Well-dispersed Cu2FeSnSe4 (CFTSe) nanoparticles were first synthesized using the hot-injection method. The structure and phase purity of as-synthesized CFTSe nanoparticles were examined by X-ray diffraction (XRD) and Raman spectroscopy. Their morphological properties were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average particle sizes of the nanoparticles were about 7-10 nm. The band gap of the as-synthesized CFTS nanoparticles was determined to be about 1.15 eV by ultraviolet-visible (UV-Vis) spectrophotometry. Photoelectrochemical characteristics of CFTSe nanoparticles were also studied, which indicated their potential application in solar energy water splitting.

It has become necessary to change from a traditional system to an automated system in production processes, because it has high advantages. The most important of them is improving and increasing production. But there is still a need to improve and develop the work of these systems. The objective of this work is to study time reduction by combining multiple sequences of operations into one process. To carry out this work, the pneumatic system is designed to decrease\ increase the time of the sequence that performs a pick and place process through optimizing the sequences based on the obstacle dimensions. Three axes are represented using pneumatic cylinders that move according to the sequence used. The system is implemented and

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

Piracy on phonograms is now, rightly, the crime of the electronic age. Despite the protection sought by States to provide for such registrations, whether at the level of national legislation or international agreements and conventions, but piracy has been and continues to pose a significant threat to the rights of the producers of those recordings, especially as it is a profitable way for hackers to get a lot of money in a way Illegal, which is contrary to the rules of legitimate competition. Hence, this research highlights the legal protection of producers of phonograms in light of the Iraqi Copyright Protection Act No. (3) of 1971, as amended.

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p