The socioeconomic role of guanxi networks among individuals has been widely recorded, yet macro-level analysis has been sparse in empirical research. This research fills that gap by presenting the first nationally representative evidence illustrating the connection between regional guanxi culture and population mobility among cities in China, with a particular focus on instrumental guanxi culture. To quantify guanxi culture, we employ online search indices related to gift giving, a measure which is challenging to capture through traditional survey data. Applying matched prefecture-level data spanning from 2011 to 2019, the panel model reveals a strong negative correlation between a city's instrumental guanxi culture and inbound migration, while sentimental guanxi culture exhibits a positive correlation with inbound mobility. This research not only adds to the existing theories by exploring the macro-level effects of both instrumental and sentimental guanxi practices but also introduces an innovative method for quantifying guanxi culture through big data analysis.

Segmenting dark-field images of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance but rely on a large number of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially underactivated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method, continuous gradient class activation mapping (CAM) and its nonlinear multiscale fusion (continuous gradient fusion CAM). The method redesigns backpropagating gradients and nonlinearly activates multiscale fused heatmaps to generate more fine-grained class activation maps with an appropriate activation degree for different damage site sizes. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms.

Inkjet printing, originally invented for text and pattern printing, is now central to many industrial applications, such as printed electronics, flexible electronics, 3-D printing of mechanical and even biological devices. However, constrained by the droplets’ ejection mechanism, the accuracy of traditional inkjet printing is limited by the size of its orifice, and it is difficult to achieve a volume of droplets at the femtolitre scale, which hinders its further application in the above fields. To this end, we propose the confined interface vibration inkjet printing (CIVIJP) technique, which is capable of printing patterns in a liquid environment with droplet size much smaller than the orifice from which they are dispensed. Here, further systematic study of the mechanism of printing in a liquid environment was carried out with the assistance of a high-speed imaging technique. It is found for the first time that a single pulse stimulation applied on the piezo-ceramic of the inkjet nozzle can trigger damping oscillations of the oil/water interface confined by the orifice, which can last more than 500 $\mathrm {\mu }$s. By adjusting the intensity of single pulse stimulation, the size and quantity of the dispensed droplets can be controlled in a wide range, which is obviously different from traditional droplet ejection in a gaseous environment. This work reveals the underlying physics between the pulse stimulation and the interface behaviours, as well as the physics between the interface behaviours and the size and number of dispensed droplets, enriching the fundamental theory of the inkjet printing in liquid phase.

Streptococcus agalactiae (S. agalactiae) infection is a significant cause of mastitis, resulting in loss of cellular homeostasis and tissue damage. Autophagy plays an essential function in cell survival, defense, and the preservation of cellular homeostasis, and is often part of the response to pathogenic challenge. However, the effect of autophagy induced by S. agalactiae in bovine mammary epithelial cells (bMECs) is mainly unknown. So in this study, an intracellular S. agalactiae infection model was established. Through evaluating the autophagy-related indicators, we observed that after S. agalactiae infection, a significant quantity of LC3-I was converted to LC3-II, p62 was degraded, and levels of Beclin1 and Bcl2 increased significantly in bMECs, indicating that S. agalactiae induced autophagy. The increase in levels of LAMP2 and LysoTracker Deep Red fluorescent spots indicated that lysosomes had participated in the degradation of autophagic contents. After autophagy was activated by rapamycin (Rapa), the amount of p-Akt and p-mTOR decreased significantly, whilst the amount of intracellular S. agalactiae increased significantly. Whereas the autophagy was inhibited by 3-methyladenine (3MA), the number of intracellular pathogens decreased. In conclusion, the results demonstrated that S. agalactiae could induce autophagy through PI3K/Akt/mTOR pathway and utilize autophagy to survive in bMECs.

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

In this work the quantitative and qualitative ability of a kinetic-theory-based two-fluid model (KT-TFM) is assessed in a state of fully periodic sedimentation (fluidization), with a focus on statistically steady, unstable (clustered) states. The accuracy of KT-TFM predictions is evaluated via direct comparison to direct numerical simulation (DNS) data. The KT-TFM and DNS results span a rather wide parameter space: mean-flow Reynolds numbers on the order of 1 and 10, mean solid volume fractions from 0.1 to 0.4, solid-to-fluid density ratios from 10 to 1000 and elastic and moderately inelastic (restitution coefficient of 0.9) conditions. Data from both KT-TFM and DNS display a rich variety of statistically steady yet unstable structures (clusters). Instantaneous snapshots of KT-TFM and DNS demonstrate remarkable qualitative agreement. This qualitative agreement is quantified by calculating the critical density ratio at which the structure transitions from a chaotic, dynamic state to a regular, plug-flow state, with good overall comparisons. Further quantitative assessments of mean and fluctuating velocities show good agreement at high density ratios but weaker agreement at intermediate to low density ratios depending on the mean-flow Reynolds numbers and solid fractions. Deviations of the KT-TFM results from the DNS data were traced to a breakdown in one of the underlying assumptions of the kinetic theory derivation: high thermal Stokes number. Surprisingly, however, even though the low Knudsen number assumption, also associated with the kinetic theory derivation, is violated throughout most of the parameter space, it does not seem to affect the good quantitative accuracy of KT-TFM simulations.

In this work, we investigated the influence of N-polar wet etching on the properties of nitride-based hexagonal pyramids array (HPA) vertical-injection light emitting diodes (V-LEDs). The cathodeluminescence images showed the randomly distribution of hexagonal pyramids with isolated active regions. The transmission electron microscopy images demonstrated the reduced density of threading dislocations. The IQE was estimated by temperature dependence of photoluminescence, which showed 30% increase for HPA V-LEDs compared with broad area (BA) V-LEDs. The improved extraction efficiency was verified by finite difference time domain simulation, which was 20% higher than that of roughened BA V-LEDs. The electrical properties of HPA V-LEDs were measured by conductive atomic force microscopy (CAFM) measurements. HPA V-LEDs exhibited much lower leakage current due to the improved crystal quality.

SnO2@carbon nanofibers were synthesized by a combination of electrospinning and subsequent thermal treatments in air and then in argon to demonstrate their potential use as an anode material in lithium ion battery applications. The as-prepared SnO2@carbon nanofibers consist of SnO2 nanoparticles/nanocrystals encapsulated in a carbon matrix and contain many mesopores. Because of the charge pathways, both for the electrons and the lithium ions, and the buffering function provided by both the carbon encapsulating the SnO2 nanoparticles and the mesopores, which tends to alleviate the volumetric effects during the charge/discharge cycles, the nanofibers display a greatly improved reversible capacity of 420 mAh/g after 100 cycles at a constant current of 100 mA/g, and a sharply enhanced reversible capacity at higher rates (0.5, 1, and 2 C) compared with pure SnO2 nanofibers, which makes it a promising anode material for lithium ion batteries.

The hypothetical wurtzite structure chromium chalcogenides were investigated through first-principle calculation within density-functional theory. All compounds are predicted to be true half-metallic ferromagnets with an integer Bohr magneton of 4 μB per unit. Their half-metallic gaps are 1.147, 0.885, and 0.247 eV at their equilibrium volumes for wurtzite-type CrM (M = S, Se, and Te), respectively. The half-metallicity can be maintained even when volumes are expanded by more than 20% for all compounds and compressed by more than 20%, 20%, and 5%, for CrS, CrSe, and CrTe, respectively.