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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.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.

Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.

Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.

To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。

使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。

综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。

本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。

Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.

Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.

La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.

Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.

This study aims to investigate the effect of supervisor bottom-line mentality (SBLM) on subordinate work well-being using self-determination theory. Furthermore, it examines the mediating role of subordinate harmonious work passion (HWP) and obsessive work passion (OWP), as well as the moderating role of subordinate family motivation on the indirect effect of SBLM on subordinate work well-being.

The authors conducted two studies, an experiment and a field study, to test the hypotheses. In Study 1, the authors conducted an experimental study using a sample of 127 undergraduate students to examine how family motivation moderates the relationship between SBLM and subordinate work passion. Concurrently, in Study 2, the authors conducted a time-lagged field study involving 261 corporate employees in China to validate the findings derived from Study 1, as well as test the entire conceptual model.

The authors find in Study 1 that family motivation moderates the effects of SBLM on subordinate HWP and OWP. Nevertheless, Study 2 uncovers a negative association between SBLM and subordinate work well-being, with HWP and OWP mediating this relationship. Besides, family motivation moderates the mediating effect of HWP on the relationship between SBLM and subordinate work well-being.

The main contribution of this study is that the negative effect of SBLM impacts subordinate work well-being, thereby building an accurate and fine-grained knowledge base of the detrimental effects of bottom-line mentality (BLM). Additionally, this study expands the frontiers of knowledge in this area by investigating the mediating mechanisms and boundary conditions of SBLM on subordinate work well-being, effectively addressing a theoretical gap in BLM research.

In order to develop novel high EP S‐containing additives and to meet the need of formulating GL‐5 gear oil or other high EP lubricating oils, aims to investigate the tribological behaviors and mechanism of a di(iso‐butyl)polysulfide (DIBPS), which was synthesized from some cheap materials at low temperature and under normal atmospheric pressure, as an additive in some mineral base oils compared with the traditional sulfurized olefin (SO) additive.

The DIBPS additive was designed and synthesized, of which the main composition is the di(iso‐butyl)trisulfide. Its load‐carrying capacity, anti‐wear and friction reduction properties as additive in some mineral base oils, compared with the traditional SO additive, were investigated using a four‐ball machine and a Timken tester according to relative testing standards. The tribological mechanism was discussed according to the SEM and XPS analytical data.

The results indicate that the four‐ball P_D value and the Timken OK value of the prepared DIBPS in VHVIS500 is clearly better than that of the traditional SO; the anti‐wear property of DIBPS is equivalent to the traditional SO and the friction reduction effect of DIBPS is better than that of the traditional SO. The SEM and XPS data show that the DIBPS additive experiences different tribochemical reaction during tribological process compared with the traditional SO. The S active element of DIBPS reacted with surface metal mainly to form FeSO₄ and/or Fe₂(SO₄)₃ inorganic film, but the S active element of the traditional SO reacted with the surface metal mainly to form FeS inorganic film. This may be the chief reason why the prepared DIBPS possesses better EP properties than the SO.

The results show that the polysulfide additive (DIBPS) possesses better extreme pressure property than the traditional SO. However, more experimental study such as the synergic effect with other additives must be performed, from which it will be clearly shown whether the novel polysulfide can be applied in industrial oils.

These results may be useful for the researchers to formulate some high EP industrial oils.

This paper proves that the designed polysulfide additive, of which the main composition is the trisulfide, possesses better extreme pressure property than the traditional SO, and its tribological mechanicsm is also different with that of SO. It is noticed that the preparative method of this novel polysulfide additive has some superiorities, such as: low‐experimental temperature, low‐experimental pressure and cheap materials.

The purpose of this paper is to make objective descriptions on various money‐laundering techniques and to put forward countermeasures in order to combat money laundering more effectively and efficiently.

This paper based on 20 simplified money‐laundering cases, describes various money‐laundering techniques, analyses the reasons why these methods prevail, and points out the future efforts to be made in the fight against money laundering.

As usual, the ways of money laundering include cash smuggling, making use of banks or insurance company, or making use of shell‐company or front‐company. Nowadays, criminals also turn to real estate, lottery, international trade, offshore company to launder money. Sometimes lawyers, accountants are exploited by money launderers. With the wide use of electronic money and internet, criminals prefer to launder money through non‐face to face transactions. The fight against money laundering is the fight between justice and evil. It is of great importance to pierce the secret veil of money laundering so that we can combat money laundering more effectively and efficiently.

This paper prevents a comprehensive description of, and comments on, various money‐laundering techniques and future efforts to be made in the fight against money laundering, which would be beneficial to policy makers, enforcement authorities, and judicial professionals.

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied to incompressible flows at a low Reynolds number, which is not sufficient to satisfy the needs of aircraft engineering. Devoted to tackling the defect, the paper proposes a developed LBM combining the subgrid model and the multiple relaxation time (MRT) approach. A multilayer adaptive Cartesian grid method to improve the computing efficiency of the traditional LBM is also employed.

The subgrid model and the multilayer adaptive Cartesian grid are introduced into MRT-LBM for simulations of incompressible flows at a high Reynolds number. Validated by several typical flow simulations, the numerical methods in this paper can efficiently study the flows under high Reynolds numbers.

Some numerical simulations for the lid-driven flow of cavity, flow around iced GLC305, LB606b and ONERA-M6 are completed. The paper presents the investigation results, indicating that the methods are accurate and effective for the separated flow after icing.

LBM is developed with the addition of the subgrid model and the MRT method. A numerical strategy is proposed using a multilayer adaptive Cartesian grid method and its treatment of boundary conditions. The paper refers to innovative algorithm developments and applications to the aircraft engineering, especially for iced wing simulations with flow separations.

This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment.

It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models.

The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns.

This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

In order to encourage the high-water-consumption (HWC) manufacturers to carry out water-saving transformation relying on self-strength or outsourcing to a water-saving service company (WSSC) during production processes, government subsidies are provided according to water-saving efforts (WSE) or investment cost. In this context, the authors derive the participant's equilibrium decisions and the manufacturer's water-saving strategy. Additionally, the effects of subsidies on WSE and stakeholders' profits are discussed.

Mathematical models including optimization model and Stackelberg game model are constructed under different subsidy schemes.

The study finds that (1) there exists a threshold related to the subsidy coefficient for the HWC manufacturer when choosing between self-saving and outsourcing-saving. (2) When the technological competitive advantage between WSSC and manufacturer is within a certain range, government's subsidy promotes HWC enterprises to choose outsourcing-saving. (3) Given a water-saving mode, subsidy on investment cost is more effective for the government to achieve more environmental performance.

First, subsidy endogeneity can be considered to explore the optimal interval for government subsidies to maximize social welfare. Second, in outsourcing-saving, other types of contract can be discussed. Another extension is about model uncertainties. Finally, other policies on improving water efficiency can be also examined.

The paper includes implication for HWC manufacturers to select the best water-saving mode under subsidy, and it allows policymakers to understand the efficiency of proposed subsidies.

Decisions on water-saving efforts, selection of water-saving modes and operational planning are also regarded as business strategies in the paper. Particularly, the influences of different government subsidies are also considered and compared.

The purpose of this paper is to understand the effect of base media on the tribological performance and tribochemistry of bismuth thiophosphate additive.

The oil‐water double soluble additive bismuth dithiophosphate was prepared and identified. The contributions of the two base media on the additive tribological behavior and the tribofilm components were comparatively studied.

The extreme pressure (EP) and friction‐reducing properties are remarkably improved with water substituted for paraffin as the base medium. The EP performance of the lubricating media containing this additive mainly results from the tribochemical reaction film on the rubbing surface, not from the viscosity of the base media. In water or paraffin medium, the adsorption process of this additive from the lubricant bulk onto the rubbing surface and the components and the properties of the tribochemical reaction films formed are different, which have important effect on the tribological performance.

The paper mainly focuses on how the water medium with polarity and the liquid paraffin base medium with non‐polarity affect on the tribological performance of the bismuth thiophosphate additive.

The research has found a water‐oil double soluble lubrication additive with outstanding EP and friction‐reducing performance.

The designed experiment provides a new approach to further learn the action mechanism of thiophosphate additive.