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Secure computational geometry (SCG) is an important type of secure multi-party computation (SMC) problem, which studies how to calculate the relationship of several geometric objects securely. Quantum SCG (QSCG) can achieve higher security than classical SCG protocols, but the achievements of QSCG are still limited at present. In this paper, we define a new SCG problem: the secure clockwise sorting (SCS) problem, and propose a quantum protocol to solve it. We first propose a quantum secure clockwise comparison subprotocol, where the cross and scalar products of two vectors are calculated by using a quantum secure two-party scalar product protocol, and then the relative clockwise order of two points is determined. Based on the subprotocol, we then give a quantum SCS protocol to determine the clockwise order of a series of points. Finally, we show the correctness, security, and efficiency of our protocol through detailed performance analysis. [ABSTRACT FROM AUTHOR]

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate. [ABSTRACT FROM AUTHOR]

• A digital twin-assisted dual transfer method was proposed with information and model adaptation for bearing fault diagnosis. • DTa-DT aims to simultaneously synthesize information transfer and model transfer for domain transfer error minimization. • DTd-IT method was proposed to information transfer with low distribution difference of twin data adapted. • DAd-MT method, composed of DBTN, was proposed to model transfer with feature distribution difference of domain-model adapted. • Experiments and ablation study verified that the proposed DTa-DT method can be well applied to small sample learning. Rolling bearing fault diagnosis is of great importance to the safety management of mechanical equipment. The scarcity of labelled fault data makes it difficult to adequately perform the training process of intelligent diagnosis models, and this will result in these intelligent models not being effectively and widely used in practice. Although some recent studies have verified that the addition of dynamic model response to the training process will greatly improve the ability of the model with low cost and high efficiency, it is still stuck in poor effect caused by large information distribution difference between dynamic model response and real measured data. Focusing on this issue, a digital twin-assisted dual transfer (DTa-DT) method with information and model adaptation was proposed for rolling bearing fault diagnosis. Different from the traditional digital-analogue driven transfer methods, the proposed DTa-DT aims to simultaneously synthesize data information transfer and feature model transfer together with domain transfer error minimization. In particular, it should be noted that the DTa-DT architecture consists of a dual transfer learning process, including digital twin-driven information transfer (DTd-IT) and digital-analogue-driven model transfer (DAd-MT), where the information is collaborated with the model to improve the integrated transfer diagnosis effect under sampling. On one aspect, with the employment of bearing dynamic model responses, DTd-IT is innovatively designed to establish the transfer of dynamic information and measured information. The information distribution difference between these twin data and real measured data is effetely adjusted with the introduced actual inference components, where the twin data with low information distribution difference can be well fusion generated by the information transfer digital twin (ITDT) model. On the other aspect, considering the truth that there are still small sample cases of real measured data and information distribution differences will affect the quality of the twin data, a digital-analogue driven model transfer (DAd-MT) method is further proposed, where the deep branch transfer network (DBTN) model with improved convolutional neural network (CNN) is used to achieve an accurate fault diagnosis effect with the help of digital twin data. Experiments and wear analysis verified that the proposed DTa-DT can significantly reduce the distribution difference between the dynamic model response and the real measured data, thus achieving low-cost and efficient rolling bearing transfer diagnosis compared to other ten state-of-the-art deep learning models. It can be predicted that the proposed dual transfer architecture provides more opportunities for the practical application of intelligent fault diagnosis under small sample sizes. [ABSTRACT FROM AUTHOR]

In this paper, we propose an acceleration slip regulation (ASR) method for multi-wheel distributed electric drive vehicles, which considers the torque loss factor and combines dynamics and stability control. The accurate estimation of the peak adhesion coefficient and optimal slip ratio on complex roads is a challenging task, resulting in uncertainties in the wheel slip domain, the timing of ASR activation, and the target slip ratio. To address these issues, a method for determining the wheel's slip domain based on the torque loss factor is first proposed. Subsequently, a variable-step optimal slip ratio search method is introduced that incorporates the concept of stability boundaries, aligning with the Lyapunov stability criterion. Finally, an ASR strategy is proposed that seamlessly considers both dynamics and stability. To validate the proposed ASR, simulations and test vehicle experiments were conducted using the joint simulation platform of MATLAB/Simulink and TruckSim, as well as an 8-wheel distributed electric drive vehicle developed by our research group. These experiments encompass a range of roads, including low-adhesion coefficient roads, split roads, and docking roads. The results clearly demonstrate that the proposed ASR can effectively intervene when wheels enter the slip non-stability domain, achieve variable step-size optimization of the slip ratio, ensure rapid and stable convergence of the slip ratio for each wheel under complex road conditions, and significantly enhance the vehicle's acceleration and straight-line driving capabilities. • Determination of wheel slip domain based on torque loss factor. • Optimal slip ratio search with variable step size considering stability boundaries. • The ASR strategy that combines dynamics and stability. [ABSTRACT FROM AUTHOR]

The thermochromic properties of vanadium dioxide (VO 2) hold great promise for applications in smart glass windows. VO 2 films exhibit a high phase transition temperature (T c), low visible transmission (T lum), and low solar modulating ability (ΔT sol), making them inadequate for the requirements of smart glass windows. While some improvement in VO 2 film performance can be achieved through single-element doping, these limited enhancements still fall short of ideal results. Therefore, there is a pressing need for the improvement of VO 2 films. In this study, we utilized the sol-gel method to prepare VO 2 samples simultaneously doped with three elements: W, Sn, and Zr. Microscopic characterization confirmed the successful incorporation of these elements. Optical measurements, including ultraviolet–visible–near-infrared spectroscopy, were conducted to assess the optical properties of the films. Ultimately, outstanding T lum (69%) and ΔT sol (14%) were achieved, along with an appropriate T c (27.6 °C). Energy consumption simulations were performed using the EnergyPlus model with experimental data, revealing that compared to conventional glass, VO 2 glass reduces total cooling energy consumption by 9.8%. In comparison to previous work, the multi-element-doped films push the performance limits of VO 2 films, offering a viable approach for fabricating superior VO 2 smart glass windows. [Display omitted] [ABSTRACT FROM AUTHOR]

Developing chiral electrode catalysts for enantioselective electrosynthesis is a great challenge, as it requires catalysts that possess both high activity and enantioselectivity. Precise synthesis of nanoclusters and single atoms coexisting chiral catalysts provide a promising pathway for enhancing asymmetric catalytic performance. Herein, chiral electrode catalysts are fabricated comprising gold clusters (R‐AuC) and single atoms (R‐AuS) on graphene oxide (R‐AuC/S@GO) through an assembly‐irradiation strategy. Thereinto, the R‐Aus is in situ generated from R‐AuC under light irradiation. The monoatomization process can be precisely regulated by changing the wavelength of the light, resulting in four Au‐based chiral electrode (R‐Au@GO) catalysts with different ratios of nanoclusters and single atoms. These chiral electrodes are applied in the electrocatalytic enantioselective hydrogenation of methyl benzoylformate (MB) to chiral methyl mandelate (S‐MM), and the R‐AuC/S‐2@GO with ≈26% R‐AuC and 74% R‐AuS achieve the highest catalytic activity (35 µmol cm−2 h−1 productivity) and enantioselectivity [97% enantiomeric excess (ee)]. Detailed experimental analysis and density functional theory calculations reveal that the R‐AuS on GO promotes the in situ generation of H* species, and R‐AuC mainly drives the enantioselective conversion of MB by transferring the H* species to the carbonyl group of MB, ultimately yielding chiral S‐MM. [ABSTRACT FROM AUTHOR]

Resource utilization of spent refractory material (SRM) is beneficial for achieving environmental protection and resource recycling. We developed a modified agent for asphalt modification, incorporating "bayberry"-like hexamethyldisilazane and tannic acid for sutured modification of SRM(HMDS-TA-SRM). This includes the mechanism of low concentration (6.17 mg/L) occurring in process F-, and the mechanism of hydrophobicity and "bayberry"-like nano-rough surface acquisition is explained from multiple perspectives. The modifier also forms a dual protection layer with the asphalt, leveraging the surface-specific hydrophobic capillary structure, demonstrating excellent safety performance in neutral, acidic, alkaline, and high salt four simulated solutions for up to 1 year of soaking (the highest leaching rate being 0.486%). Through simulation experiments, it was found that the HMDS-TA-SRM modified asphalt maintains good barrier ability (180d leaching rate of 1.05%) and fatigue resistance even after 10 years. The material modification method is green and efficient, and the SRM-asphalt new system has feasibility and long-term safety. [Display omitted] • Explanation of the origin of nano-rough surfaces. • Explanation of fluorine ion solidification using the solubility product theory. • Adsorption mechanism of oily components on modifier's surface explored. • Exploration for enhancing long-term stability of SRM modified asphalt. [ABSTRACT FROM AUTHOR]