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The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control.

First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle’s invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability.

The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests.

The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

Are franking credit balances priced? This paper aims to investigate the valuation of franking credit balances via a determinant analysis and value relevance analysis.

The determinant analysis examines the factors that contribute to the increasing cumulative level of franking credit balances. Value relevance studies explore whether franking credit balances are priced in the market.

The results provide strong evidence of a size effect that the level of franking credit balances increases with firm size and weak evidence of an international focus effect that the level of franking credit balances increases with international ownership. They also find an individual dividend clientele effect that the level of franking credit balances decreases with individual ownership. They find significant evidence that franking credit balances are priced in the market. One dollar of franking credit is worth 1.4 dollars in firm value. That franking balances are capitalized at more than their face value suggests that franking credits signal firms' future dividend policy. They also find that the market valuation of franking balances increases with firm size but decreases with international focus.

This study provides direct evidence that franking credit balances are capitalized into equity prices. In the determinant analysis, this paper improves Heaney's (2009) model by using the percentage of international ownership as the proxy of international focus, thus addressing the limitation of his measure. In the value relevance tests, the study uses a modified model that includes log-transformation to reduce the skewness of variables based on Tanza's (2014) value relevance model. Moreover, the study suggests that the market valuation of franking credit balances increases with firm size, which contradicts Heaney's (2009) findings.

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

The social and economic importance esports has gained over the past decades has led to a quickly evolving academic interest in the topic. Yet, current perspectives on esports frequently are not precise enough, too context-specific and/or focus on the question whether esports is sports or not. This means that no precise structural concept has been provided thus far. Such a conceptualization as well as a categorization of esports and related types of video gaming are provided in this paper.

This paper is based on a narrative review considering academic literature from 2000 to 2021 and publications of relevant esports organizations. The review is conceptually extended by structural parallels to traditional sports.

The central outcome of this research is conceptualizing esports as a victory-driven, organized performance comparison of exclusively human players playing video games in a competitive setting. This comparison is based solely on the performance achieved during a defined time frame according to fixed rules with comparably equal team (starting) conditions. This conceptualization is embedded in a general categorization of video gaming based on structural similarities with and differences to esports. Moreover, characteristics that were rejected in regards to the conceptualization and the categorization are discussed.

This paper provides a comprehensive categorization of esports and other types of video gaming based on structural similarities and differences. It is thus of high relevance for academia and sport management practice alike and can further the development in both fields.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

This study aims to investigate whether gender diversity in audit committees affects the purchase of nonaudit services in China. Results from family and nonfamily firms are compared and the critical mass participation of females are further examined.

The sample comprises 1,834 Chinese listed companies from 2012 to 2021, among which 910 are family firms. The Heckman (1979) two-stage model is used to mitigate the potential endogeneity issue in the selection of gender diversity. Propensity score matching is also used to further alleviate the endogeneity problem in relation to family firms.

Results show a significant and negative correlation between the gender diversity in audit committees and nonaudit service fees. This association is more apparent in nonfamily than in family firms. Findings are consistent and robust to endogeneity tests and sensitivity analyses. The analysis of critical mass and symbolic participation shows that three female directors can more significantly restrain nonaudit fees than one to two females on the board.

This study contributes to literature on resource dependence theory, which posits that audit committees help enterprises establish contact with auditors, improve the company legitimacy, assist in communication and provide relevant expertise. This study also relates to agency theory, which holds that differences in the severity of types I and II agency problems between family and nonfamily firms lead to differences in auditor selection and related costs.

Extending from previous research on the relation between the gender diversity in audit committees and nonaudit fees, the present study delves into this connection within the context of China, an emerging economy. As a result, this investigation offers novel insights and expands upon current knowledge. In addition, the correlation between the gender diversity of audit committees and nonaudit fees is explored for family and nonfamily firms.