The Love of Money and Pay Level Satisfaction: Measurement and Functional Equivalence in 29 Geopolitical Entities around the World

Demonstrating the equivalence of constructs is a key requirement for cross-cultural empirical research. The major purpose of this paper is to demonstrate how to assess measurement and functional equivalence or invariance using the 9-item, 3-factor Love of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor Pay Level Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in six continents (N = 5973). In step 1, we tested the configural, metric and scalar invariance of the LOMS and 17 samples achieved measurement invariance. In step 2, we applied the same procedures to the PLSS and nine samples achieved measurement invariance. Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurement invariance criteria for both measures. In step 3, we found that for these two measures, common method variance was non-significant. In step 4, we tested the functional equivalence between the Love of Money Scale and Pay Level Satisfaction Scale. We achieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurement equivalence in cross-cultural studies. Suggestions for remedying measurement non-equivalence are offered

RESEARCH ON TRAJECTORY PLANNING OF QUADRUPED ROBOT BASED ON BEZIER CURVE (MT)

In order to improve the stability of diagonal trot gait for quadruped robot, a foot end trajectory planning method based on Bezier curve is proposed. Aiming at the diagonal trot gait of quadruped robot, the kinematics model of parallel legs of quadruped robot is established, the forward and inverse kinematics are solved by geometric method, and the foot end trajectories of swing phase and support phase are optimized based on zero impact principle and PD controller. The comparative test is carried out by using Webots simulation platform and experimental prototype. The results show that when the proportional coefficient in X direction is 20, the differential coefficient is 0.75 s, the proportional coefficient in Y direction is 20 and the differential coefficient is 0.5 s, the foot end trajectory of quadruped robot is smooth without sudden change of speed and acceleration, and the gait of diagonal trot is more stable

Origami-inspired carbon fiber-reinforced composite sandwich materials - Fabrication and mechanical behavior

A folding fabrication method inspired by origami for carbon fiber-reinforced composites to fabricate three-dimensional structures is presented. PMI foams serve as substrates, making it possible to manufacture this kind of origami-inspired composite sandwich materials in a single-step process. Analytical models are proposed to provide upper and lower bounds of the out-of-plane compressive strength of the origami-inspired composite sandwich materials. Finite element analysis (FEA) and experiments are conducted to characterize the compressive behaviors including the deformation history, failure mode, strength, etc., and verify the validity of the analytical models. By incorporating PMI foams, the out-of-plane compressive failure mode of the composite sandwich material at low relative density changes from buckling to wrinkling or crushing, thus greatly increasing the strength. Additionally, the energy absorption capacity is significantly enhanced due to the PMI foams and their interaction with the composite origami core. This work creates a new method to fold composites into lightweight sandwich materials with high strength and high energy absorption, paving the way for their applications in transport, energy and communication

Design and foldability of Miura-based cylindrical origami structures

A methodology for designing cylindrical origami with different patterns and different cross-sectional shapes is presented. Planar and cylindrical origami with curved-crease patterns are designed and proved to be foldable and developable regardless of the type of crease line. Specifically, cylindrical origami structures with arc-Miura pattern and circular cross section, arc pattern and circular cross section, arc-Miura pattern and triangular cross section, arc pattern and triangular cross section are designed. The relationships between the two-dimensional (2D) crease lines in a sheet and three-dimensional (3D) spatial origami structures are derived. Fabrications of cylindrical origami by a folding process are performed to validate the design theory for 2D crease lines. This work paves the way for applications for cylindrical origami in engineering, such as energy absorption

Luminescent Sensors Based on the Assembly of Coinage Metal Nanoclusters

Coinage metals, such as Cu, Ag and Au, can form nanoclusters, which, when functionalized with ligands, have unique electronic and optical properties and are widely used in biomedical imaging, remote sensing, labeling, catalytic, etc. The mechanisms, structures and properties of nanocluster assemblies have been well reviewed. However, the collections and analyses of nanocluster assemblies for sensor application are few. This review examines different nanocluster sensor platforms with a focus on the assembly and analysis of the assembly processes and examples of applications

A novel alpine land cover classification strategy based on a deep convolutional neural network and multi-source remote sensing data in Google Earth Engine

Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation

A novel alpine land cover classification strategy based on a deep convolutional neural network and multi-source remote sensing data in Google Earth Engine

Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation.</p