Bird populations are important bioindicators, as the diversity of their habitats and their outstanding mobility make them sensitive to ecosystem changes. They also contribute to ecosystems by providing valuable services such as pest control, seed dispersal, and pollination, which are vital for maintaining ecological balance. Hence monitoring the composition of bird populations is essential for assessing ecosystem health and guiding conservation efforts. However, traditional observer-based surveys are expensive, time-consuming, and impractical for large-scale and hardly accessible applications. Our study proposes an approach by integrating automated acoustic monitoring with machine learning applications to develop a system that achieves high identification accuracy while keeping the pipeline simple. Our model design emphasizes a balance between efficiency and scalability, enabling deployment in resource-constrained environments. We provide a tool for low-complexity ecological monitoring by prioritizing lightweight and simple architectures. This work bridges the gap between ecological research and practical, scalable conservation technologies.

Reduced capacity on motorways can easily lead to significant congestion. This congestion is a major contributor to environmental pollution, harming the livability of the peri-urban environment and public health. In this study, we have addressed the congestion caused by lane closures on motorways, one of the many difficulties encountered in the lane closure problem. To overcome this problem, the so-called variable speed limit control, a traffic management system is a helpful tool that improves overall traffic flow characteristics - travel time, waiting time, and queue length - and reduces critical sustainability indicators such as fuel consumption and CO2and NOxemissions. Deep Learning has repeatedly been shown to be an excellent solution to this problem. Hence, this study aims to use Reinforcement Learning to address the traffic management system and to find a general solution to congestion caused by the reduction of highway capacity to apply the model regardless of the number of lanes, improving and surpassing the results achieved in the literature in several aspects.