This study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a

This study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Isolation of fungi was performed from February to July, 2019. One hundred clinical specimens were collected from King Abdullah Hospital (KAH) Bisha, Saudi Arabia. Samples were collected from twenty patients of different ages (30 - 70 years old) ten males and ten females. The samples were collected from patients with the two types of diabetics. Specimens included blood, hair, nail, oral swabs and skin.  Specimens were inoculated on Sabourauds Dextrose agar containing chloramphenicol. Thirteen fungal species were isolated and identified. The isolated species were: Aspergillus flavus, A. niger, A. terrus, A. nidulans, A. fumigatus, Candida albicans, C. krusei, C. parapsilosis, C. Tropicalis, Curvularia lunata, Fusarium solani, Penicill