The dependence of the cross-section of the coherent and incoherent radiation peaks in the X-ray absorption experiment of different energies (20-800 Kev) was investigated. Cross-sectional dependence on the atomic number Z was included from the published data for (8) elements, ranging from carbon to silver (C-Ag). The proportional constant K was obtained between (σc/σi), with the atomic number Z from (6-47). The results show that the value of K exponentially changes with energy.

This study aims to determine the exposure of dentists to radiation resulting from the use of light therapy units and to assess their risk and impact on dental clinics.  This study was conducted in private dental clinics in the city of Erbil in northern Iraq. Surveys were conducted to collect information about light-curing units. The results were analysed using the multi-response logistic regression to determine the factors affecting the radiation values of light-curing units. The results of the study showed that five major variables have a major effect by radiation.  This is shown with a value of P ≤ 0.05. Typical treatment times with radiant light, with a typical number of daily restorations, may exceed the risk limits for blue light reflected on eyes. This is given that the responding dentists did not protect their eyes with enough blue light .Technology in dentistry requires the operator to have knowledge of basic technical specifications and the safe use of devices and tools routinely used in dental treatment.

This research presents a statistical study of radiation generated from communication towers in the Nineveh Plain region Baghdeda. The intensity of radiation energy was measured at 10 meters away from the communication tower in different locations, using a (1PC XH-901 Dosimeter/ Personal Dose Alarm / Radiation Detector, dosage rate: 0.01 μSv/h to 150μSv/h) to measure the amount of radiation at various times. Energy densities were measured and compared with standard limits provided by other authorities, such as the International Committee for Radiation Protection. Results were analyzed using SPSS version 26 to implement the data. The results show that the means of the radiation levels measured at all the zones do not statistically differ from the highest values determined globally 0.50-1.70 μSv/y; they lie within the radiation-free zones. Civilians may not always have a choice where the mobile tower will place. As a result, it may rely on some quick fixes, such as certified radiation protection items that offer all-around protection from mobile tower irradiance. The radiation shielding technology used in these goods alters the nature of irradiation from a constant to a variable waveform, rendering it useless.

One of the most prevalent problems with big data is that many of the features are irrelevant. Gene selection has been shown to improve the outcomes of many algorithms, but it is a difficult task in microarray data mining because most microarray datasets have only a few hundred records but thousands of variables. This type of dataset increases the chances of discovering incorrect predictions due to chance. Finding the most relevant genes is generally the most difficult part of creating a reliable classification model. Irrelevant and duplicated attributes have a negative impact on categorization algorithms’ accuracy. Many Machine Learning-based Gene Selection methods have been explored in the literature, with the aim of improving dimensionality reduction precision. Gene selection is a technique for extracting the most relevant data from a series of datasets. The classification method, which can be used in machine learning, pattern recognition, and signal processing, will benefit from further developments in the Gene selection technique. The goal of the feature selection is to select the smallest subset of features but carrying as much information about the class as possible. This paper models the gene selection approach as a binary-based optimization algorithm in discrete space, which directs binary dragonfly optimization algorithm «BDA» and verifies it in a chosen fitness function utilizing precision of the dataset’s k-nearest neighbors’ classifier. The experimental results revealed that the proposed algorithm, dubbed MI-BDA, in terms of precision of results as measured by cost of calculations and classification accuracy, it outperforms other algorithms