Iraqi Journal of Science

In the present study, new metal complexes were synthesized using the reactive chemicals 2-amino anthraquinone and 3-acetyl furan to produce novel Schiff base ligands in molar ratios of 1:1 and 1:2 (metal to ligand) with the resultant complexes having the formulas [MLCl] and [ML2Cl2]. Several physicochemical techniques (Elemental analysis, molar conductivity, IR, UV-Vis spectroscopy and magnetic measurements) were used to characterize the complexes. Measurements of conductivity showed that none of the compounds were electrolytes. According to magnetic moment and electronic spectra, certain complexes have a tetrahedral geometry, while others have an octahedral geometry. In both circumstances, the ligand functions as a bidentate chelating ligand. Furthermore, biological assays reveal that several of the newly synthesized complexes demonstrate antibacterial properties, effectively inhibiting the proliferation of specific bacterial strains.

The current study uses mesoporous silica magnetic Fe3O4@mSiO2 nanoparticles as core-shell materials to deliver the drug diphenhydramine (DPH). To investigate the DPH drug's adsorption behavior in aqueous solutions, the batch approach has been employed. The adsorption evaluations that were obtained were fitted with the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherms models. According to the results, the Freundlich model was the best fit for the data, and the type of adsorption process used was physical. After kinetic analysis, it was discovered that the data agreed quite well with the pseudo-second-order kinetic equation. The Fe3O4@mSiO2 carrier's DPH drug loading capacity and release kinetics were also examined. The calculated amount of DPH loaded on the sample was 13.33 mg drug/mg sample. The release profiles of the loaded Fe3O4@mSiO2 sample show that the DPH release percentage in the water and (phosphate buffer solution) PBS solution is 57.23 and 97.27%, respectively. The release kinetics was also studied using three models: Korsmeyer-Peppas, first-order kinetic release, and Kopcha. The results indicate that the Kopcha model conforms more closely to the data release than other models

A novel Schiff base ligand (DBC) synthesized from 4-chlorobenzoic acid, along with its Cu (II) and Co (II) complexes, was prepared and characterized using FT-IR, 1H and 13C-NMR, UV-Vis spectroscopy, as well as magnetic and conductivity measurements. Based on this, a tetrahedral structure of [M(DBC)Cl2] was proposed for the complexes. Antioxidant activity of the compounds was assessed and compared to ascorbic acid, revealing that the copper complex exhibited superior antioxidant properties compared to the cobalt complex and the ligand. Furthermore, the antibiofilm potential of the copper and cobalt complexes was assessed against five clinically relevant bacterial species (P.aeruginosa, E.coli, K.pneumoniae, S.aureus and S.typhi) using various concentrations. Biofilm inhibition was quantified through optical density measurements, revealing that the copper complex had a higher mean biofilm inhibition compared to the cobalt complex at the maximum concentration of 1000 mg/ml. Optical density measurements also indicated reduced biofilm formation with increasing metal concentrations, particularly for the copper complex. Additionally, agarose gel electrophoresis showed significant alterations in plasmid DNA profiles of bacterial isolates upon exposure to the complexes, indicating potential genetic changes and implications for bacterial adaptation.

A Schiff base ligand was synthesized through the reflux reaction between 2-hydroxy-5-nitrobenzaldehyde and o-aminobenzenethiol in a 1:1 molar ratio, resulting in the formation of a novel compound. New copper (II) and silver (I) complexes from this ligand were synthesized also by the reflux reaction between the synthesized ligand and Cu(II) and Ag(I) salts in 1:1 molar ratio (metal: ligand). The coloured synthesised complexes were characterized by 1HNMR, 13CNMR, mass spectra, FTIR, UV-Visible and molar conductivity techniques. In this work the synthesized ligand acts as a tridentate ligand coordinate with the metal ions through its imine, hydroxyl and the thione groups. The structure of both complexes is estimated from the mass spectra which suggest the octahedral and tetrahedral chemical structures of copper (II) and silver (I) complexes respectively. The anticorrosive properties of the synthesized compounds were investigated using an electrochemical corrosion cell, with experiments conducted at two different temperatures (298 K and 308 K) and two varying concentrations (0.01 M and 0.05 M) for each compound. Both complexes show a higher anti- corrosion activity (92%) in comparison with the ligand (90%).

The present study focuses on creating and engineering novel compounds based on five established medications: Sulfamethoxazole, Ampicillin, Cefotaxime, Sulfamethazine, and Trimethoprim. The research involves incorporating a biologically active component, specifically the 3,4-dimethyl maleimidyl sulfonamide group, into these drug molecules to produce the target compounds. Many steps were performed on this target; the first one involved a reaction between 3,4-dimethyl maleic anhydride and aniline to produce compound (1) N-phenyl-3,4-dimethyl maleimide which reacts with chlorosulfonic acid in the second step producing compound (2) 4-[N-(3՝,4՝-dimethyl maleimidyl)phenyl sulfonyl chloride. The third step involved compound (2) reaction with the selected drugs, producing the target- developed compounds (3-7). The newly synthesized compounds were tested for their antibacterial and antifungal activities. Results showed they exhibit high levels of activity against both bacteria and fungi.

Breast cancer is a complex and multifaceted disease, comprising diverse subtypes that can be characterized by distinct histopathological, molecular, and clinical features. The dysregulation of β-catenin, a key transcription factor in the Wnt/β-catenin signaling pathway, plays a critical role in the early stages of tumorigenesis, ultimately influencing clinical outcomes. This study aimed to determine the correlation between sera and urine β-catenin levels in Iraqi patients with breast cancer comparing with control and first relative groups and to assess whether urine can be utilized as a substitute to sera to measure this status. The levels of β-catenin in fasting sera and urine were measured in a cohort of 29 breast cancer patients, as well as in 29 of their first-degree relatives and 30 age-matched healthy individuals serving as controls. All patients and first relatives were attending to the Oncology Teaching Hospital in Medical City. Sera and urine β-catenin concentration were measured using enzyme-linked immunosorbent assay. Urine creatinine concentrations were measured by colorimetric assay using automatically Roche/Hitachi cobas c111 System. Sera levels of β-catenin were significantly lower in breast cancer patients than in first relative and controls (p < 0.01). While in urine, levels of β-catenin were significantly higher (p <0.001) in breast cancer than in control and first relative groups. Low levels of β-Catenin in sera and high levels of this marker in urine of breast cancer patients comparing with control and first relative degree groups pointed out that β-catenin may serve as a diagnostic and prognostic biomarker, more studies needed to comfier these results.

Leech saliva contains numerous compounds, primarily consisting of proteins and peptides. This research focused on two leech species, Hirudo verbana and Hirudo orientalis, which were obtained from various alternative medicine clinics and scientific centers located in Baghdad, Iraq. Saliva samples were collected from the leehches after 5 and 6 weeks of food deprivation. Bradford proteins assay, UV absorption technique, Gel electrophoresis and RP-HPLC were used to analyze their saliva chemically. Anticoagulant activity of their saliva was also assessed using prothrombin time assay. Results showed that the concentration of saliva proteins were higher in the 5 weeks of starvation than the concentration of proteins in the 6 weeks of starvation for both species. The gel electrophoresis analysis revealed the presence of up to 16 distinct bands. Three molecular weights were found to be closely related to known proteins from leech species through comparison of the gel electrophoresis data with the literatures. Additional proteins found in the saliva extract by gel electrophoresis may possibly be associated to totally new biologically active proteins and peptides in the saliva extract or to a modification (isoforms) of the present ones or to a mixture of both. It was found that the crude saliva secretions of both H. verbana and H. orientalis have anticoagulant activity. The active action of H. verbana was stronger than the H. orienralis. The means of prothrombin time for H. verbena were higher than the means of prothrombine time for H. orientalis although no significant differences were noticed between them.

This research examined the presence of the rs10759931 single nucleotide polymorphism (SNP) in the TLR4 gene among COVID-19 patients. Additionally, it evaluated how this genetic variation influenced cognitive difficulties in individuals recovering from the disease. This Case–control study (60 patients, 30 recovered, and 50 controls) investigates the G/A genotype of rs10759931 in all studied groups, using a conventional polymerase chain reaction with an allele-specific primer method. Results showed a median of ages was significant raising in age accompanied by severe cases (p =0.003) and more than 50% of the moderate infections in the youth category. Regarding the recovered individual’s occurrence six cases along with ages median of 49.5 (45.8-57) males and females with a history of mild to moderate infection. When comparing the clinical severity, allele and genotype frequencies of the G/A rs10759931 TLR4 polymorphism showed no statistically significant differences between COVID-19 patients and controls (OR = 1.22; 95% CI = 0.60–2.49; p = 0.715). In recovered individuals with cognitive impairment, despite the occurrence of 66.7% of cognitive impairment in the GG genotype, there is no significant difference compared to intact recovered individuals (OR = 0.21; 95% CI = 0.03–1.39; p = 0.163). In conclusion, G/A rs10759931 of TLR4 required more information to explore their role in cognitive impairment and infection severity.

It is estimated that about 20% of all hospital-acquired infections can be at-tributed to environmental contamination. Acinetobacter baumannii has been identi-fied as the primary pathogen, posing a significant hazard to human health. This work aims to evaluate the frequency, and pattern of antibiotic resistance, as well as the correlation between biofilm production and the distribution of biofilm-related genotypes in multidrug-resistant A. baumannii samples isolated from a tertiary hos-pital environment in Baghdad, Iraq. Conventional methods, biochemical analysis, the VITEK-2 compact system, and blaoxa-51 gene amplification were employed to identify suspected isolates. The biofilm-forming capability and antibiotic suscepti-bility were assessed using the microtiter plate technique and disk diffusion. Isolates were then subjected to PCR technique targeting genes associated with biofilms, in-cluding ompA, csuE, bap and blaPER-1. Of the 300 samples, 21.16% were identi-fied and verified as A. baumannii samples by the presence of the blaoxa-51 gene in all isolates. The highest distribution of environmental isolates was recorded in ven-tilator (21.8%) followed by pillow and bed linens (14.5%), trolleys, bedside tables (11.5%), floor (10.3%), food tables (9.2%), sink area (8%), Staff's hand (6.7%), in-cubators (5.7%), doors knobs (4.6%), surgical blades (3.4%), Curtains (2.2%) and the lowest distribution was observed in chairs (1.1%). The greatest percentage of resistance was observed against Cefepime and Ciprofloxacin (100%), Ceftazidime (97.7%), and Ceftriaxone (90%). The lowest degree was reported against Sulbac-tam, representing 33.33%. Out of all the isolates, a significant majority (91.95%) could form biofilms. These biofilm formers were further categorized based on their level of biofilm production, with 20% classified as weak producers, 27.5% classi-fied as moderate producers, and 52.5% classified as strong producers. The csuE gene was the most common biofilm gene in 95.4% of the samples. The ompA gene was found in 83.9% of the samples, followed by the bap gene in 80.5%. The bla-PER-1 gene was the least prevalent, detected in only 10.6% of the samples. How-ever, only biofilm-forming isolates contained blaPER-1, bap and ompA. Converse-ly, all biofilm-producing and non-biofilm-producing A. baumannii were found to harbor the csuE gene. In various samples obtained from hospital environments, our investigation revealed a substantial incidence of multi-drug-resistant A. baumanii. Additionally, we observed a significant presence of genes associated with biofilm formation. Therefore, it is crucial to enforce control measures to limit the dissemi-nation and growth of A. baumannii.

White skin has long been considered the standard of beauty; however, women who use skin-whitening creams may be unaware that these products contain toxic metals, posing significant health risks to consumers. Therefore, this research aimed to detect the concentration of some toxic metals in thirty whitening creams by flame atomic absorption spectroscopy and to evaluate the carcinogenic and non-carcinogenic risks for all tested samples. The results showed that the average concentrations for all samples were in the order Cd < Co < Cr < Ni < Hg < Pb. All samples exhibited elevated concentrations of Hg, reaching levels up to ten times higher than the permissible limits set by the World Health Organization (WHO) and three times according to the Europe Union (Eu), and Canada. Non−carcinogenic risk results for the current study showed that the hazard quotient (HQ) and hazard index (HI), for all samples were <1. While, carcinogenic risks for Cd, Cr, Ni, and Pb were within the permissible limits, which indicates no harmful health effects, and consumers are safer using the product, but daily exposure can lead to accumulation that may cause cancer.

Molecular epidemiology techniques played a crucial role during the COVID-19 pandemic, as the genotyping of emerging variants was essential for effective surveillance, predicting transmission rates, and understanding the evolutionary dynamics of the virus. In Iraq, the epidemiological status before and after the 4th wave of Omicron emerging variant is unknown as it was preceded by a mass gathering event of Arbaeen pilgrimage. In this study, TaqPath RT-PCR assay was exploited along with epidemiological approaches to monitor and track dominant mutation and to identify hot spot region in Iraq through analyzing 3,748 SARS-CoV-2 positive specimens collected from different Iraqi governorates from September 5th, 2021, to March 28th, 2022. Current analysis showed that Karbala province represents a hot spot region of infection rather than the capital, Baghdad. Furthermore, the results showed high prevalence of histidine/valine deletion at codon 69/70 (H69/70V) of SARS-CoV-2 spike gene. This genetic signature is a basic characteristic of Alpha and Omicron variants. In silico analysis of genomic data retrieved from Global Initiative on Sharing All Influenza Data (GISAID) repository showed high prevalence of Delta variants and descended sublineages and confirmed the existence of Alpha variants during the studied period. The current study demonstrated that mass gathering events contribute to an increased transmission rate, elevating the prevalence of the H69/70 deletion found in local viral isolates, and offering a thorough epidemiological perspective on the factors that led to the rapid surge of the Omicron fourth wave in Iraq.

In recent years, drinking water in bottles or sachets has become increasingly accepted in major towns and cities in Ekiti and Ondo states, southwestern Nigeria. Selected sachet and bottled water based on a wide range of consumer samples were collected from various manufacturers and distributors in both states. Activity concentrations of natural radioactivity, including 40K, 226Ra and 232Th, were measured using a gamma ray spectrometer with a high-purity germanium detector (HPGe). The activity concentration of40K, 226Ra and 232Th ranged from 0.46 ± 0.02 to 4.74 ± 0.44 Bq L-1, 0.14± 0.01 to 1.62 ± 0.30 Bq L-1, and 0.16 ± 0.11 to 1.42 ± 0.21 Bq L-1, respectively in sachet water. Activity concentration values in bottled water for 40K, 226Ra and 232Th ranged from 0.72 ± 0.10 to 2.32 ± 0.14 Bq L-1, 0.15± 0.01 to 0.82 ± 0.03 Bq L-1, and 0.19 ± 0.03 to 1.42 ± 0.10 Bq L-1, respectively. Determined ingested annual effective dose on the scale of International Commission on Radiological Protection (ICRP) for the age groups of 0-1y, 1-2y, 2-7y, 7-12y, 12 -17y and > 17y from consumption of the water samples are 0.831 mSvy-1, 0.258 mSvy-1, 0.204 mSvy-1, 0.245 mSvy-1, 0.620 mSvy-1, 0.261 mSvy-1 respectively for sachet water, it is 0.613 mSvy-1, 0.118 mSvy-1, 0.156 mSvy-1, 0.175 mSvy-1, 0.428 mSvy-1, 0.203 mSvy-1, respectively for bottled water. The mean contributions of 40K, 226Ra and 232Th activities of both water type samples in the study area from an annual volume consumption is higher than the recommended tolerable limit of 0.1 mSvy-1 or lower as reported by WHO. It is therefore strongly recommended that nursing mothers should guard the lactating populace from the consumption of the surveyed samples to minimize the stochastic risk of radiation hazards in infants.

Honey and hibiscus flower nanoparticles have numerous unique characteristics, such as biocompatibility; they are excellent agents for biological applications. The aim of this study is to investigate honey and hibiscus flower nanoparticles for 2, 2- diphenyl-1-picryl-hydrazyl-hydrate (DPPH) free radical scavenging activity and their ability to offer protection against ionizing radiation (gamma ray). In this research, natural nanoparticles prepared from honey and plants (hibiscus flower) were used for the purpose of studying their effectiveness as antioxidants in water by scavenging free radicals resulting from radiation. In vitro antioxidant activity study of honey and hibiscus flower nanoparticles has shown good free radical scavenging activity for DPPH radical assayed within a concentration range of (0.5 - 2.5) x10-4 g/l, for honey nanoparticles and range (0.1-0.5)x10-4g/l for hibiscus flower nanoparticles. The absorbance and inhibition indicating this finite range of concentration is suitable for scavenging free radicals, also nanoparticles were found to have significant natural antioxidant capacity and thus can be used as potential radical scavenger against deleterious damages caused by the free radicals. Hibiscus flower nanoparticles have shown higher ability for free radical inhibition of 93.40% at 0.5x10-4 g/l, than honey nanoparticles of 49.80% at 2.5×10-4g/l.

The present study covers a sedimentological study of the gravel deposited in Al-Rahaliyah quarries for building materials in Al-Rahaliyah city within Al-Anbar Governorate. Ten samples of sediments were collected from ten sites, Dry sieving was done to separate the gravel fraction. A sedimentological analysis was performed on the gravel samples, covering the grain size fractions and morphology of gravel samples. The morphology included determining the form, roundness, and spherisity of these samples.The main size fraction of the studied gravel was pebbles (4-64mm), while the main form types were bladed and plated formTexturally the studied gravel sample of Al-Rahalyiah quarries is classified as Muddy Sandy Gravel and Sandy Gravel. About the shape of the studied gravels, the total average roundness of the studied samples is 4.2 (rounded), while the total average sphericity was 2.3 (Sub Discoidal). The roundness and sphericity of the studied gravels are affected by the fact that the original limestone rocks are deposited in horizontal sedimentary beds, and this stratification affected the shape of the studied gravels.The gravel was identified manually and using thin sections. It was found that the main lithology is carbonate type, while the type of carbonate rocks includes fossiliferous packstone, peloidal packstone, dolomitic limestone, and lithoclastic mudstone types, and the origin of these gravels are the Euphrates, Dammam, Anah, and Nfayil Formations, which are located in the west, and southwest related to the slope of the valleys in the study area.

The late Turonian to early Campanian Kometan Formation is widespread in northern and central Iraq. The study section is located in Northern Iraq, northwest Kirkuk. This formation is composed primarily of limestone, dolomitic limestone, and black shale, with nodules of chert distributed throughout the formation. The lower contact of the formation with the Gulneri with the Mushorah Formations is unconformable. The current study used 80 thin sections that showed the presence of planktonic foraminifera and 55 samples. The black shale beds in the middle formation were determined through the Gamma log. And also five nannobiozones identified, they are Eiffellithus eximius Interval Zone (CC12) Marthasterites furcatus Interval Zone (CC13), Micula staurophora Interval Zone (CC14), Reinhardites anthophorus Interval Zone (CC15), Lucianorhabdus cayeuxii Interval Zone (CC16). Depending on planktonic foraminifera and the biostratigraphy of calcareous nannofossils, evidence of Bai Hassan-86 well determined the age of the Oceanic Anoxic Event (OAE3) was recorded in Upper Turonian – lower Santonian.

The first goal in this paper is using the definition of convex fuzzy normed space to present the definition of convex fuzzy compact space. After that an example is introduced to improve the existence of such space. The second aim is to introduce important concepts and prove properties of convex fuzzy compact space. Moreover, we proved if ???? is convex fuzzy compact then ???? is totally convex fuzzy bounded, as well as we prove if ???? is convex fuzzy compact then ???? is convex fuzzy complete.

The objective of this paper is to examine the outcomes of third-order differential subordination and superordination for analytic functions within the region ????={ᶎ:ᶎ∈ℂ ???????????? |ᶎ|<1}, specifically focusing on the utilization of the novel Hadamard operator Ƙ????,????????????(ᶎ). The results are derived by analyzing relevant categories of allowable functions. New findings have been found about differential subordination and superordination, along with the discovery of several sandwich theorems. Furthermore, other specific instances are also seen. The qualities and outcomes of differential subordination exhibit symmetry with the properties of differential superordination, leading to the formulation of the sandwich theorems

The generalized Ramos-Louzada distribution (GRL) is a potential option for modeling the survival data which considers flexibility while modeling data with decreasing, increasing, reversed-J shaped, and J-shaped hazard rate functions. It should however be noted that the most widely used technique of parameter estimation of GLR is the maximum likelihood technique (MLE). While the MLE is quite efficient in large samples, they are known to be highly biased for small samples. We are therefore forced to come up with essentially nonbiased estimators for GRL parameters. Particularly, we investigate two procedures of bias correction: bootstrap and analytical methods in order to minimize MLE bias up to the second-degree. Two real-world data applications and Monte Carlo simulations are used to countercheck the conclusions of these methods. Simulation results highlight the proposed approaches’ performance, which is significantly less biased than the MLE, which is highly Biased. In small sample sizes, the bias is eliminated to about the half of the original amount.

Suppose that R be a ring, the non-zero divisor graph of R denoted by (R) and defined as a simple graph has vertex set R\{0,±1} and different vertices ????,????∈????((R)) are adjacent if and only if ????????≠0 or ????????≠0. For the prime ring R, we aim to investigate the structure of the graph (R). This involved the behavior of the nilpotent elements of the prime ring inside the graph as well as proving the graph is connected with girth of three. Furthermore, characterize certain universal vertex in the graph.

In this paper closed submodules and extending modules are generalizing to pure closed submodules and pure extending modules, respectively by using pure submodules. In this work, some properties of these concepts are consideration.

Throughout this paper we depend on the concept of μ- small submodule to generalized the Jacobson radical called μ-Radical that lead us to present a generalization of (GP*) property.

In this work, we obtain some differential subordination and superordination results defined by Hadamard product for multivalent analytic functions with Borel distribution and Ruscheweyh derivative in the open unit disk. We applied these results and obtain sandwich results.

Age-stage prediction models based on facial images are essential for various applications, including forensic investigations, medical imaging, social media adver-tising, and compliance with age-related regulations. These models provide valuable insights and predictions, helping inform decision-making and improve outcomes. The proposed model estimates the human age stage by analyzing a face image, extracting various facial features and wrinkles, and then connecting these extracted feature pat-terns to determine the age stage. To address potential limitations in facial images, such as variations in lighting, pose, expression, and image quality, which can impact prediction performance, the proposed model utilizes a neural network architecture. On the other hand, the proposed CNN model has adopted a new light architecture to make it usable with reasonable computational capabilities. The practical results of this model resulted in an accuracy of 98.86% when implemented on the UTKFace dataset, which consists of over 20,000 face images.

Today, the recognition of brain tumors is considered an essential topic in medicine. Brain neoplasm is an acute form of cancer caused by abnormal and uncontrollable cell division. Recent advancements in medical imaging-based deep learning have greatly assisted the healthcare industry in diagnosing a variety of diseases. To achieve image recognition and visual learning, a deep convolutional neural network (CNN) has been selected for implementing brain neoplasm recognition. CNN is a widely used and typical machine learning algorithm. This paper introduces a CNN-based approach along with data augmentation for categorizing magnetic resonance imaging (MRI) brain scan images into natural and unnatural categories. The proposed model achieves close-to-real-time recognition without sacrificing performance. Additionally, this paper describes the steps involved in setting hyperparameters, as well as the entire pipeline of the suggested pattern. After testing the system several times to find the optimal configuration that produces statistically more trustworthy results, each hyperparameter is chosen. Empirical results demonstrate that the proposed model achieves an accuracy of 99.55%. This model exhibits a low level of complexity and delivers more effective, accurate results when compared to other pre-trained models.

Eye iris classification and recognition are important in pattern recognition applications. Traditional image segmentation techniques and classification using machine learning techniques still have not achieved the best results. Different image-capturing methods, low-resolution photos, images with a high noise ratio, and other factors contribute to the extraction of low-quality features. This study proposed the Alex net model with absolute rectified linear unit (absRelu) and rectified linear unit (Relu) activation functions. First, the images are read and resized, then passed to convolutional neural networks (CNN) and absRelu or Relu layers to extract the best features. The next step is size reduction using Maxpooling techniques. The output is passed to the dense layers and then to the Softmax classifier. The proposed model was evaluated using three datasets: MMU, AMF, and IITD. Data augmentation was applied to increase the number of samples. For the MMU dataset, the proposed model with absReLu and ReLu yields 0.9866% and 0.9778% in sequence. For the AMF dataset, the proposed model with absRelu and Relu yields 1.0000% and 0.9981% in sequence. With the ITTD dataset, the best results for both absRelu and Relu are 0.9958% and 1.00%, respectively.

In today's data-driven business environment, organizations' dependence on cutting-edge technologies is constantly increasing in order to gain meaningful information from big data. Raw data's size and complexity make it unusable for decision-making. To address this, BI systems transform raw data into clear, insightful information by leveraging the Hadoop framework to process big data. This study is concentrated on the creation of a business intelligence (BI) system depending upon deep learning (DL) approaches, especially one-dimensional convolutional neural networks (1-D CNN) and long short-term memory (LSTM). The proposed approach takes advantage of the DL algorithms for examining and picking up intricate patterns in sequential data, which is helpful in accurately anticipating the results and offering relevant insights. The predictive capabilities of the proposed system are enhanced through a combination of the 1D CNN and LSTM models, enabling it to grasp spatial and temporal data dependencies. Parallel computing and distributed processing made possible by the Hadoop model have increased the efficiency of big data management, which ensures performance and scalability while working with large datasets. This study aims to show how well the proposed business intelligence system is based on hybrid deep learning to make predictions by utilizing big data analyses. The results show the superiority of the integrated CNN-LSTM model, which operates on a data block size of 512 MB, over a data block size of 64 MB for home data sets, in addition to its superiority over two machine learning models (decision tree and booster regression) at the same block size.

The increasing number of untruths on social media has become a critical concern, affecting public sentiment and confidence. The broad spread of misleading information on the Internet and other social platforms presents a substantial barrier, exerting an impact on public sentiment, influencing political discussions, and eroding the reliability of information sources. Identifying false information on the X platform, previously known as Twitter, is an intricate task because of the network's attributes, such as conciseness, swift spread, and varied user engagements. Extracting crucial information from brief texts, such as tweets, is challenging, even with precise labeling. This study focuses on recognizing misinformation on social media platforms. The CIC Truth-Seeker Dataset 2023, one of the most extensive datasets in its category, contains over 134,000 labeled tweets. The study introduces novel methods in the field of short text classification, incorporating machine learning and natural language processing techniques (NLP). These techniques involve feature extraction using the term frequency-inverse document frequency (TF-IDF) algorithm after the dataset is preprocessed. The study then tests a number of machine learning models, including Random Forest RF, K-Nearest Neighbor KNN, Decision Tree DT, Logistic Regression LR, Naive Bayes NB, and stochastic gradient descent SGD, to see which ones can tell the most accurate difference between real and fake tweets. The findings demonstrated significant advancements in models designed to handle short text effectively, effectively addressing a practical issue such as automatically identifying fake content on social media platforms. Furthermore, we have achieved a significant advantage over previous research on the same dataset. When implementing the models on the news data, the random forest method attained the utmost accuracy at 93%, while the K-Nearest Neighbor strategy yielded a lower accuracy of 68%. This research paper aims to offer helpful information and practical answers to recognizing and reducing false news on social media platforms, specifically focusing on the X platform. Through a Truth-Seeker dataset, we will utilize machine learning methods to enhance previous text classification models.

This study analyzes thunderstorm activity based on archived data from daily meteorological observations over 10 years at three meteorological stations in Iraq (Khanaqin, Baghdad Airport, and Basra Airport) and at two meteorological stations in Russia (Rostov-on-Don and Taganrog). The analysis showed that the greatest frequency of thunderstorms occurred in March and April, 55% of them were in the northern mountainous part of Iraq. Analysis of the fronts for six months per year and for a 10-year period showed 75 baroclinic and 77 barotropic cases for Khanaqin station, 45 baroclinic and 36 barotropic cases for Baghdad Airport station, and 33 baroclinic and 22 barotropic cases for Basra Airport station. But in Russia (Rostov-on-Don and Taganrog), the results showed that the greatest frequency of thunderstorms occurred in June and July, 53.58% of them are in Taganrog due to the warming from the sea and the arrival of a cold mass from the north (from the polar). Analysis for six months per year and for a period of 10 years showed that there were 76 baroclinic and 60 barotropic cases in Rostov-on-Don and 80 baroclinic and 76 barotropic cases in Taganrog. The aim of the study is to analyze thunderstorms and the reasons for their recurrence in some stations more than others, and to predict the storm to reduce or avoid the damage caused by thunderstorms.

Quantitative analysis of morphometric drainage systems is crucial for understanding hydrological and environmental interactions of watersheds. This study uses geospatial techniques to analyze the morphometric characteristics of watersheds, relying on ALOS World 3D DEM and topographic maps of Iraq. Key features identified include main basins, sub-basins, slopes, drainage networks, topographical patterns, flow sequences, and flow lengths. ArcGIS Pro V.3.0.2 was used to determine these characteristics. The analysis includes hydrological processes like runoff frequency, bifurcation ratio, circularity ratio, and surface runoff length. The study aims to understand how these parameters affect the regional morphological landscape and the role of watersheds in the water cycle. Quantitative measurements provide insights into watersheds\' shape, size, and hydrological status. The Hasab basin is a seventh-order basin, consisting of seven sub-basins that form an almost rectangular. Hypsometric analysis reveals geomorphological characteristics and basin maturity. The Hasab basin and most sub-basins are in the late youth or maturity stage, except for SB2 and SB4, which are in the old stage. The morphometric parameters indicate that geological and geomorphological structures significantly influence the drainage network type and basin shape.