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The purpose of this paper is to study the unit train make-up scheme for loaded direction in the heavy haul railway.

A 0-1 nonlinear integer programming model with the aim of minimizing the idling period between actual train arrival time and expected train arrival time for all loaded unit trains are proposed.

The proposed model is applied into a case study based on Daqin heavy haul railway. Results show that the proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

The proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

The purpose of this study is to discover the uniqueness of China's online livestreaming industry and to make preliminary predictions about its future.

Providing a panoramic description of China's online livestreaming industry and a comparative analysis with other countries' online livestreaming industries.

China's online livestreaming industry is world leader in general. In terms of user size and age structure, although China has a high proportion of online livestreaming users, the proportion of anchors is not high, while the age structure of users in both China and other countries shows the characteristics of younger users. In terms of policy, the difference between China and foreign support in online livestreaming is small, but developed countries have stricter regulations.

The research in this paper has great significance in gaining an in-depth understanding of the development status and trends of China's online livestreaming industry and in designing accurate support policies.

This study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).

Using the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.

The study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.

The novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.