Yuting Lv, Jiawei Guo, Weimin Huang, Yaojie Liu, Wentao Liu and Guijiang Wei
The purpose of this paper is to improve the bioactivity of variable gradient TC4 porous scaffolds prepared by selective laser melting (SLM) through the micro-arc oxidation (MAO…
Abstract
Purpose
The purpose of this paper is to improve the bioactivity of variable gradient TC4 porous scaffolds prepared by selective laser melting (SLM) through the micro-arc oxidation (MAO) technique.
Design/methodology/approach
Variable gradient TC4 porous scaffolds were prepared by SLM, then treated with MAO at different oxidation voltages. The microstructure, thickness and composition of MAO coatings were characterized by scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS) and X-ray diffraction. The bioactivity of the MAO coatings was tested by simulated body fluid (SBF) immersion test.
Findings
SEM and EDS results show that with the increase of oxidation voltage, the content of Ca and P elements and the thickness of the MAO coatings increases. The thickness of the coating inside the scaffold is smaller than that of the outside regions. SBF immersion experiments showed that MAO-treated TC4 porous scaffolds had highest bioactivity at 440 V.
Originality/value
The variable gradient porous scaffolds were treated with MAO in the electrolyte containing Ca and P elements for the first time. The effect of oxidation voltages on the different region of porous scaffolds was studied in detail.
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Jiaying Chen, Cheng Li, Liyao Huang and Weimin Zheng
Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
目的
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。
设计/方法/途径
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。
研究结果
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。
实践意义
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。
原创性/价值
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。
Objetivo
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.
Diseño/metodología/enfoque
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.
Conclusiones
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.
Implicaciones prácticas
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.
Originalidad/valor
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.
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Manzoor Ahmad, J. Luo, Ben Xu, Hendra Purnawali, Peter King, Paul Chalker, Yongqing Fu, Weimin Huang and Moshen Miraftab
Shape memory polyurethanes (SMPUs) are typically synthesized using polyols of low molecular weight, Mw, and high hydroxyl number as it is believed that high density of cross-links…
Abstract
Shape memory polyurethanes (SMPUs) are typically synthesized using polyols of low molecular weight, Mw, and high hydroxyl number as it is believed that high density of cross-links in these polyols are essential for high performance shape memory polymers. In this study, polyethylene glycol (PEG-6000) with Mw ~ 6000 g/mol and low hydroxyl number (OH ~ 18 mg K OH/g) as the soft segment and diisocyanate as the hard segment were used to synthesize SMPUs. It revealed that although the PEG-6000 based SMPUs have lower maximum elongation at break (425%) and recovery stress than those of PCL-2000 polyol based SMPUs, they have much better shape recovery ratio (98%) and shape fixity (95%). Furthermore, these SMPUs showed a much shorter actuation time of <10sec for up to 85% shape recovery, much shorter than those low Mw SMPUs, clearly demonstrated their great potential for applications.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
目的
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。
设计/方法/方法
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。
研究结果
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。
原创性/价值
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。
Propósito
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.
Diseño/metodología/enfoque
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.
Hallazgos
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.
Originalidad/valor
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.
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Liyao Huang, Cheng Li and Weimin Zheng
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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).
Practical implications
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.
Originality/value
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.
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Abstract
Purpose
The purpose of the study was to investigate both the positive and negative effects of workplace loneliness on innovative behavior. By applying the unified theory on contingencies of self-worth, the study aimed to integrate these effects into a single framework, thereby confirming the presence of the double-edged sword effect of workplace loneliness on innovative behavior.
Design/methodology/approach
A survey was conducted among enterprises across China, involving 246 employees. Hierarchical regression analysis was utilized to test the moderating hypotheses. Additionally, the mediating effects and the moderated mediation effects were further explored using the bootstrapping method.
Findings
The results indicated that workplace loneliness positively influenced innovative behavior through the desire to prove ability, with the promotion regulatory focus enhancing this relationship. Conversely, workplace loneliness negatively influenced innovative behavior through self-handicapping, with the prevention regulatory focus intensifying this relationship.
Practical implications
The findings revealed that workplace loneliness exerts a double-edged effect on innovative behavior. Lonely employees can enhance their sense of self-worth by engaging in domain switching, thereby alleviating feelings of loneliness.
Originality/value
The research confirmed a novel perspective: workplace loneliness can promote innovative behavior by influencing employees’ desire to prove ability. It also revealed the double-edged sword effect of workplace loneliness on innovative behavior. Based on these findings, employees experiencing loneliness can enhance their self-worth and alleviate feelings of loneliness through domain switching.
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Li Zhang, Haiyan Fang, Weimin Bao, Haifeng Sun, Lirong Shen, Jianyu Su and Liang Zhao
X-ray pulsar navigation (XPNAV) is an autonomous celestial navigation technology for deep space missions. The error in the pulse time of arrival used in pulsar navigation is large…
Abstract
Purpose
X-ray pulsar navigation (XPNAV) is an autonomous celestial navigation technology for deep space missions. The error in the pulse time of arrival used in pulsar navigation is large for various practical reasons and thus greatly reduces the navigation accuracy of spacecraft near the Earth and in deep space. This paper aims to propose a novel method based on ranging information that improves the performance of XPNAV.
Design/methodology/approach
This method replaces one pulsar observation with a satellite observation. The ranging information is the difference between the absolute distance of the satellite relative to the spacecraft and the estimated distance of the satellite relative to the spacecraft. The proposed method improves the accuracy of XPNAV by combining the ranging information with the observation data of two pulsars.
Findings
The simulation results show that the proposed method greatly improves the XPNAV accuracy by 70% compared with the conventional navigation method that combines the observations of three pulsars. This research also shows that a larger angle between the orbital plane of the satellite and that of the spacecraft provides higher navigation accuracy. In addition, a greater orbital altitude difference implies higher navigation accuracy. The position error and ranging error of the satellite have approximately linear relationships with the navigation accuracy.
Originality/value
The novelty of this study is that the satellite ranging information is integrated into the pulsar navigation by using mathematical geometry.
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Shujun Zhou, Bingzhen Sun, Weimin Ma and Xiangtang Chen
The purpose of this paper is to present a new method and model for determining the optimal decision-making for the pricing strategy to the Fuji apple in Shaanxi of Chain which is…
Abstract
Purpose
The purpose of this paper is to present a new method and model for determining the optimal decision-making for the pricing strategy to the Fuji apple in Shaanxi of Chain which is representing fresh agricultural products under the e-commerce environment.
Design/methodology/approach
Considering the rapid development of information technology as well as internet that actually motivate the e-commerce, Fuji apple is a distinctive product in China’s Shaanxi; its sales channels have extended to online sales under the wave of e-commerce. Internet trading platforms make it possible to trade online in real time between suppliers and customers who live in different geographical areas. In this paper, the authors study how to price online to maximize the total revenue. The challenge is to optimally price two different qualities of apple. Based on the consumer surplus theory, the authors use the method that builds the function of the relationship between the proportion of consumers purchasing different qualities of products and price.
Findings
This paper presents a generalized model to determine the optimal pricing that maximizes the total revenue of a fruit grower over a finite planning horizon. The authors divided discount into two intervals and discussed the optimal discounting and pricing at both intervals. Then they determined the optimal pricing strategy for Fuji apple in Shaanxi of Chain under the e-commerce environment.
Originality/value
This paper makes up for the lack of existing studies of pricing under the e-commerce environment. A new method and approach to the traditional pricing strategy is established and applied to a management decision-making problem with Chinese characteristics in reality.
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Chien-Hsiung Chen and Weimin Zhai
The implementation of interaction design concepts to facilitate communication between users and shopping websites has gained increasing attention in recent years. Mouse hover is a…
Abstract
Purpose
The implementation of interaction design concepts to facilitate communication between users and shopping websites has gained increasing attention in recent years. Mouse hover is a vital interaction method for users to access shopping sites and significantly impacts their decision-making experience. A well-designed mouse hover function can effectively enhance the user's search performance and improve the user experience. The purpose of this study is to investigate whether the dynamic prompt designs at the hover position and the degree of feedback transparency may affect the user's task performance and personal feelings when operating the hover function on the shopping website.
Design/methodology/approach
The study employed two independent variables in the experiments: dynamic prompt and background transparency of hover feedback. A between-subject design of 2 (single flicker and continuous flicker) × 3 (transparency at 0%, 25%, and 50%) was adopted in the experiment. A total of 60 participants were invited to participate in the experiment using the purposive sampling method. Participants were asked to complete four operation tasks, and the time of each was recorded. They were then asked to complete the system usability scale (SUS) questionnaire and conduct subjective evaluations before they were briefly interviewed.
Findings
The generated results revealed that: (1) In the interaction with a shopping website, the degree of background transparency of hover feedback affected the participants' task performance. (2) The effect of a single flicker had significantly higher subjective evaluation results regarding the degree of rationality compared with a continuous flicker. (3) The participants' perceptions of the information clarity were affected when the background transparency of hover feedback was 50%. (4) The effect of a continuous flicker was better than that of a single flicker when the background transparency of hover feedback was 25%. The participants' attraction to a continuous flicker was significantly higher than that of a single flicker. Nonetheless, when the degree of background transparency was at 0% or 50%, the results were the opposite.
Originality/value
The findings generated from the research can be a reference for the development of hover operation in the user interface design for shopping websites.
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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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.