Klebsiella pneumoniae is one of the most common infectious disease affecting chicks, causing great economic losses. Also, possess food safety and antimicrobial resistance threats, as it may act as a contamination source of poultry meat and eggs. We aim to isolation and identification of klebsiella organisms causing death and loss in chicks. To perform this aim, it is necessary to full following points: Isolation of klebsiella causing respiratory manifestations and death in chicks purification and identification (morphologically, culturally, biochemically) of recovered klebsiella, antibiotic sensitivity test, PCR detection of Klebsiella genus specific gene (GyrA), PCR detection of klebsiella carbapenem resistance gene (kpc) and PCR detection of two of important virulence genes ; Mucoviscocity attached gene (MagA) and Iron uptake system gene (Kfu). A total of 150 chicks from different locations were examined clinically for respiratory symptoms lung, liver , trachea , intestine and samples (dropping and oro-pharyngeal swab) were collected for bacteriological examination. The results of biochemical tests for detection of biochemical characteristics of isolated Klebsiella; where all the examined isolates cleared that Klebsiella, gave positive reaction for catalase test, voges proskeure, urease and citrate test, meanwhile the isolates were negative for oxidase, Indole and Methyl red tests. Cultivation of isolate on macConkey gives lactose fermenting colonies, more or less dome shaped,3-4 mm diameter after overnight incubation at 37C .The sixteen klebsiella isolates subjected for PCR detection and the results for detection of Klebsiella genus specific gene (GyrA) were positive for all isolates. Also ten of sixteen subjected to PCR for detection of Klebsiella pneumonia carbapnamase gene (KPC) and the results were negative , ten of sixteen subjected to PCR for detection of Mucoviscocity attached gene (Mag A)and the results were positive for seven out of ten isolates,eight out of sixteen isolates subjected to PCR for detection of Iron uptake system gene (Kfu) and the results were positive for eight out of ten isolates. The 16 Klebsiella isolates were tested for the resistance to antibiotics and the results indicates that Meropenem, Impienem, Amikacin , Cefotaxime sodium, Ceftazidime , Ciproflaxin and Tobramycin had high effect on klebsiella isolates. Meanwhile, Gentamycin and Cefepime have moderate effect on Klebsiella. Moreover, antibiotics have less effect on the isolated Klebsiella were Azithromycin and Erythromycin.

With the rise of the IoT, there has been a growing demand for people counting and occupancy estimation in Intelligent buildings for adapting their heating, ventilation and cooling systems. This can have a significant impact on energy consumption at a global scale as such systems consume about 40% of electricity and create about 36% of the CO2 emissions in Europe. Previous approaches to occupancy estimation either utilize methods that do not ensure people's privacy when obtaining high accuracy estimations, such as RGB cameras, or utilize thermal or radar sensors with lower accuracy. Thermal vision for people detection has several advantages. It protects people's privacy while being less affected by changes in the environment. In addition, most of the previous image processing approaches rely on streaming the data to the cloud to be analyzed. However, with the development of the more distributed network paradigms edge and fog computing, there has been a trend in moving computation towards the edge of the network. This process of embedding intelligence into end-devices enables more efficient energy consumption and network load distribution. In this work, we present an embedded algorithm for room occupancy estimation based on a thermal sensor with accuracy over the state-of-the-art. We study the performance of a variety of deep learning models on different embedded processors. We achieve a prediction accuracy of 98.9% for people counting estimation with minimal 2 KB RAM utilization. Furthermore, the proposed algorithm has very low latency achieving execution times under 14 ms.