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To solve the problem of slow convergence seen in the traditional fine alignment algorithm based on linear Kalman filtering, a forward–forward backtracking fine alignment algorithm for SINS is proposed after reanalyzing the fine alignment model in this paper. First, the forward–forward backtracking fine alignment model in initial navigation frame was derived. The displacement vector of the carrier in the initial navigation frame solved by GNSS positioning was utilized as the observation of the fine alignment model. Second, under the premise of storing only part of the navigation data, the initial alignment convergence speed was improved by backtracking and reusing the navigation data. The experimental results of the simulation and vehicle tests showed that each backtracking alignment can improve the accuracy of the fine alignment to the performance requirements of the initial alignment, which proved the effectiveness and feasibility of the backtracking fine alignment algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson's feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600–700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with R 2 = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages. [ABSTRACT FROM AUTHOR]

Active layer morphology optimization is a widely employed and effective method to enhance the photovoltaic performance of organic solar cells. In this study,D18:L8-BO bulk heterojunction organic solar cells were prepared, and chloroform was utilized as the primary solvent and halogen-free toluene was used as the secondary solvent to optimize the active layer morphology by regulating the solvent evaporation process. Morphological characterization indicates that the optimized volume ratio of chloroform : toluene significantly improves the molecule stacking and domain purity of the donor and acceptor materials, and rational phase separation facilitates exciton dissociation and charge transfer. Electrical property characterization of the transient photovoltage/transient photocurrent, open-circuit voltage and short-circuit current density dependence on light intensity, and the relationship between net photocurrent density and effective voltage indicate that the chloroform : toluene solvent effectively suppresses carrier recombination as well as greatly improves charge transport in the active layer. The proposed strategy provides a practical pathway to improve the power conversion efficiency obviously from 17.89% to 18.92% for the D18:L8-BO organic solar cells. The results highlight the important role of solvent evaporation dynamics in determining the active layer morphology and photovoltaic performance of organic solar cells. Without additives or other complicated processing, the solvent strategy not only contributes to the preparation of more efficient organic photovoltaic devices, but also plays important roles in the large-scale commercialization of organic solar cells. [ABSTRACT FROM AUTHOR]

Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves' chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R 2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring. [ABSTRACT FROM AUTHOR]

The pathogenesis of irritable bowel syndrome (IBS) is multifactorial, characterized in part by increased intestinal permeability, and visceral hypersensitivity. Increased permeability is associated with IBS severity and abdominal pain. Tenapanor is FDA-approved for the treatment of IBS with constipation (IBS-C) and has demonstrated improvements in bowel motility and a reduction in IBS-related pain; however, the mechanism by which tenapanor mediates these functions remains unclear. Here, the effects of tenapanor on colonic pain signaling and intestinal permeability were assessed through behavioral, electrophysiological, and cell culture experiments. Intestinal motility studies in rats and humans demonstrated that tenapanor increased luminal sodium and water retention and gastrointestinal transit versus placebo. A significantly reduced visceral motor reflex (VMR) to colonic distension was observed with tenapanor treatment versus vehicle in two rat models of visceral hypersensitivity (neonatal acetic acid sensitization and partial restraint stress; both P < 0.05), returning VMR responses to that of nonsensitized controls. Whole cell voltage patch-clamp recordings of retrogradely labeled colonic dorsal root ganglia (DRG) neurons from sensitized rats found that tenapanor significantly reduced DRG neuron hyperexcitability to capsaicin versus vehicle (P < 0.05), an effect not mediated by epithelial cell secretions. Tenapanor also attenuated increases in intestinal permeability in human colon monolayer cultures caused by incubation with proinflammatory cytokines (P < 0.001) or fecal supernatants from patients with IBS-C (P < 0.005). These results support a model in which tenapanor reduces IBS-related pain by strengthening the intestinal barrier, thereby decreasing permeability to macromolecules and antigens and reducing DRG-mediated pain signaling. NEW & NOTEWORTHY: A series of nonclinical experiments support the theory that tenapanor inhibits IBS-C-related pain by strengthening the intestinal barrier. Tenapanor treatment reduced visceral motor responses to nonsensitized levels in two rat models of hypersensitivity and reduced responses to capsaicin in sensitized colonic nociceptive dorsal root ganglia neurons. Intestinal permeability experiments in human colon monolayer cultures found that tenapanor attenuates increases in permeability induced by either inflammatory cytokines or fecal supernatants from patients with IBS-C. [ABSTRACT FROM AUTHOR]

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson's correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season. [ABSTRACT FROM AUTHOR]