In study carried out in the cold storage in college of Agric./Univ. of Baghdad at 8 ? C. shows that Alternaria , Pencillium , Rhizoctonia , Mucor , are the fungi that causes tomato fruits decay. This is the first record of Rhizoctonia and Mucor as a Tomato fruits rot under 8º c in Iraq. There is no fungal infection on cucumber fruits under 8 ? C. . Waxing tomato fruits reduced the severity of the fungi infection and gave shelflife (19 days) under 8 ? C. There is an infection with Mucor was found in tomato fruits kept in perforated polyethylene bages with 16 bores prevent the infection and the lowest severity and frequency of infection was found in waxed tomato fruits. Part of M.Sc thesis of the Second author.

Some metal ions (Mn+2, Co+2, Ni+2, Cu+2,Zn+2 and Cd+2) complexes of quodridentats Schiff base derived from (2-hydroxy benzaldehyde and 4,4'-methylenedianiline as primary ligand and 3-picoline (3-pic) secondary ligand have been synthesized and characterized on the basis of their 1H ,13C-NMR, FT-IR, UV-Vis spectroscopy, conductivity measurements, elemental analysis, and magnetic moments, metal to ligands ratio in all complexes has been found to be (1:1:2) (M:Schiff base:3-pic), Schiff base behaves as neutral tetra dentate ligand with (N2,O2) system from the results obtained, the following general formula has suggested for the prepared complexes [M+2(2-mbd)(3-pic)2] and octahedral stereochemistry, Where M+2 = (Mn , Co , Ni , Cu , Zn and Cd), 2

Abstract: This research was performed to study the effect of some amino acids and vitamins on the growth of bacteria Staphylococcus aureas and its sensitivity against UV light. The results showed low inhibition in bacterial growth because amino acids repairs the damges caused by UV light. Besides the effect of two groups of antibiotics (β-lactame and tetracycline) on the growth of S. aureus and the possible interference of amino acids and vitamins in the activity of the antibiotics against this bacteria in the presence of UV light were studied. The result show increase in the sensitivity towards these antibiotics and provided protection against the antibiotics.

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat