Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network

Exposure to particulate materials (PM) is known to cause respiratory and cardiovascular diseases. Respirable particles generated in closed spaces, such as underground parking garages (UPGs), have been reported to be a potential threat to respiratory health. This study reports the concentration of pollutants (PM, TVOC, CO) in UPGs under various operating conditions of heating, ventilation and air-conditioning (HVAC) systems using a real-time monitoring system with a prototype made up of integrated sensors. In addition, prediction of the PM concentration was implemented using modeling from vehicle traffic volumes and an artificial neural network (ANN), based on environmental factors. The predicted PM concentrations were compared with the level acquired from the real-time monitoring. The measured PM10 concentrations of UPGs were higher than the modeled PM10 due to short-term sources induced by vehicles. The average inhalable and respirable dosage for adult was calculated for the evaluation of health effects. The ANN predicted PM concentration showed a close correlation with measurements resulting in R2 ranging from 0.69 to 0.87. This study demonstrates the feasibility of the use of the air quality monitoring system for personal-exposure to vehicle-induced pollutant in UPGs and the potential application of modeling and ANN for the evaluation of the indoor air quality

Multi-Mode Compact Microscopy for High-Contrast and High-Resolution Imaging

We report a multi-mode compact microscope (MCM) for high-contrast and high-resolution imaging. The MCM consists of two LED illuminations, a magnification lens, a lift stage, and a housing with image processing and LED control boards. The MCM allows multi-modal imaging, including reflection, transmission, and higher magnification modes. The dual illuminations also provide high-contrast imaging of various targets such as biological samples and microcircuits. The high dynamic range (HDR) imaging reconstruction of MCM increases the dynamic range of the acquired images by 1.36 times. The microlens array (MLA)-assisted MCM also improves image resolution through the magnified virtual image of MLA. The MLA-assisted MCM successfully provides a clear, magnified image by integrating a pinhole mask to prevent image overlap without additional alignment. The magnification of MLA-assisted MCM was increased by 3.92 times compared with that of MCM, and the higher magnification mode demonstrates the image resolution of 2.46 μm. The compact portable microscope can provide a new platform for defect inspection or disease detection on site

Human Inhalation Exposure to Aerosol and Health Effect: Aerosol Monitoring and Modelling Regional Deposited Doses

Since poor air quality affects human health in the short and long term, much research has been performed on indoor and outdoor aerosol exposure; however, there is a lack of specific data on the exposure and health risks of inhalable aerosols that contain bioaerosol in different environments of human life. To investigate the potential exposure to inhalable aerosols (in the monitoring of particulate matter (PM) based on R modeling, variations of PM depend on the ventilation system and bioaerosols based on size distribution) in various environments, the special viability and culturability of bioaerosols and their deposition doses in the respiratory system were evaluated. We conducted exposure assessments on inhalable aerosols in various indoor environments (childcare facilities, schools, commercial buildings, elderly and homes). The fractions of PM (PM10, PM4 and PM2.5) were investigated and, for the bioaerosol, the viability, culturability, inhalation daily dose and the deposited dose of the aerosol in the respiratory system were calculated to evaluate the human health effects. For two years, the distribution of the indoor PM concentration was high in all PM fractions in schools and commercial buildings, and low in the elderly and at homes. For airborne bacteria, the highest concentrations were shown in the childcare facility during the four seasons, while airborne fungi showed high concentrations in the buildings during the spring and summer, which showed significant differences from other investigated environments (between the buildings and elderly and homes: p < 0.05). The viability and culturability for the bioaerosol showed no significant difference in all environments, and the correlation between inhalable PM and bioaerosol obtained from the six-stage impactor showed that the coefficient of determination (R2) between coarse particles (PM10–2.5, the size of stage 2–3) and cultivable airborne bacteria ranged from 0.70 (elderly and homes) to 0.84 (school) during the summer season

Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination (R2) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid

Compact SnO2/Mesoporous TiO2 Bilayer Electron Transport Layer for Perovskite Solar Cells Fabricated at Low Process Temperature

Charge transport layers have been found to be crucial for high-performance perovskite solar cells (PSCs). SnO2 has been extensively investigated as an alternative material for the traditional TiO2 electron transport layer (ETL). The challenges facing the successful application of SnO2 ETLs are degradation during the high-temperature process and voltage loss due to the lower conduction band. To achieve highly efficient PSCs using a SnO2 ETL, low-temperature-processed mesoporous TiO2 (LT m-TiO2) was combined with compact SnO2 to construct a bilayer ETL. The use of LT m-TiO2 can prevent the degradation of SnO2 as well as enlarge the interfacial contacts between the light-absorbing layer and the ETL. SnO2/TiO2 bilayer-based PSCs showed much higher power conversion efficiency than single SnO2 ETL-based PSCs

Genetic Relationships of Carcass Traits with Retail Cut Productivity of Hanwoo Cattle

This study aimed to estimate genetic correlation between carcass grading and retail productivity traits and to estimate the correlated response on retail productivity traits through selection for carcass grading traits in order to assess the efficacy of indirect selection. Genetic parameters were estimated with the data from 4240 Hanwoo steers using mixed models, and phenotypes included carcass weight (CWT), back fat thickness (BFT), eye muscle area (EMA), marbling (MAR), and estimated lean yield percentage (ELP) as the carcass grading traits, and weight and portion of retail cuts (RCW and RCP), trimmed fats (TFW and TFP) and trimmed bones (TBW and TBP) as the lean productivity traits. The CWT had positive genetic correlations with RCW (0.95) and TFW (0.73), but its genetic correlation with RCP was negligible (0.02). The BFT was negatively correlated with RCP (−0.63), but positively correlated with TFW and TFP (0.77 and 0.70). Genetic correlations of MAR with TFW and TFP were low. Among the carcass grading traits, only EMA was positively correlated with both RCW (0.60) and RCP (0.72). The EMA had a relatively strong negative genetic correlation with TFW (−0.64). The genetic correlation coefficients of ELP with RCP, TFW, and TFP were 0.76, −0.90, and −0.82, respectively. These correlation coefficients suggested that the ELP and EMA might be favorable traits in regulating lean productivity of carcass

Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

With the use of batteries increases, the complexity of battery management systems (BMSs) also rises. Thus, assessing the functionality of BMSs and performance of the BMS hardware is of utmost importance. Testing with embedded boards at an early stage of BMS development is a pragmatic approach for developing a BMS because it is cost- and time-efficient and considers hardware performance. In this study, we tested and analyzed the real-time state-of-charge (SOC) estimation using a test platform with limited CPU performance as well as memory resources of the embedded board. We collected battery data on a single-cell basis using a first-order RC equivalent circuit and achieved an accuracy of 95% compared to the measured data obtained using actual battery tests. The SOC estimation method applies the extended Kalman filter (EKF) and unscented Kalman filter (UKF). The experiment was performed on the real-time test platform, with 1%, 2%, and 5% noise in the measurement data. The algorithm complexity and hardware implementation were evaluated in terms of the resources used and processing speed. Although the EKF is cost-effective, its error rate increases by 5% with noise interference. The UKF exhibits high accuracy and noise robustness; however, it has a high resource occupancy

Enhancing Thermal Oxidation Stability of Silver Nanowire Transparent Electrodes by Using a Cesium Carbonate-Incorporated Overcoating Layer

Despite their excellent electrical and optical properties, Ag nanowires (NWs) suffer from oxidation when exposed to air for several days. In this study, we synthesized a Cs carbonate-incorporated overcoating layer by spin-coating and ultraviolet curing to prevent the thermal oxidation of Ag NWs. Cs incorporation increased the decomposition temperature of the overcoating layer, thus enhancing its thermal resistance. The effects of the Cs carbonate-incorporated overcoating layer on the optoelectrical properties and stability of Ag NWs were investigated in detail. The Ag NW electrode reinforced with the Cs carbonate-incorporated overcoating layer exhibited excellent thermal oxidation stability after exposure to air for 55 days at 85 °C and a relative humidity of 85%. The novel overcoating layer synthesized in this study is a promising passivation layer for Ag NWs against thermal oxidation under ambient conditions. This overcoating layer can be applied in large-area optoelectronic devices based on Ag NW electrodes