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Fitness games, as a medium that combines playfulness and usefulness for exercise, face challenges in sustaining long-term user engagement. Currently, there is limited research exploring factors influencing users' continued intention to use from the perspective of user experience. Therefore, this study aims to investigate the priority of various user experience attributes of fitness games in promoting users' sustained engagement and to construct a user behavior model, offering theoretical guidance for designers and businesses.

This study distributed 441 survey questionnaires and, based on the fundamental characteristics of external games, established a model for users' continued intention to use external games. It explores the impact of various gaming elements on users' continued intention to use fitness games and the relationships between these elements.

The study indicates that usefulness, functional quality, and ease of use directly influence players' intention to continue playing external games. Social interactions, technical quality, and playfulness do not have an impact on the continued intention to use.

This research breaks away from the bias of previous studies overly focusing on playfulness in games. It fills the research gap regarding the continued intention to use fitness games and provides insights into the design and operation of fitness games.

The rise of interactive fitness games in the post-epidemic era has resulted in the need to establish a quality evaluation index system. This study aims to develop such a system and provide a reference for enhancing the quality of interactive fitness games.

To achieve this, interviews and questionnaires were conducted to identify the factors that influence the quality of interactive fitness games. The Kano model and SII (Satisfaction Increment Index)-Dissatisfaction Decrement Index (DDI) two-dimensional quadrant analysis were then used to explore differences in quality judgment between males and females, as well as their priorities for improving interactive fitness games.

The study revealed that males and females have different quality judgments for “rich and diverse content,” “motivational value,” “sensitive motion recognition detection” and “portability.” However, both genders share similar views on the other quality factors. In addition, the study identified differences in the priority of improvement between men and women. “Very interesting,” “effective fitness achievement,” “motivating fitness maintenance,” “sensitive motion recognition detection,” “portability” and “educational value” were found to be of higher priority for men than women.

These findings provide a valuable theoretical reference for developers and designers of interactive fitness games seeking to enhance the user experience.

The purpose of the study is to develop a cloud supporting model for green computing. In today's contemporary world, information technology (IT) plays a significant role. Because of the rapid growth of the IT business and the high level of greenhouse gas emissions, salient data centers are increasingly considering green IT techniques to reduce their environmental impacts. Both developing and underdeveloped countries are widely adopting green infrastructure and services over the cloud because of its cost-effectiveness, scalability and guaranteed high uptime. Several studies have investigated the fact that cloud computing provides beyond green information and communication technology (ICT) services and solutions. Therefore, anything offered over clouds also needs to be green to reduce the adverse influence on the environment.

This paper examines the rationale for the use of green ICT in higher education and finds crucial success variables for the implementation of green ICT on the basis of an analysis of chosen educational organizations and interviews with key academic experts from the Universities of Ethiopia, in general, and BuleHora University, in particular.

Finally, this paper described the design and development of a green cloud selection supporting model for green ICTs in higher educational institutions that helps cloud service customers choose the most green cloud-based ICT products as well as services.

This study may be a significant source of new information for green ICT design and implementation in higher education institutions to preserve the environment and its impact on human life.

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

Key core technology is the most important weapon of the country, and breaking through the “strangled” problem is one of the real problems that China’s emerging industries and enterprises must solve. Accurately identifying the “strangled” problem will help China accelerate the realization of high-level scientific and technological self-reliance and win the battle against key core technologies.

Combined with the characteristics of key core technologies, the key core technology evaluation system was constructed from four dimensions: technology innovation, technology radiation, technology economy and technology safety. We adopt the entropy TOPSIS method to evaluate the patents, and the patents with the top 5% scores are identified as key core technology patents. Then, this study identifies key core technology “strangled” problems in three dimensions: technology value advantage, competitive advantage and quantitative advantage.

Taking the patent data of the global new generation information technology industry from 2011 to 2023 as a sample, 178 moderately “strangled” technologies and 49 severely “strangled” technologies are selected. The study results are consistent with the current situation of the new generation information technology industry’s development, and verify the feasibility and reliability of the key core technology “strangled” problem identification model.

This study uses patent data to identify key core technologies and “Strangled” in the new generation information technology industry. It can provide a reference for relevant national departments and agencies, as well as universities and enterprises.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

The purpose of this study is to explore the influence of venture capital participation on corporate innovation and to assess how internationalization strategies (including both the internationalization scope and speed) in mediating serve as intermediaries in the relationship between venture capital and corporate innovation.

Using hierarchical regression analysis, this research tests the hypothesized framework using survey data collected from 442 high-tech enterprises listed on the A-share markets in Shanghai and Shenzhen, China, spanning from 2010 to 2019.

The study reveals a non-linear (U-shaped) correlation between venture capital investment and innovation. This non-linear linkage is facilitated through the execution of enterprises’ strategies for international expansion. The primary finding suggests that venture capital participation positively influences the rapidity and extent of internationalization. Additionally, a U-shaped relationship is observed between corporate innovation and both the speed and scope of internationalization.

This document contributes insights into the micro-level mechanisms that explain the effects of venture capital and internationalization strategy on corporate innovation. The results offer multinational corporations practical guidance for executing their internationalization strategies effectively and fostering innovation.

This research is aimed to examine how environmental pollution affects consumers' preference for self-improvement products.

Through a series of three experimental studies, this research substantiates our hypotheses by employing various manipulations of environmental pollution and examining different types of self-improvement products.

The research demonstrates that environmental pollution enhances consumers' preference for self-improvement products via the mediation of perceived environmental responsibility. And the effect is negatively moderated by social equity sensitivity.

The recurrent incidence of environmental pollution has elicited significant concern among the general public and academic scholars. An overwhelming majority of research examining the impact of pollution on consumer behavior has concentrated on its influence on environmentally friendly and healthy consumption patterns. Nevertheless, the current research proposes that pollution fosters a preference for products associated with self-improvement, mediated by perceived environmental responsibility, with the effects being moderated by social equity sensitivity.

With the help of a quasi-natural experiment on Chinese policies, this study aims to understand the actual contribution of Smart City (SC) policies to the development of information and communications technology (ICT) in different cities. It also discusses the social and digital differences that such policies may generate, with a particular focus on the potential for exacerbating urban inequalities.

To achieve this, the study employs a principal component analysis (PCA) to develop an ICT development indicator system. It then employs a difference-in-differences (DID) model to analyze panel data from 209 Chinese cities over the period from 2007 to 2019, examining the impact of SC policies on ICT development across various urban settings.

Our findings show that SC policies have significantly contributed to the enhancement of ICT development, especially in ICT usage. However, SC policies may inadvertently reinforce developmental disparities among cities. Compared to less developed areas, the benefits of SC policies are more pronounced in economically booming cities. This is likely due to the agglomeration of the ICT industry and the strong allure of developed urban centers for high-caliber talent.

This study contributes to the related literature by explaining the role of SC policies in driving ICT development and by focusing on the often-overlooked impact of SC policies on urban inequality. These findings can provide guidance to policymakers on the need to recognize and address existing urban inequalities.

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.