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Considering the role of analysts in disseminating information, the paper explains the idiosyncratic volatility puzzle of China's stock market. As the largest developing country, China's research can provide meaningful reference for the research of financial markets in other new countries.

From the perspective of behavior, establishing a direct link between individual investor attention and stock price overvaluation.

The authors find that there is a significant idiosyncratic volatility puzzle in China's stock market. Due to the role of mispricing, individual investor attention significantly enhances the idiosyncratic volatility effect, that is, as individual investor attention increases, the greater the idiosyncratic volatility, the lower the expected return. Attention can explain the idiosyncratic volatility puzzle in China's stock market. In addition, due to the role of information production and dissemination, securities analysts can reduce the degree of market information asymmetry and enhance the transparency of market information.

China is the second largest economy in the world, and few scholars analyze it from the perspective of investors' attention. The authors believe this paper has the potential in contributing to the academia.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

This study aims to develop a green, economical and efficient ultrasonic-/microwave assisted extraction (UMAE) process for the extraction of anthocyanins.

After optimizing the extraction conditions by response surface methodology, three assays including DPPH, ABTS·+, FRAP were applied to analyze the antioxidant activity of the extracted anthocyanins. The stability under different temperatures, reductant concentrations and pHs was also discussed. The components of anthocyanins in blueberry were analyzed by HPLC-QTOF-MS2.

The optimal extraction parameters were ultrasonic power of 300 W, microwave power of 365.28 W and solid–liquid ratio of 30 (g/mL). The possible structures can be speculated as Delphinidin-3-O-galactoside, Delphinidin, Petunidin, Delphinidin-3-O-glucoside, Petunidin-3-O-glucoside, Cyanidin-3-O-glucoside. The results demonstrated that the UMAE can improve the yield of anthocyanins in shorter extraction time with higher activity.

The present study may provide a promising and feasible route for extracting anthocyanins from blueberries and studying their physicochemical properties, ultimately promoting the utilization of blueberry anthocyanins.

This paper aims to investigate the corrosion kinetics and corrosion behavior of NdFeB magnets with the addition of heavy rare earth dysprosium (Dy) for its inhibitory activity on poor corrosion resistance of NdFeB magnets.

To study the effect of dysprosium addition on corrosion behavior of NdFeB magnets and investigate its mechanism, potentiodynamic polarization, scanning electron microscopy (SEM), electrochemical impedance, energy dispersion spectrum (EDS) and scanning Kelvin probe force microscopy (SKPFM) were applied in the research. Besides, microstructures were observed by SEM equipped with EDS. Atomic force microscopy was introduced to analyze the morphology, potential image as well as the contact potential difference. The SKPFM mapping scan was applied to obtain the contact potential around Nd-rich phase at 0.1 Hz. The magnets were detected via X-ray diffraction.

Substitution of Nd with Dy led to improvement of corrosion resistance and reduced the potential difference between matrix and Nd-rich phase. Corrosion resistance is Nd-rich phase < the void < metal matrix; maximum potential difference between matrix and Nd-rich phase of Dy = 0, Dy = 3 and Dy = 6 Wt.% is 411.3, 279.4 and 255.8 mV, respectively. The corrosion rate of NdFeB magnet with 6 Wt.% Dy is about 67% of that without Dy at steady corrosion stage. The addition of Dy markedly enhanced the corrosion resistance of NdFeB magnets.

This research innovatively investigates the effect of adding heavy rare earth Dy to NdFeB permanent magnets on magnetic properties, as well as their effects on microstructure, phase structure and most importantly on corrosion resistance. Most scholars are studying the effect of element addition on magnetic properties but not on corrosion resistance. This paper creatively fills this research gap. NdFeB magnets are applied in smart cars, robotics, AI intelligence, etc. The in-depth research on corrosion resistance by adding heavy rare earths has made significant and outstanding contributions to promoting the rapid development of the rare earth industry.

The purpose of this study is to enrich the literature on employee relations with a new model focusing on the effect of institutional structure and that of employees’ organizational identification on the relationship between institutional structure in an organization and employees’ pro-environmental behaviors, which represents an alternative approach for understanding employees’ pro-environmental performance.

We collect multi-level and multi-source data from 52 four- or five-star hotels in China (N = 963). For data analysis, we adopt the approach of multilevel structural equation modeling.

The results suggest that organizations’ green institutional structure (G-structure) can significantly influence employees’ organizational identification, which in turn can increase their pro-environmental performance.

We propose a new multi-level theoretical perspective to explain employees’ pro-environmental behaviors. While prior studies on the issue mainly consider only the effects of such micro-level variables as ability, motivation and personality, we focus on the effect of organizational institution and its interaction with micro-level variables so that we can evaluate the effect a commonly-studied contextual variable, i.e. green institutions, on the behaviors. Moreover, in this new theoretical model, we also take into account the effect of another insufficiently-tested micro-level variable, i.e. employees’ identification, which has not been considered as frequently as other micro-level variables in studying employees’ pro-environmental performance. Our results highlight the importance of all these variables and suggest a valuable alternative model for more comprehensive research of employees’ green performance.

The human-following task is a fundamental function in human–robot collaboration. It requires a robot to recognize and locate a target person, plan a path and avoid obstacles. To enhance the applicability of the human-following task in various scenarios, it should not rely on a prior map. This paper aims to introduce a human-following method that meets these requirements.

For the identification and localization of the target person (ILTP), this paper proposes an approach that integrates data from a camera, a light detection and ranging (LiDAR) and a ultra-wideband (UWB) anchor. For path planning and obstacle avoidance, a modified timed-elastic-bands (TEB) algorithm is introduced.

Compared to the UWB-only method, where only UWB is used to locate the target person, the proposed ILTP method in this paper reduces the localization error by 41.82%. Experimental results demonstrate the effectiveness of the ILTP and the modified TEB method under various challenging conditions. Such as crowded environments, multiple obstacles, the target person being occluded and the target person moving out of the robot’s field of view. The complete experimental videos are available for viewing on https://youtu.be/ZKbrNE1sePM.

This paper offers a novel solution for human-following tasks. The proposed ILTP method can recognize the target person among multiple individuals, determine whether the target person is lost and publish the target person’s position at a frequency of 20 Hz. The modified TEB algorithm does not rely on a prior map. It can plan paths and avoid obstacles effectively.

Based on theory Z of leadership, this research aims to investigate the direct relationship between participative leadership (PL) and creative idea validation (CIV) fostering both radical (RC) and incremental creativity (IC). Additionally, by utilizing attribution theory, it explores the moderating effect of coworkers’ knowledge-sharing behavior (KSB) on both the direct and indirect relationships.

Data were collected through a descriptive approach and convenient sampling from three sources – leaders, subordinates and coworkers – in R&D departments at multi-levels within 97 high-tech firms in China. Data comprised 446 employees (subordinates and coworkers) and 94 leaders, and multilevel path analysis was conducted using Mplus software.

The results indicate that PL exhibits both a direct and indirect positive association with RC and IC through the CIV. Moreover, the relationship is enhanced by coworkers’ high-KSB.

Our study offers implications that managers can leverage to foster employee creativity. Leaders are encouraged to embrace a PL style for collective idea validation. However, to overcome coworkers’ reciprocal behavior, they may facilitate trust and team-building exercises, enabling employees to strengthen relationships and share critical information and knowledge resources for the development and validation.

This study is the first to empirically extend the relationship between PL and CIV, utilizing a multilevel approach to assess its impact on distinctive types of creativity – namely, radical and incremental. Further, it testifies the significance of coworkers’ knowledge as an attribution effect influencing the relationships.

This study examines the impact of corporate social responsibility (CSR) on innovation, considering the role of the three dimensions of intellectual capital (human capital, structural capital and relational capital). Specifically, the analysis explores the direct effect of CSR and intellectual capital on innovation, the effect of CSR on intellectual capital, and the mediating effect of intellectual capital on the relationship between CSR and innovation.

Data were collected from a sample of 101 Tunisian firms operating in various industries. The conceptual model of direct and indirect effects was tested with partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 software.

CSR is positively related to innovation, as well as all dimensions of intellectual capital. Structural capital is the only dimension of intellectual capital that has a significant effect on innovation. CSR affects innovation through its impact on structural capital.

Most studies have examined the direct effect of CSR on innovation in firms in developed countries. In contrast, this research sheds light on the mediating role of intellectual capital in this relationship, underlining the specific role of human capital, relational capital and structural capital. In addition, the study focuses on a developing country, which thus differentiates it from previous studies.

Compared to quad-rotor unmanned aerial vehicle (UAV), the tilting dual-rotor UAV is more prone to instability during exercises and disturbances. The purpose of this paper is using an active balance tail to enhance the hovering stability and motion smoothness of tilting dual-rotor UAV.

A balance tail is proposed and integrated into the tilting dual-rotor UAV to enhance hovering stability and motion smoothness. By strategically moving, the balance tail generates additional force and moment, which can promote the rapid stability of the UAV. Subsequently, the control strategy of the UAV is designed, and the influence of the swing of the balance tail at different installation positions with different masses on the dual-rotor UAV is analyzed through simulation. The accompany motion law and the active control, which is based on cascade Proportion Integration Differentiation (PID) control to enhance the hovering stability and motion smoothness of the UAV, are proposed.

The results demonstrate that active control has obvious adjustment effectiveness when the UAV moves to the target position or makes an emergency stop compared with the results of balance tail no swing and accompany motion.

The balance tail offers a straightforward means to enhance the motion smoothness of tilting dual-rotor UAV, rendering it safer and more reliable for practical applications.

The novelty of this works comes from the application of an active balance tail to improve the stability and motion smoothness of dual-rotor UAV.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.