Replication Data for: Critical Mass Condition of Majority Bureaucratic Behavioral Change in Representative Bureaucracy: A Theoretical Clarification and A Nonparametric Exploration

Replication Data for: Critical Mass Condition of Majority Bureaucratic Behavioral Change in Representative Bureaucracy: A Theoretical Clarification and A Nonparametric Exploratio

Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS

Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves

To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS

Estimation of Anthocyanins in Leaves of Trees with Apple Mosaic Disease Based on Hyperspectral Data

Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient (R2) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology

Facile and Controllable Ultrasonic Nebulization Method for Fabricating Ti3C2Tx‐Based Strain Sensor and Monitoring of Human Motion and Sound Wave

Abstract Flexible and wearable electronic devices hold great potential in electronic skins, health monitoring systems and soft robotics. Among them, flexible strain sensors with high performance are key components for wearable health monitoring devices. However, the facile and controllable preparation of highly sensitive sensors still faces significant challenges. By virtue of excellent conductivity of 2D transition metal carbids (MXenes), this work reports a facile and low‐cost fabrication strategy for large‐scale production of strain sensors. The sensitive layer is deposited on flexible interdigital electrodes by ultrasonic nebulization of Ti3C2Tx nanosheets. By controlling the nebulization time, different thicknesses of Ti3C2Tx films has a great influence on the performance of strain sensors. The Ti3C2Tx‐based strain sensor exhibits good sensing performances such as high GF (19.1) in the low strain range (≈0.25%–1.14%), short response time (0.7 s), and stable durability (over 1000 cycles). In practice, the potential applications of the strain sensor in sound frequency detection, human physiological signal monitoring and facial expression recognition are demonstrated. Finally, this work integrates the strain sensor with a miniaturized analyzer to assemble a wearable motion monitoring device for mobile healthcare. This study provides a facile strategy for fabricating flexible strain sensors in the field of wearable electronics

Deep learning from “passive feeding” to “selective eating” of real-world data

Abstract Artificial intelligence (AI) based on deep learning has shown excellent diagnostic performance in detecting various diseases with good-quality clinical images. Recently, AI diagnostic systems developed from ultra-widefield fundus (UWF) images have become popular standard-of-care tools in screening for ocular fundus diseases. However, in real-world settings, these systems must base their diagnoses on images with uncontrolled quality (“passive feeding”), leading to uncertainty about their performance. Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images are transferred to the subsequent AI diagnostic system (“selective eating”). In three independent datasets from different clinical institutions, the DLIFS performed well with sensitivities of 96.9%, 95.6% and 96.6%, and specificities of 96.6%, 97.9% and 98.8%, respectively. Furthermore, we show that the application of our DLIFS significantly improves the performance of established AI diagnostic systems in real-world settings. Our work demonstrates that “selective eating” of real-world data is necessary and needs to be considered in the development of image-based AI systems