Influence of Nesting Material Composition on Tick Tube Use by Peromyscus leucopus

Host-targeted acaricides are a valuable tool for the reduction of ticks and tick-borne disease. Tick tubes (also known as tick control tubes) are commercially available products containing permethrin-treated nesting materials. Through superficial acaricide application to Peromyscus mice, tick tubes reduce populations of the blacklegged tick, Ixodes scapularis Say. Results of prior field trials have varied, suggesting that mouse behavior as well as the scale of the intervention and the composition of the local host community are important determinants of efficacy. Here we evaluated behaviors related to nest material collection by P. leucopus. Two forms of nest materials used in commercial tick tube products (cotton batting and balls) were assessed through side-by-side comparisons over a four-week period. We quantified cotton uptake by monitoring weekly changes in material weight and used video surveillance to categorize and assess mouse behaviors. The odds of cotton batting being taken from tubes was 2.14 times greater than cotton balls but the process was less efficient; mice removed 0.35 g less cotton batting for each removal event and required 2.17 times longer to complete the removal. While cotton balls were readily carried in the jaws of mice, batting required separating smaller fragments from the mass before placement in the oral cavity. Video surveillance suggested that a small number of mice were super users and responsible for 22% of the 119 visits in which material was removed. Combined, material weight loss and video-captured removal events improve our understanding of host usage of nest materials but also raise questions about dissemination of the material in nests of the local mouse community.

An Effective Video Surveillance System by using CNN for COVID-19

An effective video surveillance system is a challenging task in the COVID-19 pandemic. Building a model proper way of wearing a mask and maintaining the social distance minimum six feet or one or two meters by using CNN approach in the COVID-19 pandemic, the video surveillance system works with the help of TensorFlow, Keras, Pandas, which are libraries used in Python programming scripting language used in the concepts of deep learning technology. The proposed model improved the CNN approach in the area of deep learning and named as the Ram-Laxman algorithm. The proposed model proved to build the optimized approach, the convolutional layers grouped as ‘Ram', and fully connected layers grouped as ‘Laxman'. The proposed system results convey that the Ram-Laxman model is easy to implement in the CCTV footage.

Semi-supervised generative adversarial networks for anomaly detection

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.