JOURNAL OF EDUCATION AND SCIENCE

This research deals with the preparation number of composite materials by casting the base alloy (AlCo-Ni) by adding chromium oxide (Cr2O3) as reinforcement material, by weight ratios of (5, 10, 15
wt%) for the base alloy, with heat treatment of the prepared alloys. The basic alloys and composite
materials were prepared by using solution casting method and manual mixing method to disperse
reinforcement grains in the base alloy floor and with pressure of 5 ton in purpose of forming. The
materials were melted and poured into molds and suddenly cooled in cold water, after the molten
samples were frozen. The attenuation factor of gamma rays was founded the prepared alloys and their
homogeneity was examined by shining a narrow beam of single energy gamma rays emitted from the
radiating source (137Cs) with different energies (511, 662, 1173, 1284, 1333) keV respectively, gamma
ray system (UCS-20) was being used which bounded to NaI(Tl) scintillation detector. The homogeneity
of base alloy and composite material was determined by studying the contrast of gamma ray intensity,
the linear attenuation coefficient at seven different locations at any samples and the percentage standard
deviation. The results showed that the highest value of the linear attenuation coefficient was 0.252 mm-1
for sample [5%Cr2O3+Al+Co+Ni] with the energy (511 keV) and that the lowest value for the ratio of
linear attenuation coefficient was 0.062 mm-1
at the energy of sample [15%Cr2O3+Al+Co+Ni] with the
energy (1333 keV) indicating that the homogeneity of the sample [15%Cr2O3+Al+Co+Ni] at energy
(1333 KeV).

Human activity recognition has an important role in the interaction between human and human
relationships because it provides information about a person's identity, personality, activities,
psychological state, and health, all this information is difficult to extract due to the difficulty of a person's
ability to identify the activities of another person and is considered one of the basic research topics in the
scientific fields in the field of computer vision and machine learning. the purpose of human activity
recognition (HAR) is to identify the different human activities throw monitoring and register the human
activates and the various surrounded environment, by using computers, the human activity recognition
researches which depending on visions is the basics of lots of applications even video monitoring or health
care and security monitoring and the interaction between the human and the computers.
In this research, a review of the newest development in the human activity recognition branch have been
studied, and the different ways to recognize the human actions, an important detail have been shown to
preview the HAR researches and the methodologies used to represent the human activates and its
classifications, to provide an overview of the HAR methods and comparing them.

Data mining (DM) is an amazing developing with incredible chances to advantage institutions centre
of main data of information accumulated of conduct their customer and expected customer. DM
identified data included in information which questions and summaries cannot viably discover. DM
is a straight way to examining data of periodic data records and summing up in useful information -
information could be used in expand outputs, reduction costs, or both. DM allows clients to verify
data of various measurements or points, classify it, and sum up the connections recognized. There are
four types of DM: 1) Classification and regression, 2) Clustering, 3) Association Rule Mining, and 4)
Outlier/Anomaly Detection. Tending to the velocity part of Big Data (BD) has as of late pulled in a
lot of revenue in the investigation local area because of its critical effect on information from pretty
much every area of life like medical services, financial exchange, and interpersonal organizations,
and so on. A lot of paper works verified the velocity challenge via stream mining data. The majority
of streaming mining data articles centres around adjusting primary classifications of algorithms,
methods and techniques of classic information to the modified information circumstance. This
research explores widely the latest literature of mining stream data field recognizes the essential
ready nodes supporting variance founded methods. This study not simply benefits examiner to make
strong assessment subjects and separate gaps in the field yet moreover helps specialists for DM and
BD application structure headway.

The uranium concentration and radioactivity of radon gas were measured in Al-Kasik refinery and Ain
Zala field using the CR-39 detector. Soil and water samples associated with the production stages of oil,
sludge and crude oil were collected. The levels of uranium concentration in soil ranged from 0.703 to
1.480ppm, in water samples from 0.681 to 0.716 ppm, in sludge samples from 0.849 to 1.014 ppm, and
in crude oil from 0.785 to 0.933 ppm. As for the radioactivity of radon gas, when comparing the radon
rate in the samples we obtained with the global values, it was found that it falls within the internationally
permissible limit, where the radon rate in the soil was 12.81 Bq/kg and when compared with the United
Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) which has a value of 32
Bq/kg, and in the produced water it was 8.66 Bq/kg compared to (UNSCEAR)which has a value of 50
Bq/kg. In sludge samples 11.81 Bq/kg and when compared with the International Atomic Energy
Agency (IAEA) whose value is(8 − 5 × 105
) Bq/kg, and in crude oil samples 10.56 Bq/kg and when
compared with the International Federation of Oil and Gas Producer (IOGP) whose value is (800 −
4 × 105
) Bq/kg. As for the alpha ray hazard index, the results showed that it is within the permissible
limits internationally, where the results were less than 1 and therefore does not pose a threat to the health
of workers and environment.

In the current research, 23 wells were chosen from some residential neighborhoods located
in the city of Mosul . To study some of the physical properties of well water represented by
temperature, electrical conductivity, and total dissolved salts, as well as, the study of chemical
analyzes are: (sulfates, chlorides, phosphates, nitrates, sodium, potassium, calcium, magnesium,
dissolved oxygen and pH).
The results of the study refer to the height of the electrical conductivity, which ranged between (791-
2456) µmhos/cm. The results showed that a lot of water is free of dissolved oxygen, as it recorded
the highest value of 4.6 mg/L. Whereas, the temperature of the studied water ranged between (20-
28.4) Cº, and it is considered warm water. In the current study, the calcium ion concentration was
greater than the magnesium ion concentration in all well water, and the highest concentration reached
(264 and 134) mg/L at well 22 and 23, respectively. There was an increase in the concentration of
chloride ion, which reached (204) mg/L and the lowest concentration (32) mg/L at wells 11 and 7,
respectively.
When comparing the values of the electrical conductivity of the well water with the global
determinants of drinking, it was determined that they are not suitable for drinking.
When applying the relationship between the value of the electrical conductivity and the ratio of
sodium adsorption to well water, it was found that all water from wells Class (C3 - S1) except for
wells (11 and 19) are classified as Class (C4 - S1)

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the
curvelet transform and co-occurrence matrix to recognize a hand's palmprint. The suggested system is
composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint
image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version
of the image, and finally, unsharp masking process and sobel filtering were performed for edge
detection. The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features,
while the fourth stage inclusion is the training and testing of the suggested approach. The algorithm
ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal. CASIA
PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the
performance of the proposed system. ARR and EER metrics have been adopted to assess the
performance of the proposed system. The experimental results showed a very high recognition rate
(ARR) that reaches 100% for the right hand of a male and the left hand of a female. The overall
accuracy rate (ARR) reaches 98.5% and EER equals 0.015.

Vertical cavity surface emitting laser VCSEL is currently the main solution for many technological aspects, ultrafast
optical interconnecting, Gigabit Ethernet, etc. In this paper we present the simulation results by using Optiwave™
software version 7, of the effects of optical mode confinement factor on the modulation properties )which inspected
by eye diagram of the received signals) of the vertical cavity surface emitting laser VCSEL, with the range (8-
20)Gbps of pseudo random bit sequence PRBS. The quality of the VCSEL modulation have been inspected by time
domain signals, spectrums and eye diagram. Simulation results appeared an improvement in the characteristics of
received bit sequences of (8, 10, 12.5, 16 and 20) Gbps bit rates, represented by the rising the value of quality factor
QF (1.77 to 4.81) versus increasing the value of optical mode confinement factor Γ(0.2 to 0.5) respectively, and a
decreasing in jitter time of superimposed traces of eye pattern and well opining eye pattern. And in the
corresponding, the bit error rates BERs of the received signals have been decreased, with rising the value of mode
confinement factor Γ of the laser at constant modulation index and constant temperature of the laser. Also, the
VCSEL’s modulation response differences with different bitrates, causes different values of QF and BER
for individual value of mode confinement factor Γ.

Every day, there is great growth of the Internet and smart devices connected to the network. Additionally,
there is an increasing number of malwares that attack networks, devices, system and applications. One of
the biggest threats and newest attacks in cybersecurity is Ransom Software (Ransomware). Although there
is a lot of research on detecting malware using machine learning (ML), only a few focus on ML-based
ransomware detection, especially attacks targeting smartphone operating systems (e.g., Android) and
applications. In this research, a new system was proposed to protect smartphones from malicious
applications through monitoring network traffic. Six ML methods (Random Forest (RF), k-Nearest
Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision tree (DT), Logistic Regression (LR), and
eXtreme Gradient Boosting (XGB)) are applied to CICAndMal2017 dataset which consists of benign and
various kinds of android malware samples. 603288 benign and ransomware samples were extracted from
this collection. Ransomware samples were collected from 10 different families. Several types of feature
selection techniques have been used on the dataset. Finally, seven performance metrics were used to
determine the best feature selection and ML classifiers for ransomware detection. The experiment results
imply that DT and XGB outperform other classifiers with best detection accuracy at more than (99.30%)
and (99.20%) for (DT) and (XGB) respectively.

This research work include studying one of the natural mineral ores available in Al-Hawy
area (Mosul city – Northern Iraq) by chemical analysis and X-ray fluorescence (XRF) to identify its
components of the elements as oxides, X-ray diffraction was carried out to determine the
percentages of clay minerals (natural zeolites) and non-clay minerals in the natural ore. The natural
zeolites were concentrated by removing carbonate , iron and separating the convertible silica into
sodium silicate, the prepared zeolite was converted into (H-form) by treated with ammonium nitrite
solution (1M) ,then grafted with ruthenium chloride (RuCl3.6H2O) .The properties and specifications
of the prepared zeolite (grafted and non-grafted) were studied using techniques of (XRF)
,(XRD),(BET) , (SEM) and (differential& thermo gravimetric analysis(DTA)&(TGA) ) ,it was found
that have a chemical and crystalline composition within the specifications of zeolites, as well as a
good surface area , thermal stability and selective porous channels.

The detection and tracking of head movements have been such an active area of research during
the past years. This area contributes highly to computer vision and has many applications of computer
vision. Thus, several methods and algorithms of face detection have been proposed because they are
required in most modern applications, in which they act as the cornerstone in many interactive projects.
Implementation of the detected angles of the head direction is very useful in many fields, such as
disabled people assistance, criminal behavior tracking, and other medical applications. In this paper, a
new method is proposed to estimate the angles of head direction based on Dlib face detection algorithm
that predicts 68 landmarks in the human face. The calculations are mainly based on the predicated
landmarks to estimate three types of angles Yaw, Pitch and Roll. A python program has been designed
to perform face detection and its direction. To ensure accurate estimation, the particular landmarks were
selected, such that, they are not affected by the movement of the head, so, the calculated angles are
approximately accurate. The experimental results showed high accuracy measures for the entire three
angles according to real and predicted measures. The sample standard deviation results for each real and
calculated angle were Yaw (0.0046), Pitch (0.0077), and Roll (0.0021), which confirm the accuracy of
the proposed method compared with other studies. Moreover, the method performs faster which
promotes accurate online tracking.

The objective is to use AI techniques to build a citrus image recognition system and to produce an
integrated program that will assist plant protection professionals in determining whether the disease is
infected and early detection for the purpose of taking the necessary preventive measures and reducing its
spread to other plants. In this research, the RBF and FRBF networks were used and applied to 830 images,
to detect whether citrus fruits were healthy or ill. At first, the preprocessing of these images was done, and
they were reduced to 250 x 250 pixels, and the features were extracted from them using the co-occurrence
matrix method (GLCM) after setting the gray level at 8 gradients and 1 pixel distance, 21 statistical
features were derived, and then these features were introduced to RBF after determine the number of input
layer nodes by 21 , 20 for the hidden layer and 1 node for output layer, the centers were randomly selected
from the training data and the weights were also randomly selected and trained using the Pseudo Inverse
method. The RBF network was hybridized with the fuzzy logic using the FCM method, the fuzziness
parameter = 2.3 was selected, and a new network called FRBF was acquired. These networks were trained
and tested in training data (660 images) and testing (170 images) for citrus fruits. The detection rate was
then calculated, and the results showed that the (FRBF) had a higher accuracy of 98.24% compared to
RBF of 94.71%.

A simple and sensitive spectrophotometric method has been suggested for the estimation of pure paminophenol(p-AMPL), and p-AMPL results from the hydrolysis of paracetamol (PARL). The
suggested method is based on oxidative coupling reaction of p-AMPL with 4-chlororesorcinol and
potassium periodate to produce a stable, and water-soluble colored product with maximum absorption
at wavelength 556 nm. Beer's law is followed over the range of concentration from 2 to 20 µg. ml-1.
The molar absorptivity value is equal to 1.0356×104 l.mol-1cm-1. All factors responsible for the
completed reaction and highest intensity of the product have been studied, and the optimal of each
factor has been selected. The suggested method was applied in an indirect method for the determination
of paracetamol in tablets and injection after acidic hydrolysis to p-AMPL. The common excipients
added did not interfere in the estimation of paracetamol.

Cinnamaldehyde was used to condense with substituted acetophenone then with acetone to form
substituted 2,4- diene -1- pentanone (1- 4), after two moles of cinnamaldehyde condense with one mole
of acetone to form 1, 5- diphenyl nona- 8- tetraene -5 – one (5), for these reactions, sodium hydroxide
was used as a catalyst, the resulted compounds reduced by Luche (specific reduction agent use cerium
chloride as a catalyst with sodium borohydride to protect double bond) reaction to form 2,4- diene-1-
methoxy pentane (6-9) and -5- methoxy nona- 1,3,6,8 – tetraene – 1,9 - diyl dibenzene (10), the resulted
dienes compound converted to endoperoxides (11- 15) via singlet oxygen reaction in the present of Rose
Bengal as a catalyst with light in chlorinated solvent and finally by appling of Appel reaction condition
these compounds converted to furans (16- 20). All mechanisms of the reaction were listed. These
compounds were identified by thin layer chromatography TLC, and by their physical properties in
addition, the IR spectroscopy and 1HNMR.

Because of the importance of atmospheric nitrogen-fixing bacteria, the study aimed to isolate bacteria
from the root nodules of plants from four areas within the city of Mosul during the winter planting period,
for the season 2020-2021. Biochemical tests, phenotypic and agricultural characteristics were used to
diagnose bacterial isolates and study their sensitivity and resistance to ten types of antibiotics. The results
showed that the highest percentage of resistance was to the antibiotics Trimethoprim and Streptomycin at
100%, while it was the least resistant to the antibiotics Tetracycline and Rifampicin at 22.2%. The four
bacterial isolates were resistant to cadmium chloride salts (CdCl), while the lowest growth rate was when
treated with mercury chloride salt (HgCl). Polymerase Chain Reaction (PCR) technology was used to
diagnose samples based on the analysis of nitrogenous bases in the 16S rRNA gene and compare the
sequences generated by DNA amplification with standard isolates within NCBI to detect new isolated
bacterial isolates

The prediction of rate constant of Ethers reaction with Chloramine-T has been studied theoretically in
this work using ab-initio and statistical calculations. The first order reaction between Ethers and
Chloramine-T shows that the rate constant depends on the Ethers concentration. Therefore, ab initio
calculations were used using DFT/ b3lyp with 6-311++G** as a basis set to extract the most stable
geometry of the chosen ethers. Also, several physical parameters have been gained form these
calculations such as mulliken charge at the active atoms, dipole moment, Homo and Lomo energy
levels, Hardness, Electronic Chemical Potential and Global Electrophilicity Index. The relationship
between these physical variables plays an effective role in the reaction rate. Therefore, statistical
calculations were used to investigate the possible relationship between these physical parameters.
These relationships can be predicted from the value of the correlation coefficient (R2) and the Standard
error (SE). Simple regression analysis and multiple regression analysis were performed between these
physical variables as independent variables and rate constant as a dependent variable using SPSS
software. The results show high value of R2 (0.993) of the regression analysis which reflects the high
relationship between these variables that affects the reaction rate concentration. The out data illustrates
negligible differences (about 0.008) between the experimental and calculated rate constant value.