This study investigates how digital transformation influences visitor satisfaction at 12 World Heritage Sites (WHS) across eight coastal provinces in Eastern and Southern China. Utilizing 402 valid survey responses, it explores the impact of demographic factors—education, age, and income—on visitors’ perceptions of digital services, particularly focusing on usability, quality, and overall experience. The findings reveal that younger, higher-income, and STEM-educated visitors express significantly higher satisfaction with digital services, while older, lower-income visitors report lower levels of engagement and satisfaction. This research highlights the need for tailored digital strategies that cater to diverse demographic groups, ensuring the balance between technological innovation and the preservation of cultural authenticity at heritage sites. The originality of this study lies in its focus on non-Western contexts, particularly China’s rapidly developing coastal regions, which have been largely overlooked in the global discourse on digital tourism. By applying established theoretical frameworks—such as the Technology Acceptance Model (TAM) and Expectation-Confirmation Theory (ECT)—to a non-Western setting, this research fills a crucial gap in the literature. The insights provided offer actionable recommendations for heritage site managers to enhance visitor engagement, adapt digital services to demographic variations, and promote sustainable tourism development.

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

The increase in energy consumption is closely linked to environmental pollution. Healthcare spending has increased significantly in recent years in all countries, especially after the pandemic. The link between healthcare spending, greenhouse gas emissions and gross domestic product has led many researchers to use modelling techniques to assess this relationship. For this purpose, this paper analyzes the relationship between per capita healthcare expenditure, per capita gross domestic product and per capita greenhouse gas emissions in the 27 EU countries for the period 2000 to 2020 using Error Correction Westerlund, and Westerlund and Edgerton Lagrange Multiplier (LM) bootstrap panel cointegration test. The estimation of model coefficients was carried out using the Augmented Mean Group (AMG) method adopted by Eberhardt and Teal, when there is heterogeneity and cross-sectional dependence in cross-sectional units. In addition, Dumitrescu and Hurlin test has been used to detect causality. The findings of the study showed that in the long run, per capita emissions of greenhouse gases have a negative effect on per capita health expenditure, except from the case of Greece, Lithuania, Luxembourg and Latvia. On the other hand, long-term individual co-integration factors of GDP per capita have a positively strong impact on health expenditure per capita in all EU countries. Finally, Dumitrescu and Urlin’s causality results reveal a significant one-way causality relationship from GDP per capita and CO₂ emissions per capita to healthcare expenditure per capita for all EU countries.

This study determines the efficiency and productivity of Mexico’s urban and rural municipalities in generating economic welfare between 1990 and 2020. It establishes the incidence of context and space on efficiency, using Data Envelopment Analysis, the Malmquist-Luenberger Metafrontier Productivity Index, and Nonparametric Regression. The results indicate that 4 of the 2456 municipalities analyzed were efficient, that productivity increased, and that context and space influenced efficiency. This highlights the need for policies that optimize resource utilization, enhance investment in education, stimulate local business development, encourage inter-municipal cooperation, reduce rural-urban disparities, and promote sustainability.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.