Small Gold-Thiolate Clusters Au3(SMe)3: Benchmarking of Density Functionals and Bonding Analysis

We present a benchmark study on popular density functionals for their efficiency and accuracy in the geometry and relative stability of gold-thiolate nanoclusters taking Au3(SMe)3 isomers. We have used normalized mean absolute error (NMAE) analysis as a parameter to compare the results with the reference methods - DLPNO-CCSD(T) and RI-SCS-MP2. We have also compared the performance on the thiolate interaction energy of the stable geometries using the results from our benchmark study. One of the promising functional is PBE that shows robust performance for geometry optimization. On the other hand, M06-2X stands out as the proper choice for the relative energies of the clusters. With the selected methods, we have analyzed the gold-sulfur interaction in Au3(SMe)3 and a comparison is made with AuSMe. The bonding analysis has revealed a partial covalency between gold and sulfur atoms in general. On going from AuSMe to Au3(SMe)3, a substantial flow of charge from gold atoms to thiolate ligands as a result of the increase in gold s-d hybridization. As the s-d mixing in Au increases, the main character of Au-S interaction shifts from covalent to ionic. Hence, a covalent-charge-transfer interaction dominates in gold-sulfur bonding and gives rise to a charge-shift bonding.

Practical Dynamical-Statistical Reconstruction of Ocean’s Interior from Satellite Observations

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction.

DETERMINING ROBUST PARAMETERS IN STABILIZING SET OF BACKSTEPPING BASED NONLINEAR CONTROLLER FOR SHIP COURSE KEEPING

In order to solve the problem that backstepping method cannot effectively guarantee the robust performance of the closed-loop system, a novel method of determining parameter is developed in this note. Based on the ship manoeuvring empirical knowledge and the closed-loop shaping theory, the derived parameters belong to a reduced robust group in the original stabilizing set. The uniformly asymptotic stability is achieved theoretically. The training vessel “Yulong” and the tanker “Daqing232” are selected as the plants in the simulation experiment. And the simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Robustness Analysis of an Electrohydraulic Steering Control System Based on the Estimated Uncertainty Model

The impossibility of replacing hydraulic drives with other type drives in heavy duty machinery is the main reason for the development of a system for controlling hydraulic power steering. Moreover, the need for remote automatic control of the steering in specific types of mobile machinery leads to significant scientific interest in the design of embedded systems for controlling electro-hydraulic steering units. This article introduces an approach, which has been developed by authors, for robust stability and robust performance analysis of two embedded systems for controlling electro-hydraulic power steering in mobile machinery. It is based on the suggested new more realistic “black box” SIMO model with output multiplicative uncertainty. The uncertainty is obtained by parameters confidence interval and Gauss approximation formula. The embedded control systems used a linear-quadratic Gaussian (LQG) controller and H∞ controller. The synthesis of the controllers was performed on the basis of a nominal part of an uncertainty model. Robust stability and robust performance analyses were performed in the framework of a so-called structured singular value, μ. The obtained theoretical results were experimentally approved by real experiments with both of the developed control systems, which were physically realized as a laboratory test rig.

What Drives Commuters to Pay for Autonomous Vehicles?

Although commuters have been identified as potential early adopters of autonomous vehicles (AVs) that can boost the adoption rate of this technology, there is a lack of knowledge on their willingness to pay (WTP) for this technology and the attitude that influences this decision. Using data from a consumer survey conducted in the United States, this study presents a comprehensive analysis of the decision to pay for AVs among commuters. An integrated choice and latent variable (ICLV) model was applied in this study, considering its robust performance in modeling choice behavior for integrating users’ attitudes. The results showed that commuters with a favorable view toward multitasking tended to put a higher value on driverless cars. On the other hand, although a favorable view toward technology motivated commuters to pay more for AVs, data privacy and trust issues with the technology could outweigh this factor and discourage commuters from adopting and paying for AVs. This study also provides in-depth insights and comprehensive views on the impacts of commuters’ socioeconomic and demographic attributes on the decision to pay for AVs. Notably, although age and educational attainment did not directly affect WTP behavior, they played important roles in this decision, with significant effects mediated through latent attitudes. These in-depth analyses provide useful insights that can help develop customized marketing strategies for different market segments according to their specific and unique preferences and concerns.

Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data

Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-visible objects in some datasets. In this paper, we propose to use top-down visual attention and color cues to boost performance of a state-of-the-art method on occluded scenarios. More specifically, color information is employed to detect potential points in the scene, improve feature-matching, and compute more precise fitting scores. The proposed method is evaluated on the Linemod occluded (LM-O), TUD light (TUD-L), Tejani (IC-MI) and Doumanoglou (IC-BIN) datasets, as part of the SiSo BOP benchmark, which includes challenging highly occluded cases, illumination changing scenarios, and multiple instances. The method is analyzed and discussed for different parameters, color spaces and metrics. The presented results show the validity of the proposed approach and their robustness against illumination changes and multiple instance scenarios, specially boosting the performance on relatively high occluded cases. The proposed solution provides an absolute improvement of up to 30% for levels of occlusion between 40% to 50%, outperforming other approaches with a best overall recall of 71% for the LM-O, 92% for TUD-L, 99.3% for IC-MI and 97.5% for IC-BIN.