Pollen morphology of 4 species (A. aucheri , A. auriculata, A. caucasica, A. nova) belonging to the genus Arabis L. in Iraq was examined by light microscope and scanning electron microscope to determine the significance of pollen features as a taxonomic characters. The results showed pollen grains of the species were monades, homopolar, tricolpate, and with medium size, but the species varied in shapes (polar and equatorial view), colpus length and width, exine thickness and exine ornamentation. Pollen colors were brown convert to brownish yellow.

In this research was study the effect of increasing the number of layers of the semiconductor films as PbS on the average grain sizes and illustrate the relationship between the increase in the average grain size and thickness of the membrane, and membrane was prepared using the easy and simple and does not need the complexity of which is that the chemical bath , and from an X-ray diffraction found that the material and the installation of a random cubic and when increasing the number of layers deposited note the emergence of a number of vertices of a substance and PbS at different levels but the level is more severe (200) as well as the value is calculated optical energy gap and found to be not affected by increase thickness and from th

AI in teaching English is reshaping language learning. While interest in AI-supported education is growing worldwide, research in this area is still emerging in Iraq. This review synthesizes empirical AI-based intervention studies to enhance English language learning in Iraqi higher education, and the perceptions of stakeholders regarding AI tools in language instruction. The reviewed intervention studies, comprising studies employed different AI platforms to support grammar instruction, speaking fluency, writing feedback, and pragmatic competence. These interventions yielded improvements in learners’ performance, motivation, and communicative confidence. In parallel, perception-focused studies revealed positive attitudes toward A

In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the sho

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the ene