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The authors examine whether Chinese banks use loan loss provisions (LLPs) for capital management, income smoothing and signaling purposes, and assess the effect of the recent regulatory changes following the implementation of Chinese Basel III on such behavior.

The authors use a unique set of hand-collected data on bank capital combined with financial data downloaded from the China Stock Market and Accounting Research (CSMAR) database. Multivariate regression models are used to test our hypotheses.

The authors find that while there is no evidence to suggest capital management practice before the Chinese Basel III, the implementation of the new regulations induced listed banks to manage tier-1 capital via LLPs. The authors also find strong support that Chinese banks engage in income smoothing via LLPs management, and there is no change in such tendency following the issuance of Chinese Basel III. Lastly, the authors do not find support for the signaling behavior by Chinese banks using LLPs.

The authors’ evidence suggests that elevated tier-1 capital and provisioning requirements may induce capital management by banks, which indicates a potential unintended effect brought forth by the new Basel regulations.

To the best of authors’ knowledge, this study is the first to examine Chinese banks' behavior relating to LLPs in terms of capital management, income smoothing and signaling. In particular, the authors use a sample containing a large number of Chinese commercial banks – previously a major data issue in other studies.

This paper aims to examine how auditors respond through audit fees and audit quality following disciplinary actions imposed by audit regulators in an emerging market setting.

This paper uses the disciplinary actions in 2017 against two major audit firms in China as an exogenous shock to examine the effect of tougher enforcement actions on auditor behavior as reflected in their emended audit fees and audit quality. This paper sampled from publicly listed firms in China with requisite data for the period 2015 through 2018. Using a difference-in-differences model, this paper examines whether the enforcement action (i.e. the suspension of audit firms) significantly impacted the audit fees and audit quality for clients of the disciplined audit firms (hereafter, suspended audit firms) in the two-year period postsuspension relative to audit firms that were not disciplined (hereafter, nonsuspended audit firms).

This paper finds evidence of increased audit fees and improved audit quality by the suspended audit firms relative to the nonsuspended audit firms in the two-year period postsuspension. These findings suggest that in contrast to symbolic disciplinary actions such as public censures documented in prior literature (Boone et al.,2015), tougher punitive disciplinary actions are followed by an increase in audit fees and an improvement in audit quality by the suspended audit firms. This paper also finds that the deterrent effect from the audit firm suspension is exclusive to the penalized audit firms and had no positive spillover effects on their peers.

A limitation of this study is the focus on the effect of audit firm suspension against two large local audit firms in China. Given the unique characteristics of the Chinese audit market and the Chinese regulatory environment, our findings may not be generalizable to audit firms in other countries and jurisdictions, especially where the audit market is dominated by the international Big 4 auditors that possess greater brand name capital than second-tier local audit firms.

This paper provides novel evidence on the impact of strengthened enforcement on auditor behavior in an emerging market setting. This paper contributes to the existing literature examining the impact of regulatory interventions on financial reporting outcomes and audit quality. While there is evidence on how regulations affect financial statement preparers’ demand for high audit quality, there is limited research on how regulatory interventions affect auditor’s incentive to supply higher audit quality. This paper also contributes to the scant existing evidence on the effect of disciplinary actions against audit firms in emerging economies.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues.

In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel.

The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.

The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

The equations take into account structural and geometrical parameters (sizes and a configuration of the synapse, geometrical properties of the neuron membrane, local changes of concentrations and potential) and determine changes of concentrations and the potential on the membrane and in hemispheres for discrete areas of the cytoplasm and the adjacent membrane part. Their investigation demonstrated that the equations may form the basis of sufficiently complete neuron description with due allowance for the fundamental physico‐chemical laws of motion and for the structural properties of separate parts of the nerve‐cell.

Existing tourism review articles have limited review topics and cover a relatively short period. This review paper aims to extend the coverage of the previous literature and enhances the completeness of tourism-related studies to provide comprehensive tourism-related literature from 1945 (World War II onward) to 2022. Specifically, this paper reveals the major research themes present in published tourism research during this time period and highlights the evolution of tourism research from the preliminary phase, the transversal phase, to the growth phase.

The present study visualizes tourism research through networks of coauthors and their countries and regions, cocitation analysis of keywords and explores the thematic evolution of tourism research after the World War II (i.e., 1945–2022) from Web of Science and Google Scholar through bibliometric analysis.

Findings reveal that the themes of tourism research in the past years can be divided into seven major research themes. The tourism research evolution from World War II to 2022 can be categorized into three stages: preliminary (1945–1970), transversal (1971–2004) and growth (2005–2022). In addition, the research themes of tourism are not static but evolve according to the dynamics of the society and the industry, and that seven main research themes have been formed, namely, “heritage tourism,” “medical tourism,” “adventure tourism,” “dark tourism,” “sustainable tourism,” “rural tourism” and “smart tourism.”

The present study expands and refines the comprehensive literature in tourism research, as well as reveals the trends and dynamics in tourism research through network analysis and thematic evolution research methods.

现有的旅游评论文章在审查主题方面有限, 并且涵盖的时间相对较短。本综述文章扩展了先前文献的涵盖范围, 增强了与旅游相关研究的完整性, 提供了从1945年（第二次世界大战之后）到2022年的全面旅游相关文献。具体而言, 本文揭示了此期间发表的旅游研究中的主要研究主题, 并突出了旅游研究从初步阶段、横向阶段到增长阶段的演变。

本研究通过共同作者及其国家的网络、关键词的共同引用分析, 将旅游研究可视化, 并探索二战后旅游研究的主题演变。本研究通过文献计量学分析, 将 Web of Science (WoS) 和 Google Scholar 中的旅游研究（即 1945–2022 年）可视化。

研究结果显示, 过去几年的旅游研究主题可分为七大研究主题。从第二次世界大战到 2022 年的旅游研究演变可分为三个阶段：初步阶段（1945–1970 年）、横向阶段（1971–2004 年）和成长阶段2005–2022 年）。此外, 旅游的研究主题并不是静态的, 而是根据社会和行业的动态而演变, 形成了七个主要研究主题, 即“遗产旅游”、“医疗旅游”、“冒险旅游”、“黑暗旅游”、“可持续旅游”、“乡村旅游”和“智慧旅游”。

本研究通过网络分析和主题演变研究方法扩展和完善了旅游研究方面的综合文献, 并揭示了旅游研究的趋势和动态。

Los artículos de revisión existentes sobre turismo tienen temas de revisión limitados y cubren un periodo relativamente corto. Este artículo de revisión amplía la cobertura de la bibliografía anterior y mejora la exhaustividad de los estudios relacionados con el turismo para ofrecer una bibliografía exhaustiva sobre el turismo desde 1945 (Segunda Guerra Mundial en adelante) hasta 2022. En concreto, este documento revela los principales temas de investigación presentes en la investigación turística publicada durante este periodo de tiempo y destaca la evolución de la investigación turística desde la fase preliminar, la fase transversal, hasta la fase de crecimiento.

El presente estudio visualiza la investigación turística a través de redes de coautores y sus países y regiones, análisis de co-citación de palabras clave, y explora la evolución temática de la investigación turística después de la Segunda Guerra Mundial (es decir, 1945–2022) a partir de Web of Science y Google Scholar mediante análisis bibliométricos.

Los resultados revelan que los temas de la investigación turística de los últimos años pueden dividirse en siete grandes temas de investigación. La evolución de la investigación turística desde la Segunda Guerra Mundial hasta 2022 puede clasificarse en tres etapas: preliminar (1945–1970), transversal (1971–2004) y de crecimiento (2005–2022). Además, los temas de investigación del turismo no son estáticos, sino que evolucionan según la dinámica de la sociedad y de la industria, y que se han formado siete temas principales de investigación, a saber: “turismo patrimonial”, “turismo médico”, “turismo de aventura”, “turismo oscuro”, “turismo sostenible”, “turismo rural” y “turismo inteligente”.

El presente estudio amplía y perfecciona la amplia bibliografía existente en el campo de la investigación turística, además de revelar las tendencias y la dinámica de la investigación turística mediante el análisis de redes y los métodos de investigación de evolución temática.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

The purpose of this research paper was to determine which needs for incentives influence positive word-of-mouth (PWOM) among international tourists and how these needs differ based on the cultural orientation toward individualism and collectivism.

This research used online surveys to gather data from 959 Australian, US, UK, Japanese and Korean respondents who had visited Beijing. A random sampling method was used, and data were analyzed using SmartPLS 4.0. By adopting the existence relatedness growth theory, the findings explain how cultural orientation affects the impact of needs for incentives in generating PWOM.

Three hypothesized relationships were significant for Australia/UK/USA and Japan/Korea – the effect of needs for incentives on motivation, the effect of motivation on PWOM and the effects of needs for incentives on PWOM were significant and positive for Australia/UK/USA and Japan/Korea. The effect of needs for incentive type on motivation varied across national populations, and the intensity of the effects of needs for incentive type on PWOM was also different. For Australian, UK and the US tourists, the honorary title was the most influential need to stimulate word-of-mouth motivation. The need for cultural learning was the most influential for Japanese and Korean tourists.

This research contributes to the literature by exploring and comparing the needs for incentives that influence PWOM among tourists from the perspective of individualism and collectivism. The results also increase the understanding of the relationships among needs for incentives, motivation and PWOM.

本研究旨在确定哪些激励需求会影响国际旅游者的积极口碑（PWOM）, 以及这些激励方式的影响在个人主义和集体主义文化取向的群体中有何不同。

本研究通过在线调查收集了 959 名曾到访北京的澳大利亚、美国、英国、日本和韩国的国际游客数据。研究采用了随机抽样的方法, 使用 SmartPLS 4.0 对数据进行了分析。基于ERG理论, 本研究解释了文化如何影响激励类型对积极口碑的影响。

研究结果显示, 三个假设关系在澳大利亚/英国/美国和日本/韩国两个群组中均显著, 即激励需求对动机的影响、动机对积极口碑的影响、以及激励需求对积极口碑的影响在澳大利亚/英国/美国和日本/韩国群组中都是显著和正向的。激励需求类型对动机和积极口碑的影响在不同国家群体中存在差异。对于澳大利亚、英国和美国的国际游客, 满足名誉需求的激励对产生积极口碑动机的影响更大。对于日本和韩国的国际游客, 满足文化学习需求的激励对产生积极口碑动机的影响更大。

本研究从个人主义和集体主义的角度, 探讨比较了影响国际旅游者积极口碑的激励需求, 为相关领域研究做出了贡献。研究结果加深了对激励需求、动机和积极口碑之间关系的理解。

El objetivo de esta investigación era determinar qué necesidades de incentivación influyen en el boca a boca positivo (PWOM por sus siglas en inglés) entre los turistas internacionales y cómo difieren estas necesidades en función de la orientación cultural hacia el individualismo y el colectivismo.

Esta investigación utilizó encuestas en línea para recopilar datos de 959 encuestados australianos, estadounidenses, británicos, japoneses y coreanos que habían visitado Pekín. Se empleó un método de muestreo aleatorio y los datos se analizaron con SmartPLS 4.0. Adoptando la teoría del Crecimiento de la Relación con la Existencia (ERG por sus siglas en inglés), los resultados explican cómo afecta la orientación cultural al impacto de las necesidades de incentivos en la generación del PWOM.

Tres relaciones hipotetizadas resultaron significativas en los dos grupos de Australia/Reino Unido/Estados Unidos y Japón/Corea: el efecto de las necesidades de incentivación en la motivación, el efecto de la motivación en la PWOM y los efectos de las necesidades de incentivación en la PWOM fueron significativos y positivos para Australia/Reino Unido/Estados Unidos y Japón/Corea. El efecto del tipo de necesidad de incentivación sobre la motivación varió entre las poblaciones nacionales, y la intensidad de los efectos del tipo de necesidad de incentivos sobre la PWOM también fue diferente. Para los turistas australianos, británicos y estadounidenses, el título honorífico fue la necesidad más influyente para estimular la motivación WOM. La necesidad de aprendizaje cultural fue la más influyente para los turistas japoneses y coreanos.

Esta investigación contribuye a la bibliografía al explorar y comparar las necesidades de incentivación que influyen en la motivación boca a boca entre los turistas desde la perspectiva del individualismo y el colectivismo. Los resultados también aumentan la comprensión de las relaciones entre las necesidades de incentivación, la motivación y el PWOM.

The purpose of this paper is to study the heat transfer effect of copper sensor and skin simulant on skin.

For the sensor, the physical and mathematical models of the thermal sensors were used to obtain the definite conditions in the heat transfer process of the sensor, and the heat transfer models of the two sensors were developed and solved respectively by using ANSYS WORKBENCH 19.0 software. The simulation results were compared with the experimental test results. For the skin, the numerical model of the skin model was developed and calculated. Finally, the heat transfer simulation performance of the two sensors was analyzed.

It is concluded that the copper sensor is more stable than the skin simulant, but the material and structure of the skin simulant is more suitable for skin simulation. The skin simulant better simulates the skin heat transfer. For all the factors in the model, the thermal properties of the material and the heat flux level are the key factors. The convective heat transfer coefficient, radiation heat transfer rate and the initial temperature have little influence on the results, which can be ignored.

The results show that there are still some differences between the experimental and numerical simulation values of the skin simulant. In the future, the thermal parameters of skin simulant and the influence of the thermocouple adhesion should be further examined during the calibration process.

The results suggest that the skin simulant needs to be further calibrated, especially for the thermal properties. The copper sensor on the flame manikin can be replaced by the skin simulant with higher accuracy, which will be helpful to improve the accuracy of performance evaluation of thermal protective clothing.

The application of computational fluid dynamics (CFD) technology can help to analyze the heat transfer simulation mechanism of thermal sensor, explore the influence of thermal performance of thermal sensor on skin simulation, provide basis for the development of thermal sensor and improve the application system of thermal sensor. Based on the current research status, this paper studies the internal heat transfer of the sensor through the numerical modeling of the copper sensor and skin simulant, so as to analyze the effect of the sensor simulating skin and the reasons for the difference.

In this paper, the sensor itself is numerically modeled and the heat transfer inside the sensor is studied.