The object of research is studying Raman scattering technique, photoluminescence and some optical properties of silver nanoparticles created by eco-friendly technique which independent on a long time, effort, energy and high temperatures, and with the highest adsorption capacity in order to achieve a high inhibition to paralyze the activity of the bacterial wall, by achieving the highest surface plasmon resonance (SRR). Silver nanoparticles were prepared using Matricaria Flower extract. Characterization of silver nanoparticles and detection of their effectiveness against microbial using two types of bacteria (Escherichia Coli and Staphylococcus aureus ), these nanoparticles were measured using a number of measurements, X-ray diffrac

The problem of generated waste as a result of the implementation of construction projects, has  been aggravated recently because of construction activity experienced by the world, especially Iraq, which is going through a period of reconstruction, where construction waste represents (20-40%) of the total generated waste and has a negative effect on the environment and economic side of the project. In addition, the rate of consumpted  amounts of natural resources are estimated to be about 40% in the construction industry, so it became necessary to reduce waste and to be manage well. This study aims to identify the key factors affecting waste management through the various phases of the project, and this is accom

To produce Zinc Oxide NanoParticles, ZnO-NPs, different methods can be used. However, the utilization of Liquid-Phase Pulsed Laser Ablation, LP-PLA, methodology of three distinct environment of aqueous using pure zinc plate will be one of the approaches for this job. Thus, in this work, concentrates on the influence of the results after employing some changes on the environment in other words, the influence of the NPs size and/or the NPs availability/appearance. Cetyltrimethylammonium Bromide, CTAB, is one of the three surfactants that have been used in the water-based solution. That is, the Sodium Dodecyl Sulfate, SDS, besides the Distilled Water, DW, the three surfactants will be ready when the molarity of the DW is around 10− 3 M. Th

         This study investigated the effect of using brainstorming as a teaching technique on the students’ performance in writing different kinds of essays and self regulation among the secondary students. The total population of this study, consisted of (51) female students of the 5^th Secondary grade in Al –kawarzmi School in Erbil during the academic year 2015-2016. The chosen sample consisted of 40 female students, has been divided into two groups. Each one consists of (20) students to represent the experimental group and the control one. Brainstorming technique is used to teach the experimental group, and the conventional method is used to teach the control group. The study inst

Magnesium Phthalocyanine (MgPc) was deposited on a glass substrate by pulsed laser deposition (PLD) using Q-Switching Nd:YAG laser with wavelength 1064 nm, repetition rate 6 Hz, at room temperature (300K) and different annealing temperatures (373, 473, and 573)K under vacuum condition of 10-3 torr. All films were annealed for one hour to attain crystallinity. X-ray diffraction (XRD) of MgPc powder indicated that MgPc crystallizes in polycrystalline with a monoclinic structure. While comparing the MgPc films, it was observed that the intensity of the characteristic peak increases with temperature, and the crystallization exhibited a monoclinic structure typical of the β-form. The Miller indices, hkl, values for each diffraction peak

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

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

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

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit