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Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

The purpose of this study is to explore the influence of the electronic throttle (ET) dynamics and the average speed of multiple preceding vehicles on the stability of traffic flow.

An extended car-following model integrating the ET dynamics and the average speed of multiple preceding vehicles is presented in this paper. The novel model’s stability conditions are obtained by using the thought of control theory, and the modified Korteweg–de Vries equation is inferred in terms of the nonlinear analysis method. In addition, some simulation experiments are implemented to explore the properties of traffic flow, and the results of these experiments confirm the correctness of theoretical analysis.

In view of the results of theoretical analysis and numerical simulation, traffic flow will become more stable when the average speed and ET dynamics of multiple preceding vehicles are considered, and the stability of traffic flow will also be enhanced by increasing the number of preceding vehicles considered.

This study leaves the factors such as the mixed traffic flow, the multilane and so on out of account in real road environment, which more or less influences the traffic flow’s stability, so the real traffic environment is not fully reflected.

There is little research integrating ET dynamics and the average velocity of multiple preceding vehicles to study the properties of traffic flow. The enhanced model constructed in this study can better reflect the real traffic, which can also give some theoretical reference for the development of connected and autonomous vehicles.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

Smiles displayed at varying intensities by service providers may result in different social judgments by customers, affecting decision-making. This study investigates the joint effect of customers' sense of power (low vs. high) and service providers' smile intensity (slight vs. broad) on their warmth and competence perceptions in service encounters.

The authors conducted four experiments based on the Stereotype Content Model (SCM) of social judgments and the agentic-communal model of power, and assessed the impact of perceived power and smile intensity in different service encounter contexts.

The interaction effect of customers' sense of power (low vs. high) and service providers' smile intensity (slight vs. broad) influences customers' social judgments (warmth perceptions vs. competence perceptions). A service provider who displays a broad smile is more likely to be perceived as warmer by customers with a low sense of power, but less competent by those with a high sense of power. Furthermore, mediation analysis revealed that the combined effect of customers' sense of power and service providers' smile intensity on customers' subjective well-being and purchase intentions might be attributed to their social judgments.

This study reveals the intrinsic mechanism behind the interaction effect between smile intensity and sense of power affecting customers' purchase intentions and subjective well-being, namely, warmth/competence perceptions.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

In the context of an evolving digital-based global economy, this study aims to investige the impact of digital transformation (DT) on a firm’s internal control (IC) quality. It also explores how the personal traits of (CEOs) – such as age, gender and educational background – intersect with DT to shape the IC quality in various types of state-owned enterprises (SOEs).

The study uses the data from China A-shares non-financial enterprises, listed on Shanghai and Shenzhen stock exchanges between 2007 and 2020. Using the fixed effect regression method alongside various statistical techniques, such as propensity score matching, alternative analysis and instrumental variables analysis, yields robust findings. These methods effectively address issues related to functional form misspecification and potential biases from omitted explanatory variables.

The findings reveal a positive impact of DT on firm IC quality, and this impact is more pronounced in firms when the CEO is female, young and possesses a higher level of education. Notably, the study also distinguishes between central and local state-owned enterprises (SOEs), highlighting that DT has a greater influence on IC quality in central SOEs, where CEOs often have higher political ranks and closer to government monitoring. Overall, the findings are robust and consist to alternative variable and other statistical methods.

Following are the significant implications for both academia and business. First, firms that effectively adopt DT to enhance IC not only gain a strategic advantage over competitors but also establish efficient risk management practices and a robust IC system. Second, better IC resulting from DT can enhance investor and stakeholder confidence. This is particularly important for publicly traded companies, where investors and analysts closely scrutinize the robustness of IC systems. Third, DT could result in cost savings over time, as automation and streamlined processes may reduce the need for manual efforts and resource-intensive tasks associated with IC.

The findings are contributed to the literature in multiple ways. It enhances our comprehension of the intricate DT-IC quality relationship, and provides valuable insights into the transformative impact of DT on organizational operations and risk management. It also introduces a novel perspective by investigating how CEOs personal traits intersect with DT to shape IC quality, contributing to upper echelons theory. Furthermore, it expands the discussions on firm ownership by considering the types of SOEs (central vs. local), in the DT-IC quality context.

This study aims to analyze the relationship between corporate social responsibility (CSR) and quality of accounting report, especially on earnings management (EM). In addition, potential moderators of this relationship are examined.

After a comprehensive study of potential mechanisms, the authors obtain plenty of empirical results to open the black box of the link between CSR and EM. Meta-analysis is applied on 51 studies from 35 papers. Further analysis is also carried out to determine the moderating effects, such as the cultural and sample selection differences in these papers.

CSR is negatively associated with EM. In addition, this effect is moderated by cultural difference, CSR measurement, and year of sample selection.

Two patterns of the hypothesis between CSR and EM are confirmed based on agency cost theory, a theoretical shift of corporate ethics based on organizational moral perspective. Several useful suggestions are also provided for future studies on the empirical model and sample selection. Further research is necessary to clarify the agency cost behind the two theoretical patterns.

CSR is not a tool for firms to market but rather a strategy to ensure their consistency with moral principles, indicating that management should pay more attention to the potential damage of the incongruence between CSR and accounting reporting quality. CSR reporting quality remains an important issue for legislature to guarantee continued firm operations.

To the best of the authors’ knowledge, this study is the first to analyze the CSR and EM link using a meta-analysis and to consider its underlying mechanism under the global environment. Previous method design and sample selection are reviewed to provide reference for future studies.

The purpose of this paper is to present current state of understanding on jet electrodeposition manufacturing; to compare various experimental parameters and their implication on as deposited features; and to understand the characteristics of jet electrodeposition deposition defects and its preventive procedures through available research articles.

A systematic review has been done based on available research articles focused on jet electrodeposition and its characteristics. The review begins with a brief introduction to micro-electrodeposition and high-speed selective jet electrodeposition (HSSJED). The research and developments on how jet electrochemical manufacturing are clustered with conventional micro-electrodeposition and their developments. Furthermore, this study converges on comparative analysis on HSSJED and recent research trends in high-speed jet electrodeposition of metals, their alloys and composites and presents potential perspectives for the future research direction in the final section.

Edge defect, optimum nozzle height and controlled deposition remain major challenges in electrochemical manufacturing. On-situ deposition can be used as initial structural material for micro and nanoelectronic devices. Integration of ultrasonic, laser and acoustic source to jet electrochemical manufacturing are current trends that are promising enhanced homogeneity, controlled density and porosity with high precision manufacturing.

This paper discusses the key issue associated to high-speed jet electrodeposition process. Emphasis has been given to various electrochemical parameters and their effect on deposition. Pros and cons of variations in electrochemical parameters have been studied by comparing the available reports on experimental investigations. Defects and their preventive measures have also been discussed. This review presented a summary of past achievements and recent advancements in the field of jet electrochemical manufacturing.