We present a comparative study of CO and polycyclic aromatic hydrocarbon (PAH) emission toward a region including the massive star-forming regions of NGC 6334 and NGC 6357. We use the NANTEN 12CO(J=1{0) data and the AKARI 9 m All-Sky diffuse map in order to evaluate the calibration accuracy of the AKARI data. We conrm that the overall CO distribution shows a good spatial correspondence with the PAH emission, and their intensities exhibit a good power-law correlation with a spatial resolution down to 4′ over the region of 10◦10◦. We also reveal poorer correlation for small scale structures between the two quantities toward NGC 6357, due to strong UV radiation from local sources. Larger scatter in the correlation toward NGC 6357 indicates higher ionization degree and/or PAH excitation than that of NGC 6334.

Six sigma has been the most influential management innovation tool in order to achieve the customer’s satisfaction and keep the competition in the age of limitless competition. The success in six sigma is to find the correct CTQ (Critical to Quality). QFD

Six sigma has been the most influential management innovation tool in order to achieve the customer's satisfaction and keep the competition in the age of limitless competition. The success in six sigma is to find the correct CTQ(Critical to Quality). QFD(Quality Function Deployment) is the efficient too ever created to tie product and service design decisions directly to customer wants and needs. One of the mistakes in QFD is to analyze using an inconsistent HOQ(House of Quality) chart. An inconsistent HOQ chart is one in which the information from the correlation matrix is inconsistent with that from the relationship matrix. This study presents the consistency check and inconsistency check in case of failing the consistency check. Also we propose the procedures using the Limiting Probability in correlation matrix and the Median Rank in relationship matrix in order to be consistent in HOQ chart.

This study evaluates the quality of surface air temperature, relative humidity, and precipitation detection observed by 22 internet of thing (IoT)-based mini-weather stations in Seoul in 2020 summer. The automatic weather station (AWS) closest to each IoT-based station is used as reference. The IoT-based observations show surface air temperature and relative humidity are about 0.2-4.0°C higher and about -1--22% lower than the AWS observations, respectively. However, they exhibit temporal variability similar to the AWS observations on both diurnal and daily time scales, with daily correlations greater than 0.90 for temperature and 0.82 for relative humidity. Given these strong linear relationships, it show that temperature and relative humidity biases can be effectively corrected by applying a simple bias correction method. For IoT-based precipitation detection, we found that precipitation conductivity value (PCV) during precipitation events is well separated from that during non-precipitation events, providing a basis for distinguishing precipitation events from non-precipitation events. When the PCV threshold is set to 250 for precipitation detection, the highest critical success index and the bias score index close to one, suitable for operational precipitation detection, are obtained. These results demonstrate that IoT-based mini-weather stations can successfully measure surface air temperature, relative humidity, and precipitation detection with appropriate bias corrections.