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Our previous study using 41 tree rings of one Abies alba Mill. disc indicated that the superellipse equation can accurately fit its tree-ring shape. This study further used the superellipse equation (xan+yβn=1 ) to model the geometries of 1090 tree rings of A. alba discs collected from five sites in Denmark. The adjusted root-mean-square-error (RMSEadj) was calculated to assess the goodness of fit between observed and predicted tree-ring boundaries. The results showed that RMSEadj ranged between 0.0038 and 0.0591, with a mean value of 0.0141. This verified that the superellipse equation sufficiently describes the A. alba tree-ring shape. In the polar coordinate system, the superellipse equation can be expressed as r=a(cosφn+sinφ/kn)−1/n. Where r and φ are the polar radius and polar angle, respectively. k=β/a, where a and β are the major and minor semi-axes of the superellipse. The mean value of k was 0.95, 94% of tree rings had k-values between 0.90 and 1.00, and only 67 tree rings had k-values between 0.71 and 0.90. n-value ranged from 1.62 to 2.81, with an average value of 2.04. 59% of the tree rings had n-values between 1.90 and 2.10, and 62% showed n-values greater than 2.0. This means that most tree rings are a hyperellipse approached to an ellipse. Sites with different soil moisture conditions influenced the size but not the shape of tree rings. This study verified that the tree-ring shape of A. alba tends to be bilaterally symmetric and hyperellipse approached ellipse. Its variation was reflected more in inter-annual differences in k- and n-values.

A viable pathway towards achieving shared prosperity is made possible by the growth of tourism, which encourages the movement of urban and rural elements. This harmonious alignment of tourism development and urban–rural integration also helps to narrow the gap between urban and rural areas. This study uses a set of panel regression models to investigate whether tourism growth promotes urban–rural integration within 41 cities in the Yangtze River Delta Region of China from 2010 to 2020. The findings show that the effect of tourism development on urban–rural integration is significantly positive, displaying significant heterogeneity across various times and city sizes. Furthermore, tourism development exhibits a threshold effect and city-type heterogeneity concerning urban–rural integration. The effect demonstrates significant continuous growth along with the expansion of economic growth. However, the impact of tourism development on urban–rural integration demonstrates a distinct promotional threshold effect, and its positive effect appears to be weakened.

This paper proposes a high-sensitivity microstrip differential sensor for measuring the complex permittivity of liquids. The prototype of the differential sensor was formed by cascading two LC resonators on a microstrip transmission line based on stepped impedance. A strong electric field was found to be distributed in the circular patch of the LC resonator; therefore, a cylindrical micropore was set in the center of the circular LC resonator to measure the dielectric sample, which maximized the disturbance of the dielectric sample on the sensor. By optimizing the size of the circular LC resonator, a high-sensitivity sensor circuit was designed and manufactured. The complex permittivity of the test sample was calculated by measuring the transmission coefficient of different molar concentrations of ethanol–water solutions. The experimental results show that the designed differential sensor can accurately measure the complex permittivity of liquid materials with an average sensitivity of 0.76%.

In this paper, we propose a user-friendly integral inequality to study the coupled parabolic chemotaxis system with singular sensitivity under the Neumann boundary condition. Under a low diffusion rate, the classical solution of this system is uniformly bounded. Our proof replies on the construction of the energy functional containing ∫Ω|v|4v2 with v>0. It is noteworthy that the inequality used in the paper may be applied to study other chemotaxis systems.

While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and inshore ships, the traditional methods are unable to achieve effective detection for inshore ships. To improve the detection performance of inshore ships, we propose a novel saliency enhancement algorithm based on the difference of anisotropic pyramid (DoAP). Considering the limitations of IoU in small-target detection, we design a detection framework based on the proposed Bhattacharyya-like distance (BLD). First, the anisotropic pyramid of the SAR image is constructed by a bilateral filter (BF). Then, the differences between the finest two scales and the coarsest two scales are used to generate the saliency map, which can be used to enhance ship pixels and suppress background clutter. Finally, the BLD is used to replace IoU in label assignment and non-maximum suppression to overcome the limitations of IoU for small-target detection. We embed the DoAP into the BLD-based detection framework to detect inshore ships in large-scale SAR images. The experimental results on the LS-SSDD-v1.0 dataset indicate that the proposed method outperforms the basic state-of-the-art detection methods.

Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar’s author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.