• Journal of Wetlands Research
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• v.24 no.4
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• pp.345-353
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• 2022

A wastewater treatment plant (WWTP) is a major gateway for the engineered nano-particles (ENPs) entering the water bodies. However existing studies have reported that many WWTPs exceed the No Observed Effective Concentration (NOEC) for ENPs in the effluent and thus they need to be designed or operated to more effectively control ENPs. Understanding and predicting ENPs behaviors in the unit and \the whole process of a WWTP should be the key first step to develop strategies for controlling ENPs using a WWTP. This study aims to provide a modeling tool for predicting behaviors and removal efficiencies of ENPs in a WWTP associated with process characteristics and major operating conditions. In the developed model, four unit processes for water treatment (primary clarifier, bioreactor, secondary clarifier, and tertiary treatment unit) were considered. Additionally the model simulates the sludge treatment system as a single process that integrates multiple unit processes including thickeners, digesters, and dewatering units. The simulated ENP was nano-sized TiO₂, (nano-TiO₂) assuming that its behavior in a WWTP is dominated by the attachment with suspendid solids (SS), while dissolution and transformation are insignificant. The attachment mechanism of nano-TiO₂ to SS was incorporated into the model equations using the apparent solid-liquid partition coefficient (Kd) under the equilibrium assumption between solid and liquid phase, and a steady state condition of nano-TiO₂ was assumed. Furthermore, an MS Excel-based user interface was developed to provide user-friendly environment for the nano-TiO₂ removal efficiency calculations. Using the developed model, a preliminary simulation was conducted to examine how the solid retention time (SRT), a major operating variable affects the removal efficiency of nano-TiO₂ particles in a WWTP.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.4
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• pp.83-89
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• 2020

For the past few decades, as part of efforts to protect the environment where fossil fuels, which have been a key energy resource for mankind, are becoming increasingly depleted and pollution due to industrial development, ecofriendly secondary batteries, hydrogen generating energy devices, energy storage systems, and many other new energy technologies are being developed. Among them, the lithium-ion battery (LIB) is considered to be a next-generation energy device suitable for application as a large-capacity battery and capable of industrial application due to its high energy density and long lifespan. However, considering the growing battery market such as eco-friendly electric vehicles and drones, it is expected that a large amount of battery waste will spill out from some point due to the end of life. In order to prepare for this situation, development of a process for recovering lithium and various valuable metals from waste batteries is required, and at the same time, a plan to recycle them is socially required. In this study, we introduce a nanoscale pattern transfer printing (NTP) process of Li2CO3, a representative anode material for lithium ion batteries, one of the strategic materials for recycling waste batteries. First, Li2CO3 powder was formed by pressing in a vacuum, and a 3-inch sputter target for very pure Li2CO3 thin film deposition was successfully produced through high-temperature sintering. The target was mounted on a sputtering device, and a well-ordered Li2CO3 line pattern with a width of 250 nm was successfully obtained on the Si substrate using the NTP process. In addition, based on the nTP method, the periodic Li2CO3 line patterns were formed on the surfaces of metal, glass, flexible polymer substrates, and even curved goggles. These results are expected to be applied to the thin films of various functional materials used in battery devices in the future, and is also expected to be particularly helpful in improving the performance of lithium-ion battery devices on various substrates.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.2
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• pp.17-28
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• 2022

The application of supplemental activated biochar pellet fertilizers (ABPFs) was evaluated by investigating key factors such as changes of surface paddy water and soil chemical properties and rice growth responses during the growing season. The treatments consisted of control, activated rice hull biochar pellet (ARHBP-40%), and activated palm biochar pellet (APBP-40%) applications. It was shown that the lowest NH₄⁺-N and PO₄^--P concentrations were observed in surface paddy water to the ARHBP-40%, while the NH₄⁺-N concentration in the control was abruptly decreased until 30 days after transplant in the soil. However, the lowest NH₄⁺-N concentration in the blended biochar application was 9.18 mg L^-1 at 1 day of transplant, but its ABPFs application was observed to be less than 1 mg L^-1 at 56 days after transplant. The lowest PO₄^--P concentration in paddy water treated ARHBP-40% ranged from 0.06 mg L^-1 to 0.08 mg L^-1 until 30 days after transplant among the treatments. For the paddy soil, the NH₄⁺-N concentration in the control was abruptly decreased from 177.7 mg kg^-1 to 49.4 mg kg^-1, while NO₃^--N concentration was highest, 13.2 mg kg^-1 in 14 days after transplant. The P₂O₅ concentrations in the soils increased from rice transplants until the harvesting period regardless of the treatments. The highest K₂O concentration was 252.8 mg kg^-1 in the APBP-40% at 84 days after transplant. For the rice growth responses, plant height in the control was relatively high compared to others, but grain yield was not significantly different between the control and ARHBP-40%. The application of ARHBP-40% can minimize nitrogen and phosphorous application rates into the agro-ecosystem.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Korean Journal of Organic Agriculture
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• v.22 no.4
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• pp.731-742
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• 2014

In the current study, we investigated effects of a combination of earthworm casting, environment-friendly by-product fertilizer, and cultivation soil of Gymnocalycium mihanovichii in a heavy fertilizing culture on diameter, height, numbers of tubercles, and chemical properties of soil thereby elucidating optimal mixture ratio for securing production as well as providing nutrients throughout cultivation period. The Gymnocalycium mihanovichii var 'Ihong', one of grafted cactus for export (Rootstock: 9 cm, Scion: $1.5{\times}1.3cm$ grafted cactus) was cultured in plastic houses of Agricultural Technology Center located in Naegok-dong, Seocho-gu, Seoul from June, 2013 through December, 2013. For the control group, a mixture of sand and fertilizer (50:50) was used as this ratio is widely utilized in farmhouses. In contrast, a variety mixtures of sand and earthworm casting that was produced with food wastes was compared; the mixture ratios were 80:20, 60:40, 40:60, 20:80, and 0:100 and pH for these mixtures were found to be similar each other (ranging between 7.1 and 7.4) which is in an appropriate range (pH 6.5-7.5) for cultivation of G. mihanovichii. The organic content was increasing along with increasing contents of earthworm casting ratio while it was lower than the treatment practice group (32-43 mg/kg vs. 55 mg/kg). The content of exchangeable cation was also increasing as the ratio of earthworm casting was elevated; although levels of $K^+$, $Na^+$, and $Mg^{2+}$ were lower than the treatment practice group, the level of $Ca^{2+}$ was higher ($9.1cmol^+/kg$ and $11.5-33.7cmol^+/kg$ in the treatment practice group and the earthworm casting group, respectively). Three months after grafting, diameters of G. mihanovichii were compared with the control group; consequently, there was a significant difference noted in between the earthworm casting group and the control group (31.39 mm vs. 32.46-37.59 mm). After 5 months, growth characteristics of G. mihanovichii were evaluated. Similarly, the diameter of G. mihanovichii was significantly increasing in the group with higher ratio of earthworm casting treatment (32.63 mm vs. 32.49-37.59 mm). The height of tubercles was 2.63 mm in the control group while it was significantly elevating along with the ratio of earthworm casting mixture. The more numbers of tubercles, the more incomes for farm-houses; as results, higher mixture ration of earthworm casting resulted more numbers of tubercles compared to the control group (2.7 vs. 3.2-8.3 ea). In particular, in the earthworm casting groups with 80% and 100% ratios, the numbers of tubercles were 6.2 and 8.3 ea, respectively, which is 2.5 times more than those of the control group. These results indicate that earthworm casting treatment may be utilized in G. mihanovichii farming houses for short term production of tubercles. In the group with 40% and 60% of earthworm casting mixture, the numbers of tubercles were found to be 4.5 and 4.8 ea, respectively which is higher than the control group as well; in these groups, there were no issues with soil drainage as well as moss formation. Given the analysis results of growth characteristics of G. mihanovichii, it was concluded that 40% and 60% of earthworm casting mixture might be the optimal ratios.