The motivation for this study stems from the United Nations Sustainable Development Goals (UN-SDGs) and their impact by 2030. The UN highlights 17 SDGs that address pertinent local and global issues, one of which-SDG-10-has been devoted to reducing inequality. This study investigates the nexus between trade openness, foreign direct investment (FDI), and income inequality in sub-Saharan Africa using panel data from 2000 to 2015 and the generalized method of moment (GMM) technique approach. The findings show that FDI and income have a negative, statistically significant relationship with income inequality, signifying that as FDI and income per capita increase, the level of income inequality decreases. However, trade openness, education, political stability, corruption, and rule of law have a positive, statistically significant relationship with inequality. This study, therefore, offers some recommendations that will help policymakers. First, develop good policies to attract more foreign investors, which will contribute to creating employment opportunities in the region. Second, create more infrastructures to provide good quality education. Third, implement a good policy to motivate local production which will contribute to creating jobs. Fourth, build a strong institution(s) to fight against corruption.

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Abstract. This paper aimed to establish the model of detecting straw composition rapidly based on OSC (Orthogonal Signal Correction) straw pretreatment. The study select soybean straw as research subjects, building predictive models for its main ingredient, namely, cellulose. Compared to the traditional denoising method respectively, calibration set model processed by the second derivative + smoothing and OSC has significantly higher determination coefficients. Applying OSC-PLS compared to the second derivative-smoothing denoising resulted in removal of non-correlated variation in spectra and improved interpretative ability of variation. Meanwhile, analysis and convergence velocity has improved significantly.

Orthogonal signal correction (OSC)and partial least squares(PLS)were used during the pretreatment of straw to reduce environmental noise and prediction models were established for near-infrared detection of straw cellulose. Tests were run with soybean stalk as the object of study. Test results indicated that compared to a model established using a traditional denoising method, the determination coefficients for calibration set models established by second derivative+smoothing and OSC were 0.9318595 and 0.9328905 respectively while the root mean square error for calibration (RMSEC) were 0.6762902 and 0.6696454. For an OSC-PLS regression model with a factor of 8, the relative standard deviation of a prediction model was less than 5%. In the OSC denoising process, the root mean square error fluctuated with the increasing number of PLS factors. Compared to the second derivative-smoothing denoising, OSC-PLS denoising removed the non-correlated variation from spectra and improved interpretation ability of variation while the analysis and convergence were expedited. It was therefore concluded that OSC-PLS denoising could be used to realize the rapid and accurate near-infrared detection of straw cellulose.