The Lamiaceae L. family grows and widely distributed in Iraq. The study aimed to enumerate the species that has been preserved in several botanical herbariums: National Herbarium of Iraq- Ministry of Agriculture (BAG), University of Baghdad Herbarium (BUH), Iraq Natural History Research Center& Museum- University of Baghdad Herbarium (BUNH), College of Agricultural Engineering- University of Baghdad Herbarium (BUG), College of Agricultural Engineering Sciences- Duhok Province University Herbarium (DPUH) and College of Science - Salahddin University Herbarium (SUH). This family has not yet been registered in the Flora of Iraq. After examining more than 1000 herbarium specimens, the study found 139 species belonging to 33 genera h

The following dilutions -710X46, -610X46, -510X46 of Bacillus thuringiensis were used for bioassay against the different larval instar of the potato tuber moth Phthorimaea operculella by the spraying method, the results showed that there was no significant influence in the percentage of egg hatching in comparison with the control. The sensitivity of larval stages was reduced with the increasing the age and exposure period. The study also showed that the larvae infected with B.t. stopped feeding, movement and a general paralysis causing the death of larva after (24-48) hours , and the larva color was changed from the natural waxy colour to brown finally to the black after death.

   The current study included collection for many fresh specimens, the observation of specimens in some Iraqi herbaria have been studied, although morphological characters of roots, stems, flowers, inflorescences and fruits were included in this work, however indumentums  and pollen grains were studied

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c