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Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot's ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site's lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results. [ABSTRACT FROM AUTHOR]

In Rhodobacter capsulatus , hydrogen production is predominantly limited by nitrogenase activity and light conversion efficiency. Modifying related genes can potentially mitigate these limitations. In this study, partial deletions of the mopAB and mdtB genes were performed in the R. capsulatus JL1 (WT) genome. Resultant mutants, JL11 (mopAB - ) and JL15 (mopAB - , mdtB - ), demonstrated favorable growth on MedA in photosynthetic anaerobic conditions. However, the growth of JL12 (mdtB - ) was inhibited. Batch fermentation experiments revealed an augmented hydrogen production in JL15, achieving a remarkable 177.89 ± 9.98 mmol L−1. Furthermore, the light conversion efficiencies of JL15 reached 4.6% and 3.3% under 3 ± 0.5 klux and 7 ± 0.5 klux, respectively. In comparison, JL1 (WT) achieved efficiencies of 3.3% and 1.8% with nitrogenase activities of 6.16 ± 0.11 and 6.90 ± 0.30 nmol min−1·mg−1. These findings highlight the promising potential of co-deletion as a strategy for enhancing hydrogen production. • Double-gene inactivation of mopAB and mdtB was carried out to construct mutant JL15 with improved H 2 production. • The LCE of JL15 was increased up to 83.3% compared with that of wild-type at high light conditon. • The nitrogenease activity 6.90 ± 0.30 nmol min−1·mg−1 of JL15 was increased by 55.4% to JL1 (WT) at high light conditon. [ABSTRACT FROM AUTHOR]

Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot’s ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site’s lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results.

The ability to quickly and accurately determine soil organic matter (SOM) content is critical for effective soil management decisions. Using the colour of soil images captured by smartphones to predict SOM content has emerged as a promising alternative to traditional wet chemistry methods. However, natural environments can present a complex array of light conditions that can compromise the accuracy and consistency of soil image colour acquisition, thus limiting the method's applicability. To address this issue, we propose five colour calibration patterns (C0 (no calibration), C1 (neutral grey), C2 (RGB), C3 (RGBCMY), and C4 (24 colours)), based on a 24-colour standard card. These patterns were used to calibrate the images of 352 original soil samples obtained from smartphones in three different lighting environments - L1 (100–2000 lx), L2 (35,000–40,000 lx), and L3 (75,000–80,000 lx). Random forest models were used to construct predictive models of soil organic matter (SOM) content based on images. Our findings indicate that smartphones exhibit complex spectral response characteristics, which result in poor image accuracy and stability of uncalibrated (C0) images under varying lighting conditions. The uncalibrated (C0) soil images in different lighting environments exhibited high colour difference (∆E mean = 14.11), resulting in poor SOM model sharing performance (R2 mean = 0.52 and RMSE mean = 20.33 g/kg). The use of colour calibration methods reduced the colour difference between soil images (∆E mean = 8.19) and improved the shared accuracy of the model (R2 mean = 0.61 and RMSE mean = 12.00 g/kg). The pattern of colour calibration has a key impact on the performance of the model application. The model sharing accuracy was found to be higher for the same or similar colour calibration pattern combination compared to different combinations of colour calibration patterns. Overall, the richer the colour calibration blocks, the better the model's shared performance. The findings of this research can enhance the application performance of soil attribute prediction models based on the colour of objects captured by smartphones in natural environments. • Differences in light intensity affect the colour stability of smartphone images. • Colour calibration reduces image colour differences in different light for phones. • Colour calibration boosts the shared ability of the attribute prediction model. • Colour calibration patterns with rich colours for better model-sharing ability. [ABSTRACT FROM AUTHOR]