Validation of the German Five-Factor Narcissism Inventory and construction of a brief form using Ant Colony Optimization

Narcissism is a multifaceted construct commonly conceptualized as comprising grandiose and vulnerable aspects in a two-factor model. While the manifold correlates of these aspects imposed a challenge for research on the structure of narcissism, recent models converge in a three-factor structure of agentic-extraverted, antagonistic, and neurotic aspects, capturing variance in different conceptualizations and correlates of narcissism. We construct and validate a German adaptation of the Five-Factor Narcissism Inventory (FFNI; Glover et al., 2012), a measure assessing these aspects based on the Five-Factor Model. In eight samples (N = 2,921), we found the German FFNI to align with both, two- and three-factor models. The factors display good criterion validity with other narcissism measures, (non-)clinical personality dimensions, interpersonal styles, and (mal-)adaptive adjustment. Neurotic and antagonistic narcissism discriminated between individuals with/without mental disorder diagnoses, and displayed a characteristic profile in incarcerated offenders. Since the FFNI is comprehensive but long, we constructed a 30-item brief-form (FFNI-BF) optimizing the internal structure and external validity using Ant Colony Optimization. The FFNI-BF displayed good psychometric characteristics and similar, in certain aspects even advantageous criterion validity. We conclude that the German FFNI validly measures key aspects of narcissism, and the FFNI-BF captures these in a concise manner.

HCNET: A Point Cloud Object Detection Network Based on Height and Channel Attention

The use of LiDAR point clouds for accurate three-dimensional perception is crucial for realizing high-level autonomous driving systems. Upon considering the drawbacks of the current point cloud object-detection algorithms, this paper proposes HCNet, an algorithm that combines an attention mechanism with adaptive adjustment, starting from feature fusion and overcoming the sparse and uneven distribution of point clouds. Inspired by the basic idea of an attention mechanism, a feature-fusion structure HC module with height attention and channel attention, weighted in parallel, is proposed to perform feature-fusion on multiple pseudo images. The use of several weighting mechanisms enhances the ability of feature-information expression. Additionally, we designed an adaptively adjusted detection head that also overcomes the sparsity of the point cloud from the perspective of original information fusion. It reduces the interference caused by the uneven distribution of the point cloud from the perspective of adaptive adjustment. The results show that our HCNet has better accuracy than other one-stage-network or even two-stage-network RCNNs under some evaluation detection metrics. Additionally, it has a detection rate of 30FPS. Especially for hard samples, the algorithm in this paper has better detection performance than many existing algorithms.

Analysis on Vibration Characteristics and Suppression Strategy of Vehicle Mounted GPR Detection Device for Tunnel Lining

The GPR antenna is installed on the high-speed railway tunnel detection vehicle through the detection device to carry out non-contact nondestructive and rapid detection of the lining condition. Through simulation calculation and field vibration test data analysis, the vibration characteristics of the detection device are obtained. Vibration excitation is applied to GPR antennas to explore the amplitude-frequency sensitivity of echo signal affected by vibration when GPR detects tunnel lining, thus as to provide a basis for mechanism design and vibration control strategy. Through the optimization of the material properties, structural design, and control program of the detection device, the vibration frequency and amplitude of the detection device are optimized, the adaptive adjustment stability of the control system is improved, and the quality of GPR detection data is enhanced.

Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

Problems and methods of a closed-loop blood glucose control system construction

The automation of the insulin therapy in the closed-loop blood glucose control system is accompanied by such problems as delays of the feedback and the controlling action effect on blood glucose dynamics, difficulties in low and high glucose levels advance detection and timely compensation, a human factor, complexity of a mathematical model of control object building, evaluation of blood glucose control efficiency. In the paper, these automation problems and methods for their compensation are considered. The closed-loop system based on the model predictive control, adaptive adjustment of the controlling action and decision-making support was developed. Experimental testing with UVA/Padova Type 1 Diabetes Mellitus Simulator showed that the application of suggested methods in the closed-loop allows increasing the efficiency of the blood glucose maintaining in the target range.