Journal of Basrah Researches (Sciences)

This article is review the first group of Cartan’s
structure equations for certain classes of almost contact
metric manifolds. These classes are divide into two
collections, the first involve the irreducible classes such
as cosymplectic class, Kenmotsu class, Sasakian class,
????9- class, ????11- class, and ????12- class. The second include
normal class of Killing type (CNK-class), nearly
Kenmotsu class, ????????10 - class, ????????11 - class, nearly
cosymplectic class, and Kenmotsu type class

In petroleum processes oil and gas wells need to be
cased and cemented to ensure the wellbore's stability
and to divide trouble zones. To accomplish these tasks
and increase the stability of a cement matrix, numerous
substances are added to the cement slurry. A two green
co-polymers Poly (citric acid-co-ethylene glycol)
(PCCE), and Poly (Tartaric acid-co-Glycerol) (PTCG)
as a cement retarder was prepared by polycondensation
reaction. The prepared co-polymers were characterized
by proton nuclear magnetic resonance (H1-NMR),
Fourier transform infrared spectroscopy (FT-IR), and
thermogravimetric analysis (TGA). Results showed that
all prepared copolymers have excellent thermal
stability. PCCE and PTCG copolymer shows a
promising thickening time reached to 195 and 175 min
compares to free cement -G (110 min) under schedule 5
(sch.5) conditions. This finding indicates the capability
of copolymers to use as retarders in oil well cementing.

The research includes the preparation of ligand E-H, and it
was identified by spectroscopic methods Infra Red IR,
Nuclear magnatic Reasonance NMR, Mass spectra· It was
also prepared from the ligand a complexes with copper (11)
and characterized by Infra Red spectra. Central metals
ions and Schif base coordinated via O and N donor
sites which was observed from IR spectra. The liquid
crystal characteristics of the ligand was studied using
polarized optical microscope POM and the ligand is a liquid
crystal by showed nematic phase only, while no liquid
crystal phase was detected in the prepared complex, and it
disintegrated at 250°C.

Internet of Things (IoT) connects smart devices to the
internet, but it poses significant security challenges. ‘
Security measures like access control, authentication, and
encryption must be implemented during IoT system
development to safeguard sensitive information and ensure
secure transmission. Regular security updates, ongoing
assessment, and improved measures are necessary to
address evolving threats. Data security is crucial for
overall IoT system security, given its unique features like
perception, transmission, and processing. In this paper, it
is suggested using encryption for data sent over Wi-Fi to
improve Internet of Things (IoT) security. Advanced
Encryption Standard (AES) commonly used encryption
standard, and it is the encryption technique employed.
AES algorithm uses one bio-key (variable key every
minute) to maximize security and the capacity to fend off
attacks. It is added the owner's biometric to the bio-key for
authentication and security in order to authenticate the
system. Our technology stands out with robust encryption
and authentication against numerous assaults because the
owner's biometrics are encrypted and it is added to the bio-
key made up of the minute-by-minute changes to the year,
month, day, hour, and minute. The data is encoded in
ESP32 and sent to the local server and then stored in a
Python language database using the HTTP protocol. The
database is hosted on XAMPP, which is a web server
package that allows easy database management.

The Interacting Boson Model is used to study the
level structure of ground state, beta, and gamma
bands in150Nd, 152Sm, 154Gd and 156Dy (N = 90)
isotones. The energy ratios ????????+2⁄2 and ????02⁄21 are
compared to the predictions of U(5), SU(3), and X(5)
symmetry. The calculated reduced transition
probabilities as well as quadrupoles moments were
compared to experimental data. IBM-1 investigates
the properties of the potential energy surface to
determine the nuclear shape of the Nd-Dy (N=90)
isotones.

The current experiment was conducted in the Fish
Laboratory belonging to Aquaculture Unit, Agriculture
College, Basrah University. At the beginning of feeding
experiment average larvae weight was 0.031 g. Common
carp (Cyprinus carpio) larvae brought from Fish Hatchery
of Marine Science Center. The experiment used to detect
the protein ratio effects on growth and survival of common
carp larvae. Three fish feed with protein ratio (T1 35, T2
40, T3 45)% and only fish meal of 55% protein (T4) were
used to feeding 25 larvae in each aquarium to satiation six
days a week. Twelve aquaria with dimensions of
60×40×30 cm were used for current experiment. Feeding
experiment lasted for 54 days from 10th may to 3th July
2022. Results of current experiments revealed that the
highest growth criteria were achieved by larvae fed on diet
with protein ratio of 40% followed by larvae fed on diet
with 45% protein ratio, while lowest growth criteria
achieved by larvae fed on fish meal only. Highest survival
rate (96.0%) was achieved by larvae fed on diet with 45%
protein ratio and lowest survival rate (78.7%) was
achieved by larvae fed on diet with 40% protein ratio.
There were significant differences P≤0.05 between T2
with T3 and T4 in the survival rate, while there were no
significant differences P≥0.05 between T1, T3 and T4.

The current experiment was conducted in the Fish Laboratory be-
longing to Aquaculture Unit - College of Agriculture. It included
the study of the effect of betaine (peptine hydrochloride BeHCl)
on the growth performance of common carp (Cyprinus carpio L.).
Five fish were used in three replicates for each treatment in the
experiment with an average weight of 18.49 ± 1.08 g after accli-
mation for seven days. Fishes were fed on diets of C (0% addi-
tive), T1 (0.20% BeHCl) and T2 (0.25% BeHCl). The experiment
lasted for 42 days and all fish were weighed in each replicate
every two weeks in order to estimate daily feed. The results
showed that the highest final weight (30.06 g) and weight gain
(10.80 g) were achieved by fish fed on a diet containing 0.20%
BeHCl, followed by the diet containing 0.25% BeHCl, while the
lowest weight gain (7.46 g) was achieved in the control diet with
a significant difference (P≤0.05) among all treatments, as the
fish fed a diet containing 0.20% BeHCl outperformed the rest.
Also fish fed 0.20% BeHCl was superior in the daily, relative and
specific growth rate, as it reached 1.29 g/day, 56.10 % and
1.06 %/day respectively. The results indicated that the best feed
conversion rate was 3.23 for fish fed on a diet containing 0.20%
BeHCl, while the other treatments showed conversion rates of
3.66 and 4.35 for 0.25% treatment and control respectively, as
well as the highest protein efficiency recorded in fish fed on diet
containing 0.20% BeHCl amounted to 0.93, followed by the diet
containing 0.25% BeHCl (0.82). Statistical analysis of the results
proved that the above differences between the treatments were all
significant (P≤0.05).

The utilization of blockchain technology (BT) has arisen
as a viable remedy for tackling crucial challenges in
financial system (bitcoin) and the healthcare sector,
including data security, interoperability, and
transparency. The integration of BT in the healthcare
industry has significant promise, but it is a complex
process that is impacted by several variables. This
systematic review seeks to provide a thorough
comprehension of the models, obstacles, and forthcoming
research pertaining to the use of BT in the healthcare
sector. The paper examines the fundamental ideas and
models that are crucial in comprehending the
implementation of BT in the healthcare sector. The
theories encompassed in this list are the Technology
Acceptance Model (TAM), Innovation Diffusion Theory,
Trust and Transparency Theories. These theories together
provide a systematic framework for evaluating the
process of adoption. The systematic review results
provide an overview of the present state of blockchain
implementation in the healthcare sector. The challenges
that have been highlighted include obstacles related to
regulations, difficulties in achieving interoperability,
concerns over data privacy, and the need for a strong and
reliable infrastructure. Furthermore, the advantages of
using BT, including heightened data protection and
increased healthcare services.

This paper investigates Content-Based Image Retrieval
(CBIR) using an ensemble of three cutting-edge deep
learning architectures: Xception, MobileNet, and
Inception. This ensemble approach demonstrated
exceptional retrieval accuracy, with Xception and
Inception models achieving an accuracy of 92.375%,
precision and recall of 93% and 92% respectively, and an
F1-score of 92%. The MobileNet model also showed
strong performance, with an accuracy of 87.125%,
precision and recall of 88% and 87%, and an F1-score of
87%.Beyond mere retrieval accuracy, the study places a
significant emphasis on the security of the image database.
A dual-layer encryption method was employed,
integrating visual cryptography with the Advanced
Encryption Standard (AES) to ensure robust protection of
sensitive data. This approach guarantees efficient image
retrieval based on content while securing the data against
potential breaches.The results underscore the efficiency of
the ensemble model in balancing high retrieval accuracy
with stringent security measures. This balance is
particularly relevant for applications in digital libraries,
historical research, fingerprint identification, and crime
prevention. The paper’s findings advocate for the critical
need to integrate strong security protocols in future CBIR
systems, ensuring optimal performance without
compromising data security.

Keywords:
Deep Learning, Content-Based Image
Retrieval, Visual Cryptography,
Advanced Encryption Standard.

The An earthquake is a devastating natural disaster that causes
great economic and human losses because it occurs without
warning. The increase in earthquakes in Iraq has raised concerns
about the future of the region. It is necessary to study earthquake
prediction and determine the location, size and time of the
earthquake. A machine learning model was proposed to predict
earthquakes in Iraq using two sources: the first is a catalog of data
from 1900 to 2019, which includes 36,663 earthquakes, and the
second is from the USGS for one year from 2022 to 2023, which
includes 25,000 earthquakes. Preliminary processing of the data
was done, removing outliers and integrating Date and time data
in timestamps, and the five important features for prediction were
identified and the data was divided into 80% for training and 20%
for testing. After applying several attempts in using different
models, the best results were achieved using NN and the accuracy
was about 0.7. The most important reason for this result is
training over many years that may change geologically. The
study compared its results with other studies to predict
earthquakes across different regions of the world

Transmitting video over Mobile Ad hoc Networks
(MANETs) is the most challenge because of the dynamic
of its topology and the mobility of its nodes. These
networks operate without a fixed physical infrastructure,
allows wireless nodes to seamlessly connect or disconnect
at any time. Additionally, the nodes in these networks are
capable of self-organization. That makes routing process
more difficult than traditional networks in addition to
needing high bandwidth requirements to complete video
transmitting process. This research investigates and
assesses the performance of several commonly utilized
standard routing protocols in MANETs, including AODV,
DSDV, OLSR, and DSR. The evaluation is conducted
across a range of wireless node quantities, spanning from
10 to 100, and tested on many performance metrics such as
PDR, ETE delay, Packets Delay Variation (jitter), Data
Dropped (DD), Throughput, and routing Overhead, the
simulation results are analyzed to determine the most
suitable routing protocols among them and overcome the
video transmitting. Results shows that OLSR routing
protocol is better from others tested protocols in almost
performance metrics for video transmitting. Standard
routing protocols have many resources constraints that may
cause a link failure, when using it in multimedia
applications it needs more power consumption and more
bandwidth. The performance study simulated with ns3
simulator.

This study included the preparation of a new heterocyclic compound
namely 5-benzyl-5-((5-nitropyridin-3-yl) methyl)-1,3,5-dithiazinan-5-
ium (BNPD) by a simple powerful method and an environmentally
acceptable. The BNPD compound contains O, N, and S heteroatoms
that have been inserted into the chemical structure by one step
consisting of several transformations. The BNPD structure has been
characterized using several techniques like FT-IR, 1H-NMR, 13C-
NMR, and G-C mass. The prepared molecule has been studied as an
inhibitor of carbon steel corrosion (N-80), which is generally used in
oil fields. The BNPD effectiveness has been studied as an anti-
corrosive material in saline water that accompanies the extraction of
crude oil. Saline water contains dissolved solids estimated at
239581mg/l, conductivity 254.23 ms/cm, and pH 6.1. The retardant
has been evaluated by electrochemical impedance spectroscopy
technique (EIS), potentiodynamic polarization technique (PP),
scanning electron microscopy-energy dispersive x-ray spectroscopy
(SEM/EDX), and weight loss method (WL) at 308K(±1). It was noted
that inhibition efficiency increases with increasing BNPD
concentration. The results of potentiodynamic polarization showed
that the studied inhibitor molecules act as a mixed-type inhibitor, while
the results of weight loss showed that these molecules adsorbed on the
carbon steel surface through physical and chemical adsorption
simultaneously. The results of the SEM/EDX indicated that slight
corrosion occurred at the inhibitor was added, where the EDX
spectrum of N-80 at 80 mg.dm-3 of inhibitor was noticed
approximately similar to the polished carbon steel spectrum.
Furthermore, the electronic and physic-chemical characteristics of
BNPD have been calculated like EHOMO, ELUMO, energy gap, total
energy, lipophilicity coefficient, molecule surface area, molecular
volume, molecular polarizability, molar refractivity, dipole moment,
solvation energy, theoretical inhibition efficiency, and electrostatic
potential surfaces. The BNPD compound has shown wonderful results
in corrosion inhibiting, where its efficiency optimum reached 91.14%,
93.60%, and 93.46% for WL, EIS, and PP techniques, respectively.
Either theoretical efficiency reached 90.25%.

In this paper, we have constructed AESN, a database for
regions of human faces. AESN is an improved dataset of
human eyes, noses, and mouths that are clipped from facial
photos. It was constructed to identify people, in addition to
recognizing expressions of individuals. This database was
collected from several resources, some from the Internet,
and others taken by our cameras. We have got it through
several types of multimedia, either directly from pictures or
by extracting frames from videos. The purpose for needing
a dataset for eyes was due to the emergence of the Corona
pandemic, which obligated people to wear masks. In our
work, we need AESN during developing a monitoring
system to increase their ability to discover the identity of
people through the eyes area only. It was considered a great
challenge due to the loss of a large part of the face. We have
accomplished promising results when using AESN images
in our studies. In addition to the eyes, the remaining parts of
the face (nose and mouth) have many interests due to the
diversity of the shape of the nose, which mainly serves
medical applications and the gestures that occur in the
mouth as a result of emotional reflections that help us clarify
the emotional state of the person, as well as helping
researchers in finding biometrics features for recognition
systems.

The early diagnosis and treatment of diabetes can
contribute to the mitigation of associated risks and
effects. Therefore, it is imperative to anticipate and
identify the ailment at an early stage through the
utilization of dependable procedures that can offer
forecasts characterized by a substantial level of
dependability and precision. This study utilizes the
Diabetes Readmission Dataset, comprising 101,766
records and encompassing 50 features. After selecting the
most pertinent features, the dataset is partitioned into a
training set and a test set. Subsequently, a sequential
model employing deep learning, specifically a
convolutional neural network (CNN) with three hidden
layers, is constructed for prediction purposes. The
correctness of the model was assessed through the
utilization of performance testing metrics, resulting in a
recorded accuracy rate of 99.53%. The findings of this
study have the potential to inform the development of
personalized treatment approaches that address the
individualized requirements of patients, hence enhancing
the quality of healthcare provide.

Image segmentation is a way which applied to split an
image for many parts. It will generate image smooth and
easy to evaluate. A useful image segmentation technique
is help the area authority in medical images. Also, a
good segmented images are important in many medical
fields like radiologist, pathologist of quick and
successful analysis. It has used effectively in diagnosis
for many disease. Nature inspired algorithms imitate the
mathematical and innovative techniques for non-linear
and actuality problems and can be achieved to segment
or analyses the images. In this paper, a watershed as
segmentation technique (WAWOA) has applied to
segment the medical dataset image then proposed a
whale based segmentation technique which can be used
on medical bones image anyways the nature or style of
the image. For fair analysis of these techniques, peak
signal-to-noise ratio (PSNR), structural similarity index
measure (SSIM) and Universal Image Quality Index
(UIQI) of segmented images are analyzed by using
MATLAB. The success of the proposed technique has
confirmed with evaluated via comparing the obtained
with outed results beside a standard watershed
technique. The results showed this technique develops
the segmentation of medical images and can help with
better diagnosis. The evaluation results of our proposed
technique show the success and efficiency of segmented
images in high rates.

The structural, electric, and optical characteristics of the rock
salt (RS) VBi monolayer are studied, together with the phonon
transport, using first-principles simulations based on density
functional theory (DFT). In addition to its unique electronic
properties, the (RS) VBi monolayer also displayed a broad
absorption spectrum ranging from visible light to the
ultraviolet region. Furthermore, its convergent phonon
scattering rate was found to be reduced. These findings
highlight the potential of further theoretical and experimental
investigations into the electronic structure, optical
characteristics of the VBi monolayer. The experimental
outcomes have shown that the monolayer of (RS) VBi
possesses the characteristic of being a half-metal. In the case
of the spin-up configuration, the material exhibited metallic
properties as the energy levels intersected the Fermi level. On
the other hand, for the spin-down configuration, a gap in
energy appeared on both sides of the Fermi level, resulting in
the formation of a semiconductor. This is indicated by the
presence of a gap between the conduction band and the
valence band, with the total energy gap of the compound being
determined by the combined magnitude of both gaps. The
specific energy gap of the (RS) VBi monolayer was measured
to be 0.55 eV, which is half the size of a typical metal's energy
gap (0.3 eV). Moreover, the (RS) VBi monolayer exhibited a
magnetic moment per unit cell equivalent to 2µB,
demonstrating a strong polarization at the Fermi level due to
its half-metal nature.