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Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

Online complaints have emerged as a pivotal avenue for customers to voice their dissatisfaction. In this context, bystanders, as third-party observers, actively engage in evaluating and judging these complaints. However, studies pertaining to bystanders in online customer complaints remain limited. Therefore, this study aims to integrate deontic justice theory and attribution theory to construct a research model of bystanders’ support for online customer complaints.

Leveraging a questionnaire and two scenario experiments, SPSS 24.0 and AMOS 24.0 were used to examine the relationship between bystanders’ moral outrage and their support for online customer complaints, the mediating role of responsibility attribution and the moderating role of experience similarity and online anonymity.

Based on the statistical analysis, the results show that bystanders’ moral outrage significantly enhances their support for online customer complaints; responsibility attribution plays a mediating role between moral outrage and bystanders’ support for online customer complaints; experience similarity and online anonymity can moderate the relationship between moral outrage and bystanders’ support for online customer complaints.

The findings of this study not only enrich the literature on online customer complaints but also provide valuable insights for companies to understand the diffusion of online complaints and effective strategies with which to address them.

The purpose of this systematic literature review (SLR) is to examine the theoretical landscape of knowledge hiding (KH) research, identifies prevailing theories, the different ways KH is understood within these theories and the underlying assumptions that shape these views. Based on this, ideas for further research are derived to advance the theoretical basis of KH studies.

Using a theory-based SLR, the authors analysed 170 scientific papers from Scopus and Web of Science. This involved thematic analysis to categorise theories frequently applied in KH research and a detailed examination to link core assumptions to these theoretical perspectives.

The analysis revealed a reliance on 86 distinct theories, with a notable emphasis on social exchange theory and conservation of resources theory. KH is predominantly conceptualised as a negative, objective, reactive and relational behaviour rooted in social reciprocity and resource conservation. The review uncovers the multifaceted nature of KH, challenging the field to incorporate broader theoretical views that encompass positive aspects, subjective experiences, strategic intentions and non-relational determinants of KH.

To the best of the authors’ knowledge, this is the first study to systematically map and analyse the theoretical underpinnings of KH research. It offers a unique contribution by categorising the diverse theories applied in KH studies and explicitly linking these theories to their inherent assumptions about KH. This approach provides a comprehensive overview that not only identifies gaps in the current research landscape but also proposes alternative theoretical perspectives for exploring KH, thereby setting a new direction for future studies in this field.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Customer value co-creation behavior is promising but undertheorized. To bridge this gap, this study examines the viability of a social cognitive theory positing that customers' value co-creation behavior is shaped by their co-creation experience, self-efficacy, and engagement.

Using healthcare as a case, a stratified random sample comprising 600 patients from 40 hospitals across eight metropolitan cities in an emerging economy was acquired and analyzed using co-variance-based structural equation modeling (CB-SEM).

Customers' co-creation experience has a positive impact on their co-creation self-efficacy, co-creation engagement, and value co-creation behavior. While co-creation self-efficacy and engagement have no direct influence on value co-creation behavior, they do serve as mediators between co-creation experience and value co-creation behavior, suggesting that when customers are provided with a co-creation experience, it enhances their co-creation self-efficacy and engagement, ultimately fostering value co-creation behavior.

A theory of customer value co-creation behavior is established.

Drawing on the conservation of resources (COR) theory, we propose a mediated moderation showing how proactive personality (PP) and job crafting toward interests (JC-interests) influence the relationship between interest incongruence and cyberloafing.

We used a three-wave survey and collected data from 429 full-time employees working in different industries in China.

We found that interest incongruence was positively related to cyberloafing. Furthermore, this positive relationship was more significant when employees were low in PP or engaged in low levels of JC-interests. In addition, the moderating effect of PP was mediated by JC-interests.

These findings are helpful for organizations in figuring out how to mitigate the detrimental effects of interest incongruence by providing more support to proactive employees and implementing various JC interventions.

This study suggests that PP and JC-interests (resource gain strategy) could mitigate the positive effect of interest incongruence on employees’ cyberloafing.

This research aims to investigate the trajectory tracking problem for a four-wheel independent drive autonomous vehicle (4WID) and propose an integrated, coordinated control strategy to address the mutual interference between trajectory tracking and stability control in extreme cases.

The authors establish an adaptive preview model that modifies the preview distance based on vehicle speed. They utilize a three-degrees-of-freedom vehicle model and employ model predictive control to calculate the necessary front wheel angle for trajectory tracking. In terms of longitudinal control, a longitudinal coordinated control mechanism is established to achieve the two conflicting objectives of trajectory tracking accuracy and dynamic stability through early deceleration. A stability controller based on sliding mode control (SMC) is designed, considering tire constraints and tracking the optimal yaw angle and sideslip angle. Furthermore, a lateral coordinated control strategy is developed, considering the weight coefficient of stability control, and the yaw moment is calculated and distributed based on the vehicle torque requirements.

The proposed integrated, coordinated control strategy successfully addresses the mutual interference between trajectory tracking and stability control in extreme cases for the 4WID vehicle. The strategy achieves trajectory tracking accuracy, dynamic stability and reduced energy consumption while taking into account tire constraints.

We have proposed a cooperative control strategy for the trajectory tracking problem of autonomous driving vehicles. This strategy is different from previous methods in that we have taken into account the integrated dynamic control in both longitudinal and lateral directions, balancing the conflicting control requirements and reducing energy consumption, improving trajectory tracking accuracy and vehicle dynamic stability. We have verified the feasibility of this strategy through joint simulation under different driving conditions.

Based on affective events theory, this research attempted to investigate how negative gossip about organizational change drives employees to experience negative emotions and direct their aggression toward customers.

We conducted a scenario-based experiment (Study 1) and a multiwave field survey (Study 2) to test our hypotheses.

The results show that (1) negative emotions mediate the relationship between change-related negative gossip and displaced aggression toward customers; (2) perceived organizational constraints strengthen the relationship between change-related negative gossip and negative emotions; (3) future work self-salience weakens the relationship between change-related negative gossip and negative emotions; and (4) change-related negative gossip has a strengthened (weakened) indirect effect on displaced aggression via negative emotions when employees have high perceived organizational constraints (future work self-salience).

The study expands research on organizational change and displaced aggression and provides practical implications for managing organizational change.