Iraqi Journal of Science

Cytokines are polypeptides with several functions that are produced by a variety of bodily cells. They are clinically significant for illness diagnosis, treatment, and prevention. Interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) are the most important cytokines for cell division and proliferation. Vascular endothelial growth factor (VEGF) is produced in excess in mesenchymal, epithelial, and especially in tumor cells. In this study the serum levels of IL-6, TNF-α and VEGF were detected for 41 colorectal cancer patients and 41 healthy control group at Nanakaly Hospital by comparing their serum concentrations for patients paining from colorectal carcinoma (CRC) with those of the control subjects. The result shows a significant increase in the cytokines TNF-α, IL-6 and VEGF in patients with CRC when compared with the healthy group. A significant positive correlation (p<0.0001) was found between TNF-α with IL-6 (r= 0.8200) and VEGF (r= 0.7305). According to this study, there is a direct correlation between CRC and blood levels of (TNF-a, IL-6, and VEGF). This suggests that these cytokines are crucial for the development and spread of colorectal cancer.

One of the health issues that a coronavirus can induce is blood clotting. Coronavirus can be prevented in a number of ways. Vaccination is one of the critical methods for preventing illness or lessening its impact. This study seeks to estimate a few blood coagulation variables. 147 samples were collected from the Baghdad Governorate in the autumn of 2021. The samples were split into three groups: COVID-19 patients, healthy individuals before and after receiving the (Pfizer-BioNTech) vaccine, and healthy individuals only. Prothrombin Time (PT), Partial Thromboplastin Time (PTT), Protein C (PTN-C), Protein S (PTN-S), and International Normalized Ratio (INR) for 49 samples were measured and computed for each group. The results have shown that the PTN-C and PTN-S concentrations were significantly decreased in the COVID-19 patients compared to unvaccinated healthy individuals. While INR of COVID-19 patients showed a highly significant increase when compared with unvaccinated healthy individuals. At the same time, there was no significant difference for each PT and PTT between these studied groups. The parameters exhibit identical findings when COVID-19 patients are compared to those who have had vaccinations, with the exception of PT, which reveals a highly significant rise in COVID-19 patients when compared to those who have received vaccinations. Conclusion: COVID-19 causes blood clots and may be recognized by a decrease in PTN-C and PTN-S content.

One of the most prevalent illnesses in developing countries is myocardial infarction (MI), which develops when the heart's blood supply is suddenly interrupted and causes tissue damage. It is connected to several metabolic risk factors, including diabetes, hypertension, and obesity. The objective of this study was to assess how oxidative stress (OS) contributed to the pathophysiology of MI and T2DM. The current study examined 152 male samples, including 52 MI patients without T2DM (G1), 50 MI patients with T2DM (G2), and 50 seemingly healthy men as controls (C). The levels of fasting blood sugar (FBS), C-reactive protein (CRP), total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), malondialdehyde (MDA), and total peroxidase activity were assessed. Based on the results, CRP levels increased significantly in both the G1 and G2 groups compared to the C group, with G2 reporting greater significant increases than G1. Both the G1 and G2 groups considerably exceeded the C group in terms of TOS, OSI, MDA, and total peroxidase activity. In contrast to the C group, the TAS was found to be much lower in the G1 and G2 groups. Additionally, there were no noticeable variations in the TOS, TAS, OSI, MDA, or total peroxidase activity between the G1 and G2 groups. According to our findings, individuals with MI and T2DM had an increased OS and a diminished antioxidant system. Consequently, OS may be crucial to the pathophysiology and prognosis of MI and T2DM.

The present study explored the possible role of adenosine deaminase and guanine
deaminase in diabetic foot ulcers which is considered one of the main chronic
complications of diabetes mellitus. Serum adenosine deaminase, guanine deaminase,
and some biochemical parameters were investigated in 54 patients with diabetic foot
ulcers and 40 healthy individuals. According to our findings, adenosine deaminase
and guanine deaminase activities are much higher in diabetic foot ulcer patients than
in healthy individuals. A strong positive correlation was found between adenosine
deaminase and guanine deaminase activities. Adenosine deaminase and guanine
deaminase had 100% sensitivity and specificity for diagnosing diabetic foot ulcers,
and their area under the receiver operating characteristic curve was 1.0. Kinetic study
displayed significant changes in Vmax and Km between patients and healthy
individuals for both adenosine deaminase and guanine deaminase. Based on results,
it can be concluded that the increase of adenosine deaminase and guanine deaminase
activities may negatively contribute in diabetic foot ulcers progression.

Design and synthesis of novel poly heterocycles together using same heterocyclic compound is the main task of the present paper. The target compounds entitled 4,4’-[benzene-1,4-diylbis[ethylidenehydrazine-2-ylidene]bis[4-[3,5-di(5-substitutedpyridin-2-yl)-3,3a-dihydro[1,3]thiazolo[4,5-c][1,2]oxazol-6(5H)-yl]-4H-3-yl-1,2,4-triazole-3-thiol] have been synthesized starting from the reaction of 1,4-diacetylphenyl and carbohydrazide to give Schiff base derivatives then 1,2,4- triazole derivatives from the reaction with CS2 and an excess of hydrazine hydrate. The same applies for the condensing of these newly heterocyclic amines with different pyridine-2-carbaldehydes, which resulted in the synthesis of some new Schiff bases, which were then cyclized to develop new thiazolidinones by thioglycolic acid. Additionally, by fusing thiazolidinones with various pyridine-2-carbaldehydes, a novel chalcone was formed. To produce the desired fused isoxazolines, the later compounds underwent cyclization with hydroxylamine hydrochloride and potassium hydroxide. From the examination of the (CHN), FTIR, 1H NMR, and 13C NMR spectra, the structural identities of all recently synthesized compounds were determined. The newly synthesized chemicals were also successfully tested as an antibacterial agent.

Polluted water has been considered a critical issue nowadays, threatening the environment and lives of living creatures. Because of technological and industrial advancements, as well as increased social activities of humans in various countries, pollution sources have multiplied. To reduce the impact of this problem, many techniques have been developed in order to reach zero discharge pollution. In the last decade, graphene oxide (GO) - a member of the graphene nanomaterials family, has been the focus of many research efforts in the water treatment sector because of its extraordinary properties. This review highlights the research efforts conducted to investigate GO as a novel adsorbent for water treatment applications and recent fulfilments in the last 3 years. The synthesis techniques, properties, and efficiency of this material in water treatment will be explained. All results confirm the future role of GO as an efficient absorbent to solve wastewater purification challenges, but the big challenge is to reduce time and simplify the complicated extent of synthesis stages, besides reducing the high cost of production methods. According to the review, Iraqi researchers' efforts to use this nanomaterial in water purification are still in their early stages.

Many species are resistant to heavy metals in their surrounding polluted
environment and Staphylococcus sp. is an example. This study aimed to isolate and
characterize bacteria resistant to heavy metals in the Shatt Al-Arab River in southern
Basra, Iraq. Based on the morphology and using Vitek II system, and due to their
high resistance to heavy metals (mercury and chromium), two species of
Staphylococcus (Staphylococcus lentus and Staphylococcus lugdunensis) were
chosen and isolated. The minimum inhibitory concentration (MIC) of the isolates
against Hg and Cr was determined after 72 h. of incubation in solid media. All
isolates were resistant to Hg (2000 mgL-1) and Cr (4000mgL-1). Living biomass of S.
lentus and S. lugdunensis was used to remove the heavy metal ions in various
concentrations (5, 10 and 25 mgL-1) of the solutions of aqueous metals. After 72
hours incubation, the removal percentage of S. lugdunensis was 98.91 and 78.78%
for Hg and Cr respectively. That for S. lentus it was 77.83% for Cr after 72 hours,
and 98.84% for Hg after 24 h. of incubation. The scanning electron microscope
approved that the removal of these metals causes morphological changes in bacteria.

Depending on the high resistance to antibiotics, five isolates of Pseudomonas
aeruginosa and 7 isolates of Serratia fonticola were selected out of 150 bacterial
isolates from burn wards in Baghdad hospitals, which were later identified by
VITEK2. A susceptibility test was done by using 15 antibiotics. The results showed
that all the selected isolates were resistant to antibiotics: AMP, CTX, CAZ, GEN, PIP,
TIC and TMP especially, while they were sensitive to IPE. The essential oils of
Aloysia citrodora (Family: Verbenaceae), Rosmarinus officinalis (Family:
Lamiaceae) and Thymus vulgaris (Family: Lamiaceae) were extracted by the
Clevenger. The synergistic effect of essential oils of these plants on bacterial growth
were studied by determining the MIC of essential oils for P. aeruginosa and S.
fonticola. The MIC concentration of P. aeruginosa was: 2500 ppm of A. citrodora,
2500 ppm of R. officinalis, 2500 ppm of T. vulgaris, whilst the synergistic effect of
each two plants was:1250 ppm of A. citrodora with 2500 ppm of R. officinalis, 1250
ppm of A. citrodora with 312.5 ppm of T. vulgaris, 1250 ppm of R. officinalis with
156.25 ppm of T. vulgaris, whereas the synergistic effect of three plants was: 156.25
ppm of A. citrodora with 156.25 ppm of R. officinalis and 39.0625 ppm T. vulgaris.
While the MIC concentration of S. fonticola was: 1250 ppm of A. citrodora, 5000
ppm of R. officinalis, 1250 ppm of T. vulgaris, whilst the synergistic effect of each
two plants was: 1250 ppm of A. citrodora with 2500 ppm of R. officinalis, 1250 ppm
of A. citrodora with 312.5 ppm of T. vulgaris, 1250 ppm of R. officinalis with 156.25
ppm of T. vulgaris, whereas the synergistic effect of three plants was: 312.5 ppm of
A. citrodora with 312.5 ppm of R. officinalis and 78.125 ppm T. vulgaris. The results
of bacterial gene expression showed that three isolates of P. aeruginosa had a mexB
gene expression that ranged between 2.5 - 0.4 (concentration 1) and 2.8 to 0.6
(concentration 2). It increased in two isolates and decreased in one isolate. The
melting point ranged between 90.91 - 91.280C compared with 89.89 to 90.330C for
fbp gene. Whereas S. fonticola did not have blaKPc gene, instead it had blaTEM gene
with the gene expression ranging between 2.4 - 0.1 (concentration 1) and 4.5 - 0.2
(concentration 2). The gene expression increased in two isolates and decreased in five
isolates. The melting point ranged between 83.63 - 84.200C compared with 75.82 -
85.560C for gyrB gene.

Anemia is a major significant public health issue that affects both pregnant and non-pregnant women throughout the world. This study aimed to determine the prevalence and severity of anemia, as well as its classification, based on MCV values in non-pregnant women residing in the Garmian province in the east of Iraq. Blood samples were collected from non-pregnant women between the ages of 16 and 49 years. Anemia is classified as mild, moderate and severe, and as microcytic, normocytic and macrocytic depending on the level of Hb or mean corpuscular volume (MCV) respectively. In this study sociodemographic and hematological characteristics were also evaluated. The results showed that 56% of the study samples were anemic, and of the total anemic patients, 54 (35%), 86 (55%) and 16 (10%) had mild, moderate, and severe anemia respectively. Also, of the total anemic patients, 102 (65%) had microcytic type of anemia, 51 (33%) had normocytic anemia and only 3 (2%) had macrocytic anemia. The majority of anemic women (77.6%) had normal RBC counts but low Hct, MCH and MCHC values of 76.3%, 79.5% and 81.4%, respectively. The results showed also that there was no significant difference between the severity of anemia and age groups, occupation, blood pressure, bleeding, and haemorrhoids. However, diabetics showed a significant difference in anemia severity with <0.0004 P-value. The results showed that anemia had become a major public health issue in the study area.

The research aimed to study the vaccination efficiency against the emerging virus SARS-CoV-2, which includes Pfizer, AstraZeneca and Sinopharm vaccines that are used exclusively in Iraq, by measuring the level of immune cells CD4+ as well as vitamin D3 using ELISA technology. Taking serums were taken from vaccinated individuals: 20 uninfected before individuals who got a vaccination and 20 people with prior infections who had the vaccine (for each type of the three vaccines). Samples were collected 21 days following the second dose as well as 3 to 6 months thereafter. The results showed that the most effective vaccine was AstraZeneca and the results of T-cells were the highest compared to Pfizer and Sinopharm

The nanoparticles biosynthesis is disclosing a new route of exploration concerned with nanotechnology. Silver nanoparticles (AgNPs) are integrated in familiar techniques via chemical routs, for having utterly toxic natures. Syzygium aromaticum is an aromatic plant which belongs to the genus Eugenia. Three species of the genus Streptococcus were tested using LB for their ability to produce AgNPs and all of them had a brown appearance that confirmed their involvement in AgNPs production. AgNPs were estimated by utilizing a spectrophotometer. The antimicrobial enterprise (AgNPs) of the three species was tested against several types of dangerous bacteria. To determine the MIC, three separate concentrations (v/v) of 0.5%, 5% and 10% of clove oil with dimethyl sulfoxide (DMSO) were prepared (well diffusion technique). Clove oil extract together with AgNPs biosynthesized from three kinds of Streptococcus, were shown to have antimicrobial effects on unusual clinical microorganisms.

Bitter substances are identified by protein receptors located on surface of taste cell membranes. Mutational polymorphism of the bitter taste receptor (TAS2R38) is a significant determinant in phenylthiocarbamide (PTC) threshold perception. This research's objectives were to find TAS2R38 polymorphisms in Iraqi people and investigate any correlations between genotype and the PTC taste sensitivity. Bitterness sensitivity was determined by assessing the capacity to differentiate and the responsiveness to a representative strip of PTC. Cheek cells samples were collected for DNA extraction, PCR amplification and genotyping. PCR was performed to amplify the short region of the TAS2R38 gene containing the initial polymorphisms of interest (145G > C, rs713598). Amplified samples were digested by the restriction enzyme (HaeIII) to study the genetic variations in TAS2R38 which is involved in PTC bitter sensitivity.
This particular study included a total of 32 different cohorts. The phenotypic frequency of PTC strong-tasters and non-tasters was identical at 34.375% which was a greater value than the frequency of weak-tasters (31.25%). While genotypic data analysis showed that weak-tasters had a genotypic frequency of 45.16% which was higher than the genotypic frequencies of strong-taters (22.58%) and non-tasters (32.26%) respectively. In addition to this, 87.1% of the projected phenotypic and genotypic frequencies were in agreement with one another.
Even though more detailed statistical analysis with a bigger group is needed. The results of this study suggest that allelic variation in the single locus TAS2R38 rs713598 works as a crucial genetic marker for bitterness sensitivity and has vital roles in the bitter tasting ability among Iraqi population.

This experiment was carried out in the greenhouse of Al-Adawi area of the Damascus Governorate, and labs of the Horticulture Department, Faculty of Agriculture, Damascus University during the period 2020-2021, to study the effect of biofertilizer, Em1 (0, 4, 8 ml/l), and seaweed extract, Alga 600 (0, 1, 2 g/l) and their interactions on growth and productivity of strawberry plant cv. Festival. data were calculated after six months of culture. The combination treatment of Em1, (8 ml/l) and Alga 600 (2 g/l) resulted in the best morphological parameters (23,93 cm, 27.87, 138.81 cm² for plant height, leaf number, and area respectively), physiological parameters (10.30 %, 60.00 mg/100 gm wet weight, 0.59 % for total soluble solids, vitamin C and titratable acidity respectively), and productivity parameters (20.07, 16.4, 250.72 g/ plant for flowers and fruits number/plant and plant yield respectively). The lowest values, however, were obtained in control non-treated plants.

Keratin is a fibrous, insoluble structural protein that is highly cross-linked with hydrophobic, hydrogen, and disulfide bonds. Keratinases are enzymes that belong to the category of serine hydrolases that are capable of breaking down keratin. The re-sults of the determination of the better fermentation system showed that the produc-tion of keratinase from local A.terreus A13 isolate by submerged fermentation (SmF) system was the best system to give the highest specific activity (113.4 U/mg) of keratinase compared with solid-state fermentation (SSF). The optimum conditions for keratinase production by SmF, were determined via cultivation conditions, including carbon source, nitrogen source, temperature, pH of the medium, and time of incuba-tion were optimized to enhance the production of total keratinase production in a cul-ture of A.terreus A13 with incubator shaker. The highest product of total keratinase was achieved in feather broth with 2 % sucrose, and 0.5 % soya bean, with a pH of 5.5 at 28 °C for 8 days. Separation and purification of keratinase from a local isolate of A.terreus A13 was done by precipitating with 0-75 % saturated ammonium sulfate, then by ion-exchange chromatography on DEAE-Cellulose column and sephadex G-150 gel. Partially purified keratinase gave an activity of 5.1 U/ml, protein concentra-tion of 0.004 mg/ml, and specific activity of 1275 U/mg with purification fold of 4.96 and 49 % as yield. The aim of the present study was to optimize the production of keratinase from A. terreus A13, cultivated using optimum conditions, and its use for the biodegradation of feathers.

In this study, dark and various light qualities (white, red, green, and blue) were applied to evaluate their effects on growth characteristics, chemical content, and callus characteristics of Rosa damascene Mill. and Rosa hybirda L .
Explant (single-node and shoot tips) cultured on MS media supplemented with sucrose, agar, and plant growth regulators ( Kin 0.5 mg/l and IBA 1 mg/l for whole plant formation experiment or 1 mg/l kin with 0.5 mg/l IBA for callus experiment), incubated in a growth chamber .
The results of the whole plant formation experiment showed variation in growth characteristics in two types of Rosa, Green and white light caused the height ratio of shoot growth compared with dark, while red and green light caused the height ratio of root growth to compare with dark. Rosa growing in white or green has a higher content of chlorophyll, as well as carbohydrates and proteins in shoot and root parts compared with dark .
The results of the callus experiment explained variation in callus characteristics (shape, texture, color, and weight), the highest fresh weight (1.83 and 1.99) gm and dry weight (0.207 and 0.233) mg of callus Rosa damascene and Rosa hybirda, respectively in dark conditions compared with least in blue light.

The nuclear structure for the positive ( 1,2,3 2 + ) States and negative ( 13− ) states of
36,40Ar nuclei have been studied via electromagnetic transitions within the framework
of shell model. The shell model analysis has been performed for the electromagnetic
properties, in particular, the excitation energies, occupancies numbers, the transition
strengths B(CL) and the elastic and inelastic electron scattering longitudinal form
factors. Different model spaces with different appropriate interactions have been
considered for all selected states. The deduced results for the (CL) longitudinal form
factors and other properties have been discussed and compared with the available
experimental data. The inclusion of the effective charges are essential for obtaining a
reasonable description for the data. The results of sdpf-model space with sdpfk and
sdpfmu-interactions have a good improved for the ground state form factors for 36,40Ar
and for excitation properties of
12+ and 13 − state of 36Ar nucleus.

Engineering project assessment at Al-Muthana Airport in Baghdad, Iraq, has been
studied using a 3D electrical resistivity imaging survey. The site investigation is
crucial for assessing the future of the region's infrastructures since it reveals the
location of buried facilities or weak zones below the surface and measures localized
groundwater levels. Wenner-Schlumberger array was used to conduct four parallel 2D
electrical resistivity spreads (MU1 to MU4). Each spread line was 100 m in length
with 1 m electrode spacing and an average spacing of 9 meters between any two
adjacent lines. The depth of the investigation was around 23.8 m. Survey lines were
drawn going from northwest to southeast. These spreads were combined to provide a
3D image of a 2700 m2 space.The robust inversion method and the inverse model
generated using the standard least-squares method showed horizontal slices identified
three zones with resistivity distribution ranging from 2 to 45.5 ohm.m. The first zone,
from surface to 3.37 m, had relatively high resistivity of sandy silty clay soil with
relatively low moisture content; the second zone, approximately from 3.37 to 12.2 m,
had very low resistivity representing groundwater table; and the third zone, from 12.2
to 23.8 m representing high stiffness and density and relatively high resistivity due to
gravel presence in the deposits. The second zone highlights potential risk zones for
construction projects; as a result, it is advised that the zone surrounding the foundation
be filled with a low permeability layer and that the towers be built on deep
foundations.

This study dealt with the reality of groundwater before Sulfur production in the Al-
Mishraq field-1 and after production stopped in the field, by measuring the
groundwater table for (44) wells in 2021, and comparing it to the groundwater table
measured by the Polish company Centrozap in 1971, the groundwater table was a
range between (187.71-205.8)m in 1971, but in 2021 it ranged was between (188.51-
196.55)m.
Maps of the groundwater movement and water table were created using these data. It
turned out that there was little change in the direction of groundwater flow; in both
cases, the flow is from the west and northwest towards the east with a slight slope
toward the southeast and the Tigris River. As for hydraulic properties, it was analyzed
in (29) wells, depending on pumping test data from the reports of Al-Mishraq, It was
noted that the values of Transmissivity ranged between (1557.5-24.4) m2/day before
production and (5020.6-39.4) m2/day after production, As for hydraulic conductivity,
it ranged between (0.26-14.68) m/day before production, and after production, it was
between (0.4-48.9) m/day.

The carbonate ramp facies of the Late Albian-Early Cenomanian Mauddud Formation were studied in the Ratawi Oilfield, Basra Governorate, south of Iraq using integrated borehole data set that included, core and cutting samples in three drilled wells to analyze the petrography of the Mauddud Formation, two hundred and eighty-one (281) thin sections were prepared and examined for the three selected wells. The results show that the formation is composed of light grey dolomitized limestone and pseudo-oolitic creamy limestone with green to bluish shale. The petrographic observations results show four facies’ associations in the Mauddud Formation. These include: Mid–Ramp environment which is represented by Argillaceous mudstone microfacies, Argillaceous wackestone microfacies and orbitolinid wackestone microfacies; the shallow open marine environment which is assimilated by foraminiferal wackestone microfacies and foraminiferal packstone microfacies; a restricted marine environment that represented by bioclastic fossiliferous wackestone microfacies and miliolid wackestone microfacies and Shoal environment is represented by bioclastic packstone microfacies, bioclastic grainstone microfacies, peloidal foraminiferal pack-grainstone microfacies, and peloidal packstone-grainstone microfacies with diverse skeletal grains. The porosity includes Vuggy, Interparticle, Intraparticle and Fracture porosity.

This study deals with the interpretation of structural 3D seismic reflection of the
Kumait oil field in southern Iraq within the administrative boundaries of the Maysan
Governorate. Synthetic seismograms are prepared by using available data of the Kt-1
oil field by using Petrel software to define and pick the reflector on the seismic section
of the Zubair Formation, Which represents the Cretaceous Age. The study shows that
the Kumait structure is an anticline fold. It is thought to be a structure trap caused by
the collision of the Arabian and Iranian plates and trending in the same direction as
driving factors in the area, which are from the northwest to the southeast, and the
overall trend of strata is north and northeast. Seismic structure interpretation results
show the top of the Zubair Formation and several structure closures. The stratification
Phenomenon of the study area was clear evidence of the existence of closures which
are probable to be hydrocarbon traps.

The Hartha Formation has been investigated from a biostratigraphic view in three subsurface sections in the Nasiriyah Oil field, wells Ns1, Ns3, and Ns4, South of Iraq. Hartha Formation is composed of limestone and has various areas of intense dolomitization alternating with marly limestone. The formation ranges in thickness from 126 to 182 meters. Thirteen large and small benthic foraminifer species and genera are identified from Hartha Formation. Based on the large benthic foraminifer's assemblage, one distinct biozone was recognized after an examination of the paleontological datum in the investigated area showed that the studied wells contained a diversity of foraminiferal species, the larger foraminifers biozone was proposed: Orbitoides medius- Orbitoides tissoti - Orbitoides gensacicus -Orbitoides apiculatus - Omphalocyclus macroporus assemblage zone (Late Campanian- Early Maastrichtian), this biozone divided in two range subzone: Orbitoides medius Partial Range Zone (Late Campanian) and Omphalocyclus macroporus Range Zone (Early Maastrichtian). This biozone indicated that the Hartha Formation deposited through (Late Campanian- Early Maastrichtian).

We introduce the class of analytic and univalent functions of the form
????(????)=????+Σ????????????????∞
????=2 for z∈△:={z:|z|<1} that satisfies
1+1μ{(????λ????????(????))′−1}≺ (1+Lz)(1+Mz), z∈△,
such that L and M are fixed numbers where satisfy −1≤M τ≥0,λ>−1 and μ is arbitrary complex number. We denote this subclass by ????μ(τ,λ,L,M). In this paper, we determine coefficients estimates, distortion theorem and maximization theorem for the new subclass ????μ(τ,λ,L,M).

In this article, we introduce a class of modules that is analogous of generalized
extending modules. First we define a module M to be a generalized ECS if and only
if for each ec-closed submodule A of M, there exists a direct summand D of M such
that
????
????
is singular, and then we locate generalized ECS between the other extending
generalizations. After that we present some of characterizations of generalized ECS
condition. Finally, we show that the direct sum of a generalized ECS need not be
generalized ECS and deal with decompositions for be generalized ECS concept.

In this paper, the oscillation of a Hematopoiesis model in both cases delay and non-delay are discussed. The place ????(????) and ????(????) are continuous positive ????-periodic functions. In the non-delay case, we will exhibit that a nonlinear differential equation of hematopoiesis model has a global attractor ℋ
̅(????) for all different positive solutions. Also, in the delay case, the sufficient conditions for the oscillation of all positive solutions of it about ℋ
̅(????)are presented and we establish sufficient conditions for the global attractive of ℋ
̅(????). To illustrate the obtained results some examples are given.

This paper uses classical and shrinkage estimators to estimate the system reliability (R) in the stress-strength model when the stress and strength follow the Inverse Chen distribution (ICD). The comparisons of the proposed estimators have been presented using a simulation that depends on the mean squared error (MSE) criteria.

The main object of this article is to study and introduce ℧????????(ℎ,∝,????)a subclass of meromorphic univalent functions with fixed second positive defined by q-differed operator. Coefficient bounds, distortion and Growth theorems, and various are the obtained results.

The variational iteration method is used to deal with linear and nonlinear differential equations. The main characteristics of the method lie in its flexibility and ability to accurately and easily solve nonlinear equations. In this work, a general framework is presented for a variational iteration method for the analytical treatment of partial differential equations in fluid mechanics. The Caputo sense is used to describe fractional derivatives. The time-fractional Kaup-Kupershmidt (KK) equation is investigated, as it is the solution of the system of partial differential equations via the Boussinesq-Burger equation. By comparing the results that are obtained by the variational iteration method with those obtained by the two-dimensional Legendre multiwavelet, the optimal homotopy asymptotic method (OHAM), the q-homotopy analysis transform method, the Laplace Adomian Decomposition Method, and the homotopy perturbation method, the first method proved to be very effective and convenient. The main methodology in this work is anticipated to be applied to various fractional calculus, linear, and nonlinear problems.

The conjugate coefficient optimal is the very establishment of a variety of
conjugate gradient methods. This paper proposes a new class coefficient of conjugate
gradient (CG) methods for impulse noise removal, which is based on the quadratic
model. Our proposed method ensures descent independent of the accuracy of the line
search and it is globally convergent under some conditions, Numerical experiments
are also presented for the impulse noise removal in images.

In the present study, the cluster concept was adopted to find points parallel to the cumulative points of any subset in topology cluster proximity spaces. The takeoff set term was given by the researcher to the set of all points. Also, an opposite definition was found for it, which is the follower set. The relation between them was found and their most important properties were highlighted. Through these two sets, new sets were built that are called, ????????−???????????? ,????????????−???????????? ,????????????−???????????? ,????????????ℎ???? ????????????,???????????????????? ???????????? .

In the complex field, special functions are closely related to geometric holomorphic functions. Koebe function is a notable contribution to the study of the geometric function theory (GFT), which is a univalent function. This sequel introduces a new class that includes a more general Koebe function which is holomorphic in a complex domain. The purpose of this work is to present a new operator correlated with GFT. A new generalized Koebe operator is proposed in terms of the convolution principle. This Koebe operator refers to the generality of a prominent differential operator, namely the Ruscheweyh operator. Theoretical investigations in this effort lead to a number of implementations in the subordination function theory. The tight upper and lower bounds are discussed in the sense of subordinate structure. Consequently, the subordinate sandwich is acquired. Moreover, certain relevant specific cases are examined.

Dropping packets with a linear function between two configured queue thresholds in Random Early Detection (RED) model is incapable of yielding satisfactory network performance. In this article, a new enhanced and effective active queue management algorithm, termed Double Function RED (DFRED in short) is developed to further curtail network delay. Specifically, DFRED algorithm amends the packet dropping probability approach of RED by dividing it into two sub-segments. The first and second partitions utilizes and implements a quadratic and linear increase respectively in the packet dropping probability computation to distinguish between two traffic loads: low and high. The ns-3 simulation performance evaluations clearly indicate that DFRED algorithm significantly controls the average queue occupancy and yields a reasonable gain in end-to-end-delay under different network conditions.

Measuring goat weight on a farm is carried out by two methods. The first method conventionally measures the chest girth (CG) and body length (BL), while the second is performed using a scale. This study aimed to facilitate the CG and BL measurements to estimate goat weight more efficiently through image processing. The endpoints of CG and BL were determined automatically using binary large object (BLOB) segmentation. The images of 120 goats were taken at three different shooting distances of 50 cm, 70 cm, and 90 cm. The statistical testing of the three scenarios in this study explained that the ideal distance to take pictures is the basis for determining whether or not the size of the circumference and BL is appropriate as a feature for estimating the weight of goats based on image processing. Statistical analysis showed that the features of CG and BL at a distance of 50 cm and 70 cm were not significantly different from the measurements of CG and BL of goats carried out directly (actual data). In comparison, the results of testing the feature prediction data at a distance of 90 cm are significantly different from the actual data.

Student dropout is a problem for both students and universities. However, in the crises that Lebanon is going through, it is becoming a serious financial problem for Lebanese private universities. To try to minimize it, it must be predicted in order to implement the appropriate actions. In this paper, a method to build the appropriate prediction system is presented. First, it generates a data source of predictor variables from student dataset collected from a faculty of economic sciences in Beirut between 2010 and 2020. Then, it will build a prediction model using data classification techniques based on identified predictor variables and validate it. Using open-source software and free cloud environments, a prediction program was developed. It consolidates, corrects, and normalizes the student's data. Then, it applies simple linear regression to show the correlation between the different variables and the student dropout, which allows us to select the factors that are highly correlated. From this point on, the program tries to predict the student dropout using different classification algorithms by machine learning on student dataset who left their courses either in success or in failure. Lastly, it measures the accuracy of the results and determines the best algorithm. In this study, the Artificial Neural Networks - Multilayer Perceptron showed an accuracy of 98.1% using only five variables. Finally, we evoke new avenues to further research and improve the model.

Cloud computing is a pay-as-you-go model that provides users with on-demand access to services or computing resources. It is a challenging issue to maximize the service provider's profit and, on the other hand, meet the Quality of Service (QoS) requirements of users. Therefore, this paper proposes an admission control heuristic (ACH) approach that selects or rejects the requests based on budget, deadline, and penalty cost, i.e., those given by the user. Then a service level agreement (SLA) is created for each selected request. The proposed work uses Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA) to schedule the selected requests under budget and deadline constraints. Performances of PSO and SSA with and without ACH are evaluated and compared using CloudSim. The simulation results prove that admission control maximizes profit by minimizing the number of task rejections and SLA violations. It also improves resource utilization while balancing makespan.

Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes of textures. The achieved results showed that the proposed method is accurate and fast concerning classification accuracy with low computational complexity. The achieved Recognition Rate (RR) was 100%.

Blockchain technologies have grown in popularity over the last few years, with various experts touting the technology's potential applications in a range of businesses, markets, organizations, and governmental institutions. In the brief history of blockchain, an astounding number of incredible implementations have been done in terms of how it may be utilized and the potential effect it may have on a range of sectors. And, because of the great number and complexity of these characteristics, addressing the blockchain's potential and complications can be difficult, especially when seeking to address its purpose and fit for a certain activity. The blockchain's practical skills in fixing multiple challenges that are currently preventing further progress in various industrial fields are significant benefits. Securing and sharing transactional data, automating and optimizing supply chain procedures, and enhancing transparency throughout the value chain are just a few of the issues that companies are concerned about. Blockchain technology efficiently overcomes these challenges by leveraging distributed, shared, secure, and permission-based transactional ledgers. This paper concentrates on the many industrial application domains that blockchain technology offers. In addition, it examines and investigates the benefits, drawbacks, and challenges of incorporating blockchain into various industry applications, as well as defines the criteria for using blockchain in multiple industry applications and provides a brief overview of the technology.

Cyberbullying is one of the biggest electronic problems that takes multiple forms of harassment using various social media. Currently, this phenomenon has become very common and is increasing, especially for young people and adolescents. Negative comments have a significant and dangerous impact on society in general and on adolescents in particular. Therefore, one of the most successful prevention methods is to detect and block harmful messages and comments. In this research, negative Arabic comments that refer to cyberbullying will be detected using a support vector machine algorithm. The term frequency-inverse document frequency vectorizer and the count vectorizer methods were used for feature extraction, and the results were improved using the cuckoo search algorithm. The resulting accuracy before and after optimizing the support vector machine’s hyperparameters is 85.8% and 87.1%, respectively.

Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedures, the majority of hybrid algorithms enhance classification accuracy.

Due to the spread of “Deepfake” in our society and the impact of this phenomenon on politicians, celebrities, and the privacy of individuals in particular, as well as, on the other hand, its impact on the electoral process as well as financial fraud, all these reasons prompted us to present a research paper dealing with this phenomenon. This paper presents a comprehensive review of Deepfake, how it is created, and who has produced it. This paper can be used as a reference and information source for the methods used to limit deepfake by detecting forgeries and minimizing its impact on society by preventing it. This paper reviews the results of much research in the field of deepfake, as well as the advantages of each method, as well as finding points of weakness such as complexity, time consumption, and the environment that is applied to it. Methods to avoid or reduce these obstacles are also suggested.

Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on a transfer learning-based model that uses the Inception-V3 network model to significantly reduce the amount of training data and computing costs. The proposed model was trained and tested in the laboratory for the intelligent and safe automobiles (LISA) traffic light dataset, which has been augmented by several pre-processing methods. Then, using different convolution and pooling techniques, the retrieved layer-wise features were compared and analyzed. Lastly, great reliability and repeatability are seen based on statistical analysis when the transfer learning-based model is frequently retrained utilizing precise tuning parameters. The results demonstrate that a transfer learning-based model is capable of high-level recognition performance in the proposed model, with an accuracy rate of 98.6%.

Smishing is the delivery of phishing content to mobile users via a short message service (SMS). SMS allows cybercriminals to reach out to mobile end users in a new way, attempting to deliver phishing messages, mobile malware, and online scams that appear to be from a trusted brand. This paper proposes a new method for detecting smishing by combining two detection methods. The first method is uniform resource locators (URL) analysis, which employs a novel combination of the Google engine and VirusTotal. The second method involves examining SMS content to extract efficient features and classify messages as ham or smishing based on keywords contained within them using four well-known classifiers: support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The best results of the proposed method were 98.5%, 96.9%, 93.1%, and 95.05% in terms of accuracy, precision, detection rate, and F1-score, respectively. Furthermore, the evaluation results of the proposed method outperformed the state-of-the-art and showed that the proposed method is effective in detecting smishing messages.

In-Band Full-Duplex (IBFD) systems have the capability of simultaneously transmitting and receiving signals through the channel and require the same resources as half-duplex systems. Unfortunately, IBFD systems have self-interference (SI) issues that prevent the system from gaining double throughput with respect to half-duplex systems. Therefore, the IBFD system will be more reliable if SI is mitigated more. This contribution will look at SI cancellation in wireless radio and underwater acoustic systems. The reviewed documents cover all types of SI cancellations, including passive, analog, and digital cancellations. In a practical full-duplex system, the SI cancellation for all domains must cancel the SI below the receiver noise floor.

The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network.

Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy.

With the growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper, presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI benchmark dataset was used. The proposed model produced recall, precision, F-measure, and accuracy values of 98.7%, 93.3%, 95.9%, and 98.2%, respectively.

This paper studies the effect of the solar indices (total sunspot no., solar wind density and coronal mass ejection linear speed) on F2- layer electron density, as well as, the comparison of seasonal variations of electron density of F2- layer, NmF2, for Mars at local noontime and for maximum and minimum solar cycles.
Results show that there is an inverse correlation between sunspot number, solar wind density, CME linear speed and ionosphere electron density for Mars, at maximum and minimum solar cycles. This correlation is clear when electron density is at minimum value.
The maximum value of NmF2 was in month 4 (Summer), month 21 (Winter) during maximum solar cycle, and month 1 (Spring), month 24 (Spring) during minimum solar cycle. The minimum value of NmF2 was in month 13 (Autumn) during maximum solar cycle, and month 9 (Summer) during minimum solar cycle. NmF2 was approximately constant in months 1- 8 (Spring and Summer) and gradually decreased in months 9- 13 (Summer and Autumn), then, gradually increased in months 14- 18 (Autumn and Winter) during maximum solar cycle (2012, 2013). In other hand, NmF2 was approximately constant in months 1- 5 (Spring and Summer) and gradually decreased in months 6- 9 (Summer), then, gradually increased in months 10- 16 (Autumn and Winter) during minimum solar cycle (2016, 2017).