Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.

The use of the required navigation performance (RNP) procedure has been increasing for aircraft navigation, since it allows for better optimisation of the airspace, which is increasingly congested. The present work aims to investigate the application of the functional resonance analysis method (FRAM), combined with the quantitative analysis provided by the Bayesian belief network (BBN), to demonstrate the existing variability in functions that are part of the complex navigation system based on the RNP procedure, specifically when the aircraft approaches the airport (approach phase). As a result, it is possible to analyse the variability that occurs in the studied system and the BBN complemented the study by allowing a quantitative interpretation of the functions considered most important for the execution of an RNP approach procedure.

A Bayesian Belief Network (BBN) for assessing the potential risk of dengue virus emergence and distribution in Western Australia (WA) is presented and used to identify possible hotspots of dengue outbreaks in summer and winter. The model assesses the probabilities of two kinds of events which must take place before an outbreak can occur: (1) introduction of the virus and mosquito vectors to places where human population densities are high; and (2) vector population growth rates as influenced by climatic factors. The results showed that if either Aedes aegypti or Ae. albopictus were to become established in WA, three centres in the northern part of the State (Kununurra, Fitzroy Crossing, Broome) would be at particular risk of experiencing an outbreak. The model can also be readily extended to predict the risk of introduction of other viruses carried by Aedes mosquitoes, such as yellow fever, chikungunya and Zika viruses.

In recent years, cooperation of heterogeneous multiagents (HMAs) has achieved formidable results and gained an increasing attention by researchers. This paper presents an entertainment robot system based on the HMAs' cooperation. This robot is used to distinguish the basic information of a person from the audience and entertain him in the exhibition hall. The main agents include the Arbitration System (AS), the Face Recognition System (FRS), the Position Cognition System (PCS), and the Behavior Planner (BP), which are carved up according to their respective functions. Each agent completes its own task independently and the robot finishes the whole mission through their cooperation. The hybrid control architecture and the data-fusion algorithm based on Bayesian Belief Network are also discussed in this paper.