Baghdad Science Journal

The common systemic skeletal disorder known as osteoporosis (OP) is characterized by low bone mass and microarchitectural deterioration of bone tissue, which makes bones brittle and prone to breaking. Because of Cystatin D (CST5) peptide potential to regulate a significant class of proteolytic enzymes that play a crucial part in pathophysiological processes, cystatins have drawn a lot of attention in the past ten years, as well as their real efficacy in preventing the growth of bacteria and viruses in vivo. This study evaluated the serum cystatin D (CST5) diagnostic accuracy in osteoporosis and osteopenia patients. One hundred twenty participants were involved, (40) patients with osteoporosis, (40) with osteopenia, and (40) as healthy individuals. The age range for all patients was (40-60) years for both females and males whom did not suffer from any significant diseases and underwent an examination of osteoporosis activity by using dual energy x-ray absorptiometry (DXA scan), and the determination of level of serum CST5 and vitamin D3 (Vit D3) by ELISA technique. Calcium (Ca) was measured using a spectrophotometer. The results of this study showed significantly higher CST5 concentration in patients with osteoporosis and osteopenia compared with healthy individuals with P-value < 0.001. Serum CST5 may be useful for diagnosing osteoporosis because ROC curve analysis indicated that it can reliably distinguish osteoporosis patients from healthy individuals with great area under the curve (AUC = 1.000, p < 0.001). Finally, serum CST5 is an accurate marker that is able to differentiate osteoporosis and osteopenia patients from control.

Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease with a global prevalence of 0.24%–1%, requiring early diagnosis and aggressive treatment. It is associated with synovial and cartilage destruction. This study is designed to investigate the evaluation of anti-acetylated peptide antibodies (AAPA), acetylated lysine peptide antibodies (AcLys,), anti-acetylated ornthinine peptide antibodies (AcOrn), liver function test, and body mass index (BMI) in Rheumatoid arthritis patients. One hundred-ten RA patients (86.36% female, 13.63% male) in comparison with forty healthy controls. Anti-acetylated- peptide Antibodies (AAPA), Anti-acetylated Lysine peptide Antibodies (AcLys), Anti-acetylated-ornthinine peptide Antibody (AcOrn) were determined by Enzyme Linked Immunosorbent Assay (ELISA) and liver function levels were used to measure by spectrophotometer in the serum of RA patients and healthy controls. The mean∓SE of age in patients and healthy controls were 51 0.89 years and 49 1.48 years respectively. According to treatment, the patients of the study were divided into three subgroups [biology treatment (bDMARDs), conventional treatment (cDMARDs) subgroup and combined (DMARDs) subgroup]. The mean SE of AAPA in RA patients and healthy controls were 51.05 5.613 ng/mL and 19.98 3.29 ng/mL respectively. Results showed there was a significant increase between all subgroups of AAPAs (p<0.001), AcOrns (p<0.001) and AcyLs (p<0.001) as compared to healthy control whereas there were no significant differences in AAPA between subgroups with each other’s. While AcOrns levels showed a significant increase in chemotherby as compared to biology subgroup (p<0.0149), in addition levels of AcLys showed a significant increase in biology subgroup and biochemistry as compared to chemotherapy subgroups

A successful synthesis of SBA-15 with semi-spherical morphology and suitable texture properties was achieved using hydrothermal technique. Sodium silicate, derived from silica sand from Iraq, was used as a silica precursor, and Pluronic p123 triblock copolymer was used as a template with a strong acid media (pH<1). The effect of adding (1-3) g of either polyvinyl alcohol (PVA), Polyvinyl pyrrolidone (PVP –k30), or Sodium dodecyl sulfate (SDS) dispersant agents on the texture properties of the synthesized SBA-15 was studied. The SBA-15 sample underwent various characterization techniques, including X-ray diffraction (XRD), Fourier transform infrared (FTIR), atomic force microscopy (AFM), nitrogen adsorption-desorption using Brunauer Emmett Teller (BET), and field emission scanning electron microscopy (FESEM).
XRD analysis showed a single peak in the 2θ = 20-25°, indicating the presence of pure SBA-15. The FTIR spectroscopy results demonstrated presence of key functional groups like silanone, siloxane, and silanol. Using these dispersant agents had a discernible effect on the dispersion of particles to an average particle size distribution in a nanoscale range producing a high surface area of the prepared SBA-15 material, which ranged between 349 to 871 cm2/g, while not influencing the pore volume, and pore size, which are ranged between 0.17 to 0.43 cm3/g, and between 2 to 2.5 nm, respectively. Also, all prepared samples had a nano-scale range.
The optimum texture properties were obtained using 2 g of PVP-k30 dispersion agent. Finally, it was observed that the spherical shape of SBA-15 particles was distorted by introducing more than 1g of dispersion agents.

The following work involved synthesizing a new series of substituted Pyrrolidine/4H-Pyrazole-4-carbaldehyde derivatives [IV]a-e derived from captopril known as an angiotensin converting enzyme inhibitor drug, using the Vilsmeier-Haack reaction. There were some steps involved for synthesizing new hydrazone compounds [III]a-e via refluxing equal mole of captopril hydrazide [II] with different substituted aromatic ketone (acetophenone, 4-hydroxyacetophenone, 4-aminoacetophenone, 4-nitroacetophenone, and 4-bromoacetophenone) in benzene via condensation reaction. The newly substituted 4H-pyrazole-4-carbaldehyde derivatives [IV]a-e are obtained via a cyclization reaction of substitute hydrazones [III]a-e heating with POCl3/DMF, under Vilsmeier–Haack reaction. The prepared pyrrolidine derivatives-based pyrazole moiety compounds were designed, synthesized, and confirmed by spectral methods (FT-IR, 1H-NMR, and Mass Spectra). Screening of synthesized compounds against two types of pathogenic display very good efficacy opposite these classes of bacteria utilizing Ampicillin as a traditional antibiotic.

Biochemical markers like brain natriuretic peptide (BNP) are being identified in patients with heart failure (HF) to indicate the severity of the condition. Little is known about Urokinase plasminogen activator (uPa) and wingless type 5A(Wint5a) markers. The study investigates the role of serum BNP concentration in diagnosing HF and its impact on survival and prognosis in patients with impaired renal function.150 individuals participated in this study (100 as patients and 50 as control), patients were classified according to renal dysfunction into two groups 50 patients with HF and renal dysfunction (RD), 50 patients with HF without RD. According to the area under curve 81 %, indicated BNP is a good indicator to determine HF with RD p-value <0.05, cutoff value 0.35 at which HF is diagnosed, sensitivity 0.78 and specificity 0.73, CI 95% (0.74 – 0.88). While uPa and Wint5a revealed fair indicator. BNP and Wint5a showed significant correlations with urea, creatinine, and uric acid but not with uPa. BNP levels of patients with RD showed a positive significant correlation with creatinine levels, and the critical point of BNP level for diagnosis of heart failure was 0.855 ng/mL while in Wint5a was 0.877 ng/ml and fair result with uPa. As the survival rate in patients with BNP level above the critical point was significantly low, this level was a useful indicator for predicting their prognosis. Care should be taken in interpreting BNP and Wint5a level because patients with RD may show a high concentration of BNP with and without heart failure.

With the increasing technological development, the problem of hydrocarbon pollution has become one of the urgent problems of our current era. Many techniques have been proposed and applied to address this phenomenon, including the use of polymer-based nanocomposites. This study involved the preparation of a few wrinkled layers of graphene oxide from waste pencils (wp-GO) as proved by the High-resolution transmission electron microscopy HRTEM image. Besides, magnetic iron oxide nanoparticles were synthesized and showed superparamagnetic properties due to the magnetic properties of 40 emu/g saturation magnetization and close to zero coercivity, the structure is mostly spherical particles with a diameter of 8-10 nm. These nanomaterials were exploited as a composite along with melamine-formaldehyde polymer MF. The HRTEM results showed that the prepared wp-GO-MF composite has a stable structure with many porous. The wp-GO-MF composite was applied for several hydrocarbons adsorption. Hydrocarbons adsorption study was accomplished using benzene, toluene, m-xylene, hexane, heptane, kerosene, and diesel oil. The findings revealed that the wp-GO-MF composite performs the highest sorption capacity (Qe) for kerosene at 71 g g-1. Adsorption capacity was maintained at around 90% for the hydrocarbons up to 10 absorption cycles. Therefore, it can be concluded that the wp-GO-MF composite successfully showed an excellent sorption capacity for all tested hydrocarbons with superb recycle performance.

The inhibitive action of Pulicaria Jaubertii essential oil (PJEO), as green inhibitor, has been investigated on the carbon (C38) steel corrosion in 1M HCl by using inductively coupled plasmas spectrometry (ICP), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization and gravimetric measurements. The increasing of oil concentrations increases the carbon steel corrosion resistance (The inhibition efficiency), which rose to 94% in the optimal concentration (2.5g/l). The specimen surfaces were analysed by using Infra-Red spectroscopy (IR) and scanning electronic microscope (SEM). The obtained results showed the presence of oil molecules adsorbed onto the specimen surface. Langmuir adsorption model was followed by PJ essential oil compounds which adsorbed onto the carbon steel surface to form a protective layer. PJEO was analysed by using gas chromatography coupled with mass spectra before it was used as green inhibitor. Several DFT parameters such as LUMO and HOMO energy, gap energy (∆E), electronegativity (χ), softness (????) and global hardness (η) were derived for all major molecules found in PJ oil. The theoretical results showed that there is a good agreement with the experimental data.

In this research, technological knowledge for the production of carbon black type semi-reinforcing furnace (SRF) was achieved by designing and building an experimental unit consisting of a furnace, cooling tower, cyclone, filtration and separation unit, and drying and physical treatment unit. Gas oil and fuel oil were used as raw materials in the production of carbon black-type SRF. Partial combustion technology was adopted to meet its technical requirements locally. Economically, partial combustion and oxidative pyrolysis have become mainstream methods. These methods involve the use of hydrocarbons that play a dual role; in addition to being a source of heat, they also help in the production of carbon black. Carbon black is primarily used as a filler or reinforcing agent in various rubber mixtures, especially in the tire and other rubber industries. It also serves as a black pigment in printing inks, surface coatings, paper, plastics, and other applications. The raw materials used in this research produced a good yield and exhibited similar properties to the commercial sample when appropriate operating conditions were selected. The production of carbon black from gas oil was successfully distinguished in this pilot plant, providing better specifications and a more straightforward production method than fuel oil.

This study aims at detecting the level of BRCA1 protein as a marker of breast cancer in Iraqi women previously suffering from type 2 diabetes mellitus. The study includes: 80 participants they were divided into three subgroups based on drugs strategy and duration of disease, with ages (40–70) years. Blood samples were collected from Ibn AL-Bitar Hospital in Baghdad / Iraq between the period from November 2022 to February 2023. Some biochemical parameters were measured for all study groups, which include: -Fasting blood sugar (FBS) and Lipid profile, which were measured by enzymatic oxidation method. Using an enzymatic process, Insulin levels and Insulin-like Growth Factors (IGF-1) and Estrogen-receptor-alpha (ERα) levels, and Breast Cancer Susceptibility Protein1(BRCA-1) were measured via an ELISA. Glycated Hemoglobin (HbA1c) level was measured with a sandwich-immunodetection method. Finally, Homeostasis model assessment-insulin resistance (HOMA-IR) was calculated according to the specific formula. The result showed that the P-value for the comparison of groups were not significant for FBS, HbA1c, and lipid profile, while BRCA1, ERα, Insulin, IGF-1, and HOMA IR showed significance with P-value of 0.0001. The main finding is that patients taking metformin only less than one year which contributed to increase the level of BRCA1. There is a positive association between BRAC1 level with ERα, insulin, and IGF-1 levels. According to ROC-analysis, BRCA1 showed high sensitivity and specificity. We conclude that BRCA1protin is a good parameter to predict the incidence of breast cancer in women with type 2 diabetes and metformin increases the concentration of BRCA1-portien.

In this research, the focus was on preparing a number of chalcone derivatives using the Claisen-Schmidt reaction method through the reaction of some benzaldehyde derivatives with acetophenone substitutes in the presence of a basic medium represented by (NaOH) at a concentration of 10%. The benzaldehyde derivatives and acetophenone derivatives were also dissolved in ethanol at a concentration of 98%. Before starting the preparation, a number of chalcone derivatives were banned, which were prepared and referred to as [Ch1, Ch2,Ch3, Ch4, Ch5]. The substitutes that were used in the preparation were 4-chlorobenzaldehyde, 4-aminoacetophenone, benzaldehyde, 3-Nitroacetophenone, 4-Nitrobenzaldehyde. Acetophenone ,2-4-dichlorobenzaldehyde, and 4-(dimethylamino)benzaldehyde. The compounds were spectroscopically studied using FT-IR , 1H-NMR ,13C NMR, and UV-Vis spectroscopy After that, the theoretical studies of the prepared compounds as corrosion inhibitors were conducted. They were studied theoretically using the Gaussian is a general purpose computational chemistry software package. Chalconate derivatives have the capacity to serve as electron donors as anti-corrosion agents. and it was found that derivative Ch5 is more effective theoretically as a corrosion inhibitor

Pesticides are a major global problem because of their effects on living organisms and their bodily functions. This study was conducted from August 25, 2022, to October 25, 2022. It aimed to know the toxic effects of Chlorpyrifos on common carp (Cyprinus carpio L.), and the potential of Moringa oleifera leaves to reduce this toxicity by evaluating their effect on growth performance in fish exposed to the pesticide. The LC50 for Chlorpyrifos has been estimated at 0.60 mg/L over 96 hours. Use 1/10 of the LC50 in the pesticide toxicity test treatment; Moringa leaf powder (MOL) was also added to the food ( ready-made commercial feed granules) in three proportions: 5, 10, and 15 g/kg, in addition to using feed the control treatment. During the experiment, the fish weight rates (WR), weight gain (WG), daily weight rate (DGR), relative growth rate (RGR), and specific growth rate (SGR) were studied in the exposed fish to Moringa and/or Chlorpyrifos. The results indicated that all growth indicators studied decreased with exposure to Chlorpyrifos CPF. The study achieved significant results regarding the use of Moringa oleifera leaves in treatment, as treatment with leaf powder improved growth performance at 10 g/kg and 5 g/kg From MO respectively. Adding 15 g/kg of Moringa leaves also reduced growth performance. We concluded through the study that using moderate proportions of Moringa leaves has anti-toxic effects, and raising these proportions has negative effects due to an increase in anti-nutritional compounds. Therefore, we suggest that the active substances be extracted and used as additives for fish feed.

Ankylosing spondylitis (AS) is a type of arthritis that causes inflammation in the joints and ligaments of the spine. The current study aimed to determine the level of serum interleukin-33 (IL-33), soluble stromelysin-2 (sST2) interleukin, and HLA-B27 typing among Iraqi AS patients. The whole blood samples of the patients were collected from medical hospitals in Baghdad city. The ELISA technique was used to measure the serum levels of IL-33 and sST2, and the HLA-B27 typing was performed by sequence-specific primer PCR (SSP-PCR). The patients were divided into patients newly diagnosed with the disease and patients receiving biological treatment (etanercept) in addition to the healthy control individuals. The mean serum IL-33 levels for newly diagnosed patients, patients treated with etanercept, and healthy control individuals were (82.84 ± 3.59), (55.91 ± 2.11), (37.32 ± 1.69) respectively. The mean serum sST2 levels for newly diagnosed patients, patients treated with etanercept, and healthy control individuals were (165.62 ± 8.92), (78.82 ± 5.84), (48.01 ± 2.11) respectively. In addition, the positive results of HLA-B27 for newly diagnosed patients, patients treated with etanercept, and healthy control were n=18 (60.0%), n=22 (73.3%), n=0 (0.00%), respectively. In conclusion the present research confirmed the significance of interleukin-33 (IL-33), sST2, and HLA-B27 allele as a rapid marker for diagnosis of AS also in Iraqi As patients.

The objective of the work is to examine the impacts of Stevia leaf aqueous extract, after chronic administration (60 days), on some physiological aspects and fertility in male albino mice. .twenty-four adult e mice were categorized into four groups, each involving six animals. Three treatment groups were separately exposed to three respective concentrations of the extract (25, 50, and 100 mg/kg, he remainder is a control group. The animals were given daily oral doses of 0.1 ml/ 10 gm body weight of the group-respective extract concentration for 60 consecutive days. Several physiological and fertility-related parameters were tested based on the serum's activities or levels of selected functional enzymes. Enzymatic activities of AST, ALT, and ALP were significantly elevated in animals that received treatments of 50 and 100 mg/kg compared to those that received 25 mg/kg and control doses. A similar elevation was observed in the average concentration of MDA, whereas those of CAT and GST were significantly reduced in all treated groups in the average concentrations of the enzymes CAT and GST in all concentrations of Stevia extract, compared with the control groups. The serum levels of FSH, LH or ICSH, and T in response to all studied concentrations of stevia extract were significantly decreased. Also, our data indicated that the number of epididymal sperm was significantly decreased in the experimental animals, a result also observed in the average percentage of normal and deformed sperms. Morphological sperm examination demonstrated several forms of deformation in response to the leaf extract, where sperms with two tails, broken tails, and curled tails appeared, in addition to tailless sperms and those having cytoplasmic droplets. These results show consistency with the potentiality of Stevia aqueous extract in reducing fertility in male mice.

Sediment samples were collected from five sites at Tigris River from Baghdad to Wasit Governorates River from December 2021 to November 2022. Sixteen polycyclic aromatic hydrocarbons (PAHs) in these sediment samples were analyzed. The results show that the total PAH concentrations ranged from 957.37 to 1805.05 μg/kg dry weight (dw) with mean values of 1368.034 μg/kg. Furthermore, the high molecular weight compounds (four, five, and six rings) of PAH in the analyzed sediment samples suggest that combustion sources such as fuel, gasoline, and vehicle emissions are the primary sources of PAHs in the environment. The sediment quality guidelines (SQG) based on the effects range low (ERL) and the effects range median (ERM) and risk quotient (RQ) were used to evaluate the ecological risks of polycyclic aromatic hydrocarbons (PAHs). The results showed that the risks were low to moderate, with potential biological effects of PAHs. The result of the current research work indicated that ongoing monitoring of PAH levels in sediments is needed.

In this work, a pulse laser deposition device was fabricated to synthesize nano-coated plastic optical fiber for sensing application. a plastic optical fiber of 1000 µm diameter, coated with different materials such as aluminum and titanium oxide. Using a field emission scanning electron microscope (FESEM), the coated samples' shape, surface properties, and chemical makeup were verified., an energy dispersion X-ray (EDX), UV-vis absorption spectroscopy, atomic force Microscope (AFM). The cross section EDX indicated that, a small amount of uniform distribution of aluminum and titanium on the surface of plastic optical fibers. The FESEM surface morphology of TiO2 coated on plastic optical fiber exhibited porous and granular structure of about 19.33 to 103.1nm. Also, the uv-vis shows that the absorption spectra increased with an increase in the lasing energy and number of shots. AFM measurements indicated that, the average grain size, roughness and thickness increased with increased lasing energy and number of shots.

The study seeks to investigate the role of Eu substitution in improving the characteristics of bismuth-based superconductors, employing the sol-gel technique as a flexible synthesis methodology. The study is focused on superconductor samples of the composition Bi1.7-xPb0.3EuxSr1.9Ba0.1Ca2Cu3O10+δ, where x ranged from 0.0 to 0.5 through systematic investigation where the Pb and Ba are maintained at fixed values 0.3 and 0.1 respectively. The influence of partially substituting Eu for Bi on superconducting characteristics was thoroughly explored. Electrical resistivity measurements unveiled a notable enhancement in critical temperature (Tc) with the incorporation of Eu, peaking at x=0.2 with a Tc of 114 K. X-ray diffraction (XRD) analysis demonstrated an orthorhombic structure, primarily comprising the high-Tc phase (2223), which exhibited a substantially higher ratio compared to the low phase (2212) within the same sample. Field emission scanning electron microscopy (FE-SEM) unveiled plate-like nanostructures, with discernible variations in morphology corresponding to varying levels of Eu content. The oxygen content (δ) was meticulously calculated for all prepared samples, showcasing consistency with the critical temperature values increasing proportionally with the concentration of Eu in the compound. The highest δ value, reaching 0.320, was observed at x = 0.2, aligning with the peak Tc value. The study elucidated the intricate relationship between Eu doping, structural configuration, and superconducting properties in BSCCO materials. These findings not only deepen our understanding of the underlying mechanisms governing superconductivity in complex oxide systems but also offer valuable insights for optimizing of superconducting materials for practical applications.

Maxillofacial silicone is the most popular and commonly used material for the construction of maxillofacial prostheses. However, the material failed to satisfy the expected features of a maxillofacial prosthesis due to major limitations, particularly in mechanical properties. The goal of this study is to evaluate the mechanical properties of the VST-50F silicone elastomer with the addition of hexagonal boron nitride (H-BN). A total of 120 specimens were prepared and divided into three groups control and two experimental (0.5 wt% and 0.7 wt% H-BN). Each group was divided into four identical subgroups. For each subgroup, 10 specimens were utilized for each test (tear strength, tensile strength, Shore A hardness, and surface roughness). The H-BN powder and specimens from each group were studied using field emission scanning electron microscopy (FE-SEM) and Fourier transform infrared spectroscopy (FTIR). The data were analyzed using one-way ANOVA and post-hoc Tukey's tests, and a P-value < 0.05 indicated statistical significance. The FE-SEM test showed evenly dispersed H-BN within the VST-50F silicon matrix. The FTIR test revealed no chemical interaction between the silicone and H-BN. Both experimental groups showed significantly increased tear strength and decreased Shore a hardness and surface roughness (P < 0.05). The 0.5 wt% group exhibited no significant decrease in tensile strength, but the 0.7 wt% group showed a significant decrease compared to the control group (P < 0.05). As the quantity of H-BN increased, tear strength improved, whereas tensile strength, Shore a hardness, and surface roughness reduced.

Mathematical modeling with the help of numerical coding of graphs has been used in different fields of science, especially in chemistry for the studies of molecular structures. A special type of graph invariant called the topological index is the collection of data on algebraic graphs and provides a mathematical way to understand chemical structural features. In chemical graph theory, the topological index is a type of molecular descriptor calculated based on the graph of chemical compounds. Topological indices help us collect information about algebraic graphs and give us a mathematical approach to understand the properties of algebraic structures. With the help of topological indices, we can guess the properties of chemical compounds without experimenting in a wet lab. In this study, the conglomerate researchers present the exponential dominance of graphene and armchair polyhex nanotube structures in first and second reduced (a, b)-KA indices. Also calculate the Zagreb index, hyper Zagreb index, reduced hyper Zagreb index, reciprocal reduced product connectivity index, and general reduced Zagreb index. Additionally, we get specific graph indices directly as exceptional values of a and b when introducing these structures.

One of the most important factors in preventing mild steel from deteriorating in acidic conditions is corrosion inhibition. This study examined the anti-corrosion properties of two benzodiazepine compounds, namely Dimethyl-[4-(4-phenyl-2,3-dihydro-1H-benzo[b][1,4] diazepin-2-yl] L-[4-(4-methyl-2,3-dihydro-1H-benzo[b][1,4]diazepin-2-yl)-phenyl] and dimethy-amine (DPBD) and toluene (DMBD), employing density functional theory (DFT)-based quantum chemical computations. Determining the corrosion inhibitors' active locations and contrasting their efficacy were the goals. The probable active sites of DPBD and DMBD as corrosion inhibitors were identified by the DFT calculations. The total corrosion inhibition performance of these two compounds did not significantly differ, according to the data. However, a slightly better performance was observed for DPBD compared to DMBD. These findings contribute to the understanding of the molecular-level interactions between benzodiazepine corrosion inhibitors and mild steel surfaces. The insights obtained from this study can aid in the design and development of more effective corrosion inhibitors for practical applications in the field of materials protection.

Iraq has been suffering from a rising surface air temperature (SAT), causing a general deterioration in ecosystems. Climate sensitivity (CS) which refers to the increase in average SAT caused by a duplication of Carbon Dioxide Concentration [CO2], was estimated by transient local climate response (TLCR) and equilibrium local CS (ELCS). The time series of the spatial annual averages for both [CO2] and air temperature were analyzed and a linear regression model was also used to find annual trends in SAT and [CO2]. Based on historical yearly data for CO2 concentrations (2003-2016), CO2 emissions (2000-2021), and SAT (1971-2022), CS is evaluated concerning three main areas in Iraq: Basra, Baghdad, Mosul and the whole of Iraq. The results showed that yearly mean SAT and [CO2] in Iraq were well fitted by the regression model with the values of 2.11 ppm/year and 0.07 °C/year, respectively. Among the selected provinces, the highest upward trends in SAT and [CO2] were observed in Basra with the values of 2.15 ppm/year and 0.09 °C/year, respectively, mainly caused by the doubling of CO2 emissions from 70 Gt in 2000 to 150 Gt in 2021. The result also shows that the TLCR and ELCS values in Iraq are 2.55 and 3.8 °C, respectively, within the range suggested by the IPCC 5th Assessment Report for Iraq.

In many instances, acquiring a solution for an operator equation by using conventional analytical techniques, even after it has been shown that such a solution exists. One has to know the approximate value of this solution to solve situations like these. In order to do this, the operator equation must first be restructured to take the form of a fixed-point equation (FP). On the FP equation, the most convenient iterative method is used, and the limit of the sequence that is created by this algorithm is, in fact, the value of the FP that is sought for the FP equation, as well as the solutions to the operator equation. The numerical computation of FPs for nonlinear operators is now an interesting research subject in nonlinear analysis owing to its applicability in several fields. Many researchers have developed a wide range of techniques to estimate the FP for various sorts of applications. The primary objective of this paper is to present new iteration schemes for approximating the best proximity point (BPP). The convergence of BPP for M-T cyclic contraction mappings (MTCC-mapping) has been investigated in the context of uniformly convex Banach spaces (UCB-space). The iterations approach proposed by Mann and Ishikawa was taken into account and as a result, some strong convergence results were obtained for the BPP for MTCC-mapping. Furthermore, numerical examples supporting the primary conclusion are provided, and the convergence behavior of the iterations is compared.

The primary objective of this paper is to propose a Crank-Nicolson scheme tailored to analyze a time-space fractional stochastic diffusion equation subject to additive noise. Using the Caputo definition for time fractional derivatives and the shifted Grunwald-Letnikov definition for space fractional derivatives, a fractional order Crank-Nicolson finite difference scheme is devised to obtain a numerical solution. Considerable attention has been paid to examining the stability and convergence properties of this fractional order Crank-Nicolson method. To validate the effectiveness of the developed method, a comprehensive investigation using a test problem is conducted to assess its accuracy. The study harnesses the power of Python software to perform numerical simulations, demonstrating the efficiency and effectiveness of the proposed scheme. These simulations indicate that the numerical method developed effectively provides approximate solutions for the time-space fractional stochastic diffusion equation, especially under the influence of additive noise. Furthermore, the results gleaned from surface plots of the numerical solution for the test problem unveil a notable trend: as the intensity of the noise increases, disturbances in the surface of the mean solution escalate, underscoring the impact of noise on the system\'s behavior. By offering a thorough analysis and validation of the proposed scheme, this research contributes valuable insights to the broader fields of applied mathematics and computational sciences.

One of the key concepts in operations research (OR) is the transportation problem (TP). The transportation problem involves distributing products from several sources to different destinations while minimizing shipping expenses. To achieve minimum transportation costs, the transportation problem is examined in two phases. An initial basic feasible solution (IBFS) is the first phase for achieving the lowest price, with the help of IBFS, one can calculate the optimal solution derived as the second phase. The literature review indicates that the authors developed a novel algorithm for the IBFS, incorporating several mathematical tools and some new models yet possessing certain limitations. To overcome this, we proposed a new algorithm for the IBFS, a collaborating statistical tool and a new penalty model. An exponential distribution illustrates the probability of cost or time required to reach each source to the destination. Furthermore, a novel penalty model is formulated to distribute essential supply to requisite demand. A modified distribution method (MODI) is employed to calculate the optimal solution. Eleven numerical examples were illustrated to develop the proposed algorithm. The results are compared with classical Vogel’s approximation method (VAM) and ten previously published papers. In addition, three randomly generated problems were performed. Finally, with the reduction in total transportation cost over Vogel’s approximation method, the demand-based allocation method (DBAM) and the existing ten published studies. Our suggested method of getting 10 optimal solutions in IBFS recorded a 71.42% accuracy.

For a commutative ring, a cross monic zero divisor graph is discussed, whose vertices are nonzero zero divisors of the commutative ring, then the two vertices x and y are adjacent if and only if xy=0. Sum of absolute auxiliary eigenvalues of Laplacian matrix of a simple graph is known as Laplacian energy of a simple graph. Eccentricity sum matrix of a graph is a positive matrix, whose entries are sum of the eccentricities of two vertices and 0 for diagonals of the eccentricity sum matrix. Then the sum of absolute eigenvalues of eccentricity sum matrix is known as eccentricity sum energy. Laplacian sum eccentricity energy is the absolute difference between the eigenvalues of Laplacian sum eccentricity matrix and twice the edge cardinality by vertex cardinality. Maximum eccentricity energy is the sum of absolute eigenvalues of maximum eccentricity matrix. For a simple graph, the entries of average degree eccentricity matrix, if the two vertices are adjacent, are average sum of degree and eccentricity of their two vertices, otherwise 0. Then the sum of absolute eigenvalues of average degree eccentricity matrix is known as average degree eccentricity energy. This paper discusses the degree squence, vertex cardinality, and edge cardinality of the cross monic zero divisor graph for commutative rings. For a certain family of cross monic zero divisor graphs for commutative ring, moveover, Laplacian energy, eccentricity sum energy, Laplacian sum eccentricity energy, maximum eccentricity energy, and average degree-eccentricity energy are also briefly covered.

In healthcare, maintaining both the accuracy and privacy of medical diagnoses collaboratively is a significant challenge. To the best of our knowledge, this research proposes the first end-to-end Privacy-Preserving Federated Transfer Learning (PPFTL) framework for 2-D ECG arrhythmia classification. Incorporating Transfer Learning (TL) narrows the gap between the encrypted and non-encrypted framework versions in the training step. It involves the transformation of raw Electrocardiogram (ECG) signals into 2-D ECG grayscale images. The dataset is disseminated after transformation to images and then fed as input into the local models, where MobileNetV2 serves as a feature extractor. The training process for each client incorporates data balance and augmentation techniques to improve the model’s performance. Deep Learning (DL) models are subject to various privacy attacks to gain sensitive data. As a result, the Homomorphic Encryption Cheon-Kim-Kim-Song (HE-CKKS) scheme encrypts only model parameters to protect deep models from adversary attacks, preventing the sharing of sensitive raw data. Experimental results on the MIT-BIH Arrhythmia dataset achieved 88.12% accuracy. Incorporating HE-CKKS increased computation times by 1.08%, 1.27%, and 1.43% for 2, 3, and 4 clients, respectively.

Cybersecurity is a crucial aspect of modern information technology, encompassing safeguarding computer systems, network resources, and information from cyberattacks. Wazuh is an open-source security information and event management (SIEM) technology for real-time attack detection but faces challenges like log analysis complexity and alert overloading, which raises the number of false-positive alerts and affects accuracy. This paper presents three hierarchical models for real-time attack prediction using Wazuh to gather real-time dataset from various network endpoints and then preprocess them using a variety of tools. Subsequently, mutual-information, principal component analysis (PCA), and independent component analysis (ICA) are utilized for feature reduction and selection to extract important features for each model individually. Finally, for training and testing purposes, the types of attacks are classified using the long-short-term memory (LSTM) method. In the second stage, real-time attacks are utilized to assess the performance of the suggested models for real-time attack detection. The experimental findings demonstrate that the suggested approaches performed better on binary and multiclass classification in terms of accuracy, recall, precision, and F-measures and were superior to previous methods in terms of accuracy.

Despite remarkable advancements in medical technology, cardiovascular disease persists as a significant contributor to global mortality. This research addresses the imperative need for timely disease identification through the proposition of a heart disease prediction model, utilizing a merged 1D Convolutional Neural Network (CNN). The main aim of the research is to mitigate the inherent drawbacks of single-layer designs, with a particular emphasis on enhancing hierarchical feature extraction, broadening the model's receptive fields, and facilitating more efficient non-linear transformations. The dataset has been collected from UCI data repository. The study methodology includes a new feature selection strategy that combines Mutual Information and Fuzzy Logic approaches, offering a subtle viewpoint that is not well covered in the literature at the moment. After undergoing extensive training, the output of two 1D CNNs are merged to provide an impressive average accuracy rate of 85%. The integrated 1D CNN model and Explainable AI methodologies yield promising outcomes in heart disease prediction, affirming the model's potential as a screening instrument for early identification and intervention. By providing a strong framework for proactive cardiovascular health care, the study makes a substantial contribution to the nexus between medical science and machine learning.

To support the best path among several paths, the network must choose the path with the best path to support Quality of Service (QoS), especially for real-time interactive video applications, such as distance learning and video conferencing. As long as the chosen routing protocol path is fixed by network topologies such as integrated services (ISs), which may not provide the highest possible performance. In addition, software-defined networking (SDN) intelligence with web-based real-time communications (WebRTC) can determine the most efficient data packet routing, which reduces latency and improves real-time communications. The main objective of this paper is to build and implement different contributions using SDN with WebRTC for real-time interactive video to determine the quality of service using Python language based on Oracle Virtual Box Manager. Thus, a new environment called WRTSDN is achieved using WebRTC with SDN technology to choose the network architecture to obtain the best path and wide area of the network. The WRTSDN protocol was implemented on 13 nodes of 11 switches and 2 hosts (sender and receiver). The results of the WRTSDN prototype implementation were shown using several metrics such as: network delay, network throughput, and message complexity. Also, this technique is novel using WebRTC with SND to obtain better paths with low latency, jitter, bandwidth, and delay. Moreover, this work faced many challenges by integrating WebRTC with SDN, applying metrics, analyzing the results.

Hypertensive Retinopathy (HR), a serious consequence of systemic hypertension, manifests through specific changes in the retinal vasculature observable in fundus images. There might not be any symptoms or complaints at the early stage of systemic hypertension. The complication of the systemic hypertension sets in vital organs of the body including the eyes over time. An automated method for HR detection can help in early detection and enhance diagnosis and management and thus reduce the risk of severe ocular and systemic complications. Subjective evaluation of the retinal images by ophthalmologist is the conventional diagnostic method, which might be inconsistent and time consuming. This paper presents an innovative deep neural network model AV-EffiCapsNet which integrates EfficientNet and Capsule Networks to detect HR in fundus images automatically. EfficientNet provides an efficient and scalable Conventional Neural Network framework and Capsule Networks enhance the representation of spatial hierarchies and part-whole relationships. The annotated fundus images of the datasets VICAVR and INSPIRE AVR were used to train and test the model AV-EffiCapsNet. The results showed superior precision of 97.7%, accuracy of 98.8% and recall of 95.5% compared to current models. These results indicate that AV-EffiCapsNet is effective in detecting subtle signs of HR, ensuring it a valuable tool for telemedicine and clinical screening.

Heart failure is a critical medical condition with significant global morbidity and mortality rates. Early prediction of Heart Failure risk is paramount for timely intervention and improving patient outcomes. Leveraging the power of machine learning, this study conducts an in-depth examination of Heart Failure prediction using a comprehensive dataset. The dataset encompasses clinical, demographic and laboratory data of Heart Failure patients. A variety of machine learning techniques like logistic regression, decision tree and random forest are harnessed to construct predictive models for Heart Failure. The analysis integrates essential data preprocessing techniques like missing value imputation, feature scaling and feature selection to enhance model robustness. Metrics over evaluation consist of precision, recall, accuracy, F1-score and area under receiver operating characteristic curve employed to gauge model efficiency. This study comparing the performance of Logistic Regression, Random Forest and Decision Tree to predict heart failure data. The results are provided that the Random Forest algorithm is the finest performance with accuracy 0.99, precision 0.99, recall and F1 score 0.99 in detecting heart failure than Logistic Regression and Decision Tree. This study highlights the potential of machine learning, especially Random Forest, for early heart failure risk assessment, enabling timely interventions, reducing healthcare costs, and supporting personalized patient care. Exploring advanced machine learning techniques and real-time predictive models could further enhance accuracy and clinical applicability.