To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.

This study examines the bottleneck effect of logistics performance on Vietnam’s imports, utilizing bilateral trade data from 2007 to 2022. We evaluate the impact of logistics performance on imports of Vietnam using the augmented gravity model and a random effects estimator. Our findings reveal that the minimum logistics performance between Vietnam and its trading partners has a significantly positive impact on the Vietnamese imports. The magnitude of its bottleneck effects is much larger than the influence of Vietnam’s individual logistics performance or deviations in performance with its trading partners. Recognizing the impact of logistics bottlenecks on international trade enables policymakers to develop more effective and efficient logistics-related policies for enhancing bilateral trade with trading partners.

This paper explores the role of the agile approach in managing interorganizational relationships in innovation networks. Design/methodology/approach. Relevant literature related to agile team management, network theory, innovation theory and knowledge management was studied. Based on collaboration between different approaches, a conceptual model for agile management of an innovation network was generated. Conceptual modeling was supplemented with graphical notation (diagram) of the main elements of the model. At the stage of testing the conceptual model, the action research method was applied, which provides an opportunity for organizational innovations to be carried out with the participation of researchers. The object of the pilot implementation of the conceptual model is the Bulgarian division of a global non-governmental organization (NGO) dedicated to community service. The organizational innovation applied in the testing of the model is related to improving the communication environment between individual teams (clubs), which are autonomous, but in the conditions of a network can generate projects for common, large-scale initiatives for community service. Findings. The pilot testing of the model shows its applicability, insofar as a traditionally managed structure switches to an agile communication model, in which horizontal connections become more frequent and knowledge between individual participants is transferred more efficiently. The possibility of decentralized decision-making creates the potential for generating numerous new and larger-scale initiatives for the benefit of the final beneficiaries. The participants in the network have also outlined some shortcomings, such as the need for better preliminary preparation when introducing organizational innovations in order to adequately explain and accept them.

Since the Reform and Opening up, GDP of the cities on eastern bank of the Pearl River Estuary in Guangdong Province were higher than the eastern bank cities. Therefore, this article aims to modify the urban gravity model combines it with the entropy weight method to calculate urban quality and applies it to measure the degree of connectivity between cities over the past decades. The research aims to explore whether cities with higher economic output have a greater attraction for surrounding cities, and whether the eastern bank cities can also promote the development of the west. Through detailed data collection and analysis, this essay reveals the dynamic changes of the gravity among cities and its influence factors such as economic, transportation and urban development. The research results indicate that the strongest gravitational force between cities on the east and west banks is between Dongguan and Zhongshan, rather than between Shenzhen and cities on the west bank. This demonstrates that the connection between cities on the east and west banks is primarily constrained by geographical factors, and the geographical location of a city influences on surrounding cities significantly. In particular, Dongguan and Zhongshan play a key role in connecting the eastern and western bank of the Pearl River Estuary, rather than Shenzhen, which is traditionally considered to have the highest economic aggregate. In addition, the study also found that the COVID-19 epidemic has had a significant impact on inter-city communication, resulting in a decline in inter-city gravity in recent years.

The Mass Rapid Transit (MRT) Purple Line project is part of the Thai government’s energy- and transportation-related greenhouse gas reduction plan. The number of passengers estimated during the feasibility study period was used to calculate the greenhouse gas reduction effect of project implementation. Most of the estimated numbers exceed the actual number of passengers, resulting in errors in estimating greenhouse gas emissions. This study employed a direct demand ridership model (DDRM) to accurately predict MRT Purple Line ridership. The variables affecting the number of passengers were the population in the vicinity of stations, offices, and shopping malls, the number of bus lines that serve the area, and the length of the road. The DDRM accurately predicted the number of passengers within 10% of the observed change and, therefore, the project can help reduce greenhouse gas emissions by 1289 tCO₂ in 2023 and 2059 tCO₂ in 2030.