Blockchain technology is changing conventional online transaction systems by eliminating payment gateway firms. The blockchain technology is highly attractive and has earned a lot of attention from investors and firms. To protect blockchain technology, firms acquire a patent of blockchain for enhancing the value of their blockchain technology. However, the sustainable value for a patent of blockchain has not been clearly explored. For this reason, our research attempted to explore the relationship between a patent of blockchain and firm value. We used a real options theory and built robust empirical tests based on United State Patents and Trademark Office (USPTO) data. We collected the patents of blockchains from 2014 to 2018 and matched financial data from the Compustat database. In total, we found 153 panel observations. Our results suggest that a firm’s patent of blockchain originality and t-1 lagged effects for a firm’s patent of blockchain generality are positively associated with firm value in general. In addition, the sustainable value for the patent of blockchain affects firms differently based on their industry. We found that the sustainable value for the patent of blockchain originality was positively and exclusively associated with the software industry, while the sustainable value for the patent of blockchain generality was positively and exclusively associated with the hardware industry.
Abstract. Low-cost sensors are considered to exhibit great potential to complement classical air quality measurements in existing monitoring networks. However, the use of low-cost sensors poses some challenges. In this study, the behavior and performance of electrochemical sensors for NO and NO2 were determined over a longer operating period in a real-world deployment. After careful calibration of the sensors, based on co-location with reference instruments at a rural traffic site during 6 months and by using robust linear regression and random forest regression, the coefficient of determination of both types of sensors was high (R2 > 0.9), and the root mean square error (RMSE) of NO and NO2 sensors was about 6.8 and 3.5 ppb, respectively, for 10 min mean concentrations. The RMSE of the NO2 sensors, however, more than doubled when the sensors were deployed without recalibration for a 1-year period at other site types (including urban background locations), where the range and the variability of air pollutant concentrations differed from the calibration site. This indicates a significant effect of relocation of the sensors on the quality of their data. During deployment, we found that the NO2 sensors are capable of distinguishing general pollution levels, but they proved unsuitable for accurate measurements, mainly due to significant biases. In order to investigate the long-term stability of the original calibration, the sensors were reinstalled at the calibration site after deployment. Surprisingly, the coefficient of determination and the RMSE of the NO sensor remained almost unchanged after more than 1 year of operation. In contrast, the performance of the NO2 sensors clearly deteriorated as indicated by a higher RMSE (about 7.5 ppb, 10 min mean concentrations) and a lower coefficient of determination (R2 = 0.59).
Abstract. Low-cost sensors are considered as exhibiting great potential to complement classical air quality measurements in existing monitoring networks. However, the use of low-cost sensors poses some challenges. In this study, the behavior and performance of electrochemical sensors for NO and NO2 were determined over a longer operating period in a real-world deployment. After careful calibration of the sensors, based on co-location with reference instruments at a rural traffic site during six months and by using robust linear regression and random forest regression, the coefficient of determination of both types of sensors were high (R2 > 0.9) and the root mean square error (RMSE) of NO and NO2 sensors were about 6.8 ppb and 3.5 ppb, respectively, for 10-minute mean concentrations. The RMSE of the NO2 sensors, however, more than doubled, when the sensors were deployed without re-calibration for a one-year period at other site types (including urban background locations), where the range and the variability of air pollutant concentrations differed from the calibration site. This indicates a significant effect of the re-location of the sensors on the quality of their data. During deployment, we found that the NO2 sensors are capable of distinguishing general pollution levels, but they proved unsuitable for accurate measurements, mainly due to significant biases. In order to investigate the long-term stability of the original calibration, the sensors were re-installed at the calibration site after deployment. Surprisingly, the coefficient of determination and the RMSE of the NO sensor remained almost unchanged after more than one year of operation. In contrast, the performance of the NO2 sensors clearly deteriorated as indicated by a higher RMSE (about 7.5 ppb, 10-minute mean concentrations) and a lower coefficient of determination (R2 = 0.59).
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