Two-way in-/congruence in three components of paternalistic leadership and subordinate justice: The mediating role of perceptions of renqing

This paper examines the effects of two-way congruences and incongruences between three components of paternalistic leadership, namely, benevolence, morality, and authoritarianism, on overall subordinate justice perceptions. We hypothesize that these dyad in-/congruences would differentially predict subordinate overall justice perceptions, with perceptions of renqing as a mediator. With data collected from two-wave surveys in the People’s Republic of China, the results indicate that dyad congruences and incongruences between benevolence, morality, and authoritarianism have significant impacts on subordinate perceptions of renqing and, ultimately, their overall justice perception. Our findings underscore that to fully understand the influencing processes of paternalistic leadership on subordinate outcomes, it is important to take into account the context and the different combinations of its three dimensions

MEMS Biomimetic Acoustic Pressure Gradient Sensitive Structure for Sound Source Localization

The parasitoid fly Ormia ochracea shows an astonishing localization ability with its tiny hearing organ. A novel MEMS biomimetic acoustic pressure gradient sensitive structure was designed and fabricated by mimicking the mechanically coupled tympana of the fly. Firstly, the analytic representation formulas of the resultant force and resultant moment of the incoming plane wave acting on the structure were derived. After that, structure modal analysis was performed and the results show that the structure has out-of-phase and in-phase vibration modes, and the corresponding eigenfrequency is decided by the stiffness of vertical torsional beam and horizontal beam respectively. Acoustic-structural coupled analysis was performed and the results show that phase difference and amplitude difference between the responses of the two square diaphragms of the sensitive structure are effectively enlarged through mechanical coupling beam. The phase difference and amplitude difference increase with increasing incident angle and can be used to distinguish the direction of sound arrival. At last, the fabrication process and results of the device is also presented

An Improved Fast Affine Motion Estimation Based on Edge Detection Algorithm for VVC

As a newly proposed video coding standard, Versatile Video Coding (VVC) has adopted some revolutionary techniques compared to High Efficiency Video Coding (HEVC). The multiple-mode affine motion compensation (MM-AMC) adopted by VVC saves approximately 15%-25% Bjøntegaard Delta Bitrate (BD-BR), with an inevitable increase of encoding time. This paper gives an overview of both the 4-parameter affine motion model and the 6-parameter affine motion model, analyzes their performances, and proposes improved algorithms according to the symmetry of iterative gradient descent for fast affine motion estimation. Finally, the proposed algorithms and symmetric MM-AMC flame of VTM-7.0 are compared. The results show that the proposed algorithms save 6.65% total encoding time on average, which saves approximately 30% encoding time of affine motion compensation

Large Language Model Guided Reinforcement Learning Based Six-Degree-of-Freedom Flight Control

As artificial intelligence (AI) technology advances rapidly, its increasing involvement in military defense fosters intelligent air combat domain development. The Intelligent Flight Controller (IFC) is a crucial technology and foundation for intelligent air combat decision-making systems. Controlling 6 Degree-of-freedom (DOF) aircraft in close-to-real-world environments requires an adaptable and dynamic decision-making controller. Most IFC researches focus on simplistic flight trajectory design and validation, while air combat requires aircraft that can perform complex tactical maneuvers. Deep reinforcement learning (DRL) provides a suitable technical paradigm. However, DRL suffers from sparse rewards, insufficient supervisory signals, low sampling efficiency, and slow convergence. In contrast, Large Language Model (LLM) possesses abundant knowledge about the real world, contextual understanding, and reasoning capabilities. By leveraging this, LLM can serve as prior knowledge for DRL, thereby reducing DRL training time. This paper proposes an LLM-guided deep reinforcement learning framework for IFC, which utilizes LLM-guided deep reinforcement learning to achieve intelligent flight control under limited computational resources. LLM provides direct guidance during training based on local knowledge, which improves the quality of data generated in agent-environment interaction within DRL, expedites training, and offers timely feedback to agents, thereby partially mitigating sparse reward issues. Additionally, we present an effective reward function to comprehensively balance the aircraft coupling control to ensure stable, flexible control. Finally, simulations and experiments show that the proposed techniques have good performance, robustness, and adaptability across various flight tasks, laying a foundation for future research in the intelligent air combat decision-making domain

Development and Application of a Novel High Precision Six-Axis Force/Torque Sensor

Improving the accuracy of the six-axis force sensor Six-axis force sensors (hereafter collectively referred to as 6a-sensors) is a systematic problem, which mainly involves the optimization and improvement of three aspects: the design of the elastic body of the 6a-sensors, the acquisition and processing of the weak signal, and the decoupling of the inter-dimensional data of the force and torque of each dimension. To attain high stiffness, sensitivity, and minimal coupling, a design scheme of cross beam elastomer based on titanium alloy material is proposed. In order to realize the acquisition and processing of multi-channel weak differential signals with high gain, high linearity and precision, a high-speed programmable weak signal processing circuit design scheme is proposed. To efficiently address the interdependence issue among signal dimensions in the 6a-sensors. This paper innovatively proposes a BP-PSO decoupling algorithm using Particle Swarm Approach (PSO) to optimise the optimiser of BP neural network. To validate the algorithm’s effectiveness, comparative experiments are conducted with the 6a-sensors. designed and studied in this paper by sampling and self-designed calibration system. The sensitivity of the 6a-sensor designed in this paper reaches 4mV/V. In terms of accuracy, there is an improvement of about 1%. In terms of crosstalk, there is an improvement of about 0.2%. providing a new and improved idea for the six-dimensional force decoupling algorithm. In order to further verify the practicability of the algorithm, a flexible surface grinding robot based on six-axis force is developed based on Siasun robot platform. The grinding strength control is accurate, and the running trajectory is uniform and smooth, which can effectively meet the practical application requirements of flexible and intelligent control

Design and Analysis of a New Tuning Fork Structure for Resonant Pressure Sensor

This paper presents a micromachined resonant pressure sensor. The sensor is designed to optimize the sensitivity and reduce the cross-talk between the driving electrodes and sensing electrodes. The relationship between the sensitivity of the sensor and the main design parameters is analyzed both theoretically and numerically. The sensing and driving electrodes are optimized to get both high sensing capacitance and low cross-talk. This sensor is fabricated using a micromachining process based on a silicon-on-insulator (SOI) wafer. An open-loop measurement system and a closed-loop self-oscillation system is employed to measure the characteristics of the sensor. The experiment result shows that the sensor has a pressure sensitivity of about 29 Hz/kPa, a nonlinearity of 0.02%FS, a hysteresis error of 0.05%FS, and a repeatability error of 0.01%FS. The temperature coefficient is less than 2 Hz/°C in the range of −40 to 80 °C and the short-term stability of the sensor is better than 0.005%FS

Ionic liquid surfactant-derived carbon micro/nanostructures toward application of supercapacitors

N-Doped mesoporous hollow carbon spheres (MHCSs) with different inner structures have attracted increasing attention in energy storage devices due to their low density and high electrical conductivity. Herein, vesicles formed by the molecular self-assembly of the ionic liquid surfactant derived from its unique properties (e.g. lower charge density and pi-pi interaction between the polar heads) are employed as soft templates to synthesize two kinds of uniform MHCSs (e.g. mesoporous hollow carbon spheres and core-shell structured carbon nanospheres) with high nitrogen (N) doping and various inner layouts including hollow, yolk-shell and Momordica grosvenori-like structures. The elaborate tailoring in the architectures of full sized MHCSs (55 nm-2 mu m) endows them with welcome features as electrode materials for symmetrical supercapacitors. In particular, the representative sample of as-prepared N-doped MHCSs exhibits excellent electric double-layer capacitor performance with an electrochemical specific capacitance (308 F g(-1) at 0.2 A g(-1)), superb capacitance retentions (233 F g(-1) at 20 A g(-1) and 174 F g(-1) at 100 A g(-1)), and high cycling stability (85% capacitance retention at 10 A g(-1) after 5000 cycles) in a 6 M KOH electrolyte. The full-sized carbon spheres with tunable architectures are expected to find more applications in catalysis, adsorption, biomedicine and energy storage and conversion devices

Genetic Diversity of Fish in Aquaculture and of Common Carp (<i>Cyprinus carpio</i>) in Traditional Rice–Fish Coculture

The genetic diversity of cultured species (e.g., plants and fish) has decreased as intensive agriculture and aquaculture have increased in recent decades. Maintaining genetic diversity in agriculture is a significant concern. To test whether aquaculture affects the genetic diversity of aquatic animals and whether traditional agriculture could help maintain genetic diversity, we conducted a meta-analysis to quantify the genetic diversity of cultured and wild populations. We also examined the genetic diversity and population genetic structure of common carp (Cyprinus carpio) in the traditional rice–fish coculture in the south of Zhejiang Province, China, using 20 microsatellite loci. The results of the meta-analysis showed a negative overall effect size of all cultured aquatic animals that were tested both when weighted by population replicate and when weighted by the inverse of variance. Aquaculture has caused a general decline in the genetic diversity of many cultured aquatic animals. The results from the survey of a traditional rice–fish coculture system in the south of Zhejiang Province of China showed high levels of genetic diversity in all 10 sampled populations (mean Na = 7.40, mean Ne = 4.57, mean I = 1.61, mean He = 0.71, and mean Ho = 0.73). Both the conventional analysis and a model-based analysis revealed a high and significant genetic divergence among the 10 sampled populations all over the three counties (FST value ranged from 0.00 to 0.13, and Nei’s genetic distance ranged from 0.07 to 0.62). Populations within Yongjia and Jingning counties were also genetically differentiated, respectively. Furthermore, molecular variance (AMOVA), membership coefficients estimated by STRUCTURE, PCoA, and migration network analysis supported the findings from pairwise FST values. Our results suggest that the traditional rice–fish coculture plays an important role in maintaining the genetic diversity of carp cocultured in rice paddies and future policies should favor the conservation of the rice–fish system and raise the awareness of farmers on methods to maintain carp genetic diversity