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The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB's three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component's error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network. [ABSTRACT FROM AUTHOR]

The role of spatial factors in reducing carbon emissions has been receiving increasing attention from researchers; however, these impacts may involve spatial heterogeneity. In this study, 337 prefecture-level cities in China were taken as the research object. Based on national-level urban data, the global impact of urban spatial form on carbon emissions was then investigated using ordinary least squares regression, the spatial error model, and the spatial lag model. The local effects of urban spatial form on carbon emissions in different cities were then investigated using geographically weighted regression. The findings are as follows. Overall, the larger the urban built-up area and the more fragmented and decentralized the urban land use, the greater the carbon emissions. Conversely, the more centralized the urban center of a city, the lower its carbon emissions. Locally, for some Chinese cities, the total area, landscape shape index, and mean Euclidean nearest-neighbor distance were found to have significant positive effects on carbon emissions, while the largest-patch index had a significant negative impact. For all Chinese cities, the patch density was found to have no significant effect on carbon emissions. In 29% of the cities in which the landscape division index was found to significantly affect carbon emissions, this effect was positive, while it was negative in the remaining 71%. The policy implications emerging from this study lie in the need for decision-makers and urban planners to guide the shaping of low-carbon urban spatial forms. [ABSTRACT FROM AUTHOR]

Craniofacial bone defects induced by congenital malformations, trauma, or diseases frequently challenge the orthodontic or restorative treatment. Stem cell‐based bone regenerative approaches emerged as a promising method to resolve bone defects. Microenvironment physical cues, such as the matrix elastic modulus or matrix topography, regulate stem cell differentiation via multiple genes. We constructed gelatin methacryloyl (GelMA), a well‐known scaffold, to investigate the impact of elastic modulus on osteogenic differentiation in a three‐dimensional environment. Confocal microscope was used to observe and assess the condensates fission and fusion. New bone formation was evaluated by micro‐computed tomography at 6 weeks in calvarial defect rat. We found that the light curing increased elastic modulus of GelMA, and the pore size of GelMA decreased. The expression of osteogenic markers was inhibited in hBMSCs cultured in the low‐elastic‐modulus GelMA. In contrast, the expression of YAP, TAZ and TEAD was increased in the hBMSCs in the low‐elastic‐modulus GelMA. Furthermore, YAP assembled via liquid–liquid phase separation (LLPS) into condensates that were sensitive to 1′6‐hexanediol. YAP recruit TAZ and TEAD4, but not RUNX2 into the condensates. In vivo, we also found that hBMSCs in high‐elastic‐modulus GelMA was more apt to form new bone. This study provides new insight into the mechanism of osteogenic differentiation. Reagents that can regulate the elastic modulus of substrate or LLPS may be applied to promote bone regeneration. [ABSTRACT FROM AUTHOR]

The vibration signal of a pump is often used for analysis in the study of hydraulic-pump fault diagnosis methods. In this study, for the analysis, sound signals were used, which can be used to acquire data in a non-contact manner to expand the use scenarios of hydraulic-pump fault-diagnosis methods. First, the original data are denoised using complete ensemble empirical mode decomposition with adaptive noise and the minimum redundancy maximum relevance algorithm. Second, the noise-reduced data are plotted as mean spectrogram bar graphs, and the datasets are divided. Third, the training set graphs are input into the ResNet-50 network to train the base model for fault diagnosis. Fourth, all the layers of the base model are frozen, except for the fully connected and softmax layers, and the support set graphs are used to train the base model through transfer learning. Finally, a fault diagnosis model is obtained. The model is tested using data from two test pumps, resulting in accuracies of 86.1% and 90.8% and providing evidence for the effectiveness of the proposed method for diagnosing faults in hydraulic plunger pumps. [ABSTRACT FROM AUTHOR]