover, flow was identified as the main factor affecting water quality and had a predominant effect on BOD and TP concentrations in tributaries and the main stream, respectively. Furthermore, among the 24 sites, BOD and TP targets were evidently attained at 18 and 17 sites, respectively

qPCR-Based Monitoring of 2-Methylisoborneol/Geosmin-Producing Cyanobacteria in Drinking Water Reservoirs in South Korea

Cyanobacteria can exist in water resources and produce odorants. 2-Methylisoborneol (2-MIB) and geosmin are the main odorant compounds affecting the drinking water quality in reservoirs. In this study, encoding genes 2-MIB (mic, monoterpene cyclase) and geosmin (geo, putative geosmin synthase) were investigated using newly developed primers for quantitative PCR (qPCR). Gene copy numbers were compared to 2-MIB/geosmin concentrations and cyanobacterial cell abundance. Samples were collected between July and October 2020, from four drinking water sites in South Korea. The results showed similar trends in three parameters, although the changes in the 2-MIB/geosmin concentrations followed the changes in the mic/geo copy numbers more closely than the cyanobacterial cell abundances. The number of odorant gene copies decreased from upstream to downstream. Regression analysis revealed a strong positive linear correlation between gene copy number and odorant concentration for mic (R2 = 0.8478) and geo (R2 = 0.601). In the analysis of several environmental parameters, only water temperature was positively correlated with both mic and geo. Our results demonstrated the feasibility of monitoring 2-MIB/geosmin occurrence using qPCR of their respective synthase genes. Odorant-producing, gene-based qPCR monitoring studies may contribute to improving drinking water quality management

Evaluation of Organic Matter Contribution Using Absorbance and Chromatographic Parameters in Lake Paldang, Republic of Korea

Organic matter in lakes is categorized into allochthonous organic matter, such as leaves and sewage effluent, and autochthonous organic matter, generated by microorganisms within the water system. In this study, organic matter composition was analyzed using UV-vis spectroscopy and liquid chromatography-organic carbon detection (LC-OCD). Several allochthonous natural organic matter substances were collected including leaves, green leaves, forest soils, and paddy soils. The organic matter composition analysis in our study sites revealed that humic substances comprised the highest proportion (36.5–42.3%). Also, individual samples at each site exhibited distinct characteristics. This study used a humic substance-diagram (HS-diagram) and principal component analysis (PCA) to trace the sources affecting the river water quality and identify their origins. The humic substances of soil origin predominantly influenced the water quality, with the impact of organic matter significantly pronounced during the July rainfall period. Compared with the PCA results, the contribution of the humic substance (HS, 48.9%) and building block (BB, 42.0%) indices appeared higher between June and July in summer, likely due to non-degradable substances released by heavy rain. In fall, the contribution of low molecular weight neutrals increased from 71.2% to 85.2%, owing to a humic substance influx and decomposition. This study demonstrated the application of estimating the relative contributions of source materials in lakes utilized for drinking and agricultural water to identify sources, aiding in the development of efficient watershed management plans

Water Quality, Source Identification, and Risk Assessment of Heavy Metals Using Multivariate Analysis in the Han River Watershed, South Korea

This study aimed to investigate the irrigation water quality, major pollution sources, and human health hazards by focusing on heavy metal concentrations in the surface water of the Han River watershed, South Korea that supplies water for consumption and irrigation. Here, Mn was found to have the highest mean concentration, with the maximum concentration recorded at IH-2. The heavy metal concentrations were higher during summer and fall than that during spring. The mean concentration of heavy metals was relatively high in the downtown area (1.8 times) and downstream of the wastewater treatment facilities (1.3 times), indicating that the wastewater treatment facilities (WTFs) may be the primary source of pollution. Water at most of the sites were found to be suitable for irrigation. However, the sodium absorption ratio and soluble sodium percentage indicated that IH-2 was unsuitable. The results of the principal component analysis indicated that anthropogenic (vehicle and industrial) activities were the primary sources of metal pollution. Ingestion was identified as the primary exposure pathway in terms of health risks. However, the hazard quotients and hazard index for all pathways were below the safety limit (<1) for children and adults

A genetic characterization of Korean waxy maize (Zea mays L.) landraces having flowering time variation by RNA sequencing

Maize is the second-most produced crop in the Korean peninsula and has been continuously cultivated since the middle of the 16th century, when it was originally introduced from China. Even with this extensive cultivation history, the diversity and properties of Korean landraces have not been investigated at the nucleotide sequence level. We collected 12 landraces with various flowering times and performed RNA-seq in the early vegetative stage. The transcriptomes of 12 Korean landraces have been analyzed for their genetic variations in coding sequence and genetic relationships to other maize germplasm. The Korean landraces showed specific genetic characteristics and were closely related to a Chinese inbred line. Flowering-time related gene profiles pointed to multiple causes for the variation of flowering time within Korean landraces; the profiles revealed significant positive and negative correlations among genes, allowing us to infer possible mechanisms for flowering time variation in maize. Our results demonstrate the value of transcriptome-based genetic and gene expression profiles for information on possible breeding resources, which is particularly needed in Korean waxy landraces.Y