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Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Apert syndrome is a congenital craniosynostosis syndrome comprising of bilateral coronal synostosis , symmetric syndactyly of hands and feet and midface hypoplasia. We present an atypical phenotype of this syndrome with right sided unilateral coronal synostosis. However, type I apert hand and other clinical and radiological features suggestthe diagnosis. Genetic analysis revealed an absence of the specific missense mutations in the FGFR 2 gene that is found in patients with this syndrome. We conclude that this patient represented a rare atypical variant of Apert syndrome. Further analysis is required to map the associated genotype.