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The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).

The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.

This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.

The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.

This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.

With the accelerated technological advancement, innovation has become a critical factor, which affects the core competitiveness of a company. However, studies about the relationship between internal stock option mechanisms and innovation productivity remain limited. Therefore, this paper aims to examine the impact of stock options and their elements design on innovation output from an internal mechanism perspective.

Using a sample of 302 stock option incentive plans announced and implemented between 2006 and 2016, this study uses the propensity score matching and difference-in-difference model to find out whether the implementation of stock options improves the innovation outputs of enterprises.

Based on the statistical analysis, it is concluded that: stock options can stimulate corporate innovation; a stock option may drive innovation outputs through two ways, performance-based incentives and risk-taking incentives, with the latter one playing a more dominant role and the risk-taking incentives of stock options, could be optimised when the non-executives granting proportion is larger, the granting range is limited, the incentive period is longer, the exercisable proportion is increasing, the price-to-strike ratio is lower and relatively loose performance assessment criteria are applied.

The conclusion reached in the study may provide valuable information to listed firms in designing and implementing the stock option plans.

This study aims to explore the dimensions and sub-factors of Airbnb hosts’ affinity-seeking strategies. It also aims to build a conceptual framework of hosts’ affinity-seeking strategies and their impact on Airbnb guests’ feelings of affection toward their host and/or the homestay and their behavioral intentions.

Based on 150,161 Inside Airbnb online reviews in three major US cities, this study uses semantic analysis to explore processes through which hosts’ affinity-seeking strategies are constructed.

A conceptual framework is proposed to identify two dimensions (warmth and competency) and their sub-factors (“presenting friendly attitudes,” “showing personality traits,” “providing service and help” and “promoting social interaction and sharing”) of Airbnb homestay hosts’ affinity-seeking strategies. The framework shows a positive relationship between these strategies and guests’ affection and behavior in response.

The research findings provide valuable insights to hosts for improving their affinity and strengthening their competitive advantages. They also offer guidance to destination management organizations on how to build a positive destination image.

To the best of the authors’ knowledge, this study is the first to conceptualize Airbnb homestay hosts’ affinity-seeking strategies. It contributes to the literature by incorporating social cognition theory and service theory in the analysis of these strategies.

Online medical teams (MTs), involving collaboration between remote healthcare workers, can provide comprehensive and rapid healthcare to patients. The growth in MTs is continuing, with popularity growing among doctors and patients, but some MTs disband, which could break the continuity of healthcare services provided. We aim to address this pressing issue by exploring the effects of team diversity and leadership types on team status (i.e. team disbandment (TD)). This paper systematically investigates the influences of team diversity, including separation, variety and disparity diversity and the effects of leadership types, including strong, equal and weak types.

A data set consisting 1,071 online MTs was collected from the Good Doctor website, a leading Chinese online health community (OHC), on January 10, 2018. The data captured included 206 teams which disbanded after 3 months collaboration. Logistic regression and maximum likelihood estimation (MLE) were used to examine their effects.

The results show that variety diversity, related to departments, positively affects TD, but disparity diversity, referring to clinician titles, negatively affects TD. Separation diversity, in terms of team member attitudes, exerts a negligible influence on disbandment. Although strong and equal leadership types negatively influence TD, they are seen to strengthen the positive effect of variety diversity, suggesting stable structure combinations of strong or equal-type leadership and low department diversity, as well as the match of weak-type leadership and high department diversity.

This paper extends the current understanding of virtual teams and OHCs by examining the role of leadership types and team diversity, and their influencing role on team status. The pairwise combinations are obtained to effectively reduce the disbandment probability of medical teams operating in OHCs, which could help platform managers, team founders and those connected with MTs deal with the team-disbandment crisis, providing both theoretical and practical implications to healthcare providers and researchers alike.

In China, healthcare services have historically been expensive and difficult to access, with resources being unfairly distributed, often being centralized in large hospitals in major cities. In rural regions, hospitals often suffer from limited supplies, including human capital and equipment. E-health technologies have received significant attention from governments and citizens, with online healthcare communities (OHCs) providing easier communication between patients and doctors. Although doctors play a pivotal role in the success of OHCs, they are often unsure how to attract patients, with limited research focusing on this. The purpose of this paper is to explore how doctors can take initiatives in OHCs, from the joint perspectives of individual effort (i.e. intrapersonal factor) and identity in medical teams (MTs) (i.e. interpersonal factor), based on attribution theory.

Hierarchical linear regression was conducted on data from 3,170 doctors participating in 865 online MTs, to examine the effects of individual effort and identity in MTs on individual performance. Individual effort included central effort (log-in frequency to OHC) and peripheral effort (articles published on doctors' homepages). Identity in MTs was represented as the identity of team leader and multiple team membership (MTM).

This study found that the main variables – central and peripheral effort, and leader and MTM identity – all had significant and positive impacts on the service quantity (SQ) of both written and telephone consultations. Although positive effects could be experienced in most conditions that were congruent with the logic of identity theory, the interaction terms demonstrated complex influences. Specifically, leader identity did not moderate the effect of article effort in written consultation, while MTM identity could not moderate the relationship between frequency effort and SQ in telephone-consultation services. Further, the leader identity negatively moderated the relationship between article effort and SQ in telephone consultations. Thus, for doctors with the leader identity, the impact of article effort on SQ was weaker. In summary, both aspects were proved to play important roles in individual SQ.

This study provides empirical findings through focusing on the SQ of both written and telephone consultations in OHCs, thereby enabling healthcare providers to take initiatives and ultimately improve the efficiency and provision of delivered healthcare services. It is worth mentioning that doctors possessing the identity of team leader should be cautious that the more articles published by them may not lead to envisaged telephone-consultation performance, according to the negative moderating effect of leader identity on the relationship between article effort and SQ during telephone consultations.

This study contributes to the OHC literature by investigating how doctors' efforts and identity in OHCs affect individual performance, based on attribution theory and identity theory. Further, we provide healthcare practitioners with an improved understanding of these dimensions to improve autonomy regarding service provision in OHCs.

The purpose of this study is to examine the mechanism of benevolent leadership on employee cheating behavior through two paths – employee uncertainty and perceived acceptability of norm violation – and also reveal the possible dark side of benevolent leadership. Meanwhile, the moderating effects of leader behavioral integrity in the cognition dual path process are also discussed.

This study invites 383 employees and their superiors in seven Chinese enterprises as the research objects and conducts a paired survey at three time points, and then Mplus 7.4 software is used to analyze the empirical data.

The results are shown as follows. Benevolent leadership plays a positive role on uncertainty and perceived acceptability of norm violation. Uncertainty and perceived acceptability of norm violation mediate the relationship between benevolent leadership and cheating behavior, respectively. Leader behavioral integrity moderates the positive role of benevolent leadership on uncertainty and perceived acceptability of norm violation. Leader behavioral integrity moderates the indirect effect of benevolent leadership on employees' cheating behavior through uncertainty and perceived acceptability of norm violation.

This study reveals the mechanism behind the negative role of benevolent leadership through the cognition reaction of employees to benevolent leadership and broadens the research scope of benevolent leadership. Meanwhile, it provides some practical inspiration for leaders to effectively use the benevolent leadership style and restrain employees' cheating behavior.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Based on the moral licensing theory, this study aims to reveal the mechanism of self-sacrificial leadership inducing abusive supervision from two paths of leader moral credit and leader moral credential. At the same time, it also discusses the moderating effect of leader behavioral integrity on the two paths.

In this study, 434 employees and their direct leaders from six Chinese companies were investigated in a paired survey at three time points, and the empirical data was analyzed using Mplus 7.4 software.

Self-sacrificial leadership has a positive effect on leader abusive supervision through the mediating role of leader moral credit and leader moral credential. In addition, this study also finds that leader behavioral integrity is the “gate” for self-sacrificial leadership to promote abusive supervision, and the leader behavioral integrity has a moderating effect on the process of self-sacrificial leadership influencing on leader moral credit and leader moral credential.

This study explores the evolution of self-sacrificial leadership from “good” to “bad” from the perspective of moral licensing and broadens the research on the mechanism and boundary conditions of self-sacrificial leadership. At the same time, it also provides important reference value for preventing the negative effects of self-sacrificial leadership in organizations.

This paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.

This paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.

A series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.

The proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

This paper aims to obtain a texture dental model with real images and improve the rendering effect of the dental model.

The paper proposes a semiautomatic method to construct a realistic dental model with real images based on two-dimensional/three-dimensional (2D/3D) registration. First, a 3D digital dental model and three intraoral images are obtained by a 3D scanner and digital single-lens reflex camera. Second, the camera projection poses for every intraoral images are calculated by using the single-objective optimization algorithm. Third, with camera poses, the preliminary projection texture mapping is performed; besides, the seam between two textures is marked. Finally, the marked regions are fused based on the image pyramid to eliminate obvious seams.

The paper provides a method to construct a realistic dental model. The method can map three intraoral images to the dental model. The experimental results show that the textured dental model without obvious distortion, dislocation and seams is constructed with simple interactions.

The proposed method can be applied to the digital smile design system to improve the communication efficiency between doctors, patients and technicians.