Journal of Basrah Researches (Sciences)

The study was devoted to calculating and interpreting the electronic spectra of the RDX molecule. The six isomers of the RDX molecule were geometry optimized with the MP2/cc-pVTZ level of theory. The twist isomer was shown to be the most stable isomer. The electronic spectra of the six isomers were calculated with acetonitrile as a solvent. The electronic spectra were calculated using the PBE0/def2-tzvppd level of theory in acetonitrile as a solvent. The combined PBE0/def2-tzvppd//MP2/cc-pVTZ methods succeeded in reproducing fairly the experimentally measured main band at 236 nm. The calculated wavelength was 237 nm, and the band was shown to mainly originate from the HOMO to LUMO transition with a transition probability of 0.49688, which is 49.4% of the overall transitions responsible for this band. The calculated spectrum of the most stable isomer (twist) was most relevant to the experimental spectrum. In order to predict the detonation velocity of explosives, the Quantity Structural-Property Relationship calculations were done, and a statistical empirical equation was built based on the measured detonation velocity of well-known explosives and several structural and electronic descriptors.

Role of malic acid in enhancing the efficiency of barley (Hordeum vulgare L.) and alfalfa (Medicago sativa L.) plants for phytoremediation of salt affected soil

A total of 84 pygmy cormorant (Phalacrocorax pygmaeus) and 18 great cormorant (carbo) birds were hunted from the eastern Hammar marsh in Basrah province. These birds were examined, and it was found that they were infected with three types of nematodes: Contracaecum Phalacrocorax sp., C.microcephalum, C.rudolphii. The C.rudolphii species was compared with previous studies, while the other two species were considered new records.

In this paper, we study two estimation methods for a three-way repeated measurements model. We estimated the variance components by using the maximum likelihood method and penalized likelihood method. We determine the moments of each estimator, also we define the likelihood ratio test statistic. We look at the various factors that contribute to accuracy, such as sample size, and biased estimation

Object detection is an essential field within computer vision, focusing on identifying objects' presence and category within image or video data. The significance of this issue is paramount in numerous domains that directly impact people's lives, including autonomous driving, healthcare systems, and security monitoring. In contrast to traditional methodologies employed for object detection, deep learning-based algorithms have demonstrated substantial progress in computational efficiency and precision in recent years. This study aims to provide a comprehensive review of object detection by methodically employing deep learning to facilitate a comprehensive and in-depth comprehension of the fundamental principles in this field. The discussion has encompassed various subjects, such as the obstacles and complexities associated with object detection and the traditional and deep learning detectors. The detection of objects within images and videos, the real-time detection of objects, detection of 3D objects, commonly used datasets, and the metrics employed for evaluating object detection performance. This study will likely yield scientific benefits for academics working in the field of object detection and deep learning.

In the present study, we propose a physical model to study spin transport through DNA system and provide apparent physical mechanism for spin dependent phenomenon. The system considered in our work is DNA bases guanine-cytosine coupled to two ferromagnetic leads (FM-(G/C)10-FM) in parallel and anti-parallel configuration case, throughout magnetic quantum contacts. Our treatment is based on the tight binding model to derive obvious formula for the transmission spectrum which is employed to investigate the spin dependent current-bias voltage characteristics and the temperature - Conductance dependence. Our calculations of for strong, weak and without backbone regimes. Various factors are involved in our -study. These are the electrical contacts between DNA molecules and electrodes, the structure of DNA molecule and the environment around DNA molecule. The system spin dependent factors, that are investigated extensively in our study include the spin dependent coupling between subsystems, the quantum contacts between active region and electrodes, majority and minority electrons spin in the ferromagnetic leads as well as externally applied bias voltage. Variation of these factors can enhanced or suppressed spin transport through (G/C)10 molecule. The transmission spectrum calculations conform that the spin transport throughout (G/C)10 originates by a coherent tunneling process between neighboring bases through the overlapping of the LUMO orbitals of the bases. Our results showed that the spin-polarized transport that can be effectively regulated by the type of regime as well as the spin configuration in the leads which can exhibit efficient spin filtering and spin switching by employing the spin blockade phenomenon.

The growing prevalence of face recognition technology in various applications, including mobile devices, access control, and financial transactions, highlights its importance. However, the vulnerability of face recognition systems to attacks has been demonstrated, underscoring the necessity of addressing potential weaknesses that attackers may exploit. The paper delves into face presentation attack detection (PAD) within biometric systems, which is crucial for ensuring the reliability and security of face recognition algorithms. To address this issue, the paper proposes a method for face presentation attack detection using ResNet-50 in conjunction with multi-modal data, incorporating RGB, depth, infrared (IR), and thermal channels. The method explores diverse strategies to combine results from each modality, investigating various fusion techniques such as majority voting, weighted voting, average pooling, and a stacking classifier. The system has been tested on the WMCA dataset. It exhibits strong performance compared to existing methods, notably achieving an impressive ACER ratio of 0.087% with the stacking classifier. This approach proves effective by consolidating multiple modalities without requiring individual scenario-specific models, indicating promise for real-world applications.

The electronic band structure for the stacked multilayer armchair graphene nanoribbon (MLAGNRs) is presented theoretically by using the generalized effective long-wave Hamiltonian and the tight-binding approximation. The relation between the energy gap and the number of layers in a wide range of energies around Fermi's energy level is calculated numerically. The energy of the electron depends on the momentum is investigated for an arbitrary number of layers for the armchair graphene nanoribbon having number of layers n = 1, 2 and 3 with the stacking ABC. We find, in agreement with previous calculations, that MLAGNRs are changeable from conducting to semiconducting according to the number of stacked layers and the width of the armchair graphene nanoribbons. Our results revealed the behavior of the flat electronic bands for ABC-stacked multilayer armchair graphene nanoribbon at the K-point around Fermi’s energy level. This study may be useful in various forms of graphene’s physics. Thus, it emphasized the possibility of controlling the electronic properties as required by the techniques based on these nanomaterials.

Four different concentrations of a plant inhibitor (dry red pepper) were created to investigate its effectiveness in preventing corrosion of a N80 carbon steel alloy used in petroleum pipe manufacturing. This was done in a 1M HCl acidic solution at various temperatures using electrochemical techniques and surface analysis. The inhibitor was discovered to be effective and efficient. As inhibitor concentrations rise, inhibition also increases because the protective layer becomes thicker. At a concentration of 200 ppm at a temperature of 298 K, the inhibition efficiency reached 92.69%. Corrosion rate decreases in the presence of the plant inhibitor. Despite the presence of the plant inhibitor, the corrosion rate in the acidic media (HCl 1M) was 15.58 mpy lower than when the inhibitor was absent. It reached 1.138 when it was. This is because of the reduction in the current density of Icorr. The polarisation results showed that the addition of the inhibitor reduced the corrosion current density of the steel. The Carbon N80 corrosion rate ranges from 34.22 µA/cm2 for the sample without inhibitor to 2.5 µA/cm2 for the sample including 200 ppm of inhibitor (C). The polarisation curves and electrochemical investigations suggest that the corrosion inhibitor exhibits both anodic and cathodic inhibition processes, indicating a mixed kind of inhibition. FT-IR technology analysed the inhibitor's chemical composition and revealed the presence of functional groups and heteroatoms. SEM was utilised to locate the inhibitor layer on the carbon steel alloy surface, which serves to prevent corrosion.

The present study aims to detect specialized E-type antibodies against ovomucoid, and ovotransferrin in the serum of individuals with egg allergy. Current study included collecting 88 samples, including 70 samples from food allergy patients and 18 control samples, during the period from September 2023 to December 2023. Age range of the samples was from 3 to 61 years. Study was conducted using ELISA technique, where the ovomucoid, and ovotransferrin proteins were prepared according to the Vidal method. Results of the current study showed that among the samples studied, 12.85% had antibodies against the egg white ovomucoid antigen, which showed a significant difference at the probability level of P ≥0.05 compared to the control samples. In addition, 30% of the studied samples were allergic to ovotransferrin antigen, which also indicates a significant difference at P<0.05 compared to the control samples.

Using first principles calculations, which were conducted within the framework of Density Functional Theory (DFT) and General Gradient Approximation (GGA), which were implemented using the WIEN2k code program, in order to calculate the structural, electronic, magnetic, and optical properties of the KCrS compound. The results show that the compound achieves the property of half-metal at the equilibrium constant (6.63), where it behaves like a metal in the spin up channel while it behaves like a semiconductor in the spin down channel. The total magnetic moment of KCrS is 5, and the energy gap of this compound is (3,29). In addition, the compound showed distinctive optical properties, which makes it a strong choice for optical and optoelectronic uses. High reflection of light appears in UV, and this is important in laser technology and stimulated emission.

Using a spectral Petrov-Galerkin approach (SPGM), the main objective of this research is to propose a numerical solution for the two-dimensional (2D) linear Volterra and mixed Volterra-Fredholm integral equations (VIEs and MIEs, respectively) type. We solving four examples that illustrate the results of the method compared with other methods in terms of excellent accuracy, powerful method and less error it provides for this type of integral equations (IEs).

In this investigation, a new method for studying the effect of non-Newtonian fluid on the flow and temperature distribution when cooling the turbine disk is presented. The new method is based on the homotopy perturbation method developed with the Chebyshev series. The results of the proposed method were compared with the results obtained using numerical methods in previous literature to ensure the validity of the method, as it showed good agreement. The effect of several physical parameters on flow velocity and temperature diffusion, such as the Reynolds number, cross viscosity parameter, Prandtl number, and power law, was explored. The results obtained using the proposed method were more accurate than other methods used to solve the current problem. Moreover, figures and error tables show the new method's efficacy and efficiency.

This study compares three different pre-trained deep learning models specifically designed for fingerprint identification. The first model uses Convolutional Neural Network (CNN), the second includes Residual Network (ResNet), and the third employs the Visual Geometry Group (VGG) approach. The subsequent comparative assessment reveals the CNN-based model\'s superior performance, with an impressive F1 score of 96.5%. In contrast, the ResNet and VGG models achieve F1 scores of 94.3% and 92.11%, respectively. These findings highlight the CNN model\'s ability to accurately identify fingerprints. Furthermore, a comparative analysis is performed between the obtained results and those reported in recent studies using the same dataset. This analysis evaluates the performance of the proposed models and compares them to previous research, increasing confidence in the results. In conclusion, this study shows that in terms of fingerprint identification, the CNN-based model performs better than the other models.

Azo compound was prepared with ethyl-4-aminobenzoate reactor with (2-amino-3(4-hydroxyphenyl) propanoic acid) and this symbol (J25) was suggested. The compound was purified and Recrystallization with absolute ethanol and then performed diagnostic and analytical techniques where the compound was diagnosed with infrared spectrometry and mass spectrometry. Optical spectrometry to pH values within the range (2-12), which included determining the highest absorption value of the compound and identifying Isopstic points. Different polarized solvents also had an impact on electronic spectrometry. The biological effectiveness of the compound (J25) was studied against two types of bacteria (E. coli) and (staphylocococcus Aureuse) and the results showed the positive effect of the compound in inhibiting the growth of bacteria. This study has been legalized and presented for medical, chemical, physical, biological and other applications

The measurement of magnesium ion in homemade ph samples has been designed using a fully automated microfluidic device. This study involved the design of two channel microchips (45 × 3 cm). Arduino mega microcontrollers were used to operate the suggested system. The DIY micro-peristaltic pumping was operated by the first type that was used for remove specimens and chemical agents extracted from the microchip and transfer them to the UV. Visible spect. We are use the apparatus a 7-microliter flow cell in our lab, also the second type called mega, was employed as logger’s data to alter, it was received and record the outcomes since highest point corresponding to concentrations using an Office Excel 2016 application. A correlation coefficient (R2) 0.9997 and the linearity varied from 0.5 to 10 µg/mL. Ten measurements of Mg ion 8 µg/mL had a relative standard deviation of (0.872%).

The organic salts are a broad class of ionic chemicals with a variety of properties. Scientists have an interest in violurate salts because of their bright color and crystal structure. In this study, 4-chloroanilinium violurate salt was synthesized and identification using infrared spectroscopy, 13C NMR spectroscopy and Electrospray ionization (ESI). Density functional theory (DFT) calculations have been performed using B3LYP/6-31++G**, and CAM-B3LYP/6-31++G** levels of theory. Practically, chloroanilinium ion (M+H)+ has a relative abundance of 100% in ESI+, indicating its excellent stability. The protonation formula for violuric acid (VA+H)+ shows instability, despite the observation of a low abundance of the (VA+3H)3+ ion. DFT calculations demonstrate the exothermic nature of the salt formation reaction. The calculated enthalpy change is -26.861kJ in the B3LYP/6-31++G** level of theory and -31.82kJ in the CAM-B3LYP/6-31++G** level of theory.

The goal of this work was to evaluate the thermoluminescence features of Himalayan salt (HS) for the purpose of radiation dosimetry. As a result of its low cost, widespread availability in the market, and similarity to the tissues found in the human body. Irradiation of the samples was performed with a radiation source that utilised 137Cs. With a heating rate of 15 degrees Celsius per second and a final temperature of 320 degrees Celsius, the samples were subjected to TL analysis with the help of the Harshaw Model 2000B/C TLD Reader. In commercial salt samples, the glow curve (gwc) reveals the presence of a strong glow peak at temperatures ranging from 204 to 282 degrees Celsius. For the salt sample, the grain size (gz) is exactly proportional to the total light intensity (TL) of the glow curve shown in the graph. Not only that, but the findings also demonstrate that the TL intensity of the HS has a linear relationship with dosage throughout a wide range, spanning from 250 mGy to 20 Gy. The post- irradiated fading rate are investigated and shows the rate of thermal fading of HS, found to be equal 32% at storage time for 24 days. These results indicate that this material is very interesting for useful for TL-dosimetric applications.

Vehicular ad hoc networks (VANETs) provide the potential to improve transportation efficiency by facilitating the sharing of traffic information among vehicles. Acceptance of VANET depends on communication speed and accuracy as well as privacy protection guaranteeing an individual's safety. Vehicle authentication is necessary to ensure message correctness. This necessitates the implementation of an effective privacy-preserving authentication scheme, as well as the need for both secrecy and timebound delivery of messages. Various privacy-preserving authentication schemes have been suggested to guarantee the integrity of messages in communications. However, most of the schemes are not able to solve issues related to computing costs, communication, security, privacy, threats, and vulnerabilities. In this review, we focus on cryptographic strategies that are suggested to accomplish privacy and authentication, such as identity-based, public key cryptography-based, pseudonym-based, and blockchain-based schemes. We provide a thorough analysis of schemes, including their categorizations, advantages, and drawbacks. The study demonstrates that the majority of current authentication techniques necessitate trusted authorities that lack transparency in their operations. Additionally, authentication process incurs substantial computational and communication overhead, leading to a considerable impact on the timely delivery of messages. More efforts are required to enhance the development of efficient authentication schemes in VANETs.

In this study, two Schiff base ligands, L1 and L2derived from ortho-phthalaldehyde and primary amines (4-aminopyridine and 4-aminobenzothiazole) were studied using infrared spectroscopy (IR), proton nuclear magnetic resonance spectroscopy ( 1HNMR), carbon- . 13 atomic magnetic coupled resonance spectroscopy (13 CNMR ), mass spectrometry, and also were performed by elemental analysis ,and then reacted with tetrathiocyanates in a 1:1 molar ratio with Schiff base ligands synthesized to produce diatomic mixed-metal-bridge complexes as (L MCd(SCN) 4), where M represents Co(II) or Ni(II) .These complexes were characterized by spectroscopic methods and magnetic susceptibility measurements .The results showed that all the complexes had the same coordination number of four. In addition, the cobalt complex exhibited paramagnetic properties, while the nickel complex exhibited diamagnetic properties. The synthesized Schiff base ligands were evaluated as an inhibitor of carbon steel corrosion in acidic medium and showed good inhibitory activity.

This paper presented an improvement of the order of approximation by the sequence of Bernstein-Kantorovich type operators in three ways. The approximation order obtained by these sequences is O(n^(-1) ),O(n^(-2)) and O(n^(-3)) respectively. Some theoretical results related to the convergence theorem and Voronovskaja asymptotic formula of the improvement sequences are presented. Then, some numerical examples for these sequences are given. The numerical results are supported by the improvement of the order of approximation. These improvements are done based on the research idea [9].

Klebsiella pneumoniae belongs to the family Enterobacteriaceae, and it is the most clinically pertinent species, also it causes both nosocomial and communities-acquired infections, such as liver abscesses, urinary infections, pneumonia, and bacteremia. The current study aimed to detect the virulence iroN gene in Klebsiella pneumoniae for patients with urinary tract infections. One hundred fifty collected 75 samples of urine and 75 samples of blood samples from patients with urinary tract infections from Ibn Al-Baladi Hospital/ Baghdad -Iraq from November 2023 to February 2024. The results showed the diagnosis of 60 isolates of bacteria with an 80% prevalence of Klebsiella pneumoniae observed percentage for the age (more than 60 years) was 80% higher than for age (less than 60 years) was 20%. Moreover, females recorded 55% and males 45%. Also, blood group O has a percentage (33.3% ) more than other groups A (23.3%), AB(30%), and B (13.3%). The results of antibiotic resistance of Klebsiella pneumoniae showed 100% resistance toward Cefoxitin Screen, Amoxicillin/ Clavulanic acid, Ticarcillin/ Clavulanic Acid, Piperacillin, Piperacillin/Tazobactam, Cefmetazole, Ceftazidim, Cefepime, Aztreonam, Amikacin, Ciprofloxacin). In contrast, it was 63.3 % for (Meropenem, Gentamicin, Tobramycin),66.7%for Trimethoprim / Sulfamethoxazole, and 61.6% for Imipenem. Moreover, these isolates were sensitive to Ticarcillin (100%), Minocycline (83%). Besides, the results of the virulence gene showed iroN gene was found in 38(63.3%) isolates out of 60 isolates. In conclusion, this study appeared to the prevalence resistance of K. pneumoniae in urinary infections with the presence of iroN gene.

Healthcare offers several advantages for actual-time smart healthcare. security concerns are growing due to its constrained computing power, storage capacity, and self-defense capabilities. The tamper-resistant decentralized architecture of more recent blockchain-based authentication solutions gives them significant security features, but they come with a high resource cost because they need a lot of processing power, additional storage, and lengthy authentication processes. Therefore, these challenges offer impediments to achieving the optimal degrees of temporal efficiency and scalability, which are critical for the effective operation of large-scale, time-sensitive IoHT systems. Our work provides an authentication solution specifically created for healthcare systems to address these issues. We work in three phases: initializing, registering, logging in, and authenticating. The suggested system combines blockchain technology, Firebase Framework, Bloom Filter, Multi-Factor authentication, and other elements to improve security and efficiency at the same time. We use the Python programming language to simulate the work, and our findings indicate that the Bloom filter decreases the amount of time it takes to determine whether a person is in the system compared to the previous way. Moreover, using Firebase may reduce transaction numbers by up to 73%. Using the Scyther tool, a security analysis of the proposed scheme proved that the suggested plan is safe from possible threats and maintains the IoHT system's scalability.

Data owners seeking to boost processing power, storage, or bandwidth can take advantage of cloud computing services. However, this shift poses new challenges related to privacy and data security. Searchable Encryption (SE), which combines encryption and search techniques, addresses these issues (violation of data users' privacy) by allowing user data to be encrypted, transmitted to a cloud server, and searched using keywords. Despite its benefits, several recent real-world attacks have raised concerns about the security of searchable encryption. Ensuring forward and backward privacy is likely to become a standard requirement in the development of new SE systems. To address these issues, we propose a scheme that exclusively uses symmetric cryptographic primitives, achieving high communication efficiency and forward and backward privacy. In addition, we emphasize improved I/O efficiency because only the results of subsequent updates are loaded when searching. The time required to retrieve results is so significantly reduced compared to existing SE methods that we have shown that our scheme achieves superior efficiency. Moreover, by integrating blockchain network services with cloud services, we have developed a searchable intelligent cryptosystem suitable for lightweight smart devices. In our study conducted on the Ethereum network, we found our method to be both efficient and secure, especially when compared to methods such as PPSE and Jiang. The results indicate that our system delivers results in terms of performance and privacy within dynamic cloud environments making it a solution for protecting confidential information.

The study involved the isolation, purification and culturing of the cyanobacterial Chroococcidiopsis cubana from the Shatt al-Arab River in the Basrah Governorate, southern Iraq. The growth curve of Ch. cubana was schemed after its cultivation in the liquid culture medium BG-11. The growth analysis indicated a growth constant (k) = 1.3 and a generation time (G) = 0.5 days. Ch. cubana ability to produce hepatoxic microcystins (MCs) was detected after isolating and purifying the toxins. The toxins were determined quantitatively and qualitatively using the technique of High-Performance Liquid Chromatography (HPLC), which revealed that Ch. cubana exhibited a high production capacity of these toxins, with a concentration rate = 4.053 ug/l.

Electrocardiogram (ECG) classification plays a crucial role in the diagnosis and management of cardiovascular diseases. Deep learning-based approaches have shown promising results in automated ECG classification. However, the complexity of ECG signals, including variations in morphology, duration, and amplitude, poses significant challenges for existing deep learning models. In this regard, recent advancements in vision transformer models have shown remarkable performance in images processing and computer vision tasks. In this paper, we propose a deep vision transformer-based approach for ECG classification, which combines the power of convolutional neural networks and self-attention mechanisms. Our proposed model was tuned and enhanced by four hyper-parameters of the proposed model, it can effectively detect internally the main features of ECG images and achieve performance on benchmark ECG datasets. The proposed model can aid in the early detection and diagnosis of cardiovascular diseases, thus improving patient outcomes the final accuracy was 98.23% in dataset https://data.mendeley.com/datasets/gwbz3fsgp8/2 .

SQL injection attacks cause significant threats to the security of online applications. It leverages vulnerabilities in database systems and can result in unauthorized access to and compromising sensitive data. This study investigates the use of bio-inspired algorithms to tackle such attacks, assessing their applications and potential for enhancing cybersecurity measures against SQL injection attacks. In this review, we describe the basic definition, causes, types, and prevention mechanisms of SQL injection attacks. In addition, we examine the use of various bio-inspired algorithms to solve the problem of SQL injection attacks. This study concludes the importance of continuously improving detection methods, particularly those adopting bio-inspired algorithms since they achieved promising results.

The study involved the isolation, purification and culturing of the cyanobacterial Chroococcidiopsis cubana from the Shatt al-Arab River in the Basrah Governorate, southern Iraq. The growth curve of Ch. cubana was schemed after its cultivation in the liquid culture medium BG-11. The growth analysis indicated a growth constant (k) = 1.3 and a generation time (G) = 0.5 days. Ch. cubana ability to produce hepatoxic microcystins (MCs) was detected after isolating and purifying the toxins. The toxins were determined quantitatively and qualitatively using the technique of High-Performance Liquid Chromatography (HPLC), which revealed that Ch. cubana exhibited a high production capacity of these toxins, with a concentration rate = 4.053 ug/l.