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Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem. [ABSTRACT FROM AUTHOR]

Renewable energy sources are cheap to produce and have no upfront costs. It can take many different forms, including solar, wind, geothermal, hydroelectric, oceanic, and biological. There are numerous renewable energy resources in Oman, especially wind and solar. The kinetic energy of moving air is transformed into electrical energy to create wind power. This electrical energy has been used to pump water from the sea to tiny artificial ponds close to the wind farms, where the water is desalinated, used for agriculture, and then stored. The research attempts to identify the best locations for windmills that transfer seawater to small ponds. The various parameters are considered to select a suitable site for wind energy mapping, such as wind speed, elevation, distance to the main road, distance to an urban area, and land cover or land use. In order to choose appropriate locations for windmill mapping, data mining techniques are used. Based on parameters applied to classification and clustering techniques, a few wind mill sites are identified as suitable.