The anatomic characteristics of the leaves of 22 taxa of Stachys L. representing seven of the currently recognized sections distributed in northern Iraq, were examined. The study did not found any variations in the characteristics of the stomata system of the leaves.Therewere variations with a good taxonomic value in other anatomic characteristics.Thevariations in characters of the mesophyll tissue in S.kurdica var.brevidens Bom ex Bhattacharjeewas bifacial but in S.kurdica var.kurdica Boiss. & Hohen.was unifacial .The study also found that the taxa S.benthamiana Bioss., S.lanigera (Bornm.) Rech.f. andS.kotscyi Bioss. with 3 vascular bundles in the middle vein while the taxa S.ballotiformis Vatke., S.megalodonta Hausskn. &Bo

The anatomic characteristics of the leaves of 22 taxa of Stachys L. representing seven of the currently recognized sections distributed in northern Iraq, were examined. The study did not found any variations in the characteristics of the stomata system of the leaves.Therewere variations with a good taxonomic value in other anatomic characteristics.Thevariations in characters of the mesophyll tissue in S.kurdica var.brevidens Bom ex Bhattacharjeewas bifacial but in S.kurdica var.kurdica Boiss. & Hohen.was unifacial .The study also found that the taxa S.benthamiana Bioss., S.lanigera (Bornm.) Rech.f. andS.kotscyi Bioss. with 3 vascular bundles in the middle vein while the taxa S.ballotiformis Vatke., S.megalodonta Hausskn. &Bornm.exP.H.Davis

A comparative investigation of the anatomical characters through a microscopical examination of the prepared transverse sections of the stem was carried out. Six plates with 32 photomicrographs were provided to convincingly show the considerable variations of anatomical characters within the nine examined species. The matrix of 18 anatomical characters which included nine quantitative and nine qualitative was applied for the clustering analysis (CA) followed by the principal component analysis (PCA) using the Multivariate Analysis of Ecological Data, PC-ORD.
The results exhibited significant variations among the species resulting in the construction of an artificial key; this key accurately represents a sufficient tool to display the

Ursolic acid (UA, 3 ?-hydroxy-urs-12-en-28-oic acid) are isomeric triterpenic acids. The high quantities of pentacyclic triterpenoids in Scabiosa species seems to be obvious and there is an evidence that most of pentacyclic triterpenoids that have been isolated are saponins. This is one of the most important characteristic of the genus Scabiosa, the main aglycones are ursolic acid and oleanolic acid. In the current study, isolation from the aerial part and roots of Scabiosa palaestina L. was performed using Preparative HPLC. Furthermore, detection and quantitation of ursolic acid was performed by high performance thin layer chromatography (HPTLC). The identification of isolated triterpenoid involves two methods including FT-IR coupl

It is from the greatness of God Almighty and from him that he facilitated for me to delve into the immersion of the hadith of the Prophet in my doctoral dissertation, then I found it a fertile ground for the cultivation of scientific results.
Through this work, I chose the topic (Replies of Al-Aini to Ibn Malik in his book Umdat Al-Qari Sharh Sahih Al-Bukhari) because of its scientific impact, as such topics inspire in the soul of the follower and the student a lot of contemplation and great care to understand the texts and then come up with a result that is almost close. It is correct, and my current situation in this research is the condition of many scholars, not without errors and omissions. Whatever was correct is by the grace of

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.