This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

ABSTRACTBackground: In Medical ethics education, improving medical student’s attitudes toward respecting the right of patients is an essential task. The medical students’ attitude has been affected by social, educational and personality background factors.Objective: To investigate medical student’s attitudes regarding medical ethics courses.Method: The study was conducted in Al-Kindy College of Medicine on academic year (2013 -2014) for the period from January to September. A cross- sectional study design was adopted with a self- administered questionnaire form distributed to medical students in the 5th-6th under graduate grades. The questionnaire consisted of 31 items relevant to student’s opinion about attitudes concerning ethi

The syntheses, characterization and experimental solid state X-ray structures of five low-spin paramagnetic 2-pyridyl-(1,2,3)-triazole-copper compounds, [Cu(Ln)2Cl2], are presented in this study, for the following five Ln ligands: L1 = 2-(1-(p-tolyl)-1H-(1,2,3-triazol-4-yl)pyridine), L2 = 2-(1-(4- chlorophenyl)-1H-(1,2,3-triazol-4-yl)pyridine), L3 = 4-(4-(pyridin-2-yl)-1H-(1,2,3-triazol-4-yl)benzonitril), L4 = 2-(1-phenyl-1H-(1,2,3-triazol-4-yl)pyridine) and L5 = 2-(1-(4-(trifluoromethyl)phenyl)-1H-(1,2,3- triazol-4-yl)pyridine). These five [Cu(Ln)2Cl2] complexes each contain two bidentate 2-pyridyl-(1,2,3)- triazole (Ln) and two chloride ions as ligands, with the Cu–N(pyridine) bonds, Cu–N(triazole) and Cu–Cl bonds trans to each othe

This study  was  conducted  with the  aim to  extract and  purify  a  polyphenolic  compound  â€œ Resveratrol” from the skin of black grapes Vitis vinifera cultivated in Iraq. The purified resveratrol is obtained after ethanolic extraction with 80% v/v solution for fresh grape skin, followed by acid hydrolysis   with   10%   HCl   solution then   the  aglycon   moiety   was  taken  with   organic   solvent

( chloroform). Using silica gel G60 packed glass column chromatography with mobile phase benzene: methanol: acetic acid  20:4:1 a

The best proximity point is a generalization of a fixed point that is beneficial when the contraction map is not a self-map. On other hand, best approximation theorems offer an approximate solution to the fixed point equation . It is used to solve the problem in order to come up with a good approximation. This paper's main purpose is to introduce new types of proximal contraction for nonself mappings in fuzzy normed space and then proved the best proximity point theorem for these mappings. At first, the definition of fuzzy normed space is given. Then the notions of the best proximity point and - proximal admissible in the context of fuzzy normed space are presented. The notion of α ̃–ψ ̃- proximal contractive mapping is introduced.

Inelastic magnetic electron scattering M1 at Ex =10.23 MeV form factors in Ca-48 have been investigated. The fp shell model space with four orbits and eight neutrons have been considered and FPD6 has been selected between 32 model space effective interactions to generates the model space vectors for the M1 transition with excitation energy Ex =10.23 MeV and for constructing OBDM. Discarded space (core and higher configuration orbits) has been included through the first order perturbation theory to couple the partice-hole pair of excitation in the calculation of the total M1 form factor and regarding the realistic interaction M3Y as a core polarization interaction with six sets of fitting parameters. Finally the theoretical calculations h

In the present study, MCM-41 was synthesis as a carrier for poorly drugs soluble in water, by the sol-gel technique. Textural and chemical characterizations of MCM-41 were carried out by X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscope (SEM), and thermal gravimetric analysis (TGA). The experimental results were analyzed mesoporous carriers MCM-41. With maximum drug loading efficiency in MCM-41 determined to be 90.74%. The NYS released was prudently studied in simulated body fluid (SBF) pH 7.4 and the results proved that the release of NYS from MCM-41 was (87.79%) after 18 hr. The data of NYS released was found to be submitted a Weibull model with a correlation coefficient of (0.995). The Historical

Background: The overuse and inappropriate use of antibiotics cause antimicrobial resistance (AMR). The antibiotic stewardship program (ASP) plays a crucial role in improving prescribing antibiotics. Although the Iraqi Ministry of Health has issued ASP legislation, its full implementation in clinical practice remains incomplete. Objectives: To explore physicians' perspectives regarding the current and potential implementation of ASP in Al-Nasiriya hospitals. Methods: A qualitative study was conducted in Al-Nasiriyah public hospitals from December 17, 2023, to January 21, 2024, using face-to-face interviews. Physicians who prescribe antibiotics, work in Al-Nasiriyah public hospitals, and consent to participate in the study were recru

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s