Browsing by Author "Lu B"

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A leap of faith that echoes well? The value impact of Chinese firms starting up business overseas
(Elsevier Inc, 2023-08) Lu B; Hao W; Liao J; Wongchoti U
We investigate the impact of greenfield outward foreign direct investment (GODI) by Chinese firms on their subsequent Tobin's Q. Our findings indicate that Chinese listed companies from 2003 to 2019 generally experience a significantly positive boost in perceived firm value (or growth prospects) when engaging in overseas business start-ups (i.e., with no foreign partners) when compared to their inactive peers. The positive GODI effect is more prominent among privately owned enterprises vs. state-owned enterprises (SOEs). Our mechanism tests indicate that lowered effective tax rates and reduced illiquidity due to conducting greenfield ODI serve as the value-enhancing channels. Possibly driven by political objectives, SOEs tend to prioritize developing and Belt-Road countries as the destination for their greenfield overseas endeavors.
A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data
(MDPI (Basel, Switzerland), 2023-05-09) Dehghan-Shoar MH; Pullanagari RR; Kereszturi G; Orsi AA; Yule IJ; Hanly J; Berger K; Croft H; Liu T; Lu B; Yin D
The increasing number of satellite missions provides vast opportunities for continuous vegetation monitoring, crucial for precision agriculture and environmental sustainability. However, accurately estimating vegetation traits, such as nitrogen concentration (N%), from Landsat 7 (L7), Landsat 8 (L8), and Sentinel-2 (S2) satellite data is challenging due to the diverse sensor configurations and complex atmospheric interactions. To address these limitations, we developed a unified and physically based method that combines a soil–plant–atmosphere radiative transfer (SPART) model with the bottom-of-atmosphere (BOA) spectral bidirectional reflectance distribution function. This approach enables us to assess the effect of rugged terrain, viewing angles, and illumination geometry on the spectral reflectance of multiple sensors. Our methodology involves inverting radiative transfer model variables using numerical optimization to estimate N% and creating a hybrid model. We used Gaussian process regression (GPR) to incorporate the inverted variables into the hybrid model for N% prediction, resulting in a unified approach for N% estimation across different sensors. Our model shows a validation accuracy of 0.35 (RMSE %N), a mean prediction interval width (MPIW) of 0.35, and an R (Formula presented.) of 0.50, using independent data from multiple sensors collected between 2016 and 2019. Our unified method provides a promising solution for estimating N% in vegetation from L7, L8, and S2 satellite data, overcoming the limitations posed by diverse sensor configurations and complex atmospheric interactions.
Human-Machine Function Allocation Method for Submersible Fault Detection Tasks
(MDPI (Basel, Switzerland), 2024-11-19) Yang C; Pang L; Wu W; Cao X; Lu B; Piao M
The operation and support (OS) officer is responsible for buoyancy regulation and fault detection of onboard equipment in the civil submersible. The OS officer carries out the above tasks through the human-machine interface (HMI) of a submersible buoyancy regulation and support (SBRS) system. However, the OS officer often faces uneven task frequency produced by fault tasks, which leads to an unbalanced mental workload and individual failures. To address this issue, we proposed a human-machine function allocation method based on level of automation (LOA) taxonomy and submersible task complexity (STC), aimed at improving human-machine cooperation in submersible fault detection tasks. Based on this method, we identified the LOA2 as the optimal human-computer function allocation scheme. In this study, three measurement techniques (subjective scale, work performance, and physiological status) were used to test 15 subjects to validate the effectiveness of the proposed optimal human-machine function allocation scheme. The GAMM test results also indicate that the proposed optimal human-machine function allocation scheme (LOA2) can improve the work performance of the operating system officials under low or high workloads and reduce the subjective workload.