Undesirable behaviors among students  are consider one of the danger problems threating societies and educational and scientific institutions of countries  because its one of the way to express aggression , so the instructors consider one of the most important people could be trust their evaluation and logical view , therefore the present research aimed to :

1. Recognize undesirable behavior in students of University by the view of their instructors
2. Measure the level of undesirable behavior in students of university by the view of their instructors
3. Recognize more common  undesirable behavior in students of University by the view o

This paper constructs a new linear operator associated with a seven parameters Mittag-Leffler function using the convolution technique. In addition, it investigates some significant second-order differential subordination properties with considerable sandwich results concerning that operator.

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

A new benzylidene derivative, namely N-benzylidene-5-phenyl-1,3,4-thiadiazol-2-amine (BPTA), has been synthesized and instrumentally confirmed with Elemental Analysis (CHN), Nuclear Magnetic Resonance (NMR), and Fourier Transform Infrared Spectroscopy (FT-IR). Titanium Dioxide (TiO2) nanoparticles (NPs) were synthesized and characterized by X-ray. The mutualistic complementary dependence of BPTA with TiO2 nanoparticles as anti-corrosive inhibitor on mild steel (MS) in 1.0 M hydrochloric acid has been tested at various concentrations and various temperatures. The methodological work was achieved by gravimetric measurement methods complemented with surface analysis. The synthesized inhibitor concentrations were 0.1 mM to 0.5 mM and the temper

 We conclude that this study has dealt with an important aspect of the book that was not clearly defined in the book as it was derived through the book's translations, namely, the extraction of those urban structures of the city of Baghdad, which were of great importance in the religious, cultural, economic and social life at that time, The important link that reflected the development of the Islamic city in general and Baghdad in particular, as he addressed such urban facilities through the translations of the book that did not offer the outline or information about those facilities, but only the name only, and here lies the difficulty in addressing Such as T. As subjects because they need the most accurate analysis of facilities t

Active compounds were extracted from Anethum gravoelens to produce safety vegetable treat for diabetic, the results showed that alcoholic extract of Anethum gravoelens contain Alkaloid, glycosides, phenols, resins, saponins, coumarins, flavonoide, terpenes, steroids and volatile oils. after that it was studied the effect of alcoholic extract at dose 50, 100 mg/kg of body weight in reduced glucose level in serum of diabetic rats induced by alloxan, after the end of experiment for period 30 days the rats fasting 12 hours to measure the level of glucose in serum, the result showed asignificant decrease in serum glucose level of rats treated with extract in comparison with positive group (alloxan), So biochemical tests showed significant dec

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F