In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. Background: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and...

Social network platforms like Reddit are increasingly impacting real-world economics. Meme stocks are a recent phenomena where price movements are driven by retail investors organising themselves via social networks. To study the impact of social networks on meme stocks, the first step is to...

In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the...

Current research on evaluating tourism’s ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can...

Current research on evaluating tourism's ecological efficiency using multi-source data fusion and graph neural networks has notable limitations. At the data level, integrating diverse sources is difficult due to differences in format, quality, and meaning. Data cleaning and preprocessing can...

This study explores the dynamics of industrial value chains from the perspective of directed graph neural networks (DGNNs). This study focuses on the effects of environmental, social, and governance (ESG) factors on industrial value extension. The industrial value chain, which encompasses a...

Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel...

The study quantifies the impact of resilience enablers on various business outcomes affected during a supply chain (SC) disruption faced by the Indian automobile industry. This impact is captured using the resilience index that helps in benchmarking resilience across SCs.Graph theoretic and...

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph...

Urban functional areas, as an important fundamental component of urban planning, hold significant importance for optimizing urban layouts, resource allocation, and the rational organization of socio-economic activities. Urban functional area division using traditional methods often has...

This study presents a prescriptive analytics framework for optimal intelligent predictive maintenance of railway tracks. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway infrastructure and enhance operational efficiency and safety....

This study examines the evolution of public debt among the 27 EU member states using Graph Theory tools; the Threshold Weighted-Minimum Dominating Set (TW-MDS) and the k-core decomposition method, alongside a standard network quantitative metric, the density. By separating the data into three...

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based...

Ride-hailing services are part of intermodal transport systems, allowing passengers to use various transport modes for their trip. The optimal choice for a request in the intermodal system depends on the passenger's waiting time for the ride-hailing service. Estimating this waiting time is...

The growing popularity of shared electric moped scooters (E-mopeds) as a sustainable and convenient transport option faces a fundamental challenge: demand often exceeds supply, necessitating efficient real-time relocation strategies. To address this, we propose an unmet demand-driven relocation...

To support energy transition and climate action, China has announced its "dual carbon" goals and introduced a series of related policies. However, these policies remain disorganised and scattered across various platforms and formats, posing significant challenges for accessibility and effective...

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local...

The global crude oil trade network, a cornerstone of energy supply chains, suffers from structural inefficiencies that amplify environmental damage and geopolitical risks. This study aims to (1) develop an optimization model reconciling logistical costs with CO2 emissions, (2) quantify energy...

The growing geopolitical tensions and regional instabilities in Eurasia raise urgent questions about the resilience of trade routes and infrastructure connectivity. This paper applies a graphbased approach to model the EU-Asia trade network and explores Armenia's potential as a transit hub...