The green method was chosen for the preparation of nano iron oxide due to its simplicity, ease of preparation, and purity, compared to other methods. Nano iron oxide was made using a substance that causes precipitation and a coating from the alcoholic extract of orange leaves from Iraq. It was examined structurally and spectrally using several techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, field-emission scanning microscopy (FESEM), energy-dispersive X-ray spectroscopy, and UV-Vis spectroscopy. The diagnosis proved that the nano iron oxide was successfully prepared in a spherical form and with an average size of 71.1 nm. The nano iron oxide particles were tested for their ability to remove crystal

Effect of [Cu/In] ratio on the optical properties of CuInS2 thin films prepared by chemical spray pyrolysis on glass slides at 300oC was studied. The optical characteristics of the prepared thin films have been investigated using UV-VIS spectrophotometer in the wavelength range (300-1100 nm). The films have a direct allow electronic transition with optical energy gap (Eg) decreased from 1.51 eV to 1.30 eV with increasing of [Cu/In] ratio and as well as we notice that films have different behavior when annealed the films in the temperature 100oC (1h,2h), 200oC (1h,2h) for [Cu/In]=1.4 . Also the extinction coefficient (k), refractive index (n) and the real and imaginary dielectric constants (ε1, ε2) have been investigated

In this study, silver nanoparticles (AgNPs) were synthesized using a cold plasma technique and a plasma jet. They were then used to explore how photothermal treatment may be used to treat lung cancer (A549) and normal cells (REF) <i>in vitro</i>. The anti-proliferative activity of these nanoparticles was studied after A549 cells were treated with (AgNPs) at various concentrations (100&#37;, 50&#37;, or 25&#37;) and exposure times (6 or 8 min) of laser after 1 h or 24 h from exposed AgNPs. The highest growth inhibition for cancer cells is (75&#37;) at (AgNPs) concentration (100&#37;) and the period of exposure to the laser is (8 min). Particle size for the prepared samples varied according to the diameter o

Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a