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Purposes The sparse feature map established by using VSLAM cannot be directly used for navigation, and the existing methods of converting sparse feature maps into grid maps for navigation suffer from low accuracy of map construction, resulting in the failure to achieve accurate navigation. An improved ORB-SLAM3 navigation method is proposed based on multi-convex combined maximum correntropy criterion (MCMCC), which can realize the construction, positioning, and navigation of indoor real-time mapping on the embedded device with only a RGB-D camera. Methods The proposed method uses the back-end optimization algorithm based on MCMCC to combat the influence of non-Gaussian noises and to further improve the mapping accuracy of ORB-SLAM. Subsequently, in the process of converting the sparse feature map into a grid map, the grid map is updated with the loop detection information to improve the grid map accuracy for accurate navigation. Findings Furthermore, simulation and real scene experiments have been carried out to verify the effectiveness of the proposed method.

In the field of collaborative visual simultaneous localization and mapping (CVSLAM), efficient data communication poses a significant challenge, particularly in environments with limited bandwidth. To address this issue, we introduce a method aimed at reducing communication consumption. Our approach starts with a strategic culling of map points, aiming at maximizing pose-visibility and expanding spatial diversity to effectively eliminate redundant data in CVSLAM. We achieve this by formulating the problem of maximizing pose-visibility and spatial diversity as a minimum-cost maximum-flow graph optimization problem. Subsequently, we apply finite state entropy encoding for the compression of visual information, further alleviating bandwidth constraints. To verify the proposed method, we implement it within a centralized collaborative monocular simultaneous localization and mapping (SLAM) system. Our approach has been tested on publicly available datasets and in real-world scene. The results show a prominent reduction in bandwidth usage by 49% while maintaining mapping accuracy and without introducing additional latency, confirming its effectiveness in a multi-agent system setting. [ABSTRACT FROM AUTHOR]

In order to obtain a strawberry fruit wine with strong aroma and typical flavor, the non-fermentative yeast, Torulaspora delbrueckii (Td), was sequentially co-fermented with four commercial wine yeasts (D254, K1, F15, and Aq) at a 1:1 inoculation ratio.The effects of the four mixed fermentation combinations on the physicochemical properties, volatile aromatic compounds, and consumer preferences of strawberry wines were evaluated using SPME-GC-MS and a 9-point hedonic scale.The results show that the co-fermentation of Td with different wine yeasts significantly affects the color and aroma components of strawberry wine.Sequential inoculation with Td and Aq or D254 resulted in a wine with a higher redness and saturation.For aroma, a total of 67 volatile compounds, mainly esters, alcohols and acids, were identified in the four strawberry wines.Based on the Venn diagram analysis, 19 aroma compounds were common to all four wine samples, with the Td+D254 combination producing the highest number of aroma compounds (48).The principal component analysis of major aroma compounds (ROAV>0.1) revealed that the volatile active compounds produced by Td+K1 group and Td+D254 group were similar, and closely related to octanoic acid, nonanal, and S-(-) -2-methyl-1-butanol, etc.The consumer preference test showed that the Td+D254 group of strawberry wine had strong fruity, refreshing, and floral aromas, achieving the highest preference score 6.7±1.2.Overall, the findings suggest that the combined fermentation of Td and Saccharomyces cerevisiae D254 has the potential to enhance the aromatic qualities of strawberry wine. [ABSTRACT FROM AUTHOR]

This study focuses on the different impacts of platform training and learning by doing on gig workers' platform income. Based on survey data of China's delivery riders on the platform in 2020, via quantitative methods combined with the case study, it is found that the platform training is negatively correlated with riders' incomes, while learning by doing is positively correlated with their incomes. Workers with a high level of platform-income dependence earn more than those with an average level of dependence under the same platform training, or learning by doing. Overall, the incomes of the former are significantly lower than those of the latter, where the difference is mainly due to unobservable factors. Both platform training and learning by doing significantly reduce the income gap. In addition, the instrumental variable and the propensity score matching approaches are introduced to handle the endogeneity problem, and robust results are obtained. Plain language summary: Influence of learning by doing and platform training on gig workers' incomes This study looks at how two different ways of learning affect gig workers' earnings on platforms. We used data from a survey of delivery riders in China in 2020. We found that training provided by the platform tends to lower riders' earnings, while learning from actual work experience tends to increase earnings. Riders who rely more on their platform income earn more than those who don't, even after training or learning by doing. Both types of learning help reduce the income gap. We used special methods to make sure our results are accurate. [ABSTRACT FROM AUTHOR]

Root biomass and length growth, functional traits, and their responses to soil nutrient availability are crucial for resource acquisition under environmental change. Previous studies have focused on the response of root morphological, architectural, and chemical traits to fertilization, while less attention has been given to root biomass and length growth, as well as mycorrhizal symbiosis, impeding a full understanding of root resource acquisition strategies. Here, using a nutrient addition experiment (control, inorganic, and organic nutrients), we explored the responses of function-based root (absorptive fine roots [AFRs] versus transport fine roots [TFRs]) growth, functional traits (morphological, architectural, and chemical traits), and mycorrhizal colonization of C. lanceolat and Q. acutissim, which are the dominant tree species in subtropical China. The results showed that the fine root biomass and total root length of AFRs for both tree species basically decreased significantly after nutrient addition, but TFRs responded less sensitively than AFRs. Nutrient addition significantly increased the mycorrhizal colonization rate in C. lanceolata but decreased it in Q. acutissima. The diameter of AFRs for C. lanceolata increased significantly, while the branch ratio and branch intensity decreased significantly in both inorganic nutrients (NPK) and organic nutrients (F); however, the opposite response pattern occurred for Q. acutissima. Fine root biomass, total root length, and root nutrient concentration exhibited the most plastic responses to changes in nutrient availability. The magnitude of the plastic response of fine root traits was slightly higher in the NPK treatment than in the F treatment. Our findings suggest that the responses of fine root traits and mycorrhizal fungi to nutrient addition were species-specific: C. lanceolata depended on mycorrhizal fungi for resource acquisition, while Q. acutissima could acquire soil nutrient resources by increasing root branching. The contrasting nutrient acquisition strategies between tree species may facilitate plant species coexistence and distribution under soil nutrient change. Fine root biomass and total root length emerged as more pivotal indicators for nutrient acquisition strategies compared to morphological traits. [ABSTRACT FROM AUTHOR]